United States Department of Agriculture

Modeling Historical Range of Variability and 

Alternative Management Scenarios in the 

Upper Yuba River Watershed, Tahoe National 

Forest, California

Kevin McGarigal, Maritzza Mallek, Becky Estes, Marilyn Tierney, Terri Walsh, 

Travis Thane, Hugh Safford, Samuel A. Cushman

Forest Service Rocky Mountain 

Research Station

General Technical Report 

RMRS-GTR-385

October 2018
McGarigal, Kevin; Mallek, Maritza; Estes, Becky; Tierney, Marilyn; Walsh, Terri; 

Thane, Travis; Safford, Hugh; Cushman, Samuel A. 2018. Modeling historical range 

of variability and alternative management scenarios in the upper Yuba River 

watershed, Tahoe National Forest, California. Gen. Tech. Rep. RMRS-GTR-385. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Stations. 346 p. 

Abstract

This report describes modeling of historical range of variability and alternative 

management scenarios in the upper Yuba River watershed, Tahoe National Forest, 

California. We discuss the need for this study with respect to the historical and 

contemporary context of the northern Sierra Nevada landscape, including background 

on the range of variability concept and the use of simulation modeling to quantify it. We 

simulated the dynamics in vegetation driven by wildfire during the historical reference 

period (ca. 1550–1850). Based on the output, we quantified the range of variability in 

composition and configuration of the landscape mosaic, and compared the results to 

the current landscape to quantify departure. We also created a set of eight alternative 

management scenarios reflecting different objectives and applying different treatment 

types and intensities. We conducted 20 replicate 100-year simulations of each of these 

management scenarios and quantified the range of variability in landscape composition 

and configuration, as before, for each scenario. We compared the range of variation in 

each landscape attribute among management scenarios and with the historical range of 

variability and current landscape to determine the potential for management scenarios to 

move the current landscape toward its historical range of variability. We provide a synopsis 

of the major findings or “take-home” messages of this study and their management 

implications. For example, our scenario analysis demonstrates that active vegetation 

management involving a combination of mechanical and prescribed fire treatments has 

the potential to emulate many aspects of landscape structure that would occur under a 

natural disturbance regime, but it would require a much higher intensity of treatment than 

we are accustomed to—perhaps as much as 10 times the current treatment rate.

Keywords: range of variability, historical range of variability, range of natural variability, 

natural range of variability, future range of variability, landscape disturbance-succession 

modeling, alternative management scenarios, northern Sierra Nevada

All Rocky Mountain Research Station publications are published by U.S. Forest Service employees 

and are in the public domain and available at no cost. Even though U.S. Forest Service publications 

are not copyrighted, they are formatted according to U.S. Department of Agriculture standards 

and research findings and formatting cannot be altered in reprints. Altering content or formatting, 

including the cover and title page, is strictly prohibited.

Cover images—top: Upper Yuba River watershed from Sierra Buttes (photo by: Becky Estes, USDA 

Forest Service); bottom left: simulated point-specific fire return interval for the simulated historical 

range of variability (ca. 1550–1850); bottom right: simulated trajectory in the percentage of the 

Sierran Mixed Conifer – Ultramafic cover type in each seral stage. 
i

Authors

Kevin McGarigal is a Professor of Landscape Ecology at the University of Massachusetts, 

Amherst and Proprietor of LandEco Consulting. He received a B.S. degree in forestry and 

wildlife from Virginia Tech, an M.S. degree in wildlife science from Oregon State University, 

and a Ph.D. in forest science from Oregon State University.

Maritza Mallek is currently an Ecologist and Data Manager with the U.S. Fish and Wildlife 

Service, Northeast Region, National Wildlife Refuge System in Hadley, Massachusetts. 

She was an Ecologist with LandEco Consulting in Amherst, Massachusetts at the time 

of writing. She received a B.A. degree in geography from the University of Chicago and 

an M.S. degree in environmental conservation from the University of Massachusetts, 

Amherst.

Becky Estes is the Central Sierra Province Ecologist (Tahoe, Eldorado, Stanislaus, and 

Lake Tahoe Basin Management Unit) in the USDA Forest Service, Pacific Southwest 

Region in Placerville, California. She received a Ph.D. in forestry from Auburn University 

and is a Certified Ecologist with the Ecological Society of America.

Marilyn Tierney is the District Biologist on the Yuba River Ranger District, Tahoe National 

Forest, USDA Forest Service, Pacific Southwest Region in Camptonville, California. She 

received a B.S. degree in biology and an M.A. degree in biology with an emphasis in 

ecology and systematics from San Francisco State University.

Terri Walsh is the Timber Management Officer on the Yuba River Ranger District, Tahoe 

National Forest, USDA Forest Service, Pacific Southwest Region in Camptonville, 

California. She is a Certified Silviculturist and received a certificate in forestry from 

California Polytechnic State University, San Luis Obispo. 

Travis Thane is a District Fire Management Officer on the Placerville Ranger District, 

Eldorado National Forest, USDA Forest Service, Pacific Southwest Region in Camino, 

California. He received a B.S. degree in agriculture business from California State 

University, Chico. In 2010, he completed a Technical Fire Management Program from the 

Washington Institute. 

Hugh Safford is the Region 5 Regional Ecologist with the USDA Forest Service, Pacific 

Southwest Region in Vallejo, California, and a research faculty affiliate in the Department 

of Environmental Science and Policy, University of California, Davis. 

Samuel A. Cushman is a Research Scientist with the USDA Forest Service, Rocky 

Mountain Research Station in Flagstaff, Arizona. He received a B.S. degree in ecology 

from The Evergreen State College, an M.S. degree in terrestrial ecology from Western 

Washington University, and a Ph.D. in landscape ecology from the University of 

Massachusetts, Amherst.
ii

Acknowledgments

We appreciate careful reviews of our cover type descriptions and associated stateand-transition models by Ramona Butz, Michelle Coppoletta, Shana Gross, Kyle Merriam, 

Marc Meyer, and Michele Slaton. Alan Doerr served as GIS Analyst in early phases of this 

project. Prior to retiring, he provided several spatial data layers used in our model. In some 

cases he collated existing Forest Service corporate datasets, and in others he performed 

analysis and interpretation to enhance data utility. For example, he interpreted legacy 

forestry datasets so they could be used to infer stand ages and categorized national forest 

transportation spatial datasets into size classes used by RMLands. We also thank the 

national forest staff and leaders for their support of this project. Eduard Ene served as a 

programmer and implemented several new algorithms in RMLands to accommodate the 

needs of this project. Robert Keane and Eric Gustafson provided useful review comments 

on a draft of this report.
iii

CONTENTS

INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Purpose and Need . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Historical Reference Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Why Range of Variability? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Why Simulation Modeling? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Scope and Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

PROJECT AREA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Physical Geography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Land Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

METHODS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Historical Range of Variability and Departure . . . . . . . . . . . . . . . . . . . . . . . . 14

 RMLands Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

 HRV Spatial Input Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

 HRV Model Parameterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

 HRV Model Calibration and Verification . . . . . . . . . . . . . . . . . . . . . . . . . . 30

 HRV Model Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

 Alternative Management Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

 Management Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

 Wildfire Disturbance Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

 RMLands Vegetation Treatments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

 Model Calibration and Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

 Model Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Analysis of Landscape Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

 Landscape Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

 Landscape Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Historical Range of Variability and Departure . . . . . . . . . . . . . . . . . . . . . . . . 51

Disturbance Regime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

 Fire Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

 Fire Rotation Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

 Fire Severity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
iv

Landscape Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

 Developmental Stage Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

 Canopy Cover Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

 Seral Stage Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Landscape Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

 Developmental Stage Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

 Canopy Cover Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

 Seral Stage Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Alternative Management Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

 Disturbance Regime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

 Landscape Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

 Landscape Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

MAJOR FINDINGS AND MANAGEMENT IMPLICATIONS . . . . . . . . . . . . . . . 99

NEXT STEPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

APPENDIX A: Spatial Data Layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

APPENDIX B: Cover Type Description and State-and-Transition Model 

for 12 Cover Types and Variants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

APPENDIX C: Parameterization of Selected Landscape Metrics . . . . . . . 271

APPENDIX D: Detailed Supplemental Results . . . . . . . . . . . . . . . . . . . . . . . 276
USDA Forest Service RMRS-GTR-385. 2018 1

INTRODUCTION

Purpose and Need

The upper Yuba River watershed project area (181,556 hectares, or 448,625 acres) on the 

Tahoe National Forest in northern California (fig. 1) is a spatially and temporally dynamic 

mosaic of ecological systems. This dynamic stems from a complex natural and human land 

use history that characterizes much of the northern Sierra Nevada ecological region. This 

dynamic is largely driven by the interplay of disturbance regimes, especially fire and vegetation 

succession. Cycles of fire and vegetation recovery occur variably over large extents, as well as 

over long periods of time, and produce a constantly shifting mosaic of ecosystem conditions. 

Understanding this dynamic is essential to the management of this landscape.

It is generally believed that prior to Euro-American settlement in the mid-1800s the

Sierra Nevada landscape was shaped by a set of environmental conditions—including climate, 

topography, vegetation, and management by Native Americans—that over thousands of years 

had led to high resilience to major ecological change (Van Wagtendonk and Fites-Kaufman 

2006). Although climate is always changing, the general outlines of modern Sierra Nevada 

ecosystems have been in place for approximately the last 4,000 years (Millar and Woolfenden 

1999). Before Euro-American settlement, fire was the major source of disturbance in Sierran 

forests, shaping the composition and spatial configuration of vegetation communities (Safford 

and Stevens 2017). Fires were primarily lightning caused, although indigenous peoples set 

fires for a variety of purposes, especially at lower elevations (Safford and Stevens 2017). Fires 

during this period were exceptionally frequent, resulting in an overall fire rotation period 

(i.e., the time it takes to burn an area equivalent to the total area under consideration) of about 

30 years (Agee 1993), although fire rotation varied considerably among vegetation types in 

relation to moisture and elevation gradients (Mallek et al. 2013). In general, regardless of 

Figure 1— The upper Yuba River watershed project area on the Tahoe National Forest located 

within the North Sierran CALVEG mapping zone in the Sierra Nevada ecological region.
2 USDA Forest Service RMRS-GTR-385. 2018

vegetation type, fires during the presettlement period are thought to have burned primarily at 

low intensities. High mortality (>75 percent overstory canopy mortality) fire was relatively 

uncommon (Collins et al. 2007; Safford and Stevens 2017) and, when it did occur, it most 

likely occurred as relatively small (<1 to several hectares) patches embedded within a 

mosaic of lower severity and unburned areas (Collins et al. 2007). When stand-replacing fire 

did occur, it initiated early-development conditions on the landscape. Most fires, however, 

were surface fires that removed only understory fuels and tree regeneration. In some cases, 

moderate overstory mortality opened forest canopies without resetting stand development, 

especially in more xeric parts of the forest or areas of dense patches of trees (Mallek et al. 

2013; Safford and Van de Water 2014; SNEP 1996a,b). Where fires did not recur frequently or 

occurred only at very low severity levels, succession processes such as infilling and overstory 

growth led to a gradual closing of the canopy. For most of the ecological communities, highseverity fire rates were low enough to allow most stands to succeed into late-development and 

old-growth conditions characterized by a variety of (mostly open) canopy structures (Mallek 

et al. 2013; Safford and Van de Water 2014; SNEP 1996a,b). Thus, it is generally believed 

that during the presettlement period the landscape was in a dynamic equilibrium—a relatively 

stable shifting mosaic of vegetation conditions—and highly resilient to permanent change 

(Hessburg et al. 2005; North et al. 2009). 

Since Euro-American settlement, grazing, logging, mining, and fire exclusion have

interacted to greatly and rapidly alter the historical fire regime and vegetation patterns (Knapp 

et al. 2013; Stephens et al. 2015). Heavy grazing in the late 19th and early 20th century altered 

fine fuels and probably reduced understory flammability (Hessburg et al. 2005). Widespread 

timber harvest in the past, especially of fire-tolerant species such as ponderosa pine (Pinus 

ponderosa) and sugar pine (P. lambertiana), accelerated the increased cover of fire-intolerant 

species such as white fir (Abies concolor) and incense cedar (Calocedrus decurrens) and 

selectively removed most of the late-seral forests (Hessburg et al. 2005; McKelvey and 

Johnston 1992; McKelvey et al. 1996). 

Timber harvesting continues today, but the emphasis has switched from commercial 

timber production to fuels management. Hydraulic mining in the past had a long-lasting effect 

on fire through the local removal of both vegetation (i.e., fuels) and soils, which prevented or 

dramatically altered the potential for vegetation development after mining ceased (Storer and 

Usinger 1963). For oak woodlands, yellow pine, and mixed conifer forest types, frequent fires 

(usually having low mortality) were historically common (Mallek et al. 2013; Safford and Van 

de Water 2014; Van de Water and Safford 2011). After large-scale fire exclusion in the second 

half of the 19th century, less fire-tolerant species such as white fir, incense cedar, and Douglasfir (Pseudotsuga menziesii) have become more dominant in areas where they were once a 

minor part of the vegetation community (Beaty and Taylor 2007; Knapp et al. 2013; Safford 

and Stevens 2017; Stephens et al. 2015). In addition, fire exclusion has allowed the buildup 

of surface fuels and ladder fuels, which promotes larger and hotter fires when they do occur 

(Knapp 2015). Moreover, the lack of natural fires has increased the contiguity in fuel loading 

that allows fires to spread over very large areas under the right conditions (Beaty and Taylor 

2007; Hessburg et al. 2005; Meyer et al. 2008). Thus, the current landscape is now dominated 

by fuel-rich, early- to mid-seral stage, overstocked forests composed disproportionately 

of less fire-tolerant species (Hessburg et al. 2005; Knapp et al. 2013; Stephens et al. 2015; 

Storer and Usinger 1963). Given the uncharacteristically high canopy cover, tree density, and 

continuity of abundant surface fuels, it is believed that the landscape has become less resilient 

to the occurrence of future fires and other disturbance agents and is especially susceptible to 

extensive and uncharacteristically severe fires (Beaty and Taylor 2007; Hessburg et al. 2005; 

Meyer et al. 2008).
USDA Forest Service RMRS-GTR-385. 2018 3

The Tahoe National Forest is the principal land manager in the Yuba River watershed. 

Based on fire return interval departure mapping (which compares current fire frequencies 

versus historical frequencies), the Tahoe National Forest has one of the highest levels of 

departure among national forests in California, reflecting extremely effective fire suppression 

efforts (Safford and Van de Water 2014). In 1999, however, the Pendola fire burned 4,734 

hectares (11,698 acres) around Bullards Bar Reservoir and west of Camptonville, California, 

in the upper Yuba River watershed. The final fire perimeter included a total of 1,565 hectares 

(3,867 acres) on the Tahoe and Plumas National Forests. Postfire analysis of the burn on the 

national forests determined that 70 percent of the area burned at high severity, prompting a 

need for restoration actions (USDA Forest Service 1999). 

Triggered by the Pendola fire and its uncharacteristic proportion of high-severity fire, 

Tahoe National Forest managers determined that to better guide restoration planning efforts 

within the fire perimeter, and more generally on the forest as a whole, it was prudent to gain 

a better understanding of the natural range of variability (NRV, also called range of natural 

variability, or RNV) in vegetation composition and configuration. More specifically, pursuant 

to the 2012 Forest Planning Rule (NFMA, 2012 Planning Rule 2015), managers on the Tahoe 

decided to develop a quantitative assessment of the NRV in landscape structure to serve as a 

reference against which to evaluate current landscape conditions and provide a framework for 

deriving potential desired future conditions and planning future management. They decided 

to use the historical (i.e., pre-Euro-American settlement) range of variability (HRV) as the 

measure of NRV because it offered the best reference for evaluating the current and future 

landscapes (Keane et al. 2009) and because the historical data were deemed sufficient to 

reliably characterize landscape conditions during the historical reference period. In addition, 

Tahoe managers sought to explore the potential of alternative land management scenarios to 

move the landscape toward HRV. To meet these needs of the Tahoe National Forest, this study 

was conducted with the following specific objectives:

1. Synthesize empirical and expert knowledge on disturbance and succession processes

characteristic of the pre-Euro-American settlement period in the northern Sierra Nevada 

ecoregion, which contains the upper Yuba River watershed.

2. Quantify HRV in landscape structure (i.e., vegetation land cover composition and 

configuration) in the upper Yuba River watershed by using the RMLands landscape 

disturbance-succession model.

3. Quantify current departure of the upper Yuba River watershed landscape structure from its 

HRV.

4. Quantify range of variability in landscape structure in the upper Yuba River watershed 

under several alternative management scenarios and compare them to the current 

landscape and HRV.

5. Synthesize simulation modeling results and summarize the implications for land

management.

Historical Reference Period

We defined the historical reference period as the 300 years prior to Euro-American 

settlement (ca. 1550–1850). This is because our understanding of past climate and vegetation 

patterns becomes less clear as we move back in time, but 300 years is sufficient to capture 

notable variability (Meyer 2013a; Safford and Stevens 2017; Van de Water and Safford 

2011). Because this reference period captures landscape changes over hundreds of years, 

far longer than the typical forest planning cycle, the HRV results allow managers to develop 

near-term plans and expectations within a broader temporal context. Indeed, in restoration 
4 USDA Forest Service RMRS-GTR-385. 2018

planning efforts, it is logical to look back to the last known period during which a dynamic 

but resilient landscape existed. The arrival of Euro-American settlers in the Sierra Nevada 

led to sweeping ecological changes that now have greatly altered many Sierran landscapes 

through fire exclusion, grazing, road-building, timber cutting, recreation, and other activities 

(Hessburg et al. 2005; Knapp et al. 2013; Stephens et al. 2015; Storer and Usinger 1963). The 

period before Euro-American settlement, then, is a suitable reference condition against which 

we can compare current landscape structure and dynamics. Moreover, it is frequently used in 

the western United States as the historical reference period for restoration planning (Meyer 

2013a; Safford and Stevens 2017; Van de Water and Safford 2011). This reference period is 

also several times the length of fire rotation periods identified for well-understood cover types 

within the project area. Finally, it is a timeframe for which we have sufficient information to 

have some confidence in model results.

We are mindful that this reference period overlaps the “Little Ice Age,” which may temper 

the utility of the results as specific management targets, but does not diminish their usefulness 

in other ways (Minnich 2007; Safford and Stevens 2017). The chosen reference period was not 

a time of stasis climatically, ecologically, or culturally. The oscillation of the Palmer Drought 

Severity Index, a measure of climate variability in terms of precipitation and temperature, over 

time illustrates this (see Methods section). Although the Late Holocene was characterized by 

general cooling and gradual increases in precipitation, multiyear droughts and El Niño and La 

Niña events also occurred over this timeframe (Minnich 2007). The Medieval Drought periods 

were extremely dry and warm and are postulated to be climatic analogs to a warmer, drier 

future (Safford and Stevens 2017). Even though fire frequencies moderated during the Little 

Ice Age, frequencies were still much higher than today, permitting fire- and drought-tolerant 

species to dominate much of the landscape (Safford and Stevens 2017). During the reference 

period, several Native American tribes lived in the project area. Debate continues among 

scientists and other researchers about the extent to which those peoples managed vegetation 

through setting fires (Anderson and Morrato 1996). Because we lack empirical evidence to 

distinguish between lightning-caused and human-caused fires during this period, we decided 

not to exclude any fire frequency or rotation data on the basis of not being reflective of 

“natural” conditions.

We emphasize that our choice of reference periods does not suggest that it should be our 

goal in management to recreate all of the ecological conditions and dynamics of this period. 

Such a goal may not be possible, nor potentially desirable, in light of current climate change, 

ecological shifts, and social realities. However, the chosen reference period allows us to 

compare current conditions to a baseline set of data on ecosystem conditions (composition, 

configuration, and disturbance processes) to develop an idea of the level of departure of 

altered ecosystems from their “natural” state (Safford and Stevens 2017). The results of this 

study complement the NRV assessments compiled by the Forest Service, U.S. Department of 

Agriculture, Pacific Southwest Region Ecology group for the Bioregional Assessment process 

necessary for forest planning (e.g., Estes 2013a,b; Gross and Coppoletta 2013; Merriam 

2013; Meyer 2013a,b; Safford and Stevens 2017), and provides a basis for forest management 

policies and associated actions that seek to emulate natural disturbance patterns (Perera et al. 

2004; Romme et al. 2000).

Why Range of Variability?

Since the late 1970s, ecologists have increasingly focused on community and ecosystem

dynamics rather than static endpoints such as the climax. Disturbance and response to 

disturbance are now recognized as natural processes that lie at the core of ecosystem 
USDA Forest Service RMRS-GTR-385. 2018 5

dynamics, a concept that plant ecologists have variously expressed as “patch dynamics” 

(Pickett and Thompson 1978), the “shifting mosaic” (Bormann and Likens 1979), and the 

“mosaic cycle” (Remmert 1991). This is more generally known as the “dynamic view,” 

reflecting the increasing recognition that disturbances and other factors mitigate against 

ecological communities attaining a lasting constancy in species composition. This dynamic 

view of ecosystems and landscapes has had a tremendous impact on land management. It 

is now universally accepted that ecosystems and landscapes are dynamic in space and time 

and that disturbance and successional processes interact to affect the range of variability in 

ecosystem structure, composition, and function. 

Although the notion of “natural” in ecological systems has multiple interpretations

(Sprugel 1991), many scientists and land managers believe that NRV can provide a useful 

framework for managing dynamic ecosystems and landscapes, especially as human activity 

is increasingly seen as the causal agent of large-scale environmental changes resulting in 

departure of the ecosystem or landscape from its “natural” range of variability. For our 

purposes, NRV can be defined as “[t]he ecological conditions, and the spatial and temporal 

variation in these conditions, that are relatively unaffected by people, within a period of 

time and geographical area appropriate to an expressed goal” (Landres et al. 1999: 1180). 

Management use of NRV concepts began in earnest out of a search for a legally defensible 

strategy for maintaining biological diversity and sustaining the viability of threatened 

and endangered species, pursuant to the requirements of laws such as the National Forest 

Management Act of 1976 (https://www.fs.fed.us/emc/nfma/includes/NFMA1976.pdf) and the 

Endangered Species Act of 1973 (https://www.fws.gov/endangered/esa-library/pdf/ESAall.

pdf). Eventually, it was realized that incorporating ecological variability into management 

is key to maintaining ecosystem integrity because it: (1) ensures optimal biodiversity, (2) 

recognizes the roles of disturbance, (3) widens the options for management, and (4) maintains 

resilience. Consequently, the NRV concept gradually evolved and expanded into a general 

“coarse filter” strategy for sustaining ecological integrity and as a benchmark for evaluating 

the impacts of human activities on ecosystems and landscapes (Christensen et al. 1996). 

By 2000, the Planning Rule explicitly called for the Forest Service to estimate and

describe the range of variability under natural disturbance regimes, and manage for those 

characteristics (NFMA, 2000 Planning Rule 2000). Decisions were to be grounded in the 

context of “maintain[ing] or restor[ing] ecological conditions that are similar to the biological 

and physical range of expected variability” (NFMA, 2000 Planning Rule 2000: 36 CFR § 

219.4(b)(2)(vi)). The need to consider the NRV was maintained through various amendments 

to the rule, and is prominent in the current 2012 Planning Rule (finalized in early 2015): “Plan 

decisions affecting ecosystem diversity must provide for maintenance or restoration of the 

characteristics of ecosystem composition and structure within the range of variability that 

would be expected to occur under natural disturbance regimes of the current climatic period,” 

(NFMA, 2012 Planning Rule 2015: 36 CFR § 219.2o(b)(1)). Consequently, NRV concepts 

guide most current land management activities on national forests and other public lands.

The NRV approach required a baseline or reference, and historical ecology appeared 

to provide the best reference, so historical range of variability (HRV) quickly became the 

standard for applying NRV concepts (Keane et al. 2009; Swetnam et al. 1999). A prerequisite 

to the use of HRV, however, is the ability to define a meaningful historical reference period 

during which human activities had relatively minor effects on overall landscape structure and 

function, and the ability to examine the range of variation in key landscape attributes during 

this period. This range of variation can then be used as a benchmark for comparison with 

contemporary or potential future conditions. Assuming the prerequisites can be met, HRV can 

be a useful tool for understanding and evaluating change and communicating the concept that 
6 USDA Forest Service RMRS-GTR-385. 2018

landscapes are dynamic to the public. Moreover, a quantitative characterization of HRV can 

help determine whether the current landscape is “outside” its natural range of variability and 

provide detailed, specific, and quantitative criteria for establishing desired future conditions 

that are intended to emulate natural disturbance patterns. HRV can also help to develop 

hypotheses about the drivers and mechanisms of ecosystem change that can then be tested 

with spatial and temporal data. The understanding obtained from these analyses can aid in 

predicting how ecosystems might change in the future (i.e., future range of variability, FRV) 

in response to novel structures and processes (e.g., accelerated climate change) (Romme et al. 

2012). 

Despite the appeal of HRV, it is not without some major challenges that should be 

addressed up front.

1. Is HRV relevant today given the following? 

• Native and contemporary people have so altered natural systems that there are no pristine 

natural areas left on our planet, making information derived from the past difficult to 

interpret or irrelevant. 

• Each point in time and space is unique, and dominant climate patterns are continually 

changing. Therefore, a description of past patterns and processes is largely irrelevant today 

or in the future.

• Management goals based on HRV seek to recreate past environments and then maintain 

those environments in a static condition.

We submit that the use of HRV does not require pristine conditions during the reference 

period, nor does it suggest that it should be our goal in management to recreate all the 

ecological conditions and dynamics of the historical reference period. Complete achievement 

of such a goal would be impractical in many cases given the current and future ecological, 

sociocultural, economic, and political climate. In general, understanding past conditions 

and the natural processes that influenced those conditions, regardless of the level of human 

impact, yields insight into why and how current conditions developed, and what changes 

may be expected in the future. In addition, the use of HRV is not necessarily an attempt to 

simply mimic or recreate the processes that occurred on a site long ago, or to return managed 

landscapes to a single and unchanging past condition. Rather, it is an attempt to improve 

understanding about the ecological context of an area and the landscape-scale effects of 

disturbance in an effort to make existing and future conditions more ecologically sustainable.

2. Are there sufficient data for generating a reliable reconstruction of HRV given the 

following?

• Site-specific data are lacking for most areas, requiring extrapolation from other areas and a 

great deal of expert opinion to fill the knowledge gaps.

• There is insufficient temporal depth of data for most areas, precluding a reconstruction 

of historical conditions for a sufficiently long reference period, and the estimates derived 

from paleo-reconstructions become more uncertain further back in time for several reasons, 

making the analysis of long-term trends difficult.

• The spatial configuration and severity of disturbances are not usually identified with 

confidence from historical data, resulting in a general lack of information about the spatial 

variation of past conditions.

Our understanding of spatial and temporal dynamics in ecological systems will never

be complete and will always be based on imperfect data. Consequently, in applying HRV 

concepts, multiple sources of information are needed, ranging from site-specific data and 

simulation models, to expert opinions and judgments. These disparate types of information 
USDA Forest Service RMRS-GTR-385. 2018 7

allow for the forming and testing of hypotheses about how HRV concepts can best be applied 

to managing ecological systems. Of course, HRV results must be interpreted within the 

scope and limitations of the information sources, but the ultimate issue is whether we are 

able to make more informed decisions with or without an HRV based on the incomplete and 

imperfect data.

3. Is HRV practical for managing dynamic, especially nonequilibrium, systems in today’s 

society given the following?

Management systems are not geared to managing moving targets or coping with the

uncertainty and surprise that are inherent and fundamental aspects of dynamic ecological 

systems. Consequently, managing dynamic systems will always be difficult and fraught with 

surprises. 

Even in those cases where there is sufficient ecological understanding of how to manage

the processes that drive dynamic systems, there may be insufficient social or political will to 

maintain or restore these processes given people’s unwillingness to accept high uncertainty in 

the protection of life and property.

Even if there is sufficient ecological understanding and sociopolitical will to manage

for variability, HRV derived under dynamic equilibrium conditions may not help us manage 

future landscapes under nonequilibrium conditions being driven by rapidly changing climate 

and human land use.

HRV is not a panacea for dealing with uncertainty and surprises, but no other approach 

will eliminate the uncertainties and surprises that are inherent in all dynamic systems. HRV 

merely helps to understand and communicate these uncertainties as “normal” behavior of 

the system. Moreover, using the concept of HRV to discuss planned forest management with 

the public can put change into contex. The concept of HRV can help people to understand 

and expect the changes brought about by disturbance events and to view them as important 

and integral to the resiliency of the system. Last, despite the possibility of novel and 

nonequilibrium conditions in the future, characterizing HRV can nonetheless help to determine 

when those future conditions might be forcing the system to operate outside its natural range 

of variability. This knowledge can either aid in building expectations for different future 

outcomes or serve as a “call to arms” for proactive management to promote greater resiliency.

Why Simulation Modeling?

Range of variability analyses have been conducted by using literature searches

exclusively, including within the Sierra Nevada (e.g., Safford and Stevens 2017). Results of 

such analyses depend on the assumption that an aggregation of many small studies is sufficient 

to address long-term, broad-scale questions, and require researchers to accept many unknowns 

about research methodologies. Moreover, in landscapes severely impacted by European 

settlement, such as those of the northern Sierra Nevada, we can never observe trajectories in 

which fire suppression is not part of the equation (Keane 2012). In the absence of consistent 

and complete data, simulations can be used to incorporate the data available and generate new 

datasets of otherwise unobservable landscape trajectories (Keane et al. 2009; Mladenoff and 

Baker 1999; Swetnam et al. 1999). From these new datasets, statistical analyses can be used 

to describe the landscape quantitatively and subsequently make inferences about the HRV of 

an area, as well as compare current conditions to the HRV. Within the western United States, 

the Rocky Mountains and Oregon Coast Range in particular have been the focus of several 

simulated HRV studies. To our knowledge only one has been conducted in the Sierra Nevada, 

and it took place in Sequoia National Park in the southern Sierra (Miller and Urban 1999).

Models for simulating HRV (and NRV in general), as well as FRV, have proliferated since 
8 USDA Forest Service RMRS-GTR-385. 2018

the early 1990s. By 2004, some 45 landscape disturbance and succession models alone had 

been developed (Keane et al. 2004). Many of these, such as SAFE-FORESTS (Sessions et al. 

1997), LANDIS (He and Mladeno 1999), ZELIG-L (Miller and Urban 1999), BFOLDS 

(Perera et al. 2003), SIMPPLLE (Chew et al. 2004), LANDSUM (Keane 2012), and RMLands 

(McGarigal and Romme 2005a,b, 2012) are still in use today. Such models are used to create 

spatially explicit simulations of both of these key forest processes, typically generating a set 

of GIS layers for each timestep of the model that can then be analyzed to quantify trajectories 

and patterns in the disturbance regime and vegetation over time (Gustafson et al. 2010; Keane 

et al. 2004). 

Scope and Limitations

This study relied on computer models, so the scope and limitations of the modeling must 

be understood from the outset.

First, we used a “phenomenological” modeling approach that sought to emulate the 

statistical properties of disturbance and succession processes consistent with the historical 

data. This contrasts with a “mechanistic” modeling approach, which tries to simulate the 

actual physical, chemical, or biological mechanism of the process, such that the outcomes 

are emergent properties of the mechanism governing the process (Gustafson 2013). Of 

course, there are advantages and disadvantages to both approaches. The major advantage of 

the phenomenological approach is that it does not require a complete understanding of the 

mechanisms associated with the processes, requires far fewer model parameters, and can be 

more easily parameterized to reflect the limited observations of real-world behavior (which are 

more often statistical rather than mechanical). The major disadvantage is that the algorithms 

can be somewhat arbitrary, and thus may not have an intuitive ecological interpretation or may 

be viewed as somewhat of a “black box.” The major advantage of the mechanistic approach 

is that if the mechanisms are well understood and parameterized correctly, it allows for the 

projection of landscape changes under novel environmental conditions (e.g., future climate) 

for which we have no observational data. Because we used a phenomenological modeling 

approach, we limited our simulations of wildfire disturbance to reflect the historical and 

modern fire record. In particular, we did not project wildfire disturbance under future climate 

conditions.

Second, the model results should be viewed as “fuzzy” estimates, not as exact answers. 

Models have many uses and advantages over strictly empirical studies, but they are 

fundamentally abstract and simplified representations of reality. This is especially true for 

landscape disturbance-succession models. The processes that drive real landscapes to change 

are far too many and complex to model comprehensively and accurately. Therefore, our 

goal in modeling these systems is to capture the most important drivers well enough that our 

results, in a very general sense, reflect our real-world expectations. Although it is tempting to 

view the results as exact, given their quantitative nature and apparent numerical precision, we 

should resist overinterpreting them.

Third, as long as the model gets it right most of the time, it still can have great utility. The 

results of our model, as with any model, are constrained by the quality of input data, which are 

not perfect. For example, the vegetation cover layer is subject to human interpretation errors 

and objective classification errors, and is further limited by the spatial resolution of the grid. 

Consequently, there will be places where the model gets it wrong, not necessarily because 

the model itself is wrong, but rather because the input data are wrong. Getting it wrong in 

some places, however, should not undermine the utility of the results as a whole. In the end, 

the results should be used and interpreted with the appropriate degree of caution and an 
USDA Forest Service RMRS-GTR-385. 2018 9

appreciation for the limits of the available data. 

Fourth, the model results are subject to change as new scientific understanding or better 

data become available. RMLands, like all landscape disturbance-succession models, requires 

substantial parameterization before it can be applied to a particular landscape. To the extent 

possible, we used local empirical data. However, we also drew on relevant scientific studies, 

often from other geographic locations, and relied heavily on expert opinion when scientific 

studies and local empirical data were not available. Despite our best efforts to incorporate the 

most relevant data and scientific findings, it is clear that we have a very limited understanding 

of the disturbance and succession processes being modeled. This does not undermine the 

utility of the results. The key question is whether the results lead to more informed and thus 

better decisions. Moreover, the model should lead to new insights that might at first seem 

counterintuitive or inconsistent with our limited observations, because the model is able to 

integrate a large amount of data over broad spatial scales and long timeframes in a consistent 

manner and thus provide a perspective not easily obtained via direct observation.

Fifth, the model results reflect landscape dynamics as driven by the selected succession 

and disturbance processes—in our case, wildfire and forest vegetation treatments (both 

mechanical and prescribed fire). The results do not reflect the influence of other disturbance 

processes or all of the complex interactions among them that characterize real landscapes 

and drive the full range of variability. Other kinds of natural disturbances also occur in the 

project area, including insects and disease, windthrow, wild ungulate and beaver herbivory, 

avalanches, and other forms of soil movement, but the impacts of these other natural 

disturbances tend to be localized in time or space and have far less impact on vegetation 

patterns over broad spatial and temporal scales than does fire. In addition, other kinds of 

anthropogenic disturbances also occur, including domestic livestock grazing and both rural 

and urban development. These also tend to be extremely limited within the project area, 

however; they thus also have limited impact on coarse vegetation patterns and dynamics, at 

least within the project area. 

Sixth, the model results pertaining to individual cover types should be interpreted with 

caution for cover types having limited extent within the project area. The Sierra Nevada 

vegetation is extremely diverse and complex in its spatial arrangement of ecological settings 

and conditions; accordingly, the accuracy of maps of each unique ecological setting (i.e., cover 

type) and condition varies considerably. In general, because the model results are statistical in 

nature, confidence in the model results should decline as the extent of a cover type declines. 

Consequently, we limit our interpretation of the results to cover types that extend across 1,000 

hectares (2,500 acres) or more of the project area. 

Last, extrapolating the model results for the upper Yuba River watershed to other 

landscapes should be done with caution. Landscapes are idiosyncratic; in other words, they 

have a unique internal structure and history such that no two landscapes are the same. Our 

general findings pertain to other similar surrounding landscapes and can probably be safely 

extrapolated to some extent. However, to the extent that detailed quantitative results are 

desired for other geographies, a separate modeling exercise should be undertaken.
10 USDA Forest Service RMRS-GTR-385. 2018

PROJECT AREA

Physical Geography

The Sierra Nevada is a major North American mountain range and ecological region,

located east of California’s Central Valley and extending from Fredonyer Pass in the north to 

southern Kern County in the south. Much of the Sierra Nevada is reserved as Federally held 

public land, managed by the Forest Service, Bureau of Land Management, and National Park 

Service. The Tahoe National Forest is located in the northern portion of the Sierra Nevada. 

The project area is located on the northern part of the Tahoe National Forest, on the Yuba 

River and Sierraville Ranger Districts, and encompasses 181,556 hectares (448,625 acres) (fig. 

1). It is defined by a set of three Hydrologic Unit Code level-5 watersheds, the Upper North 

Yuba River, the Middle Yuba River, and the Lower North Yuba River, which are collectively 

referred to in this document as the “upper Yuba River watershed.”

The topography of the project area consists of rugged mountains incised by two major and

a few minor river drainages. Elevation ranges from about 350 to 2,500 meters (1,100–8,200 

feet). The area receives 30–260 centimeters (12–102 inches) of precipitation annually, with 

snow exceeding rain above about 1,750 meters (5,740 feet) elevation (Storer and Usinger 

1963). A summer drought typically persists from May to September, increasing the importance 

of developing a significant snowpack during the winter because snowmelt runoff is a key 

source of soil moisture during the late spring and summer (Minnich 2007). In the Sierra 

Nevada, the heaviest precipitation occurs to the east and north of the San Francisco Bay area; 

our project area is within this region (Van Wagtendonk and Fites-Kaufman 2006). Datasets 

of the 30-year normal (1981–2010) precipitation at 800-meter (2,600-foot) resolution for the 

northern Sierra illustrate that particularly high amounts of precipitation fall across the middle 

elevations of the project area compared to the larger region (Oregon State University 2014). 

This increased moisture contributes to the occurrence of exceptionally productive patches of 

forest (Alan Doerr, Tahoe National Forest, Camptonville, California, personal communication, 

May 2013; Littell et al. 2012).

Land Use

The arrival of Euro-Americans in the 1850s sparked a transformation of this landscape as

people harvested timber, extracted gold using hydraulic mining techniques, and suppressed 

wildfires (Storer and Usinger 1963). Forestry, mining, grazing, and dozens of recreational 

activities, including hunting, mountain biking, and hiking, are all important uses in the project 

area. Fifteen cattle and sheep grazing allotments exist within the project area. In addition, 

57,117 hectares (141,136 acres) inside of the project area have non-Forest Service ownership. 

Many of these lands were privately held, often by timber companies, before the Tahoe was 

created. In addition, many public lands were given to the Central Pacific Railroad in the late 

19th century and subsequently returned to the public land system under the Bureau of Land 

Management, resulting in a “checkerboard” ownership pattern persisting today (fig. 2). Mining 

of gold and other minerals also continues. These activities affect and interact with ongoing 

vegetation succession and disturbance processes in the area (USDA Forest Service 2014).

Although many uses of the forest led to changes in vegetation structure and composition,

logging and wildfire suppression since the early 19th century in combination have altered the 

historical fire regime and vegetation patterns the most (Hessburg et al. 2005; Knapp et al. 

2013; McKelvey et al. 1996; Safford and Stevens 2017; Stephens et al. 2015). The Tahoe 

National Forest has active timber and fire management programs. Clearcutting, shelterwood, 
USDA Forest Service RMRS-GTR-385. 2018 11

Figure 2—Map of land ownership within the upper Yuba River watershed located on the Tahoe 

National Forest. This image also depicts the 10-km (6-mi) buffer around the project area that was 

included in the simulations.

salvage cutting, and plantation management have been major components of timber 

management on the forest in the past. Between 1988 and 2002, timber sales in the Sierra 

Nevada dropped drastically, but on the Tahoe National Forest timber sale levels have 

fluctuated both up and down (although annual sawtimber sold has decreased similarly to other 

Sierran national forests) (USDA Forest Service 2004). Although the area burned in the Sierra 

Nevada has been increasing over the past several decades (Miller et al. 2009b), relatively 

few large fires have impacted the upper Yuba River watershed in the last 100 years (USDA 

Forest Service 1990). Total burned area is low despite fairly high fire starts (both human and 

lightning caused), indicating that suppression efforts have been very successful (USDA Forest 

Service 1990).

Vegetation

Vegetation in the project area is tremendously diverse and changes slowly along an 

elevation gradient and in response to local changes in microclimate and soils, and includes the 

following major ecological zone groupings (Van Wagtendonk and Fites-Kaufman 2006) (see 

also Appendices A and B; capitalized cover types and variants are defined and described in 

the latter):

• Foothill shrubland and woodland—This zone lies directly adjacent to and west of the 

project area. A small part of the buffer that we used around the project area (see HRV Model 

Execution) includes this zone, which is represented by the Oak Woodland cover type. This 

type is characterized by savannas, woodlands, or forests of either monospecific or mixed 

stands of various broadleaf species, including: blue oak (Quercus douglasii), valley oak 

(Q. lobata), interior live oak (Q. wislizenii), canyon live oak (Q. chrysolepis), madrone 

(Arbutus menziesii), California buckeye (Aesculus californica), and bigleaf maple (Acer 

macrophyllum). Foothill pine (Pinus sabiniana) is also an important tree in this zone, and 

chaparral shrubland stands are common as well.
12 USDA Forest Service RMRS-GTR-385. 2018

• Lower montane forest—This zone includes Oak-Conifer Forests and Woodlands and Mixed 

Evergreen Forests at lower elevations, developing into Sierran Mixed Conifer Forests with 

increasing elevation. All three of these cover types are typified by a combination of both 

coniferous and broadleaved trees. 

Mixed Evergreen Forest is characterized on very moist sites by dense stands of tanoak 

(Notholithocarpus densiflorus) and madrone with a variable component of Douglas-fir. In 

mesic sites, Douglas-fir will often dominate, and broadleaf species are dominated by canyon 

live oak and California black oak (Q. kelloggii). In dry sites Douglas-fir is less common, and 

is replaced by patchy stands of foothill pine and (mostly evergreen) oaks (canyon live oak 

and interior live oak), interspersed with chaparral and occasional stands of knobcone pine (P. 

attenuata). Historically, low- and mixed-severity fires were fairly common in this cover type 

(mean fire return interval, FRI = ~30 years), and vegetation would quickly recover even from 

severe fires because the hardwood dominants resprout after fire. 

Oak-Conifer Forests and Woodlands are characterized by ponderosa pine and one or more 

oaks, such as California black oak, interior live oak, or canyon live oak. Lower amounts of 

incense cedar, Douglas-fir, and sugar pine are also found in these forests. Historically, lowseverity fires were extremely common. Fire is integral to the ecology of yellow pines, and this 

cover type is one of the most altered by fire suppression and urban development.

Sierran Mixed Conifer Forests are characterized by multiple conifer and hardwood 

species, including: ponderosa pine, Jeffrey pine (Pinus jeffreyi) (in higher, colder, or drier 

sites and on the east side of the Sierra crest), sugar pine, white fir, Douglas-fir, incense cedar, 

California black oak, canyon live oak, and other broadleaf species. At least three conifers 

are typically present in any given stand. Today white fir and Douglas-fir tend to be the most 

ubiquitous species, especially on moister aspects and drainages. Pines were historically the 

dominant species under the previous frequent low-severity fire regime and are still common 

on south slopes. Pines (primarily ponderosa pine) are present continuously from the OakConifer Forest and Woodland zone below it in elevation. Historically, low- to moderateseverity fires were very frequent in this forest type (mean FRI = ~11–16 years), and fire 

suppression and widespread logging have greatly altered forest structure and composition 

(Safford and Stevens 2017).

• Upper montane forest—This zone is defined by the presence of Red Fir Forests, which 

occur within and above the level of maximum precipitation and maximum snowfall on the 

Sierra Nevada westslope (Potter 1998; Safford and Van de Water 2014). The Red Fir cover 

type is dominated by red fir (A. magnifica), but other species do co-occur, for example 

western white pine (P. monticola) and lodgepole pine (P. contorta ssp. murrayana). On 

xeric sites, Jeffrey pine can occur. Historically, wildfires were less common in these 

forests than those of the lower montane zone (mean FRI = ~40 years), and stands were 

characterized by complex patches of even-aged trees within a single stand arising from 

localized disturbance events. The boundaries between Sierran Mixed Conifer and Red Fir 

Forests are fuzzy, and the types tend to co-occur, particularly in the ecotones. Similarly, Red 

Fir Forests of the upper montane zone blend into the lodgepole pines and subalpine conifers 

of the subalpine forest zone.

• Subalpine forest—This extremely heterogeneous zone is composed of patches of dense 

trees, rock outcrops, meadows and lakes, and open forest. This zone includes two major 

cover types: Lodgepole Pine and Western White Pine. Lodgepole pine is found throughout 

the subalpine zone in a variety of habitats, including in wetter soils, such as along meadow 

edges, or in very rocky soils. Unlike the subspecies of lodgepole pine found in the Rocky 

Mountains, the lodgepole pine subspecies found here does not have serotinous cones. 
USDA Forest Service RMRS-GTR-385. 2018 13

Wildfires in lodgepole pine occur in a mixture of severities, from low-severity underburning 

in drier subalpine stands, to high severity in dense stands in drainages. Mountain hemlock 

(Tsuga mertensiana) is common on north slopes where heavy snows accumulate. Red 

fir is often an associate. Drier slopes support western white pine at lower elevations and 

whitebark pine (P. albicaulis) at higher elevations. Trees often grow as krummholz forms at 

the highest elevations. Wildfire is relatively rare in this cover type (mean FRI > ~100 years). 

Western white pine sometimes occurs in sufficiently continuous patches to be classified 

as its own type, separate from the Subalpine Conifer group. Typified by western white pine, 

species from the Subalpine Conifer and Red Fir cover types sometimes co-occur as well. 

This cover type tends to occur on drier soils. Most fires are low severity, which promotes the 

development of late-successional forests.

• Eastside forest and woodland—Although the project area does not include any lands east of 

the Sierra crest, the buffer around the project area created for purposes of the simulation (see 

HRV Model Execution) does, and we classified and described the Eastside Yellow Pine cover 

type to capture most of this vegetation community. It is characterized by Jeffrey pine (with 

some ponderosa pine), but other conifers and oaks, as well as western juniper (Juniperus 

occidentalis) may occur. Under historical conditions, wildfires were extremely common and 

were almost always low severity; fire is integral to the ecology of yellow pines. Oak-Conifer 

Forests and Woodlands, as described previously, are also present in this zone. Also east of the 

crest are two shrub community types typified by either big sagebrush (Artemisia tridentata) or 

a mixture of black sagebrush (A. arbuscula) and low sagebrush (A. nova).

• Other cover types and variants—Some cover types not already listed can be found in 

any zone. Montane riparian vegetation occurs throughout the project area and is usually 

dominated by willow (Salix spp.), alder (Alnus spp.), and cottonwood (Populus spp.). 

Curl-leaf Mountain Mahogany (Cercocarpus ledifolius), a shrubland cover type, is most 

common in the upper montane zone and above, but may occur in the lower montane zone 

as well. Several of the major forest cover types described earlier include variants growing 

on ultramafic (“serpentine”) soils, which are characterized by low levels of macronutrients 

and high levels of toxic heavy metals (Alexander et al. 2006). Vegetation on ultramafic 

soils is almost always more open than on neighboring, more fertile substrates and therefore 

supports a more diverse understory. Chaparral stands are common on ultramafic sites, but 

open forests are also found, dominated by Jeffrey pine, incense cedar, and other conifers 

usually at low density. Many endemic species grow on ultramafic soils (Safford et al. 2005), 

and biomass and fuels accumulation is lower than on other soil types (DeSiervo et al. 2015). 

Several of the major forest cover types include an aspen (Populus tremuloides) variant that 

is seral to the corresponding conifer forest type in the absence of fire, thereby maintaining 

the aspen component. 
14 USDA Forest Service RMRS-GTR-385. 2018

METHODS

Historical Range of Variability and Departure

RMLands Overview

To simulate fine-grained disturbance and succession processes representative of the 

historical reference period within the project area, we used the RMLands (Rocky Mountain 

Landscape Simulator) landscape disturbance-succession model. RMLands was originally 

developed to simulate HRV of forests in southwestern Colorado (McGarigal and Romme 

2005a,b, 2012), but it has been similarly applied to forests in the northern Rocky Mountains in 

Montana (Cushman et al. 2011). Here, we adapted the software for use in the Sierra Nevada. 

It is beyond the scope of this document to fully describe RMLands (a detailed technical 

document is forthcoming). Here, we provide only a brief overview of the model structure and 

a brief description of the major components pertaining to the simulation of natural disturbance 

and succession processes:

• Grid-based—The data format is a grid or raster. Each cell or pixel has a suite of attributes 

that record its biophysical condition and disturbance history over time. Disturbance and 

succession act to change these attributes over time. The grain (cell size) and extent of the 

grid is limited only by computer memory. 

• Spatially explicit—Location matters, particularly in the disturbance model, in which 

disturbance events are modeled as a contagious process—that is, one that spreads from cell 

to cell. For example, the location of a cell will affect whether it gets disturbed during an 

event because the disturbance may spread faster uphill, in the direction of the prevailing 

wind, or through more susceptible vegetation.

• Stochastic—Each step of the disturbance and succession processes has a stochastic 

component that adds uncertainty to the outcome. Consequently, the model outcomes from 

replicate simulations will never be exactly the same. As a result, the specific outcomes are 

not intended to be predictive of what will actually happen in any particular place at any 

point in time. Rather, the outcomes are intended to be representative, in a statistical sense, 

of the kinds of outcomes that could happen.

• Process-oriented—The model simulates disturbance and succession as processes. 

Specifically, the model treats each process as having a series of steps that reflect our 

understanding of how these processes play out in the real world. For example, disturbance 

events are initiated, spread, terminate, and then affect the vegetation. 

• Phenomenological—The model seeks to emulate the statistical properties of disturbance 

and succession processes, in other words produce simulated behavior that is consistent with 

the statistical properties of real-world observations (e.g., mean disturbance patch size, mean 

return interval). In this statistical or phenomenological modeling approach, the mechanics 

of the process are deemed unimportant so long as the observed statistical properties of 

the phenomenon are emulated well. For example, the actual mechanism by which a fire 

of a given intensity determines whether a tree (or stand) is killed (i.e., severity) is not 

represented explicitly in the algorithm. Instead, the algorithm is designed to produce the 

desired result of, say, a 20-percent chance of high severity. 

Given this overall modeling structure, succession occurs at the beginning of each timestep 

in the simulation and represents the gradual growth or development, or both, of vegetation 

communities over time. Succession is implemented with a stochastic state-and-transition 

approach in which vegetation cover types transition probabilistically between discrete states 

(seral stages). Transition pathways and rates of transition between states are defined uniquely 
USDA Forest Service RMRS-GTR-385. 2018 15

for each cover type and can be conditional on any number of biophysical and disturbance 

history attributes of a cell tracked by the model. We forced succession to occur in patches, 

defined as spatially contiguous cells having the same cell attributes (e.g., identical disturbance 

history and age). Succession patches represent something akin to forest stands. Most cover 

types progress through a series of seral stages (states) over time as a result of successional 

processes (albeit at different rates due to the stochastic nature of succession). In some cases, 

these transitions are affected by the occurrence of certain disturbances (e.g., low-severity fire) 

or are regulated by management (e.g., silviculture). Other cover types (e.g., Meadow, Barren, 

Water) are treated as nonseral and remain in the same state over time (i.e., they are static).

Disturbance follows succession in each timestep in the simulation. RMLands uses a 

generic disturbance algorithm that can be meaningfully parameterized to represent a variety 

of natural disturbances, including fire, insects and pathogens, and wind, although only fire 

was simulated in this project. Each disturbance process is implemented separately, but affects 

and is affected by other disturbance processes operating concurrently to produce changes 

in landscape conditions. The common disturbance algorithm consists of the following key 

components:

• Climate—Climate can play a significant role in determining the temporal and spatial 

characteristics of the disturbance regime. Climate is specified as an optional global 

parameter that can affect initiation, spread, and mortality for all disturbance events within 

a timestep. Climate can be specified as constant with a user-specified level of temporal 

variability, a trend over time (with variability), or a user-defined trajectory reflecting the 

climate conditions during a specific reference period.

• Initiation—Disturbance events are initiated at the cell level. Each cell has a relative 

probability of initiation in each timestep that is a function of its susceptibility to the 

disturbance, which can be specified as a function of any of the spatial attributes of the 

cell (e.g., cover type, seral stage, time since last disturbance, and topographic position). 

However, the overall initiation rate is governed by a single global parameter that determines 

the number of disturbance attempts per unit area.

• Spread—Once initiated, disturbance spreads to adjacent cells (or nearby cells via 

“spotting”) in a probabilistic fashion. Each cell has a probability of spread during each 

iteration of the spread process that is a function of its susceptibility to disturbance (as 

defined in the preceding paragraph), which can be further modified in a variety of ways 

including, for example, its relative position (e.g., relative elevation or wind direction), the 

influence of potential barriers (e.g., roads and streams), and the maximum size of the event. 

• Termination—An event terminates either due to the probabilistic spread (i.e., fails to spread 

further by chance due to the low spread probabilities in adjacent cells) or because of a 

limit placed on spread to reflect variable weather conditions at the time of the event. This 

event modifier limits the final size of the disturbance and is specified as a user-defined size 

distribution.

• Mortality—Following spread, each cell is evaluated to determine the magnitude of 

ecological effect of the disturbance. Each cell can exhibit either high mortality (>75 percent 

of the dominant overstory plants are killed) or low mortality.

• Seral stage transition—Following mortality, each cell is evaluated for potential immediate 

transition to a new seral stage (state). Transition pathways and rates of transition between 

states are defined uniquely for each cover type and can be conditional on any attribute of 

the cell. Note: These immediate disturbance-induced transitions are differentiated from the 

successional transitions that occur at the beginning of each timestep in response to gradual 

growth and development of vegetation over time.
16 USDA Forest Service RMRS-GTR-385. 2018

HRV Spatial Input Data

We compiled several spatial input data layers needed for the HRV simulation. All layers 

were represented as 30-meter (100-foot) rasters. See Appendix A for a detailed description of 

these layers. We provide a brief description of three important layers here.

• Cover type—We developed a system of land cover classification based on LandFire (2007) 

biophysical settings and the presettlement fire regimes of Van de Water and Safford (2011), 

crosswalked to the Forest Service corporate spatial data based on the North Sierran CALVEG 

classification using the Forest Service Region 5 existing vegetation layer (EVeg). We also 

considered information from the updated A Guide to Wildlife Habitats of California (Mayer 

and Laudenslayer 1988), popularly known as the “Wildlife Habitat Relationship” cover types. 

Our classification included 31 cover types, although many of these were variants of the major 

types based on microclimate (mesic versus xeric) and soils (ultramafic) (table 1). Cover types 

included both potential natural vegetation types (i.e., the expected vegetation community 

Table 1—Cover types in the upper Yuba River watershed project area, including the formal project area and the simulation area 

(project area plus 10-km [6-mi] buffer), given in rank order of extent within the core project area.

Cover type Abbreviation

Project 

area (ha)

Project 

area (ac)

Simulation 

area (ha)

Simulation 

area (ac)

Sierran Mixed Conifer – Mesic SMC_M 57,853 142,955 133,920 330,916

Sierran Mixed Conifer – Xeric SMC_X 52,198 128,981 91,443 225,956

Oak-Conifer Forest and Woodland OCFW 23,279 57,522 56,987 140,815

Red Fir – Mesic RFR_M 8,563 21,159 19,626 48,496

Red Fir – Xeric RFR_X 7,493 18,515 9,989 24,683

Mixed Evergreen – Mesic MEG_M 7,273 17,972 13,548 33,477

Mixed Evergreen – Xeric MEG_X 6,768 16,724 13,774 33,036

Sierran Mixed Conifer – Ultramafic SMC_U 4,124 10,190 9,774 24,152

Watera WAT 4,058 10,027 8,157 20,156

Barrena BAR 2,665 6,585 8,751 21,624

Grasslanda GRASS 1,379 3,408 4,617 11,409

Meadowa MED 1,201 2,968 3,435 8,488

Oak-Conifer Forest and Woodland – Ultramafic OCFW_U 1,060 2,619 2,185 5,399

Lodgepole Pine LPN 837 2,068 2,816 6,958

Montane Riparian MRIP 732 1,809 2,216 5,476

Subalpine Conifer SCN 638 1,576 2,044 5,051

Mixed Evergreen – Ultramafic MEG_U 604 1,492 1,655 4,090

Red Fir – Ultramafic RFR_U 294 726 321 793

Western White Pine WWP 273 675 510 1,260

Urbana URB 114 282 782 1,932

Sierran Mixed Conifer with Aspen SMC_ASP 58 143 121 299

Red Fir with Aspen RFR_ASP 31 77 34 84

Oak Woodland OAK 19 47 4,192 10,358

Curl-Leaf Mountain Mahogany CMM 18 45 41 101

Agriculturea AGR 16 40 5,416 13,383

Lodgepole Pine with Aspen LPN_ASP 8 20 31 77

Yellow Pine YPN 0 0 10,499 25,943

Big Sagebrush SAGE 0 0 1,600 3,954

Subalpine Conifer with Aspen SCN_ASP 0 0 6 15

Black and Low Sagebrush LSG 0 0 5 12

Yellow Pine with Aspen YPN_ASP 0 0 3 7

Total 181,556 448,625 408,498 1,009,399

a

 Cover types treated as nonseral.
USDA Forest Service RMRS-GTR-385. 2018 17

in the presence of natural disturbance regimes, such as Sierran Mixed Conifer) and current 

anthropogenic cover types (e.g., Agriculture). The anthropogenic cover types were retained 

for the HRV simulation because we had no way to predict the potential natural vegetation 

type for these areas with any certainty. We assigned each cell to a single cover type, which we 

treated as static (i.e., constant over time) during the simulation; cover type served as a fixed 

spatial template upon which disturbance and succession processes played out over time.

• Seral stage—We classified each cover type, except those treated as nonseral such as 

Barren and Meadow (table 1), into seral stages based on a combination of developmental 

stage (early, mid-, late development based on expected tree size of the dominant and 

codominant individuals), and canopy cover class (open, moderate, and closed canopy based 

on percent canopy cover from above) derived primarily from the EVeg layer (table 2). 

The specific tree sizes and percent canopy cover for each seral stage varied among cover 

types as given in Appendix B. Our classification was similar to LandFire except that we 

added a moderate canopy cover stage to the mid- and late-development stages. In addition, 

we created a unique classification scheme for the Mixed Conifer-Aspen cover types. We 

initially assigned each cell to a single seral stage, but unlike with cover type, we treated 

seral stages as dynamic (i.e., changing over time) in response to simulated succession and 

disturbance. Because RMLands does not model individual trees, we used age in the model 

as a proxy for tree size to determine transitions from one developmental stage to the next. 

The combination of cover type and seral stage formed the primary basis for characterizing 

vegetation patterns and dynamics. 

• Topographic position index—We created a topographic position index (TPI) that 

combined heat load, which was based on aspect and slope, with slope position. We scaled 

the index to the project area (including the buffer; see HRV Model Execution) such that 

the values were relative to topography of the project area. We assigned a TPI value to 

each cell, which was of course treated as static during the simulation. High values of TPI 

were associated with locations on steep, south- and west-facing, upper slopes. Low values 

were associated with locations on gentle, north- and east-facing, lower slopes, and valley 

bottoms. Values in between occurred along a gradient of these characteristics. We used 

TPI to affect wildfire susceptibility and mortality as described next. 

Table 2—Seral stages defined for cover types in the upper Yuba River watershed. Seral stages 

represent a combination of vegetation development stage (based on size of the dominant and 

codominant individuals) and canopy cover class. The tree sizes and percent canopy cover for each 

seral stage varied among cover types as given in Appendix B.

Seral stage Abbreviation

Nonseral NS

Early - all structures ED

Mid-open MDO

Mid-moderate MDM

Mid-closed MDC

Late-open LDO

Late-moderate LDM

Late-closed LDC

Early - Aspen ED-A

Mid - Aspen MD-A

Mid - Aspen and Conifer MD-AC

Late - Conifer and Aspen LD-CA
18 USDA Forest Service RMRS-GTR-385. 2018

HRV Model Parameterization

RMLands has many model parameters that govern the disturbance and succession 

processes, and these must be specified by the user to represent a particular scenario. For the 

HRV scenario, we parameterized the model to reflect our best understanding of the succession 

and disturbance processes characteristic of the historical reference period in the project area. 

Most of the model parameters were associated with state-and-transition models defined for 

each of the cover types, although some parameters were global and applied to all cover types. 

Here, we describe the global parameters and the general parameterization of the state-andtransition models; see Appendix B for the detailed parameterization of each cover type model.

Each state-and-transition model defined the alternative states (i.e., seral stages) of the 

cover type and the rules for transitioning among states due to both succession and disturbance 

processes. Not all cover types had the seven-state model depicted in figure 3. The Mixed 

Conifer-Aspen cover types had either a three-state model (Subalpine Conifer with Aspen, 

Yellow Pine with Aspen) or five-state model (Lodgepole Pine with Aspen, Red Fir with 

Aspen, and Sierran Mixed Conifer with Aspen). The shrubland cover types (Black and Low 

Sagebrush, Curl-leaf Mountain Mahogany, and Big Sagebrush) all had a three-state model. In 

general, however, our state-and-transition models followed those defined by the corresponding 

LandFire models, except for the additional moderate-canopy cover state. 

The state-and-transition model for each cover type included a set of rules that governed 

the succession process. In general, these rules specified how long a cell remained in a seral 

stage, subject to certain contingencies, before probabilistically transitioning to the next 

seral stage. We used LandFire models to determine the initial transition probabilities, but 

we subsequently evaluated and refined these probabilities with input from local experts and 

current literature to capture subtle changes in succession at the project scale. Four important 

characteristics of the final succession rules are worth noting:

Early

development

Mid

development

75-100%

Canopy cover

Mid

development

40-75%

Canopy cover

Mid

development

0-40%

Canopy cover

Late

development

75-100%

Canopy cover

Late

development

40-75%

Canopy cover

Late

development

0-40%

Canopy cover

High-mortality fire

Low-mortality fire

Succession

Figure 3—Generic state-and-transition model for forested cover types. Each box represents one 

of the seven standard seral stages (or states) for a forested cover type. Each column of boxes 

represents a stage of vegetation development. Each row of boxes represents a different level of 

canopy cover. Transitions between seral stages may occur as a result of high-mortality fire, lowmortality fire, or succession, each of which is indicated by a colored arrow between boxes. 
USDA Forest Service RMRS-GTR-385. 2018 19

1. Succession transitions between developmental stages (from early to mid- to late

development) were governed by development age (i.e., how long the cell was in the 

current developmental stage) because, as noted previously, RMLands uses age as a proxy 

for tree size. For example, if a cell was in the early-development stage for, say, 20 years, it 

was given some probability of transitioning to the mid-development stage. 

2. Succession transitions between canopy cover states (from open to moderate to closed

canopy) were governed by a combination of seral stage age (i.e., how long the cell was in 

the current seral stage) and the time since the last low-mortality fire. For example, if a cell 

was in the mid-development, open-canopy cover seral stage for, say, 20 years without the 

occurrence of low-mortality fire, it was given a probability of transitioning to the middevelopment, moderate-canopy cover seral stage. 

3. All transitions were probabilistic once minimum required conditions were met (e.g., seral

stage age and time since low-mortality fire). For probabilities less than 1, it was therefore 

possible for a cell to remain in a state for many timesteps. As a whole, cells eligible for 

transition did so gradually. This allowed us to capture the range of postfire behaviors, 

from rapid tree growth to the presence of a multidecade chaparral stage.

4. Succession pathways were sometimes branching; that is, one seral stage transitioned with

some probability to two or more different seral stages. For example, in some models if a 

cell was in the early-development seral stage for some years it was given some probability 

of transitioning to either the mid-development open-canopy, moderate-canopy, or closedcanopy seral stage. As a result, some percentage of the cells would transition into each of 

the mid-development canopy cover seral stages, perhaps reflecting the relative likelihood 

of successful establishment of trees following disturbance.

The state-and-transition model for each cover type included several parameters that

governed the occurrence of wildfire in that cover type as well as a set of rules that governed 

the disturbance-induced seral stage transitions (i.e., immediate transitions between seral stages 

caused by the disturbance). 

• Climate—The climate modifier was governed by a global parameter (C) that operated 

across all cover types within a timestep (t); thus, a single climate value was assigned to each 

timestep and it affected the rate of wildfire initiation, spread, and mortality. The climate 

parameter was based on a rescaling of the Palmer Drought Severity Index (PDSI). PDSI is 

a long-term measure of drought, on the scale of months to years. It is based on precipitation 

and temperature and incorporates soil moisture. Reconstructed PDSI values for the summer 

months during the historical reference period (1550–1850) are available from the National 

Oceanic and Atmospheric Administration (www.ncdc.noaa.gov/paleo/pdsi.html). We used 

datasets from Zhang et al. (2004) and Cook et al. (2004). These data are summarized at 

broad scales; for example, the Cook et al. (2004) data are calculated for a grid with points 

spaced at 2.5°. We selected the five points closest to the center of the project area from 

these two datasets and calculated the inverse distance-weighted mean of the values. We 

then converted the yearly data into 5-year averages to align with the 5-year timesteps in 

our model, centered the mean value on 1, rescaled the values to range between about 0.5 

and 1.5, and took the inverse so that values less than 1 represented wetter-than-normal 

timesteps, and values greater than 1 represented drier-than-normal timesteps (fig. 4).

• Susceptibility—Susceptibility (S), which affected both wildfire initiation and spread, was 

determined by two major factors: (1) topographic position (T), and (2) fuel characteristics 

(F). Specifically, the S of the i

th cell in the current timestep (t) was determined by the 

product of these two factors, each represented as probabilities (0–1):

Sit = Ti x Fit

Topographic position was computed as a logistic transformation of TPI and was treated as 

having a constant (over time) and universal effect on all cells of certain cover types (table 

3) regardless of seral stage or disturbance history. We allowed topographic position to affect 
20 USDA Forest Service RMRS-GTR-385. 2018

Figure 4—Climate modifier parameter derived from the Palmer Drought Severity Index, rescaled, 

inverted, and represented as a 5-year average for the historical reference period (ca. 1550–1850).

Lodgepole Pine Red Fir – Mesic

Mixed Evergreen – Mesic Red Fir – Xeric

Mixed Evergreen – Xeric Red Fir – Ultramafic

Mixed Evergreen – Ultramafic Sierran Mixed Conifer – Mesic

Oak Woodland Sierran Mixed Conifer – Xeric

Oak-Conifer Forest and Woodland Sierran Mixed Conifer – Ultramafic

Oak-Conifer Forest and Woodland – Ultramafic Western White Pine

Yellow Pine

Table 3—Cover types whose susceptibility to fire and mortality following fire was modified by 

topographic position (based on the topographic position index).

susceptibility for cover types that occurred across a broad range of topographic positions. 

For unaffected cover types, T was set to 1 and thus had no effect on S. For affected cover 

types, all other things being equal, susceptibility decreased by 30 percent as the TPI 

decreased over its full range (+3 to -3) according to the four-parameter logistic function 

depicted in figure 5. However, because the bulk of the landscape varied over a much smaller 

range of TPI values, the effect on susceptibility was typically much less than 30 percent. 

Thus, topographic position had no effect on susceptibility when TPI was maximum (i.e., on 

steeper, south- and west-facing, upper slopes), but acted to reduce susceptibility by up to 30 

percent when TPI was minimum (i.e., on gentle, north- and east-facing, lower slopes, and 

valley bottoms). There was no empirical basis or published scientific studies that could be 

used directly to parameterize this function; thus, it was based on expert opinion and general 

support in the scientific literature (North 2012; Taylor and Skinner 2003).

Fuels were represented by vegetation cover type, seral stage, and recent disturbance

history, and were treated as having a dynamic (i.e., changing over time) effect on the relative 

susceptibility of a cell to wildfire. Fuels varied among cover types and seral stages in 

relation to the number of years since the last fire according to the function shown in figure 6. 
USDA Forest Service RMRS-GTR-385. 2018 21

Figure 5—Susceptibility (relative probability) of a cell to wildfire as a logistic function of 

topographic position (as measured by the topographic position index; see text for description).

Figure 6—Susceptibility of a cell to wildfire given as a cumulative Weibull function of the 

number of years since the last wildfire, shown here for different mean return intervals and a 

shape parameter of 3.
22 USDA Forest Service RMRS-GTR-385. 2018

Here, we used the cumulative form of the Weibull distribution, which gave the cumulative 

probability of a fire for any number of years since the last fire. Thus, the probability increased 

from 0 immediately following a fire to approaching 1 after a certain number of years since 

the last fire, depending on the specified mean return interval (MRI) and shape parameters 

of the Weibull function. Holding Shape constant, and all other things being equal, as MRI 

increases the curve shifts to the right, resulting in a lower probability for any given number 

of years since the last fire. The shape parameter controls whether the probability of fire 

decreases (Shape <1), stays constant (Shape = 1), or increases (Shape >1) with the time since 

the last fire. We specified a Shape of 3 for all cover types, which represents a moderately 

strong feedback between the time since the last fire (as a proxy for fuel accumulation) and 

susceptibility (Collins and Stephens 2010).

We specified an MRI for each cover type and seral stage based on the corresponding 

LandFire models using the Vegetation Dynamics Development Tool (VDDT) models (LandFire 

2007), as modified by H. Safford and B. Estes (Appendix B). We obtained single probabilities of 

wildfire for each cover type and each seral stage by summing the probabilities of a replacement, 

mixed, and surface fire (given by the corresponding VDDT model) and took the inverse (i.e., 1/

probability of fire) (e.g., table 4). Because we held the shape parameter of the Weibull function 

constant across all cover types and topographic position was a constant, susceptibility of the 

various seral stages was determined by MRI. However, these return intervals should not be 

interpreted literally, as the concept of a return interval does not meaningfully apply to a dynamic 

seral stage. Moreover, these MRIs were derived from the LandFire BpS descriptions and 

modified VDDT models. Taken collectively, these values do not necessarily agree with the target 

fire rotation periods for the cover types (see HRV Model Calibration and Verification), which 

were based on values reported by Van de Water and Safford (2011) and Mallek et al. (2013) and 

expert input from Safford and Estes. Thus, the MRIs assigned to each cover type and seral stage 

should be interpreted as relative values that affected the relative susceptibility of the various 

vegetation states. 

• Initiation—The relative probability of wildfire initiation (I) for the i

th cell in the current 

timestep (t) was determined by the product of susceptibility (S) and climate (C), as follows:

Iit = max{Sit ×Ct

,1}

Thus, the relative probability of initiating a wildfire within a timestep increased across

all cells when the climate modifier was greater than 1 (to a ceiling of 1) and decreased 

across all cells when the climate modifier was less than 1, and otherwise varied among 

cells based on their relative susceptibility as determined by cover type, seral stage, and 

Table 4—Weibull function parameters (mean return interval, and Shape) associated with the 

susceptibility of a cell to wildfire based on fuels (i.e., vegetation cover type, seral stage, and the 

number of years since the last fire) and the probability of a high-mortality wildfire by seral stage 

for the Sierran mixed conifer – Mesic cover type (original values from Vegetation Development 

Dynamic Tool models in parentheses). 

Weibull parameters

Seral stage

Mean return 

interval (years) Shape

Probability of 

high-mortality fire

Early - all structures 44 3 0.67 (1.00)

Mid-open 10 3 0.06 (0.14)

Mid-moderate 13 3 0.09 (0.17)

Mid-closed 19 3 0.16 (0.23)

Late-open 8 3 0.03 (0.08)

Late-moderate 13 3 0.06 (0.14)

Late-closed 34 3 0.19 (0.37)
USDA Forest Service RMRS-GTR-385. 2018 23

time since the last fire. The overall disturbance rate within each timestep was governed 

by a single parameter, which controlled the number of disturbance attempts per unit area. 

This parameter determined how many wildfire events were attempted, but the number that 

actually occurred was determined by the Iit of the selected cells. We treated this overall 

disturbance rate parameter as a calibration parameter, which we tuned to achieve the target 

overall fire rotation period of 30 years (see HRV Model Calibration and Verification).

• Spread—The relative probability of spread (Z) for the i

th cell during the jth wildfire event in 

the current timestep (t) was determined by several factors: susceptibility (S) and climate (C), 

as described earlier, wind direction and strength (W), relative elevation (E), the presence 

of rivers and streams (R) as a potential impediment, and a calibration coefficient for each 

cover type (G). The relative weight (w) of susceptibility, wind, and relative elevation as well 

as the strength of the road or stream effect varied with the size of the event, as described 

later. These factors were combined as follows:

where distance = 1 for the spread to an orthogonal neighboring cell (i.e., shares a full side) 

and distance = 1.4 for the spread to a diagonal neighboring cell. 

Wind was incorporated in two parts. First, a prevailing wind direction for an individual 

wildfire event was selected probabilistically from the eight cardinal directions. To derive the 

wind distribution values, we summarized all available historical wind direction data from six 

local weather stations (Rice Canyon, Saddleback, Downieville, White Cloud, Emigrant Gap, 

and Blue Canyon) (fig. 7) for the fire season (May 15–October 15) and burning period times 

(1,000–1,800 hours) (fig. 8). Data from all weather stations were weighted equally. After the 

wind direction was selected for the particular event, the fire was able to grow in all directions, 

but was relatively more likely to spread with wind than against it (i.e., directional bias). We 

used expert opinion to parameterize the strength of this directional bias, which caused a 

reduction in the probability of spread as the angle increased from the direction the wind was 

blowing (fig. 8). Note that the realized effect of wind on the probability of spread depended on 

the weight assigned to wind and the maximum size of the fire (see Termination).

Relative elevation was defined as the percent slope uphill or downhill from the “burning” 

cell to the focal cell. A positive relative elevation meant that the focal cell was uphill of the 

burning cell. Conversely, a negative relative elevation meant that the focal cell was downhill 

of the burning cell. We used expert opinion to parameterize the strength of the relative 

elevation effect, which caused a reduction in the probability of spread from the maximum (1) 

when the focal cell was uphill at a slope of more than 50 percent from the burning cell (fig. 9). 

Probability of spread decreased for shallower slopes and cells downhill of the burning cell. 

Note that, as with wind, the realized effect of relative elevation on the probability of spread 

depended on the weight assigned to relative elevation and the maximum size of the fire.

Streams were treated as a potential impediment to spread, depending on stream size and 

maximum fire size. We used expert opinion to parameterize the magnitude of the effect, which 

caused a reduction in the probability of spread. Regardless of size, streams had no effect on 

spread for the largest fires, but their effect increased as the fire size decreased and stream 

size increased (fig. 10). Consequently, large streams were usually an effective barrier for the 

smallest fires, whereas small streams had only a minor effect even on the smallest fire.

Spread calibration coefficient is an arbitrary calibration parameter that we used to increase 

or decrease the probability of spread in each cover type. Here, we used it to decrease the 

probability of spread in some cover types to achieve the target overall fire rotation periods (see 

HRV Model Calibration and Verification).
24 USDA Forest Service RMRS-GTR-385. 2018

Figure 7—Weather stations in or near the upper Yuba River watershed. Data from these stations 

were used to inform wind direction parameters for modeling wildfires.

Figure 8—Distribution of wind directions (i.e., direction the wind is coming from) during the fire 

season (May 15–October 15) and burning period times (1,000–1,800 hours) derived from weather 

stations in or near the project area (fig. 7) and the strength of directional bias in spread (0o 

represents the direction the wind is blowing, which is opposite of the wind direction).
USDA Forest Service RMRS-GTR-385. 2018 25

Figure 9—Effect of relative elevation (i.e., percent slope uphill or downhill from a burning cell) 

on the probability of wildfire spread.

Figure 10—Effect of streams by size class on the probability of wildfire spread as a function of 

fire size.
26 USDA Forest Service RMRS-GTR-385. 2018

We also allowed for “spotting” during spread, that is, the fire leaping ahead and initiating 

in cells ahead of the advancing front. Specifically, during each iteration of the fire spread there 

was a Bernoulli probability of spotting (i.e., yes or no) that varied with fire size (fig. 11). If 

the Bernoulli trial was successful, a cell on the fire front was selected at random to serve as 

the source of the spotting. Then a spotting distance was selected at random from a Uniform 

distribution between 1 and a maximum spotting distance that also varied with fire size (fig. 

11). Next, the cell at the randomly selected distance from the source cell and in the direction 

the wind was blowing for this particular event was subjected to a Bernoulli trial based on its 

probability of initiation. If initiation was successful, the cell became part of the active fire 

front for the next spread iteration. We used expert opinion to parameterize spotting so that as 

fires became larger, their probability of spotting and spotting distance increased as depicted in 

figure 11. Thus, spotting was important only for the largest fires, where its main effect was to 

facilitate fire spread past potential barriers such as large rivers, barren areas, and water bodies. 

A complete description of the spread algorithm is beyond the scope of this document. 

Briefly, based on the parameterization just described, after a successful initiation, fire spread 

contagiously to adjacent cells based on their probability of spread (Zijt) and occasionally 

spotted ahead of the burning front. Fire continued to spread in an iterative manner outward 

from all “burning” cells until either it stopped by chance due to low spread probabilities or it 

reached the randomly selected maximum fire size (see Termination). As given in the spread 

equation, the probability of spreading from a “burning” cell to an adjacent cell was largely 

determined by the maximum of the cell’s current susceptibility (Sit) (which was a function 

of topographic position and fuels as determined by cover type, seral stage, and time since 

last fire) and a weighted combination of current susceptibility, wind, and relative elevation 

(wj

v

Sit+wj

wWij+wje

Eij), where the weights varied as a function of the randomly selected 

maximum fire size for the current event. We parameterized these spread weights such that the 

larger the selected maximum fire size (see Termination), the more weight that was given to 

wind and relative elevation (fig. 12). For the largest fires (~100,000 hectares; 250,000 acres), 

wind was the dominant factor determining the local probability of spread. Rivers and streams 

(Rij) were incorporated as a multiplier such that they acted to suppress the probability of 

spread across a stream, but with the strength of the effect decreasing with increasing fire size 

(fig. 10). Next, the climate modifier (Ct

) was incorporated as a multiplier such that it acted to 

increase (to a ceiling of 1) or decrease the probability of spread for all cells within the current 

timestep (t). Last, the spread calibration coefficient for each cover type (Gi

) was incorporated 

as a multiplier so as to variably decrease the probability of spread through some cover types 

in order to achieve the target overall fire rotation periods for each cover type.

Termination—The spread of an individual wildfire event was terminated when either 

it stopped by chance due to low spread probabilities or it reached a randomly selected 

maximum fire size. In this context, maximum fire size functioned as a surrogate for weather 

conditions (e.g., rain event) that limit the spread of a fire regardless of the underlying 

fuel conditions. We determined the maximum fire size distribution by analyzing the size 

distribution of all mapped fires in the North Sierran CALVEG mapping zone and west of the 

Sierran crest, available from the Forest Service and the California Department of Forestry and 

Fire Protection, Fire and Resource Assessment Program for 1908–2010 (fig. 13). Although 

these data do not stem from the historical reference period, local experts agreed that they were 

an acceptable representation of the expected fire size distribution for the reference period with 

the exception that they do not contain the very large fires that probably occurred during the 

historical reference period. 
USDA Forest Service RMRS-GTR-385. 2018 27

Figure 11—Probability of wildfire “spotting” ahead of the burning front and the spotting distance 

as a function of maximum fire size.

Figure 12—Spread weights assigned to susceptibility, wind, and relative elevation factors as a function 

of maximum fire size. Spread weights sum to 1 across factors for a given maximum fire size.
28 USDA Forest Service RMRS-GTR-385. 2018

Figure 13—Estimated historical fire size distribution of wildfires occurring between 1900 and 

2010 in the northern Sierra Nevada west of the Sierran crest (from USDA Forest Service and 

California Department of Forestry and Fire Protection, http://frap.fire.ca.gov/projects/fire_data/

fire_perimeters_index).

Mortality—Mortality was defined as the level of mortality of the dominant overstory 

vegetation caused by the disturbance (i.e., fire severity), and it was classified as either high 

(>75 percent mortality) or low (≤75 percent mortality) at the cell level. Although researchers 

have differed on whether 75 percent or 95 percent overstory tree mortality is a more 

appropriate cutoff point for defining a “stand-replacing” event (Fulé et al. 2014; Mallek et al. 

2013), we used 75 percent as our cutoff as it is widely accepted in the literature (Agee 1993, 

2007; Baker 2014; Miller et al. 2009a). Importantly, RMLands does not classify individual 

fires as a whole by “low,” “mixed,” or “high” severity status. Some fire ecologists combine 

fire attributes such as flame length and fire size into their interpretation of the relative 

“severity” of a particular fire (e.g., Agee [1993]). Ecologists working at other scales and not 

working with models often describe “mixed severity” regimes (e.g., Kane et al. [2013]), 

which Collins and Stephens (2010) define as “stand-replacing patches within a matrix of 

low to moderate fire induced effects.” At the 30-meter cell size of our application, nearly all 

fires would be classified as “mixed severity” by the prior definition. Instead, we focused on 

defining conditions under which transitions among potential states within a given cover type 

occurred or not. All burned cells were evaluated probabilistically and assigned to a highmortality outcome or not. All nonhigh-mortality outcomes were considered low mortality. 

If a cell burned at high mortality, then it transitioned to the early-seral stage; otherwise, it 

either remained in the same seral stage or transitioned to another seral stage based on a set of 

disturbance transition rules.

The probability of a high- versus low-mortality response to fire was determined by four

major factors: (1) topographic position (T), (2) disturbance history (D), (3) vegetation (V), and 

(4) climate (C). Specifically, the probability of a high-mortality response following wildfire for 

the i

th cell in the current timestep (t) was determined by the product of these four factors (the 
USDA Forest Service RMRS-GTR-385. 2018 29

first three factors were each represented as probabilities, 0–1), but with a ceiling of 1 because 

C was not a probability and thus the product could exceed 1:

Pit = max{Tit ×Di ×Vit ×Ct ,1}

Vegetation was represented by vegetation cover type and seral stage. We specified an 

overall probability of high-mortality response to fire for each cover type and seral stage. To 

derive these probabilities, we used the LandFire VDDT models, as modified by Safford and 

Estes (Appendix B). Specifically, from the VDDT models, we used the probabilities of a 

transition to the early-seral stage. We ignored the classified type of fire (given as replacement, 

mixed, or surface), focusing instead on the outcome of fire in terms of the seral stage, if any, to 

which a cell transitioned after fire. High-mortality fires were those that resulted in conversion 

to early-seral stage (regardless of whether they were called replacement or mixed-severity 

fire). All other fires were considered low mortality. The probability of a high-mortality 

outcome from fire was calculated by dividing the summed probabilities of high-mortality fires 

as defined earlier by the summed probabilities of all fires (e.g., table 4).

Topographic position was computed as a logistic transformation of TPI as described 

previously (fig. 5) and applied only to certain cover types (table 3). As before, topographic 

position had no effect on mortality when TPI was maximum (i.e., on steeper, south- and westfacing, upper slopes), but acted to reduce high mortality by up to 30 percent when TPI was 

minimum (i.e., on gentle, north- and east-facing, lower slopes, and valley bottoms). Note that 

because T was incorporated as a multiplier, it acted to depress the probability of high mortality 

from the nominal value set by the vegetation factor (V) described in the preceding paragraph.

Disturbance history was computed as a logistic transformation of the age (years) since 

the last fire. We applied disturbance history only in forested cover types; for nonforested 

cover types this parameter was set to 1 and thus had no effect. We varied the effect between 

low-elevation forest cover types and high-elevation forest cover types according to the threeparameter logistic function depicted in figure 14. Specifically, for low-elevation cover types 

the effect of burning on subsequent mortality was shorter lived, with the inflection occcurring 

at 15 years and a slope such that after 25 years there was no residual effect. For high-elevation 

cover types (e.g., Red Fir, Lodgepole Pine, and Subalpine Conifer), the effect was longer 

lived, with the inflection occurring at 20 years and a slope such that after 40 years there was 

essentially no residual effect. Note that because D was incorporated as a multiplier, it acted to 

depress the probability of high mortality from the nominal value set by the vegetation factor 

(V) described earlier. Thus, for a cell of low-elevation forest, the probability of high mortality 

was depressed to 0.1 times its nominal value immediately following a fire; at 15 years after 

the fire the probability was depressed by 0.5, and after about 25 years the probability of high 

mortality was back to its nominal value.

Climate (C) acted globally to modify the probability of high mortality within each timestep. 

Thus, the probability of a high-mortality outcome within a timestep increased across all cells 

when the climate modifier was greater than 1 (to a ceiling of 1), decreased across all cells when 

the climate modifier was less than 1, and varied among cells based on their relative probabilities 

of high-mortality fire as determined by topographic position and vegetation. 

• Seral stage transitions—Following the mortality determination, each cell was evaluated 

for potential immediate transition to a new seral stage (state). Transition pathways and 

rates of transition between states were defined uniquely for each cover type and coded in 

a set of disturbance transition rules analogous to the succession transition rules described 

previously. In general, these rules specified the probability of transitioning to a new seral 

stage following high- and low-mortality fire. A high-mortality fire always caused a transition 

to the early-seral stage (probability = 1). The outcome of a low-mortality fire varied among 
30 USDA Forest Service RMRS-GTR-385. 2018

cover types and seral stages, but generally resulted either in no change or in a transition to a 

more open-canopy seral stage (within the same developmental stage). We used the LandFire 

models, as modified by Safford and Estes (Appendix B), to determine the transition 

probabilities following low-mortality fire. Specifically, we used the probabilities of a 

transition to a more open-canopy seral stage or of no transition. We ignored the classified 

type of fire (given as replacement, mixed, or surface), focusing instead on the outcome from 

fire in terms of the seral stage, if any, to which a cell transitioned after fire. The probability 

of transitioning to a more open-canopy seral stage was calculated by dividing the summed 

probabilities of transitioning to a more open-canopy seral stage by the summed probabilities 

of all possible low-mortality outcomes. Thus, given a low-mortality fire, the probability of 

transitioning to a more open-canopy seral stage and the probability of no transition always 

summed to 1. We further modified the probabilities to accommodate our state-and-transition 

models, which have three canopy-cover stages instead of LandFire’s two. We did this by 

evenly allocating the original probability of a single transition from a closed canopy to open 

canopy between the two possible transitions in our model: closed to moderate canopy and 

closed to open canopy.

HRV Model Calibration and Verification

Although RMLands is a process-oriented model with individual parameters sourced 

from either data, the literature, or expert opinion, the model outcomes reflect the complex 

spatiotemporal interactions of the stochastic processes. Consequently, it is necessary to calibrate 

the model by tuning (i.e., adjusting up or down) one or more of the model parameters to produce 

outcomes that are consistent with the data, the literature, and expert opinion. In this context, 

we considered the parameters associated with the disturbance process to be the “independent” 

variables and the vegetation conditions (e.g., seral stage distribution) to be the “dependent” 

outcomes. Thus, we calibrated the disturbance parameters to achieve the targeted disturbance 

regime, but without attention to the vegetation response, which we treated as the dependent 

outcome of the disturbance regime.

Figure 14—Modifier to the probability of high mortality for a cell burned in a wildfire as a 

logistic function of the number of years since the last fire (either wildfire or prescribed fire) for 

low-elevation and high-elevation forests. 
USDA Forest Service RMRS-GTR-385. 2018 31

We focused model calibration on one major disturbance regime attribute: fire rotation period 

(FRP), which is the number of years required to burn a cumulative area equal to the extent of 

the landscape or of a single cover type. FRP combines fire frequency and fire size into a single 

integrated measure of how much fire occurred. Regardless of whether there are more smaller 

fires or fewer larger fires, FRP provides an effective measure of how much fire is actually 

applied to a unit of ground. FRP is equivalent to the “point-specific” or “population” mean fire 

return interval (FRI)—the average number of years between fires to a single point on the ground 

(or pixel)—and is roughly equivalent to the oft-reported “grand mean” FRI computed from a 

large sample of point-specific intervals (e.g., from a set of fire-scarred trees). Importantly, FRP 

is not equivalent to the “composite” FRI, which is computed based on the average interval 

between fires to an area of some specified size larger than a single point or pixel (e.g., a forest 

stand). The FRP or point-specific FRI is generally much longer than the composite FRI, because 

the interval between fires to a single point is generally much greater than the interval between 

fires to anywhere within an arbitrarily defined larger site. A major limitation with composite 

FRIs is that the unit area is arbitrary and varies considerably from one study to the next, making 

comparisons of published composite FRIs very difficult. In this report, we use FRP to describe 

the amount of fire applied to the landscape, in part because it is unambiguous in meaning and 

well understood by managers, but also because the simulation modeling gives us a complete 

census of simulated wildfires, which lends itself well to the calculation of FRP.

We tuned the overall disturbance rate parameter (see Initiation), which controlled the 

number of disturbance attempts per unit area within each timestep, and the spread calibration 

coefficients (Gi) for each cover type, which affected the probability of fire spread in each 

cover type, and measured calibration success based on conformity to prespecified target FRPs 

at the cover type level. We set our calibration goal as within 10 percent of the target FRPs 

for the 11 cover types with 1,000 hectares or more in the project area (excluding Barren and 

Water). Target values were based on empirical published values (Mallek et al. 2013; Van de 

Water and Safford 2011) and expert input from Safford and Estes. With one exception, we 

achieved this calibration goal (table 5).

Table 5—Simulated and target historical fire rotation periods (FRPs) for dynamic cover types with 1,000 ha (2,500 ac) or more 

in the upper Yuba River watershed, and the delta (percent difference) between simulated and target FRPs. The row for Total 

includes all cover types. Note that a positive delta indicates that the target FRP was greater than the simulated FRP, and a 

negative delta indicates that the target FRP was less than the simulated FRP. 

Fire rotation period

Cover type Area (ha) Area (ac) Simulated Target Delta

Sierran Mixed Conifer – Mesic 57,853 142,955 27 29 6.9%

Sierran Mixed Conifer – Xeric 52,198 128,981 24 22 -9.1%

Oak-Conifer Forest and Woodland 23,279 57,522 25 21 -19.0%

Red Fir – Mesic 8,563 21,159 63 60 -5.0%

Red Fir – Xeric 7,493 18,515 43 40 -7.5%

Mixed Evergreen – Mesic 7,273 17,972 48 50 4.0%

Mixed Evergreen – Xeric 6,768 16,717 37 40 7.5%

Sierran Mixed Conifer – Ultramafic 4,124 10,190 57 60 5.0%

Grassland 1,379 3,408 58 60 3.3%

Meadow 1,201 2,968 61 60 -1.7%

Oak-Conifer Forest and Woodland – Ultramafic 1,060 2,619 40 42 4.8%

Total 171,191 423,006 29 30 3.3%
32 USDA Forest Service RMRS-GTR-385. 2018

In addition to model calibration, as confirmation of the correct model implementation, 

we also verified that the model generated the expected outcomes for three other disturbance 

regime attributes: 

• Simulated fire patterns—We visually inspected output grids depicting wildfire events for 

both small and large fires and verified that their shapes and conformity to the topography 

and patterns of mortality were reasonably similar to actual wildfires (fig. 15).

• Fire size distribution—We compared the simulated fire size distribution (i.e., the resulting 

fire sizes) against the expected distribution (see Termination) and verified that they were 

similar (fig. 16). The simulated fire size distribution included a small proportion of very 

large fires that exceeded the largest fires in the historical dataset used to create the target 

distribution, but this was expected as noted previously.

• Topographic position—We examined our use of TPI to affect both susceptibility to 

wildfire and the probability of a high-mortality outcome. In the state-and-transition 

models, early-development and more open-canopy seral stages result from fire. We 

expected that an increase in the occurrence of fire and in the likelihood of high-mortality 

fire for cells with large TPI values would lead to a decrease in the average canopy cover 

for those cells (i.e., on steeper, south- and west-facing, upper slopes). We verified that 

this was in fact the case. Across all dynamic cover types for which we applied TPI (table 

3), we observed an 18-percent absolute (or 28-percent relative) decrease in average 

canopy cover across the full TPI gradient (-3 to 3), although it varied somewhat among 

the dominant cover types (table 6). Although there was a clear negative relationship 

between TPI and average canopy cover, there was also considerable variability about this 

trend (fig. 17), reflecting the complex spatiotemporal interactions of the disturbance and 

succession processes.

Figure 15—Typical simulated wildfires of average total extent during a single 5-year timestep 

depicting mortality level (low versus high) and the individual fire events (inset) in the upper 

Yuba River watershed.
USDA Forest Service RMRS-GTR-385. 2018 33

Figure 16—Distribution of simulated fire sizes within the upper Yuba River watershed and the 

target fire size distribution derived from fire size data in the North Sierran ecoregion (1900–

2010). Note that the x-axis is geometrically scaled.

Table 6—Average percent canopy cover at the minimum and maximum values of the topographic position index (TPI) for 

dynamic cover types with 1,000 ha (2,500 ac) or more and whose susceptibility to fire and mortality following fire was modified 

by topographic position (see table 3) in the upper Yuba River watershed, the delta (difference) in percent canopy cover between 

minimum and maximum TPI values, and the percentage change in canopy cover (defined as delta/average canopy cover at 

min TPI, times 100). Note: The negative delta indicates canopy cover decreased as TPI increased. 

Cover type Min TPI Max TPI

Average 

canopy cover 

at min TPI

Average 

canopy cover 

at max TPI Delta

Percent 

change 

canopy cover

Mixed Evergreen – Mesic -3.0 3.0 73.3 65.6 -7.7 -10.5

Mixed Evergreen – Xeric -3.0 3.0 73.0 66.6 -6.4 -8.8

Oak-Conifer Forest and Woodland -3.0 3.0 57.1 53.0 -4.1 -7.2

Oak-Conifer Forest and Woodland – Ultramafic -3.0 3.0 31.1 28.9 -2.2 -7.1

Red Fir – Mesic -3.0 3.0 76.4 70.2 -6.2 -8.1

Red Fir – Xeric -2.1 3.0 56.3 42.3 -14.0 -24.9

Sierran Mixed Conifer – Mesic -3.0 3.0 61.5 56.9 -4.6 -7.5

Sierran Mixed Conifer – Ultramafic -3.0 3.0 48.2 36.4 -11.8 -24.5

Sierran Mixed Conifer – Xeric -3.0 3.0 45.5 41.7 -3.8 -8.4

Total -3.0 3.0 63.6 45.7 -17.9 -28.1
34 USDA Forest Service RMRS-GTR-385. 2018

Figure 17—Relationship between topographic position index (TPI) and simulated average 

percent canopy cover for cells classified as Sierran Mixed Conifer – Xeric, showing an average 

decrease of 8.4 percent in percent canopy cover over the full range of TPI (red line).

HRV Model Execution

We used RMLands to simulate fire disturbance and succession representative of the 

historical reference period (ca. 1550–1850). We used a 5-year timestep in the model based on 

the short FRIs recorded in the literature. After calibrating the model, we ran a single simulation 

for 500 timesteps (i.e., 2,500 years) and generated several GIS data layers representing 

disturbance and vegetation state at each timestep. We treated the first 40 timesteps (200 years) 

as the model equilibration period based on preliminary simulations, which indicated that it took 

the landscape up to 200 years to reach dynamic equilibrium for all of the attributes of interest. 

Some attributes reached dynamic equilibrium much sooner, but for consistency we used a 

single equilibration period. Thus, we kept the last 460 timesteps (41–500) as snapshots of the 

landscape and used these samples to quantify HRV in various landscape attributes (see Analysis 

of Landscape Structure). Note that the use of a single 2,500-year simulation rather than several 

300-year simulations was merely for processing efficiency to obtain a large sample of landscape 

snapshots representing multiple disturbance and succession cycles; it did not change the fact that 

the landscape shapshots were all stochastic realizations of the disturbance regime representative 

of the 300-year historical reference period.

Although our primary interest was simulating HRV for the 181,556-hectare project 

area, we included a 10-kilometer (6-mile) buffer around the project area for purposes of the 

simulation to avoid landscape boundary effects, resulting in a much larger landscape for the 

simulation (408,498 hectares; 1,009,399 acres). Thus, wildfires were allowed to spread across 

the project area boundary unimpeded. However, all of the results were calculated for the core 

project area (i.e., excluding the buffer). 
USDA Forest Service RMRS-GTR-385. 2018 35

Alternative Management Scenarios

To simulate the range of variability in landscape structure under alternative management 

scenarios, we used RMLands as described previously to simulate wildfire disturbance and 

succession processes. In several ways, the model parameterization for the management 

scenarios was similar to that of the HRV scenario described earlier. For instance, many of 

the parameters were given by the state-and-transition models defined for each cover type 

(Appendix B). In particular, the succession process was identical for both the HRV and 

management scenarios. What differed was the disturbance processes. For the management 

scenarios, we modified the wildfire disturbance regime to reflect modern conditions 

(reflecting aggressive fire suppression), and we added a variety of vegetation treatments to the 

disturbance process to reflect alternative vegetation management scenarios.

Management Scenarios

We created the following scenarios to examine how the range of variability in landscape 

structure may deviate from the current landscape and the HRV under alternative vegetation 

management strategies representing a wide range of treatment intensities and treatment types:

• MS1: no treatment—In this no treatment scenario, we simulated wildfire disturbance 

based on our best estimate of the modern fire regime reflecting aggressive fire suppression 

and no vegetation treatments.

• MS2: current land management plan—In this “business as usual” scenario, we simulated 

wildfire disturbance as in MS1 in combination with a suite of vegetation treatments 

modeled after the 1990 land management plan (LMP), as amended in 2004. We 

emphasized thinning; hand cut, pile, and burn; and mastication treatments. Realized 

overall treatment intensity was 3,458 hectares (8,545 acres) (2.8 percent of eligible area) 

per 5-year timestep. 

• MS3: prescribed fire only—In this scenario, we simulated wildfire disturbance as in MS1 

in combination with extensive prescribed fire treatments (10 times the amount in MS2) 

and no mechanical treatments. Realized overall treatment intensity was 34,191 hectares 

(84,486 acres) (27.6 percent of eligible area) per 5-year timestep. We simulated two 

versions of this scenario: MS3a included only “cool” burns typical of modern prescribed 

fire prescriptions (~2–5 percent canopy mortality); MS3b included “hotter” burns 

producing about three times the severity (i.e., probability of canopy mortality) as the cool 

burn scenario (~5–15 percent canopy mortality, as per Safford et al. 2012).

• MS4: LMP moderate intensity—In this scenario, we simulated wildfire disturbance as 

in MS1 in combination with the same distribution of vegetation treatments as MS2 (i.e., 

emphasizing thinning; hand cut, pile, and burn; and mastication treatments) but with 

about five times the realized overall treatment intensity at 15,572 hectares (38,478 acres) 

(12.6 percent of eligible area) per 5-year timestep.

• MS5: Sierra Conservancy—In this scenario, we simulated wildfire disturbance as in 

MS1 in combination with intensive vegetation treatment at the realized overall treatment 

intensity of 30,798 hectares (76,102 acres) (24.8 percent of eligible area) per 5-year 

timestep. Treatments consisted of both prescribed fire (based on the “cool” burns as in 

MS3a) and a variety of mechanical treatments, but with a strong emphasis on prescribed 

fire treatments. Note that this vegetation treatment scenario was developed by the staff 

of the Yuba River Ranger District in collaboration with the Sierra Nevada Conservancy 

(SNC). Briefly, in 2015 Tahoe National Forest managers took the lead for the region in 

helping the SNC display its vision of the environmental, social, and economic needs for 

restoration within the Sierra Nevada. District managers estimated a level of treatments 

that they felt should be implemented across the district to restore resiliency within the 
36 USDA Forest Service RMRS-GTR-385. 2018

landscape. They identified priority treatment areas focusing on the wildland-urban 

interface (WUI) and areas that contain larger proportions of forests with moderate to high 

canopy cover of mid- to late-successional forests, referred to as “old forest.” Restoring 

resiliency to “old forests” first was selected because, once lost, these forests contain 

values that take the longest to replace. Treatments followed the existing LMP: excluding 

treatments in roadless areas, restricting mechanical treatments within California spotted 

owl (Strix occidentalis occidentalis) and northern goshawk (Accipiter gentilis) Protected 

Activity Centers (PACs), restricting mechanical treatments to primarily slopes of 30 

percent or less, and maintaining canopy cover of more than 40 percent where it already 

exists above this level. Based on these priorities, district managers determined the total 

area to be treated each year and allocated the acreage among several different treatment 

types. The only modification we made here was to adjust upward the total treatment 

intensity from the original approximately 28,000 hectares (69,000 acres) per 5 years to 

about 31,000 hectares (77,000 acres) per 5 years.

• MS6: balanced—In this scenario, we simulated wildfire disturbance as in MS1 in 

combination with intensive vegetation treatment at the realized overall treatment intensity 

of 24,198 hectares (59,793 acres) (19.5 percent of eligible area) per 5-year timestep, 

consisting of an equal emphasis (i.e., “balanced”) on prescribed fire (based on the “cool” 

burns as in MS3a) and mechanical treatments. This scenario was created after reviewing 

the results of the previous scenarios in an attempt to construct a scenario that would better 

emulate the HRV. 

• MS7: final—In this, our final, scenario, we modified MS6 slightly to better emulate the 

HRV; specifically, we shifted the distribution between thinning treatments and matrix thin 

and group cut treatments in favor of thinning, switched to “hotter” burns for prescribed fire 

treatments (as in MS3b), and adjusted the dynamic priorities among cover types and seral 

stages somewhat (see Constraints and Priorities). We realized an overall treatment intensity 

of 22,174 hectares (54,792 acres) (17.9 percent of eligible area) per 5-year timestep 

allocated among treatment types as follows: 39.5 percent prescribed fire; 23.5 percent 

thinning; 17.3 percent matrix thin and group cut; 7.8 percent thin and burn; 3.9 percent 

matrix thin, group cut, and burn; 2.6 percent thin, masticate, and burn; 2.6 percent hand cut, 

pile, and burn; 1.5 percent mastication; and 1.2 percent thin, hand cut and pile, and burn.

Wildfire Disturbance Parameters

For all management scenarios, we kept the wildfire disturbance parameters the same 

as the HRV scenario except for the fire size distribution and the overall disturbance rate 

calibration parameter (which controls the number of disturbance attempts per timestep; see 

previous discussion). We adjusted these two parameters to produce a target overall FRP of 152 

years for the no treatment scenario (MS1) instead of 30 years for the HRV scenario. Thus, we 

simulated many fewer fires, but the behavior of the fires was generally the same.

To determine the target overall FRP for the management scenarios, we used the Forest 

Service Fire Program Analysis-Fire Occurrence Data (FPA-FOD) records on fire occurrence 

between 1992 and 2013 in the North Sierran ecoregion (N = 13,336). Specifically, we 

summed the total area burned during this 21-year period, converted this to an average annual 

burned area, and computed FRP as the total area of the ecoregion divided by the average 

annual burned area. This resulted in a modern FRP of 152 years. We opted to use the overall 

landscape FRP as our model calibration target for the management scenarios rather than 

specifying a separate FRP target for each cover type for two reasons. First, the cover type 

classification does not include all the cover types or variants that we defined for this project. 

Thus, we do not have a one-to-one crosswalk between the empirically derived modern FRPs 
USDA Forest Service RMRS-GTR-385. 2018 37

for the cover types and our larger set of cover types for our project area. Second, because 

landscape histories are idiosyncratic, the cover type FRPs computed from this one landscape 

trajectory may not be as reliable for forecasting purposes as the overall landscape FRP. For 

these reasons, we calibrated the disturbance rate parameter to achieve an overall FRP and kept 

the relative differences among cover types the same as for the HRV scenario.

In addition to tuning the overall disturbance rate parameter, we also adjusted the target 

fire size distribution to match the modern distribution derived from the FPA-FOD record. A 

comparison between the HRV fire size distribution derived from the longer record of 1908–

2010 and the modern record of 1992–2013 reveals that the modern fire size distribution has 

a higher proportion of fires less than 1 hectare (2 acres), presumably reflecting successful fire 

suppression efforts as well as better detection and recording efforts (table 7). Accordingly, the 

average fire size in the modern record is considerably less than in the full record. However, the 

area-weighted mean fire size is substantially larger in the modern record, which suggests that 

the modern FRP, though much longer than historically (152 versus 30 years), is being driven 

by fewer but relatively larger fires.

RMLands Vegetation Treatments

It is beyond the scope of this document to fully describe the vegetation treatment 

module in RMLands (a detailed technical document is forthcoming). Here, we provide only 

a brief description of the vegetation treatment module as applied in this study. Like natural 

disturbances, vegetation treatments follow succession in each timestep in the simulation, and 

there are many parameters that control the treatment process. 

• Management zones—We subdivided the project area into three management zones 

representing major ownerships (fig. 2) to reflect differences in goals and objectives:

1. National forest lands—124,030 hectares (306,478 acres). In this zone, we varied 

treatment intensity and treatment types among management scenarios, as detailed in 

Treatment Amount and Allocation.

2. Private industrial forest lands—32,768 hectares (80,970 acres). In this zone, we 

implemented a constant treatment regime across all scenarios, reflecting a goal of 

maximizing commercial timber production, as detailed in Treatment Amount and 

Allocation.

3. Other lands—24,763 hectares (61,189 acres). In this zone, we implemented a constant 

treatment region across all scenarios, reflecting a variable but low intensity of 

treatments, as detailed in Treatment Amount and Allocation.

Table 7—Target fire size distribution specified for the historical range of variability scenario (ca. 

1550–1850) and the management scenarios based on the modern fire record for the period 

1992–2013 in the North Sierran ecoregion. 

Size (ha) Size (ac) Historical Modern

0–1 0–2 83.55% 92.80%

>1–10 >2–25 6.33% 4.96%

>10–100 >25–250 4.79% 1.27%

>100–1,000 >250–2,500 4.13% 0.65%

>1,000–10,000 >2,500–25,000 1.06% 0.27%

>10,000 >25,000 0.14% 0.05%

max 30,500 ha (75,400 ac) 30,523 ha (75,422 ac)

mean 72 ha (178 ac) 22 ha (54 ac)

area-weighted mean 8,356 ha (20,658 ac) 12,686 (31,347 ac)
38 USDA Forest Service RMRS-GTR-385. 2018

• Management types—We divided all the forest and woodland cover types into two 

management types and specified a unique management regime for each type (detailed as 

follows):

1. Conifer forests and woodlands—Conifer forests and woodlands of various types 

(excluding subalpine conifer forest) make up about 94 percent (170,636 hectares; 

421,642 acres) of the project area. 

2. Mixed conifer-aspen forests—Mixed conifer-aspen forests of various types compose 

about 0.05 percent (97 hectares; 240 acres) of the project area and occurred only on 

national forest lands (at least as mapped).

• Treatment amount and allocation—Within each management zone and management 

type, we allocated a total treatment intensity (i.e., number of hectares treated per 5-year 

timestep) among the following treatment types, which varied among management 

scenarios as given in table 8:

1. Clearcut—Removal of more than 90 percent of the overstory designed to regenerate 

the stand and create temporary early-seral stage conditions.

2. Thinning—Single-tree selection designed to maintain an uneven-aged stand structure, 

promote continuous regeneration, and reduce canopy cover. Thinning involved partial 

overstory removal uniformly distributed throughout the treatment unit and always 

resulted in a transition to a more open-canopy class within the same developmental 

stage (e.g., late-development closed canopy to late-development moderate canopy) or 

maintained the stand in an open-canopy condition.

3. Mastication—Mechanical mastication of understory vegetation designed to compact 

and distribute the understory fuels and reduce the likelihood of severe fire. This 

treatment had no effect on the overstory of forests and woodlands, and thus it did not 

result in a seral stage transition.

4. Prescribed fire—Prescribed fire designed to be predominantly nonlethal surface fire 

to reduce fine fuels and reduce the likelihood of severe fire, but allowing for some 

lethal surface or crown fire in small patches aimed at opening up the stand. The 

probability of a prescribed fire causing cells and small patches to transition to a more 

open-canopy class within the same developmental stage varied among cover types and 

seral stages (Appendix B), but generally was about 2–5 percent under the “cool” burn 

scenarios. We increased the probabilities threefold for the “hotter” burn scenarios.

5. Matrix thin and group cut—Small, randomly sized group cuts with complete 

overstory removal designed to create regeneration patches and increase spatial 

heterogeneity in canopy cover, embedded within a thinned matrix designed to reduce 

overall canopy cover. Group cuts encompassed 20 percent of the area and were 

distributed randomly throughout the treatment unit. The group cuts always resulted 

in a transition to the early-seral stage, and the matrix thinning always resulted in 

a transition to a more open-canopy class within the same developmental stage or 

maintained the matrix in an open-canopy condition.

6. Thin and burn—Overstory thinning followed by prescribed fire over the entire unit, as 

previously described. This treatment always resulted in a transition to a more opencanopy class within the same developmental stage or maintained the stand in an opencanopy condition.

7. Hand cut, pile, and burn—Hand cutting and piling of small diameter material 

followed by prescribed fire designed to reduce understory fuels and reduce the 

likelihood of severe fire. Hand cutting and piling was done within a randomly selected 

75 percent of the treatment unit, followed by prescribed fire over the entire unit. Hand 

cutting and piling had no effect on the overstory, but it reduced the probability of 

canopy mortality (and subsequent seral stage transition) caused by the prescribed fire 

by 50–75 percent depending on the seral stage.
USDA Forest Service RMRS-GTR-385. 2018 39 Table 8—Realized average treatment intensity (mean number of ha [ac] treated per 5-year timestep) across management scenarios involving vegetation treatments (MS2– MS7) (see Management Scenarios under Methods) by management zone (national forest, private industrial forest, and other), management type (aggregated cover types), and treatment type for the upper Yuba River watershed. Note that realized treatment intensity was based on the average across 20 replicated 100-year simulations, and in some cases it was less than the targeted treatment intensity due to dynamic landscape conditions and the specified constraints and priorities for treatment. The realized treatment rate (percentage of eligible forest) pooled across treatment types is also given for conifer-dominated forest on national forest lands. Scenario Management zone/type Treatment type MS2 MS3a MS3b MS4 MS5 MS6 MS7 National foresta Conifer-dominated forest Thinning - thin 1,575 (3,892 ac) - - 7,328 (18,107 ac) 809 (1,999 ac) 3,954 (9,770 ac) 5,213 (12,881 ac) Matrix thin and group cut - - - - 1,206 (2,980 ac) 5,927 (14,646 ac) 3,839 (9,486 ac) Mastication 514 (1,270 ac) - - 2,275 (5,622 ac) - 365 (902 ac) 336 (830 ac) Prescribed fire 118 (292 ac) 34,183 (84,466 ac) 34,178 (84,454 ac)d 335 (828 ac) 24,371 (60,221 ac) 9,566 (23,638 ac) 8,766 (21,661 ac)d Hand cut, pile, and burn 858 (2,120 ac) - - 3,930 (9,711 ac) - 633 (1,564 ac) 579 (1,431 ac) Thin and burn - - - - 2,390 (5,906 ac) 1,880 (4,646 ac) 1,723 (4,258 ac) Thin, hand cut, pile, and burn 386 (954 ac) - - 1,697 (4,193 ac) - 282 (697 ac) 259 (640 ac) Thin, masticate, and burn - - - - 808 (1,996 ac) 637 (1,574 ac) 583 (1,441 ac) Matrix thin, group cut, and burn - - - - 1,206 (2,980 ac) 947 (2,340 ac) 869 2,417 ac) subtotal 3,451 (8,527 ac) (2.78%) 34,183 (27.58%) 34,178 (27.58%) 15,565 (38,461 ac) (12.56%) 30,790 (76,082 ac) (24.84%) 24,191 (59,776 ac) (19.52%) 22,167 (54,775 ac) (17.89%) Mixed conifer-aspen forest Prescribed fire - 13 (32 ac) 13 - - - - Thinning 7 (17 ac) - - 7 8 (20 ac) 7 7 subtotal 7 13 13 7 8 7 7 Private industryb Clearcut 1,606 (3,968 ac) 1,612 (3,983 ac) 1,596 (3,944 ac) 1,600 (3,954 ac) 1,599 (3,951 ac) 1,609 (3,976 ac) 1,592 (3,934 ac) Mastication 357 882 ac) 358 (885 ac) 355 (877 ac) 355 355 357 352 (870 ac) Thinning 1,646 (4,067 ac) 1,653 (4,085 ac) 1,634 (4,038 ac) 1,641 (4,055 ac) 1,637 (4,045 ac) 1,648 (4,072 ac) 1,630 (4,028 ac) subtotal 3,609 (8,918 ac) 3,623 (8,952 ac) 3,585 (8,859 ac) 3,596 (8,886 ac) 3,591 (8,873 ac) 3,614 (8,930 ac) 3,574 (8,831 ac) Otherc Clearcut 112 (278 ac) 111 (274 ac) 110 (272 ac) 111 111 111 112 Mastication 277 (684 ha) 273 (675 ac) 271 (670 ac) 272 (672 ac) 276 (682 ac) 274 (677 ac) 273 Thinning 274 (677 ac) 276 (682 ac) 276 275 (680 ac) 275 275 275 subtotal 663 (1,638 ac) 660 (1,631 ac) 657 (1,623 ac) 658 (1,626 ac) 662 (1,636 ac) 660 (1,631 ac) 660 a Total area eligible: conifer-dominated forest = 123,933 ha (306,238 ac); mixed conifer-aspen forest = 97 ha (240 ac) b Total area eligible: 32,768 ha (80,970 ac) c Total area eligible: 24,763 ha (61,189 ac)

d These scenarios used “hotter” burns, resulting in about three times the level of canopy mortality as the “cool” burns.
40 USDA Forest Service RMRS-GTR-385. 2018

8. Matrix thin, group cut, and burn—Matrix thinning and group cuts followed by 

prescribed fire, as previously described.

9. Thin, masticate, and burn—Mastication of small diameter material within a randomly 

selected 75 percent of the treatment unit combined with overstory thinning followed 

by prescribed fire over the entire unit. Because of the thinning, this treatment always 

resulted in a transition to a more open-canopy class within the same developmental 

stage or maintained the stand in an open-canopy condition.

10. Thin, hand cut, pile, and burn—Hand cutting and piling within a randomly selected 

75 percent of the treatment unit combined with overstory thinning followed by 

prescribed fire over the entire unit. Because of the thinning, this treatment always 

resulted in a transition to a more open-canopy class within the same developmental 

stage or maintained the stand in an open-canopy condition.

• Constraints and priorities—We specified a number of constraints and priorities for 

treatments. First, we assigned “static” constraints and priorities for each treatment type, 

whereby each cell was assigned a value of 0–1 to indicate its eligibility and priority 

for treatment. These static constraints and priorities were constant over the duration of 

the simulation (i.e., they did not change over time) and were thus defined on the basis 

of relatively static landscape features (e.g., administrative areas such as roadless areas, 

designated areas for focal wildlife, edaphic and topographic features). We constructed 

four different static spatial constraints and priorities layers associated with four groupings 

of treatments: 

1. Burn—prescribed fire (used by itself without mechanical treatments). We created two 

versions of this layer: (1) priorities applied to scenarios MS2 and MS4–MS7 (fig. 18a), 

and (2) priorities applied to the burn-only scenarios (MS3a,b) that differed in also 

allowing prescribed fire to be used in the designated roadless areas (not shown).

2. Thin—overstory thinning, applied to: (1) clearcut; (2) thinning; (3) thin and burn; (4) 

matrix thin and group cut; and 5) matrix thin, group cut, and burn (fig. 18b).

3. Mast—understory fuel mastication, applied to: (1) mastication; and (2) thin, masticate, 

and burn (fig. 18c).

4. Hand—understory fuel hand cut and pile, applied to: (1) hand cut, pile, and burn; and 

(2) thin, hand cut, pile, and burn (fig. 18d). 

To construct each layer, we combined several spatial data layers, including: WUI zones, 

spotted owl and northern goshawk PACs, riparian conservation areas, topographic position, 

road proximity, roadless areas, and cover type. First, each of these layers was transformed to 

range from 0 to 1 based on the coefficients in table 9 for national forest lands; on private lands 

we considered only slope (fig. 19) and cover type (table 9). Next, we combined the transformed 

layers with a geometric mean. Consequently, at the cell level if any transformed layer was 0, the 

final result was 0 and the cell was deemed ineligible for treatment. If all the layers were greater 

than 0, the cell received an average value that was weighted toward the smallest value. 

Second, we specified “dynamic” constraints and priorities, whereby each cell was 

assigned a value of 0–1 to indicate its eligibility and priority for treatment in the current 

timestep based on conditions that changed over the duration of the simulation. For conifer 

forests and woodlands on national forest lands, this was determined by the minimum of: (1) 

logistic function of the number of years since the last vegetation treatment of any kind (fig. 

20), (2) logistic function of the number of years since the last fire (fig. 20), and (3) priority 

assigned to each seral stage by cover type as given in table 10. The seral stage priorities for 

scenarios MS2–MS6 were assigned a priori, whereas the adjustments to these priorities for 

our final scenario (MS7) were based on scrutiny of the previous results in an attempt to more 

closely emulate HRV. For conifer forests and woodlands on private industrial forest lands 
USDA Forest Service RMRS-GTR-385. 2018 41

and other lands, this was determined by the minimum of: (1) logistic function of the number 

of years since the last vegetation treatment of any kind (fig. 20), and (2) priority assigned to 

each seral stage (across all cover types) as given in table 10. For mixed conifer-aspen forests 

(which occurred only on national forest lands, as mapped), this was based solely on seral 

stage, with treatments restricted to and assigned equal priority to the three most advanced seral 

stages (mid-aspen and conifer, late-aspen and conifer, and late-closed canopy).

Third, we computed the geometric mean of the static and dynamic constraints and 

priorities to determine the eligibility and priority of each cell for the corresponding treatment 

in each timestep. Consequently, at the cell level if either the static constraint or dynamic 

constraint was 0, the final result was 0 and the cell was deemed ineligible for treatment during 

the current timestep. If both layers were more than 0, the cell received an average value that 

was weighted toward the smallest value and prioritized for that treatment type accordingly. 

Last, based on current Tahoe National Forest policy, we constructed a minimum canopy 

cover threshold layer to serve as a constraint on vegetation treatments. We set the threshold 

at 70 percent in PACs, 0 percent (i.e., no limit) in the WUI urban core and defense zones, and 

40 percent for all other areas (fig. 21). Treatments were not allowed to occur if the average 

canopy cover within the potential treatment unit was below the average minimum canopy 

cover threshold as depicted in this layer.

Figure 18—Static spatial constraints and priorities for: (a) prescribed fire treatments, (b) overstory thinning treatments, (c) 

understory fuel mastication treatments, and (d) hand cut and pile treatments in the upper Yuba River watershed. Transparent 

areas (showing gray hillshading) were excluded from treatment.
42 USDA Forest Service RMRS-GTR-385. 2018

Figure 19—Transformation of (a) the topographic 

position index (TPI), (b) slope, and (c–e) road 

proximity for prescribed fire, thin and masticate, and 

hand cut and pile treatments, respectively, into a 

priority score (0–1) to be combined with other data 

listed in table 9 to create the static constraints and 

priorities layers for vegetation treatments (fig. 18). Note 

change in x-axis scale for road proximity figures.
USDA Forest Service RMRS-GTR-385. 2018 43

Table 9—Static spatial constraints and priorities for vegetation treatments on national forest lands in the upper Yuba River 

watershed. Each spatial layer was transformed to range from 0 to 1, whereby 0 = exclude from treatment and >0 indicates 

relative priority. Transformed layers were combined with a geometric mean for each of the four groupings of treatments: Burn = 

prescribed fire; Thin = thinning (applied to clearcut; thinning; thin and burn; matrix thin and group cut; matrix thin, group cut, and 

burn); Mast = understory fuel mastication (applied to mastication; thin, masticate, and burn); Hand = understory fuel hand cut 

and pile (applied to hand cut, pile, and burn; thin, hand cut, pile, and burn). 

Treatment type (priority 0–1)

Spatial layer Class Burn Thin Mast Hand

WUI-PACsa Urban core-PAC 0 0.1 0.1 0.8

Defense zone-PAC 0.1 0.1 0.1 0.8

Threat zone-PAC 0.6 0 0 0.6

nonWUI-PAC 0.4 0 0 0.1

Urban core-nonPAC 0 1 1 1

Defense zone-nonPAC 1 1 1 1

Threat zone-nonPAC 0.8 0.8 0.8 0.8

nonWUI-nonPAC 0.8 0.8 0.6 0.1

Riparian conservation areasb n/a 0.4 0 0 0

Topographic position n/a logistic function in figure 19 ignored

Slope n/a ignored logistic function in figure 19 ignored

Road proximity n/a logistic function as shown in figure 19

Roadless areas n/a 0 (0.8)c 0 0 0

Cover types n/a Urban, Agriculture, Barren, and Water = 0

a WUI-PACs = wildland-urban interface; Protected Activity Centers 

b Perennial streams = 30-m (100-ft) buffer; intermittent streams = 23-m (75-ft) buffer

c Coefficient in parentheses was used for the burn-only scenarios (MS3a and MS3b)

Figure 20—Logistic functions depicting the relationship between (1) age since any 

vegetation treatment and (2) age since any fire and the dynamic spatial constraints and 

priorities for vegetation treatments in conifer forests and woodlands.
44 USDA Forest Service RMRS-GTR-385. 2018

Table 10—Dynamic spatial constraints and priorities assigned to vegetation seral stages for vegetation treatments in coniferdominated forest and woodlands on national forest lands in the upper Yuba River watershed under the management scenarios 

(MS1–MS7) described in the text. 

Seral stage

National forest

Private industry and 

other private

MS2–MS6 MS7 All scenarios

Cover typea Cover typea Cover type

A B C D E F All

Early – all structures 0 0 0 0 0 0 0

Mid-open 0.05 0.1 0.1 0.1 0.05 0.05 0

Mid-moderate 0.3 0.4 0.6 0.7 0.5 0.4 0.1

Mid-closed 0.7 0.8 1 1 0.9 0.8 0.5

Late-open 0.1 0.2 0.1 0.1 0.05 0.05 0

Late-moderate 0.5 0.6 0.4 0.5 0.3 0.2 0.2

Late-closed 0.9 1 0.8 0.9 0.7 0.6 1

a

 A = Red Fir Forest (all variants); B = All other forest and woodlands; C = Sierra Mixed Conifer Forest (all variants), Western White Pine Forest, Lodgepole Pine 

Forest, and Oak Woodland; D = Oak-Conifer Forest and Woodland (all variants); E = Red Fir Forest – Xeric, Red Fir Forest – Ultramafic, and Mixed Evergreen 

Forest (all variants); F = Red Fir Forest – Mesic.

Figure 21—Minimum canopy cover threshold for vegetation treatments in the upper Yuba River 

watershed.
USDA Forest Service RMRS-GTR-385. 2018 45

• Treatment units—We created individual treatment units by randomly selecting a treatment 

type and an initial cell and spreading outward based on the relative probabilities in the 

corresponding constraints and priorities layer until meeting either a barrier (i.e., constraint = 

0) or a major topographic ridgeline, or achieving the maximum unit size. For conifer forests 

and woodlands, we constrained treatment units to be 20–120 hectares (50–300 acres) for 

mechanical treatments and 20–405 hectares (50–1,000 acres) for prescribed fires, whereas 

for mixed conifer-aspen forest we limited the units to 0.09–16 hectares (0.2–40 acres) 

to reflect the very small sizes of the existing patches. Subsequent treatment units within 

each management zone and management type were randomly assigned to treatment type 

according to the specified allocation and were implemented sequentially until the target 

overall treatment intensity was met or there were 100 failed attempts to create a viable unit. 

Within a timestep, treatment units for conifer forests and woodlands were randomly 

dispersed within a compartment randomly selected from a pool of 20 compartments that 

ranged in size from 4,650 to 15,906 hectares (11,490–39,303 acres) (fig. 22). This resulted 

in an aggregation of treatment units within a geographic area generally corresponding to 

a project area. Multiple compartments were treated in a single timestep as necessary to 

achieve the target treatment area. For mixed conifer-aspen forests, treatments were randomly 

distributed across the entire project area to reflect the limited and scattered distribution of 

this forest type. Last, all treatments were implemented as single-entry treatments. A unit was 

created and treated once; then the cells were returned to the pool of cells and made available 

for another treatment (subject to their suitability) in a subsequent timestep. 

Model Calibration and Verification

As described earlier, we adjusted the overall disturbance rate calibration parameter 

to achieve the target modern FRP of 152 years for the no treatment scenario (MS1) and 

modified the target fire size distribution to reflect the modern (1992–2013) fire record; all 

other succession and wildfire disturbance parameters were kept the same as the HRV scenario. 

Figure 22—Vegetation treatment compartments in the upper Yuba River watershed.
46 USDA Forest Service RMRS-GTR-385. 2018

Once the model was calibrated, we kept these parameters the same for all other management 

scenarios. As before, we set our calibration goal to be within 10 percent of the target overall 

FRP and limited our calibration to the overall FRP for all cover types combined rather than 

calibrating for each cover type separately. We realized an overall FRP for the no treatment 

scenario of 147 years (averaged across simulations), which was within 3 percent of the 

target. Last, as with the HRV simulation, we verified the model behavior with respect to the 

simulated fire patterns (not shown) and fire size distribution (fig. 23).

Model Execution

We used RMLands to simulate succession and fire disturbance under modern conditions 

(i.e., aggressive fire suppression) and vegetation treatments for a 100-year period (ca. 2010–

2110). We used a 5-year timestep in the model for consistency with the HRV scenario. After 

calibrating the model, for each of the management scenarios we ran 20 replicate simulations 

for 20 timesteps (i.e., 100 years) and generated several GIS data layers representing 

disturbance and vegetation state at each timestep. For the analysis of landscape structure, 

we were most interested in comparing the landscape at the end of the 100-year simulation 

to the current landscape condition and the simulated HRV. Thus, for the landscape structure 

analysis we kept only the last timestep of each replicate simulation (N = 20 for each scenario) 

as snapshots of the landscape and used these samples to quantify the range of variability in 

various landscape attributes (see next section).

As with the HRV scenario, we included a 10-kilometer buffer around the project area for 

the simulation to avoid landscape boundary effects, resulting in a much larger landscape for the 

simulation (408,498 hectares; 1,009,399 acres). Wildfire disturbances were allowed to spread 

across the project area boundary unimpeded, but we restricted vegetation treatments to the 

core project area (i.e., excluding the buffer). All results were calculated for the core project 

area only. 

Figure 23—Distribution of simulated fire sizes for the management scenarios (MS1–MS7) 

within the upper Yuba River watershed and the target fire size distribution derived from 

the modern fire record in the North Sierran ecoregion (1992–2013). Note that the x-axis is 

geometrically scaled.
USDA Forest Service RMRS-GTR-385. 2018 47

Analysis of Landscape Structure

To quantify landscape structure, we used FRAGSTATS (McGarigal et al. 2012), which is 

a software tool for quantifying the structure of patch mosaics based on hundreds of different 

landscape metrics. For the HRV scenario, we computed the variability in selected measures 

of landscape composition and configuration (see following) based on the 460 snapshots of 

the landscape. To evaluate current departure, we compared the initial landscape representing 

approximately the year 2010 (based on the source GIS data) to the corresponding HRV for 

each landscape metric. For each of the management scenarios, we computed the variability in 

the landscape metrics based on the 20 snapshots of the landscape derived from the replicate 

100-year simulations, and compared the variability in each landscape metric to both the initial 

landscape (2010) and the HRV.

Landscape Composition

For our purposes, “landscape composition” refers to the total area of each vegetation class 

(or patch type). Landscape composition is nonspatial and ignores the spatial pattern of the 

patch mosaic. We quantified landscape composition based on four different classifications of 

the landscape:

• Developmental stage—Here, we classified the landscape into none or early-, mid-, or 

late-development classes pooled across cover types (table 11). Developmental stages 

represented stages of successional development and reflected the size of the dominant 

overstory trees (which varied among cover types; Appendix B). Nonvegetated (e.g., 

Water) and nonseral (e.g., Meadow) cover types were classified as “none.”

• Canopy cover—Here, we classified the landscape into none or open-, moderate-, or 

closed-canopy classes by reclassifying seral stages and pooling across cover types (table 

11). Here, we considered any seral stage with an average canopy cover from above of less 

than 40 percent as “open,” between 40 and 75 percent as “moderate,” and greater than 75 

percent as “closed.” Nonvegetated and nonseral cover types were classified as “none.”

• Seral stage—Here, we classified the landscape into 12 seral stage classes pooled across 

cover types (table 11). Seral stages mostly represented a combination of developmental 

stage and canopy cover class, although they varied somewhat among cover types 

(Appendix B).

Table 11—Crosswalk between seral stage classes, developmental stages, and canopy cover 

classes for the simulated historical range of variability (ca. 1550–1850) and management 

scenarios in the upper Yuba River watershed.

Seral stage Developmental stage Canopy cover class

Nonseral None None

Early - all structures Early Open

Mid-open Mid Open

Mid-moderate Mid Moderate

Mid-closed Mid Closed

Late-open Late Open

Late-moderate Late Moderate

Late-closed Late Closed

Early - Aspen Early Open

Mid - Aspen Mid Moderate

Mid - Aspen and Conifer Mid Closed

Late - Conifer And Aspen Late Closed
48 USDA Forest Service RMRS-GTR-385. 2018

• Cover-seral—Here, we classified the landscape into 151 unique combinations of cover 

type and seral stage. This was the most finely resolved thematic classification and 

represented the unique states that each cell could exist in during the simulation, as defined 

in the state-and-transition models (Appendix B).

For each of these landscape classifications, we generated the following statistical summaries 

(all of which excluded the class representing nonvegetated and nonseral cover types):

• Trajectory over time—Proportion of the eligible landscape in each vegetation class at 

each timestep, depicted as a stacked bar chart such that the proportions summed to 1 at 

each timestep. We used this graphical summary to visually display the model equilibration 

period, or the period of time required for the current landscape composition to return 

to within the historical dynamic equilibrium composition for the HRV scenario, and to 

display the dynamic nature of landscape composition under historical conditions.

• Range of variability and current departure table—Tabular summary of the range of 

variability in landscape composition and current landscape departure. Specifically, 

we computed the percentage of the eligible landscape in each vegetation class at 

the minimum value; 5th, 25th, 50th, 75th, and 95th percentiles; and maximum of 

the simulated range of variability (or a subset of these for the management scenario 

comparison). Each percentile represented the percentage of time the simulated landscape 

was at or below the corresponding percent composition of the landscape. Thus, the 

5th–95th percentile range of variability, for example, represented the 90-percent range of 

variability in landscape composition, or the range of values the landscape exhibited 90 

percent of the time under the particular scenario. We also computed the current landscape 

composition and the percentile of the simulated range of variability it represented.

• Range of variability and current departure figure—Graphical summary of the range of 

variability in landscape composition and current landscape departure. Specifically, we 

created a “box-and-whisker” plot in which the box represented the 25th–75th percentile 

range, the whiskers extended out to the minimum and maximum values, the solid 

horizontal line represented the median or 50th percentile, and the dashed horizontal line 

represented the current landscape condition. The box plot merely provided a graphical 

summary of the data in the previous table. For the management scenario comparison, the 

box plot was simplified to display only the 5th, 50th, and 95th percentiles along with the 

current condition.

Landscape Configuration

For our purposes, “landscape configuration” refers to the spatial pattern of the patch 

mosaic defined by either vegetation developmental stages, canopy cover classes, seral stages, 

or unique combinations of cover type and seral stage (table 11). Landscape configuration 

is the spatial component of landscape structure. For each of the four landscape definitions, 

we computed the following suite of landscape metrics for each snapshot of the landscape, 

including the current landscape. For pragmatic reasons, we report only the landscape-level 

metrics, which describe the configuration of the entire patch mosaic without regard to 

individual land cover classes:

• Largest patch index (LPI)—This metric measures the percentage of the landscape 

composed of the single largest patch and indicates the extent to which the landscape was 

dominated by a single matrix-forming patch. As such, this metric reflects the coarse patch 

structure of the landscape.

• Mean and area-weighted mean patch size (AREA_MN/AREA_AM)—These two metrics 

measure the size (hectares) of a patch selected at random (AREA_MN) and the size 

(hectares) of the patch for a point selected at random (AREA_AM), respectively. Mean 

patch size is a better measure of the “fine-grained” heterogeneity of the landscape, as 

it is heavily influenced by the vast number of very small patches. Area-weighted mean 
USDA Forest Service RMRS-GTR-385. 2018 49

patch size is a better measure of the coarse patch structure of the landscape, as it is 

predominantly influenced by the larger patches. Furthermore, because the latter metric is 

not affected by the many small patches that can be created as an artifact of the pixelation 

associated with a raster-based disturbance and succession model, it is a more reliable 

measure of landscape configuration than the mean for evaluating current departure. 

• Correlation length (GYRATE_AM)—This metric measured the physical continuity of the 

landscape based on a measure of the extensiveness of each patch (meters), as measured 

by the radius of gyration (GYRATE), weighted by patch area. It can be interpreted as the 

average distance an organism might traverse the map from a random starting point and 

moving in a random direction without having to leave a patch. Because this is an areaweighted metric, like area-weighted mean patch size, it is a measure of the coarse patch 

structure of the landscape. Whereas area-weighted mean patch size measures the coarse 

patch structure in terms of area (hectares), correlation length measures the coarse patch 

structure in terms of distance (meters). 

• Mean and area-weighted mean shape index (SHAPE_MN/SHAPE_AM)—These two 

unitless measures reflect the shape of a patch selected at random (SHAPE_MN) and the 

shape of the patch for a point selected at random (SHAPE_AM), respectively. The shape 

index is a normalized perimeter-to-area ratio that equals 1 for a square and increases as 

the patch becomes increasingly non-Euclidean (i.e., geometrically more complex). The 

greater the shape index value, the greater the ratio of edge to area, and thus potentially 

the greater the magnitude of edge effects (both positive and negative) on ecosystems and 

species. Like area-weighted mean patch size, the area-weighted mean shape index is a 

measure of the coarse patch structure of the landscape. 

• Mean and area-weighted mean disjunct core area (DCORE_MN/DCORE_AM)—These 

two metrics measure the core area (hectares) of a disjunct patch of core area selected 

at random (DCORE_MN) and the core area (hectares) of the patch for a core area 

point selected at random (DCORE_AM), respectively. Core area is defined as the area 

that is greater than a specified distance (see Appendix C for our edge effect distances) 

from the nearest patch edge, that is, the patch interior. It is affected jointly by the size, 

shape, and specified edge depth. A single patch can have multiple disjunct core areas 

depending on its spatial character and the specified edge depths. This metric is based on 

the logic that the greater the core area, the greater the absolute amount of patch interior 

environment, and thus potentially the smaller the edge effects (both positive and negative) 

on ecosystems and species. Like area-weighted mean patch size, the area-weighted mean 

disjunct core area is a measure of the coarse patch structure of the landscape.

• Mean and area-weighted mean core area index (CAI_MN/CAI_AM)—These two metrics 

measure the percentage of a patch that is core area for a patch selected at random (CAI_MN) 

and of the patch for a point selected at random (CAI_AM), respectively. In contrast to the 

core area metric that measures the absolute core area (hectares), the core area index is a 

relative measure of core area that measures the percentage of the total area that is in core. 

This metric is useful because the greater the core area index, the greater the relative amount 

of patch interior environment, and thus potentially the smaller the edge effects (both positive 

and negative) on ecosystems and species. Like area-weighted mean patch size, the areaweighted mean core area index is a measure of the coarse patch structure of the landscape.

• Total edge contrast index (TECI)—This metric measures the relative magnitude of 

ecological differences between adjacent patches along an edge, that is, the abruptness 

of the edge. Specifically, it measures the average percentage of the maximum possible 

contrast along a randomly selected edge, where contrast weights for each pair of adjacent 

patch types are user defined (see Appendix C for our edge contrast weights). It gives the 

average contrast regardless of the total length of edge. The greater the edge contrast index, 

the greater the abruptness of the edges, and thus potentially the greater the edge effects 
50 USDA Forest Service RMRS-GTR-385. 2018

(both positive and negative) on ecosystems and species. In addition, edge contrast can 

affect the movement of organisms across the landscape (both positively and negatively). 

Thus, all other things being equal, the edge contrast index can loosely be interpreted as a 

measure of landscape connectivity.

• Interspersion and juxtaposition index (IJI)—This metric measures the interspersion 

(spatial intermixing) of patch types as a percentage of the maximum possible interspersion 

given the number of classes. This metric is interpreted as a general measure of spatial 

diversity. All other things being equal, the greater the interspersion and juxtaposition 

index, the greater the spatial diversity.

• Edge density (ED)—This metric measures the density of edges (meters per hectare) in 

the landscape, where an edge is defined as the boundary between adjacent patches. The 

greater the edge density, the greater the quantity of edge, and thus potentially the greater 

the magnitude of edge effects (both positive and negative) on ecosystems and species. 

This metric is also interpreted as a general measure of spatial heterogeneity. All other 

things being equal, the greater the edge density, the greater the spatial heterogeneity. 

In addition, edges can affect the movement of organisms across the landscape (both 

positively and negatively). Thus, all other things being equal, edge density can loosely be 

interpreted as a measure of landscape connectivity.

• Contrast-weighted edge density (CWED)—This metric measures the density of edges 

(meters per hectare) in the landscape weighted by the degree of contrast between adjacent 

patch types, where contrast weights are defined as before. It represents the equivalent 

maximum-contrast edge density (meters of maximum contrast edge per unit area). This 

metric essentially combines the total edge contrast index and edge density metric into 

a single measure that reflects both the quantity of edge and its average contrast. Similar 

to edge density, the greater the contrast-weighted edge density, the more higher-contrast 

edges, and thus potentially the greater the magnitude of edge effects (both positive and 

negative) on ecosystems and species. This metric is also interpreted as a general measure 

of spatial heterogeneity. All other things being equal, the greater the contrast-weighted 

edge density, the greater the spatial heterogeneity. In addition, edges and their contrast can 

affect the movement of organisms across the landscape (both positively and negatively). 

Thus, all other things being equal, contrast-weighted edge density can loosely be 

interpreted as a measure of landscape connectivity. 

• Aggregation index (AI)—This metric measures the degree (percent) to which patch types are 

aggregated at the cell level relative to the maximum aggregation possible given the amount 

of each patch type. The aggregation index is maximum when each patch type is maximally 

aggregated into a single compact patch such that the cells are maximally adjacent to cells 

of the same patch type. As such, this metric reflects the spatial heterogeneity and physical 

continuity of the landscape and can loosely be interpreted as a measure of fragmentation, 

with higher values indicating less fragmentation and greater continuity.

• Contagion (CONTAG)—This metric measures the clumpiness of the landscape as 

a percentage of the maximum. Contagion is affected by both the aggregation and 

interspersion of patch types; it essentially combines the aggregation index and 

interspersion and juxtaposition index into a single measure. High contagion equates to 

highly aggregated patch types that are poorly interspersed (spatially intermixed).

For each of these landscape configuration metrics, we generated the following statistical 

summaries:

• Trajectory over time—Line plot depicting the change in the metric over timesteps. As 

with the landscape composition metrics, we used this graphical summary to visually 

display the model equilibration period for the HRV scenario and to display the dynamic 

nature of landscape configuration under historical conditions.
USDA Forest Service RMRS-GTR-385. 2018 51

• Range of variability and current departure table—Tabular summary of the range of 

variability in landscape configuration and current landscape departure, similar to the table 

for the landscape composition metrics. However, to simplify the table given the number 

of scenarios, here we reported only the values of the landscape metric corresponding to 

the 5th, 50th, and 95th percentiles of the simulated range of variability. 

• Range of variability and current departure figure—Graphical summary of the range of 

variability in landscape configuration and current landscape departure, similar to the box 

plots for the landscape composition metrics. However, to simplify the plots given the 

number of scenarios, here we displayed the range of the 5th to 95th percentiles as the box 

and the median or 50th percentile as a horizontal line. 

RESULTS

Historical Range of Variability and Departure

For organizational purposes, we divided the HRV results into three major sections. In the 

first section, we describe the results of the simulated disturbance regime with respect to fire 

frequency, area burned, rotation period, and severity, recognizing that the disturbance regime 

was largely determined by the model parameterization and was calibrated to achieve certain 

targeted outcomes. In the second section, we describe the HRV in landscape composition with 

respect to the variability in vegetation developmental stage, canopy cover, and seral stage, 

and the degree of departure of the current landscape with respect to each of these landscape 

definitions. In the last section, we describe the HRV and current departure in landscape 

configuration (i.e., spatial pattern) with respect to each of the landscape definitions. 

Disturbance Regime

Fire Frequency

The simulated number of individual fires per 5-year timestep varied from 61 to 331 (mean = 

194), which translated to about 12 to 66 fires per year initiating within the 181,556-hectare 

project area. Recall that fire frequency was treated as a calibration parameter tuned to achieve 

the target overall fire rotation period of 30 years. Therefore, the absolute fire frequency is 

somewhat revealing because it indicates the number of fires that were required to achieve the 

scientifically supported overall fire rotation period. As expected given the model structure, fire 

frequency increased with the climate modifier, such that the warmer, drier timesteps realized 

on average about two to three times as many fires as the cooler, wetter timesteps (fig. 24).

Area Burned 

Approximately 96 percent of the landscape, equating to 174,830 hectares (432,005 acres), 

was eligible for wildfire disturbance (all cover types except Barren and Water). The percentage 

of the eligible landscape that burned each 5-year timestep (across multiple fires) averaged 

about 18 percent (~30,000 hectares; 74,000 acres), but varied dramatically over time, ranging 

from less than 1 percent (~100 hectares; 250 acres) to almost 74 percent (~129,000 hectares; 

319,000 acres) (figs. 25, 26). As with fire frequency, the total area burned increased with the 

climate modifier, such that, on average, much greater area burned during the warmer, drier 

timesteps, and the “bigfire” years (actually 5-year timesteps) in which more than 50 percent of 

the eligible landscape burned always occurred during these warmer and drier periods (fig. 27).
52 USDA Forest Service RMRS-GTR-385. 2018

Figure 24—Simulated number of fires per 5-year timestep in relation to the climate modifier for 

the simulated historical range of variability (ca. 1550–1850) in the upper Yuba River watershed. 

Solid line represents the average trend (R2 = 40 percent, P < 0.001) and the dotted lines 

represent the trends corresponding to the 5th and 95th percentiles of the data.

Figure 25—Simulated proportion of 5-year timesteps in which a given percentage of the 

eligible area burned for the simulated historical range of variability (ca. 1550–1850) in the 

upper Yuba River watershed.
USDA Forest Service RMRS-GTR-385. 2018 53

Figure 26—Simulated percentage of the eligible area burned per 5-year timestep and the 

proportion of high- versus low-mortality vegetation response (i.e., severity) for the simulated 

historical range of variability (ca. 1550–1850) in the upper Yuba River watershed.

Figure 27—Simulated percentage of eligible area burned per 5-year timestep in relation to the 

climate modifier for the simulated historical range of variability (ca. 1550–1850) in the upper 

Yuba River watershed. Solid line represents the average trend (R2 = 19 percent, P < 0.001) and 

the dotted lines represent the trends corresponding to the 5th and 95th percentiles of the data.
54 USDA Forest Service RMRS-GTR-385. 2018

Another useful way of describing area burned is with exceedance probabilities and 

intervals. Exceedance probability (P) refers to the probability in any one timestep of the total 

area burned exceeding a certain percentage of the landscape, and the exceedance interval (I) is 

the average number of years between timesteps in which a certain exceedance level is achieved:

I = (1⁄E)×t,

where t = length of the timestep in years (= 5 in this case). 

Figure 28 depicts both exceedance probability and exceedance interval for the observed 

range of percentages of the eligible landscape burned in a single 5-year timestep (~0–74 

percent). Exceedance probability decreases monotonically from 1 to 0 as the exceedance 

increases from the minimum to the maximum observed percentage of the landscape 

burned. Similarly, exceedance interval increases monotonically over the same range, as it is 

inversely related to exceedance probability. For convenience, table 12 lists both exceedance 

probabilities and intervals for three exceedance thresholds. It indicates that, on average, every 

20 years (four timesteps in the model) more than 25 percent of the eligible landscape burned.

Figure 28—Simulated probability of wildfires exceeding (left y-axis and solid line) a given 

percentage of eligible area in a 5-year timestep, and the corresponding average interval 

between events of that magnitude (right y-axis and dashed line) for the simulated historical 

range of variability (ca. 1550–1850) in the upper Yuba River watershed.

Table 12—Exceedance probabilities and intervals for burning greater than or equal to a certain 

percentage of the eligible landscape per 5-year timestep for the simulated historical range of 

variability (ca. 1550–1850) in the upper Yuba River watershed.

Percentage of eligible burned 

per timestep

Exceedance 

probability

Exceedance interval 

(yrs)

10 0.63 8

25 0.25 20

50 0.04 135
USDA Forest Service RMRS-GTR-385. 2018 55

Fire Rotation Period

As described previously, we calibrated the model to achieve a target overall FRP of 30 

years for the eligible portion of the landscape (96 percent) and to achieve ±10 percent of 

the target FRPs for the dynamic cover types with 1,000 hectares or more in the project area. 

Consequently, the realized FRPs were not really a result of the model because they largely 

reflected the model parameterization. They were nonetheless useful to examine because they 

allow us to describe how much disturbance was ultimately applied to each cover type and the 

landscape as a whole. 

FRPs varied as much as threefold among the dominant cover types from a low of 24 

years in Sierran Mixed Conifer – Xeric to a high of 63 years in Red Fir – Mesic (table 13). 

We observed much longer FRPs for some of the less common cover types, but these should be 

interpreted with caution due to the small sample size as noted previously. We computed FRPs 

for low- and high-mortality fires, as well as for “any” fire (inclusive of both fire mortality 

levels). FRPs for high-mortality fires were anywhere from 2 to 10 times longer than FRPs for 

low-mortality fires, but this too was largely a reflection of the model parameterization. 

Table 13—Fire rotation periods (FRPs) by mortality level (i.e., severity) for all of the dynamic cover types for the simulated 

historical range of variability (ca. 1550–1850) in the upper Yuba River watershed. Note that cover types are sorted by extent 

within the project area and we considered FRPs for cover types with extent of less than 1,000 ha (2,500 ac) as unreliable, 

even though they are reported in this table for completeness.

Fire rotation period (years)

Cover type Area (ha) Area (ac)

Low 

mortality

High 

mortality

Any 

mortality

Sierran Mixed Conifer – Mesic 57,853 142,955 31 199 27

Sierran Mixed Conifer – Xeric 52,198 128,981 26 257 24

Oak-Conifer Forest and Woodland 23,279 57,522 32 122 25

Red Fir – Mesic 8,563 21,159 84 253 63

Red Fir – Xeric 7,493 18,515 65 126 43

Mixed Evergreen – Mesic 7,273 17,972 58 277 48

Mixed Evergreen – Xeric 6,768 16,724 43 269 37

Sierran Mixed Conifer – Ultramafic 4,124 10,190 65 450 57

Grassland 1,379 3,408 737 63 58

Meadow 1,201 2,968 1,843 63 61

Oak-Conifer Forest and Woodland – Ultramafic 1,060 2,619 42 982 40

Lodgepole Pine 837 2,068 62 275 51

Montane Riparian 732 1,809 102 106 52

Subalpine Conifer 638 1,576 1,245 357 277

Mixed evergreen – ultramafic 604 1,492 132 648 110

Red fir – ultramafic 294 726 167 467 123

Western white pine 273 675 109 506 90

Urban 114 282 3,882 162 156

Sierran mixed conifer with aspen 58 143 41 131 32

Red fir with aspen 31 77 85 207 60

Oak woodland 19 47 30 137 25

Curl-leaf mountain mahogany 18 44 280 109 78

Agriculture 16 40 3,209 162 154

Lodgepole pine with aspen 8 20 59 246 48

Total 174,830 432,004 33 195 29
56 USDA Forest Service RMRS-GTR-385. 2018

It is extremely important to recognize that FRP reflects the average point-specific fire return 

interval, but says nothing about its spatial variability. The point-specific FRI is more appropriate 

for examining spatial variability in fire frequency. We noted substantial spatial variability in the 

point-specific FRI across the landscape (fig. 29), reflecting gradients in cover types with varying 

propensities to burn, but also reflecting complex interactions among fire, vegetation, and terrain 

that emerged from the simulation. The dramatic variability in point-specific FRI within each 

cover type was especially noteworthy. For example, although the point-specific FRI for the Red 

Fir – Mesic cover type averaged 63 years, it varied from about 25 to 125 years among cells 

within this cover type (fig. 30). This spatial variability within even a single cover type resulted 

from the complex interactions among the spatial processes in the model and almost certainly 

was strongly influenced by the local landscape context of the focal cell. For example, a cell of 

Red Fir – Mesic in a neighborhood dominated by subalpine conifer typically had a much longer 

FRI because of the considerably longer average FRI of subalpine conifer (277 years). Indeed, 

the variability in point-specific FRI within cover types was generally as great as the variability 

among cover types, reflecting the overriding importance of landscape context.

Fire Severity

Recall that at the cell level, fires caused either low or high mortality to the overstory 

vegetation, which we defined as less than or greater than 75 percent of the canopy, respectively. 

Furthermore, recall that one of the key model parameters was the probability of a high-mortality 

response, which we specified separately for each unique combination of cover type and seral 

stage, as modified by topographic position, as part of the state-and-transition models (Appendix 

B). The realized percentage of high-mortality fire, however, was at least partly an emergent 

property of the simulation because it reflected the unspecified proportion of time each cover type 

spent in each seral stage. Therefore, it was useful to examine because it allowed us to describe 

how often fire caused an immediate state transition to the early-development seral stage. 

Figure 29—Simulated point-specific fire return interval for the simulated historical range of 

variability (ca. 1550–1850) in the upper Yuba River watershed.
USDA Forest Service RMRS-GTR-385. 2018 57

The percentage of high-mortality fire varied over time from a low of about 2 percent to a 

high of 24 percent, but averaged around 13 percent per timestep (fig. 31), or 15 percent when 

pooled across timesteps. The 95-percent range of variability was about 7–21 percent high 

mortality. This variability was driven primarily by the variation in climate over time (via variation 

in the climate modifier parameter), but it also reflected the interaction between climate and the 

changing seral stage composition over time. For example, a warmer, drier period generally 

increased the percentage of high mortality, but a warmer, drier period that coincided with a 

greater proportion of the forest in closed-canopy conditions increased the percentage even more. 

Landscape Composition

We have defined “landscape composition” as the land cover composition of the 

project area, that is, the extent of each class (or patch type) without considering the spatial 

configuration of the patch mosaic. In this section, we describe the HRV in landscape 

composition with respect to: (1) vegetation developmental stage (none or early, mid-, or 

late development), (2) canopy cover class (none or open, moderate, or closed canopy), and 

(3) seral stage (largely a combination of developmental stage and canopy cover), and the 

degree of current landscape departure in each of these attributes (i.e., how much the current 

landscape, as it existed ca. 2010, deviates from the simulated HRV).

Importantly, although the simulated landscape composition was clearly affected by the 

model parameterization, we did not calibrate the model to achieve any prespecified vegetation 

conditions. Calibration was restricted to disturbance regime characteristics as described 

previously. Therefore, we considered the simulated landscape composition to be a result of the 

simulation from which we can make inferences about HRV and current departure. 

Figure 30—Simulated distribution of point-specific fire return intervals (or fire rotation periods) 

by fire mortality level for cells classified as Red Fir – Mesic for the simulated historical range of 

variability (ca. 1550–1850) in the upper Yuba River watershed.
58 USDA Forest Service RMRS-GTR-385. 2018

Figure 31—Simulated percentage of the area burned per 5-year timestep that resulted in high 

mortality (>75 percent canopy mortality) for the simulated historical range of variability (ca. 

1550–1850) in the upper Yuba River watershed. Red dotted line represents the average.

Figure 32—Simulated trajectory in the percentage of the eligible landscape (i.e., excluding nonseral 

and nonvegetated land cover) in each vegetation developmental stage for the simulated historical 

range of variability (ca. 1550–1850) in the upper Yuba River watershed. Note that the trajectory 

includes the current landscape (timestep = 0), the equilibration period (timesteps 1–40), and the 

period used to compute the historical range of variability (timesteps 41–500).
USDA Forest Service RMRS-GTR-385. 2018 59

Developmental Stage Dynamics

Vegetation developmental stage rapidly achieved dynamic equilibrium in the simulation, 

after which the percentage of the landscape in each developmental stage remained relatively 

constant at approximately a 10:20:70 ratio of early- to mid- to late-development stages (fig. 

32). The relatively minor variability of this ratio was indicative of the landscape achieving a 

nearly perfect shifting-mosaic equilibrium (sensu Bormann and Likens 1979), in other words, a 

constancy in landscape composition, despite the shifting spatial mosaic of stages.

The current landscape’s overall developmental stage distribution (i.e., pooled across all cover 

types) deviates considerably from the HRV (table 14, fig. 33). Specifically, the current landscape 

has a 19:47:34 ratio of early- to mid- to late-development stages, and therefore has much more in 

the early- and mid-development stages and much less in the late-development stage. This finding 

is not surprising given the land use history of the past century. The current landscape also has a 

somewhat more even distribution of developmental stages, whereas in the HRV the distribution 

was heavily skewed toward the late-development stage.

Table 14—Historical range of variability (HRV) in vegetation developmental stages, represented as the percentage of the eligible 

landscape (i.e., excluding nonseral and nonvegetated land cover) for ca. 1550–1850 in the upper Yuba River watershed. Select 

percentiles of the simulated HRV are given, as well as the current landscape condition and its corresponding percentile of the 

simulated HRV.

Percentile of HRV Current

Developmental stage 0th 5th 25th 50th 75th 95th 100th % eligible % HRV

Early 5.33 6.73 8.72 10.02 11.88 14.42 16.82 18.99 100

Mid 16.24 17.94 19.68 20.69 21.68 23.12 24.22 47.00 100

Late 62.53 65.52 67.67 69.10 70.52 72.47 74.54 34.01 0

Figure 33—Graphical summary of the data in table 14 for percentage of the eligible landscape (i.e., 

excluding nonseral and nonvegetated land cover) in each vegetation developmental stage for the 

simulated historical range of variability (HRV) (ca. 1550–1850) in the upper Yuba River watershed. 

Boxes represent the interquartile range (25th–75th percentiles) of the HRV; the median is the dark 

horizontal line in the middle of the box. Thick, solid vertical lines represent the 5th–95th percentiles 

of the HRV, and the thin, gray vertical extensions represent the full range of the simulated HRV. 

Dashed, red horizontal lines represent the current condition of the landscape.
60 USDA Forest Service RMRS-GTR-385. 2018

These general patterns were relatively consistent across the major cover types, although 

the specific HRV distribution of developmental stages (i.e., the early:mid:late ratio) differed 

somewhat among cover types, as did the magnitude of current departure from HRV (fig. 34). For 

example, the HRV median percentage area in the early-development stage varied among cover 

types from about 4 to 26 percent, and the HRV median percentage area in the late-development 

stage varied from about 47 to 91 percent, largely reflecting differences among cover types in 

the prevalence of high-mortality (i.e., stand-replacing) fire and fire rotation periods. Similarly, 

the current departure in the late-development stage is extreme in Mixed Evergreen Forest and 

Oak-Conifer Forest and Woodland and somewhat less so in Sierran Mixed Conifer Forest and 

Red Fir Forest. Despite these differences, all major cover types showed a preponderance of area 

in the late-development stage under the HRV and a deficiency of late-development forest in the 

current landscape (see table D1 in Appendix D for detailed results).

Canopy Cover Dynamics

We considered canopy cover as both a continuous variable and categorical variable by 

reclassifying seral stage into average percent canopy cover (see Appendix B for crosswalks) or 

into discrete classes. The discrete classification is more straightforward for quantifying HRV. 

Like vegetation developmental stage, the distribution of canopy cover classes rapidly achieved 

dynamic equilibrium in the simulation, after which the percentage of the landscape in each 

canopy cover class remained variable but stable about a 38:24:37 ratio of open- to moderate- to 

Figure 34—Median percentage area in early-, mid-, and late-development stages of succession for the major cover types 

(≥1,000 ha extent) for the simulated historical range of variability (ca. 1550–1850) in comparison to the distribution for the 

current landscape in the upper Yuba River watershed. Note differences in y-axis scales. 
USDA Forest Service RMRS-GTR-385. 2018 61

closed-canopy classes (fig. 35). In general, the landscape fluctuated over time between having 

an approximately equal mixture of open-, moderate-, and closed-canopy conditions and one 

that was either slightly dominated by open-canopy conditions or slightly dominated by closedcanopy conditions (e.g., fig. 36). 

The current landscape’s overall canopy cover composition (i.e., pooled across all cover 

types) does not differ much from the HRV, being within the 95-percent range of variability for 

all three canopy cover classes (table 15, fig. 37). At first, this result seems somewhat surprising 

given the popular belief that the current landscape has an uncharacteristic predominance of 

closed-canopy forest. However, it is important to recognize that this result is for the landscape as 

a whole, pooled across all cover types. It is being driven by the abundance of early-development 

stands (which are classified as open canopy) in the current landscape, which make up about 20 

percent of the landscape (fig. 33). Thus, this result for the landscape as a whole masks important 

departures within individual cover types and developmental stages, as follows. 

First, the HRV distribution of canopy cover classes varied substantially among cover types, 

with the xeric and ultramafic cover types typically maintaining a preponderance of open- and 

moderate-canopy conditions and the mesic cover types typically maintaining a preponderance 

of closed-canopy conditions (fig. 38). These patterns were partly due to lower productivity and 

slower vegetation growth on the xeric and, especially, ultramafic sites compared to the mesic 

sites, as governed by the state-and-transition models. The mixed evergreen forests maintained a 

preponderance of closed-canopy conditions in both xeric and mesic sites, presumably because 

of the relatively fast succession on both xeric and mesic sites supporting this productive forest 

type. Similarly, the magnitude of current departure varies considerably among cover types, 

with some cover types exhibiting very little departure (e.g., Mixed Evergreen Forest), others 

exhibiting moderate departure (e.g., Sierran Mixed Conifer Forest), and still others showing 

major departure (e.g., Oak-Conifer Forest and Woodland – Ultramafic and Red Fir – Mesic).

Figure 35—Simulated trajectory in the percentage of the eligible landscape (i.e., excluding nonseral 

and nonvegetated land cover) in each canopy cover class (<40 percent = “Open,” 40–75 percent = 

“Moderate,” and >75 percent = “Closed”), for the simulated historical range of variability (ca. 1550–

1850) in the upper Yuba River watershed. Note that the trajectory includes the current landscape 

(timestep = 0) and the equilibration period (timesteps 1–40) and the period used to compute the 

historical range of variability (timesteps 41–500).
62 USDA Forest Service RMRS-GTR-385. 2018

Figure 36—Snapshots of the canopy cover class mosaic for timesteps in which the landscape 

had a slight majority of (a) open-canopy versus (b) closed-canopy conditions during the simulated 

historical range of variability (ca. 1550–1850) in the upper Yuba River watershed.
USDA Forest Service RMRS-GTR-385. 2018 63

Table 15—Historical range of variability (HRV) in vegetation canopy cover classes (<40% = “Open,” 40–75% = “Moderate,” and 

>75% = “Closed”), represented as the percentage of the eligible landscape (i.e., excluding nonseral and nonvegetated land cover) 

for ca. 1550–1850 in the upper Yuba River watershed. Select percentiles of the simulated HRV are given, as well as the current 

landscape condition and its corresponding percentile of the simulated HRV.

Percentile of HRV Current

Canopy cover class 0th 5th 25th 50th 75th 95th 100th % eligible % HRV

Open 25.22 28.97 33.56 37.82 43.32 50.47 55.34 37.57 47

Moderate 20.96 22.25 23.45 24.20 25.25 27.69 30.06 27.02 93

Closed 21.19 25.62 32.42 37.35 41.63 45.98 51.77 35.40 40

Figure 37—Graphical summary of the data in table 15 for percentage of the eligible landscape (i.e., 

excluding nonseral and nonvegetated land cover) in each vegetation canopy cover class for the 

simulated historical range of variability (HRV) (ca. 1550–1850) in the upper Yuba River watershed. 

Boxes represent the interquartile range (25th–75th percentiles) of the HRV; the median is the dark 

horizontal line in the middle of the box. Thick, solid vertical lines represent the 5th–95th percentiles 

of the HRV, and the thin, gray vertical extensions represent the full range of the simulated HRV. 

Dashed, red horizontal lines represent the current condition of the landscape.
64 USDA Forest Service RMRS-GTR-385. 2018

Figure 38—Median percentage area in open-, moderate-, and closed-canopy classes for the major cover types (≥1,000 

ha extent) for the simulated historical range of variability (ca. 1550–1850) in comparison to the distribution for the current 

landscape in the upper Yuba River watershed. Note differences in y-axis scales.

Second, the result for the landscape as a whole masks important canopy cover departures

occurring within individual developmental stages (fig. 39). There are two important things 

to note about figure 39. First, for simplicity the median HRV distribution is shown here as 

a benchmark for comparison to the current distribution, but it can be somewhat misleading 

because the full HRV distribution includes (often considerable) variability in the distribution 

among canopy cover classes (see table D2 in Appendix D for detailed results). Second, 

the percentage in each canopy cover class is within each developmental stage, so that the 

percentages sum to 100 percent within each developmental stage separately. Given these 

caveats, it is apparent that the proportional distribution of canopy cover classes within the latedevelopment stage deviates considerably from the HRV. Specifically, the late-development 

stage in the current landscape contains far more area in the moderate- and closed-canopy 

conditions than in the open-canopy condition. The ratio of open to moderate to closed 

canopy in the late-development stage was roughly 1:1:1.5 in the HRV, whereas in the current 

landscape the ratio is about 1:4:6. In addition, canopy cover departure within individual 

developmental stages is more apparent in some cover types than others (fig. 40). Some of the 

notable results by cover type are as follows:
USDA Forest Service RMRS-GTR-385. 2018 65

• Mixed Evergreen (Mesic and Xeric)—Current departure is prevalent only in the middevelopment stage, with the current landscape containing much more closed-canopy and 

much less moderate-canopy cover.

• Oak-Conifer Forest and Woodland—Current departure is relatively minor in both 

developmental stages.

• Oak-Conifer Forest and Woodland – Ultramafic—Current departure is pronounced in 

both developmental stages, with the current landscape containing much more closed 

canopy and much less open canopy in both developmental stages.

• Red Fir – Mesic—Current departure is pronounced in both developmental stages, with 

the current landscape containing much more open canopy and much less closed canopy 

in both developmental stages. Note that this is opposite of the pattern observed in other 

cover types.

• Red Fir – Xeric—Current departure is pronounced in both developmental stages, with the 

current landscape containing much more closed or moderate canopy and much less open 

canopy in both developmental stages.

• Sierran Mixed Conifer – Mesic—Current departure is evident in both developmental 

stages, but the pattern differs. The current landscape contains much more closed canopy 

and much less open canopy in the late-development stage, and contains much more 

moderate canopy and much less closed canopy in the mid-development stage.

• Sierran Mixed Conifer – Ultramafic and Xeric—Current departure is pronounced in both 

developmental stages, with the current landscape containing much more closed canopy 

and much less open canopy in both developmental stages. 

Figure 39—Median percentage area of mid-development forest in open- (MDO), moderate- (MDM), 

and closed-canopy (MDC) conditions and separately of late-development forest in open- (LDO), 

moderate- (LDM), and closed-canopy (LDC) conditions for the landscape as a whole for the 

simulated historical range of variability (ca. 1550–1850) and the current landscape in the upper 

Yuba River watershed. Note that the percentage distribution among canopy cover classes is within 

each developmental stage separately.
66 USDA Forest Service RMRS-GTR-385. 2018

Thus, despite the result for the landscape as a whole, the results by cover type and 

developmental stage suggest that for several of the major cover types the current landscape 

contains much more closed or moderate canopy and much less open canopy than was observed 

for HRV.

It is important to recognize that these results are ultimately driven by the model 

parameterization, specifically pertaining to several key parameters that determined the relative 

probability of fires causing a transition to a more open-canopy seral stage and the rate of 

succession in canopy closure in the absence of fire. For example, in the Sierran Mixed Conifer 

– Mesic forest type, the dominant cover type in the project area, wildfires in the closed-canopy 

seral stages had a 13- to 19-percent chance (depending on developmental stage) of being high 

mortality and causing the stand to transition to an early-development, open-canopy condition. 

The remaining 81 to 87 percent of wildfires were low-mortality fires with an 18- to 54-percent 

chance (depending on developmental stage) of causing a transition to a more open-canopy 

condition (see Appendix B for details). However, succession in canopy closure was relatively 

rapid, with an average of only 16 years to transition from open to moderate canopy or moderate 

to closed canopy. This, coupled with a 29-year FRP for low-mortality fires and a 199-year FRP 

for high-mortality fires, produced a strong tendency for the canopy to close before another fire 

could potentially maintain the open canopy. 

Figure 40—Median percentage area of mid-development forest in open- (MDO), moderate- (MDM), and closed-canopy (MDC) 

conditions and of late-development forest in open- (LDO), moderate- (LDM), and closed-canopy (LDC) conditions for the major 

cover types (≥1,000 ha extent) for the simulated historical range of variability (ca. 1550–1850) (darker bars) and the current 

landscape (lighter bars) in the upper Yuba River watershed. Note that the percentage distribution among canopy cover classes 

is within each developmental stage separately. Also note differences in y-axis scales. 
USDA Forest Service RMRS-GTR-385. 2018 67

Last, as described previously, we verified that the model, as parameterized for the HRV, 

simulated a greater preponderance of fire (and high-mortality fire) on steeper, south- and westfacing, upper slopes, resulting in a decrease in average canopy cover (table 6). Although this 

relationship was extremely “noisy,” it was evident in the map of average canopy cover (fig. 

41). We included the average canopy cover map because it is useful for discerning where in the 

landscape the canopy cover is more likely to be relatively high or low. However, it can also give a 

false impression that the expected canopy cover composition of the landscape at any point in time 

is largely devoid of both high and low canopy cover patches, which is in fact not the case (e.g., 

fig. 36). The limitation associated with the “averaging” of canopy cover across timesteps is that it 

dampens the contrast between high and low canopy cover patches that typically exists within any 

one timestep (a problem known generally as “regression towards the mean”).

Seral Stage Dynamics

The distribution of seral stages, representing the combination of developmental stage and 

canopy cover class pooled across cover types, rapidly achieved dynamic equilibrium in the 

simulation, after which the percentage of the landscape in each seral stage remained variable 

but stable (fig. 42). In general, this was true for most individual cover types; within a few 

decades the seral stage distribution achieved dynamic equilibrium. In the ultramafic cover 

types, however, equilibration took considerably longer. For example, in the Sierran Mixed 

Conifer – Ultramafic cover type, equilibration took more than 100 years (fig. 43). The longer 

“recovery” time for the ultramafic types undoubtedly reflected the longer FRP and slower rate 

of succession. We highlight this in our management implications section because it means 

that it will be considerably more difficult and take much longer to return the ultramafic sites 

to their HRV based solely on the restoration of natural disturbances. Active management 

may facilitate restoration of the seral stage distribution in these ultramafic cover types, but 

the rate at which mid-seral stages can succeed to late-development seral stages is ultimately 

constrained by the slow rates of succession on these nutrient-poor sites.

Figure 41—Average percent canopy cover (smoothed to facilitate interpretation) for the simulated 

historical range of variability (ca. 1550–1850) in the upper Yuba River watershed. Note that the 

average canopy cover does not depict what the landscape might look like at any single point in time 

(see figure 36).
68 USDA Forest Service RMRS-GTR-385. 2018

Figure 42—Simulated trajectory in the percentage of the eligible landscape (i.e., excluding 

nonseral and mixed conifer-aspen forest land cover types) in each seral stage (see Methods for 

definitions of seral stages) for the simulated historical range of variability (ca. 1550–1850) in the 

upper Yuba River watershed. Note that the trajectory includes the current landscape (timestep = 0) 

and the equilibration period (timesteps 1–40) and the period used to compute the historical range 

of variability (timesteps 41–500).

Figure 43—Simulated trajectory in the percentage of the Sierran Mixed Conifer – Ultramafic cover 

type in each seral stage (see Methods for definitions of seral stages) for the simulated historical 

range of variability (ca. 1550–1850) in the upper Yuba River watershed. Note that the trajectory 

includes the current landscape (timestep = 0) and the equilibration period (timesteps 1–40) and the 

period used to compute the historical range of variability (timesteps 41–250).
USDA Forest Service RMRS-GTR-385. 2018 69

The current landscape’s overall seral stage distribution (i.e., pooled across all cover types) 

deviates considerably from the HRV (table 16, fig. 44), although this largely reflects the significant 

departure in developmental stage distribution. The current landscape has much more in the early – 

all structures seral stage, perhaps reflecting recent high-severity fires such as the Pendola fire and 

past land use practices (e.g., hydraulic mining). Such disturbances created large areas of earlyseral chaparral that stagnated in the shrub-dominated vegetation condition. The current landscape 

also has much more in each of the mid-development seral stages and much less in each of the 

late-development seral stages. Moreover, in the late-development seral stages the proportional 

distribution among canopy cover classes deviates considerably from the HRV. Specifically, under 

the HRV, the ratio of open- to moderate- to closed-canopy conditions in the late-development stage 

was approximately 1:1:1.5, whereas in the current landscape the ratio is about 1:4:6. In other words, 

in the current landscape within the late-development stage there is much more in the moderateand closed-canopy condition relative to the open-canopy condition. Overall, the greatest departure 

is in the late-development, open-canopy seral stage; in the current landscape this seral stage is 

dramatically underrepresented relative to the HRV due to both the paucity of late-development forest 

and the effective suppression of wildfires, which function to maintain open-canopy conditions. It is 

worth noting that although it is tempting to interpret current departure for the mixed conifer-aspen 

seral stages, we did not do so because of the small extent of these cover types in the project area. 

The distribution of seral stages and current departure within each of the dominant cover types 

(i.e., those with ≥1,000 hectares in the project area) varied considerably from the overall patterns 

described earlier, warranting a separate interpretation for each cover type, as follows (see table D3 in 

Appendix D for the detailed results for individual cover types):

• Mixed Evergreen (Mesic and Xeric)—In the HRV, moderate-canopy conditions were more 

prevalent than either open- or closed-canopy conditions in the mid-development stage, 

whereas the current landscape contains a disproportionately high amount of closed canopy in 

this developmental stage. In the HRV, closed-canopy conditions were prevalent in the latedevelopment stage, whereas in the current landscape the proportion in closed canopy is low (fig. 

45a-b).

• Oak-Conifer Forest and Woodland—In the HRV, closed-canopy conditions were more prevalent 

than open-canopy conditions in both mid- and late-development stages. Current departure is 

dominated by the paucity of late-development seral stages in the current landscape (fig. 45c).

• Oak-Conifer Forest and Woodland – Ultramafic—The HRV distribution of canopy cover 

classes within developmental stages was opposite that of Oak-Conifer Forest and Woodland, 

with the open-canopy condition being more prevalent than the closed-canopy condition. 

Current departure is dominated by relatively much more closed-canopy condition in the middevelopment stage and much less open-canopy condition in the late-development stage, with the 

latter being especially pronounced (fig. 45d).

• Red Fir – Mesic—In the HRV, closed-canopy conditions were much more prevalent than opencanopy conditions in both mid- and late-development stages. However, the current landscape 

is dominated by open- and moderate-canopy conditions in the mid-development stage and 

moderate-canopy condition in the late-development stage. Current departure is most pronounced 

in the proportional underrepresentation of closed-canopy conditions in both developmental 

stages in the current landscape (fig. 45e).

• Red Fir – Xeric—The HRV distribution of seral stages differed considerably from that of Red 

Fir – Mesic. The most notable differences are the relative preponderance of the early-seral stage 

and the prevalence of open-canopy conditions in the mid-development stage and, accordingly, 

less in the late-development, closed-canopy seral stage. Overall, current departure is much 

less pronounced in Red Fir – Xeric, with the only significant departure being the proportional 

underrepresentation of the late-development, closed-canopy seral stage (fig. 45f).

• Sierran Mixed Conifer – Mesic—Similar to Red Fir – Mesic, in the HRV closed-canopy 

conditions were more prevalent than open-canopy conditions in both mid- and late-development 
70 USDA Forest Service RMRS-GTR-385. 2018

stages. With respect to current departure, the current landscape has much more in the middevelopment, open- and moderate-canopy conditions and much less in the late-development, 

open-canopy condition (fig. 45g). 

• Sierran Mixed Conifer – Ultramafic and Xeric—In contrast to Sierran Mixed Conifer – Mesic, 

open-canopy conditions were more prevalent in the HRV than closed-canopy conditions in both 

mid- and late-development stages for the ultramafic variant and in the late-development stage 

for the xeric variant. Accordingly, with respect to current departure, the current landscape has 

proportionately less in the open-canopy conditions in both developmental stages. In addition, the 

current landscape has much more in the early-seral stage (fig. 45h-i). 

Table 16—Historical range of variability (HRV) in vegetation seral stages (excluding those associated with mixed conifer-aspen 

cover types; see table 2 for acronyms), represented as the percentage of the eligible landscape (i.e., excluding nonseral and 

nonvegetated cover types) for ca. 1550–1850 in the upper Yuba River watershed. Select percentiles of the simulated HRV are 

given, as well as the current landscape condition and its corresponding percentile of the simulated HRV.

Percentile of HRV Current

Seral stage 0th 5th 25th 50th 75th 95th 100th % eligible % HRV

ED 5.32 6.73 8.71 10.01 11.88 14.41 16.81 18.98 100

MDO 5.23 5.97 6.74 7.46 8.27 9.32 10.65 15.21 100

MDM 3.97 4.58 5.28 5.76 6.24 7.02 7.63 15.43 100

MDC 3.76 4.72 6.34 7.31 8.37 9.64 10.94 16.32 100

LDO 12.95 15.79 17.91 20.39 23.62 28.04 30.21 3.37 0

LDM 15.68 17.04 17.92 18.47 19.30 21.17 22.94 11.58 0

LDC 16.19 20.62 25.99 29.76 33.40 37.52 42.52 19.05 3

Figure 44—Graphical summary of the data in table 16 for percentage of the eligible landscape (i.e., 

excluding nonseral and nonvegetated cover types) in each seral stage (excluding those associated with 

mixed conifer-aspen cover types) for the simulated historical range of variability (HRV) (ca. 1550–1850) 

in the upper Yuba River watershed. Boxes represent the interquartile range (25th–75th percentiles) of the 

HRV; the median is the dark horizontal line in the middle of the box. Thick, solid vertical lines represent 

the 5th–95th percentiles of the HRV, and the thin, gray vertical extensions represent the full range of the 

simulated HRV. Dashed, red horizontal lines represent the current condition of the landscape.
USDA Forest Service RMRS-GTR-385. 2018 71

Landscape Configuration

We have defined “landscape configuration” as the spatial pattern of the landscape mosaic. 

In this section, we describe the HRV in landscape configuration with respect to: (1) vegetation 

developmental stage, (2) canopy cover class, and (3) seral stage, and the degree of current 

landscape departure in each of these attributes (i.e., how much the current landscape, as it 

existed ca. 2010, deviates from the simulated HRV). 

Importantly, although the simulated landscape configuration was clearly affected by the 

model parameterization, as discussed previously we did not calibrate the model to achieve 

any prespecified vegetation conditions. Therefore, we considered the simulated landscape 

configuration to be a result of the simulation, from which we can make inferences about HRV 

and current departure. However, the spatial patterns (i.e., landscape configuration) resulting 

from the simulated processes are much more sensitive to modeling artifacts than landscape 

composition. In particular, the fine-scaled spatial heterogeneity as measured by several of 

the landscape metrics is affected by the choice of cell size in the model. Consequently, we 

tempered our interpretation of the landscape configuration results as follows:

• We placed more emphasis on the interpretation of landscape metrics that measure the 

coarse-scale or larger patch structure, such as the area-weighted mean metrics, because 

Figure 45—Graphical summary of the data in table D3 for the simulated historical range of variability in each vegetation seral 

stage and current departure for the major cover types (≥1,000 ha extent) in the upper Yuba River watershed. See figure 44 

caption for details on interpretation. Note differences in y-axis.
72 USDA Forest Service RMRS-GTR-385. 2018

these metrics are much less sensitive to the scale of the grid and the grid-based modeling 

approach. In particular, our input spatial layers (e.g., Eveg)—and therefore the current 

conditions—were relatively coarse-scaled, in contrast to the relatively fine-grained 

simulated output. Despite our efforts to minimize this discrepancy by coarsening 

the simulated output, a direct comparison is still challenging. Using coarse-scaled 

measurements of landscape configuration (i.e., those based on area-weighted means) 

resolves much of this issue, and is therefore more reliable for estimating curent departure. 

• For the reasons discussed earlier, we describe a range of variability when interpreting 

the landscape metrics that measure the fine-scale spatial heterogeneity, such as those that 

are based on the mean or sensitive to the designation of edges. Accordingly, we refrained 

from drawing strong conclusions regarding current departure in these landscape metrics.

Developmental Stage Dynamics

For the developmental stage mosaic (pooled across all cover types), most of the landscape 

metrics measuring the coarse-scale structure of the mosaic achieved dynamic equilibrium in 

the simulation almost immediately (i.e., within 10–20 years), whereas the remaining metrics 

required 50–100 years to equilibrate (e.g., fig. 46). 

In general, the HRV in landscape structure was characterized by very large (~13,000–

35,000 hectares; 32,000–86,000 acres), extensive (~6,000–10,000 meters; 20,000–30,000 

feet), and geometrically very complex matrix-forming patches of predominantly latedevelopment stage vegetation; these patches varied considerably over time in response to 

periodic large disturbance events interspersed with long periods of recovery (table 17, fig. 47). 

Here, we use the term “matrix” to refer to very large and extensive patches that individually 

compose about 10 percent or more of the landscape and thus dominate the structure (and 

Figure 46—Simulated trajectory in the Contagion metric based on the landscape classified into 

vegetation developmental stages (none, early, mid, and late) for the simulated historical range of 

variability (ca. 1550-1850) in the upper Yuba River watershed. Note that the trajectory includes the 

current landscape (timestep = 0), the equilibration period (timesteps 1–40), and the period used to 

compute the historical range of variability (timesteps 41–500). The current landscape value and the 

5th (q0.05), 50th (q0.5), and 95th (q0.95) percentiles of the simulated variability are also shown.
USDA Forest Service RMRS-GTR-385. 2018 73

presumably function) of the landscape mosaic. This dynamic matrix was coupled with a 

tremendous and constant amount of fine-scale heterogeneity, including numerous highcontrast edges and small and intermediate-sized patches of early and mid-development 

embedded within the coarse-scale, late-development matrix. Thus, even though these matrixforming patches were large and extensive, they were also extremely geometrically complex, 

resulting in an average of only 33 to 44 percent in core area. This geometric complexity was 

due to the myriad small patches created by many small disturbances and the high spatial 

heterogeneity in severity of the larger disturbances. 

The current landscape configuration based on developmental stages deviates considerably 

from the HRV (table 17). Compared to the HRV, the larger patches in the current landscape are 

much smaller and less extensive. These patches are also less geometrically complex, resulting 

in a greater interior-to-edge ratio, and have less contrast along the edges. In addition, the 

developmental stages are more interspersed than under the HRV. The current landscape also 

has notably less fine-scale heterogeneity, for example as measured by edge density and the 

aggregation index, but some of this departure may be an artifact of the modeling, as discussed 

previously. Overall, therefore, the most notable departure in landscape configuration, and the 

departure we can infer with the most confidence, is the absence of very large, extensive, and 

geometrically complex matrix-forming patches of late development in the current landscape. 

Canopy Cover Dynamics

We observed patterns in the canopy cover class mosaic (pooled across all cover types) 

similar to the patterns observed for developmental stage. Most of the landscape metrics 

measuring the coarse-scale structure of the mosaic achieved dynamic equilibrium in the 

simulation almost immediately (i.e., within 10–20 years), whereas the remaining metrics 

required 50–100 years to equilibrate (e.g., fig. 48). 

Table 17—Historical range of variability (HRV) in landscape metrics (see Landscape Configuration 

under Methods for description and units for each landscape metric) computed on the basis of the 

landscape classified into vegetation developmental stages (none, early, mid, and late) for circa 1550–

1850 in the upper Yuba River watershed. Select percentiles of the simulated HRV are given, as well as 

the current landscape condition and its corresponding percentile of the simulated HRV.

Percentile of historical range of vulnerability Current

Landscape metric 5th 50th 95th % eligible % HRV

LPI 17.94 29.87 38.72 5.72 0

AREA_AM 13,564.06 22,737.68 34,698.79 1,808.16 0

GYRATE_AM 5,954.86 8,009.40 9,825.99 2,397.36 0

SHAPE_AM 42.82 54.07 69.39 8.65 0

DCORE_AM 379.98 1,149.70 2,510.95 681.58 23

CAI_AM 33.53 39.69 44.44 62.03 100

TECI 44.80 46.50 48.79 44.43 3

IJI 60.00 65.61 69.64 84.95 100

AREA_MN 2.77 3.30 3.83 21.00 100

SHAPE_MN 1.32 1.33 1.33 1.63 100

DCORE_MN 3.41 4.68 5.73 12.30 100

CAI_MN 0.83 0.98 1.11 17.92 100

ED 135.17 148.59 168.54 70.95 0

CWED 62.16 70.04 81.29 32.25 0

AI 74.69 77.68 79.70 89.35 100

CONTAG 36.91 41.30 45.24 41.51 54
74 USDA Forest Service RMRS-GTR-385. 2018

Figure 47—Snapshots of the vegetation developmental stage mosaic for (a) the current landscape 

and (b) the last timestep of the simulated historical range of variability (ca. 1550–1850) in the upper 

Yuba River watershed, shown here for a randomly selected location within the project area.
USDA Forest Service RMRS-GTR-385. 2018 75

Figure 48—Simulated trajectory in the Contagion metric based on the landscape classified into 

canopy cover classes (none, open, moderate, closed) for the simulated historical range of variability 

(ca. 1550–1850) in the upper Yuba River watershed. Note that the trajectory includes the current 

landscape (timestep = 0), the equilibration period (timesteps 1–40), and the period used to compute 

the historical range of variability (timesteps 41–500). The current landscape value and the 5th 

(q0.05), 50th (q0.5), and 95th (q0.95) percentiles of the simulated variability are also shown.

In general, the HRV in landscape structure was characterized by large (~1,500–7,500 

hectares; 3,700–18,500 acres), extensive (~2,000–4,000 meters; 7,000–13,000 feet), 

geometrically very complex, but generally not matrix-forming patches of either open- or 

closed-canopy forest that varied considerably over time between a majority of open-canopy 

forest and a majority of closed-canopy forest. This large patch structure was coupled with a 

relatively high degree of interspersion of canopy cover classes and a tremendous and constant 

amount of fine-scale spatial heterogeneity in canopy cover (table 18, fig. 49). Compared to 

the developmental stage mosaic, the canopy cover mosaic was less dominated by matrixforming patches (e.g., almost an order of magnitude smaller area-weighted mean patch size) 

and exhibited a much greater degree of fine-scale spatial heterogeneity (e.g., almost a twofold 

greater density of edges). Thus, the landscape overall exhibited much less contagion in canopy 

cover than in developmental stage. The greater degree of spatial heterogeneity in canopy cover 

compared to developmental stage was due to the large quantity of disturbances resulting in 

partial overstory mortality that did not change the developmental stage of the residual canopy. 

For example, most of the area burned in wildfires was classified as low mortality. The result 

in these areas was either no change in canopy cover class or a thinning of the overstory that 

produced a transition to a more open-canopy cover class, resulting in a spatially heterogeneous 

mosaic in canopy cover.

The current landscape configuration based on canopy cover classes deviates somewhat

from the HRV (table 18). Compared to the HRV, the larger patches in the current landscape are 

on the small and less extensive side, but nonetheless still within the HRV. However, the larger 

patches in the current landscape are much less geometrically complex than under the HRV, 

resulting in a greater percentage of the patches in core area. In addition, the contrast along 

edges is greater in the current landscape and the canopy cover classes are more interspersed 
76 USDA Forest Service RMRS-GTR-385. 2018

than under the HRV. As with developmental stage, the current landscape also has notably less 

fine-scale heterogeneity, for example as measured by edge density and the aggregation index, 

but again some of this observed departure may be due to modeling artifacts as discussed 

previously. Overall, departure in canopy cover appears to be mostly related to changes in 

the geometric complexity and fine-scale heterogeneity of the mosaic, and although we are 

confident in reporting this departure, we are hesitant to draw strong conclusions about the 

magnitude of the departure. 

Seral Stage Dynamics

The seral stage mosaic (pooled across all cover types) exhibited HRV patterns similar to 

the patterns observed for developmental stage and canopy cover class. Most of the landscape 

metrics measuring the coarse-scale structure of the mosaic achieved dynamic equilibrium in 

the simulation almost immediately (i.e., within 10–20 years), whereas the remaining metrics 

required 50–100 years to equilibrate (e.g., fig. 50).

In general, the HRV in landscape structure was characterized by the absence of large, 

extensive, matrix-forming patches of a single seral stage. Instead, the landscape maintained 

a relatively high degree of spatial heterogeneity and variability over time in the larger patch 

structure, with the area-weighted mean patch size varying between about 290 and 916 hectares 

(717–2,263 acres). This larger patch structure was coupled with a relatively low magnitude 

of average contrast along edges, moderate degree of interspersion of seral stages, and a 

tremendous and constant amount of fine-scale spatial heterogeneity in seral stages (table 19, 

fig. 51). Overall, the picture of the HRV that emerges from the suite of landscape metrics 

describing the configuration and variability over time in the seral stage mosaic is one of a 

spatially heterogeneous mosaic of seral stage patches at fine to intermediate spatial scales 

(i.e., <1- to several hundred-hectare patches); large, matrix forming patches are rarely present. 

Table 18—Historical range of variability (HRV) in landscape metrics (see Landscape Configuration 

under Methods for description and units for each landscape metric) computed on the basis of the 

landscape classified into canopy cover classes (none, open, moderate, closed) for ca. 1550–1850 in 

the upper Yuba River watershed. Select percentiles of the simulated HRV are given, as well as the 

current landscape condition and its corresponding percentile of the simulated HRV.

Percentile of historical range of vulnerability Current

Landscape metric 5th 50th 95th % eligible % HRV

LPI 4.06 8.57 16.39 5.97 25

AREA_AM 1,458.52 2,956.67 7,559.46 1,719.73 11

GYRATE_AM 2,041.19 2,654.64 4,036.39 2,302.38 21

SHAPE_AM 21.95 29.27 48.37 9.05 0

DCORE_AM 136.00 265.87 624.71 448.54 86

CAI_AM 16.47 20.22 23.97 60.70 100

TECI 42.74 43.99 44.99 46.07 100

IJI 70.30 72.27 73.02 87.14 100

AREA_MN 1.18 1.38 1.62 21.53 100

SHAPE_MN 1.29 1.32 1.34 1.68 100

DCORE_MN 1.17 1.55 1.98 10.17 100

CAI_MN 0.22 0.32 0.43 21.31 100

ED 237.86 257.78 280.16 74.08 0

CWED 105.09 113.59 124.83 34.88 0

AI 57.93 61.29 64.28 88.88 100

CONTAG 20.26 21.75 23.92 39.11 100
USDA Forest Service RMRS-GTR-385. 2018 77

Figure 49—Snapshots of the canopy cover class mosaic for (a) the current landscape and (b) the 

last timestep of the simulated historical range of variability (ca. 1550–1850) in the upper Yuba River 

watershed, shown here for a randomly selected location within the project area.
78 USDA Forest Service RMRS-GTR-385. 2018

Table 19—Historical range of variability (HRV) in landscape metrics (see Landscape Configuration

under Methods for description and units for each landscape metric) computed on the basis of 

the landscape classified into seral stage classes (see HRV Spatial Input Data under Methods

for classification) for ca. 1550–1850 in the upper Yuba River watershed. Select percentiles of 

the simulated HRV are given, as well as the current landscape condition and its corresponding 

percentile of the simulated HRV.

Percentile of HRV Current

Landscape metric 5th 50th 95th % eligible % HRV

LPI 2.50 2.58 5.06 2.50 35

AREA_AM 289.85 500.44 915.57 302.57 7

GYRATE_AM 942.18 1,138.37 1,447.33 1,054.98 27

SHAPE_AM 8.92 10.81 13.66 4.52 0

DCORE_AM 90.08 120.92 192.03 92.69 8

CAI_AM 4.29 7.04 10.36 33.16 100

TECI 26.37 27.72 29.70 37.84 100

IJI 64.81 66.96 69.26 75.31 100

AREA_MN 0.63 0.73 0.84 10.83 100

SHAPE_MN 1.25 1.27 1.28 1.63 100

DCORE_MN 0.78 1.19 1.57 4.59 100

CAI_MN 0.05 0.09 0.13 9.95 100

ED 293.88 318.64 344.07 96.30 0

CWED 79.61 88.68 100.90 37.06 0

AI 48.36 52.18 55.90 85.60 100

CONTAG 30.91 32.88 35.43 46.63 100

Figure 50—Simulated trajectory in the Contagion metric based on the landscape classified into 

seral stage classes (see Methods for classification) for the simulated historical range of variability 

(ca. 1550–1850) in the upper Yuba River watershed. Note that the trajectory includes the current 

landscape (timestep = 0), the equilibration period (timesteps 1–40), and the period used to compute 

the historical range of variability (timesteps 41–250). The current landscape value and the 5th 

(q0.05), 50th (q0.5), and 95th (q0.95) percentiles of the simulated variability are also shown.
USDA Forest Service RMRS-GTR-385. 2018 79

Figure 51—Snapshots of the seral stage mosaic for (a) the current landscape and (b) the last 

timestep of the simulated historical range of variability (ca. 1550–1850) in the upper Yuba River 

watershed, shown here for a randomly selected location within the project area. Note that for 

simplicity the four seral stages associated with mixed-conifer aspen cover types are not shown.
80 USDA Forest Service RMRS-GTR-385. 2018

The complex interaction between widely varying disturbance sizes, heterogeneous severity 

patterns within disturbance events, and varying rates of succession produces and maintains 

this heterogeneous patch mosaic.

The current landscape configuration based on seral stages deviates somewhat from the 

HRV in a manner very similar to that of canopy cover (table 19). As with canopy cover, 

the larger patches in the current landscape are on the small and less extensive side, but 

nonetheless still within the HRV. However, the larger patches in the current landscape are 

much less geometrically complex than under the HRV, resulting in a greater percentage of the 

patches in core area. In addition, the contrast along edges is greater in the current landscape, 

and the seral stages are more interspersed than under the HRV. As with both developmental 

stage and canopy cover, the current landscape also has notably less fine-scale heterogeneity, 

but this may partly be a modeling artifact as discussed previously. Overall, like canopy cover, 

departure in seral stage appears to be mostly related to changes in the geometric complexity 

and fine-scale heterogeneity of the mosaic, and although we are confident in reporting this 

departure, we are hesitant to draw strong conclusions about the magnitude of the departure.

The results were very similar for the landscape classified into 151 unique combinations 

of cover type and seral stage; the main difference was that the absolute range of values of 

the metrics reflected the greater spatial heterogeneity of the more finely resolved landscape 

mosaic (table 20). The landscape maintained a relatively high degree of spatial heterogeneity 

and variability over time in the larger patch structure, with the area-weighted mean patch size 

varying between about 126 and 196 hectares (311–484 acres), coupled with a relatively low 

magnitude of average contrast along edges, moderate degree of interspersion of seral stages, 

and a tremendous and constant amount of fine-scale spatial heterogeneity in seral stages. 

Table 20—Historical range of variability (HRV) in landscape metrics (see Landscape Configuration 

under Methods for description and units for each landscape metric) computed on the basis 

of the landscape classified into combinations of cover type and seral stage classes (see HRV 

Spatial Input Data for classification) for ca. 1550–1850 in the upper Yuba River watershed. Select 

percentiles of the simulated HRV are given, as well as the current landscape condition and its 

corresponding percentile of the simulated HRV.

Percentile of HRV Current

Landscape metric 5th 50th 95th % eligible % HRV

LPI 2.08 2.08 2.08 2.08 98

AREA_AM 126.21 151.47 196.42 118.92 2

GYRATE_AM 587.68 636.03 693.89 615.93 24

SHAPE_AM 5.17 5.63 6.19 3.27 0

DCORE_AM 96.68 121.21 152.13 45.27 0

CAI_AM 5.22 7.42 10.28 38.78 100

TECI 21.72 22.22 22.98 26.14 100

IJI 54.90 55.81 56.78 63.80 100

AREA_MN 0.51 0.58 0.67 3.60 100

SHAPE_MN 1.23 1.24 1.26 1.42 100

DCORE_MN 0.89 1.17 1.46 2.26 100

CAI_MN 0.16 0.28 0.48 18.78 100

ED 312.13 335.58 360.03 139.71 0

CWED 69.20 75.04 81.42 36.96 0

AI 46.11 49.79 53.32 79.33 100

CONTAG 44.65 45.54 46.92 50.89 100
USDA Forest Service RMRS-GTR-385. 2018 81

The absolute range of variability in contagion was greater for the cover-seral mosaic than 

the pooled seral stage mosaic. This was because the cover-seral mosaic exhibited much less 

interspersion (55–57 percent) than the pooled seral stage mosaic (65–69 percent), which more 

than compensated for the lower aggregation of the cover-seral mosaic. 

As with seral stage pooled across cover types, the larger patches in the current landscape 

are on the small and less extensive side, but nonetheless still within the HRV, and the larger 

patches in the current landscape are much less geometrically complex than under the HRV, 

resulting in a greater percentage of the patches in core area. In addition, the contrast along 

edges is greater in the current landscape and the seral stages are more interspersed than under 

the HRV. The current landscape also has notably less fine-scale heterogeneity, but again this 

may partly be a modeling artifact. 

Alternative Management Scenarios

For consistency with the HRV section, we divided the results of the alternative 

management scenarios into three major sections corresponding to the disturbance regime, 

landscape composition, and landscape configuration. Here, we focus on the comparison 

among the management scenarios (MS1–MS7) and HRV, and the degree to which the 

management scenarios moved the current landscape toward the HRV in each of the measured 

attributes. We report the disturbance results because they represent how much disturbance 

was actually realized in the simulation, but do not analyze them further because they are a 

direct reflection of our model parameterization. The simulated landscape composition and 

configuration represent a set of results of the simulation from which we can make inferences. 

We have higher confidence in the comparative results than in the absolute results of any single 

scenario, because our focus here is on the comparison among scenarios that were subject to 

the same modeling assumptions and limitations. Thus, even if our modeling assumptions 

are not accurate, the biases resulting from those assumptions should be consistent across 

scenarios, making the comparative results reliable.

Disturbance Regime

In this section, we distinguish between “wildfires” and “prescribed fires.” The HRV 

and no treatment (MS1) scenarios involved only wildfires, whereas the other management 

scenarios (MS2–MS7) involved both wildfires and prescribed fires.

Wildfire Frequency

The simulated number of individual wildfires per 5-year timestep differed between the 

HRV and management scenarios as expected given the model parameterization (fig. 52). 

Taking into account the modern fire size distribution, we reduced the disturbance rate 

calibration coefficient in the management scenarios as necessary to approach the target 

modern wildfire FRP of 152 years for MS1. The net result was a reduction in the average 

number of wildfires per 5-year timestep initiating within the 181,556-hectare project area from 

194 in HRV to 119 in MS1—a 39-percent reduction. The average number of wildfires per 

timestep in the management scenarios was directly related to the intensity of prescribed fire 

treatments, with the fewest wildfires occurring in the scenarios with the greatest intensity of 

prescribed fire (MS3a,b). This additional reduction to as low as 78 in M3a (34 percent fewer 

than MS1) was due solely to the reduced vegetation susceptibility to wildfire caused by the 

vegetation treatments (including prescribed fire). 
82 USDA Forest Service RMRS-GTR-385. 2018

Figure 52—Mean number of wildfires per 5-year timestep for the simulated historical range of 

variability (ca. 1550–1850; mean is across 460 timesteps) and management scenarios with a 

modified fire regime (MS1) and varying intensities and types of vegetation treatments (MS2–MS7) 

(mean is across 20 replicate 100-year simulations with 10-year timesteps; N = 200) in the upper 

Yuba River watershed (see Methods for description of scenarios).

Area Burned

The percentage of the eligible landscape (174,830 hectares) burned by wildfires in each

5-year timestep under the FRV scenarios was more than 80 percent lower than under the 

HRV, as expected given the model parameterization (table 21). The area burned by wildfires 

was reduced even further under the management scenarios involving vegetation treatments 

(MS2–MS7). As with wildfire frequency, the reduction was directly related to the intensity 

of prescribed fire treatments. As before, this reduction was due solely to the effect of the 

vegetation treatments (including prescribed fire) on susceptibility to wildfire. In addition, 

the simulated fire size distribution shifted between the HRV and management scenarios, as 

expected given the model parameterization, such that a greater proportion of wildfires was 

in the smallest size class under the management scenarios (fig. 53). The dramatic reduction 

in area burned by wildfires under the management scenarios was due to the reduction in fire 

frequency (fig. 52) and the reduced average fire size (table 22), both of which were the direct 

result of model parameterization. However, even though the total area burned and the mean 

wildfire size were dramatically smaller under the management scenarios, the area-weighted 

mean wildfire size was greater (table 22), indicating that more of the total area that burned 

under the management scenarios did so during larger wildfires. This result was not directly 

due to model parameterization but rather emerged as an outcome of the interaction between 

fire and vegetation. 
USDA Forest Service RMRS-GTR-385. 2018 83

The same patterns were evident in the exceedance curves, which represent the probability

in any one timestep of the total area burned by wildfires exceeding a certain percentage of 

the landscape. Exceedance probability decreased much more rapidly under the management 

scenarios compared to the HRV scenario (fig. 54). For example, under the HRV scenario there 

was a 25-percent chance of more than 25 percent of the eligible landscape burning in wildfires 

in any one timestep, but under the management scenarios there was less than a 3-percent 

chance of more than 25 percent of the eligible landscape burning in any one timestep. 

Table 21—Percentage of the eligible landscape burned by wildfires per 5-year timestep for the 

simulated historical range of variability (ca. 1550–1850) and management scenarios with a modified 

fire regime (MS1) and varying intensities and types of vegetation treatments (MS2–MS7) (see 

Management Scenarios under Methods for descriptions) in the upper Yuba River watershed.

Percentage burned per timestep

Scenario Min Mean Max

HRV 0.06 17.52 73.63

MS1 0.02 3.39 44.02

MS2 0.03 2.94 49.89

MS3a 0.01 1.69 40.98

MS3b 0.01 1.99 37.97

MS4 0.03 3.13 53.09

MS5 0.01 2.25 34.25

MS6 0.02 2.19 35.65

MS7 0.03 2.96 46.97

Figure 53—Distribution of simulated wildfire sizes for the simulated historical range of variability (ca. 

1550–1850) and management scenarios with a modified fire regime (MS1) and varying intensities 

and types of vegetation treatments (MS2–MS7) in the upper Yuba River watershed (see Methods

for description of scenarios). Note that the x-axis is geometrically scaled.
84 USDA Forest Service RMRS-GTR-385. 2018

Table 22—Wildfire sizes under the simulated historical range of variability (ca. 1550–1850) and management scenarios 

with a modified fire regime (MS1) and varying intensities and types of vegetation treatments (MS2–MS7) (see Management 

Scenarios under Methods for descriptions) in the upper Yuba River watershed.

N Fire size 

Scenario (ha) (ac)

Mean 

(ha) (ac)

Area-weighted mean 

(ha) (ac)

Max 

(ha) (ac)

HRV 220,900 545,844 181 447 38,146 94,259 139,369 344,381

MS1 107,534 265,717 56 138 53,662 132,599 147,460 364,374

MS2 106,706 263,671 48 119 38,925 96,184 125,865 311,012

MS3a 91,052 224,989 48 119 40,078 99,033 126,139 311,689

MS3b 92,047 227,448 55 136 49,484 122,275 128,659 317,916

MS4 106,342 262,771 54 133 46,615 115,186 147,810 365,239

MS5 94,774 234,187 55 136 53,268 131,625 136,874 338,216

MS6 103,193 254,990 47 116 44,370 109,638 140,839 348,013

MS7 103,459 255,647 57 141 54,360 134,324 125,205 309,382

Figure 54—Exceedance probability of simulated wildfires (i.e., probability of ≥any percent of the 

eligible landscape being burned in any timestep) for the simulated historical range of variability (ca. 

1550–1850) and management scenarios with a modified fire regime (MS1) and varying intensities 

and types of vegetation treatments (MS2–MS7) in the upper Yuba River watershed (see Methods

for description of scenarios).

Fire Rotation Period

Reflecting model parameterization, wildfire FRP increased fivefold from the HRV 

scenario (29 years) to the MS1 scenario (147 years) (table 23). The wildfire FRPs under 

the other management scenarios increased even further—to as high as 296 years under the 

MS3a scenario—and these increases were due to the reduced vegetation susceptibility to 

wildfire caused by the vegetation treatments. Thus, despite having the same wildfire regime 

parameters, the vegetation treatments reduced the total occurrence of wildfire by up to 100 

percent, depending on the intensity of treatment.
USDA Forest Service RMRS-GTR-385. 2018 85

Table 23—Fire rotation periods (FRPs) (yr) for wildfires of “any” mortality level (i.e., severity) for all of the dynamic cover types 

for the simulated historical range of variability (HRV) (ca. 1550–1850), and management scenarios with a modified fire regime 

(MS1) and varying intensities and types of vegetation treatments (MS2–MS7) (see Management Scenarios under Methods 

for descriptions) in the upper Yuba River watershed. Note that we considered FRPs for cover types with <1,000 ha (2,500 ac) 

extent in the project area as unreliable, but we included them here for completeness.

Scenario

Cover type Area (ha) Area (ac) HRV MS1 MS2 MS3a MS3b MS4 MS5 MS6 MS7

Sierran Mixed Conifer – 

Mesic

 57,853 142,288 27 140 156 274 213 151 197 209 156

Sierran Mixed Conifer – 

Xeric

 52,198 128,981 24 127 144 236 209 131 184 191 143 

Oak-Conifer Forest and 

Woodland

 23,279 57,522 25 129 160 246 231 146 204 209 154

Red Fir – Mesic 8,563 21,159 63 252 302 1,031 766 305 527 491 323

Red Fir – Xeric 7,493 18,515 43 149 175 420 385 156 264 250 181

 7,273 17,972 48 268 316 711 568 370 548 496 337

 6,768 16,724 37 178 225 495 468 217 403 321 239

 4,124 10,190 57 430 543 1,186 1,096 518 1,105 895 585

 1,060 2,619 40 257 355 803 949 366 705 586 422

 837 2,068 51 330 358 748 410 348 337 370 285

 732 1,809 52 250 280 518 375 281 295 342 258

 638 1,576 277 1,443 1,616 2,568 1,291 1,466 1,185 1,485 1,223

 604 1,492 110 767 1,124 4,367 3,951 1,134 4,460 2,055 1,716 

 294 726 123 740 730 5,554 4,213 800 2,553 1,177 956

 273 675 90 884 722 1,517 804 866 580 769 573

 58 143 31 140 169 493 285 140 209 241 164

 31 77 60 256 356 1,624 1,880 230 1,274 456 336

 19 47 25 132 170 233 260 150 237 217 169

 18 45 78 591 569 849 476 590 308 656 428

 8 20 48 235 342 913 620 286 414 396 310

 174,830 29 147 170 296 251 159 222 229 169

Mixed Evergreen – Mesic

Mixed Evergreen – Xeric

Sierran Mixed Conifer – 

Ultramafic

Oak-Conifer Forest and 

Woodland – Ultramafic

Lodgepole Pine 

Montane Riparian 

Subalpine Conifer 

Mixed Evergreen – 

Ultramafic 

Red Fir – Ultramafic 

Western White Pine 

Sierran Mixed Conifer with 

Aspen 

Red Fir with Aspen 

Oak Woodland 

Curl-Leaf Mountain 

Mahogany 

Lodgepole Pine with Aspen 

Totala

All fires (wild and prescribed) 174,830 432,005 29 147 134 24 23 76 27 49 49

a

 Total includes static vegetated cover types that were allowed to burn (e.g., grasslands, meadows).

Importantly, the longer wildfire FRPs under the management scenarios do not take into 

account prescribed fires. The combined fire (wild and prescribed) FRPs for the management 

scenarios with intensive use of prescribed fire (MS3a,b and MS5) were in fact comparable 

to the HRV (table 23). Thus, prescribed burning roughly compensated for the reduction in 

wildfires under these particular management scenarios. These differences among scenarios 

were generally consistent across all major cover types, although Oak-Conifer Forest and 

Woodland – Ultramafic and Sierran Mixed Conifer – Ultramafic exhibited a 6.5- and 7.5-fold 

(rather than the average fivefold), respectively, increase in wildfire FRP from the HRV to MS1 

scenario (table 23).

Recall that FRP reflects the average point-specific fire return interval (FRI), but says 

nothing about its variance. After pooling wildfires and prescribed fires, we noted substantial 

spatial variability in FRIs across the landscape both within and among scenarios (fig. 55). 
86 USDA Forest Service RMRS-GTR-385. 2018

Figure 55—Simulated point-specific fire return interval 

for wildfires and prescribed fires combined under (a) the 

simulated historical range of variability (ca. 1550–1850) 

and a variety of management scenarios with a modified 

fire regime reflecting aggressive fire suppression in the 

upper Yuba River watershed: (b) MS1 — no treatment; 

(c) MS2 — current Land Management Plan (LMP); (d) 

MS3a — prescribed fire only; (e) MS3b — prescribed 

fire only, with “hotter” burns; (f) MS4 — LMP moderate 

intensity; (g) MS5 — Sierra Conservancy; (h) MS6 — 

balanced scenario; (i) MS7 — our final scenario (see 

Methods for description of scenarios).
USDA Forest Service RMRS-GTR-385. 2018 87

This variability resulted not only from the choice of treatments, but also from gradients in 

cover types with varying propensities to burn, reflecting complex interactions among fire, 

vegetation, and terrain that emerged from the simulation. The difference between the HRV and 

MS1 scenario was largely one of reduced frequency of fire under the latter scenario resulting 

in considerably longer fire FRIs, but the spatial heterogeneity in FRIs was similar between 

scenarios. The low-intensity mixture of mechanical and prescribed fire treatments in the MS2 

scenario resulted in little overall difference from MS1 even though the average FRI was 23 

years longer (fig. 55c). In contrast, the high-intensity prescribed fire-only scenarios (MS3a,b) 

homogenized the short FRIs across national forest lands and exacerbated the differences 

between national forest lands and other ownerships (fig. 55d–e). 

The moderate-intensity mixture of mechanical and prescribed fire treatments in MS4 

resulted in a striking pattern of short FRIs along roads where the hand cut, pile, and burn 

treatments were conducted and much longer FRIs elsewhere (fig. 55f). The high-intensity 

treatments dominated by prescribed fire in MS5 resulted in a pattern similar to the prescribed 

fire-only scenarios (MS3a,b), with the exception of the roadless areas that remained untreated 

in MS5 and thus exhibited considerably longer FRIs (fig. 55g). Last, the high-intensity 

treatments in MS6 and MS7 consisting of an equal mixture of mechanical and prescribed fire 

resulted in very high spatial heterogeneity in FRIs (fig. 55h; note that MS7 is not shown but is 

almost indistinguishable from MS6).

Wildfire Severity

Recall that at the cell level, fires caused either low or high mortality of the overstory 

vegetation, which we defined as less than or greater than 75 percent of the canopy, 

respectively. Also recall that one of the key model parameters was the probability of a highmortality response, which we specified separately for each unique combination of cover type 

and seral stage, as modified by topographic position, as part of the state-and-transition models 

(Appendix B). Therefore, the realized percentage of high-mortality fire largely reflected the 

model parameterization. Nonetheless, we report it as a description of how often fire caused 

an immediate state transition to the early-development seral stage. The realized percentage 

of high-mortality fire was at least partly an emergent property of the simulation because it 

reflected both the model parameterization and the unspecified proportion of time each cover 

type spent in each seral stage. Thus, scenarios that increased the proportion of time vegetation 

was in a closed-canopy condition were associated with greater realized percentages of highmortality wildfire.

The realized percentage of high-mortality wildfire differed slightly among scenarios (table 

24). The overall percentage of high-mortality wildfire increased from 14.6 percent under the 

HRV scenario to a high of 20.2 percent under the MS2 scenario, but when averaged across 

all management scenarios it increased only slightly to 17.2 percent. The increased propensity 

for high-mortality wildfire under the no treatment scenario (MS1) was driven primarily by the 

increased vegetation susceptibility to fire. The greater extent of closed-canopy forest, which 

resulted from less fire, led to higher susceptibility to fire. The 90-percent range of variability 

over time increased between the HRV (7–21 percent) and the management scenarios (5–25 

percent), with the latter reflecting the wide range of vegetation conditions resulting from 

different treatment intensities and types. 

Vegetation Treatment

The realized treated area and allocation among treatment types for the various 

management scenarios are given in table 8. Treament intensities were almost constant across 

all scenarios for private industry and other lands and for mixed conifer-aspen forest on 

national forest lands. Treatment intensities for conifer-dominated forest on national forest 
88 USDA Forest Service RMRS-GTR-385. 2018

Table 24—High-mortality wildfire rates (percentage of burned cells exhibiting high mortality effect: >75 percent canopy cover 

mortality) for the forested cover types for the simulated historical range of variability (HRV) (ca. 1550–1850) and management 

scenarios with a modified fire regime (MS1) and varying intensities and types of vegetation treatments (MS2–MS7) (see 

Management Scenarios under Methods for descriptions) in the upper Yuba River watershed.

Scenario

Cover type HRV MS1 MS2 MS3a MS3b MS4 MS5 MS6 MS7

Lodgepole Pine 18.5 13.4 18.1 22.7 22.4 14.1 12.3 17.2 30.3

Lodgepole Pine with Aspen 19.4 75.0 17.2 NA 26.7 36.2 0.0 42.6 100.0

Mixed Evergreen – Mesic 17.4 14.9 19.6 19.6 22.5 21.1 23.9 31.5 19.3

Mixed Evergreen – Ultramafic 16.9 22.5 23.7 7.4 5.9 6.2 24.7 23.3 9.4

Mixed Evergreen – Xeric 13.8 13.9 14.9 8.4 17.0 15.8 13.4 20.7 13.3

Montane Riparian 49.2 36.6 49.3 55.7 46.9 52.1 44.5 47.3 38.1

Oak Woodland 18.2 33.5 40.2 0.0 47.7 39.9 0.0 5.9 NA

Oak-Conifer Forest and Woodland 20.7 20.7 22.6 25.5 23.1 15.7 22.5 21.9 23.4

Oak-Conifer Forest and Woodland – Ultramafic 4.1 17.5 12.4 8.8 9.4 5.1 4.6 4.9 6.7

Red Fir with Aspen 29.2 47.6 42.0 91.7 0.0 48.8 43.3 0.0 0.0

Red Fir – Mesic 25.0 40.9 25.8 37.1 37.7 24.9 27.5 27.6 24.0

Red Fir – Ultramafic 26.4 0.0 26.2 100.0 0.0 21.0 53.4 11.1 7.1

Red Fir – Xeric 34.2 35.1 24.9 30.7 23.8 24.9 24.2 28.9 29.3

Subalpine Conifer 77.7 76.6 78.8 79.8 79.1 76.0 71.7 79.2 72.7

Sierran Mixed Conifer with Aspen 24.1 45.0 33.9 55.6 37.1 24.1 70.8 20.7 60.0

Sierran Mixed Conifer – Mesic 13.4 17.4 22.9 12.6 20.9 14.9 15.6 17.7 16.2

Sierran Mixed Conifer – Ultramafic 12.6 9.9 13.7 15.7 9.7 6.5 19.0 6.5 13.8

Sierran Mixed Conifer – Xeric 9.2 10.8 12.4 9.0 9.6 9.1 11.5 13.1 13.7

Western White Pine 17.7 51.0 20.1 17.1 13.3 15.6 11.0 12.2 2.3

Total 14.6 16.2 20.2 13.6 18.8 15.3 16.9 19.4 17.3

lands differed considerably among scenarios as intended. Treatment intensities ranged from 

an average of 3,451 hectares (8,527 acres) treated per timestep over the replicate 100-year 

simulations, representing a treatment rate of approximately 2.5 percent every 5 years (or 0.5 

percent per year), in the current LMP scenario (MS2) to about 34,180 hectares (84,459 acres) 

treated per timestep, representing a treatment rate of approximately 27.5 percent every 5 years 

(or 5.5 percent per year) for the prescribed fire-only scenarios (MS3a,b). 

Landscape Composition

Developmental Stage Dynamics

All of the management scenarios moved the current landscape closer to the HRV 

distribution of developmental stages pooled across cover types. In fact, with the exception of 

the no treatment scenario (MS1), most of the management scenarios roughly emulated the HRV 

(fig. 56). In both the HRV and management scenarios, we observed a shift from the current middevelopment forest-dominated landscape to a landscape dominated by late-development forest. 

This was due to the high number of low-mortality wildfires and prescribed fires, as well as the 

use of largely non-stand replacing vegetation treatments in the management scenarios, which 

allowed the mid-development stands in the current landscape to succeed to late development. 

The no treatment scenario least emulated the HRV, resulting in less early- and mid-development 

and more late-development forest than the HRV. Conversely, all of the management scenarios 

involving active vegetation management emulated the HRV fairly well, although the Sierra 

Conservancy scenario (MS5) probably came the closest to emulating the HRV across all three 

developmental stages. 
USDA Forest Service RMRS-GTR-385. 2018 89

These patterns were generally consistent across the dominant cover types (see table D4 in 

Appendix D for cover type-specific results), with the following exceptions. The management 

scenarios did not emulate the HRV very closely in the Oak-Conifer Forest and Woodland – 

Ultramafic and Sierran Mixed Conifer – Ultramafic cover types. In both cases, the management 

scenarios produced less area in late-development and more area in mid-development forest than 

the HRV. This was in part due to the priorities for treatments that resulted in creating a large 

number of early-development patches via group cuts in these ultramafic cover types, and is 

something that could be rectified by adjusting the priorities accordingly.

Canopy Cover Dynamics

Recall that the current landscape was well within the HRV in the overall canopy cover 

class distribution (i.e., pooled across all cover types). The management scenarios differed 

considerably in how much they moved the current distribution of canopy cover classes away 

from the HRV (fig. 57). In particular, the no treatment scenario (MS1) caused the current 

landscape to move far outside the HRV in all canopy cover classes, producing much less openand moderate-canopy cover and much more closed-canopy cover than we observed under 

the HRV. This shift to a dominance of closed-canopy conditions was due to the reduction in 

the rate of wildfire disturbance and the natural succession to closed canopy in the absence of 

disturbance. The active management scenarios (MS2–MS7), on the other hand, progressively 

improved in emulating HRV. Our final scenario (MS7) emulated HRV closely, producing 

a 35:25:40 ratio of open to moderate to closed canopy compared to the 38:24:37 ratio we 

observed under the HRV. Overall, the mixture of mechanical and prescribed fire treatments 

in our final scenario more than doubled the proportion of the landscape in open and moderate 

canopy cover conditions over the no treatment scenario. 

Figure 56—Range of variability in vegetation developmental stages, represented as the percentage 

of the eligible landscape (i.e., excluding nonseral and nonvegetated land cover), for the simulated 

historical range of variability (ca. 1550–1850) and management scenarios with a modified fire 

regime (MS1) and varying intensities and types of vegetation treatments (MS2–MS7) in the 

upper Yuba River watershed (see Methods for description of scenarios). Box represents the 

5–95th percentiles, solid horizontal line the 50th percentile, and dashed horizontal line the current 

landscape condition.
90 USDA Forest Service RMRS-GTR-385. 2018

These patterns were generally consistent across the dominant cover types (see table D5 in 

Appendix D for cover type-specific results), with two notable exceptions:

• Among cover types, our final scenario resulted in less area in closed canopy in Mixed 

Evergreen (mesic and xeric variants) and Red Fir – Mesic forests than we observed under 

the HRV. This resulted from an excess of thinning and prescribed fire treatments that 

reduced canopy cover in these cover types, which could easily be rectified by adjusting 

the treatment priorities.

• In contrast, our final scenario resulted in less area in open canopy and, conversely, more 

area in closed canopy in Oak-Conifer Forest and Woodland – Ultramafic forests than 

we observed under the HRV. This resulted from a paucity of thinning and prescribed fire 

treatments in this cover type, which could easily be rectified by adjusting the treatment 

priorities. 

Seral Stage Dynamics—The current landscape’s overall seral stage distribution (i.e., 

pooled across all cover types) deviates considerably from the HRV, most of which is due 

to significant departure in the developmental stage distribution, as described previously. 

It is perhaps more instructive to compare the HRV scenario to the management scenarios 

(fig. 58). In general, with the exception of the no treatment scenario (MS1), all of the active 

management scenarios (MS2–MS7) emulated the HRV relatively well in the early- and midseral stages, but they varied considerably in emulating HRV in the late-seral stages. Our final 

scenario (MS7), however, emulated HRV relatively well in all of the seral stages, suggesting 

that an active management strategy involving a mixture of mechanical treatments and 

prescribed fire can accomplish the goal of emulating HRV in terms of landscape composition. 

These patterns differed somewhat across the dominant cover types as reported in table D6 in 

Appendix D. 

Figure 57—Range of variability in vegetation canopy cover classes, represented as the percentage 

of the eligible landscape (i.e., excluding nonseral and nonvegetated land cover), for the simulated 

historical range of variability (ca. 1550–1850) and management scenarios with a modified fire 

regime (MS1) and varying intensities and types of vegetation treatments (MS2–MS7) in the 

upper Yuba River watershed (see Methods for description of scenarios). Box represents the 

5–95th percentiles, solid horizontal line the 50th percentile, and dashed horizontal line the current 

landscape condition.
USDA Forest Service RMRS-GTR-385. 2018 91

Figure 58—Range of variability in vegetation seral stages, represented as the percentage of 

the eligible landscape (i.e., excluding nonseral and nonvegetated land cover), for the simulated 

historical range of variability (ca. 1550–1850) and management scenarios with a modified fire 

regime (MS1) and varying intensities and types of vegetation treatments (MS2–MS7) in the 

upper Yuba River watershed (see Methods for description of scenarios). Box represents the 

5–95th percentiles, solid horizontal line the 50th percentile, and dashed horizontal line the current 

landscape condition.

Landscape Configuration

Developmental Stage Dynamics

A complete summary of the landscape metrics by scenario for the developmental stage 

mosaic is presented in table D7 in Appendix D. Here we only briefly summarize the major 

results as illustrated by a smaller set of the metrics. 

Compared to HRV, the no treatment scenario (MS1) produced considerably less spatial 

heterogeneity in the developmental stage mosaic (fig. 59). In contrast, the active management 

scenarios varied considerably in how well they emulated the HRV in both coarse- and finegrained spatial heterogeneity in developmental stage. Our final scenario (MS7), in particular, 

did well in emulating both the coarse- and fine-grained patch structure of the HRV (fig. 59). 

These differences among scenarios are reflected well in the landscape metrics (fig. 60). 

In particular, compared to HRV the coarse patch structure was much larger and geometrically 

simpler under the no treatment scenario, with the area-weighted mean patch size roughly 

twice, and the area-weighted mean shape index roughly half, that produced under the HRV 

scenario. This simplification resulted in roughly a doubling of the percentage of the larger 

patches in core (i.e., patch interior) under the no treatment scenario. In addition, the finescale heterogeneity was considerably less pronounced under the no treatment scenario, as 

exemplified by a sevenfold increase in the mean patch size. Overall, the no treatment scenario 

produced a much simpler patch mosaic than the HRV scenario due to the reduced rate of 

disturbance. 
92 USDA Forest Service RMRS-GTR-385. 2018

Figure 59—Snapshots of the vegetation developmental stage mosaic for (a) the current landscape and the last timestep of the 

simulated (b) historical range of variability (ca. 1550–1850) and range of variability under (c) the no treatment scenario (MS1), 

representing a modified wildfire regime without vegetation treatments, and (d) our final scenario (MS7), representing a modified 

wildfire regime combined with moderately intensive mechanical and prescribed fire treatments. Snapshots are shown for a 

randomly selected location in the upper Yuba River watershed and the last timestep of the respective simulations.

Compared to the no treatment scenario, our final scenario involving moderately intensive

mechanical and prescribed fire treatments produced considerably less variability in the 

patch structure over time (fig. 60). Moreover, the large patches were considerably more 

complex geometrically, with the area-weighted mean shape index roughly double that of 

the no treatment scenario, which resulted in much less in core area under our final scenario. 

The vegetation treatments also produced much greater fine-scale heterogeneity than the no 

treatment scenario, as exemplified by the mean patch size metric. Overall, the vegetation 

treatments in our final scenario emulated HRV in all aspects of landscape configuration, 

with the only notable difference being a reduced range of variability in the metrics. The 

reduction in variability over time is perhaps not surprising given the regularity of treatments 

implemented in the model, compared to the more episodic nature of wildfire disturbances 

under the HRV. Overall, these results demonstrate that an active management scenario has the 

potential to emulate the HRV in landscape configuration (as well as landscape composition, as 

demonstrated previously). It is also worth noting that active management scenarios can also be 

devised that poorly emulate the HRV.
USDA Forest Service RMRS-GTR-385. 2018 93

Figure 60—Range of variability in selected landscape 

metrics for the developmental stage mosaic (none, 

early, mid, and late) for the simulated historical range of 

variability (ca. 1550–1850) and management scenarios 

with a modified fire regime (MS1) and varying 

intensities and types of vegetation treatments (MS2–

MS7) in the upper Yuba River watershed (see Methods

for description of scenarios): (a) area-weighted mean 

patch size, (b) area-weighted mean shape index, (c) 

area-weighted mean core area index, (d) mean patch 

size, and (e) contagion (see Methods for description 

and units for each landscape metric). Box represents 

the 5–95th percentiles, solid horizontal line the 50th 

percentile, and dashed horizontal line the current 

landscape condition.
94 USDA Forest Service RMRS-GTR-385. 2018

Canopy Cover Dynamics 

A complete summary of the landscape metrics by scenario for the canopy cover class 

mosaic is presented in table D8 in Appendix D. Here we provide only a brief summary of the 

major results, as illustrated by a smaller set of the metrics. 

As with the developmental stage mosaic, compared to the HRV the no treatment scenario 

(MS1) produced considerably less spatial heterogeneity in the canopy cover mosaic (fig. 61). 

Similarly, the active management scenarios varied considerably in how well they emulated the 

HRV in both coarse- and fine-grained spatial heterogeneity. As with the developmental stage 

mosaic, our final scenario (MS7), in particular, did well in emulating both the coarse- and 

fine-grained canopy cover mosaic of the HRV (fig. 61); only the prescribed fire-only scenario 

involving “hotter” fires (MS3b) did a better job of emulating the HRV in the canopy cover 

mosaic. 

These differences among scenarios are reflected well in the landscape metrics (fig. 62). In 

particular, compared to the HRV the coarse patch structure was much larger and the landscape 

was more contagious under the no treatment scenario, with the area-weighted mean patch size 

and contagion index about two to several times greater than that produced under the HRV. 

Figure 61—Snapshots of the vegetation canopy cover class mosaic for (a) the current landscape and the last timestep of the 

simulated (b) historical range of variability (ca. 1550–1850) and range of variability under (c) the no treatment scenario (MS1), 

representing a modified wildfire regime without vegetation treatments, and (d) our final scenario (MS7), representing a modified 

wildfire regime combined with moderately intensive mechanical and prescribed fire vegetation treatments. Snapshots are 

shown for a randomly selected location in the upper Yuba River watershed and the last timestep of the respective simulations.
USDA Forest Service RMRS-GTR-385. 2018 95

This simplification of the landscape pattern resulted in about two to three times more core area 

under the no treatment scenario. In addition, the fine-scale heterogeneity was considerably 

less pronounced under the no treatment scenario, as exemplified by a several-fold increase in 

mean patch size. Overall, the no treatment scenario produced a much simpler canopy cover 

mosaic than the HRV, and one dominated by closed-canopy conditions due to the reduced 

rate of disturbance. Perhaps as important, under the no treatment scenario the canopy cover 

configuration fluctuated much more dramatically over time (i.e., greater range of variation) 

than it did under the HRV. 

Figure 62—Range of variability in selected landscape 

metrics for the canopy cover class mosaic (none, open, 

moderate, and closed) for the simulated historical 

range of variability (ca. 1550–1850) and management 

scenarios with a modified fire regime (MS1) and varying 

intensities and types of vegetation treatments (MS2–

MS7) in the upper Yuba River watershed (see Methods

for description of scenarios): (a) area-weighted mean 

patch size, (b) area-weighted mean shape index, (c) 

area-weighted mean core area index, (d) mean patch 

size, and (e) contagion (see Methods for description 

and units for each landscape metric). Box represents 

the 5–95th percentiles, solid horizontal line the 50th 

percentile, and dashed horizontal line the current 

landscape condition.
96 USDA Forest Service RMRS-GTR-385. 2018

As with the developmental stage mosaic, compared to the no treatment scenario our final 

scenario involving moderately intensive mechanical and prescribed fire treatments produced 

considerably less variability in the canopy cover mosaic over time (fig. 62). Moreover, the 

large canopy cover patches were considerably smaller with less core area than under the no 

treatment scenario, resulting in significantly less contagion. In addition, our final scenario 

produced much greater fine-scale heterogeneity than the no treatment scenario, as exemplified 

by the mean patch size metric. 

Overall, the vegetation treatments in our final scenario moved the current landscape 

much closer to the HRV in the configuration of the canopy cover mosaic, although most of 

the landscape metrics fell short of closely emulating the HRV. In particular, the large patch 

structure of the canopy cover mosaic was smaller than under the HRV, but the patches were 

less complex geometrically. They contained a greater proportion of core area, creating a more 

contagious canopy cover mosaic than under the HRV. Larger, geometrically more complex 

treatment units embedded with an increased number of small patches of varying canopy 

cover classes would be likely to move our final scenario to within the HRV in all aspects of 

landscape configuration. It is perhaps not too surprising that only the prescribed fire-only 

scenario employing “hotter” burns (MS3b) closely emulated the HRV in most configurational 

aspects of the canopy cover mosaic, because this is the only scenario using approximately the 

same disturbance regime as the HRV. However, it is important to note that our final scenario 

moved the current landscape very close to the HRV but with considerably less area treated 

(MS7 = 22,167 hectares [54,775 acres] vs. MS3b = 34,178 hectares [84,454 acres]).

Seral Stage Dynamics

A complete summary of the landscape metrics by scenario for the seral stage mosaic is 

presented in table D9 in Appendix D. Here we provide only a brief summary of the major 

results, as illustrated by a smaller set of the metrics. 

As with the developmental stage and canopy cover mosaics, compared to the HRV the no 

treatment (MS1) scenario produced considerably less spatial heterogeneity in the seral stage 

mosaic (fig. 63). Similarly, the active management scenarios varied considerably in how well 

they emulated the HRV in both coarse- and fine-grained spatial heterogeneity. As with the 

developmental stage mosaic, our final scenario (MS7), in particular, did well in emulating both 

the coarse- and fine-grained canopy cover mosaic of the HRV (fig. 63). In contrast to the results 

for the canopy cover mosaic, with the seral stage mosaic our final scenario was superior to the 

prescribed fire-only scenario involving “hotter” fires (MS3b).

These differences among scenarios are reflected well in the landscape metrics (fig. 64). 

As with the developmental stage and canopy cover mosaics, compared to the HRV the coarse 

patch structure was much larger and the landscape was more contagious under the no treatment 

scenario, with the area-weighted mean patch size as much as 50 times greater and the contagion 

index 2 to several times greater than that produced under the HRV. This simplification in 

landscape pattern resulted in roughly four to five times more core area under the no treatment 

scenario. In addition, the fine-scale heterogeneity was considerably less pronounced under 

the no treatment scenario, as exemplified by a several-fold increase in the mean patch size. 

Overall, the no treatment scenario produced a much simpler seral stage mosaic than the HRV, 

and one dominated by late-development, closed-canopy conditions, due to the reduced rate of 

disturbance. Perhaps as important, as with the developmental stage and canopy cover mosaics, 

the seral stage configuration fluctuated much more dramatically over time (i.e., greater range of 

variability) under the no treatment scenario than under the HRV.

As with the developmental stage and canopy cover mosaics, our final scenario involving 

moderately intensive mechanical and prescribed fire treatments produced considerably less 

variability in the seral stage mosaic over time compared to the no treatment scenario. Moreover, 

the large seral stage patches were considerably smaller with less core area than under the no 
USDA Forest Service RMRS-GTR-385. 2018 97

treatment scenario, resulting in significantly less contagion. In addition, our final scenario 

produced much greater fine-scale heterogeneity than the no treatment scenario, as exemplified 

by the mean patch size metric. 

Overall, the vegetation treatments in our final scenario emulated the HRV in seral stage 

configuration reasonably well. Although the larger, matrix-forming patches were slightly less 

complex geometrically, resulting in proportionately more core area, the overall configuration 

of the seral stage mosaic was remarkably close to the HRV.

These patterns were also generally true for the landscape mosaic defined on the basis of 

unique combinations of cover type and seral stage—the most refined thematic resolution that 

we have available. The detailed results are available in table D10 in Appendix D. Naturally, 

given the much finer thematic resolution of the cover-seral stage mosaic, the grain of the 

patch mosaic was much finer across all of the scenarios (fig. 65). Nevertheless, the relative 

comparison among scenarios was essentially the same. The range of variability for many 

of the landscape metrics under our final scenario was similar to or overlapped that of the 

HRV scenario, indicating that the vegetation treatments were able to create patterns roughly 

comparable to that of the HRV even though the landscape composition differed between 

scenarios, as described previously.

Figure 63—Snapshots of the vegetation seral stage mosaic for (a) the current landscape and the last timestep of the 

simulated (b) historical range of variability (ca. 1550—1850) and range of variability under (c) the no treatment scenario 

(MS1), representing a modified wildfire regime without vegetation treatments, and (d) our final scenario (MS7), representing a 

modified wildfire regime combined with moderately intensive mechanical and prescribed fire vegetation treatments. Snapshots 

are shown here for a randomly selected location in the upper Yuba River watershed and the last timestep of the respective 

simulations.
98 USDA Forest Service RMRS-GTR-385. 2018

Figure 64—Range of variability in selected landscape 

metrics for the seral stage mosaic (see text for classes) 

for the simulated historical range of variability (ca. 

1550—1850) and management scenarios with a 

modified fire regime (MS1) and varying intensities 

and types of vegetation treatments (MS2—MS7) in 

the upper Yuba River watershed (see Methods for 

description of scenarios): (a) area-weighted mean 

patch size, (b) area-weighted mean shape index, (c) 

area-weighted mean core area index, (d) mean patch 

size, and (e) contagion (see Methods for description 

and units for each landscape metric). Box represents 

the 5—95th percentiles, solid horizontal line the 50th 

percentile, and dashed horizontal line the current 

landscape condition.
USDA Forest Service RMRS-GTR-385. 2018 99

Figure 65—Snapshots of the vegetation cover type and seral stage mosaic (without a legend due to the number of classes) for 

(a) the current landscape and the last timestep of the simulated (b) historical range of variability (ca. 1550—1850) and range 

of variability under (c) the no treatment scenario (MS1), representing a modified wildfire regime without vegetation treatments, 

and (d) our final scenario (MS7), representing a modified wildfire regime combined with moderately intensive mechanical and 

prescribed fire vegetation treatments. Snapshots are shown here for a randomly selected location in the upper Yuba River 

watershed and the last timestep of the respective simulations.

MAJOR FINDINGS AND MANAGEMENT 

IMPLICATIONS

Our intent in this section is to provide a synopsis of what we learned from this project

in the form of major “take-home” lessons for public land management professionals. We 

emphasize general findings that pertain to the landscape as a whole, as derived from results 

pooled across cover types. Specific findings pertaining to individual cover types are not 

covered in this section due to the excessive detail required, except as used to illustrate general 

findings. Readers seeking these more detailed cover type findings are referred to the detailed 

Results section and Appendix B. 

We base our major findings and their management implications on the underlying 

assumption that the historical range of variability (HRV) is a useful reference for evaluating 

the ecological condition of the current landscape (i.e., current departure). Accordingly, we 

posit that native biodiversity is well adapted to the fluctuating ecological conditions of the 

historical reference period and that any significant departure from the HRV is therefore 
100 USDA Forest Service RMRS-GTR-385. 2018

likely to have significant ecological consequences to native biodiversity. We also posit that 

conditions close to the HRV are most likely to maximize ecological integrity, which is a major 

focus of Forest Service management. Consequently, if management goals include maintaining 

native biodiversity and maximizing ecological integrity, the HRV may provide useful 

guidance to inform the description of desired future conditions. Although there are always 

socioeconomic, political, and even ecological (e.g., changing climate) reasons for establishing 

desired future conditions that do not closely conform to the HRV, we posit that moving 

the current landscape toward the HRV offers the best chance of maintaining the ecological 

integrity and native biodiversity of the landscape. 

Major Findings 

1. The study landscape during the historical reference period was best characterized as a 

shifting mosaic of vegetation types and conditions.

One of the most useful outcomes of this project is a robust depiction of the landscape as 

a constantly shifting mosaic of vegetation composition and structure occurring at multiple 

scales that provides a clear example of the range of variability concept. Figure 66 depicts the 

historical landscape as a shifting mosaic of vegetation developmental stages (nonseral, early, 

mid-, and late development), which can be illustrated equally well for the other landscape 

definitions (e.g., canopy cover classes, seral stages). 

Figure 66—Snapshots of the vegetation developmental stage mosaic taken at (a–d) four different (random) timesteps of the 

simulated historical range of variability (ca. 1550—1850) for a randomly selected location in the upper Yuba River watershed.
USDA Forest Service RMRS-GTR-385. 2018 101

Management Implications

Although this shifting mosaic in vegetation composition and structure is simply a 

reflection of the spatially explicit simulation of stochastic disturbance and succession 

processes (and thus not too surprising), it nonetheless illustrates and highlights the 

dynamic nature of the landscape to the public, which often perceives landscapes as static. 

Communicating this information to the public is critically important because it helps to build 

understanding and support for disturbance (both natural and anthropogenic) as a positive force 

for maintaining resilient landscapes that sustain native biodiversity and ecological integrity.

2. During the historical reference period, the study landscape was subject to a 

remarkably high wildfire disturbance rate.

Another important outcome of this project is the depiction of how prevalent wildfire was 

during the historical reference period. Under the HRV, the percentage of the landscape eligible 

to burn (i.e., supporting vegetation) that burned every 5 years averaged about 18 percent 

(~30,000 hectares [74,130 acres] of the 174,830 hectares [432,005 acres] eligible to burn). But 

this proportion varied dramatically over time, ranging from less than 1 percent (~100 hectares 

[250 acres]) to almost 74 percent (~129,000 hectares [319,000 acres]) (fig. 26), driven largely 

by fluctuations in climate and vegetation conditions. This translates to roughly 3.5 percent 

of the landscape, on average, burning every year. Another way of looking at this is that in 

any given 5-year period there was a 63-percent chance of burning more than 10 percent of 

the eligible landscape and a 4-percent chance of burning more than 50 percent of the eligible 

landscape. 

The disturbance rate resulted in a fire rotation period (FRP), or the time required to 

burn a cumulative area equal to the project area, of 29 years. Note that this is equivalent to 

the average point-specific fire return interval (FRI), or the average (expected) time between 

fires at a single point on the ground. The average FRI is often much longer than the composite 

fire return intervals (for varying units of space) as often reported in the literature. Importantly, 

even though the overall FRP was 29 years, the realized average FRI to points on the ground 

varied dramatically across the landscape, reflecting spatial variability in vegetation and terrain 

(fig. 29).

The percentage of high-mortality fire (>75 percent canopy mortality) varied over time 

from a low of about 2 percent to a high of 24 percent, but averaged around 13 percent 

per timestep (fig. 31). The 95-percent range of variability was about 7 to 21 percent high 

mortality. This variability was driven primarily by the variation in climate over time, but it 

also reflected the interaction between climate and the changing seral stage composition over 

time.

Management Implications

These results quantify the very high rate of wildfire disturbance that was necessary to 

achieve the scientifically derived (largely from fire history studies) historical fire disturbance 

rates for this landscape. Although these rates were based on information in the fire history 

studies, their values may surprise the casual observer. The primary implications are as follows: 

• Historically, wildfire was a dominant driver of the landscape dynamics, and therefore the 

local vegetation communities coevolved with wildfire and were presumably well adapted 

to a frequent fire regime. Consequently, it is reasonable to assume that major deviations 

in the historical fire regime are likely to have major ecological consequences. The highly 

modified contemporary fire regime resulting from a century of fire suppression and both 

past and present human land use is therefore likely to have significant ecological effects.

• Given the preceding implication, any desired future conditions intended to restore the 
102 USDA Forest Service RMRS-GTR-385. 2018

historical landscape structure and function will require the extensive use of fire and fire 

surrogates, ideally involving prescribed fire or managed wildfire for resource benefit—

most likely both. Of course, the magnitude of fire needed to restore the historical 

landscape structure (see following) would be challenging to implement given today’s 

socioeconomic and political constraints. Unfortunately, assuming those constraints remain 

fixed, silvicultural treatments alone would almost certainly be insufficient to restore the 

historical landscape structure. 

3. The current landscape departs from the historical range of variability in the 

composition of the vegetation mosaic, and more so in some attributes than others.

A major outcome of this project is a detailed, specific, and quantitative description of 

the HRV and current departure in landscape composition—referring to the amount of each 

vegetation class without consideration of the spatial arrangement of the mosaic. 

Under the HRV, the landscape exhibited very little variability in the composition of 

vegetation developmental stages, maintaining a nearly constant 10:20:70 ratio of earlyto mid- to late-development stages. Although this ratio differed somewhat among cover 

types, reflecting differences in the prevalence of high-mortality (i.e., stand-replacing) fire 

and fire rotation periods, overall the landscape across all cover types was dominated by 

late-development vegetation. The current landscape deviates dramatically from this HRV, 

containing much more than the HRV in the early- and mid-development stages and much less 

than the HRV in the late-development stage (fig. 33). This departure undoubtedly reflects the 

human land use history of this landscape, including intensive timber harvesting on private 

lands, and past widespread timber harvesting on public lands in which the older, mature 

forest was extensively harvested during the early and mid-20th century. Other practices such 

as hydraulic mining created large areas of early-seral chaparral that stagnated in a shrubdominated (i.e., early-seral) vegetation condition. In addition, the recent human-caused 

Pendola fire created large areas of early-seral vegetation. The legacy of these human land use 

practices and human disturbances is a landscape today that is dominated by early- and midseral forest.

Under the HRV, the percentage of the landscape in each canopy cover class was variable 

but stable around a 38:24:37 ratio of open to moderate to closed canopy. In general, the 

landscape fluctuated over time between having an approximately equal mixture of open, 

moderate, and closed canopy and one slightly dominated by either open canopy or closed 

canopy (fig. 36). The current landscape as a whole does not differ much from this HRV, falling 

within the 95-percent range of variability for all three canopy cover classes (fig. 37). At first, 

this result seems somewhat surprising given the popular belief that the current landscape 

has an uncharacteristic predominance of closed-canopy forest. However, it is important to 

recognize that this result applies to the landscape as a whole, pooled across all cover types 

and ownerships, which is being driven by the excess of early-development stands (which 

are classified as open canopy) in the current landscape. This result masks some important 

differences among individual cover types and developmental stages, as follows.

First, the HRV distribution of canopy cover classes varied substantially among cover 

types, with the xeric and ultramafic cover types typically maintaining a preponderance of 

open- and moderate-canopy conditions and the mesic cover types typically maintaining a 

preponderance of closed-canopy conditions, although this was not true for all cover types. 

Similarly, the magnitude of current departure also varies considerably among cover types, 

with some cover types exhibiting little departure (e.g., Mixed Evergreen Forest), others 

exhibiting moderate departure (e.g., Sierran Mixed Conifer Forest), and still others showing 

major departure (e.g., Red Fir – Mesic). 
USDA Forest Service RMRS-GTR-385. 2018 103

Second, this result for the landscape as a whole is masking important canopy cover 

departures occurring within individual developmental stages (fig. 39). In particular, in 

late-development stands, the proportional distribution of canopy cover classes deviates 

considerably from the HRV. Specifically, under the HRV the ratio of open to moderate to 

closed canopy in the late-development stage was roughly 1:1:1.5, whereas in the current 

landscape the ratio is roughly 1:4:6. In other words, within the late-development stage the 

current landscape contains far too much in the moderate- and closed-canopy condition 

relative to the open-canopy condition. In addition, canopy cover departure within individual 

developmental stages is more apparent in some cover types than others. Thus, despite the 

result for the landscape as a whole, the results by cover type and developmental stage suggest 

that for several of the major cover types the current landscape contains relatively too much 

closed and moderate canopy and too little open canopy.

Management Implications

These results indicate that the current landscape composition departs dramatically 

from the HRV in developmental stage distribution and, depending on the cover type and 

developmental stage, departs considerably in canopy cover class distribution as well. These 

findings have important management implications, as follows.

• Restoring the HRV in the landscape composition of vegetation developmental stages 

would require the following:

◦ Decreasing the amount of early-development vegetation—moving from the current 

approximately 20 percent of the landscape to about 5 to 15 percent of the landscape.

◦ Decreasing the amount of mid-development vegetation—moving from the current 

approximately 50 percent of the landscape to about 15 to 25 percent of the landscape.

◦ Increasing the amount of late-development vegetation—moving from the current 

approximately 35 percent of the landscape to about 65 to 75 percent. 

• Achieving a restoration of the HRV would require allowing existing mid-development 

stands to succeed to late-development conditions, which could take several decades. But it 

could also be facilitated by the following management actions:

◦ Limit the occurrence of high-mortality disturbances by controlling the amount of standreplacing silvicultural treatments (e.g., group cuts) and managing fuels to reduce the 

future occurrence of high-mortality wildfire. Wildfire will continue to create early-seral 

conditions on the landscape, so the focus on national forest lands should mostly be the 

provision of the late-development vegetation.

◦ Promote (with the intention of accelerating) the succession of early- and middevelopment stands to late-development stage structure via appropriate silvicultural 

treatments (e.g., thinning to promote diameter growth) and managing fuels to minimize 

the risk of high-mortality wildfire.

• Restoring the HRV in the landscape composition of canopy cover classes would require 

the following:

◦ Examining canopy cover departure for individual cover types and developmental 

stages, because the landscape pooled across all cover types and developmental stages 

exhibits no apparent departure. In general, the results by cover type and developmental 

stage suggest that for several of the major cover types (e.g., Sierran Mixed Conifer – 

Mesic, the dominant cover type) the current landscape contains relatively too much 

closed or moderate canopy and too little open canopy, and this is especially true in the 

late-development stage.

• Achieving this goal through active management would require judicious application 

of appropriate silvicultural treatments in each cover type. Moreover, it would require 
104 USDA Forest Service RMRS-GTR-385. 2018

examing current departure for the entire seral stage distribution (not just a single seral 

stage), and any management actions aimed at adjusting the seral stage distribution would 

also need to account for the expected successional changes over time. For example, the 

HRV seral stage distribution and current departure in the Sierran Mixed Conifer – Mesic 

cover type is shown in figure 67. Given the apparent departure of each seral stage and 

the expected successional changes over the next several decades (i.e., succession of 

early to mid-, and mid- to late development), mechanical thinning and prescribed fire 

treatments should be focused on maintaining the open- and moderate-canopy cover in 

the mid-development stands, while over time succession should rectify the departures in 

the late-development stage. Similarly, given the expected succession of early-seral stands 

to mid-development closed-canopy stands on private lands, national forest lands should 

focus treatments in early-seral stands on maintaining open-canopy conditions as these 

stands succeed to mid-development. 

4. The current landscape departs from the historical range of variability in the spatial 

configuration of the vegetation mosaic, and more so in some attributes than others.

One of the unique outcomes of this project is a detailed, specific, and quantitative 

description of the HRV and current departure in landscape configuration—referring to the 

spatial pattern of the vegetation mosaic. Most past efforts to quantify the HRV and current 

departure have focused solely on landscape composition and ignored the importance of 

landscape configuration. Here, because we used a spatially explicit landscape disturbancesuccession model, we were able to quantify landscape configuration as well. 

Figure 67—Range of variability in vegetation seral stages (ED = early all, MDO = middevelopment open-canopy, MDM = mid-development moderate-canopy, MDC = mid-development 

closed-canopy, LDO = late-development open-canopy, LDM = late-development moderate 

canopy, and LDC = late-development closed-canopy) in the Sierran Mixed Conifer – Mesic 

cover type for the simulated historical range of variability (HRV) (ca. 1550–1850) and current 

landscape in the upper Yuba River watershed. Boxes represent the interquartile range (25th–75th 

percentiles) of the HRV; the median is the dark horizontal line in the middle of the box. Thick, solid 

vertical lines represent the 5th–95th percentiles of the HRV, and the thin, gray vertical extensions 

represent the full range of the simulated HRV. Dashed, red horizontal lines represent the current 

condition of the landscape.
USDA Forest Service RMRS-GTR-385. 2018 105

Under the HRV, the general picture that emerges of the shifting landscape mosaic is as 

follows:

• The landscape maintained a matrix of late-development vegetation, with individual matrix 

patches extending for up to 10 kilometers (6 miles) and encompassing tens of thousands 

of hectares. 

• Within these matrix-forming late-development patches there existed large patches 

of either open- or closed-canopy forest that extended for up to a few kilometers and 

encompassed up to several thousand hectares. The larger patches of open canopy mostly 

(but not exclusively) existed on steeper, south- and west-facing, upper slopes, and the 

larger patches of closed canopy mostly (but not exclusively) existed on gentle, north- and 

east-facing, lower slopes and valley bottoms. 

• Over time, the late-development matrix shifted from being slightly dominated by patches 

of closed-canopy forest to being dominated by patches of open-canopy forest and back 

again, but at no time was the matrix composed of mainly one canopy cover class. Indeed, 

our simulations suggest the landscape as a whole probably never fell below about 25–30 

percent in each of the three canopy cover classes. These fluctuations were probably driven 

by periodic large disturbance events interspersed with longer periods of recovery.

• The matrix-forming late-development vegetation and the extensive patches of open or 

closed canopy embedded within the matrix were geometrically complex; they contained 

highly convoluted edges and were themselves embedded with many small and irregularly 

shaped patches of differing developmental stages and canopy cover classes. Indeed, the 

late-development matrix maintained only about 30–40 percent in core area (defined as the 

interior of patches farther than a specified depth of edge effect from the patch boundary, 

with edge depths appropriately defined; a detailed technical report is forthcoming), and 

the large open- or closed-canopy patches maintained only about 15–25 percent in core 

area. Thus, most of the landscape was affected by the edges formed by adjacent patches of 

different developmental stages and canopy cover classes.

• In comparison to the developmental stage mosaic, the canopy cover mosaic was less 

dominated by extensive, matrix-forming patches (e.g., almost an order of magnitude smaller 

area-weighted mean patch size) and exhibited a much greater degree of fine-scale spatial 

heterogeneity (e.g., almost a twofold greater density of edges). Thus, the landscape overall 

exhibited much less contagion (i.e., overall aggregation or clumpiness) in canopy cover 

than in developmental stage. The greater degree of spatial heterogeneity in canopy cover 

was undoubtedly due to the preponderance of wildfire disturbances that resulted in partial 

overstory mortality without changing the developmental stage of the residual canopy. 

For example, most of the area burned in wildfires had low mortality and did not undergo 

stand initiation; instead, wildfires more often acted to thin the overstory and create more 

moderate- and open-canopy conditions, resulting in a spatially heterogeneous mosaic in 

canopy cover but not changing the overall dominance by late-development vegetation.

• Although this characterization helps to describe the landscape pattern at any one point 

in time, it is important to recognize that as patches of early-seral forest succeeded to 

mid- and then late-development stands, the late-development matrix shifted in spatial 

configuration over time. Similarly, as patches of open canopy sometimes succeeded to 

moderate and then closed canopy after escaping subsequent wildfires for a prolonged 

period, the larger patches of open- or closed-canopy forest embedded within the latedevelopment matrix also shifted in configuration over time. Moreover, although these 

general patterns held true for the landscape as a whole, these patterns varied somewhat 

across the landscape in relation to the distribution of major cover types.
106 USDA Forest Service RMRS-GTR-385. 2018

The current landscape deviates considerably from the HRV picture described, but more so 

in some attributes than others. Compared to the HRV, the larger developmental stage patches 

in the current landscape are much too small, less extensive, and less geometrically complex 

(resulting in a much greater interior-to-edge ratio), and have slightly less contrast (i.e., the 

magnitude of differences in vegetation structure) along the edges (fig. 59). In addition, the 

developmental stages are more interspersed today than under the HRV, probably due to the 

more regular distribution of anthropogenic disturbances (e.g., timber harvesting) than occurs 

with natural disturbances. The current landscape also has notably less fine-scale heterogeneity. 

We must note, however, that it is unclear how much of this departure is real versus an artifact 

of the modeling despite our attempts to minimize these modeling artifacts by coarsening 

the simulation output grids to more closely match the resolution of the input grids. Overall, 

therefore, the most notable and reliable departure is the absence of very large, extensive, and 

geometrically complex matrix-forming patches of late development in the current landscape. 

The magnitude of current departure for the canopy cover and seral stage mosaics is 

less pronounced. Compared to the HRV, the larger canopy cover and seral stage patches in 

the current landscape are somewhat smaller and less extensive but nonetheless still within 

the HRV (figs. 61, 63). However, these patches are much less geometrically complex than 

under the HRV, resulting in a greater percentage of the patches in core area. In addition, the 

contrast along edges is greater in the current landscape and the canopy cover and seral stage 

classes are more interspersed than under the HRV, again probably owing to the distribution 

of anthropogenic disturbances and management practices. As with developmental stage, 

the current landscape also has notably less fine-scale heterogeneity in canopy cover and 

seral stage, but again it is unclear how much of this departure is real versus an artifact of 

the modeling. Overall, therefore, departure in the canopy cover and seral stage mosaics is 

less clear. Most of the observed major departure is in the fine-scale heterogeneity that we 

cannot be certain is real given the limitations of the input spatial data layers (although other 

considerations of Sierra Nevada mixed conifer forest have also focused on a modern lack of 

fine-scale heterogeneity, e.g., North et al. 2009; Safford and Stevens 2017). 

Management Implications

The preceding results describe the ways in which the current landscape configuration 

departs from the HRV. These findings have important management implications, as follows.

• Restoring the HRV in landscape configuration would require the following:

◦ Promoting the development of very large (~13,000–35,000 hectares) and extensive 

(~6–10 kilometers) matrix-forming patches of late-development stage vegetation. 

◦ Creating geometrically very complex matrix-forming patches resulting in about 30 to 

40 percent in core area (i.e., interior forest environment). This could be achieved by 

creating small (~1–200 hectares; 2–500 acres), irregularly shaped patches of earlyseral vegetation that would eventually succeed to mid-development forest, within and 

between these larger, matrix-forming late-development patches.

◦ Creating relatively large (~1,500–7,500 hectares; 3,700–18,500 acres) and extensive 

(~2–4 kilometers; 1–2 miles), but not necessarily matrix-forming, patches of both openand closed-canopy forest well interspersed with each other and with moderate-canopy 

cover.

◦ Creating geometrically very complex large patches of open- and closed-canopy forest 

resulting in about 15 to 25 percent in core area (i.e., interior forest environment). 

◦ Within and between these large canopy cover patches, creating much fine-scale spatial 

heterogeneity in the canopy cover mosaic.
USDA Forest Service RMRS-GTR-385. 2018 107

• Restoring the HRV in landscape configuration is likely to take a great deal of time 

(decades), effort (management), and creative landscape design. In the absence of natural 

disturbances to recreate the spatial and temporal heterogeneity in landscape structure, it 

would require substantial active management as a surrogate, which could be facilitated by 

the following management actions:

◦ Locating vegetation treatments that promote late-development vegetation (e.g., 

thinning, fuels reduction in mid-development stands) so as to eventually connect 

existing late-development stands and create larger, continuous stands of latedevelopment vegetation to form a matrix.

◦ Within and between these matrix-forming late-development patches, creating numerous 

small patches of early- and mid-development stands of varying sizes and shapes. The 

most practical way to do this would be to create small irregularly shaped patches of 

early development, especially embedded within mid-development stands. However, 

given the current surplus of early-development vegetation, to do so would exacerbate 

the current departure in the amount of early-development vegetation. Thus, it would be 

more prudent in the short term to design silvicultural treatments in mid-development 

stands aimed at promoting late-development conditions. This step would be intended 

to eventually create a matrix of late-development stands embedded with many small 

irregularly shaped residual patches of dense, mid-development forest.

◦ Locating vegetation treatments that create large and geometrically complex patches of 

open- or closed-canopy forest where these conditions are most likely to occur under a 

natural disturbance regime. In this regard, a map of the average percent canopy cover 

under the HRV (fig. 41) could provide a useful spatial template for where to promote 

the larger patches of open- and closed-canopy forest through active management.

• Our modeling suggests (but does not confirm) that the HRV landscape contained far 

more fine-scale heterogeneity than exists today. This was consistent regardless of how 

we defined the landscape (i.e., based on developmental stage, canopy cover, seral stage, 

or even unique combinations of cover type and seral stage). This agrees with other recent 

studies of forest structural conditions, such as North (2012), North et al. (2009), and 

Safford and Stevens (2017). Emulating this fine-scale heterogeneity would require some 

changes in common vegetation treatment prescriptions. In particular, prescribed fire and 

low- and moderate-severity managed wildfire, with or without mechanical treatment of 

fuels, are the treatment types most likely to increase fine-scale heterogeneity. Thus, given 

the area affected, modifying prescribed fire prescriptions to promote variable overstory 

mortality is likely to have the greatest effect on increasing the fine-scale heterogeneity in 

the vegetation mosaic. 

• Overall, emulating the HRV in landscape configuration through active forest management 

would require translating the strategic guidance provided by this study into a spatially 

and temporally explicit landscape design that specifies what treatments to do where and 

when. In particular, the landscape design would specify the location of treatments (and the 

type of treatments needed) to promote the creation of the late-development matrix and the 

large open- and closed-canopy patches embedded within the matrix that conforms to the 

terrain and existing distribution of vegetation types.

5. Scenario analysis revealed the comparative effects of alternative management 

strategies on landscape composition and configuration.

In this study we demonstrated how scenario analysis can be used to examine the potential 

effects of alternative management strategies on landscape structure. Not only can scenario 

analysis inform us as to the potential consequences of a no treatment or “business as usual” 
108 USDA Forest Service RMRS-GTR-385. 2018

management strategy, but it also allows us to derive management scenarios that strive to 

achieve detailed, specific, and quantitative desired outcomes. In this regard, we examined 

eight different management scenarios, each of which was simulated for 100 years. At the end 

of the 100 years we summarized the variability in landscape structure among 20 replicate runs. 

One of the scenarios (MS1) explored a no treatment alternative involving only aggressive 

fire suppression, reflecting the modern fire regime. A second scenario (MS2) explored a 

“business as usual” alternative in which treatment intensity and types followed the existing 

land management plan (LMP) guidelines. The remaining six scenarios explored more 

intensive treatment options that varied in the amounts and types of treatments, ranging from 

prescribed fire only (MS3a,b) to varying mixtures of mechanical and prescribed fire treatments 

(MS4–MS7). These latter scenarios were devised to explore various “what if” alternatives that 

sought to determine: (1) What management actions would be required to move the current 

landscape to within the HRV in landscape composition and configuration, and (2) what 

mixture of mechanical and prescribed fire treatments would best achieve this goal. Although 

we realize that these latter scenarios far exceed the current level of active management on 

public lands, and may seriously challenge what is currently deemed socially and politically 

acceptable, we believe that they provide a useful barometer for how much disturbance is 

needed to move the current landscape closer to the HRV. 

Our results demonstrated that both the no treatment scenario and the current LMP (or 

“business as usual”) scenario not only failed to move the current landscape closer to the HRV 

in landscape structure in most attributes, but actually caused the current landscape to move 

even further away from the HRV in many attributes. The no treatment scenario did allow the 

landscape to restore a more “natural” distribution of developmental stages, but ultimately it 

produced much less in the early- and mid-development stages and much more in the latedevelopment stage compared to the other scenarios (figs. 56, 59). This scenario also moved the 

current landscape furthest away from the HRV distribution in canopy cover classes, producing 

far less open- and moderate-canopy forest and far more closed-canopy forest (figs. 57, 61). 

In addition, this scenario resulted in a greatly simplified (i.e., more homogeneous) landscape 

configuration that differed dramatically from the HRV, as represented by any of the landscape 

definitions and landscape metrics (e.g., fig. 64). None of these outcomes was surprising 

given the dramatic reduction in wildfire occurrence as the sole source of disturbance. The 

“business as usual” scenario involved 3,451 hectares (8,524 acres) of treatments per 5-year 

timestep, representing a treatment rate of 2.78 percent of the national forest land area every 

5 years, or 0.5 percent per year. This relatively low intensity of treatments in addition to the 

reduced wildfire occurrence did result in moving the current landscape closer to the HRV, but 

ultimately it failed to closely emulate the HRV in most attributes.

The higher intensity treatment scenarios (MS3–MS7) were comparatively more successful 

in moving the current landscape closer to the HRV and produced several important findings:

• The intensity and types of treatments affected how well a scenario emulated the HRV 

in each of the landscape attributes. For example, all of the higher intensity treatment 

scenarios did a reasonably good job of emulating the HRV in the distribution of 

developmental stages (figs. 56, 59), but they varied considerably with respect to most 

other attributes (e.g., figs. 57, 61, 64).

• The prescribed fire-only scenarios (MS3a,b) required a much greater treatment intensity 

than the scenarios involving a mixture of mechanical and prescribed fire treatments 

(MS4–MS7) to achieve a similar outcome, and the “hotter” prescribed fires (MS3b) did 

a much better job of emulating the HRV than the “cool” prescribed fires (MS3a) (figs. 

59, 61, 64). Note that the “hotter” prescribed fires involved allowing three times as much 

canopy mortality than is typically prescribed. 

• It is possible to achieve the desired outcome with lower treatment intensity through 

careful selection of treatment types and regimes. In general, our balanced and final 
USDA Forest Service RMRS-GTR-385. 2018 109

scenarios (MS6–MS7), which involve a suite of mechanical and prescribed fire treatments 

at the rate of 3.6–3.9 percent of the Tahoe National Forest treated per year did as well 

or better in emulating the HRV in most landscape attributes than the higher intensity 

scenarios (MS3 and MS5) treated at the rate of 5.0–5.5 percent of the Forest per year 

(figs. 59, 61, 64).

• It is possible to construct a management scenario that closely emulates the HRV in most 

attributes of landscape structure. Our final scenario was constructed after examining the 

results of the a priori scenarios and the results of our first attempt to design a scenario 

(MS6) that would emulate the HRV in all landscape attributes. Our final scenario involved 

a mixture of nine different treatment types, including “hotter” prescribed fires, in which 

the dynamic constraints and priorities were adjusted slightly to rectify the shortcomings 

of the “balanced” scenario (a detailed technical document is forthcoming). Although our 

final scenario did not perfectly emulate the HRV across all attributes (e.g., figs. 56–65), 

especially at the level of individual cover types (see tables D4–D10 in Appendix D), it 

came very close to doing so and thus represents an approximation of what it might take 

to move the current landscape close to the HRV. Moreover, it would certainly be feasible 

to further tune this scenario in order to home in on emulating the HRV closely in all 

landscape attributes, even at the level of individual cover types. 

Management Implications

These findings have several important management implications:

• If the desired future condition is to move the current landscape closer to HRV, then the 

no treatment strategy is not an option. The modern fire regime alone, assuming continued 

effective fire suppression, is likely to eventually result in too much of the landscape in a 

late-development, closed-canopy seral stage and would almost certainly result in too little 

spatial heterogeneity compared to the HRV in landscape structure. Similarly, the “business 

as usual” strategy based on the current LMP is likely to fall far short of the goal of closely 

emulating the HRV. The relatively low rate of treatment under this scenario (0.5 percent 

per year), even in combination with the expected wildfire disturbances under the modern 

fire regime, falls far short of the rate of natural wildfire disturbance characteristic of the 

historical reference period. If we choose to adopt this management scenario, then we must 

be willing to accept dramatic departure from the HRV in many landscape attributes and 

accept the ecological consequences. Note also that our scenarios assume continuation 

in the current rate of success in suppressing fires and do not include insect outbreaks. 

Current trends in fire occurrence and severity and insect outbreaks suggest that the even 

greater forest densities and canopy cover that would occur under the no treatment and 

“business as usual” scenarios could ultimately lead to large losses of forest cover in highseverity disturbances.

• If prescribed fire treatments are to play an important role in the mixture of treatments 

used to restore the HRV in landscape structure, then we must recognize that “hotter” fires 

than are typical of current prescriptions are needed to create the spatial heterogeneity in 

developmental stage and canopy cover that is typical of the low- and moderate-severity 

fires common under the HRV. For example, in Sierran Mixed Conifer – Mesic forests, 

our “cool” burn scenarios allowed for about 3–5 percent canopy mortality, whereas our 

“hotter” burn scenarios allowed for about 6–15 percent canopy mortality. Although this 

falls short of what we estimated the mortality rate to be for wildfires under the HRV, it 

comes much closer to creating the fine-scale heterogeneity in developmental stage and 

canopy cover that was probably characteristic of the landscape under the HRV.

• Our scenario analysis demonstrates that active vegetation management involving a 

combination of mechanical and prescribed fire treatments has the potential to emulate 
110 USDA Forest Service RMRS-GTR-385. 2018

many aspects of landscape structure that would occur under a natural disturbance regime, 

but it would require a much higher intensity of treatment than we are accustomed 

to—perhaps as much as 10 times the current treatment rate. Although this is a highly 

ambitious goal given contemporary socioeconomic, political, and logistical constraints on 

planning and implementing vegetation treatments, it is nonetheless important to realize 

what it would take to meet the goal of emulating the HRV—even if we choose to do 

otherwise.

• Given the mixed ownership of our study landscape—in which 32 percent of the land 

is privately owned and managed—it is perhaps not realistic to believe that any public 

land management strategy could completely emulate the HRV in landscape structure. 

Indeed, given these constraints, it is in fact remarkable how well our final scenario did in 

emulating the HRV in landscape structure. Thus, land managers should perhaps not expect 

to completely emulate the HRV, if that is the ultimate goal, but instead strive to move the 

landscape steadily closer to the HRV. In addition, in designing the landscape to achieve 

desired future conditions, public land managers may want to account for the management 

occurring on private lands to the extent possible and strive to complement those activities. 

For example, it is reasonable to assume that in our study landscape the private industrial 

forest lands will continue to be managed to maximize economic returns from timber 

production. Managers of these lands are likely to continue to use clearcutting as the 

principal regeneration method and strive to maintain full stocking levels as stands grow 

to economic maturity. Consequently, we might expect the private industrial forest lands 

to sustain the level of mid-development closed-canopy forest (and some of the early-seral 

vegetation, where heavy brush control is not implemented) needed to emulate the HRV. 

Then management of public lands could focus on providing late-development and opencanopy conditions. 

6. The quantitative approach used here demonstrates the feasibility of creating detailed, 

specific, and quantitative desired future conditions, and monitoring progress toward 

achieving those conditions.

The 2012 Planning Rule (NFMA, 2012 Planning Rule 2015) and the final planning 

directives in Forest Service Handbook 1909.12 (https://www.fs.usda.gov/detail/planningrule/

home/?cid=stelprd3828310) are largely couched in an adaptive management framework 

requiring detailed, specific, and quantitative desired future conditions and periodic monitoring 

of progress toward achieving those conditions. Moreover, the planning directives recommend 

using “natural” range of variability (where “natural” is generally interpreted as some 

appropriately defined historical reference period, or an appropriate alternative) as the basis 

for establishing desired future conditions and using the composition and configuration of 

ecosystems (i.e., landscape structure) as key state variables (among others). Importantly, 

adaptive management requires that the desired future conditions be detailed, specific, and 

quantitative so as to allow for unambiguous effectiveness monitoring and feedback.

In this project, we used the HRV in several detailed, specific, and quantitative measures 

of landscape composition and configuration to describe the ecological conditions that are 

presumed to confer landscape ecological integrity and sustain native biodiversity. We used 

these measures to evaluate the current landscape condition relative to the HRV (i.e., current 

departure). In addition, we used the HRV in each of these measures to establish desired future 

conditions and then simulated alternative management strategies aimed at achieving these 

desired future conditions. Because these measures were detailed, specific, and quantitative, we 

were able to quantitatively evaluate the success of each management scenario in achieving the 

desired future conditions.

Although this exercise focused solely on landscape structure dynamics, and did not 

consider other important ecological variables (e.g., focal wildlife species, ecosystem services), 
USDA Forest Service RMRS-GTR-385. 2018 111

it nonetheless demonstrates the feasibility of using a landscape disturbance-succession 

model to help inform the establishment of detailed, specific, and quantitative desired future 

conditions. Moreover, it demonstrates the feasibility and added value of using a spatially 

explicit landscape disturbance-succession model to quantify both landscape composition and 

configuration. 

NEXT STEPS

In this project we demonstrated the practicality of using a spatially explicit landscape 

disturbance-succession model to characterize the historical range of variability in landscape 

structure and compare it to the current landscape (i.e., current departure) and several 

alternative management scenarios in the upper Yuba River watershed. Although these 

results are based on a model, and thus limited by the quality of the input data and modeling 

framework as discussed previously, we believe that these results are useful and can lead to 

more informed and better management. However, there are some important next steps to 

improve the quality and reliability of these results and to extend their applicability, as follows:

• One of the obvious next steps is to expand the geographic scope of the modeling to the 

entire district, forest, or other ecological or administrative units. Strictly speaking, because 

landscapes are idiosyncratic (i.e., have unique ecological settings and history), our 

specific results pertain only to the upper Yuba River watershed project area. However, our 

general findings certainly pertain to other similar surrounding landscapes and probably 

can be safely extrapolated to some extent. Nevertheless, to the extent that detailed 

quantitative results are desired for other geographies, a separate modeling exercise should 

be undertaken. Fortunately, much of the work to develop spatial input data layers and to 

parameterize the model has been done and can easily be adapted to other nearby similar 

ecological settings.

• Another important next step is to expand the ecological scope of the modeling to include 

future climate and insect outbreaks in combination with vegetation management. We 

conducted some preliminary modeling of future climate impacts on range of variability in 

landscape structure, but not in combination with vegetation management (Mallek 2016). 

In addition, for this study we focused exclusively on wildfire as the dominant disturbance 

process, but insect outbreaks may increasingly affect vegetation patterns in the future 

(Bentz et al. 2010). Despite great uncertainty in how wildfire and insect disturbance 

regimes may vary under future climate conditions, which presents some difficult 

modeling challenges, this remains an important topic for further investigation. 

• A practical next step is to translate the strategic findings of this study into a landscape 

design to guide project-level planning within the landscape. Specifically, we described 

the HRV in landscape composition and configuration and current departure for the upper 

Yuba River watershed, and made several management recommendations for moving the 

current landscape to its HRV. These general guidelines must be translated into a spatially 

and temporally explicit design for what treatments to do where and when. This landscape 

design would provide the guidance needed for project-level layout and implementation of 

treatments aimed at restoring the HRV for the landscape as a whole.

• One of the greatest limitations of the current analysis is the relatively poor spatial 

resolution of the spatial input layers pertaining to vegetation cover type and seral stage. In 

particular, the relatively coarse spatial resolution of the seral stage data makes it difficult 

to compare the spatial configuration of the current landscape (which is relatively coarse) 

to that generated by the simulation model (which is relatively fine). This compromises the 

interpretation of current departure, which relies on the comparability of the input layers 
112 USDA Forest Service RMRS-GTR-385. 2018

(to characterize the current landscape) and the model output layers (to characterize range 

of variability). This is especially problematic for comparisons of spatial patterns—most 

notably measures of fine-scale heterogeneity; hence our caution in overinterpreting 

departure for the landscape configuration metrics that measure fine-scale heterogeneity. 

Although we took steps to minimize this bias by rescaling the simulation output grids—

dissolving patches below a minimum size—to more closely match the input grids, our 

adjustment was not entirely satisfactory. One potential solution to this problem is to 

create an accurate fine-resolution vegetation map by using a combination of spectral and 

LiDAR data to serve as the input layers for the current landscape. Indeed, such a product 

was recently developed for the Tahoe National Forest. The analyses we conducted in this 

project could be replicated with the new and improved vegetation layers. Unfortunately, 

nothing is as simple as it sounds. There is not a one-to-one crosswalk between vegetation 

seral stages (or stand structures) as currently defined and used in this project and those 

defined in the new and improved vegetation layers. Therefore, substantial work would be 

required to parameterize the model for these new cover type and seral stage descriptions, 

essentially developing new state-and-transition models for each cover type. 

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118 USDA Forest Service RMRS-GTR-385. 2018

APPENDIX A: Spatial Data Layers

RMLands uses raster GeoTiffs (.tif files) as its data structure. Rasters are based on 

uniform square units called cells (or pixels). Each cell represents an actual portion of 

geographic space. In this application, we used the Universal Transverse Mercator projection 

Zone 10 North. The extent of a raster is rectangular although the project area was not. Cells 

outside of the buffered project area were assigned a null value. For this project, each grid cell 

was 30 meters (100 feet) on a side (i.e., 900 square meters or 0.09 hectares; 10,000 square feet 

or 0.2 acres), and the input grid measured 2,910 pixels × 2,245 pixels.

RMLands requires that all input grids be perfectly aligned. We accomplished this by 

setting the Extent and Snap Raster to the same parameters whenever we manipulated the 

layers in ArcMap. This “base” spatial layer was created by taking the primary elevation layer 

used on the Tahoe and Plumas National Forests, resampling it to a 30-meter grid, and clipping 

its extent to match that of the buffered project area. Each cell was assigned a single class 

value, where valid class values were positive nonzero integers. Integer values were mapped 

to more descriptive class names by using comma-separated value files with names identical 

to the grid name. All grids were created in ArcMap and saved as GeoTiff files before being 

loaded into the model.

The following is a description of nine of the key spatial input layers and how we derived 

them.

Cover Type

Cover type was based on the potential or current natural vegetation of a site and included 

both natural and anthropogenic cover types. For example, cover types included not only 

Lodgepole Pine, Sierran Mixed Conifer, and Red Fir, but also Barren and Agriculture. 

Succession pathways were defined uniquely for each cover type, susceptibility to natural 

disturbances varied among cover types, and suitability or eligibility for various vegetation 

treatments varied among cover types. Cover was treated as a static (constant) grid and 

therefore provided a fixed template upon which disturbance and succession processes played 

out over time.

The source for the cover layer was the Forest Service Region 5 existing vegetation layer 

(“EVeg”), first mapped to the CALVEG classification developed by the region’s Ecology 

program in 1978. When we were deciding on land cover types, including determining xeric 

and mesic subtypes, our focus was to best represent the project area and the surrounding 

landscape. We used the CALVEG mapping zone boundary for the North Sierra (fig. A1) as our 

focus for defining vegetation and disturbance, including susceptibility, response to fire, and 

fire size and distribution. Within the project area, the EVeg layer was developed based on three 

separate efforts: a satellite-based imagery analysis in 2000, and two orthoimagery analyses 

completed by contracting firms in 2005. Generally, specific cover type names were derived 

from the California Fire Return Interval Departure (FRID) report by Van de Water and Safford 

(2011). We also considered information from A Guide to Wildlife Habitats of California 

(Mayer and Laudenslayer 1988, as periodically updated), popularly known as the “Wildlife 

Habitat Relationship” (WHR) cover types.

Our team originally intended to use two separate cover layers: one for the historical 

reference period, and one for the current period to be used in projections of future scenarios. 

Two layers were identified as potentially suitable for the historical analysis: a map created 

from forest survey and inventory efforts under Albert Wieslander conducted between 1928 and 

1940 (“Wieslander”) (Thorne et al. 2006), and a map of potential natural vegetation (PNV) 
USDA Forest Service RMRS-GTR-385. 2018 119

created by a Forest Service Enterprise Team for the Tahoe National Forest in the 2000s (Forest 

Service internal GIS data). Our intent was to use the PNV or Wieslander, or a combination 

thereof, to derive the land cover layer for the HRV phase of the project.

To validate the historical maps, we needed to develop a crosswalk between the vegetation 

type methodologies for the EVeg, PNV, and Wieslander maps. We also examined the spatial 

consistency in cover types across the maps. We attempted to create a crosswalk from these 

maps to the set of land cover types to be used in the project. However, we were unable to 

develop a consistent and comprehensive set of rules for this purpose. A major reason for 

this is that both the PNV and Wieslander maps used species lists, rather than assemblages 

(as in CALVEG and LandFire). For example, Sierran Mixed Conifer Forests do not appear 

as a dominant “cover type” in the PNV map. The Wieslander maps do contain an internal 

crosswalk to a mixed conifer alliance, but only occasionally. 

Figure A.1—CALVEG Mapping Zones (i.e., ecological provinces). These zones meet 

USDA Forest Service standards at national and regional levels. In addition, these 

zones are associated with dozens of vegetation alliances, which are used to classify 

vegetation in spatial data products. We used vegetation alliance definitions for the 

North Sierran zone to classify the land cover spatial data shared by the Forest Service.
120 USDA Forest Service RMRS-GTR-385. 2018

In addition, the PNV map contained a more significant error: We learned that, for the 

purposes of the modeling used to create the PNV map, “potential natural vegetation” meant 

the so-called “climax” community that would develop in the complete absence of disturbance, 

regardless of whether that disturbance was human caused or natural. We were seeking to 

mimic the natural historical range of variability, so we decided to discard this layer. The 

Wieslander map had its own issues. Most problematic was the nonsystematic spatial error of 

up to 300 meters (1,000 feet), which meant it would not be suitable for comparing specific 

locations. In addition, crosswalking precisely was impossible because coded vegetation was 

not necessarily in order of most prevalent vegetation, but instead prioritized tree species over 

shrubs, and commercially important trees over others. As an example from the handbook, 

a plot consisting of 75 percent Quercus kelloggii (California black oak), 15 percent Pinus 

ponderosa (ponderosa pine), and 10 percent Pinus lambertiana (gray pine) would be coded 

as ponderosa pine, gray pine, black oak. Finally, the Wieslander maps were developed from 

surveys done in the 1930s, decades after the huge influx of settlers in the 1850s; by the 

1930s, vegetation patterns may have already been significantly altered (Thorne et al. 2006). 

Consequently, the Wieslander map was also not a reliable predictor of land cover type without 

extensive review of the original data and maps, which was beyond the scope of this project.

To confirm these problems, we examined the overlap in land cover types between 

different maps in ArcGIS. In general, the overlap between EVeg and either the PNV or the 

Wieslander layers was no better than random, and in many cases it was worse. Consequently, 

we decided to proceed using only the EVeg map, and omit the calibration period of the model 

from our analysis of the characteristics of the HRV.

In the early stages of this project, we created a suite of land cover types based roughly 

on the WHR types used in California and by Forest Service managers and planners. These 

consisted of the WHR types with a few more types where additional specificity or refinement 

was desired. For example, Red Fir was split into two subtypes. The original concept was to 

begin with the WHR types and modify them as needed based on other attributes in the EVeg 

layer. However, creating a crosswalk from WHR to the project-specific types also proved 

problematic. First, we realized that the WHR values were actually derived from the CALVEG 

species alliances included in the EVeg layer, but the methodology used was unavailable or 

missing. The crosswalks we did find were not mutually exclusive and all-inclusive, and did 

not always make ecological sense (California Department of Fish and Game 2005; de Becker 

and Sweet 1988; Keeler-Wolf 2007). This is probably due in part to the fact that WHR is not a 

mapping classification. It is always derived secondarily. So we were unable to create consistent 

rules for mapping from WHR to other types. Others have encountered similar issues: 

WHR has been less successful in differentiating between vegetation types. Because the 

habitat types are inconsistently defined, a broad familiarity with its detailed descriptions is 

needed to differentiate among types of similar structure. Although mappers have constructed 

rules for discriminating among types, difficulties still remain because species dominance 

varies substantially within some types and broad overlaps in dominant plants occur among 

types. Other problems arise due to the small number of classes and the inconsistencies in scale 

among them. (Keeler-Wolf 2007: 23)

We decided to instead base our land cover types on, at the first order, Presettlement Fire 

Regime (PFR) types as defined in the Fire Return Interval Departure (FRID) report by Van de 

Water and Safford (2011). The PFR types, as part of the FRID, were developed through the 

scientific process and underwent peer review. We used the methodology from the FRID rather 

than using the second-order WHR classification and trying to reverse-engineer it to fit into our 

custom land cover types. Thus, we created a new structure of cover types in a nested regime, 

moving from PFR (the coarsest aggregation of CALVEG types, which included a direct 
USDA Forest Service RMRS-GTR-385. 2018 121

crosswalk from them to PFR types), to Biophysical Settings from LandFire (which were also 

crosswalked to PFR types in the FRID report), and finally to various local types not otherwise 

represented, such as xeric and mesic variants of cover types (e.g., Mixed Evergreen), and 

aspen variants (e.g., Red Fir – Aspen). A mutually exclusive and all-inclusive crosswalk 

for each land cover type used in this analysis to a single LandFire Biophysical Setting and 

Presettlement Fire Regime type thus exists.

Extensive geoprocessing was required to prepare the EVeg layer for use in RMLands. 

Beyond converting the vector data to a raster format, further analysis was required to 

distinguish eastside and westside areas from one another, and generate the cover type 

modifications that the team agreed on. Aspen types were created by overlaying an aspen layer 

onto the vegetation layer and creating combined types (“[type] – Aspen”) where appropriate. 

Areas mapped as a vegetation type characteristic of early seral vegetation (e.g., chaparral) 

were analyzed and assigned an appropriate forested cover type. Ultramafic types were created 

by overlaying a geology layer onto the vegetation layer and performing a similar processing 

step to create “[type] – Ultramafic.” Finally, for the Sierran Mixed Conifer and Red Fir cover 

types, which cover broad swaths of land across elevation and aspect, a xeric to mesic gradient 

was developed in conjunction with local experts and applied, creating “[type] – Mesic” and 

“[type] – Xeric.”

Ultimately, 31 cover types were generated for the buffered project area, although most 

occupy a small extent (see table A1). The cover types with an extent of less than 1,000 

hectares (2,500 acres) within the core project area may have statistically unreliable results; this 

problem increases as the extent of given cover type decreases. We caution against attempting 

to make inferences for these less common cover types. However, because the nine cover types 

that do occupy 1,000 or more hectares represent approximately 93 percent of the core project 

area, we have high confidence in the landscape-level results. These nine cover types were 

considered our focal cover types, and were all fully analyzed as part of the historical range of 

variability assessment. 

Seral Stage

Seral stage classes combine developmental stage and canopy cover, and were defined 

for all cover types that undergo succession. Seral stages in this application were based on 

LandFire vegetation classes, and were further modified based on local expertise. In RMLands, 

susceptibility to and mortality from natural disturbances vary among seral stages. Unlike 

the cover grid, the seral stage grid changes dynamically over time in response to simulated 

succession and disturbance events. The combination of cover type and seral stage formed the 

basis for characterizing vegetation patterns and dynamics.

The source for the seral stage layer was the Region 5 EVeg Layer. We considered 

potential attributes to be used for this classification and ultimately chose tree diameter at 

breast height and cover from above to classify pixels into early, mid-, or late development, and 

open, moderate, and closed canopy. This classification system was used for most of the forest 

cover types; aspen and shrubland cover types were classified differently. See Appendix B for 

the classification and description of the seral stages for each of the major cover types.

Extensive geoprocessing was required to prepare this layer for RMLands. Beyond 

converting the vector data to a raster format, further analysis was required to update the layer to 

a year 2010 condition. Spatial data on fire and timber management history were used to provide 

a more accurate assessment of seral stage based on estimated stand age. In addition, areas 

currently mapped as chaparral in the EVeg layer were assigned to the early-development stage.
122 USDA Forest Service RMRS-GTR-385. 2018

Age

Age represents the number of years since the last stand-replacing disturbance (highmortality fire). In the model, age affected successional transitions and susceptibility to 

disturbance. Because the characteristic species of a given cover type may not immediately 

establish after a stand-replacing fire, it is likely that the age value recorded in the model is 

larger than the actual age of the oldest individuals in a stand. Several of the cover types in this 

area may go through a chaparral-dominated early-development stage; in those cases the oldest 

trees in the stand could be decades younger than the formal stand age. Nevertheless, due to the 

lack of comprehensive fire history data, we were forced to assign initial age to each cell based 

on an interpolation of stand age in stand exams, which is based on the actual age of dominant 

and codominant individuals, but modified by data on recent fire and timber management 

history where it existed.

In the HRV analysis, the initial age value assigned to a given cell is not necessarily 

important to the outcome of the simulation, due to the exclusion of the model equilibration 

period (first 40 timesteps in our case) from the analyzed results. However, in the future 

vegetation treatment scenario analysis, the initial age value carries more weight because the 

total simulation length is only 18 timesteps.

In this application, we used data from stand exams dating to the 1960s and from recent 

Region 5 Ecology Group survey plots to estimate stand age across the buffered project area. 

We then interpolated that information across the landscape. Due to insufficient data, we were 

unable to disaggregate the data below the landscape scale to cover type or another more finely 

resolved classification. We also acknowledge that the stand exam and modern vegetation plots 

do not constitute a true sample. They were conducted almost exclusively in mid-mature and 

mature stands of commercially viable trees, thus skewing the results to some unquantifiable 

degree. We updated the interpolated data with fire and timber management history, and 

assigned ages to types coded as chaparral in the EVeg layer to the midpoint of the age spread 

of early development for the forest cover type to which it was converted. Remaining ages 

out of compliance with allowed ages for the corresponding seral stage of a given cell were 

modified to be in compliance, based on the assumption that the seral stage assignment was 

more accurate than the interpolated age information. Last, in this application, we rounded all 

modeled and derived ages to the nearest 5 years (the length of one timestep).

Seral Stage Age

Seral stage age represents the age since transitioning to the current seral stage, that is, 

how long a cell has been in the current seral stage. In RMLands, seral stage age can affect 

transitions between seral stages; typically, there is a threshold seral-stage age below which 

transitions do not occur. After creating both the seral stage and age layers, we derived 

seral stage age based on the youngest possible age for a cell of that cover and seral stage. 

For example, if we determined that a particular cell on the landscape had a cover type of 

Lodgepole Pine, seral stage of mid-development closed, and age of 50 years, we took the 

minimum age for that cover-seral stage combination (10 years old), and subtracted it from 

the age to arrive at a seral stage age of 40. Given that each seral stage also belongs to a 

developmental stage (early, mid-, or late development), we also derive development age (i.e., 

how long a cell has been in the current developmental stage) from seral stage age and track it 

in the simulation.
USDA Forest Service RMRS-GTR-385. 2018 123

Elevation

Elevation represents the height above sea level in meters. In RMLands, elevation can 

affect disturbance spread. The elevation grid used in this analysis was a digital elevation 

model provided by the Tahoe National Forest and resampled to 30-meter pixels.

Slope

Slope represents the steepness of a cell as measured in percent. In RMLands, slope can 

affect disturbance spread. The slope grid used in this analysis was derived from the elevation 

grid just described.

Aspect

Aspect represents the direction a cell is facing in terms of eight cardinal directions, plus 

flat. In RMLands, aspect can affect disturbance spread via an interaction with wind direction 

during an event. The aspect grid used in this analysis was derived from the elevation layer 

described earlier.

Topographic Position Index

Our topographic position index (TPI) combines heat load, which is based on aspect 

and slope, with slope position. High values for TPI are correlated with locations on steep, 

south- and west-facing, upper slopes. Low values are correlated with locations on gentle, 

north- and east-facing, lower slopes, and valley bottoms. Values in between occur along a 

gradient of these characteristics. Our TPI is scaled to the project area (including the buffer), 

and is therefore a local index only. In RMLands, TPI can affect disturbance susceptibility and 

mortality. 

Streams

Streams represent linear hydrological features, classified as small, medium, or large based 

on stream order. In RMLands, they can affect the spread of disturbance depending on both 

stream size and disturbance size. For this application, the streams layer was created from the 

Tahoe National Forest hydrography dataset. Note that to function as a potential barrier, cells 

coded as streams in the stream raster must share a side, rather than only a vertex. All “large” 

streams were represented as Water in the cover type layer.
124 USDA Forest Service RMRS-GTR-385. 2018 Table A.1—Cover types in the upper Yuba River watershed project area, including the formal project area and the simulation area (project area plus 10-km [6-mi] buffer), 

Ta

given in rank order of extent within the core project area (source: McGarigal et al. 2018).

ble A.1—Cover types in the upper Yuba River watershed project area, including the formal project area and the simulation area (project area plus 10-

km [6-mi] buffer), given in rank order of extent within the core project area (source: McGarigal et al. 2018). 

Cover type Abbreviation

Project area

(ha)

Project area

(ac)

Simulation area

(ha)

Simulation area

(ac)

Sierran Mixed Conifer – Mesic SMC_M 57,853 142,955 133,920 330,916Sierran Mixed Conifer – Xeric SMC_X 52,198 128,981 91,443 225,956Oak-Conifer Forest and Woodland OCFW 23,279 57,522 56,987 140,815Red Fir – Mesic RFR_M 8,563 21,159 19,626 48,496Red Fir – Xeric RFR_X 7,493 18,515 9,989 24,683Mixed Evergreen – Mesic MEG_M 7,273 17,972 13,548 33,477Mixed Evergreen – Xeric MEG_X 6,768 16,724 13,774 33,036Sierran Mixed Conifer – Ultramafic SMC_U 4,124 10,190 9,774 24,152Watera WAT 4,058 10,027 8,157 20,156Barrena BAR 2,665 6,585 8,751 21,624Grasslanda GRASS 1,379 3,408 4,617 11,409Meadowa MED 1,201 2,968 3,435 8,488Oak-Conifer Forest and Woodland – Ultramafic OCFW_U 1,060 2,619 2,185 5,399Lodgepole Pine LPN 837 2,068 2,816 6,958Montane Riparian MRIP 732 1,809 2,216 5,476Subalpine Conifer SCN 638 1,576 2,044 5,051Mixed Evergreen – Ultramafic MEG_U 604 1,492 1,655 4,090Red Fir – Ultramafic RFR_U 294 726 321 793Western White Pine WWP 273 675 510 1,260

(Table A.1 continued on next page.)
USDA Forest Service RMRS-GTR-385. 2018 125 Cover type Abbreviation Project area (ha) Project area (ac) Simulation area (ha) Simulation area (ac) Urbana URB 114 282 782 1,932 Sierran Mixed Conifer with Aspen SMC_ASP 58 143 121 299 Red Fir with Aspen RFR_ASP 31 77 34 84 Oak Woodland OAK 19 47 4,192 10,358 Curl-Leaf Mountain Mahogany CMM 18 45 41 101 Agriculturea AGR 16 40 5,416 13,383 Lodgepole Pine with Aspen LPN_ASP 8 20 31 77 Yellow Pine YPN 0 0 10,499 25,943 Big Sagebrush SAGE 0 0 1,600 3,954 Subalpine Conifer with Aspen SCN_ASP 0 0 6 15 Black and Low Sagebrush LSG 0 0 5 12 Yellow Pine with Aspen YPN_ASP 0 0 3 7 Total 181,556 448,625 408,498 1,009,399 a (Table A.1 continued) Cover types treated as nonseral.
126 USDA Forest Service RMRS-GTR-385. 2018

REFERENCES

California Department of Fish and Game. 2005. CWHR crosswalked with CalVeg 

classification. Sacramento, CA: California Department of Fish and Game, California 

Wildlife Habitat Relationships System. https://nrm.dfg.ca.gov/FileHandler.

ashx?DocumentID=65861.

de Becker, S; Sweet, A. 1988. Crosswalk between WHR and California vegetation 

classifications. In: Mayer, K.E.; Laudenslayer, E.F., eds. A guide to wildlife habitats of 

California. Sacramento, CA: California Department of Fish and Game: 21.

Keeler-Wolf, T. 2007. The history of vegetation classification and mapping in California. 

[Chapter 1]. In: Barbour, M.G.; Keeler-Wolf, T.; Schoenherr, A.A., eds. Terrestrial vegetation 

of California. Berkeley and Los Angeles, CA: University of California Press: 1–42.

Mayer, K.E.; Laudenslayer, W.F., eds. 1988. A guide to wildlife habitats of California. 

Sacramento, CA: California Department of Fish and Game. http://www.dfg.ca.gov/

biogeodata/cwhr/pdfs/SGB.pdf [Accessed 4 December 2012].

McGarigal, K.; Mallek, M.; Estes, B.; [et al.]. 2018. Modeling historical range of variability 

and alternative management scenarios in the upper Yuba River watershed, Tahoe National 

Forest, California. Gen. Tech. Rep. RMRS-GTR-385. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station. 346 p.

Thorne, J.H.; Kelsey, R.G.; Honig, J.; [et al.]. 2006. The development of 70-year-old 

Wieslander vegetation type maps and an assessment of landscape change in the central 

Sierra Nevada. Tech. Rep. CEC 500-2006-107. Sacramento, CA: PIER Energy Related 

Environmental Program.

Van de Water, K.M.; Safford, H.D. 2011. A summary of fire frequency estimates for California 

vegetation. Fire Ecology. 7(3): 26–58. https://doi.org/10.4996/fireecology.0703026.
USDA Forest Service RMRS-GTR-385. 2018 127

APPENDIX B: Cover Type Description and State-andTransition Model for 12 Cover Types and Variants 

1. Black and Low Sagebrush (LSG)

Reviewed by: (1) Hugh Safford, Regional Ecologist, USDA Forest Service; (2) Becky 

Estes, Central Sierra Province Ecologist, USDA Forest Service; and (3) Michele Slaton, GIS 

Specialist, Inyo National Forest, USDA Forest Service.

Cover Type Classification and Crosswalks

▪ EVeg: Regional Dominance Type 1:

◦ Low Sagebrush

◦ Black Sagebrush

▪ Presettlement Fire Regime Type: 

◦ Black and Low Sagebrush

▪ LandFire BpS model: 

◦ 0610790: Great Basin Xeric Mixed Sagebrush Shrubland 

Vegetation Description

The Black and Low Sagebrush cover type is a shrubland generally dominated by 

broad-leaved, evergreen shrubs of short stature, typically averaging about 15 percent 

cover. Depending on site conditions, crowns may touch. Deciduous shrubs and small trees 

are sometimes sparsely scattered within this type. The ground cover of grasses and forbs 

is typically only 5–15 percent cover (Verner 1988c). This cover type may be dominated 

by either Artemisia arbuscula or A. nova, often in association with Chrysothamnus 

viscidiflorus, Purshia tridentata, or A. tridentata; A. nova is also commonly associated 

with Krascheninnikovia and Ephedra. Juniperus occidentalis may be sparsely scattered in 

stands dominated by A. arbuscula, and J. osteosperma and Pinus monophylla are sometimes 

scattered in stands dominated by A. nova. A rich variety of forbs is usually present, including 

Eriogonum, Erigeron, Phlox, Castilleja, Sphaeralcea, and Lupinus. Common grasses include 

Poa, Pseudoroegneria, Elymus, Stipa, and Festuca. The abundance and distribution of 

associated plants is highly influenced by soils and precipitation (LandFire 2007o; Verner 

1988c).

Distribution

Communities of A. arbuscula are generally restricted to elevated arid plains along the 

eastern flanks of the Sierra Nevada. Artemisia nova can occur in subalpine areas, at elevations 

above 2,400 meters (8,000 feet). Stands dominated by A. arbuscula range in elevation from 

1,200 to 2,700 meters (4,000–9,000 feet) (Verner 1988c). Stands of A. arbuscula are usually 

found on shallow soils with impaired drainage in the transition zone between the wetter 

bottom and open timber on the mountainsides. This cover type also occurs on terraces with 

hardpan or heavy clay soils. In mosaics formed with Purshia tridentata, A. arbuscula occurs 

on harsher sites with shallow, well-drained soils, whereas P. tridentata occupies areas with 

deeper soils. Soils typically associated with stands of A. nova are shallow, contain a high 

percentage of gravel, and are rich in mineral carbonates. It is prevalent on limestone soils 

(Verner 1988c).
128 USDA Forest Service RMRS-GTR-385. 2018

Disturbances

Wildfire

Wildfires tend to be high-mortality, stand-replacing fires that initiate a process of postfire 

forest succession. High-mortality fires kill large as well as small trees, and may kill many of 

the shrubs and herbs as well, although belowground organs of at least some individual shrubs 

and herbs survive and resprout. Artemisia nova generally supports more fire than other dwarf 

sagebrushes. Stand-replacing fire is rare due to relatively low fuel loads and herbaceous cover. 

Bare ground acts as a microbarrier to fire between low-statured shrubs. Stand-replacing fires 

can occur in this type when successive years of above-average precipitation are followed 

by an average or dry year. Stand-replacing fires predominate in the late-successional class 

where the herbaceous component has diminished or where trees dominate (LandFire 2007o). 

Although it is not included in this iteration of the model, scientists have noted that Bromus 

tectorum has invaded most of these communities, altering successional pathways and 

disturbance regimes. It burns readily and is an early-season postfire colonizer (Verner 1988c).

Other Disturbances

Other disturbances are not currently being modeled, but may, depending on the seral stage 

and mortality levels, reset patches to early development, maintain existing seral stages, or shift 

or accelerate succession to a more open condition.

Seral Stages

The classification of seral stages originated from the corresponding LandFire biophysical 

setting model, but with some modifications based on expert input, as follows and as 

summarized in table B1.1. Due to the absence of relevant spatial data, the seral stage map 

corresponding to this classification for the current landscape was derived by assigning 

polygons with 50 percent canopy cover or more to the late-development seral stage, and 

polygons with less than 50 percent canopy cover to the mid-development seral stage.

Early Development (ED)

This seral stage is characterized by the dominance of herbaceous vegetation, including 

Poa, Pseudoroegneria, and Achnatherum. Shrub canopy is typically less than 20 percent. 

Fire-tolerant shrubs, such as Chrysothamnus species, are initial sprouters postfire (LandFire 

2007o). Note that scattered residual or legacy trees from the predisturbance stand may exist, 

but generally occupy less than 10 percent canopy cover.

Mid-Development – Moderate Canopy Cover (MDM) 

This seral stage is characterized by a mixture of herbaceous and shrub vegetation. 

Vegetation present is likely to include A. nova, A. arbuscula, Poa, Achnatherum, and 

Pseudoroegneria. Shrub cover is typically less than 25 percent (LandFire 2007o). Note that 

scattered residual or legacy trees from the predisturbance stand and trees established after the 

disturbance may exist, but generally occupy less than 10 percent canopy cover. The “moderate 

canopy cover” designation refers to the shrubland component of the stand and not the tree 

component, which is usually minor.

Late Development – Closed Canopy Cover (LDC)

This seral stage is characterized by an increased presence of conifer trees (up to 40 

percent cover). The degree of tree canopy closure differs depending on whether it is an A. 

arbuscula (closure likely <15 percent) or an A. nova (closure up to 40 percent) community. 
USDA Forest Service RMRS-GTR-385. 2018 129

In A. arbuscula communities, a mixture of herbaceous and shrub vegetation with over 10 

percent shrub cover would still be present. In A. nova communities the herbaceous and shrub 

component would be greatly reduced (<1 percent cover). Vegetation present includes A. nova, 

A. arbuscula, Juniperus, P. monophyla, and Achnatherum (LandFire 2007o). Note that the 

“closed canopy cover” designation is a misnomer, as neither the shrub nor tree component 

would compose more than 40 percent cover.

Model Parameterization

This section includes a listing of the model parameters that are cover type-specific. Note 

that there are additional model parameters not specific to a cover type (e.g., climate modifier) 

that ultimately affect the model processes and outcomes, and these are discussed under the 

Methods section in McGarigal et al. 2018.

Succession

The rules (i.e., parameters) governing succession for the LSG cover type are listed in 

table B1.2. These rules were based on the corresponding LandFire BpS description (LandFire 

2007o) and associated model created by using the Vegetation Dynamics Development 

Tool (VDDT). The first rule dictates that a cell in the LSG cover type which has been in 

the ED seral stage for 20 years will have a 100-percent chance of transitioning to MDM at 

the beginning of the next timestep. The second rule dictates that a cell that has been in the 

MDM seral stage for 120 years will have a 100-percent chance of transitioning to the LDC 

seral stage; thus, all stands will have transitioned to the LDC stage after 140 years since 

establishment.

Applying the succession rules listed in table B1.2 results in stands transitioning between 

seral stages in a deterministic manner, such that we know the stand age (years) for the 

transition to the next seral stage, as shown in table B1.3. For example, the first row in table 

B1.3 indicates that a cell in the LSG cover type in the ED seral stage will transition to the 

MDM seral stage at 20 years since the stand-replacing disturbance. The second row in table 

B1.3 indicates that a cell in the LSG cover type in the MDM seral stage will transition to the 

LDC seral stage after an additional 120 years. 

Wildfire Disturbance

Rotation Period

Wildfire rotation period (equivalent to the point-specific mean return interval) is not 

formally a model parameter, but rather is specified as a target value to be achieved through 

model calibration. Target fire rotation periods (FRPs) were specified by cover type (table 

B1.4). FRP for the LSG cover type was based on Van de Water and Safford (2011) and expert 

input from Safford and Estes.

Susceptibility: Fuels (Vegetation and Disturbance History) 

The only cover type-specific factor affecting susceptibility of a cell to wildfire was fuel 

characteristics, as represented by vegetation cover type, seral stage, and time since the last 

wildfire, which was represented as a relative probability. Fuels, as represented by vegetation 

cover type, seral stage, and recent disturbance history, were treated as having a dynamic (i.e., 

changing over time) effect on the relative susceptibility of a cell to wildfire. Specifically, 

susceptibility varied among cover types and seral stages in relation to the time (number of 

years) since the last fire according to the cumulative Weibull function and the parameters 

listed in table B1.4 (e.g., as illustrated in figure B.1). Note that here we use the cumulative 

form of the Weibull distribution, which gives the cumulative probability of a disturbance 
130 USDA Forest Service RMRS-GTR-385. 2018

for any number of years since the last disturbance. Thus, the probability increases from 0 

immediately following a fire to approaching 1 after a certain number of years since the last 

fire, depending on the specified mean return interval (MRI) and shape parameters of the 

Weibull function. Holding Shape constant, and all other things being equal, as MRI increases 

the curve shifts to the right, resulting in a lower probability for any given number of years 

since the last disturbance. In this manner, varying the MRI among cover types and seral 

stages affects the relative susceptibility to wildfire. The specified Weibull MRI parameters 

were based on the corresponding LandFire BpS description (LandFire 2007o) and associated 

VDDT model, as modified by Safford and Estes. 

Importantly, although susceptibility of the various seral stages is determined by MRI 

(holding Shape constant), these return intervals should not be interpreted literally, as the 

concept of a return interval does not meaningfully apply to a dynamic seral stage. Moreover, 

these MRIs were derived from the LandFire BpS description and associated VDDT model, 

as modified by Safford and Estes; taken collectively, these values do not necessarily agree 

with the target FRP for the cover type. Thus, the MRIs assigned to each seral stage should be 

interpreted as relative values that affect the relative susceptibility of the various vegetation 

states.

Mortality: Fuels (Vegetation)

The only cover type-specific factor affecting overstory mortality following wildfire (i.e., 

fire severity) was fuel characteristics. Fuels, as represented by vegetation cover type and 

seral stage, were treated as having a dynamic (i.e., changing over time) effect on the relative 

probability of a high-mortality response to wildfire. Specifically, we assigned a probability of 

high-mortality response to wildfire to each seral stage (table B1.4); values were based on the 

corresponding LandFire BpS description (LandFire 2007o) and associated VDDT model, as 

modified by Safford and Estes.

Figure B.1—Susceptibility of a cell to wildfire given as a cumulative Weibull function of the 

number of years since the last wildfire, shown here for different mean return intervals and a 

shape parameter of 3. (Source: Figure 6 in McGarigal et al. 2018).
USDA Forest Service RMRS-GTR-385. 2018 131

Disturbance Transitions

The rules (i.e., parameters) governing seral stage transitions following low-mortality 

wildfire disturbance were initially based on the corresponding LandFire BpS description 

(LandFire 2007o) and associated model created by using the VDDT, as modified by Safford 

and Estes. Accordingly, no rules were necessary; all low-mortality wildfires simply maintained 

the stand in its current seral stage. Note that rules governing transitions following highmortality wildfire are not listed here, either, because high-mortality wildfires always result in 

transition to the ED seral stage.

Table B1.1—Summary of LSG seral stage characteristics: average overstory tree diameter at breast height (d.b.h.) 

of the dominant and codominant trees, overstory tree percent cover from above (CFA), assigned average CFA value 

for classifying the landscape by percent canopy cover, and range of stand ages (number of years since the last 

stand-replacing disturbance) possible for the corresponding seral stage. Note that overstory tree d.b.h. and CFA for 

all seral stages refer to the residual or legacy overstory of trees from the predisturbance stand.

Cover type

Seral

stagea Overstory treed.b.h. (inches)

Overstory tree

CFA (%)

Assigned

average CFA (%)

Stand age

range (years)

LSG ED <10 (25 cm) <10 5 0–15

MDM ≥10 <10 5 20–135

LDC ≥10 <40 20 ≥140

a ED = Early - all structures; MDM = Mid-moderate; LDC = Late-closed. 

Table B1.2—Succession rules for LSG seral stages.

Cover type

From seral

stagea To seralstagea

No. of years in

current

successional

stage

No. of years

since lowmortality fire

Probability of

transition

LSG ED MDM 20 any 1.0

MDM LDC 120 any 1.0

a ED = Early - all structures; MDM = Mid-moderate; LDC = Late-closed.

Table B1.3—Summary of LSG seral-stage transitions: earliest, latest, and average stand age (number of years 

since the last stand-replacing disturbance) for the transition to the next seral stage; and average number of years 

without low-mortality fire to transition to the next canopy cover class.

Cover

type

From seral

stagea To seralstagea

Earliest

stand age

(years) at

transition

Latest

stand age

(years) at

transition

Average

stand age

(years) at

transition

Average no. of

years without

low-mortality

fire to

transition

LSG ED MDM 20 20 20 n/a

MDM LDC 140 140 140 n/a

a ED = Early - all structures; MDM = Mid-moderate; LDC = Late-closed. 
132 USDA Forest Service RMRS-GTR-385. 2018

Table B1.4—Weibull function parameters associated with the susceptibility of a cell to wildfire based on fuels (i.e., 

vegetation cover type, seral stage, and number of years since the last fire) and the probability of a high-mortality 

wildfire by cover type and seral stage for the LSG cover type. 

Weibull parameters

Cover

type

Seral

stagea

Target fire

rotation period

(years)

Mean return

interval (years) Shape

Probability of

high-mortality fire

LSG n/a 76 n/a n/a n/a

ED n/a 250 3 1.00

MDM n/a 63 3 0.31

LDC n/a 149 3 1.00

a ED = Early - all structures; MDM = Mid-moderate; LDC = Late-closed. 

2. Curl-Leaf Mountain Mahogany (CMM) 

Reviewed by: (1) Hugh Safford, Regional Ecologist, USDA Forest Service; and (2) Becky 

Estes, Central Sierra Province Ecologist, USDA Forest Service.

Cover Type Classification and Crosswalks

▪ EVeg: Regional Dominance Type 1:

◦ Curl-leaf Mountain Mahogany

▪ Presettlement Fire Regime Type: 

◦ Curl-leaf Mountain Mahogany

▪ LandFire BpS model: 

◦ 0610620: Inter-Mountain Basin Curl-leaf Mountain Mahogany Woodland 

and Shrubland 

Vegetation Description

The Curl-leaf Mountain Mahogany cover type is a shrubland characterized by the 

dominance or codominance of Cercocarpus ledifolius. Other shrubs such as Artemisia, 

Arctostaphylos, Ceanothus, and Ephedra may be present. Cercocarpus ledifolius is both a 

primary early-successional colonizer rapidly invading bare mineral soils after disturbance and 

the dominant long-lived species. Depending on the effects of a given fire on the seedbank, 

it could take 10 years to recolonize in some cases. Where C. ledifolius has reestablished 

quickly after fire, Chrysothamnus nauseosus may codominate. Litter and shading by woody 

plants inhibit the establishment of C. ledifolius, particularly in late-seral conditions where 

canopy cover is high. Reproduction is often more dependent on terrain variables (e.g., slope, 

aspect, and elevation) than on biotic factors. Artemisia arbuscula and A. nova are infrequently 

associated. Symphoricarpos, Amelanchier, and Ribes are present on cooler, moister sites. 

Pinus monophylla, Juniperus, Pseudotsuga menziesii, Abies magnifica, Abies concolor, and 

Pinus jeffreyi may have sporadic presence at very low densities. In older stands the understory 

may consist largely of Leptodactylon pungens (Gucker 2006; LandFire 2007n).
USDA Forest Service RMRS-GTR-385. 2018 133

Distribution

Cercocarpus ledifolius communities are usually found on upper slopes and ridges 

between 2,130 and 3,200 meters (7,000–10,500 feet), although northern stands may occur as 

low as 600 meters (200 feet). It is more common on northwestern and northeastern aspects. 

Most stands occur on rocky, shallow soils and outcrops, with mature stand cover from 10 to 

55 percent. In the absence of fire, old stands may occur on somewhat deeper soils, with more 

than 55 percent cover (LandFire 2007n).

Disturbances

Wildfire

Wildfires tend to be high-mortality, stand-replacing fires that initiate a process of postfire 

forest succession. High-mortality fires kill large as well as small trees, and may also kill 

many of the shrubs and herbs, although belowground organs of at least some individual 

shrubs and herbs survive and resprout. Cercocarpus ledifolius is easily killed by fire and 

does not resprout. However, it is a primary early-successional colonizer, rapidly invading 

bare mineral soils after disturbance. Fires are not common in early-seral stages, when there is 

little fuel, except in chaparral-dominated stands. Stand-replacing fires are more common in 

mid-seral stands, where herbs and smaller shrubs provide ladder fuels. When surface fire is 

relatively common, stands will adopt a savanna-like woodland structure with an understory 

characterized by Ribes, L. pungens, and various grasses. Trees can become very old and will 

rarely show fire scars. In late, closed stands, the absence of herbs and small forbs makes 

fire uncommon; extreme winds and drought conditions are required for fire in these stands. 

However, stands that do burn often experience high-mortality fire (LandFire 2007n).

Other Disturbances

Other disturbances are not currently being modeled, but may, depending on the seral stage 

and mortality levels, reset patches to early development, maintain existing seral stages, or shift 

or accelerate succession to a more open condition.

Seral Stages

The classification of seral stages originated from the corresponding LandFire biophysical 

setting model, but with some modifications based on expert input, as follows and as 

summarized in table B2.1. Due to the absence of relevant spatial data, the seral stage map 

corresponding to this classification for the current landscape was derived by randomly 

assigning disjunct patches of CMM to the early-, mid-, and late-development seral stages 

based on a 20:10:70 distribution. The proportional allocation was based on an analysis of past 

fire in the project area.

Early Development (ED)

This seral stage is characterized by the rapid invasion of C. ledifolius seedlings on 

bare mineral soils after fire. Litter and shading by woody plants inhibits establishment. 

Bunchgrasses and disturbance-tolerant forbs and resprouting shrubs, such as Symphoricarpos, 

may be present. Ericameria and Artemisia seedlings are likely to be present. Vegetation 

composition will affect fire behavior, especially if chaparral species such as Arctostaphylos or 

Ceanothus are present (LandFire 2007n). Note that scattered residual or legacy trees from the 

predisturbance stand may exist, but generally occupy less than 10 percent canopy cover.
134 USDA Forest Service RMRS-GTR-385. 2018

Mid-Development – Moderate Canopy Cover (MDM) 

This seral stage is characterized by a highly variable canopy cover of shrubs and 

potentially scattered residual or legacy trees from the predisturbance stand composing less 

than 10 percent canopy cover. Note that the “moderate canopy cover” designation refers to 

the shrubland component of the stand and not the residual tree component, which is usually 

minor.

Late Development – Closed Canopy Cover (LDC)

This seral stage is characterized by a moderate to high cover of large shrub- or treelike C. ledifolius and potentially scattered residual or legacy trees from the predisturbance 

stand composing less than 10 percent canopy cover. When low-mortality fire is relatively 

frequent, late-successional C. ledifolius may show evidence of infrequent fire scars on older 

trees. Patches may consist of open savanna-like woodlands with an herbaceous-dominated 

understory. Other shrub species may be abundant, but decadent. When low-mortality fire is 

absent, very few other shrubs are present, and herbaceous cover is low. Duff may be very 

deep, and scattered trees may occur. Cercocarpus ledifolius trees reach very old age in the 

absence of stand-replacing fire, potentially living more than 1,000 years (LandFire 2007n). 

Note that the “closed canopy cover” designation refers to the shrubland component of the 

stand and not the residual tree component, which is usually minor.

Model Parameterization

This section includes a listing of the model parameters that are cover type-specific. Note 

that there are additional model parameters not specific to a cover type (e.g., climate modifier) 

that ultimately affect the model processes and outcomes, and these are discussed under the 

Methods section in McGarigal et al. 2018.

Succession

The rules (i.e., parameters) governing succession for the CMM cover type are listed in 

table B2.2. These rules were initially based on the corresponding LandFire BpS description 

(LandFire 2007n) and associated model created by using the Vegetation Dynamics 

Development Tool (VDDT), but were subsequently modified based on expert input to adjust 

the rate of succession given the tree sizes defined for the various seral stages. For example, the 

first rule dictates that a cell in the CMM cover type, which has been in the ED seral stage for 

20 years, will have a 100-percent chance of transitioning to MDM at the beginning of the next 

timestep. The next rule dictates that a cell that has been in the MDM seral stage for 120 years 

will have a 100-percent chance of transitioning to the LDC seral stage; thus, all stands will 

have transitioned to the LDC stage after 140 years since establishment.

Applying the succession rules listed in table B2.2 results in stands transitioning between 

seral stages in a deterministic manner, such that we know the stand age (years) for the 

transition to the next seral stage, as shown in table B2.3. For example, the first row in table 

B2.3 indicates that a cell in the CMM cover type in the ED seral stage will transition to the 

MDM seral stage at 20 years since the stand-replacing disturbance. The second row in table 

B2.3 indicates that a cell in the CMM cover type in the MDM seral stage will transition to the 

LDC seral stage after an additional 120 years. Note that low-mortality fires simply maintain 

the stand in its current seral stage.

Wildfire Disturbance
USDA Forest Service RMRS-GTR-385. 2018 135

Rotation Period

Wildfire rotation period (equivalent to the point-specific mean return interval) is not 

formally a model parameter, but rather is specified as a target value to be achieved through 

model calibration. Target fire rotation periods (FRPs) were specified by cover type (table 

B2.4). FRP for the CMM cover type was based on Van de Water and Safford (2011) and 

expert input from Safford and Estes.

Susceptibility: Fuels (Vegetation and Disturbance History)

The only cover type-specific factor affecting susceptibility of a cell to wildfire was 

fuel characteristics. Fuels, as represented by vegetation cover type, seral stage, and recent 

disturbance history, were treated as having a dynamic (i.e., changing over time) effect on the 

relative susceptibility of a cell to wildfire. Specifically, susceptibility varied among cover 

types and seral stages in relation to the time (number of years) since the last fire according to 

the cumulative Weibull function and the parameters listed in table B2.4 (e.g., as illustrated in 

figure B.1). Note that here we use the cumulative form of the Weibull distribution, which gives 

the cumulative probability of a disturbance for any number of years since the last disturbance. 

Thus, the probability increases from 0 immediately following a fire to approaching 1 after a 

certain number of years since the last fire, depending on the specified mean return interval 

(MRI) and shape parameters of the Weibull function. Holding Shape constant, and all 

other things being equal, as MRI increases the curve shifts to the right, resulting in a lower 

probability for any given number of years since the last disturbance. In this manner, varying 

the MRI among cover types and seral stages affects the relative susceptibility to wildfire. The 

specified Weibull MRI parameters were based on the corresponding LandFire BpS description 

(LandFire 2007n) and associated VDDT model, as modified by Safford and Estes. 

Importantly, although susceptibility of the various seral stages is determined by MRI 

(holding Shape constant), these return intervals should not be interpreted literally, as the concept 

of a return interval does not meaningfully apply to a dynamic seral stage. Moreover, these MRIs 

were derived from the LandFire BpS description and associated VDDT model, as modified by 

Safford and Estes; taken collectively, these values do not necessarily agree with the target FRP 

for the cover type. Thus, the MRIs assigned to each seral stage should be interpreted as relative 

values that affect the relative susceptibility of the various vegetation states. 

Mortality: Fuels (Vegetation)

The only cover type-specific factor affecting overstory mortality following wildfire (i.e., 

fire severity) was fuel characteristics, as represented by vegetation cover type and seral stage, 

which was represented as a relative probability. Fuels, as represented by vegetation cover 

type and seral stage, were treated as having a dynamic (i.e., changing over time) effect on 

the relative probability of a high-mortality response to wildfire. Specifically, we assigned 

a probability of high-mortality response to wildfire to each seral stage (table B2.4); values 

were based on the corresponding LandFire BpS description (LandFire 2007n) and associated 

VDDT model, as modified by Safford and Estes.

Disturbance Transitions

The rules (i.e., parameters) governing seral stage transitions following low-mortality 

wildfire disturbance were initially based on the corresponding LandFire BpS description 

(LandFire 2007n) and associated model created by using the VDDT, as modified by Safford 

and Estes. Accordingly, no rules were necessary; all low-mortality wildfires simply maintained 

the stand in its current seral stage. Note that rules governing transitions following highmortality wildfire are not listed here, either, because high-mortality wildfires always result in 

transition to the ED seral stage.
136 USDA Forest Service RMRS-GTR-385. 2018

Table B2.1—Summary of CMM seral stage characteristics: average overstory tree diameter at breast height (d.b.h.) 

of the dominant and codominant trees, overstory tree percent cover from above (CFA), assigned average CFA value 

for classifying the landscape by percent canopy cover, and range of stand ages (number of years since the last 

stand-replacing disturbance) possible for the corresponding seral stage. Note that overstory tree d.b.h. and CFA for 

all seral stage refer to the residual or legacy overstory of trees from the predisturbance stand.

Cover type

Seral

stagea Overstory treed.b.h. (inches)

Overstory tree

CFA (%)

Assigned

average CFA (%)

Stand age

range (years)

CMM ED <10 (25 cm) <10 5 0–55

MDM ≥10 <10 5 20–125

LDC ≥10 <10 5 ≥60

a ED = Early - all structures; MDM = Mid-moderate; LDC = Late-closed. 

Table B2.2—Succession rules for CMM seral stages. 

Cover type

From seral

stagea To seralstage

Number of

years in current

successional

stage

Number of

years since

low-mortality

fire

Probability of

transition

CMM ED MDM 20 any 1.0

MDM LDC 120 any 1.0

a ED = Early - all structures; MDM = Mid-moderate; LDC = Late-closed. 

Table B2.3—Summary of CMM seral stage transitions: earliest, latest, and average stand age (number of years 

since the last stand-replacing disturbance) for the transition to the next seral stage, and average number of years 

without low-mortality fire to transition to the next canopy cover class.

Cover

type

From seral

stagea To seralstagea

Earliest

stand age

(years) at

transition

Latest

stand age

(years) at

transition

Average

stand age

(years) at

transition

Average no. of

years without

low-mortality

fire to

transition

CMM ED MDM 20 20 20 n/a

MDM LDC 140 140 140 n/a

a ED = Early - all structures; MDM = Mid-moderate; LDC = Late-closed. 
USDA Forest Service RMRS-GTR-385. 2018 137

Table B2.4—Weibull function parameters associated with the susceptibility of a cell to wildfire based on fuels (i.e., 

vegetation cover type, seral stage, and the number of years since the last fire) and the probability of a high-mortality 

wildfire by cover type and seral stage for the CMM cover type. 

Weibull parameters

Cover type

Seral

stagea

Target fire

rotation period

(years)

Mean return

interval (years) Shape

Probability of

high-mortality fire

CMM n/a 76 n/a n/a n/a

ED n/a 83 3 0.17

MDM n/a 17 3 0.67

LDC n/a 500 3 1.0

a ED = Early - all structures; MDM = Mid-moderate; LDC = Late-closed. 

3. Lodgepole Pine (LPN) 

Reviewed by: (1) Hugh Safford, Regional Ecologist, USDA Forest Service; (2) Becky Estes, 

Central Sierra Province Ecologist, USDA Forest Service; and (3) Shana Gross, Ecologist, 

USDA Forest Service.

Cover Type Classification and Crosswalks

Lodgepole Pine (LPN) Variant

▪ EVeg: Regional Dominance Type 1:

◦ Lodgepole Pine

▪ Presettlement Fire Regime Type:

◦ Lodgepole Pine

▪ LandFire BpS models: 

◦ 0610581: Sierra Nevada Subalpine Lodgepole Pine Forest and Woodland – Wet

◦ 0610582: Sierra Nevada Subalpine Lodgepole Pine Forest and Woodland – Dry

Lodgepole Pine With Aspen (LPN_ASP) Variant

This type was created by overlaying the NRIS TERRA Inventory of Aspen on the EVeg 

layer. Where it intersected with LPN, it was assigned to LPN_ASP.

Vegetation Description

Lodgepole Pine (LPN) Variant

Pinus contorta ssp. murrayana is the overwhelming dominant within the Lodgepole 

Pine cover type, mixing occasionally with Abies magnifica, and with scattered P. jeffreyi 

and P. monticola, and Tsuga mertensiana at higher elevations (Fites-Kaufman et al. 2007). 

Mature Sierran stands often contain significant numbers of seedlings and saplings. Understory 

characteristics are influenced by proximity to meadow and stream margins. Arctostaphylos

and Ribes are common shrubs. Stands associated with meadow edges and streams may have a 

rich herbaceous layer consisting of grasses, forbs, and sedges. Species associations are likely 
138 USDA Forest Service RMRS-GTR-385. 2018

to be very location specific. Plants present may include Cassiope, Vaccinium, Phyllodoce, 

Kalmia, Ceanothus, Chrysolepis, Carex, and others. Elsewhere, the understory may be 

virtually absent, consisting of scattered shrubs such as Quercus vaccinifolia, and herbs such as 

Antennaria, Arabis, Eriogonum, and Gayophytum. Fast-moving streams within the cover type 

are generally characterized by relatively dense populations of Salix (Bartolome 1988; FitesKaufman et al. 2007; LandFire 2007k,l).

Lodgepole Pine With Aspen (LPN_ASP) Variant 

This LPN variant occurs when Populus tremuloides co-occurs with LPN on the west side 

of the Sierran crest, and is typically found in smaller patches, often less than 2 hectares (5 

acres) in size. Mature stands in which P. tremuloides are still dominant are usually relatively 

open. Average canopy closures range from 60 to 100 percent in young and intermediate-aged 

stands and from 25 to 60 percent in mature stands. The open nature of the stands results in 

substantial light penetration to the ground (Verner 1988a).

Distribution

Lodgepole Pine (LPN) Variant

This variant consists of open stands of P. contorta ssp. murrayana, which make up a 

widespread upper montane forest/woodland. This variant tolerates both rocky soils and 

semisaturated meadow edges, in an elevational belt within and above the A. magnifica zone. 

These forests, strongly dominated by P. contorta ssp. murrayana, generally occur at elevations 

of about 1,800–2,400 meters (6,000–7,800 feet) in the northern Sierra Nevada. Stands of P. 

contorta ssp. murrayana may reach much lower, however, with cold air drainage down glacial 

canyons (Anderson 1996; Fites-Kaufman et al. 2007). On infertile soils, P. contorta ssp. 

murrayana is often the only tree species that will grow (Lotan and Critchfield 1990).

More than any other Sierran conifer, P. contorta ssp. murrayana is relatively tolerant of 

poor soil aeration, and thus grows well around the margins of wet meadows and other moist 

areas. Many upper montane and subalpine meadows in the Sierra Nevada exhibit invasion of 

young P. contorta ssp. murrayana moving inward from their drier margins. It is not clear how 

much this process has been influenced by changes in fire frequency or grazing over the last 

150 years (Fites-Kaufman et al. 2007).

Lodgepole Pine with Aspen (LPN_ASP) Variant 

Sites supporting P. tremuloides are usually associated with added soil moisture, that is, 

azonal wet sites. These sites are found throughout the LPN zone, often close to streams, lakes, 

and meadows. Other sites include rock reservoirs, springs, and seeps. Terrain can be simple 

to complex. 

Disturbances

Wildfire

Lodgepole Pine (LPN) Variant

Wildfires tend to be high-mortality, stand-replacing fires that initiate a process of postfire 

forest succession. High-mortality fires kill large as well as small trees, and may kill many of 

the shrubs and herbs as well, although belowground organs of at least some individual shrubs 

and herbs survive and resprout. Low-mortality fires tend to kill only small seedlings and 

depend on the herbaceous layer to carry fire.
USDA Forest Service RMRS-GTR-385. 2018 139

Unlike the Rocky Mountain subspecies of P. contorta (ssp. latifolia), P. contorta ssp. 

murrayana does not have serotinous cones (Fites-Kaufman et al. 2007). After high-mortality 

fire, it initially establishes in even-aged stands, but small-scale disturbances and the ability 

of the subspecies to regenerate in the absence of fire promote uneven-aged structure (Cope 

1993b; Shana Gross, Ecologist, USDA Forest Service, South Lake Tahoe, California, personal 

communication, July 2013).

High-mortality fire occurs at long intervals. Mixed-severity fire is related to fire behavior 

across the often moist areas where P. contorta ssp. murrayana is found. Surface fires are more 

common on drier sites, although in general sparse fuels limit fire ignition and spread. Most fires 

are small (<1 hectare [2.5 acres]), but very large fires covering hundreds of hectares do occur 

(LandFire 2007k,l). This is due in part to the high susceptibility to fire mortality by P. contorta 

ssp. murrayana because of its thin bark and shallower roots. Postfire conditions provide an ideal 

seedbed, and P. contorta ssp. murrayana is an early postfire colonizer (Cope 1993b).

Lodgepole Pine with Aspen (LPN_ASP) Variant

Sites supporting P. tremuloides are maintained by stand-replacing disturbances that allow 

regeneration from belowground suckers. Upland clones are impaired or suppressed by conifer 

ingrowth and overtopping and intensive grazing that inhibits growth. In a reference condition 

scenario, a few stands will advance toward conifer dominance, but in the current landscape 

scenario, where fire has been reduced from reference conditions, there are many more coniferdominated mixed aspen stands (LandFire 2007m; Verner 1988a). 

Other Disturbances

Other disturbances are not currently being modeled, but may, depending on the seral stage 

and mortality levels, reset patches to early development, maintain existing seral stages, or shift 

or accelerate succession to a more open condition. All of the tree species associated with this 

vegetation type are susceptible to a wide variety of pathogens and insects.

Seral Stages

The classification of seral stages originated from the corresponding LandFire biophysical 

setting models, but with some modifications (e.g., the addition of a moderate canopy cover 

stage in the mid- and late-seral stages) based on expert input, as follows and as summarized in 

table B3.1. The seral stage map corresponding to this classification for the current landscape 

was derived from the EVeg dataset and the rules in table B3.2 for the LPN cover types; 

LPN_ASP seral stages were mapped manually by using NAIP 2010 Color IR imagery.

Lodgepole Pine (LPN) Variant

Early Development (ED)

This seral stage is characterized by bare ground, herbs, shrubs, and varying densities 

of tree seedlings and saplings (primarily P. contorta ssp. murrayana). This condition is 

characterized by the recruitment of a new cohort of early-successional, shade-intolerant 

tree species into an open area created by a stand-replacing disturbance. A short period of 

herbaceous productivity precedes closure of the tree canopy on productive sites. The prolific 

seed output, establishment, and seedling growth of P. contorta ssp. murrayana makes the 

period of herbaceous production short (Bartolome 1988). Pinus contorta ssp. murrayana

regeneration density ranges from moderate to dog hair thickets (LandFire 2007k). Note that 

there can be residual or legacy overstory trees from the predisturbance stand making up less 

than 25 percent canopy cover.
140 USDA Forest Service RMRS-GTR-385. 2018

Mid-Development – Open Canopy Cover (MDO) 

This seral stage is characterized by a sparse ground cover of grasses, forbs, and shrubs, 

with a low canopy cover (<40 percent) of pole-sized (5–10 inches; 13–25 cm d.b.h.) 

P. contorta ssp. murrayana where surface fire or other disturbance has opened the stand 

(LandFire 2007k). Continued recruitment into stands produces overstocking and slow growth 

of the overcrowded trees. This overcrowding may make them susceptible to insects, although 

others have argued that the more vigorously growing trees are more likely to be attacked. 

Beetle infestation creates large quantities of fuel that increase the probability of wildfire 

(Bartolome 1988).

Mid-Development – Moderate Canopy Cover (MDM) 

This seral stage is characterized by a moderate canopy cover (40–70 percent) of polesized P. contorta ssp. murrayana where surface fire or other disturbance has opened the stand 

or where there is recruitment into an MDO stand during a fire-free period. This seral stage is 

otherwise similar to MDO.

Mid-Development – Closed Canopy Cover (MDC) 

This seral stage is characterized by a dense canopy cover (>70 percent) of pole-sized 

P. contorta ssp. murrayana resulting from heavy recruitment of trees following standreplacing disturbance. Overstocking results in the slow growth of the trees and may make 

them susceptible to insects, although others have argued that the more vigorously growing 

trees are more likely to be attacked. Beetle infestation creates large quantities of fuel that 

increase the probability of wildfire (Bartolome 1988). 

Late Development – Open Canopy Cover (LDO)

This seral stage is characterized by a sparse ground cover of grasses, forbs, and low 

shrubs, with a low canopy cover (<40 percent) of medium-sized (>10 inches d.b.h.) 

P. contorta ssp. murrayana where surface fire or other disturbance has opened the stand 

(LandFire 2007k). The open stand structure is maintained by low-severity fire and insectcaused tree mortality (the latter not modeled at this time).

Late Development – Moderate Canopy Cover (LDM)

This seral stage is characterized by a moderate canopy cover (40–70 percent) of mediumsized P. contorta ssp. murrayana where surface fire or other disturbance has opened the stand 

or where there is recruitment into an LDO stand during a fire-free period. This seral stage is 

otherwise similar to LDO.

Late Development – Closed Canopy Cover (LDC)

This seral stage is characterized by a high canopy cover (>70 percent) of medium-sized 

P. contorta ssp. murrayana where fire has had a minimal influence on the stand development. 

Similar to MDC, overstocking may make the trees susceptible to insects which, following 

an outbreak, can create large quantities of fuel that increase the probability of wildfire 

(Bartolome 1988).

Lodgepole Pine with Aspen (LPN_ASP) Variant 

Early Development – Aspen (ED–A)

This seral stage is characterized by the recruitment of a new cohort of early-successional, 

shade-intolerant tree species (primarily P. tremuloides) into an open area created by a standreplacing disturbance. Note that there can be a residual or legacy of overstory trees from the 

predisturbance stand making up less than 25 percent canopy cover. Following disturbance, 

succession proceeds rapidly from an herbaceous layer to shrubs and trees, which invade 

together (Verner 1988a). Populus tremuloides suckers over 2 meters (6 feet) tall develop 

within about 10 years (LandFire 2007m).
USDA Forest Service RMRS-GTR-385. 2018 141

Mid-Development – Aspen (MD–A)

This seral stage is characterized by P. tremuloides trees 5–16 inches (13–41 cm) d.b.h. 

Canopy cover is highly variable, and can range from 40 to 100 percent. Some understory 

conifers are encroaching, but P. tremuloides is still the dominant component of the stand 

(LandFire 2007m).

Mid-Development – Aspen with Conifer (MD–AC)

This seral stage is characteristic of stands that have been protected from fire since the 

last stand-replacing disturbance. Populus tremuloides trees are predominantly greater than 16 

inches d.b.h. Conifers, primarily P. contorta ssp. murrayana, are present and overtopping the 

P. tremuloides. Conifers are pole-sized (5–10 inches d.b.h.) with canopy cover greater than 40 

percent (LandFire 2007m).

Late Development – Conifer with Aspen (LD–CA)

If stands are sufficiently protected from fire such that conifer species, primarily P. contorta 

ssp. murrayana, overtop P. tremuloides, become larger (>10 inches d.b.h.), and make up more 

than 70 percent canopy cover, the conifers may be able to withstand some fire that the more 

sensitive P. tremuloides cannot. When this occurs, it creates a stand characterized by latedevelopment conifers, primarily P. contorta ssp. murrayana, but with P. tremuloides present in 

the midstory and understory at varying densities depending on the disturbance history.

Late Development – Closed (LDC)

In this seral stage, some P. tremuloides continue to be present in the understory largely 

as a legacy, but medium- to large-sized (>10 inches d.b.h.) conifers, primarily P. contorta 

ssp. murrayana, are now the dominant tree species Having overtopped the P. tremuloides, 

these conifers compose more than 70 percent canopy cover. Smaller conifers are present in 

the midstory as well (LandFire 2007k). Note that this seral stage is analogous to the LDC 

condition for the LPN variant.

Model Parameterization

This section includes a listing of the model parameters that are cover type specific. Note 

that there are additional model parameters not specific to a cover type (e.g., climate modifier) 

that ultimately affect the model processes and outcomes, and these are discussed under the 

Methods section in McGarigal et al. 2018.

Succession

The rules (i.e., parameters) governing succession for the LPN and LPN_ASP cover types 

are listed in table B3.3. These rules were initially based on the corresponding LandFire BpS 

descriptions (LandFire 2007k,l,m) and associated models created by using the Vegetation 

Dynamics Development Tool (VDDT), as modified by Safford and Estes. They were 

subsequently modified based on expert input to include probabilistic rather than deterministic 

seral stage transitions. Specifically, we modified the rules so that stands would gradually, 

instead of abruptly, transition from one seral stage to the next to reflect stochasticity in the 

real-world processes governing succession. For example, the first rule dictates that a cell in the 

LPN cover type, which has been in the ED seral stage for 10–35 years, will have a 60-percent 

chance of transitioning to MDC at the beginning of each timestep. Thus, stands will randomly 

begin transitioning to MDC stages after 10 years in the ED stage, but some stands could be 

delayed in the ED stage for as much as 40 years to reflect delayed tree establishment. Note 

that for stands currently in the ED stage and between 10 and 35 years in this stage, there is 

a 40-percent chance of remaining in the ED seral stage at each timestep. The second rule 
142 USDA Forest Service RMRS-GTR-385. 2018

dictates that a cell that has been in the ED seral stage for 40 years will have a 100-percent 

chance of transitioning to MDC: All stands will have transitioned to the MDC seral stage after 

40 years since establishment.

Applying the succession rules listed in table B3.3 results in stands transitioning between 

seral stages in a probabilistic rather than deterministic manner, such that we can compute the 

average stand age (years) for the transition to the next seral stage, as shown in table B3.4. 

For example, the first row in table B3.4 indicates that for a cell in the LPN cover type in the 

ED seral stage, the earliest stand age (i.e., number of years since the last stand-replacing 

disturbance) for transitioning to one of the MD seral stages is 10 years, the latest stand age is 

40 years, and the average stand age at the time of the transition is 13 years. The fifth row in 

table B3.4 indicates that a cell in the LPN cover type in the MDO seral stage will, on average, 

take 16 years without a low-mortality fire disturbance to transition to the MDM seral stage 

(i.e., transition from an open-canopy cover, <40 percent, to a moderate-canopy cover, 40–70 

percent, condition). Note that a low-mortality fire every 15 years will maintain the stand in the 

open-canopy condition.

Wildfire Disturbance

Rotation Period

Wildfire rotation period (equivalent to the point-specific mean return interval) is not 

formally a model parameter, but rather is specified as a target value to be achieved through 

model calibration. Target fire rotation periods (FRPs) were specified by cover type (table 

B3.5). FRP for the LPN cover type was based on Mallek et al. (2013), whereas the FRP for the 

LPN_ASP cover type was set to be equal to that of LPN based on the corresponding LandFire 

BpS model (LandFire 2007m) and expert input from Safford and Estes.

Susceptibility

The cover type-specific factors affecting susceptibility of a cell to wildfire were: (1) 

topographic position, and (2) fuel characteristics, as represented by vegetation cover type, 

seral stage, and time since the last wildfire. Each of these two factors is represented as a 

probability.

Topographic position—Topographic position, as represented by the topographic position 

index (TPI) described under the Methods section in McGarigal et al. 2018, was treated as 

having a static (i.e., constant over time) and universal effect on the relative susceptibility of 

a cell to wildfire regardless of seral stage or disturbance history. We allowed topographic 

position to affect susceptibility for the LPN cover type, but not LPN_ASP. Specifically, all 

other things being equal, for the LPN cover type, susceptibility decreased by 30 percent as the 

TPI decreased over its full range according to the four-parameter logistic function depicted in 

figure B.2. However, because the bulk of the landscape varies over a much smaller range of 

TPI values, the effect on susceptibility is typically much less than 30 percent.

The specified logistic parameters were based on consensus expert opinion about the 

strength and nature of the topographic influence on wildfire susceptibility, and reflect general 

support for such an effect in the scientific literature (North 2012; Taylor and Skinner 2003).

Fuels (vegetation and disturbance history)—Fuels, as represented by vegetation cover, 

seral stage, and recent disturbance history, were treated as having a dynamic (i.e., changing 

over time) effect on the relative susceptibility of a cell to wildfire. Specifically, susceptibility 

varied among cover types and seral stages in relation to the time (number of years) since the 

last fire according to the cumulative Weibull function and the parameters listed in table B3.5 

(e.g., as illustrated in figure B.1). Note that here we use the cumulative form of the Weibull 

distribution, which gives the cumulative probability of a disturbance for any number of years 
USDA Forest Service RMRS-GTR-385. 2018 143

since the last disturbance. Thus, the probability increases from 0 immediately following a 

fire to approaching 1 after a certain number of years since the last fire, depending on the 

specified mean return interval (MRI) and shape parameters of the Weibull function. Holding 

Shape constant, and all other things being equal, as MRI increases the curve shifts to the 

right, resulting in a lower probability for any given number of years since the last disturbance. 

In this manner, varying the MRI among cover types and seral stages affects the relative 

susceptibility to wildfire.

The specified Weibull MRI parameters were based on the corresponding LandFire BpS 

descriptions (LandFire 2007 k,l,m) and associated VDDT models, as modified by Safford 

and Estes. 

Importantly, although susceptibility of the various seral stages is determined by MRI 

(holding Shape constant), these return intervals should not be interpreted literally, as the concept 

of a return interval does not meaningfully apply to a dynamic seral stage. Moreover, these MRIs 

were derived from the LandFire BpS descriptions and associated VDDT models, as modified 

by Safford and Estes; taken collectively, these values do not necessarily agree with the target 

FRPs for the cover types. Thus, the MRIs assigned to each cover type and seral stage should be 

interpreted as relative values that affect the relative susceptibility of the various vegetation states.

Mortality

The cover type-specific factors affecting overstory mortality following wildfire (i.e., 

fire severity) were: (1) topographic position, and (2) fuel characteristics, as represented by 

vegetation cover type and seral stage. Each of these two factors is represented as a probability.

Topographic position—The effect of topographic position on mortality was treated 

identically to its effect on susceptibility (see previous description). Again, the specified 

logistic parameters were based on consensus expert opinion about the strength and nature of 

the topographic influence on wildfire severity, and reflect general support for such an effect in 

the scientific literature (North 2012; Taylor and Skinner 2003).

Figure B.2—Susceptibility (relative probability) of a cell to wildfire as a logistic function of 

topographic position (as measured by the topographic position index; see text for description) 

(source: Figure 5 in McGarigal et al. 2018).
144 USDA Forest Service RMRS-GTR-385. 2018

Fuels (vegetation)—Fuels, as represented by vegetation cover type and seral stage, were 

treated as having a dynamic (i.e., changing over time) effect on the relative probability of a 

high-mortality response to wildfire. Specifically, we assigned a probability of high-mortality 

response to wildfire to each cover type and seral stage (table B3.5); values were based on 

the corresponding LandFire BpS descriptions (LandFire 2007k,l,m) and associated VDDT 

models, as modified by Safford and Estes.

Disturbance Transitions

The rules (i.e., parameters) governing seral stage transitions following low-mortality 

wildfire disturbance for the LPN and LPN_ASP cover types are listed in table B3.6. These 

rules were initially based on the corresponding LandFire BpS descriptions (LandFire 

2007k,l,m) and associated models created by using the VDDT, as modified by Safford and 

Estes, but were subsequently modified to include the moderate canopy cover seral stages not 

present in the VDDT models. Note that rules governing transitions following high-mortality 

wildfire are not listed in table B3.6 because high-mortality wildfires always result in transition 

to the ED seral stage. In addition, conditions in which low-mortality wildfire has no effect on 

the seral stage (i.e., does not cause a transition) are not listed. For example, the first two rules 

dictate that a low-mortality wildfire in a cell of LPN in the MDC seral stage has a 50-percent 

chance of transitioning to the MDM stage, a 50-percent chance of transitioning to the MDO 

stage, and (by implication) a 0-percent chance of remaining in the MDC stage. In addition, by 

implication (given the absence of a rule), a low-mortality wildfire in the ED, MDO, or LDO 

seral stage has no effect other than to maintain the cell in that seral stage.

Vegetation Treatments

Dynamic spatial constraints and priorities affecting individual cover types were described 

under the Methods section in McGarigal et al. 2018; here we describe the rules goverming seral 

stage transitions following each unique vegetation treatment (table B3.7). Note that these rules 

were created by the principals involved in this project and reflect expectations based on the 

common prescriptions applied today.
USDA Forest Service RMRS-GTR-385. 2018 145

Table B3.2—Mapping rules for LPN seral stages. Diameter at breast height (d.b.h.) and cover from above (CFA) 

values were taken from EVeg polygons. Categories for d.b.h. are: null, 0–0.9, 1–4.9, 5–9.9, 10–19.9, 20–29.9, ≥30. 

CFA categories (%) are: null, 0–10, 10–20, ... , 90–100. Each row should be read with a Boolean AND across each 

column. Within each seral stage the rows should be read with a Boolean OR across rows.

Seral

stagea Overstory treed.b.h. 1 (inches)

Overstory tree

d.b.h. 2 (inches)

Total tree

CFA (%)

Conifer

CFA (%)

Hardwood

CFA (%)

ED 0–4.9 (0–12.4 cm) any any any any

MDO 5–9.9 (13–25.1 cm) any 0–40 any any

MDM 5–9.9 any 40–70 any any

MDC 5–9.9 any 70–100 any any

LDO ≥10 (25.4 cm) any 0–40 any any

LDM ≥10 any 40–70 any any

LDC ≥10 any 70–100 any any

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 

Table B3.1—Summary of LPN and LPN_ASP seral stage characteristics: average overstory tree diameter at breast 

height (d.b.h.) of the dominant and codominant trees, overstory tree percent cover from above (CFA), assigned 

average CFA value for classifying the landscape by percent canopy cover, and range of stand ages (number of years 

since the last stand-replacing disturbance) possible for the corresponding seral stage. Note that overstory tree d.b.h.

and CFA for the ED/EDA seral stages refer to the residual or legacy overstory from the predisturbance stand.

Cover type

Seral

stagea Overstory treed.b.h. (inches)

Overstory tree

CFA (%)

Assigned

average CFA (%)

Stand age

range (years)

LPN ED <5 (13 cm) <25 10 0–35

MDO 5–9.9 (13–25.0 cm) <40 30 10–135

MDM 5–9.9 40–70 55 10–125

MDC 5–9.9 >70 85 10–115

LDO ≥10 (25.1 cm) <40 30 ≥60

LDM ≥10 40–70 55 ≥55

LDC ≥10 >70 85 ≥50

LPN_ASP ED-A <5 <25 10 0–5 

MD-A 5–15.9 (13–40.5 

cm)

>40 60 10–105

MD-AC ≥16 (40.6 cm) >50 70 60–205

LD-CA ≥10 >70 85 160–275

LDC ≥10 >70 85 ≥230

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; 

LD-CA = Late - Conifer and Aspen. 
146 USDA Forest Service RMRS-GTR-385. 2018

Table B3.3—Succession rules for LPN and LPN_ASP seral stages. Note that for LPN cover types, number of years 

in current successional stage refers to the number of years in either early-development (ED), mid-development (MD), 

or late-development (LD) stage independent of canopy cover class, whereas for LPN_ASP it refers to the number of 

years in the corresponding seral stage. 

Cover type

From seral

stagea To seralstagea

Number of years

in current

successional

stage

Number of

years since

low-mortality

fire

Probability of

transition

LPN ED MDC 10–35 any 0.6

ED MDC 40 any 1.0

MDC LDC 40–75 any 0.6

MDC LDC 80 any 1.0

MDM LDM 45–85 any 0.55

MDM LDM 90 any 1.0

MDM MDC ≥15 ≥15 0.8

MDO LDO 50–95 any 0.5

MDO LDO 100 any 1.0

MDO MDM ≥15 ≥15 0.8

LDM LDC ≥25 ≥25 0.7

LDO LDM ≥25 ≥25 0.7

LPN_ASP ED-A MD-A 10 any 1.0

MD-A MD-AC 50–95 any 0.6

MD-A MD-AC 100 any 1.0

MD-AC LD-CA 100 any 1.0

LD-CA LDC 70 any 1.0

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; 

LD-CA = Late - Conifer and Aspen. 
USDA Forest Service RMRS-GTR-385. 2018 147

Table B3.4—Summary of LPN and LPN_ASP seral-stage transitions: earliest, latest, and average stand age 

(number of years since the last stand-replacing disturbance) for the transition to the next seral stage, and average 

number of years without low-mortality fire to transition to the next canopy cover class.

Cover

type

From seral

stagea To seralstagea

Earliest

stand age

(years) at

transition

Latest

stand age

(years) at

transition

Average

stand age

(years) at

transition

Average

number of

years without

low-mortality

fire to

transition

LPN ED MD 10 40 13 n/a

MDO LDO 60 140 68 n/a

MDM LDM 55 130 62 n/a

MDC LDC 50 120 56 n/a

MDO MDM n/a n/a n/a 16

MDM MDC n/a n/a n/a 16

LDO LDM n/a n/a n/a 27

LDM LDC n/a n/a n/a 27

LPN_ASP ED-A MD-A 10 10 10 n/a

MD-A MD-AC 60 110 63 n/a

MD-AC LD-CA 160 210 163 n/a

LC-CA LDC 230 280 233 n/a

a ED = Early - all structures; MD = Mid development; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; 

LDO = Late-open; LDM = Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = 

Mid - Aspen and Conifer; LD-CA = Late - Conifer and Aspen. 
148 USDA Forest Service RMRS-GTR-385. 2018

Table B3.5—Weibull function parameters associated with the susceptibility of a cell to wildfire based on fuels (i.e., 

vegetation cover type, seral stage, and number of years since the last fire) and the probability of a high-mortality 

wildfire by cover type and seral stage for the LPN and LPN_ASP cover types.

Weibull parameters

Cover type

Seral

stagea

Target fire

rotation period

(years)

Mean return

interval (years) Shape

Probability of

high-mortality fire

LPN n/a 52 n/a n/a n/a

ED n/a 29 3 0.03

MDO n/a 18 3 0.07

MDM n/a 27 3 0.15

MDC n/a 59 3 0.41

LDO n/a 18 3 0.07

LDM n/a 24 3 0.13

LDC n/a 37 3 0.26

LPN_ASP n/a 52 n/a n/a n/a

ED-A n/a 29 3 0.03

MD-A n/a 59 3 0.41

MD-AC n/a 27 3 0.15

LC-CA n/a 24 3 0.13

LDC n/a 37 3 0.26

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; 

LD-CA = Late - Conifer and Aspen. 
USDA Forest Service RMRS-GTR-385. 2018 149

Table B3.6—Disturbance rules for LPN cover types governing seral stage transitions following a low-mortality 

wildfire. Note that conditions in which low-mortality wildfire has no effect are not listed. 

Cover type From seral stagea

 To seral stagea Probability of transition

LPN MDC MDM 0.5

MDC MDO 0.5

MDM MDO 0.68

LDC LDM 0.5

LDC LDO 0.5

LDM LDO 0.73

LPN_ASP LDC LD-CA 1.0

a MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = Late-moderate; LDC = Lateclosed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; LD-CA = Late - Conifer and 

Aspen.
150 USDA Forest Service RMRS-GTR-385. 2018

Table B3.7—Disturbance rules for the LPN and LPN_ASP cover types governing seral stage transitions following 

vegetation treatments. Note that treatments not affecting seral stage transitions (e.g., mastication) are not included 

here.

Cover type Treatment type

From seral

stagea To seralstagea Probability oftransition

LPN Clearcut and Group cuts Any ED 1 

Thinning, including cells thinned in: 

1) matrix thin and group cut; 2) thin 

and burn; 3) thin, hand cut, pile, 

and burn; 4) thin, masticate, and

burn; and 5) matrix thin, group cut, 

and burn treatments

MDC MDM 1 

MDM MDO 1 

LDC LDM 1 

LDM LDO 1 

Prescribed fire, including cells 

burned as part of a prescribed fireonly treatment); "cool" burn/"hot" 

burn transition probabilities

MDC MDM 0.03/0.05

MDC MDO 0/0.03

MDC ED 0/0.01

MDM MDO 0.05/0.14

MDM ED 0/0.01

LDC LDM 0.02/0.03

LDC LDO 0/0.02

LDC ED 0/0.01

LDM LDO 0.04/0.11

LDM ED 0/0.01

Thin and burn, including cells 

burned only as part of: 1) thin and

burn; and 2) hand cut, pile, and

burn treatments

MDC MDM 0.03

MDM MDO 0.05

LDC LDM 0.02

LDM LDO 0.04

LPN_ASP Thinning MD-AC MD-A 1 

LDC MD-A 1 

LD-CA MD-A 1 

Prescribed fire LDC LD-CA 1 

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; LD-CA = Late - Conifer 

and Aspen. 
USDA Forest Service RMRS-GTR-385. 2018 151

4. Mixed Evergreen Forest (MEG) 

 Reviewed by: (1) Hugh Safford, Regional Ecologist, USDA Forest Service; (2) Becky Estes, 

Central Sierra Province Ecologist, USDA Forest Service; and (3) Kyle Merriam, SierraCascade Province Ecologist, USDA Forest Service.

Cover Type Classification and Crosswalks

▪ EVeg: Regional Dominance Type 1:

◦ Interior Mixed Hardwood

◦ California Bay

◦ Canyon Live Oak

◦ Madrone

◦ Bigleaf Maple

◦ Interior Live Oak

◦ Montane Mixed Hardwood 

◦ Pacific Douglas-fir

◦ Tanoak

▪ EVeg: Regional Dominance Type 2:

◦ anoak (regardless of Regional Dominance Type 1 value, and therefore inclusive of all 

potential Type 1 vegetation types)

◦ Presettlement Fire Regime Type: 

◦ Mixed Evergreen Forest

▪ LandFire BpS model: 

◦ 0610430: Mediterranean California Mixed Evergreen Forest

• Mesic Modifier (MEG_M)

This type was created by intersecting a binary xeric/mesic layer with the EVeg layer. MEG 

cells that intersected with mesic cells were assigned to the mesic modifier.

• Xeric Modifier (MEG_X)

This type was created by intersecting a binary xeric/mesic layer with the EVeg layer. MEG 

cells that intersected with xeric cells were assigned to the xeric modifier.

• Ultramafic Modifier (MEG_U)

This type was created by intersecting an ultramafic soils/geology layer with the EVeg layer. 

Where ultramafic cells intersected with MEG, they were assigned to the ultramafic modifier.

Vegetation Description

The Mixed Evergreen Forest cover type forms a complex mosaic of forest due to 

the geologic, topographic, and successional variation typical within its range. This type 

is characterized by a combination of coniferous and broad-leaved trees. Characteristic 

trees include Pseudotsuga menziesii, Quercus chrysolepis, Notholithocarpus densiflorus 

(tanoak was known as Lithocarpus densiflorus for over 90 years before botanists renamed 

it Notholithocarpus densiflorus in 2008 [Manos et al. 2008]; some sources and databases 

continue to use the old name and plant symbol), Arbutus menziesii, Umbellularia californica,

and Chrysolepis chrysophylla. Species composition is primarily determined by the 

environmental gradients of temperature and moisture availability. Quercus kelloggii is found on 

drier sites on inland portions of the range. Pinus lambertiana and P. ponderosa can be present 

in this type. These stands tend to have dense or diverse shrub understories with Ceanothus, 

Corylus, Gaultheria, Morella, Rhododendron, Ribes, Rubus, Toxicodendron diversilobum, and 

Vaccinium. Grass species include Bromus, Festuca, and Hierochloe. Polystichum munitum and
152 USDA Forest Service RMRS-GTR-385. 2018

Pteridium aquilinum var. pubescens sometimes grow abundantly. Carex species are present in 

some places (LandFire 2007i; McDonald 1988; Tappeiner et al. 1990). 

Mesic Modifier 

Deep mesic soils support aggregations that include a lower or midstory layer of dense, 

schlerophyllous, broad-leaved evergreen trees such as N. densiflorus and A. menziesii, with an 

irregular, often open, higher layer of tall needle-leaved evergreen trees, typically P. menziesii. 

A small number of pole and sapling trees occur throughout stands. On wetter sites, shrub 

layers are well developed, often with 100 percent cover. Cover of the herbaceous layer under 

the shrubs can be up to 10 percent. At higher elevations, the shrubs disappear and the herb 

layer is often 100 percent. Diversity of tree size typically increases with stand age, as does 

tree spacing. Young stands have closely spaced and uniformly distributed trees, whereas older 

stands have a more patchy stem distribution. Snags and downed logs, an important structural 

component of this cover type, increase in density or volume with stand age (Raphael 1988). 

Potential additional conifer associates include Abies concolor, P. lambertiana, Calocedrus 

decurrens, and P. ponderosa (Tappeiner et al. 1990). A large variety of shrubs, forbs, grasses, 

sedges, and ferns, along with N. densiflorus sprouts, can become aggressive on burned or 

cutover areas. This is especially true in areas where high-severity fires have locally eliminated 

conifer seed sources (Tappeiner et al. 1990).

Xeric Modifier

A pronounced hardwood tree layer is typical on xeric sites, with an infrequent and poorly 

developed shrub stratum, and a sparse herbaceous layer (McDonald 1988). Characteristic oaks 

include Q. chrysolepis, Q. wislizeni, Q. kelloggii, and Q. garryana var. breweri. The former 

two species are the most common oaks in the project area. They may individually form almost 

pure stands on steep canyon slopes and rocky ridgetops throughout the Sierra Nevada, or 

co-occur. They have tremendously variable growth forms, ranging from shrubs with multiple 

trunks on rocky, steep slopes, to magnificently spreading tall trees on deeper soils in moister 

areas. Both are evergreen with dense canopies (Allen-Diaz et al. 2007). Tree spacing is close 

(3–4 meters; 10–13 feet) on better sites, and wider (8–10 meters; 26–33 feet) on poor sites. 

In general, snags and downed woody material are sparse. Lower elevation associates are P. 

sabiniana, Pinus attenuata, N. densiflorus, A. menziesii, Q. wislizeni, C. chrysophylla, and 

scrubby U. californica (McDonald 1988).

Ultramafic Modifier

Notholithocarpus densiflorus var. echinoides, or dwarf tanoak, grows on ultramafic and 

other less productive sites. It is unclear whether the two varieties of tanoak differ genetically 

or whether the small stature of dwarf tanoak is due to unproductive site conditions. The 

scientific literature does not usually distinguish between the two infrataxa (Fryer 2007). 

However, its identification is pertinent to management decisions. While N. lithocarpus is 

generally protected as an oak species, the dwarf variety may be classified as a shrub and 

therefore subject to treatment or removal. Typically, P. menziesii attains less dominance and 

may be replaced by open stands of various conifers, such as P. ponderosa, P. sabiniana, or 

P. jeffreyi. Trees occur within a generally open grassland or shrubland. The shrub layer is 

likely to include Q. vaccinifolia, N. densiflorus, U. californica, Q. garryana var. breweri, 

and Rhamnus. Common grasses include Stipa, Festuca, and Danthonia (LandFire 2007s; 

McDonald 1988; O’Geen et al. 2007; Raphael 1988).
USDA Forest Service RMRS-GTR-385. 2018 153

Distribution

This highly variable cover type occurs in the Sierra Nevada on all aspects at elevations 

of 350 meters (1,100 feet) to over 1,700 meters (5,600 feet) (LandFire 2007i). Soil depth 

classes range from shallow to deep. The large number of species in the type, both conifer 

and hardwood, allow it to occupy and persist in a wide range of environments. Good soils 

and poor, steep slopes and slight, frequently disturbed and pristine—all are at least adequate 

habitats for one or more species (McDonald 1988). 

We derived a xeric-mesic gradient based on four variables: (1) aspect, (2) potential 

evapotranspiration, (3) topographic wetness index, and (4) soil water storage. We standardized 

the variables by z-scores such that higher values corresponded to more mesic environments. 

Thus, potential evapotranspiration was inverted to maintain this balance. We combined the 

four variables with equal weights into a topographic position index (TPI), which we split into 

xeric vs. mesic, with xeric occupying the negative end of the range up to one-fourth of the 

standard deviation below the mean (zero) and mesic occupying the remaining portion of the 

spectrum.

Mesic Modifier

This type is generally found where soils are deep, well-drained, and loamy, sandy, or 

gravelly. It is found in valleys, coves, and ravines, along streams, and on north-facing as well 

as east-facing slopes. It typically occurs in areas that are cool and on moist sites in areas where 

precipitation is highest, most likely in the form of rain and snow.

Xeric Modifier

This type occurs on a wide range of slopes, especially those that are moderate to steep. 

Soils are for the most part rocky, alluvial, coarse textured, poorly developed, and well drained. 

Quercus chrysolepis is typically more prevalent on xeric sites. 

Ultramafic Modifier

Ultramafics have been mapped at various spatial densities throughout the elevational 

range of the MEG cover types. Low to moderate elevations in ultramafic and serpentinized 

areas often produce soils low in essential minerals such as calcium, potassium, and nitrogen, 

and have excessive accumulations of heavy metals such as nickel and chromium. These sites 

vary widely in the degree of serpentinization and effects on their overlying plant communities 

(USDA Forest Service 2008). Note that the terms “ultramafic rock” and “serpentine” are broad 

terms used to describe many different but related rock types: serpentinite, peridotite, dunite, 

pyroxenite, talc, and soapstone, among others (O’Geen et al. 2007). 

Disturbances

Wildfire

Wildfires are common and frequent in the MEG cover types; vegetation mortality 

caused by wildfire depends on vegetation (i.e., fuel) characteristics and wildfire intensity. 

Low-mortality fires (≤75 percent overstory mortality) kill small trees and may consume 

aboveground portions of small oaks, shrubs, and herbs, but do not often kill large trees or 

belowground organs of most oaks, shrubs, and herbs, which promptly resprout. High-mortality 

fires (>75 percent overstory mortality) kill trees of all sizes and may kill many of the shrubs 

and herbs as well. However, high-mortality fires typically kill only the aboveground portions 

of the oaks, shrubs, and herbs; consequently, most oaks, shrubs and herbs promptly resprout 

from surviving belowground organs.
154 USDA Forest Service RMRS-GTR-385. 2018

The vast majority of fires occur in late summer or early fall and are associated with 

lightning storms. Native American burning locally increased the frequency and may have 

been extensive prior to 1850. However, research also suggests that fire frequencies actually 

increased after European settlement (Kyle Merriam, USDA Forest Service, Quincy, California, 

personal communication, July 2013). Fires in the past were often large in area due to the high 

number of ignition points associated with fire events, and created patches of varying age and 

species composition (LandFire 2007i). 

Hardwoods typically provide the greatest cover after fire due to root-crown sprouting. 

Depending on fire severity, many hardwoods may have epicormic sprouting well into the 

crown. Species composition, density, and interspecific competition within stands contribute 

to multiple pathways following disturbance. If fire has been absent from an area for a long 

time, some conifers may be able to establish and persist even with the return of frequent lowseverity fire. But if low-severity fire is frequent after a stand-replacing fire, conifers will be 

more or less excluded and hardwoods will dominate (LandFire 2007i). 

Fire severity in the MEG cover types is typically positively correlated with slope position, 

with higher mortality occurring on upper slopes and ridgetops, especially on southwest-facing 

aspects.

Mesic Modifier

Notholithocarpus densiflorus, which is prevalent on mesic sites, is adapted to ignite easily. 

In the lower montane zone of the Sierra Nevada where N. densiflorus occurs, the historical 

fire regime was characterized by dormant season fires of mostly low to moderate severity 

(Tappeiner et al. 1990). In stands with high N. densiflorus cover, N. densiflorus may dominate 

the stand for many years before conifers reestablish. Patchy, stand-replacement fires were 

most common on north-facing slopes and during extended droughts. Seedlings and saplings 

of N. densiflorus are typically top-killed by even low-severity surface fire. Large trees usually 

survive moderate-severity fire, bearing fire scars afterward. Even N. densiflorus with thick 

bark (3–10 cm; 1–4 inches) typically sustain bole damage from fire. Relative to associated 

conifers, mature P. menziesii is fairly resistant to surface fires. Crown fires cause extensive 

mortality (Tappeiner et al. 1990). 

Xeric Modifier

Quercus chrysolepis, which is prevalent on xeric sites, has loose, dead, flaky bark that 

catches fire readily and burns intensely. Occasional fire often changes a stand of Q. chrysolepis 

to Q. wislizeni–chaparral, but without fire for sufficient time, trees again develop. Where fire 

is frequent, this oak becomes scarce or even drops out of the montane hardwood community 

(McDonald 1988). 

Ultramafic Modifier

Historically, these woodland types had frequent low-severity fire. However, now there is 

higher susceptibility to stand-replacing fire because of fire exclusion. Overall, this type has a 

very limited distribution and consequently limited information for fire occurrence history.

Other Disturbances

Other disturbances are not currently being modeled, but may, depending on the seral stage 

and mortality levels, reset patches to early development, maintain existing seral stages, or shift 

or accelerate succession to a more open condition. All of the tree species associated with this 

vegetation type are susceptible to a wide variety of pathogens and insects, such as sudden oak 

death for N. densiflorus, which is caused by the pathogen Phytophthora ramorum.
USDA Forest Service RMRS-GTR-385. 2018 155

Seral Stages

The classification of seral stages originated from the corresponding LandFire biophysical 

setting model, but with some modifications (e.g., the addition of a moderate canopy cover 

stage in the mid- and late-seral stages) based on expert input, as follows and as summarized in 

table B4.1. The seral stage map corresponding to this classification for the current landscape 

was derived from the EVeg dataset and the rules in table B4.2 for the MEG cover types.

Early Development (ED)

On mesic sites, this seral stage is characterized by abundant grasses, forbs, and low 

shrubs found under sparse to moderate cover of tree (primarily P. menziesii and N. densiflorus) 

seedlings and saplings, potentially with a residual or legacy of overstory trees from the 

predisturbance stand making up less than 25 percent canopy cover. Seedling establishment of 

P. menziesii following fire depends on the spacing and number of surviving seed trees. Seedling 

establishment after large stand-replacing fires may be slow if seed trees are killed over 

extensive areas. Or, if there are numerous, well-spaced surviving seed trees within the burned 

area, a new cohort of seedlings can quickly establish (Uchytil 1991). Nearly all N. densiflorus 

burls sprout after fire, and survivorship is high. Quercus chrysolepis, if present, also sprouts 

readily, and shrubs such as Mahonia, Gaultheria, and Rhododendron may be significant. Shrub 

growth from seedbanks (e.g., Ceanothus integerrimus), can also be high (LandFire 2007i). 

Thus, N. densiflorus and other shrubs usually dominate the initial condition if P. menziesii is 

not able to seed in quickly (Raphael 1988). 

On xeric sites, grasses, forbs, low shrubs, and sparse cover of tree seedlings and saplings 

are found often under an open canopy of residual overstory trees from the predisturbance 

stand. Forest openings contain a dense cover of hardwood sprouts. Sprouting shrubs such as 

M. aquifolium, Gaultheria shallon, and Rhododendron may be significant. Shrub growth from 

seedbanks (e.g., Ceanothus integerrimus) can also be high (LandFire 2007i). On ultramafic 

sites, P. menziesii may be stunted and slow-growing, and N. densiflorus var. echinoides may be 

present. Grasses such as Festuca, Danthonia, and Acnatherum, or else chaparral shrubs, establish. 

Scattered Pinus ponderosa, P. sabiniana, or P. jeffreyi may also be present (LandFire 2007s).

Mid-Development – Open Canopy Cover (MDO) 

On mesic sites, this seral stage is characterized by a sparse ground cover of grasses, forbs, 

and shrubs, with a moderate canopy cover (<40 percent) of pole- to medium-sized (10–20 

inches [25–51 cm] d.b.h.) trees (primarily P. menziesii and N. densiflorus). Other Quercus

and Arctostaphylos species may also be present. In this stage, hardwoods are dominant, but

P. menziesii and possibly other conifers are established or establishing under the predominantly 

N. densiflorus canopy (LandFire 2007i; McDonald 1988). On xeric sites, hardwoods such as 

Q. chrysolepis and Q. kelloggii are often prevalent, whereas conifers such as P. menziesii may 

be present at low densities in emergent status. The shrub understory is still a significant presence 

(LandFire 2007i). Ultramafic sites are characterized by open P. menziesii, P. ponderosa, P. 

sabiniana, or P. jeffreyi stands with an understory composed of N. densiflorus var. echinoides or 

Q. chrysolepis as well as grasses, forbs, and shrubs (LandFire 2007s).

Mid-Development – Moderate Canopy Cover (MDM) 

This seral stage is characterized by a moderate canopy cover (40–70 percent) of pole- to 

medium-sized conifers, and is otherwise similar to MDO.

Mid-Development – Closed Canopy Cover (MDC) 

This seral stage is characterized by a dense canopy cover (>70 percent) of pole- to 

medium-sized conifers, and is otherwise similar to MDO. 
156 USDA Forest Service RMRS-GTR-385. 2018

Late Development – Open Canopy Cover (LDO)

On mesic sites, this seral stage is characterized by an overstory of low canopy cover 

(<40 percent) of large trees (>20 inches d.b.h.), primarily P. menziesii. Pinus lambertiana 

also occurs. Notholithocarpus densiflorus is tolerant of both full sun and shade, and usually 

dominates the subcanopy at this stage. Codominance of the upper canopy with P. menziesii is 

uncommon but possible after extended periods without disturbance (LandFire 2007i; Uchytil 

1991). There is also some evidence that the senescence of late-development N. densiflorus 

may cause openings in the canopy and allow for continued P. menziesii dominance. Quercus

and Arctostaphylos species may also be present in the subcanopy (LandFire 2007i). On xeric 

sites, P. menziesii, Q. chrysolepis, and Arctostaphylos mewukka may occur. Shrubs persist in 

openings, but those in shade are likely to begin senescing (LandFire 2007i). On ultramafic 

sites, large P. ponderosa, P. sabiniana, or P. jeffreyi may be present along with P. menziesii and 

N. densiflorus var. echinoides. Grass savanna persists on sites experiencing low-intensity fire 

(with Festuca, Achnatherum, and Danthonia). Where fire is less frequent, chaparral shrubland 

develops (with Arctostaphylos and Q. breweri) (LandFire 2007s).

Late Development – Moderate Canopy Cover (LDM)

This seral stage is characterized by an overstory of large trees with canopy cover 40–70 

percent, and is otherwise similar to LDO.

Late Development – Closed Canopy Cover (LDC)

This seral stage is characterized by an overstory of large trees with canopy cover greater 

than 70 percent, and is otherwise similar to LDO.

Model Parameterization

This section includes a listing of the model parameters that are cover type-specific. Note 

that there are additional model parameters not specific to a cover type (e.g., climate modifier) 

that ultimately affect the model processes and outcomes, and these are discussed under the 

Methods section in McGarigal et al. 2018.

Succession

The rules (i.e., parameters) governing succession for the MEG cover types are listed in 

table B4.3. These rules were initially based on the corresponding LandFire BpS descriptions 

(LandFire 2007i,s) and associated models created by using the Vegetation Dynamics 

Development Tool (VDDT), as modified by Safford and Estes. They were subsequently 

modified based on expert input to include probabilistic rather than deterministic seral stage 

transitions. Specifically, we modified the rules so that stands would gradually, instead of 

abruptly, transition from one seral stage to the next to reflect stochasticity in the real-world 

processes governing succession. For example, the first rule dictates that a cell in the MEG_M 

cover type, which has been in the ED seral stage for 20–35 years, will have an 80-percent 

chance of transitioning to MDM at the beginning of each timestep. Thus, stands will randomly 

begin transitioning to the MDM stage after 20 years in the ED stage, but some stands could 

remain in the ED stage for as much as 40 years to reflect delayed tree establishment. Note 

that for stands currently in the ED stage and between 20 and 35 years in this stage, there is an 

implied 20-percent chance of remaining in the ED seral stage at each timestep. The second 

rule dictates that a cell that has been in the ED seral stage for 40 years will have a 100-percent 

chance of transitioning to the MDM seral stage; thus, all stands will have transitioned to the 

MDM stage after 40 years since establishment. Note that on ultramafic sites stands can be 

delayed in the ED stage for up to 80 years, and they always transition to MDO.
USDA Forest Service RMRS-GTR-385. 2018 157

Applying the succession rules listed in table B4.3 results in stands transitioning between 

seral stages in a probabilistic rather than deterministic manner, such that we can compute the 

average stand age (years) for the transition to the next seral stage, as shown in table B4.4. For 

example, the first row in table B4.4 indicates that for a cell in the MEG_M cover type in the ED 

seral stage, the earliest stand age (i.e., number of years since the last stand-replacing disturbance) 

for transitioning to the MDM seral stages is 20 years, the latest stand age is 40 years, and the 

average stand age at the time of the transition is 21 years. Note that the average stand age at 

transition (21 years) is close to the specified earliest stand age (20 years) because of the relatively 

high rate (0.8) of transitioning beginning at the specified earliest stand age. Also, the third row 

in table B4.4 indicates that a cell in the MEG_M cover type in the MDM seral stage will, on 

average, take 16 years without a low-mortality fire disturbance to transition to the MDC seral 

stage (i.e., transition from a moderate-canopy cover, 40–70 percent, to a closed-canopy cover, 

>70 percent, condition). Note that a low-mortality fire every 15 years will maintain the stand in 

the open-canopy condition.

Wildfire Disturbance

Rotation Period

Wildfire rotation period (equivalent to the point-specific mean return interval) is not formally 

a model parameter, but rather is specified as a target value to be achieved through model 

calibration. Target fire rotation periods (FRPs) were specified by cover type (table B4.5). FRPs 

for MEG_M and MEG_X were based on Van de Water and Safford (2011), although expert input 

from Safford and Estes was used to differentiate FRPs between the mesic and xeric sites; FRP for 

MEG_U was set to be more than double that of MEG_M, based on the corresponding LandFire 

BpS model (LandFire 2007s) and expert input from Safford and Estes.

Susceptibility

The cover type-specific factors affecting susceptibility of a cell to wildfire were: (1) 

topographic position, and (2) fuel characteristics, as represented by vegetation cover type, seral 

stage, and time since the last wildfire. Each of these two factors is represented as a probability.

Topographic position—Topographic position, as represented by the topographic position 

index (TPI) described under the Methods section in McGarigal et al. 2018, was treated as having 

a static (i.e., constant over time) and universal effect on the relative susceptibility of a cell to 

wildfire regardless of seral stage or disturbance history. Specifically, all other things being equal, 

for the MEG cover types, susceptibility decreased by 30 percent as the TPI decreased over its 

full range according to the four-parameter logistic function depicted in figure B.2. However, 

because the bulk of the landscape varies over a much smaller range of TPI values, the effect on 

susceptibility is typically much less than 30 percent.

The specified logistic parameters were based on consensus expert opinion about the strength 

and nature of the topographic influence on wildfire susceptibility, and reflect general support for 

such an effect in the scientific literature (North 2012; Taylor and Skinner 2003).

Fuels (vegetation and disturbance history)—Fuels, as represented by vegetation cover type, 

seral stage, and recent disturbance history, were treated as having a dynamic (i.e., changing over 

time) effect on the relative susceptibility of a cell to wildfire. Specifically, susceptibility varied 

among cover types and seral stages in relation to the time (number of years) since the last fire 

according to the cumulative Weibull function and the parameters listed in table B4.5 (e.g., as 

illustrated in figure B.1). Note that here we use the cumulative form of the Weibull distribution, 

which gives the cumulative probability of a disturbance for any number of years since the last 

disturbance. Thus, the probability increases from 0 immediately following a fire to approaching 

1 after a certain number of years since the last fire, depending on the specified mean return 

interval (MRI) and shape parameters of the Weibull function. Holding Shape constant, and all 
158 USDA Forest Service RMRS-GTR-385. 2018

other things being equal, as MRI increases the curve shifts to the right, resulting in a lower 

probability for any given number of years since the last disturbance. In this manner, varying 

the MRI among cover types and seral stages affects the relative susceptibility to wildfire.

The specified Weibull MRI parameters were based on the corresponding LandFire BpS 

descriptions (LandFire 2007i,s) and associated VDDT models, as modified by Safford and Estes. 

Importantly, although susceptibility of the various seral stages is determined by MRI 

(holding Shape constant), these return intervals should not be interpreted literally, as the 

concept of a return interval does not meaningfully apply to a dynamic seral stage. Moreover, 

these MRIs were derived from the LandFire BpS descriptions and associated VDDT models, 

as modified by Safford and Estes; taken collectively, these values do not necessarily agree 

with the target FRPs for the cover types. Thus, the MRIs assigned to each cover type and 

seral stage should be interpreted as relative values that affect the relative susceptibility of the 

various vegetation states.

Mortality

The cover type-specific factors affecting overstory mortality following wildfire (i.e., fire 

severity) were: (1) topographic position, and (2) fuel characteristics. Each of these two factors 

is represented as a probability.

Topographic position—The effect of topographic position on mortality was treated 

identically to its effect on susceptibility (see previous description). Again, the specified 

logistic parameters were based on consensus expert opinion about the strength and nature of 

the topographic influence on wildfire severity, and reflect general support for such an effect in 

the scientific literature (North 2012; Taylor and Skinner 2003).

Fuels (vegetation)—Fuels, as represented by vegetation cover type and seral stage, were 

treated as having a dynamic (i.e., changing over time) effect on the relative probability of a 

high-mortality response to wildfire. Specifically, we assigned a probability of high-mortality 

response to wildfire to each cover type and seral stage (table B4.5); values were originally 

based on the corresponding LandFire BpS descriptions (LandFire 2007i,s) and associated 

VDDT models, but were subsequently modified based on expert input from Safford and Estes.

Disturbance Transitions

The rules (i.e., parameters) governing seral stage transitions following low-mortality 

wildfire disturbance for the MEG cover types are listed in table B4.6. These rules were 

initially based on the corresponding LandFire BpS descriptions (LandFire 2007i,s) and 

associated models created by using the VDDT, as modified by Safford and Estes. They were 

subsequently modified to include the moderate canopy cover seral stages not present in the 

VDDT models. Note that rules governing transitions following high-mortality wildfire are not 

listed in table B4.6 because high-mortality wildfires always result in transition to the ED seral 

stage. In addition, conditions in which low-mortality wildfire has no effect on the seral stage 

(i.e., does not cause a transition) are not listed. For example, the first two rules dictate that a 

low-mortality wildfire in a cell of MEG_M in the MDC seral stage has an 11-percent chance 

of transitioning to the MDM stage, an 11-percent chance of transitioning to the MDO stage, 

and (by implication) a 78-percent chance of remaining in the MDC stage. In addition, by 

implication (given the absence of a rule), a low-mortality wildfire in the ED, MDO, or LDO 

seral stage has no effect other than to maintain the cell in that seral stage.

Vegetation Treatments

Dynamic spatial constraints and priorities affecting individual cover types were described 

under the Methods section in McGarigal et al. 2018; here we describe the rules governing 

seral stage transitions following each unique vegetation treatment (table B4.7). Note that these 

rules were created by the principals involved in this project and reflect expectations based on 

the common prescriptions applied today.
USDA Forest Service RMRS-GTR-385. 2018 159

Table B4.1—Summary of MEG seral stage characteristics: average overstory tree diameter at breast height (d.b.h.) 

of the dominant and codominant trees, overstory tree percent cover from above (CFA), assigned average CFA value 

for classifying the landscape by percent canopy cover, and range of stand ages (number of years since the last 

stand-replacing disturbance) possible for the corresponding seral stage. Note that overstory tree d.b.h. and CFA for 

the ED seral stages refer to the residual or legacy overstory from the predisturbance stand.

Cover type

Seral

stagea Overstory treed.b.h. (inches)

Overstory tree

CFA (%)

Assigned

average CFA (%)

Stand age

range (years)

MEG_M ED <5 (13 cm) <25 10 0–35

MDO 5–19.9 (13–50.5 

cm)

<40 30 20–75

MDM 5–19.9 40–70 55 20–75

MDC 5–19.9 >70 85 20–75

LDO ≥20 (51 cm) <40 30 ≥40

LDM ≥20 40–70 55 ≥40

LDC ≥20 >70 85 ≥40

MEG_X ED <5 <25 10 0–35

MDO 5–19.9 <40 30 20–85

MDM 5–19.9 40–70 55 20–85

MDC 5–19.9 >70 85 20–85

LDO ≥20 <40 30 ≥50

LDM ≥20 40–70 55 ≥50

LDC ≥20 >70 85 ≥50

MEG_U ED <5 <25% 10% 0–75

MDO 5–19.9 <40% 30% 30–155

MDM 5–19.9 40–70% 55% 30–155

MDC 5–19.9 >70% 85% 30–155

LDO ≥20 <40% 30% ≥60

LDM ≥20 40-70% 55% ≥60

LDC ≥20 >70% 85% ≥60

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
160 USDA Forest Service RMRS-GTR-385. 2018

Table B4.2—Mapping rules for MEG seral stages. Diameter at breast height (d.b.h.) and cover from above (CFA) 

values were taken from EVeg polygons. Categories for d.b.h. (inches) are: null, 0–0.9, 1–4.9, 5–9.9, 10–19.9, 20–

29.9, ≥30. CFA categories (%) are: null, 0–10, 10–20, ... , 90–100. Each row should be read with a Boolean AND 

across each column. Within each seral stage the rows should be read with a Boolean OR across rows.

Seral stagea Overstory tree

d.b.h. 1 (inches)

Overstory tree

d.b.h. 2 (inches)

Total tree

CFA (%)

Conifer

CFA (%)

Hardwood

CFA (%)

ED 0–4.9 (0–12.4 cm) any any any any

MDO 5–19.9 (13–50.5 cm) any 0-40 any any

MDM 5–19.9 any 40-70 any any

MDC 5–19.9 any 70-100 any any

LDO ≥20 (51 cm) any 0-40 any any

LDM ≥20 any 40-70 any any

LDC ≥20 any 70-100 any any

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
USDA Forest Service RMRS-GTR-385. 2018 161

Table B4.3—Succession rules for MEG seral stages. Note that number of years in current successional stage 

refers to the number of years in either early-development (ED), mid-development (MD), or late-development (LD) 

stage independent of canopy cover class. 

Cover type

From seral

stagea To seralstagea

Number of years in

current

successional stage

Number of

years since

low-mortality

fire

Probability of

transition

MEG_M ED MDM 20–35 any 0.8

ED MDM 40 any 1.0

MDC LDC 20–35 any 0.8

MDC LDC 40 any 1.0

MDM LDM 20–35 any 0.8

MDM LDM 40 any 1.0

MDM MDC ≥15 ≥15 0.8

MDO LDO 20–35 any 0.8

MDO LDO 40 any 1.0

MDO MDM ≥15 ≥15 0.8

LDM LDC ≥15 ≥15 0.8

LDO LDM ≥15 ≥15 0.8

MEG_X ED MDM 20–35 any 0.7

ED MDM 40 any 1.0

MDC LDC 30–45 any 0.7

MDC LDC 50 any 1.0

MDM LDM 30–45 any 0.7

MDM LDM 50 any 1.0

MDM MDC ≥15 ≥15 0.7

MDO LDO 30–45 any 0.7

MDO LDO 50 any 1.0

MDO MDM ≥15 ≥15 0.7

LDM LDC ≥15 ≥15 0.7

LDO LDM ≥15 ≥15 0.7

MEG_U ED MDO 30–75 any 0.4

ED MDO 80 any 1.0

MDC LDC 30–75 any 0.4

MDC LDC 80 any 1.0

(Table B4.3 continued on next page.)
162 USDA Forest Service RMRS-GTR-385. 2018

MDM LDM 30–75 any 0.4

MDM LDM 80 any 1.0

MDM MDC ≥20 ≥20 0.4

MDO LDO 30–75 any 0.4

MDO LDO 80 any 1.0

MDO MDM ≥20 ≥20 0.4

LDM LDC ≥20 ≥20 0.4

LDO LDM ≥20 ≥20 0.4

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 

(Table B4.3 continued) 

Table B4.3—Succession rules for MEG seral stages. Note that number of years in current successional stage 

refers to the number of years in either early-development (ED), mid-development (MD), or late-development (LD) 

stage independent of canopy cover class. 

Cover type

From seral

stagea To seralstagea

Number of years in

current

successional stage

Number of

years since

low-mortality

fire

Probability of

transition

MEG_M ED MDM 20–35 any 0.8

ED MDM 40 any 1.0

MDC LDC 20–35 any 0.8

MDC LDC 40 any 1.0

MDM LDM 20–35 any 0.8

MDM LDM 40 any 1.0

MDM MDC ≥15 ≥15 0.8

MDO LDO 20–35 any 0.8

MDO LDO 40 any 1.0

MDO MDM ≥15 ≥15 0.8

LDM LDC ≥15 ≥15 0.8

LDO LDM ≥15 ≥15 0.8

MEG_X ED MDM 20–35 any 0.7

ED MDM 40 any 1.0

MDC LDC 30–45 any 0.7

MDC LDC 50 any 1.0

MDM LDM 30–45 any 0.7

MDM LDM 50 any 1.0

MDM MDC ≥15 ≥15 0.7

MDO LDO 30–45 any 0.7

MDO LDO 50 any 1.0

MDO MDM ≥15 ≥15 0.7

LDM LDC ≥15 ≥15 0.7

LDO LDM ≥15 ≥15 0.7

MEG_U ED MDO 30–75 any 0.4

ED MDO 80 any 1.0

MDC LDC 30–75 any 0.4

MDC LDC 80 any 1.0
USDA Forest Service RMRS-GTR-385. 2018 163

Table B4.4—Summary of MEG seral-stage transitions: earliest, latest, and average stand age (number of years 

since the last stand-replacing disturbance) for the transition to the next seral stage; and average number of years 

without low-mortality fire to transition to the next canopy cover class.

Cover

type

From seral

stagea To seralstagea

Earliest

stand age

(years) at

transition

Latest stand

age (years)

at transition

Average

stand age

(years) at

transition

Average no. of

years without

low-mortality

fire to

transition

MEG_M ED MDM 20 40 21 n/a

MD LD 40 80 42 n/a

MDO MDM n/a n/a n/a 16

MDM MDC n/a n/a n/a 16

LDO LDM n/a n/a n/a 16

LDM LDC n/a n/a n/a 16

MEG_X ED MDM 20 50 23 n/a

MD LD 50 90 56 n/a

MDO MDM n/a n/a n/a 18

MDM MDC n/a n/a n/a 18

LDO LDM n/a n/a n/a 18

LDM LDC n/a n/a n/a 18

MEG_U ED MDO 30 80 37 n/a

MD LD 60 160 74 n/a

MDO MDM n/a n/a n/a 27

MDM MDC n/a n/a n/a 27

LDO LDM n/a n/a n/a 27

LDM LDC n/a n/a n/a 27

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
164 USDA Forest Service RMRS-GTR-385. 2018

Table B4.5—Weibull function parameters associated with the susceptibility of a cell to wildfire based on fuels (i.e., 

vegetation cover type, seral stage, and number of years since the last fire) and the probability of a high-mortality 

wildfire by cover type and seral stage for the MEG cover types (original values in parentheses). 

Weibull parameters

Cover type

Seral

stagea

Target fire

rotation period

(years)

Mean return

interval (years) Shape

Probability of

high-mortality fire

MEG_M n/a 50 n/a n/a n/a

ED n/a 68 3 1.0

MDO n/a 18 3 0.05 (0.01)

MDM n/a 26 3 0.09 (0.01)

MDC n/a 46 3 0.20 (0.02)

LDO n/a 17 3 0.05 (0.005)

LDM n/a 25 3 0.09 (0.01)

LDC n/a 44 3 0.19 (0.02)

MEG_X n/a 40 n/a n/a n/a

ED n/a 85 3 1.00

MDO n/a 15 3 0.03 (0.003)

MDM n/a 22 3 0.06 (0.01)

MDC n/a 39 3 0.13 (0.01)

LDO n/a 15 3 0.03 (0.003)

LDM n/a 21 3 0.06 (0.01)

LDC n/a 37 3 0.13 (0.01)

MEG_U n/a 120 n/a n/a n/a

ED n/a 136 3 1.00

MDO n/a 35 3 0.05 (0.005)

MDM n/a 51 3 0.09 (0.01)

MDC n/a 92 3 0.20 (0.02)

LDO n/a 35 3 0.05 (0.005)

LDM n/a 50 3 0.09 (0.01)

LDC n/a 87 3 0.19 (0.02)

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
USDA Forest Service RMRS-GTR-385. 2018 165

Table B4.6—Disturbance rules for MEG cover types governing seral stage transitions following a low-mortality 

wildfire. Note that conditions in which low-mortality wildfire has no effect are not listed.

Cover type From seral stagea

 To seral stagea Probability of transition

MEG_M MDC MDM 0.11

MDC MDO 0.11

MDM MDO 0.14

LDC LDM 0.13

LDC LDO 0.13

LDM LDO 0.17

MEG_X MDC MDM 0.1

MDC MDO 0.1

MDM MDO 0.13

LDC LDM 0.12

LDC LDO 0.12

LDM LDO 0.15

MEG_U MDC MDM 0.11

MDC MDO 0.11

MDM MDO 0.14

LDC LDM 0.13

LDC LDO 0.13

LDM LDO 0.17

a MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = Late-moderate; LDC = Lateclosed. 
166 USDA Forest Service RMRS-GTR-385. 2018

Table B4.7—Disturbance rules for MEG cover types governing seral stage transitions following vegetation 

treatments. Note that treatments not affecting seral stage transitions (e.g., mastication) are not included here.

Cover type Treatment type

From seral

stagea To seralstagea Probability oftransition

MEG_M Clearcut and Group cuts Any ED 1 

Thinning, including cells thinned in: 

(1) matrix thin and group cut; (2) 

thin and burn; (3) thin, hand cut, 

pile, and burn; (4) thin, masticate, 

and burn; and (5) matrix thin, group 

cut, and burn treatments)

MDC MDM 1 

MDM MDO 1 

LDC LDM 1 

LDM LDO 1 

Prescribed fire, including cells 

burned as part of a prescribed fireonly treatment); "cool" burn/"hot" 

burn transition probabilities

MDC MDM 0.03/0.05

MDC MDO 0/0.03

MDC ED 0/0.01

MDM MDO 0.05/0.14

MDM ED 0/0.01

LDC LDM 0.02/0.03

LDC LDO 0/0.02

LDC ED 0/0.01

LDM LDO 0.04/0.11

LDM ED 0/0.01

Thin and burn, including cells

burned only as part of: (1) thin and

burn; and (2) hand cut, pile, and

burn treatments)

MDC MDM 0.03

MDM MDO 0.05

LDC LDM 0.02

LDM LDO 0.04

MEG_X; 

MEG_U Clearcut and Group cuts Any ED 1 

Thinning, including cells thinned in: 

(1) matrix thin and group cut; (2) 

thin and burn; (3) thin, hand cut, 

pile, and burn; (4) thin, masticate, 

and burn; and (5) matrix thin, group 

cut, and burn treatments

MDC MDM 1 

MDM MDO 1 

LDC LDM 1 

LDM LDO 1 

Prescribed fire, including cells 

burned as part of a prescribed fireonly treatment; "cool" burn/"hot" 

burn transition probabilities

MDC MDM 0.05/0.15

MDC MDO 0.03/0.09

MDC ED 0.01/0.03

MDM MDO 0.05/0.15

(Table B4.7 continued on next page.)
USDA Forest Service RMRS-GTR-385. 2018 167

Cover type Treatment type

From seral

stagea To seralstagea Probability oftransition

MDM ED 0.01/0.03

LDC LDM 0.04/0.12

LDC LDO 0.02/0.06

LDC ED 0.01/0.03

LDM LDO 0.04/0.12

LDM ED 0.01/0.03

Thin and burn, including cells 

burned only as part of: (1) thin and

burn; and (2) hand cut, pile, and

burn treatments

MDC MDM 0.05

MDC MDO 0.03

MDC ED 0.01

MDM MDO 0.05

MDM ED 0.01

LDC LDM 0.04

LDC LDO 0.02

LDC ED 0.01

LDM LDO 0.04

LDM ED 0.01

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 

(Table B4.7 continued) 
168 USDA Forest Service RMRS-GTR-385. 2018

5. Mountain Riparian (MRIP) 

Reviewed by: (1) Hugh Safford, Regional Ecologist, USDA Forest Service; (2) Becky Estes, 

Central Sierra Province Ecologist, USDA Forest Service; and (3) Sarah Sawyer, Assistant 

Pacific Southwest Regional Ecologist, USDA Forest Service.

Cover Type Classification and Crosswalks

▪ EVeg: Regional Dominance Type 1:

◦ Riparian Mixed Hardwood

◦ White Alder

◦ Willow

◦ Black Cottonwood

◦ Willow - Alder

◦ Mountain Alder

◦ Willow (Shrub)

▪ Presettlement Fire Regime Type: None

▪ LandFire BpS model: 

◦ 0611520: California Montane Riparian Systems

Vegetation Description

The Mountain Riparian cover type often occurs as a highly variable mosaic of multiple 

communities that are tree- or shrub-dominated. The variety of plant associations connected to 

this system reflect elevation, stream gradient, floodplain width, and flooding events. Usually, 

the montane riparian zone occurs as a narrow, often dense grove of broad-leaved, winter 

deciduous trees (but often shrub-form) with a sparse understory. At high elevations, there are 

usually more shrubs in the understory, or the type may not be well developed or may occur in 

the shrub stage only (Grenfell 1988; LandFire 2007q). Due to the methodology of assigning 

the landscape to particular cover types, the montane riparian type is limited to those sites 

determined to be dominated by the species assemblages listed in the preceding crosswalk 

section. Although we recognize that the riparian zone commonly includes areas near 

watercourses that are dominated by conifers and other trees, for the purposes of this model 

those sites have been sorted into the pertinent cover types in accordance with the dominant 

vegetation observed. We do not have the capacity at this time to groundtruth or map riparian 

zones based on understory or midstory vegetation.

Characteristic species are many, including those from the following genera: Acer, Alnus, 

Cornus, Populus, Rhododendron, and Salix. MRIP can occur as Alnus or Salix stringers along 

streams or seeps. In other situations an overstory of Populus or Alnus, or both, may be present 

(Grenfell 1988). Other tree species may include Pseudotsuga menziesii, Platanus racemosa, 

and Quercus agrifolia. At lower elevations, the riparian areas may contain Arbutus menziesii, 

Notholithocarpus densiflorus, Umbellularia californica, Cornus, Acer and Fraxinus. Salix

species are common throughout, with the predominant species changing as elevation 

increases. Overall, the cover type most typically occurs as dense, shrub-like thickets of 

riparian tree and shrub species, but with scattered and variable low canopy cover of overstory 

trees (LandFire 2007q).

Distribution

Mountain Riparian is associated with montane lakes, ponds, seeps, bogs, and meadows, 

as well as rivers, streams, and springs. Water may be permanent or ephemeral. The transition 
USDA Forest Service RMRS-GTR-385. 2018 169

between MRIP and adjacent nonriparian vegetation may be abrupt, especially where the 

topography is steep. Typically, this vegetation type occurs below 2,400 meters (8,000 feet) 

(Grenfell 1988). 

Disturbances

Wildfire

Fire frequency is highly variable within the riparian zone. Multiple factors, including 

topography, elevation, climate, dominant vegetation, and existing vegetation all affect fire 

frequency and intensity. Riparian zones are heavily influenced by the fire regime of adjacent 

cover types and thus are still susceptible to disturbance by wildfire, even frequent and highmortality fires. Streams also act as an inhibitor of fire spread, thus contributing to spatial and 

temporal diversity of landscapes beyond what their relative area would suggest (Grenfell 1988). 

In some forested riparian areas, fire return intervals (FRIs) before fire suppression were 

very likely lower than adjacent uplands, while in others, fire frequency appears to have been 

comparable in riparian and upland areas. FRIs are shorter for riparian zones bordering narrow 

streams compared to zones around wider and deeper streams. In arid ecosystems, FRIs may 

be shorter than the surrounding areas in part because the increased productivity of these sites 

results in more fuels to carry fire. Lower elevation and adjacency to fire-tolerant vegetation 

also contribute to shorter FRIs for some riparian areas (Sawyer 2013). 

Other Disturbances

Other disturbances are not currently being modeled, but may, depending on the seral stage 

and mortality levels, reset patches to early development, maintain existing seral stages, or shift 

or accelerate succession to a more open condition. All of the tree species associated with this 

vegetation type are susceptible to a wide variety of pathogens and insects.

Seral Stages

The classification of seral stages originated from the corresponding LandFire biophysical 

setting model, but with some modifications based on expert input, as follows and as 

summarized in table B5.1. The seral stage map corresponding to this classification for the 

current landscape was derived from the EVeg dataset and the rules in table B5.2 for the MRIP 

cover type.

Early Development (ED)

This seral stage is characterized by a highly variable mixture of trees and shrubs that 

is largely dependent on the predisturbance vegetation composition after a stand-replacing 

disturbance. Salix and Alnus are most common, though overall composition is highly variable 

(LandFire 2007q). Note that there can be a residual or legacy of overstory trees from the 

predisturbance stand making up less than 25 percent canopy cover.

Mid-Development – Open Canopy Cover (MDO) 

This seral stage is characterized by a highly variable mixture of shrubs and, typically, low 

canopy cover (<40 percent) of medium-sized (10–20 inches d.b.h.) trees.

Late Development – Open Canopy Cover (LDO)

This seral stage is characterized by a highly variable mixture of shrubs and, typically, low 

canopy cover (<40 percent) of large (≥20 inches d.b.h.) trees, often dominated by Populus and 

Alnus (LandFire 2007q).
170 USDA Forest Service RMRS-GTR-385. 2018

Model Parameterization

This section includes a listing of the model parameters that are cover type-specific. Note 

that there are additional model parameters not specific to a cover type (e.g., climate modifier) 

that ultimately affect the model processes and outcomes, and these are discussed under the 

Methods section in McGarigal et al. 2018.

Succession

The rules (i.e., parameters) governing succession for the MRIP cover type are listed in 

table B5.3. These rules were initially based on the corresponding LandFire BpS description 

(LandFire 2007q) and associated model created by using the Vegetation Dynamics 

Development Tool (VDDT). They were subsequently modified based on expert input to adjust 

the rate of succession given the tree sizes defined for the various seral stages. For example, the 

first rule dictates that a cell in the MRIP cover type which has been in the ED seral stage for 

20 years, will have a 100-percent chance of transitioning to MDO at the beginning of the next 

timestep. The next rule dictates that a cell that has been in the MDO seral stage for 20 years 

will have a 100-percent chance of transitioning to the LDO seral stage; thus, all stands will 

have transitioned to the LDD stage after 40 years since establishment.

Applying the succession rules listed in table B5.3 results in stands transitioning between 

seral stages in a deterministic manner, such that we know the stand age (years) for the 

transition to the next seral stage, as shown in table B5.4. For example, the first row in table 

B5.4 indicates that a cell in the MRIP cover type in the ED seral stage will transition to the 

MDO seral stage at 20 years since the stand-replacing disturbance. The second row in table 

B5.4 indicates that a cell in the MRIP cover type in the MDO seral stage will transition to the 

LDO seral stage after an additional 20 years. Note that low-mortality fires simply maintain the 

stand in its current seral stage.

Wildfire Disturbance

Rotation Period

Wildfire rotation period (equivalent to the point-specific mean return interval) is not 

formally a model parameter, but rather is specified as a target value to be achieved through 

model calibration. Target fire rotation periods (FRPs) were specified by cover type (table 

B5.5). FRP for the MRIP cover type was based on Van de Water and Safford (2011) and expert 

input from Safford and Estes.

Susceptibility

The only cover type-specific factor affecting susceptibility of a cell to wildfire was fuel 

characteristics, as represented by vegetation cover type, seral stage, and time since the last 

wildfire, which was represented as a relative probability.

Fuels (vegetation and disturbance history)—Fuels, as represented by vegetation cover 

type, seral stage, and recent disturbance history, were treated as having a dynamic (i.e., 

changing over time) effect on the relative susceptibility of a cell to wildfire. Specifically, 

susceptibility varied among cover types and seral stages in relation to the time (number of 

years) since the last fire according to the cumulative Weibull function and the parameters 

listed in table B5.5 (e.g., as illustrated in figure B.1). Note that here we use the cumulative 

form of the Weibull distribution, which gives the cumulative probability of a disturbance 

for any number of years since the last disturbance. Thus, the probability increases from 0 

immediately following a fire to approaching 1 after a certain number of years since the last 

fire, depending on the specified mean return interval (MRI) and shape parameters of the 

Weibull function. Holding Shape constant, and all other things being equal, as MRI increases 
USDA Forest Service RMRS-GTR-385. 2018 171

the curve shifts to the right, resulting in a lower probability for any given number of years 

since the last disturbance. In this manner, varying the MRI among cover types and seral stages 

affects the relative susceptibility to wildfire.

The specified Weibull MRI parameters were based on the corresponding LandFire BpS 

description (LandFire 2007q) and associated VDDT model, as modified by Safford and Estes. 

Importantly, although susceptibility of the various seral stages is determined by MRI (holding 

Shape constant), these return intervals should not be interpreted literally, as the concept of a 

return interval does not meaningfully apply to a dynamic seral stage. Moreover, these MRIs 

were derived from the LandFire BpS description and associated VDDT model, as modified 

by Safford and Estes; taken collectively, these values do not necessarily agree with the target 

FRP for the cover type. Thus, the MRIs assigned to each seral stage should be interpreted as 

relative values that affect the relative susceptibility of the various vegetation states. 

Mortality

The only cover type-specific factor affecting overstory mortality following wildfire (i.e., 

fire severity) was fuel characteristics, as represented by vegetation cover type and seral stage, 

which was represented as a relative probability.

Fuels (vegetation)—Fuels, as represented by vegetation cover type and seral stage, were 

treated as having a dynamic (i.e., changing over time) effect on the relative probability of a 

high-mortality response to wildfire. Specifically, we assigned a probability of high-mortality 

response to wildfire to each cover type and seral stage (table B5.5); values were based on the 

corresponding LandFire BpS descriptions (LandFire 2007q) and associated VDDT models, as 

modified by Safford and Estes.

Disturbance Transitions

The rules (i.e., parameters) governing seral stage transitions following low-mortality 

wildfire disturbance were initially based on the corresponding LandFire BpS description 

(LandFire 2007q) and associated model created by using the VDDT, as modified by Safford 

and Estes. Accordingly, no rules were necessary because all low-mortality wildfires simply 

maintained the stand in its current seral stage. Note that rules governing transitions following 

high-mortality wildfire are not listed here, either, because high-mortality wildfires always 

result in transition to the ED seral stage.

Table B5.1—Summary of MRIP seral stage characteristics: average overstory tree diameter at breast height 

(d.b.h.) of the dominant and codominant trees, overstory tree percent cover from above (CFA), assigned average 

CFA value for classifying the landscape by percent canopy cover, and range of stand ages (number of years since 

the last stand-replacing disturbance) possible for the corresponding seral stage. Note that overstory tree d.b.h. and 

CFA for the ED seral stage refer to the residual or legacy overstory from the predisturbance stand.

Cover type

Seral

stagea Overstory treed.b.h. (inches)

Overstory tree

CFA (%)

Assigned

average CFA (%)

Stand age

range (years)

MRIP ED <10 (25 cm) <25 10 0–55

MDO 10–19.9 (25–50.5 

cm) <40 20 20–125

LDO ≥20 (51 cm) <40 30 ≥60

a ED = Early - all structures; MDO = Mid-open; LDO = Late-open. 
172 USDA Forest Service RMRS-GTR-385. 2018

Table B5.2—Mapping rules for MRIP seral stages. Diameter at breast height (d.b.h.) and cover from above (CFA) 

values were taken from EVeg polygons. Categories for d.b.h. (inches) are: null, 0–0.9, 1–4.9, 5–9.9, 10–19.9, 20–

29.9, ≥30. CFA categories (%) are: null, 0–10, 10–20, ... , 90–100. Each row should be read with a Boolean AND 

across each column. Within each seral stage the rows should be read with a Boolean OR across rows.

Seral

stagea

Overstory tree

d.b.h. 1 (inches)

Overstory tree

d.b.h. 2 (inches)

Total tree

CFA (%)

Conifer

CFA (%)

Hardwood

CFA (%)

ED null any any any any

ED 0–9.9 (0–25.1 cm) any any any any

MDO 10–19.9 (25.4–50.5 

cm) any any any any

LDO ≥20 (51 cm) any any any any

a ED = Early - all structures; MDO = Mid-open; LDO = Late-open. 

Table B5.3—Succession rules for MRIP seral stages. Note that number of years in current successional stage 

refers to the number of years in either early-development (ED), mid-development (MD), or late-development (LD) 

stage independent of canopy cover class. 

Cover type

From seral

stagea To seralstagea

Number of years in

current

successional stage

Number of

years since

low-mortality

fire

Probability of

transition

MRIP ED MDO 20 any 1.0

MDO LDO 20 any 1.0

a ED = Early - all structures; MDO = Mid-open; LDO = Late-open. 

Table B5.4—Summary of MRIP seral-stage transitions: earliest, latest, and average stand age (number of years 

since the last stand-replacing disturbance) for the transition to the next seral stage, and average number of years 

without low-mortality fire to transition to the next canopy cover class.

Cover

type

From seral

stagea To seralstagea

Earliest

stand age

(years) at

transition

Latest

stand age

(years) at

transition

Average

stand age

(years) at

transition

Average no. of

years without

low-mortality

fire to

transition

MRIP ED MDO 20 20 20 n/a

MDO LDO 40 40 40 n/a

a ED = Early - all structures; MDO = Mid-open; LDO = Late-open. 
USDA Forest Service RMRS-GTR-385. 2018 173

Table B5.5—Weibull function parameters associated with the susceptibility of a cell to wildfire based on fuels (i.e., 

vegetation cover type, seral stage, and number of years since the last fire) and the probability of a high-mortality 

wildfire by cover type and seral stage for the MRIP cover type.

Weibull parameters

Cover type

Seral

stagea

Target fire

rotation period

(years)

Mean return

interval (years) Shape

Probability of

high-mortality fire

MRIP n/a 53 n/a n/a n/a

ED n/a 50 3 1.0

MDO n/a 50 3 0.5

LDO n/a 50 3 0.5

a ED = Early - all structures; MDO = Mid-open; LDO = Late-open. 

6. Oak Woodland (OAK)

Reviewed by: (1) Hugh Safford, Regional Ecologist, USDA Forest Service; and (2) Becky 

Estes, Central Sierra Province Ecologist, USDA Forest Service.

Cover Type Classification and Crosswalks

▪ EVeg: Regional Dominance Type 1:

◦ Gray Pine

◦ Blue Oak

◦ Valley Oak

▪ Presettlement Fire Regime Type: 

◦ Oak Woodland

▪ LandFire BpS model: 

◦ 0611140: California Lower Montane Blue Oak-Foothill Pine Woodland and Savanna

Vegetation Description

The Oak Woodland cover type is characterized by savannas, woodlands, or forests of 

either monospecific or mixed stands of various oak species. Quercus douglasii, Q. lobata, 

Q. wislizenii, and Q. garryana var. breweri are the major dominants. In oak forests where 

mixtures of tree oak and conifer species exist, Q. kelloggii and Q. chrysolepis occur along 

with Pinus sabiniana (Allen-Diaz et al. 2007). 

Both Q. douglasii and Q. lobata are endemic to California. Quercus lobata are among the 

oldest and largest oaks in North America. Tree age can exceed 500 years. Quercus douglasii 

are relatively slow-growing, long-lived trees. On Q. douglasii-P. sabiniana woodlands, 

P. sabiniana is taller and dominates the overstory, but is shorter-lived (at approximately 80 

years) than Q. douglasii (150–250 years). Quercus douglasii is usually the more abundant 

of the two trees, but P. sabiniana contributes as much basal area as Q. douglasii (Allen-Diaz 

et al. 2007).
174 USDA Forest Service RMRS-GTR-385. 2018

Typical vegetation is dominated by open oak savanna with relatively uniform mature 

trees at low densities (<40 percent cover), where understory vegetation structure is a function 

of frequent surface fire that mediates woody plant development. In some instances and 

in some sites, tree density will increase to 70 percent or more, forming a relatively stable 

hardwood forest type subject to surface fires in the hardwood litter and occasional standreplacement fire (LandFire 2007p).

In riparian forests, associates include Platanus racemosa, Juglans hindsii, Acer negundo, 

Populus fremontii, Salix, and Fraxinus latifolia. In drier areas and open woodlands, shrubs 

usually clump together in open areas with full sun. Species may include Aesculus californica, 

Ceanothus, Arctostaphylos, Rhamnus, Toxicodendron diversilobum, and Cercis occidentalis 

(Allen-Diaz et al. 2007). The shrub layer is best developed along natural drainages, becoming 

insignificant in the uplands. Ground cover consists of a well-developed carpet of grasses 

and forbs (Ritter 1988b). Common forbs include Daucus, Geranium, Madia, and Trifolium. 

Most understory cover is created by annual grasses, including Bromus, Lolium, and Hordeum

(Allen-Diaz et al. 2007).

Oak recruitment is poor in many areas today, due to both natural and human causes. 

Many stands exist as groups of medium to large trees with few or no young oaks. There is 

concern that these woodlands may be slowly changing into savannas and grasslands as trees 

die and are not replaced. Mortality of oak saplings seems to be related to competition for 

moisture with grasses and forbs, wild and domestic animals feeding on acorns and seedlings, 

fire suppression, and flood control. Most recent work suggests that recruitment is limited not 

by reproduction, but by the establishment and survival of saplings (Allen-Diaz et al. 2007).

Distribution

Oak Woodland has a patchy distribution embedded in a matrix of agriculture, urban 

development, grasslands, riparian forests, and other conifer and oak woodland types. It occurs 

in a band along the western Sierra Nevada foothills, generally below 800 meters (2,600 feet) 

in elevation, although individual species described here are capable of surviving at higher 

elevations. In general, tree density is highest along natural drainages with deeper soils, and 

lower in uplands and on steeper slopes. The transition from savanna to woodland to forest is 

largely driven by soil, precipitation, and elevation (Allen-Diaz et al. 2007).

Soils in this type vary significantly, with different types conducive to the establishment 

of differing dominant tree species. Quercus lobata is best developed on deep, well-drained 

alluvial soils, usually in valley bottoms (Ritter 1988b). Quercus wislizeni becomes more 

abundant on steeper slopes, on shallower soils, and at higher elevations. Quercus douglasii 

woodlands occur on a wide range of soils; however, they are often shallow, rocky, infertile, 

and well drained. The overstory ranges from sparsely scattered trees on poor sites to nearly 

closed canopies on good quality sites (Allen-Diaz et al. 2007; Ritter 1988a). Quercus 

douglasii-P. sabiniana woodlands are found on a variety of generally well-drained parent 

materials, ranging from gravelly loam through stony clay loam. They occupy steeper, drier 

slopes with shallower and rockier soils than pure oak woodlands (Verner 1988b). 

Disturbances

Wildfire

An overstory dominated by deciduous hardwood species results in an herbaceous surface 

fuel complex that is the primary influence on fuels and wildfire (LandFire 2007p). Because 

of the long period of human habitation of oak woodlands, it is extremely difficult to define 
USDA Forest Service RMRS-GTR-385. 2018 175

the “natural” fire regime. Lightning-caused fires certainly occurred in the past, but decades 

may pass between these events. Native Americans used fire in their stewardship of oak 

woodlands, but it is difficult to document the frequency, intensity, and extent of burning by 

Native Americans. Some estimate the fire return interval (FRI) of the pre-Euro-American 

settlement period to be around 25 years. The first European settlers continued to use fire as 

a management practice; burning intervals ranged from 8 to 15 years. Ranchers continued 

the practice through the 1950s, but since then fire suppression has emerged as the standard 

management policy (Allen-Diaz et al. 2007). 

The fire regime that produced this cover type is thought to be frequent; mortality 

depends on vegetation vulnerability and wildfire intensity. Younger oaks are fire-sensitive 

and frequently killed by even low-severity fires. However, they typically sprout following 

disturbance. Older, decadent oaks are not likely to sprout after being damaged or killed by fire. 

Therefore, younger stands are more likely to regrow after fires, and fire exclusion can have a 

significant effect on stand structure. Regeneration of P. sabiniana is dependent on regeneration 

from seed, although it, too, is fire adapted. It also grows faster than Q. douglasii and is an 

important colonizer (Allen-Diaz et al. 2007). 

Other Disturbances

Other disturbances are not currently being modeled, but may, depending on the seral stage 

and mortality levels, reset patches to early development, maintain existing seral stages, or shift 

or accelerate succession to a more open condition. All of the tree species associated with this 

vegetation type are susceptible to a wide variety of pathogens and insects.

Seral Stages

The classification of seral stages originated from the corresponding LandFire biophysical 

setting model, but with some modifications (e.g., the addition of a moderate canopy cover 

stage in the mid- and late-seral stages) based on expert input, as follows and as summarized in 

table B6.1. The seral stage map corresponding to this classification for the current landscape 

was derived from the EVeg dataset and the rules in table B6.2 for the OAK cover type.

Early Development (ED)

This seral stage is characterized by a heterogeneous ground cover of grasses, forbs, and 

shrubs and the establishment of trees, including Q. douglasii, Q. chrysolepis, Q. garryana var. 

breweri, and P. sabiniana, following a stand-replacing disturbance. Reestablishment can occur 

from basal resprouting or sexual reproduction, depending on composition, growth form, and 

seed dynamics. The density of seedlings and saplings and understory shrubs can vary widely 

depending on site conditions. Patch sizes are likely to range from very small gap recruitment 

to areas of about 40 hectares (100 acres) (LandFire 2007p). Note that there can be a residual 

or legacy of overstory trees from the predisturbance stand making up less than 25 percent 

canopy cover.

Mid-Development – Open Canopy Cover (MDO) 

This seral stage is characterized by a heterogeneous ground cover of grasses, forbs, and 

shrubs, with a low canopy cover (<40 percent) of small (5–10 inches d.b.h.) trees. The open 

stand structure is maintained by low-severity surface fire (LandFire 2007p).

Mid-Development – Moderate Canopy Cover (MDM) 

This seral stage is characterized by a moderate canopy cover (40–70 percent) of small 

trees, and is otherwise similar to MDO.
176 USDA Forest Service RMRS-GTR-385. 2018

Mid-Development – Closed Canopy Cover (MDC) 

This seral stage is characterized by a dense canopy cover (>70 percent) of small trees, and 

is otherwise similar to MDO. 

Late Development – Open Canopy Cover (LDO)

This seral stage is characterized by a heterogeneous ground cover of grasses, forbs, and 

low shrubs, with a low canopy cover (<40 percent) of medium-sized oak and conifer trees 

(>10 inches d.b.h.). If P. sabiniana occurs, it can be very large (>20 inches d.b.h.). The open 

stand structure is maintained by frequent low-severity fire (LandFire 2007p).

Late Development – Moderate Canopy Cover (LDM)

This seral stage is characterized by an overstory of medium-sized oak and conifer trees 

with canopy cover 40–70 percent, and is otherwise similar to LDO. The moderately open 

stand structure is maintained by frequent low-severity fire.

Late Development – Closed Canopy Cover (LDC)

This seral stage is characterized by an overstory of medium-sized oak and conifer trees 

with canopy cover greater than 70 percent, and is otherwise similar to LDO. If P. sabiniana 

occurs, it can be very large (>20 inches d.b.h.) and begin to shade out the oak trees (LandFire 

2007p).

Model Parameterization

This section includes a listing of the model parameters that are cover type-specific. Note 

that there are additional model parameters not specific to a cover type (e.g., climate modifier) 

that ultimately affect the model processes and outcomes, and these are discussed under the 

Methods section in McGarigal et al. 2018.

Succession

The rules (i.e., parameters) governing succession for the OAK cover type are listed in 

table B6.3. These rules were initially based on the corresponding LandFire BpS description 

(LandFire 2007p) and associated model created by using the Vegetation Dynamics 

Development Tool (VDDT). They were subsequently modified based on expert input to 

include probabilistic rather than deterministic seral stage transitions. Specifically, we modified 

the rules so that stands would gradually, instead of abruptly, transition from one seral stage to 

the next to reflect stochasticity in the real-world processes governing succession. For example, 

the first three rules dictate that a cell in the OAK cover type which has been in the ED seral 

stage for 20–55 years, will have a 20-percent chance of transitioning to MDO, a 20-percent 

chance of transitioning to MDM, and a 20-percent chance of transitioning to MDC at the 

beginning of each timestep. Thus, stands will randomly begin transitioning to one of the MD 

stages after 20 years in the ED stage, but some stands could remain in the ED stage for as 

long as 60 years to reflect delayed tree establishment. Note that for stands currently in the ED 

stage and between 20 and 55 years in this stage, the combined chance of transitioning to MD 

at each timestep is 60 percent; therefore, there is a 40-percent chance of remaining in the ED 

seral stage at each timestep. The next three rules together dictate that a cell that has been in 

the ED seral stage for 60 years will have a 100-percent chance of transitioning to one of the 

MD seral stages; thus, all stands will have transitioned to the MD stage after 60 years since 

establishment.

Applying the succession rules listed in table B6.3 results in stands transitioning between 

seral stages in a probabilistic rather than deterministic manner, such that we can compute the 
USDA Forest Service RMRS-GTR-385. 2018 177

average stand age (years) for the transition to the next seral stage, as shown in table B6.4. 

For example, the first row in table B6.4 indicates that for a cell in the OAK cover type in 

the ED seral stage, the earliest stand age (i.e., number of years since the last stand-replacing 

disturbance) for transitioning to one of the MD seral stages is 20 years, the latest stand age 

is 60 years, and the average stand age at the time of the transition is 23 years. Also, the third 

row in table B6.4 indicates that a cell in the OAK cover type in the MDO seral stage will, on 

average, take 17 years without a low-mortality fire disturbance to transition to the MDM seral 

stage (i.e., transition from an open-canopy cover, <40 percent, to a moderate-canopy cover, 

40–70 percent, condition). Note that a low-mortality fire every 40 years will maintain the 

stand in the open-canopy condition.

Wildfire Disturbance

Rotation Period

Wildfire rotation period (equivalent to the point-specific mean return interval) is not 

formally a model parameter, but rather is specified as a target value to be achieved through 

model calibration. Target fire rotation periods (FRPs) were specified by cover type (table 

B6.5). FRP for the OAK cover type was based on Mallek et al. (2013) and expert input from 

Safford and Estes.

Susceptibility

The cover type-specific factors affecting susceptibility of a cell to wildfire were: (1) 

topographic position, and (2) fuel characteristics, as represented by vegetation cover type, 

seral stage, and time since the last wildfire. Each of these two factors is represented as a 

probability.

Topographic Position—Topographic position, as represented by the topographic position 

index (TPI) described under the Methods section in McGarigal et al. 2018, was treated as 

having a static (i.e., constant over time) and universal effect on the relative susceptibility 

of a cell to wildfire regardless of seral stage or disturbance history. Specifically, all other 

things being equal, for the OAK cover type, susceptibility decreased by 30 percent as the TPI 

decreased over its full range according to the four-parameter logistic function depicted in 

figure B.2. However, because the bulk of the landscape varies over a much smaller range of 

TPI values, the effect on susceptibility is typically much less than 30 percent.

The specified logistic parameters were based on consensus expert opinion about the 

strength and nature of the topographic influence on wildfire susceptibility, and reflect general 

support for such an effect in the scientific literature (North 2012; Taylor and Skinner 2003).

Fuels (vegetation and disturbance history)—Fuels, as represented by vegetation cover 

type, seral stage, and recent disturbance history, were treated as having a dynamic (i.e., 

changing over time) effect on the relative susceptibility of a cell to wildfire. Specifically, 

susceptibility varied among cover types and seral stages in relation to the time (number of 

years) since the last fire according to the cumulative Weibull function and the parameters 

listed in table B6.5 (e.g., as illustrated in figure B.1). Note that here we use the cumulative 

form of the Weibull distribution, which gives the cumulative probability of a disturbance 

for any number of years since the last disturbance. Thus, the probability increases from 0 

immediately following a fire to approaching 1 after a certain number of years since the last 

fire, depending on the specified mean return interval (MRI) and shape parameters of the 

Weibull function. Holding Shape constant, and all other things being equal, as MRI increases 

the curve shifts to the right, resulting in a lower probability for any given number of years 

since the last disturbance. In this manner, varying the MRI among cover types and seral stages 

affects the relative susceptibility to wildfire.
178 USDA Forest Service RMRS-GTR-385. 2018

The specified Weibull MRI parameters were based on the corresponding LandFire BpS 

description (LandFire 2007p) and associated VDDT model. 

Importantly, although susceptibility of the various seral stages is determined by MRI 

(holding Shape constant), these return intervals should not be interpreted literally, as the 

concept of a return interval does not meaningfully apply to a dynamic seral stage. Moreover, 

these MRIs were derived from the LandFire BpS description and associated VDDT model. 

Thus, the MRIs assigned to each cover type and seral stage should be interpreted as relative 

values that affect the relative susceptibility of the various vegetation states. 

Mortality

The cover type-specific factors affecting overstory mortality following wildfire (i.e., 

fire severity) were: (1) topographic position, and (2) fuel characteristics, as represented by 

vegetation cover type and seral stage. Each of these two factors is represented as a probability.

Topographic position—The effect of topographic position on mortality was treated 

identically to its effect on susceptibility (see previous description). Again, the specified 

logistic parameters were based on consensus expert opinion about the strength and nature of 

the topographic influence on wildfire severity, and reflect general support for such an effect in 

the scientific literature (North 2012; Taylor and Skinner 2003).

Fuels (vegetation)—Fuels, as represented by vegetation cover type and seral stage, were 

treated as having a dynamic (i.e., changing over time) effect on the relative probability of a 

high-mortality response to wildfire. Specifically, we assigned a probability of high-mortality 

response to wildfire to each seral stage (table B6.5); values were based on the corresponding 

LandFire BpS description (LandFire 2007p) and associated VDDT model.

Disturbance Transitions

The rules (i.e., parameters) governing seral stage transitions following low-mortality 

wildfire disturbance for the OAK cover type are listed in table B6.6. These rules were initially 

based on the corresponding LandFire BpS description (LandFire 2007p) and associated model 

created by using the VDDT, but were subsequently modified to include the moderate canopy 

cover seral stages not present in the VDDT model. Note that rules governing transitions 

following high-mortality wildfire are not listed in table B6.6 because high-mortality wildfires 

always result in transition to the ED seral stage. In addition, conditions in which low-mortality 

wildfire has no effect on the seral stage (i.e., does not cause a transition) are not listed. For 

example, the third rule dictates that a low-mortality wildfire in a cell of OAK in the MDM 

seral stage has a 25-percent chance of transitioning to the MDO stage and (by implication) 

a 75-percent chance of remaining in the MDM stage. In addition, by implication (given the 

absence of a rule), a low-mortality wildfire in the ED, MDO, or LDO seral stage has no effect 

other than to maintain the cell in that seral stage.

Vegetation Treatments

Dynamic spatial constraints and priorities affecting individual cover types were described 

under the Methods section in McGarigal et al. 2018; here we describe the rules governing 

seral stage transitions following each unique vegetation treatment (table B6.7). Note that these 

rules were created by the principals involved in this project and reflect expectations based on 

the common prescriptions applied today.
USDA Forest Service RMRS-GTR-385. 2018 179

Table B6.1—Summary of OAK seral stage characteristics: average overstory tree diameter at breast height (d.b.h.) 

of the dominant and codominant trees, overstory tree percent cover from above (CFA), assigned average CFA value 

for classifying the landscape by percent canopy cover, and range of stand ages (number of years since the last 

stand-replacing disturbance) possible for the corresponding seral stage. Note that overstory tree d.b.h. and CFA for 

the ED seral stage refer to the residual or legacy overstory from the predisturbance stand.

Cover type

Seral

stagea Overstory treed.b.h. (inches)

Overstory tree

CFA (%)

Assigned

average CFA (%)

Stand age

range (years)

OAK ED <5 (13 cm) <25 10 0–55

MDO 5–9.9 (13–25.1 cm) <40 30 20–125

MDM 5–9.9 40–70 55 20–125

MDC 5–9.9 >70 75 20–125

LDO ≥10 (25.4 cm) <40 30 ≥60

LDM ≥10 40–70 55 ≥60

LDC ≥10 >70 75 ≥60

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; 

Table B6.2—Mapping rules for OAK seral stages. Diameter at breast height (d.b.h.) and cover from above (CFA) 

values were taken from EVeg polygons. Categories of d.b.h. (inches) are: null, 0–0.9, 1–4.9, 5–9.9, 10–19.9, 20–

29.9, ≥30. CFA categories (%) are: null, 0–10, 10–20, ... , 90–100. Each row should be read with a Boolean AND 

across each column. Within each seral stage the rows should be read with a Boolean OR across rows.

Seral stagea Overstory tree

d.b.h. 1 (inches)

Overstory tree

d.b.h. 2 (inches)

Total tree

CFA (%)

Conifer

CFA (%)

Hardwood

CFA (%)

ED 0–4.9 (0–12.4 cm) any any any any

MDO 5–9.9 (13–25.1 cm) any 0–40 any any

MDM 5–9.9 any 40–70 any any

MDC 5–9.9 any 70–100 any any

LDO ≥10 (25.4 cm) any 0–40 any any

LDO ≥10 any null 0–40 0–40

LDM ≥10 any 40–70 any any

LDM ≥10 any null 40–70 40–70

LDM ≥10 any null 0–70 40–70

LDC ≥10 any 70–100 any any

LDC ≥10 any null 70–100 any

LDC ≥10 any null any 70–100

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
180 USDA Forest Service RMRS-GTR-385. 2018

Table B6.3—Succession rules for OAK seral stages. Note that number of years in current successional stage refers 

to the number of years in either early-development (ED), mid-development (MD), or late-development (LD) stage 

independent of canopy cover class. 

Cover type

From seral

stagea To seralstagea

Number of years 

in current

successional stage

Number of 

years since lowmortality fire

Probability of

transition

OAK ED MDO 20–55 any 0.2

ED MDM 20–55 any 0.2

ED MDC 20–55 any 0.2

ED MDO 60 any 0.3

ED MDM 60 any 0.4

ED MDC 60 any 0.3

MDC LDC 40–65 any 0.7

MDC LDC 70 any 1.0

MDM LDM 40–65 any 0.7

MDM LDM 70 any 1.0

MDM MDC ≥15 ≥15 0.7

MDO LDO 40–65 any 0.7

MDO LDO 70 any 1.0

MDO MDM ≥15 ≥15 0.7

LDM LDC ≥15 ≥15 0.7

LDO LDM ≥15 ≥15 0.7

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 

Table B6.4—Summary of OAK seral stage transitions: earliest, latest, and average stand age (number of years 

since the last stand-replacing disturbance) for the transition to the next seral stage; and average number of years 

without low-mortality fire to transition to the next canopy cover class.

Cover

type

From seral

stagea To seralstagea

Earliest

stand age

(years) at

transition

Latest

stand age

(years) at

transition

Average

stand age

(years) at

transition

Average no. of

years without

low-mortality

fire to transition

OAK ED MD 20 60 23 n/a

MD LD 60 130 65 n/a

MDO MDM n/a n/a n/a 17

MDM MDC n/a n/a n/a 17

LDO LDM n/a n/a n/a 17

LDM LDC n/a n/a n/a 17

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
USDA Forest Service RMRS-GTR-385. 2018 181

Table B6.5—Weibull function parameters associated with the susceptibility of a cell to wildfire based on fuels (i.e., 

vegetation cover type, seral stage, and number of years since the last fire) and the probability of a high-mortality 

wildfire by cover type and seral stage for the OAK cover type.

Weibull parameters

Cover type

Seral

stagea

Target fire

rotation period

(years)

Mean return

interval (years) Shape

Probability of

high-mortality fire

OAK n/a 26 n/a n/a n/a

ED n/a 10 3 0.01

MDO n/a 10 3 0.05

MDM n/a 10 3 0.06

MDC n/a 12 3 0.07

LDO n/a 8 3 0.08

LDM n/a 12 3 0.18

LDC n/a 25 3 0.50

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 

Table B6.6—Disturbance rules for OAK cover type governing seral stage transitions following a low-mortality 

wildfire. Note that conditions in which low-mortality wildfire has no effect are not listed.

Cover type From seral stagea

 To seral stage Probability of transition

OAK MDC MDM 0.5

MDC MDO 0.5

MDM MDO 0.25

LDC LDM 0.5

LDC LDO 0.5

LDM LDO 0.14

a MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = Late-moderate; LDC = Lateclosed; 
182 USDA Forest Service RMRS-GTR-385. 2018

Table B6.7—Disturbance rules for OAK cover type governing seral stage transitions following vegetation 

treatments. Note that treatments not affecting seral stage transitions (e.g., mastication) are not included here.

Cover type Treatment type

From seral

stagea To seralstagea Probability oftransition

OAK Clearcut and Group cuts Any ED 1 

Thinning, including cells thinned in: 

(1) matrix thin and group cut; (2) 

thin and burn; (3) thin, hand cut, 

pile, and burn; (4) thin, masticate, 

and burn; and (5) matrix thin, group 

cut, and burn treatments

MDC MDM 1 

MDM MDO 1 

LDC LDM 1 

LDM LDO 1 

Prescribed fire, including cells 

burned as part of a prescribed fireonly treatment); "cool" burn/"hot" 

burn transition probabilities

MDC MDM 0.09/0.15

MDC MDO 0/0.09

MDC ED 0/0.03

MDM MDO 0.06/0.15

MDM ED 0/0.03

LDC LDM 0.02/0.03

LDC LDO 0/0.02

LDC ED 0/0.01

LDM LDO 0.04/0.11

LDM ED 0/0.01

Thin and burn, including cells 

burned only as part of: (1) thin and

burn; and (2) hand cut, pile, and

burn treatments

LDC LDM 0.02

LDM LDO 0.04

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
USDA Forest Service RMRS-GTR-385. 2018 183

7. Oak-Conifer Forest and Woodland (OCFW) 

Reviewed by: (1) Hugh Safford, Regional Ecologist, USDA Forest Service; (2) Becky Estes, 

Central Sierra Province Ecologist, USDA Forest Service; and (3) Kyle Merriam, SierraCascade Province Ecologist, USDA Forest Service.

Cover Type Classification and Crosswalks

▪ EVeg: East of the Sierra Crest: 

Regional Dominance Type 1:

◦ Black Oak

◦ Eastside Pine

◦ Jeffrey Pine

◦ Ponderosa Pine

AND

Regional Dominance Type 2:

◦ Black Oak

◦ Canyon Live Oak

◦ Madrone

◦ Montane Mixed Hardwood

◦ Scrub Oak

◦ EVeg: West of the Sierra Crest: Regional Dominance Type 1:

◦ Black Oak

◦ Eastside Pine

◦ Jeffrey Pine

◦ Ponderosa Pine

▪ Presettlement Fire Regime Type: 

◦ Yellow Pine

▪ LandFire BpS model: 

◦ 0610300: Mediterranean California Lower Montane Black Oak-Conifer Forest and 

Woodland

• Ultramafic Modifier (OCFW_U)

This type was created by intersecting an ultramafic soils/geology layer with the EVeg 

layer. Where ultramafic cells intersected with OCFW, they were assigned to the ultramafic 

modifier.

Vegetation Description

The Oak-Conifer Forest and Woodland cover type is characterized by woodlands or 

forests of Pinus ponderosa or P. jeffreyi with one or more oaks, such as Quercus kelloggii, 

Q. garryana var. breweri, Q. wislizeni, or Q. chrysolepis. Pseudotsuga menziesii and other 

conifer species are uncommon but may co-occur, especially after long-term fire suppression 

(LandFire 2007e). Pinus jeffreyi tends to dominate on ultramafic sites (Fitzhugh 1988b). In 

some areas, sites are dominated initially by oaks, which form a dense subcanopy. Eventually, 

and especially on locally mesic sites, conifers will form a persistent emergent canopy over 

the oak as a bilayered canopy (LandFire 2007e). In other cases, characteristic species occur 

in a mosaic-like pattern with small pure stands of conifers interspersed with small stands of 

broad-leaved trees. Most of the broad-leaved trees are schlerophyllous evergreen species, but 

winter-deciduous species also occur (Anderson 1988). The understory is composed of shrubs 

such as Arctostaphylos, Ceanothus, Chamaebatia, Cornus, Eriodictyon, Garrya, Prunus, 
184 USDA Forest Service RMRS-GTR-385. 2018

Rhamnus, Ribes, and Toxicodendron diversilobum. Grasses and forbs are diverse and include 

Bromus, Melica, Poa, Elymus, Carex, Collinsia, Saltugilia, Iris, Lupinus, Streptanthus, Viola,

and Pteridium aquilnum (Fitzhugh 1988b; LandFire 2007e).

• Ultramafic Modifier

Woodlands of P. ponderosa or P. jeffreyi occur mainly on low-elevation ultramafics. They 

grow on strongly serpentinized soil, and are typically adjacent to the non-ultramafic form of the 

cover type. Although P. ponderosa or P. jeffreyi dominates, it may be associated with Calocedrus 

decurrens, P. attentuata, P. lambertiana, P. sabiniana, and Q. chysolepis (O’Geen et al. 2007).

Quercus kelloggii occurs occasionally on ultramafic soils (Fryer 2007). The shrub layer is 

dominated by Arctostaphylos, Ceanothus, Eriodictyon, Heteromeles, and Pickeringia. The herb 

layer is a mix of sparse perennials and many annual grasses and forbs (O’Geen et al. 2007).

Distribution

OCFW occurs in the valleys and lower slopes of mountainous terrain, on deep, 

well-developed soils derived from a variety of parent materials including granitics and 

metamorphic and Franciscan metasedimentary parent material, although rocky soils are 

also possible. Slopes are generally steep and all aspects are included. In the northern Sierra 

Nevada, the elevational range is 240 to 1,800 meters (780 to 5,900 feet) (Anderson 1988; 

LandFire 2007e).

• Ultramafic Modifier

Ultramafics have been mapped at various spatial densities throughout the elevational 

range of the OCFW cover type. Low to moderate elevations in ultramafic and serpentinized 

areas often produce soils low in essential minerals such as calcium, potassium, and nitrogen, 

and have excessive accumulations of heavy metals such as nickel and chromium. These sites 

vary widely in the degree of serpentinization and effects on their overlying plant communities 

(USDA Forest Service 2008). Note that the terms “ultramafic rock” and “serpentine” are broad 

terms used to describe many different but related rock types: serpentinite, peridotite, dunite, 

pyroxenite, talc and soapstone, among others (O’Geen et al. 2007). 

Disturbances

Wildfire

Wildfires are common and frequent in OCFW; vegetation mortality caused by wildfire 

depends on vegetation (i.e., fuel) characteristics and wildfire intensity. Low-mortality fires 

(≤75 percent overstory mortality) kill small trees and may consume aboveground portions 

of small oaks, shrubs, and herbs, but do not often kill large trees or belowground organs of 

most oaks, shrubs, and herbs, which promptly resprout. High-mortality fires (>75 percent 

overstory mortality) kill trees of all sizes and may kill many of the shrubs and herbs as well. 

However, high-mortality fires typically kill only the aboveground portions of the oaks, shrubs, 

and herbs; consequently, most oaks, shrubs, and herbs promptly resprout from surviving 

belowground organs.

OCFW sites are fire adapted and had frequent, low-severity surface fires prior to fire 

exclusion in the late 19th century. Historically, fire return intervals (FRIs) in P. ponderosa-Q. 

kelloggii forests increased with increasing elevation in the Sierra Nevada, with a tendency 

toward shorter mean FRIs (5–15 years) on dry, west- and south-facing slopes and longer FRIs 

(15–25 years) on mesic, east- and north-facing slopes. Mid-elevation forests typically had 

mixed-severity fires that created patchy mosaics (Fryer 2007).

Fire severity in OCFW is typically positively correlated with slope position, with higher 

mortality occurring on upper slopes and ridgetops, especially on southwest-facing aspects. 
USDA Forest Service RMRS-GTR-385. 2018 185

Other Disturbances

Other disturbances are not currently being modeled, but may, depending on the seral stage 

and mortality levels, reset patches to early development, maintain existing seral stages, or shift 

or accelerate succession to a more open condition. All of the tree species associated with this 

vegetation type are susceptible to a wide variety of pathogens and insects.

Seral Stages

The classification of seral stages originated from the corresponding LandFire biophysical 

setting model, but with some modifications (e.g., the addition of a moderate canopy cover 

stage in the mid- and late-seral stages) based on expert input, as follows and as summarized in 

table B7.1. The seral stage map corresponding to this classification for the current landscape 

was derived from the EVeg dataset and the rules in table B7.2 for the OCFW cover type.

Early Development (ED)

This seral stage is characterized by coppicing oak sprouts (predominantly Q. kelloggii, 

but potentially also Q. chrysolepis). Toxicodendron diversilobum may be abundant as well. 

Bunchgrasses and associated forbs dominate the understory. Note that there can be a residual 

or legacy of overstory trees from the predisturbance stand making up less than 25 percent 

canopy cover. Localized native herbivory may maintain oak sprouts in “shrub” form for 

an extended period. Vegetation may also include conifer seedlings and saplings (LandFire 

2007e). On sites or areas that are dry or of low quality, significant pine regeneration may 

depend on concurrent disturbance of shrub species and a good pine seed crop with favorable 

weather. Thus, it may require 50–100 years for significant pine regeneration in the absence of 

intervention. Dense brush is typical in young stands, and an herbaceous layer may develop on 

some sites. On drier sites, there is less tendency for succession toward shade-adapted species. 

As young, dense stands age and attain a closed canopy, they exclude most undergrowth. When 

other adapted conifers occur in moist pine stands of medium to high site quality, they may 

form a significant understory in about 20 years in the absence of fire (Fitzhugh 1988b).

Mid-Development – Open Canopy Cover (MDO) 

This seral stage is characterized by a low canopy cover (<40 percent) of pole- to largesized (5–30 inches [13–76 centimeters] d.b.h.) trees, with hardwoods dominating the canopy 

and sporadic conifer presence at low coverage levels. Bunchgrasses and shade-intolerant 

shrubs, most notably, will be prominent on most sites (LandFire 2007e).

Mid-Development – Moderate Canopy Cover (MDM) 

This seral stage is characterized by a moderate canopy cover (40–70 percent) of pole- to 

large-sized trees, and may represent a drier, hardwood-dominated site that has gone without 

fire for an extended period, or a mesic site supporting both oak and yellow pine species that 

has been opened up by fire. Occasional P. menziesii may also occur. Sod-forming grasses and 

shade-tolerant shrubs will be prominent on most sites (LandFire 2007e).

Mid-Development – Closed Canopy Cover (MDC) 

This seral stage is characterized by a dense canopy cover (>70 percent) of pole- to largesized conifers, and is representative of the more mesic end of the environmental gradient. 

Stands support a dense canopy of oak and P. ponderosa or P. jeffreyi, or both pine species. 

Occasional P. menziesii may also occur. Sod-forming grasses and shade-tolerant shrubs will be 

prominent on most sites (LandFire 2007e). 

Late Development – Open Canopy Cover (LDO)

This seral stage is characterized by a low canopy cover (<40 percent) of very large trees 
186 USDA Forest Service RMRS-GTR-385. 2018

(>30 inches d.b.h.). Trees may be very large, but overall size classes vary with a patchy 

distribution and open canopy, and the oaks are overtopped by conifers. Thus, in this stage, 

oaks make up a smaller proportion of the stand. This seral stage develops when low-mortality 

disturbance is fairly frequent; it persists as long as low-mortality fires continue to occur 

periodically (LandFire 2007e).

Late Development – Moderate Canopy Cover (LDM)

This seral stage is characterized by an overstory of very large trees with canopy cover 

40–70 percent, and may represent a drier, hardwood-dominated site that has gone without 

fire for an extended period, or a mesic site supporting both oak and yellow pine species that 

has been opened up by fire. The oaks are overtopped by conifers and thus compose a smaller 

proportion of the stand. Shade-tolerant conifers such as P. menziesii may also occur and even 

dominate the oaks and pines (LandFire 2007e).

Late Development – Closed Canopy Cover (LDC)

This seral stage is characterized by an overstory of very large trees with canopy cover greater 

than 70 percent, and is representative of the more mesic end of the environmental gradient when 

fire has been excluded from the stand for an extended period. The oaks are overtopped by conifers 

and thus make up a smaller proportion of the stand. Shade-tolerant conifers such as P. menziesii 

may also occur and even dominate the oaks and pines (LandFire 2007e).

Model Parameterization

This section includes a listing of the model parameters that are cover type-specific. Note 

that there are additional model parameters not specific to a cover type (e.g., climate modifier) 

that ultimately affect the model processes and outcomes, and these are discussed under the 

Methods section in McGarigal et al. 2018.

Succession

The rules (i.e., parameters) governing succession for the OCFW and OCFW_ASP cover 

types are listed in table B7.3. These rules were initially based on the corresponding LandFire 

BpS description (LandFire 2007e) and associated model created by using the Vegetation 

Dynamics Development Tool (VDDT), as modified by Safford and Estes. They were 

subsequently modified based on expert input to include probabilistic rather than deterministic 

seral stage transitions. Specifically, we modified the rules so that stands would gradually, 

instead of abruptly, transition from one seral stage to the next to reflect stochasticity in the 

real-world processes governing succession. For example, the first three rules dictate that a cell 

in the OCFW cover type, which has been in the ED seral stage for 20–35 years, will have a 

40-percent chance of transitioning to MDC, a 20-percent chance of transitioning to MDM, and 

a 10-percent chance of transitioning to MDO at the beginning of each timestep. Thus, stands 

will randomly begin transitioning to one of the MD stages after 20 years in the ED stage, 

but some stands could remain in the ED stage for as much as 40 years to reflect delayed tree 

establishment. Note that for stands currently in the ED stage and between 20 and 35 years 

in this stage, the combined chance of transitioning to MD at each timestep is 70 percent; 

therefore, there is a 30-percent chance of remaining in the ED seral stage at each timestep. The 

next three rules together dictate that a cell that has been in the ED seral stage for 40 years will 

have a 100-percent chance of transitioning to one of the MD seral stages; thus, all stands will 

have transitioned to the MD stage after 40 years since establishment. Note that on ultramafic 

sites stands can be delayed in the ED stage for up to 100 years, and they always transition to 

MDO.
USDA Forest Service RMRS-GTR-385. 2018 187

Applying the succession rules listed in table B7.3 results in stands transitioning between 

seral stages in a probabilistic rather than deterministic manner, such that we can compute the 

average stand age (years) for the transition to the next seral stage, as shown in table B7.4. 

For example, the first row in table B7.4 indicates that for a cell in the OCFW cover type in 

the ED seral stage, the earliest stand age (i.e., number of years since the last stand-replacing 

disturbance) for transitioning to one of the MD seral stages is 20 years, the latest stand age 

is 40 years, and the average stand age at the time of the transition is 22 years. Note that the 

average stand age at transition (22 years) is close to the specified earliest stand age (20 years) 

because of the relatively high rate (0.7) of transitioning beginning at the specified earliest 

stand age. Also, the third row in table B7.4 indicates that a cell in the OCFW cover type in the 

MDO seral stage will, on average, take 17 years without a low-mortality fire disturbance to 

transition to the MDM seral stage (i.e., transition from an open-canopy cover, <40 percent, to 

a moderate-canopy cover, 40–70 percent, condition). Note that a low-mortality fire every 15 

years will maintain the stand in the open-canopy condition.

Wildfire Disturbance

Rotation Period

Wildfire rotation period (equivalent to the point-specific mean return interval) is not 

formally a model parameter, but rather is specified as a target value to be achieved through 

model calibration. Target fire rotation periods (FRPs) were specified by cover type (table 

B7.5). FRP for the OCFW cover type was based on values reported by Mallek et al. (2013), 

whereas the FRP for the OCFW_U cover type was set to twice that of OCFW based on the 

corresponding LandFire BpS model (LandFire 2007b) and expert input from Safford and 

Estes.

Susceptibility

The cover type-specific factors affecting susceptibility of a cell to wildfire were: (1) 

topographic position, and (2) fuel characteristics, as represented by vegetation cover type, 

seral stage, and time since the last wildfire. Each of these two factors is represented as a 

probability.

Topographic position—Topographic position, as represented by the topographic position 

index (TPI) described under the Methods section in McGarigal et al. 2018, was treated as 

having a static (i.e., constant over time) and universal effect on the relative susceptibility of 

a cell to wildfire regardless of seral stage or disturbance history. Specifically, all other things 

being equal, susceptibility decreased by 30 percent as the TPI decreased over its full range 

according to the four-parameter logistic function depicted in figure B.2. However, because 

the bulk of the landscape varies over a much smaller range of TPI values, the effect on 

susceptibility is typically much less than 30 percent.

The specified logistic parameters were based on consensus expert opinion about the 

strength and nature of the topographic influence on wildfire susceptibility, and reflect general 

support for such an effect in the scientific literature (North 2012; Taylor and Skinner 2003).

Fuels (vegetation and disturbance history)—Fuels, as represented by vegetation cover 

type, seral stage, and recent disturbance history, were treated as having a dynamic (i.e., 

changing over time) effect on the relative susceptibility of a cell to wildfire. Specifically, 

susceptibility varied among cover types and seral stages in relation to the time (number of 

years) since the last fire according to the cumulative Weibull function and the parameters 

listed in table B7.5 (e.g., as illustrated in figure B.1). Note that here we use the cumulative 

form of the Weibull distribution, which gives the cumulative probability of a disturbance 

for any number of years since the last disturbance. Thus, the probability increases from 0 
188 USDA Forest Service RMRS-GTR-385. 2018

immediately following a fire to approaching 1 after a certain number of years since the last 

fire, depending on the specified mean return interval (MRI) and shape parameters of the 

Weibull function. Holding Shape constant, and all other things being equal, as MRI increases 

the curve shifts to the right, resulting in a lower probability for any given number of years 

since the last disturbance. In this manner, varying the MRI among cover types and seral stages 

affects the relative susceptibility to wildfire.

The specified Weibull MRI parameters were based on the corresponding LandFire BpS 

description (LandFire 2007e) and associated VDDT model, as modified by Safford and Estes. 

Importantly, although susceptibility of the various seral stages is determined by MRI 

(holding Shape constant), these return intervals should not be interpreted literally, as the 

concept of a return interval does not meaningfully apply to a dynamic seral stage. Moreover, 

these MRIs were derived from the LandFire BpS description and associated VDDT model, 

as modified by Safford and Estes; taken collectively, these values do not necessarily agree 

with the target FRPs for the cover types. Thus, the MRIs assigned to each cover type and 

seral stage should be interpreted as relative values that affect the relative susceptibility of the 

various vegetation states. 

Mortality

The cover type-specific factors affecting overstory mortality following wildfire (i.e., 

fire severity) were: (1) topographic position, and (2) fuel characteristics, as represented by 

vegetation cover type and seral stage. Each of these two factors was represented as a probability.

Topographic position—The effect of topographic position on mortality was treated 

identically to its effect on susceptibility (see previous description). Again, the specified 

logistic parameters were based on consensus expert opinion about the strength and nature of 

the topographic influence on wildfire severity, and reflect general support for such an effect in 

the scientific literature (North 2012; Taylor and Skinner 2003).

Fuels (vegetation)—Fuels, as represented by vegetation cover type and seral stage, were 

treated as having a dynamic (i.e., changing over time) effect on the relative probability of a 

high-mortality response to wildfire. Specifically, we assigned a probability of high-mortality 

response to wildfire to each cover type and seral stage (table B7.5); values were based on the 

corresponding LandFire BpS descriptions (LandFire 2007e) and associated VDDT model, 

although the values for the ED seral stage were modified from their original value of 1.0 to 0.8 

based on expert input from Safford and Estes.

Disturbance Transitions

The rules (i.e., parameters) governing seral stage transitions following low-mortality 

wildfire disturbance for the OCFW cover types are listed in table B7.6. These rules were 

initially based on the corresponding LandFire BpS description (LandFire 2007e) and 

associated model created by using the VDDT as modified by Safford and Estes, but were 

subsequently modified to include the moderate canopy cover seral stages not present in the 

VDDT models. Note that rules governing transitions following high-mortality wildfire are not 

listed in table B7.6 because high-mortality wildfires always result in transition to the ED seral 

stage. In addition, conditions in which low-mortality wildfire has no effect on the seral stage 

(i.e., does not cause a transition) are not listed. For example, the first two rules dictate that 

a low-mortality wildfire in a cell of OCFW in the MDC seral stage has a 30-percent chance 

of transitioning to the MDM stage, a 30-percent chance of transitioning to the MDO stage, 

and (by implication) a 40-percent chance of remaining in the MDC stage. In addition, by 

implication (given the absence of a rule), a low-mortality wildfire in the ED, MDO, or LDO 

seral stage has no effect other than to maintain the cell in that seral stage.
USDA Forest Service RMRS-GTR-385. 2018 189

Vegetation Treatments

Dynamic spatial constraints and priorities affecting individual cover types were described 

under the Methods section in McGarigal et al. 2018; here we describe the rules governing 

seral stage transitions following each unique vegetation treatment (table B7.7). Note that these 

rules were created by the principals involved in this project and reflect expectations based on 

the common prescriptions applied today.

Table B7.1—Summary of OCFW seral stage characteristics: average overstory tree diameter at breast height 

(d.b.h.) of the dominant and codominant trees, overstory tree percent cover from above (CFA), assigned average CFA 

value for classifying the landscape by percent canopy cover, and range of stand ages (number of years since the last 

stand-replacing disturbance) possible for the corresponding seral stage. Note that overstory tree d.b.h. and CFA for 

the ED seral stages refer to the residual or legacy overstory from the predisturbance stand.

Cover type

Seral

stagea Overstory treed.b.h. (inches)

Overstory

tree CFA (%)

Assigned average

CFA (%)

Stand age

range (years)

OCFW/ 

OCFW_U ED <5 (13 cm) <25 10 0–35

MDO 5–29.9 (13–75.9 cm) <40 30 20–195

MDM 5–29.9 40–70 55 20–195

MDC 5–29.9 >70 85 20–195

LDO ≥30 (76 cm) <40 30 ≥100

LDM ≥30 40–70 55 ≥100

LDC ≥30 >70 85 ≥100

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
190 USDA Forest Service RMRS-GTR-385. 2018

Table B7.2.—Mapping rules for OCFW seral stages. Diameter at breast height (d.b.h.) and cover from above (CFA) 

values were taken from EVeg polygons. Categories for d.b.h. (inches) are: null, 0–0.9, 1–4.9, 5–9.9, 10–19.9, 20–29.9, 

≥30. CFA categories (%) are: null, 0–10, 10–20, ... , 90–100. Each row in this table should be read with a Boolean 

AND across each column. Within each seral stage the rows should be read with a Boolean OR across rows.

Seral

stagea Overstory treed.b.h. 1 (inches)

Overstory tree

d.b.h. 2 (inches)

Total tree

CFA (%)

Conifer

CFA (%)

Hardwood

CFA (%)

ED null null any any any

ED 0–4.9 (0–12.4 cm) 0–4.9 any any any

ED 0–4.9 null any any any

MDO 0–4.9 5–29.9 0–40 any any

MDO 5–29.9 (12.7–75.9 cm) null 0–40 any any

MDO 5–29.9 null null 0–40 null

MDO 5–29.9 null null null 0–40

MDO 5–29.9 null null 0–40 0–40

MDO 5–29.9 0–29.9 0–40 any any

MDO 5–29.9 0–29.9 null 0–40 0–40

MDM 0–4.9 5–29.9 40–70 any any

MDM 5–29.9 null 40–70 any any

MDM 5–29.9 null null 40–70 null

MDM 5–29.9 null null null 40–70

MDM 5–29.9 null null 40–70 0–70

MDM 5–29.9 null null 0–70 40–70

MDM 5–29.9 0-29.9 40–70 any any

MDM 5–29.9 0–29.9 null 40–70 0–70

MDM 5–29.9 0–29.9 null 0–70 40–70

MDC 0–4.9 5–29.9 70–100 any any

MDC 5–29.9 null 70-100 any any

MDC 5–29.9 null null 70–100 any

MDC 5–29.9 null null any 70–100

MDC 5–29.9 0–29.9 70–100 any any

MDC 5–29.9 0–29.9 null 70-100 any

MDC 5–29.9 0–29.9 null any 70–100

LDO ≥30 (76.2 cm) any 0–40 any any

LDO ≥30 any null 0–40 null

LDO ≥30 any null null 0–40

(Table B7.2 continued on next page.)
USDA Forest Service RMRS-GTR-385. 2018 191

LDO ≥30 any null 0–40 0–40

LDO any ≥30 0–40 any any

LDO any ≥30 null 0–40 null

LDO any ≥30 null null 0–40

LDO any ≥30 null 0–40 0–40

LDM ≥30 any 40–70 any any

LDM ≥30 any null 40–70 null

LDM ≥30 any null null 40–70

LDM ≥30 any null 40–70 0–70

LDM ≥30 any null 0–70 40–70

LDM any ≥30 40–70 any any

LDM any ≥30 null 40–70 null

LDM any ≥30 null null 40–70

LDM any ≥30 null 40–70 0–70

LDM any ≥30 null 0–70 40–70

LDC ≥30 any 70–100 any any

LDC ≥30 any null 70–100 any

LDC ≥30 any null any 70–100

LDC any ≥30 70–100 any any

LDC any ≥30 null 70–100 any

LDC any ≥30 null any 70–100

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 

Table B7.2.—Mapping rules for OCFW seral stages. Diameter at breast height (d.b.h.) and cover from above (CFA) 

values were taken from EVeg polygons. Categories for d.b.h. (inches) are: null, 0–0.9, 1–4.9, 5–9.9, 10–19.9, 20–29.9, 

≥30. CFA categories (%) are: null, 0–10, 10–20, ... , 90–100. Each row in this table should be read with a Boolean 

AND across each column. Within each seral stage the rows should be read with a Boolean OR across rows.

Seral

stagea Overstory treed.b.h. 1 (inches)

Overstory tree

d.b.h. 2 (inches)

Total tree

CFA (%)

Conifer

CFA (%)

Hardwood

CFA (%)

ED null null any any any

ED 0–4.9 (0–12.4 cm) 0–4.9 any any any

ED 0–4.9 null any any any

MDO 0–4.9 5–29.9 0–40 any any

MDO 5–29.9 (12.7–75.9 cm) null 0–40 any any

MDO 5–29.9 null null 0–40 null

MDO 5–29.9 null null null 0–40

MDO 5–29.9 null null 0–40 0–40

MDO 5–29.9 0–29.9 0–40 any any

MDO 5–29.9 0–29.9 null 0–40 0–40

MDM 0–4.9 5–29.9 40–70 any any

MDM 5–29.9 null 40–70 any any

MDM 5–29.9 null null 40–70 null

MDM 5–29.9 null null null 40–70

MDM 5–29.9 null null 40–70 0–70

MDM 5–29.9 null null 0–70 40–70

MDM 5–29.9 0-29.9 40–70 any any

MDM 5–29.9 0–29.9 null 40–70 0–70

MDM 5–29.9 0–29.9 null 0–70 40–70

MDC 0–4.9 5–29.9 70–100 any any

MDC 5–29.9 null 70-100 any any

MDC 5–29.9 null null 70–100 any

MDC 5–29.9 null null any 70–100

MDC 5–29.9 0–29.9 70–100 any any

MDC 5–29.9 0–29.9 null 70-100 any

MDC 5–29.9 0–29.9 null any 70–100

LDO ≥30 (76.2 cm) any 0–40 any any

LDO ≥30 any null 0–40 null

LDO ≥30 any null null 0–40

(Table B7.2 continued) 
192 USDA Forest Service RMRS-GTR-385. 2018

Table B7.3—Succession rules for OCFW seral stages. Note that number of years in current successional stage 

refers to the number of years in either early-development (ED), mid-development (MD), or late-development (LD) 

stage independent of canopy cover class. 

Cover type

From seral

stagea To seralstagea

Number of years in

current

successional stage

Number of years

since lowmortality fire

Probability

of transition

OCFW ED MDC 20–35 any 0.4

ED MDM 20–35 any 0.2

ED MDO 20–35 any 0.1

ED MDC 40 any 0.5

ED MDM 40 any 0.3

ED MDO 40 any 0.2

MDC LDC 80–155 any 0.3

MDC LDC 160 any 1.0

MDM LDM 80–155 any 0.3

MDM LDM 160 any 1.0

MDM MDC ≥15 ≥15 0.7

MDO LDO 80–155 any 0.3

MDO LDO 160 any 1.0

MDO MDM ≥15 ≥15 0.7

LDM LDC ≥15 ≥15 0.7

LDO LDM ≥15 ≥15 0.7

OCFW_U ED MDO 50–95 any 0.2

ED MDO 100 any 1.0

MDC LDC 120–245 any 0.2

MDC LDC 250 any 1.0

MDM LDM 120–245 any 0.2

MDM LDM 250 any 1.0

MDM MDC ≥30 ≥30 0.1

MDO LDO 120–245 any 0.2

MDO LDO 250 any 1.0

MDO MDM ≥30 ≥30 0.1

LDM LDC ≥30 ≥30 0.1

LDO LDM ≥30 ≥30 0.1

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
USDA Forest Service RMRS-GTR-385. 2018 193

Table B7.4—Summary of OCFW seral stage transitions: earliest, latest, and average stand age (number of years 

since the last stand-replacing disturbance) for the transition to the next seral stage; and average number of years 

without low-mortality fire to transition to the next canopy cover class.

Cover type

From seral

stagea

To

seral

stagea

Earliest

stand age

(years) at

transition

Latest

stand age

(years) at

transition

Average

stand age

(years) at

transition

Average no. of

years without

low-mortality

fire to

transition

OCFW ED MD 20 40 22 n/a

MD LD 100 200 114 n/a

MDO MDM n/a n/a n/a 17

MDM MDC n/a n/a n/a 17

LDO LDM n/a n/a n/a 17

LDM LDC n/a n/a n/a 17

OCFW_U ED MD 60 120 68 n/a

MD LD 180 340 208 n/a

MDO MDM n/a n/a n/a 70

MDM MDC n/a n/a n/a 70

LDO LDM n/a n/a n/a 70

LDM LDC n/a n/a n/a 70

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed.
194 USDA Forest Service RMRS-GTR-385. 2018

Table B7.5—Weibull function parameters associated with the susceptibility of a cell to wildfire based on fuels (i.e., 

vegetation cover type, seral stage, and number of years since the last fire) and the probability of a high-mortality 

wildfire by cover type and seral stage for the OCFW cover types (original values in parentheses). 

Weibull parameters

Cover type

Seral

stagea

Target fire

rotation period

(years)

Mean return

interval (years) Shape

Probability of

high-mortality fire

OCFW n/a 21 n/a n/a n/a

ED n/a 30 3 0.80 (1.0)

MDO n/a 8 3 0.05

MDM n/a 9 3 0.14

MDC n/a 11 3 0.26

LDO n/a 8 3 0.01

LDM n/a 10 3 0.08

LDC n/a 15 3 0.20

OCFW_U n/a 42 n/a n/a n/a

ED n/a 61 3 0.80 (1.0)

MDO n/a 16 3 0.05

MDM n/a 18 3 0.14

MDC n/a 22 3 0.26

LDO n/a 16 3 0.01

LDM n/a 21 3 0.08

LDC n/a 31 3 0.20

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
USDA Forest Service RMRS-GTR-385. 2018 195

Table B7.6—Disturbance rules for OCFW cover types governing seral stage transitions following a low-mortality 

wildfire. Note that conditions in which low-mortality wildfire has no effect are not listed.

Cover type From seral

stagea To seral stagea Probability of transition

OCFW MDC MDM 0.30 

MDC MDO 0.30 

MDM MDO 0.32

LDC LDM 0.29

LDC LDO 0.29

LDM LDO 0.18

OCFW_U MDC MDM 0.30 

MDC MDO 0.30 

MDM MDO 0.32

LDC LDM 0.29

LDC LDO 0.29

LDM LDO 0.18

a MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = Late-moderate; LDC = Lateclosed. 
196 USDA Forest Service RMRS-GTR-385. 2018

Table B7.7—Disturbance rules for OCFW cover types governing seral stage transitions following vegetation 

treatments. Note that treatments not affecting seral stage transitions (e.g., mastication) are not included here.

Cover type Treatment type

From seral

stagea To seralstagea Probability oftransition

OCFW Clearcut and group cuts Any ED 1 

Thinning, including cells thinned in: 

(1) matrix thin and group cut; (2) 

thin and burn; (3) thin, hand cut, 

pile, and burn; (4) thin, masticate, 

and burn; and (5) matrix thin, group 

cut, and burn treatments

MDC MDM 1 

MDM MDO 1 

LDC LDM 1 

LDM LDO 1 

Prescribed fire, including cells 

burned as part of a prescribed fireonly treatment; "cool" burn/"hot" 

burn transition probabilities

MDC MDM 0.03/0.05

MDC MDO 0/0.03

MDC ED 0/0.01

MDM MDO 0.05/0.14

MDM ED 0/0.01

LDC LDM 0.02/0.03

LDC LDO 0/0.02

LDC ED 0/0.01

LDM LDO 0.04/0.11

LDM ED 0/0.01

Thin and burn, including cells 

burned only as part of: (1) thin and

burn; and (2) hand cut, pile, and

burn treatments

MDC MDM 0.03

MDM MDO 0.05

LDC LDM 0.02

LDM LDO 0.04

OCFW_U Clearcut and Group cuts Any ED 1 

Thinning, including cells thinned in: 

(1) matrix thin and group cut; (2) 

thin and burn; (3) thin, hand cut, 

pile, and burn; (4) thin, masticate, 

and burn; and (5) matrix thin, group 

cut, and burn treatments

MDC MDM 1 

MDM MDO 1 

LDC LDM 1 

LDM LDO 1 

(Table B7.7 continued on next page.)
USDA Forest Service RMRS-GTR-385. 2018 197

(Table B7.7 continued) 

Cover type Treatment type

From seral

stagea To seralstagea Probability oftransition

Prescribed fire, including cells 

burned as part of a prescribed fireonly treatment; "cool" burn/"hot" 

burn transition probabilities

MDC MDM 0.05/0.15

MDC MDO 0.03/0.09

MDC ED 0.01/0.03

MDM MDO 0.05/0.15

MDM ED 0.01/0.03

LDC LDM 0.04/0.12

LDC LDO 0.02/0.06

LDC ED 0.01/0.03

LDM LDO 0.04/0.12

LDM ED 0.01/0.03

Thin and burn, including cells 

burned only as part of: (1) thin and

burn; and (2) hand cut, pile, and

burn treatments

MDC MDM 0.05

MDC MDO 0.03

MDC ED 0.01

MDM MDO 0.05

MDM ED 0.01

LDC LDM 0.04

LDC LDO 0.02

LDC ED 0.01

LDM LDO 0.04

LDM ED 0.01

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
198 USDA Forest Service RMRS-GTR-385. 2018

8. Red Fir (RFR) 

Reviewed by: (1) Hugh Safford, Regional Ecologist, USDA Forest Service; (2) Becky Estes, 

Central Sierra Province Ecologist, USDA Forest Service; and (3) Marc Meyer, Southern Sierra 

Province Ecologist, USDA Forest Service.

Cover Type Classification and Crosswalks

Red Fir (RFR) Variant

▪ EVeg: Regional Dominance Type 1:

◦ Red Fir

• Mesic Modifier (RFR_M)

▪ Presettlement Fire Regime Type: 

◦ Red Fir

▪ LandFire BpS model: 

◦ 0610322: Mediterranean California Red Fir Forest – Southern Sierra

 This type was created by intersecting a binary xeric/mesic layer with the EVeg layer. 

RFR cells that intersected with mesic cells were assigned to the mesic modifier.

• Xeric Modifier (RFR_X)

▪ Presettlement Fire Regime Type:

◦ Red Fir

• LandFire BpS model: 

◦ 0610322 Mediterranean California Red Fir Forest – Southern Sierra

 This type was created by intersecting a binary xeric/mesic layer with the EVeg layer. 

RFR cells that intersected with xeric cells were assigned to the xeric modifier.

• Ultramafic Modifier (RFR_U)

▪ LandFire BpS model: 

◦ 0710220 Klamath-Siskiyou Upper Montane Serpentine Mixed Conifer Woodland

 This type was created by intersecting an ultramafic soils/geology layer with the EVeg 

layer. Where ultramafic cells intersected with RFR, they were assigned to the ultramafic 

modifier.

Red Fir with Aspen (RFR_ASP) Variant

This type was created by overlaying the NRIS TERRA Inventory of Aspen on the EVeg 

layer. Where it intersected with RFR, it was assigned to RFR_ASP.

Vegetation Description

Red Fir (RFR) Variant

The Red Fir cover type is characterized by the presence of Abies magnifica. Other 

conifer species such as Pinus monticola, Pinus contorta ssp. murrayana, Tsuga mertensiana, 

Abies concolor, and Pinus jeffreyi occur at varying densities (LandFire 2007g,h). Mature A. 

magnifica stands are frequently monotypic, with very few other plant species in any layer. 

Heavy shade and a thick layer of duff tend to inhibit understory vegetation, especially in dense 

stands (Barrett 1988). However, there are many open or patchy stands on less productive soils 

that are not monotypic, but rather codominant with other tree species. These sites may have 

substantial shrub cover (Marc Meyer, USDA Forest Service, Clovis, California, personal 

communication, June 2013).
USDA Forest Service RMRS-GTR-385. 2018 199

Stand-replacing disturbances such as lightning-caused fires, windthrows, insect outbreaks, 

and disease kill groups of trees (Barrett 1988). Stand structure is complex. Most current 

(fire-suppressed) A. magnifica stands that were logged in the 19th century have an even-aged 

structure. In contrast, current unlogged and fire-suppressed stands have an uneven-aged or 

irregular age structure. Last, presettlement stands with an active fire regime had a relatively 

flat age-class structure that did not fit a classic even- or uneven-aged distribution (Marc 

Meyer, USDA Forest Service, personal communication, June 2013). That is, frequent smallscale disturbance led to small patches of even-aged trees within the average “stand,” and 

most age classes in a given stand are represented by some of these small patches (Taylor and 

Halpern 1991). After fire, A. magnifica seedlings may establish in canopy gaps, especially if 

they are small to moderate in size. Pinus contorta ssp. murrayana, as well as P. jeffreyi and 

P. monticola, may also function as postfire pioneer species (Chappell and Agee 1996; Marc 

Meyer, USDA Forest Service, personal communication, June 2013). On sites where these 

pioneering types occur under an A. magnifica canopy, the A. magnifica will dominate over the 

long term (Cope 1993a).

In openings resulting from tree mortality or logging, and under open stands on poor 

sites, many species may occur. Large shrubfields can dominate areas after severe fire, 

although conifers eventually will reclaim these sites. In some cases, particularly on xeric 

sites with significant shrub cover, reforestation can be effectively delayed for decades. Ribes, 

Arctostaphylos, and Ceanothus are the most commonly found shrubs (Laacke 1990). Other 

associated shrubs include Symphoricarpos rotundifolius, Lonicera conjugialis, and Quercus 

vaccinifolia (Marc Meyer, USDA Forest Service, personal communication, June 2013). 

Associated herbaceous genera include Carex, Lupinus, Xerophyllum, Eucephalus, Pedicularis, 

Gayophytum, Pyrola, and Monardella (Cope 1993a).

• Mesic Modifier

In addition to A. magnifica, mesic regions within the RFR cover type are associated with 

the presence of P. monticola and P. contorta ssp. murrayana. Tsuga mertensiana may 

occur on north-facing aspects. Abies concolor is uncommon, except at lower elevations 

(LandFire 2007h).

• Xeric Modifier

These sites often include and are occasionally codominated by A. concolor, P. jeffreyi, 

and P. contorta ssp. murrayana, although other conifer species (e.g., P. lambertiana) can 

also be present in lesser amounts at lower elevations. Abies concolor is more prevalent at 

lower elevations. Pinus jeffreyi is more common on shallow soils or when disturbance is 

frequent. Shrubs and herbs generally contribute less than 30 percent cover each. If shrub 

cover is higher, the shrubs are short or prostrate (LandFire 2007g).

• Ultramafic Modifier

Ultramafic soils support a number of endemic plant species. Slowly growing and often 

stunted P. contorta ssp. murrayana and P. jeffreyi occur in combinations or in nearly pure 

open stands. Abies magnifica may be less dominant. Hardwoods are usually sparse, but 

shrubs such as Arctostaphylos, Quercus, Rhamnus, Lithocarpus, Rhododendron, and 

Ceanothus may occur on these sites (LandFire 2007r; USDA Forest Service 2008).

Red Fir With Aspen (RFR_ASP) Variant

When Populus tremuloides co-occurs with RFR on the west side of the Sierran crest, 

it is typically found in smaller patches, often less than 2 hectares in size. This variant is not 

subject to the modifiers described earlier because it is found only on mesic sites with deeper 

soils. Mature stands in which P. tremuloides are still dominant are usually relatively open. 

Average canopy closures range from 35 to 95 percent. The open nature of the stands results 

in substantial light penetration to the ground (Marc Meyer, USDA Forest Service, personal 

communication, June 2013; Verner 1988b).
200 USDA Forest Service RMRS-GTR-385. 2018

Distribution

Red Fir (RFR) Variant

RFR generally forms a vegetation band ranging from 1,900 to 2,800 meters (6,200–9,000 

feet). It is bounded and intergrades with Sierran Mixed Conifer at lower elevations. Geology 

is quite variable (Barrett 1988).

We derived a xeric-mesic gradient based on four variables: (1) aspect, (2) potential 

evapotranspiration, (3) topographic wetness index, and (4) soil water storage. We standardized 

the variables by z-scores such that higher values corresponded to more mesic environments. 

Thus, potential evapotranspiration was inverted to maintain this balance. We combined the four 

variables with equal weights into a topographic position index (TPI), which we split into xeric 

vs. mesic, with xeric occupying the negative end of the range up to one-fourth of a standard 

deviation below the mean (zero) and mesic occupying the remaining portion of the spectrum.

• Mesic Modifier

These sites generally receive more moisture, either from precipitation or by virtue of 

being positioned on middle or lower slopes or drainage bottoms, or both. They may be 

adjacent to meadows or riparian areas. They are found at the highest elevations and northfacing aspects.

• Xeric Modifier

These sites are typically drier and tend to occupy the lower portion of the RFR zone. 

They are also more likely to exist on south-facing aspects and steeper slopes. 

• Ultramafic Modifier

Ultramafics have been mapped at various spatial densities throughout the elevational 

range of the RFR cover type. Low to moderate elevations in ultramafic and serpentinized 

areas often produce soils low in essential minerals such as calcium, potassium, and 

nitrogen, and have excessive accumulations of heavy metals such as nickel and 

chromium. These sites vary widely in the degree of serpentinization and effects on 

their overlying plant communities (USDA Forest Service 2008). Note that the terms 

“ultramafic rock” and “serpentine” are broad terms used to describe many different but 

related rock types: serpentinite, peridotite, dunite, pyroxenite, talc, and soapstone, among 

others (O’Geen et al. 2007). 

Red Fir With Aspen (RFR_ASP) Variant 

Sites supporting P. tremuloides are usually associated with added soil moisture, that is, 

azonal wet sites. These sites are found throughout the RFR zone, often close to streams and 

lakes. Other sites include meadow edges, rock reservoirs, springs, and seeps. Terrain can be 

simple to complex. At lower elevations, topographic conditions for this type tend toward 

positions resulting in relatively colder, wetter conditions within the prevailing climate (e.g., 

ravines, north slopes, wet depressions) (LandFire 2007m). In general, these sites lie on lower 

slope positions, and are associated with slopes under 25 percent (Potter 1998).

Disturbances

Wildfire

Red Fir (RFR) Variant

Wildfires in high-elevation A. magnifica forests are generally not as intense as those in 

the Rocky Mountains and are typically less intense than those at lower elevations. Lesser 

annual fuel accumulation, less severe fire weather conditions, and compact and patchy fuels 
USDA Forest Service RMRS-GTR-385. 2018 201

are all factors (Marc Meyer, USDA Forest Service, personal communication, June 2013). Still, 

fire has an important role in maintaining species diversity within these forests. Fire creates 

canopy openings by killing mature trees. Vegetation mortality caused by wildfire depends on 

vegetation (i.e., fuel) characteristics and wildfire intensity. Low-mortality fires (≤75 percent 

overstory mortality) kill small trees and may consume aboveground portions of shrubs and 

herbs, but do not often kill large trees or belowground organs of most shrubs and herbs, which 

promptly resprout. High-mortality fires (>75 percent overstory mortality) kill trees of all sizes 

and may kill many of the shrubs and herbs as well. However, high-mortality fires typically kill 

only the aboveground portions of the shrubs and herbs; consequently, most shrubs and herbs 

promptly resprout from surviving belowground organs.

Fire severity in RFR is typically positively correlated with slope position, with higher 

mortality occurring on upper slopes and ridgetops, especially on southwest-facing aspects.

• Mesic Modifier

Fire (of any mortality level) is relatively less common on mesic sites than xeric sites, 

resulting in a relatively longer fire rotation period. 

• Xeric Modifier

Fire (of any mortality level) is relatively more common on xeric sites than mesic sites, 

resulting in a relatively shorter fire rotation period. 

• Ultramafic Modifier

This type has a very limited distribution and consequently limited information for fire 

occurrence history.

Red Fir With Aspen (RFR_ASP) Variant

Sites supporting P. tremuloides are maintained by both low- and high-mortality wildfire 

that promote regeneration of P. tremuloides from belowground suckers. Upland clones are 

impaired or suppressed by conifer ingrowth and overtopping and intensive grazing that 

inhibits growth. In a reference condition scenario, a few stands will advance toward conifer 

dominance, but in most stands fire disturbance is frequent enough to maintain P. tremuloides 

as a dominant or codominant component of the stand. In the current landscape, where fire has 

been reduced from reference conditions, there are many more conifer-dominated mixed aspen 

stands than was typical of the reference period conditions (LandFire 2007m; Verner 1988a).

Other Disturbances

Other disturbances are not currently being modeled, but may, depending on the seral stage 

and mortality levels, reset patches to early development, maintain existing seral stages, or shift 

or accelerate succession to a more open condition. All of the tree species associated with this 

vegetation type are susceptible to a wide variety of pathogens and insects.

Seral Stages

The classification of seral stages originated from the corresponding LandFire biophysical 

setting models, but with some modifications (e.g., the addition of a moderate canopy cover 

stage in the mid- and late-seral stages) based on expert input, as follows and as summarized in 

table B8.1. The seral stage map corresponding to this classification for the current landscape 

was derived from the EVeg dataset and the rules in table B8.2 for the RFR cover types; RFR_

ASP seral stages were mapped manually by using NAIP 2010 Color IR imagery.

Red Fir (RFR) Variant

• Early Development (ED)
202 USDA Forest Service RMRS-GTR-385. 2018

This seral stage is characterized by the recruitment of a new cohort of early-successional 

tree species into an open area created by a stand-replacing disturbance. Note that there 

can be a residual or legacy of overstory trees from the predisturbance stand making up 

less than 25 percent canopy cover. Conifer associates regenerate from seed. Occasionally, 

large brush fields may develop after hot wildfires and are dominated by Ceanothus, 

Arctostaphylos, Chrysolepis, or other shrub species for many years (Barrett 1988). On 

mesic sites, A. magnifica, P. monticola and P. contorta ssp. murrayana regenerate from 

seed. Shrub cover is an important component; herb cover varies (LandFire 2007h). On 

xeric sites, there is regeneration of A. magnifica and A. concolor, and perhaps P. jeffreyi or 

P. lambertiana from seed. Shrub and herb cover varies (LandFire 2007g). Ultramafic sites 

will have similar species composition, especially at edges, but P. jeffreyi are relatively 

more common. Shrubs and herbs are sparse (O’Geen et al. 2007). Tree seedlings and 

saplings typical of the cover type can occur in either low or high density (i.e., 0–100 

percent understory cover) depending on local environmental conditions and climate 

conditions following the disturbance. In some cases (e.g., favorable climate conditions 

after the stand-replacing disturbance, coupled with a good seed source), tree seedlings 

may develop a nearly continuous canopy and succeed relatively quickly to middevelopment seral stages. In other cases, and more commonly on xeric or ultramafic 

sites, chaparral conditions may dominate and persist for long periods of time (LandFire 

2007g,h).

• Mid-Development – Open Canopy Cover (MDO)

This seral stage is characterized by a heterogeneous ground cover of grasses, forbs, and 

shrubs, with a low canopy cover (<40 percent) of pole- to medium-sized (5–20 inches 

d.b.h.) conifers (LandFire 2007g). A. magnifica is or is transitioning to become the 

dominant tree species. On mesic sites, P. monticola and P. contorta ssp. murrayana are 

present in varying amounts. Grasses, forbs, and shrubs are declining, although chaparraltype shrubs, such as Arctostaphylos or Chrysolepis can contribute to a dense understory. 

On xeric sites, A. concolor and P. jeffreyi are present in varying amounts, and shrub 

cover varies (LandFire 2007g,h). Ultramafic sites will have similar species composition, 

especially at edges, but P. jeffreyi is relatively more common (O’Geen et al. 2007).

• Mid-Development – Moderate Canopy Cover (MDM)

This seral stage is characterized by a moderate canopy cover (40–70 percent) of pole- to 

medium-sized conifers, and is otherwise similar to MDO.

• Mid-Development – Closed Canopy Cover (MDC)

This seral stage is characterized by a dense canopy cover (>70 percent) of pole- to 

medium-sized conifers, and is otherwise similar to MDO. 

• Late Development – Open Canopy Cover (LDO)

This seral stage is characterized by a heterogeneous ground cover of grasses, forbs, and 

low shrubs, with a low canopy cover (<40 percent) of large trees (>20 inches d.b.h.). 

Subdominant trees die and add to a growing layer of duff and downed woody material, 

and dominant trees continue to grow for several hundred years. Abies magnifica is the 

most common tree species. The understory of mature stands may be limited to less than 5 

percent cover (e.g., Chimaphila menziesii, Pyrola picta). Upper canopy trees may be very 

large, but overall size classes vary with a patchy distribution and open canopy. This seral 

stage develops when low-mortality disturbance is fairly frequent; it persists as long as 

low-mortality fires continue to occur periodically. Ceanothus and Arctostaphylos populate 

disturbance-generated gaps (LandFire 2007g,h). On mesic sites, P. monticola and P. 

contorta ssp. murrayana may each compose up to 20 percent of tree cover. On xeric sites,

A. concolor and P. jeffreyi are common associates of A. magnifica (Barrett 1988; LandFire 

2007g,h). Ultramafic sites will have similar species composition, especially at edges, but 

P. jeffreyi is relatively more common (O’Geen et al. 2007).
USDA Forest Service RMRS-GTR-385. 2018 203

• Late Development – Moderate Canopy Cover (LDM)

This seral stage is characterized by an overstory of large trees with canopy cover 40–70 

percent, and is otherwise similar to LDO.

• Late Development – Closed Canopy Cover (LDC)

This seral stage is characterized by an overstory of large trees with canopy cover greater 

than 70 percent, and is otherwise similar to LDO.

Red Fir with Aspen (RFR_ASP) Variant

• Early Development – Aspen (ED–A)

This seral stage is characterized by the recruitment of a new cohort of early-successional, 

shade-intolerant tree species (primarily P. tremuloides) into an open area created by a 

stand-replacing disturbance. Note that there can be a residual or legacy of overstory trees 

from the predisturbance stand making up less than 25 percent canopy cover. Following 

disturbance, succession proceeds rapidly from an herbaceous layer to shrubs and trees, 

which invade together (Barrett 1988). Populus tremuloides suckers more than 2 meters (6 

feet) tall develop within about 10 years (LandFire 2007m).

• Mid-Development – Aspen (MD–A)

This seral stage is characterized by P. tremuloides trees 5–16 inches d.b.h. Canopy cover 

is highly variable, and can range from 40 to 100 percent. Some understory conifers are 

encroaching, but P. tremuloides is still the dominant component of the stand (LandFire 

2007m).

• Mid-Development – Aspen with Conifer (MD–AC)

This seral stage is characteristic of stands that have been protected from fire since the last 

stand-replacing disturbance. Populus tremuloides trees are predominantly greater than 16 

inches d.b.h. Conifers are present and overtopping the P. tremuloides. Abies magnifica is a 

typical conifer that is successional to P. tremuloides, but other conifers including P. monticola 

and P. contorta ssp. murrayana are also possible. Conifers are pole- to medium-sized (5–20 

inches d.b.h.) with canopy cover greater than 40 percent (LandFire 2007m).

• Late Development – Conifer with Aspen (LD–CA)

If stands are sufficiently protected from fire such that conifer species overtop P. tremuloides, 

become large (>20 inches d.b.h.), and compose more than 70 percent canopy cover, these 

conifers may be able to withstand some fire that the more sensitive P. tremuloides cannot. 

When this occurs, it creates a stand characterized by late-development conifers dominated 

by A. magnifica. The stand may potentially also include P. monticola and P. contorta ssp. 

murrayana, but with P. tremuloides present in the midstory and understory at varying 

densities depending on the disturbance history.

• Late Development – Closed (LDC)

In this seral stage, some P. tremuloides continue to be present in the understory largely as 

a legacy, but large (>20 inches d.b.h.) conifers are now the dominant tree species, having 

overtopped the P. tremuloides, and compose more than 70 percent canopy cover. Smaller 

conifers are present in the midstory as well. Conifer species likely present include A. 

magnifica, A. monticola, and P. contorta spp. murrayana (LandFire 2007g,h,m,r). Note 

that this seral stage is analogous to the LDC condition for the RFR variant.

Model Parameterization

This section includes a listing of the model parameters that are cover type-specific. Note 

that there are additional model parameters not specific to a cover type (e.g., climate modifier) 

that ultimately affect the model processes and outcomes, and these are discussed under the 

Methods section in McGarigal et al. 2018.
204 USDA Forest Service RMRS-GTR-385. 2018

Succession

The rules (i.e., parameters) governing succession for the RFR and RFR_ASP varianats 

are listed in table B8.3. These rules were initially based on the corresponding LandFire BpS 

descriptions (LandFire 2007g,h,m,r) and associated models created by using the Vegetation 

Dynamics Development Tool (VDDT), as modified by Safford and Estes. They were 

subsequently modified based on expert input to include probabilistic rather than deterministic 

seral stage transitions. Specifically, we modified the rules so that stands would gradually, 

instead of abruptly, transition from one seral stage to the next to reflect stochasticity in the 

real-world processes governing succession. For example, the first rule dictates that a cell in 

the RFR_M cover type, which has been in the ED seral stage for 20–45 years, will have a 

60-percent chance of transitioning to MDC, and thus a 40-percent chance of remaining in the 

ED stage, at the beginning of each timestep. Thus, stands will randomly begin transitioning 

to MDC after 20 years in the ED stage, but some stands could remain in the ED stage for as 

much as 50 years to reflect delayed tree establishment. The second rule dictates that a cell that 

has been in the ED seral stage for 50 years will have a 100-percent chance of transitioning to 

the MDC seral stage; thus, all stands will have transitioned to the MDC stage after 50 years 

since establishment. Note that on xeric and ultramafic sites stands can be delayed in the ED 

stage for up to 90 or 120 years, respectively, and they transition to MDO instead of MDC. 

Applying the succession rules listed in table B8.3 results in stands transitioning between 

seral stages in a probabilistic rather than deterministic manner, such that we can compute the 

average stand age (years) for the transition to the next seral stage, as shown in table B8.4. 

For example, the first row in table B8.4 indicates that for a cell in the RFR_M cover type in 

the ED seral stage, the earliest stand age (i.e., number of years since the last stand-replacing 

disturbance) for transitioning to one of the MD seral stages is 20 years, the latest stand age 

is 50 years, and the average stand age at the time of the transition is 23 years. Note that the 

average stand age at transition (23 years) is close to the specified earliest stand age (20 years) 

because of the relatively high rate (0.6) of transitioning beginning at the specified earliest 

stand age. Also, the third row in table B8.4 indicates that a cell in the RFR_M cover type in 

the MDO seral stage will, on average, take 27 years without a low-mortality fire disturbance 

to transition to the MDM seral stage (i.e., transition from an open-canopy cover, <40 percent, 

to a moderate-canopy cover, 40–70 percent, condition). Note that a low-mortality fire every 15 

years will maintain the stand in the open-canopy condition.

Wildfire Disturbance

Rotation Period

Wildfire rotation period (equivalent to the point-specific mean return interval) is not 

formally a model parameter, but rather is specified as a target value to be achieved through 

model calibration. Target fire rotation periods (FRPs) were specified by cover type (table 

B8.5). FRPs for RFR_M and RFR_X were based on Mallek et al. (2013), although expert 

input from Safford and Estes was used to differentiate FRPs between the mesic and xeric sites. 

FRP for RFR_U was set to be double that of RFR_M, and FRP for RFR_ASP was set to be 

equal to RFR_M, based on the corresponding LandFire BpS models (LandFire 2007m,r) and 

expert input from Safford and Estes.

Susceptibility

The cover type-specific factors affecting susceptibility of a cell to wildfire were: (1) 

topographic position, and (2) fuel characteristics, as represented by vegetation cover type, seral 

stage, and time since the last wildfire. Each of these two factors is represented as a probability.

Topographic position—Topographic position, as represented by the topographic position 
USDA Forest Service RMRS-GTR-385. 2018 205

index (TPI) described under the Methods section in McGarigal et al. 2018, was treated as 

having a static (i.e., constant over time) and universal effect on the relative susceptibility of 

a cell to wildfire regardless of seral stage or disturbance history. We allowed topographic 

position to affect susceptibility for the RFR cover types (RFR_M, RFR_X, and RFR_U), but 

not RFR_ASP. Specifically, all other things being equal, for the RFR variants, susceptibility 

decreased by 30 percent as the TPI decreased over its full range according to the fourparameter logistic function depicted in figure B.2. However, because the bulk of the landscape 

varies over a much smaller range of TPI values, the effect on susceptibility is typically much 

less than 30 percent.

The specified logistic parameters were based on consensus expert opinion about the 

strength and nature of the topographic influence on wildfire susceptibility, and reflect general 

support for such an effect in the scientific literature (North 2012; Taylor and Skinner 2003).

Fuels (vegetation and disturbance history)—Fuels, as represented by vegetation cover 

type, seral stage, and recent disturbance history, were treated as having a dynamic (i.e., 

changing over time) effect on the relative susceptibility of a cell to wildfire. Specifically, 

susceptibility varied among cover types and seral stages in relation to the time (number of 

years) since the last fire according to the cumulative Weibull function and the parameters 

listed in table B8.5 (e.g., as illustrated in figure B.1). Note that here we use the cumulative 

form of the Weibull distribution, which gives the cumulative probability of a disturbance 

for any number of years since the last disturbance. Thus, the probability increases from 0 

immediately following a fire to approaching 1 after a certain number of years since the last 

fire, depending on the specified mean return interval (MRI) and shape parameters of the 

Weibull function. Holding Shape constant, and all other things being equal, as MRI increases 

the curve shifts to the right, resulting in a lower probability for any given number of years 

since the last disturbance. In this manner, varying the MRI among cover types and seral stages 

affects the relative susceptibility to wildfire.

The specified Weibull MRI parameters were based on the corresponding LandFire BpS 

descriptions (LandFire 2007g,h,m,r) and associated VDDT models, as modified by Safford 

and Estes. 

Importantly, although susceptibility of the various seral stages is determined by MRI 

(holding Shape constant), these return intervals should not be interpreted literally, as the 

concept of a return interval does not meaningfully apply to a dynamic seral stage. Moreover, 

these MRIs were derived from the LandFire BpS descriptions and associated VDDT models, 

as modified by Safford and Estes; taken collectively, these values do not necessarily agree 

with the target FRPs for the cover types. Thus, the MRIs assigned to each cover type and 

seral stage should be interpreted as relative values that affect the relative susceptibility of the 

various vegetation states. 

Mortality

The cover type-specific factors affecting overstory mortality following wildfire (i.e., 

fire severity) were: (1) topographic position, and (2) fuel characteristics, as represented by 

vegetation cover type and seral stage. Each of these two factors is represented as a probability.

Topographic position—The effect of topographic position on mortality was treated 

identically to its effect on susceptibility (see previous description). Again, the specified 

logistic parameters were based on consensus expert opinion about the strength and nature of 

the topographic influence on wildfire severity, and reflect general support for such an effect in 

the scientific literature (North 2012; Taylor and Skinner 2003).

Fuels (vegetation)—Fuels, as represented by vegetation cover type and seral stage, were 

treated as having a dynamic (i.e., changing over time) effect on the relative probability of a 
206 USDA Forest Service RMRS-GTR-385. 2018

high-mortality response to wildfire. Specifically, we assigned a probability of high-mortality 

response to wildfire to each cover type and seral stage (table B8.5); values were originally 

based on the corresponding LandFire BpS descriptions (LandFire 2007g,h,m,r) and associated 

VDDT models, but were subsequently modified based on expert input from Safford and Estes.

Disturbance Transitions

The rules (i.e., parameters) governing seral stage transitions following low-mortality 

wildfire disturbance for the RFR and RFR_ASP cover types are listed in table B8.6. These 

rules were initially based on the corresponding LandFire BpS descriptions (LandFire 

2007g,h,m,r) and associated models created by using the VDDT, as modified by Safford and 

Estes, but were subsequently modified to include the moderate canopy cover seral stages not 

present in the VDDT models. Note that rules governing transitions following high-mortality 

wildfire are not listed in table B8.6 because high-mortality wildfires always result in transition 

to the ED seral stage. In addition, conditions in which low-mortality wildfire has no effect 

on the seral stage (i.e., does not cause a transition) are not listed. For example, the first two 

rules dictate that a low-mortality wildfire in a cell of RFR_M in the MDC seral stage has a 

9-percent chance of transitioning to the MDM stage, a 9-percent chance of transitioning to 

the MDO stage, and (by implication) an 82-percent chance of remaining in the MDC stage. 

In addition, by implication (given the absence of a rule), a low-mortality wildfire in the ED, 

MDO, or LDO seral stage has no effect other than to maintain the cell in that seral stage.

Vegetation Treatments

Dynamic spatial constraints and priorities affecting individual cover types were described 

under the Methods section in McGarigal et al. 2018; here we describe the rules governing 

seral stage transitions following each unique vegetation treatment (table B8.7). Note that these 

rules were created by the principals involved in this project and reflect expectations based on 

the common prescriptions applied today.
USDA Forest Service RMRS-GTR-385. 2018 207

Table B8.1—Summary of RFR and RFR_ASP seral stage characteristics average overstory tree diameter at breast 

height (d.b.h.) of the dominant and codominant trees, overstory tree percent cover from above (CFA), assigned 

average CFA value for classifying the landscape by percent canopy cover, and range of stand ages (number of years 

since the last stand-replacing disturbance) possible for the corresponding seral stage. Note that overstory tree d.b.h. 

and CFA for the ED/EDA seral stages refer to the residual or legacy overstory from the predisturbance stand.

Cover type

Seral

stagea Overstory treed.b.h. (inches)

Overstory tree

CFA (%)

Assigned

average CFA (%)

Stand age

range (years)

RFR_M ED <5 (13 cm) <25 10 0–45

MDO 5–19.9 (13-50.5 

cm)

<40 30 20–185

MDM 5–19.9 40–70 55 20–185

MDC 5–19.9 >70 85 20–185

LDO ≥20 (51 cm) <40 30 ≥80

LDM ≥20 40–70 55% ≥80

LDC ≥20 >70 85% ≥80

RFR_X ED <5 <25 10 0–85

MDO 5–19.9 <40 30 40–265

MDM 5–19.9 40–70 55 40–265

MDC 5–19.9 >70 85 40–265

LDO ≥20 <40 30 ≥130

LDM ≥20 40-70 55 ≥130

LDC ≥20 >70 85 ≥130

RFR_U ED <5 <25 10 0–115

MDO 5–19.9 <40 30 60–335

MDM 5–19.9 40–70 55 60–335

MDC 5–19.9 >70 85 60–335

LDO ≥20 <40 30 ≥180

LDM ≥20 40–70 55 ≥180

LDC ≥20 >70 85 ≥180

RFR_ASP ED-A <5 <25 10 0–5 

MD-A 5–15.9 >40 60 10–105

MD-AC 5–19.9 >40 70 60–205

LD-CA ≥20 >70 85 160–275

LDC ≥20 >70 85 ≥230

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; 

LD-CA = Late - Conifer and Aspen. 
208 USDA Forest Service RMRS-GTR-385. 2018

Table B8.2—Mapping rules for RFR seral stages. Diameter at breast height (d.b.h.) and cover from above (CFA) 

values were taken from EVeg polygons. Categories for d.b.h. (inches) are: null, 0–0.9, 1–4.9, 5–9.9, 10–19.9, 20–

29.9, ≥30. CFA categories (%) are: null, 0–10, 10–20, ... , 90–100. Each row should be read with a Boolean AND 

across each column. Within each seral stage the rows should be read with a Boolean OR across rows.

Seral stagea Overstory tree

d.b.h. 1 (inches)

Overstory tree

d.b.h. 2 (inches)

Total tree

CFA (%)

Conifer

CFA (%)

Hardwood

CFA (%)

ED null any any any any

ED 0–4.9 (0–12.4 cm) any any any any

MDO 5–19.9 (13–50.5 cm) any null null null

MDO 5–19.9 any 0–40 any any

MDO 5–19.9 any null 0–40 null

MDM 5–19.9 any 40–70 any any

MDM 5–19.9 any null 40–70 null

MDC 5–19.9 any 70–100 any any

MDC 5–19.9 any null 70–100 any

LDO ≥20 (51 cm) any null null null

LDO ≥20 any 0–40 any any

LDO ≥20 any null 0–40 null

LDM ≥20 any 40–70 any any

LDM ≥20 any null 40–70 null

LDC ≥20 any 70–100 any any

LDC ≥20 any null 70–100 any

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
USDA Forest Service RMRS-GTR-385. 2018 209

Table B8.3—Succession rules for RFR and RFR_ASP seral stages. Note that for RFR cover types, number of

years in current successional stage refers to the number of years in either early-development (ED), mid-development 

(MD), or late-development (LD) stage independent of canopy cover class, whereas for RFR_ASP it refers to the 

number of years in the corresponding seral stage. 

Cover type

From seral

stagea To seralstagea

Number of years

in current

successional

stage

Number of

years since

low-mortality

fire

Probability of

transition

RFR_M ED MDC 20–45 any 0.6

ED MDC 50 any 1.0

MDC LDC 60–135 any 0.4

MDC LDC 140 any 1.0

MDM LDM 60–135 any 0.4

MDM LDM 140 any 1.0

MDM MDC ≥15 ≥15 0.3

MDO LDO 60–135 any 0.4

MDO LDO 140 any 1.0

MDO MDM ≥15 ≥15 0.3

LDM LDC ≥15 ≥15 0.3

LDO LDM ≥15 ≥15 0.3

RFR_X ED MDO 40–85 any 0.4

ED MDO 90 any 1.0

MDC LDC 90–175 any 0.3

MDC LDC 180 any 1.0

MDM LDM 90–175 any 0.3

MDM LDM 180 any 1.0

MDM MDC ≥20 ≥20 0.2

MDO LDO 90–175 any 0.3

MDO LDO 180 any 1.0

MDO MDM ≥20 ≥20 0.2

LDM LDC ≥20 ≥20 0.2

LDO LDM ≥20 ≥20 0.2

RFR_U ED MDO 60–115 any 0.2

ED MDO 120 any 1.0

(Table B8.3 continued on next page.)
210 USDA Forest Service RMRS-GTR-385. 2018

Table B8.3—Succession rules for RFR and RFR_ASP seral stages. Note that for RFR cover types, number of

years in current successional stage refers to the number of years in either early-development (ED), mid-development 

(MD), or late-development (LD) stage independent of canopy cover class, whereas for RFR_ASP it refers to the 

number of years in the corresponding seral stage. 

Cover type

From seral

stagea To seralstagea

Number of years

in current

successional

stage

Number of

years since

low-mortality

fire

Probability of

transition

RFR_M ED MDC 20–45 any 0.6

ED MDC 50 any 1.0

MDC LDC 60–135 any 0.4

MDC LDC 140 any 1.0

MDM LDM 60–135 any 0.4

MDM LDM 140 any 1.0

MDM MDC ≥15 ≥15 0.3

MDO LDO 60–135 any 0.4

MDO LDO 140 any 1.0

MDO MDM ≥15 ≥15 0.3

LDM LDC ≥15 ≥15 0.3

LDO LDM ≥15 ≥15 0.3

RFR_X ED MDO 40–85 any 0.4

ED MDO 90 any 1.0

MDC LDC 90–175 any 0.3

MDC LDC 180 any 1.0

MDM LDM 90–175 any 0.3

MDM LDM 180 any 1.0

MDM MDC ≥20 ≥20 0.2

MDO LDO 90–175 any 0.3

MDO LDO 180 any 1.0

MDO MDM ≥20 ≥20 0.2

LDM LDC ≥20 ≥20 0.2

LDO LDM ≥20 ≥20 0.2

RFR_U ED MDO 60–115 any 0.2

ED MDO 120 any 1.0

(Table B8.3 continued) 

MDC LDC 120–215 any 0.2

MDC LDC 220 any 1.0

MDM LDM 120–215 any 0.2

MDM LDM 220 any 1.0

MDM MDC ≥30 ≥30 0.1

MDO LDO 120–215 any 0.2

MDO LDO 220 any 1.0

MDO MDM ≥30 ≥30 0.1

LDM LDC ≥30 ≥30 0.1

LDO LDM ≥30 ≥30 0.1

RFR_ASP ED-A MD-A 10 any 1.0

MD-A MD-AC 50–95 any 0.6

MD-A MD-AC 100 any 1.0

MD-AC LD-CA 100 any 1.0

LD-CA LDC 70 any 1.0

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; 

LD-CA = Late - Conifer and Aspen. 
USDA Forest Service RMRS-GTR-385. 2018 211

Table B8.4—Summary of RFR and RFR_ASP seral stage transitions: earliest, latest, and average stand age 

(number of years since the last stand-replacing disturbance) for the transition to the next seral stage, and average 

number of years without low-mortality fire to transition to the next canopy cover class.

Cover type

From

seral

stagea To seralstage

Earliest

stand age

(years) at

transition

Latest

stand age

(years) at

transition

Average

stand age

(years) at

transition

Average no. of

years without

low-mortality

fire to

transition

RFR_M ED MDC 20 50 23 n/a

MD LD 80 190 90 n/a

MDO MDM n/a n/a n/a 27

MDM MDC n/a n/a n/a 27

LDO LDM n/a n/a n/a 27

LDM LDC n/a n/a n/a 27

RFR_X ED MDO 40 90 47 n/a

MD LD 130 270 149 n/a

MDO MDM n/a n/a n/a 40

MDM MDC n/a n/a n/a 40

LDO LDM n/a n/a n/a 40

LDM LDC n/a n/a n/a 40

RFR_U ED MDO 60 120 79 n/a

MD LD 180 340 219 n/a

MDO MDM n/a n/a n/a 70

MDM MDC n/a n/a n/a 70

LDO LDM n/a n/a n/a 70

LDM LDC n/a n/a n/a 70

RFR_ASP ED-A MD-A 10 10 10 n/a

MD-A MD-AC 60 110 63 n/a

MD-AC LD-CA 160 210 163 n/a

LD-CA LDC 230 280 233 n/a

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; 

LD-CA = Late - Conifer and Aspen. 
212 USDA Forest Service RMRS-GTR-385. 2018

Table B8.5—Weibull function parameters associated with the susceptibility of a cell to wildfire based on fuels 

(i.e., vegetation cover type, seral stage, and number of years since the last fire) and the probability of a highmortality wildfire by cover type and seral stage for the RFR and RFR_ASP cover types (original values in 

parentheses). 

Weibull parameters

Cover

type

Seral

stagea

Target fire

rotation period

(years)

Mean return

interval (years) Shape

Probability of

high-mortality fire

RFR_M n/a 60 n/a n/a n/a

ED n/a 58 3 1.00

MDO n/a 25 3 0.10 (0.09)

MDM n/a 34 3 0.15 (0.17)

MDC n/a 55 3 0.25 (0.35)

LDO n/a 23 3 0.08 (0.05)

LDM n/a 32 3 0.15 (0.16)

LDC n/a 52 3 0.25 (0.41)

RFR_X n/a 40 n/a n/a n/a

ED n/a 50 3 1.00

MDO n/a 50 3 0.10 (0.13)

MDM n/a 65 3 0.15 (0.25)

MDC n/a 94 3 0.25 (0.50)

LDO n/a 43 3 0.08 (0.09)

LDM n/a 55 3 0.15 (0.19)

LDC n/a 74 3 0.25 (0.38)

RFR_U n/a 120 n/a n/a n/a

ED n/a 117 3 1.00

MDO n/a 50 3 0.10 (0.09)

MDM n/a 69 3 0.15 (0.17)

MDC n/a 110 3 0.25 (0.35)

LDO n/a 46 3 0.08 (0.05)

LDM n/a 63 3 0.15 (0.16)

LDC n/a 104 3 0.25 (0.41)

(Table B8.5 continued on next page.)
USDA Forest Service RMRS-GTR-385. 2018 213

RFR_AS

P 

n/a 60 n/a n/a n/a

ED-A n/a 58 3 1 

MD-A n/a 55 3 0.35

MD-AC n/a 34 3 0.17

LD-CA n/a 32 3 0.16

LDC n/a 52 3 0.41

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM 

= Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and 

Conifer; LD-CA = Late - Conifer and Aspen. 

Table B8.5—Weibull function parameters associated with the susceptibility of a cell to wildfire based on fuels 

(i.e., vegetation cover type, seral stage, and number of years since the last fire) and the probability of a highmortality wildfire by cover type and seral stage for the RFR and RFR_ASP cover types (original values in 

parentheses). 

Weibull parameters

Cover

type

Seral

stagea

Target fire

rotation period

(years)

Mean return

interval (years) Shape

Probability of

high-mortality fire

RFR_M n/a 60 n/a n/a n/a

ED n/a 58 3 1.00

MDO n/a 25 3 0.10 (0.09)

MDM n/a 34 3 0.15 (0.17)

MDC n/a 55 3 0.25 (0.35)

LDO n/a 23 3 0.08 (0.05)

LDM n/a 32 3 0.15 (0.16)

LDC n/a 52 3 0.25 (0.41)

RFR_X n/a 40 n/a n/a n/a

ED n/a 50 3 1.00

MDO n/a 50 3 0.10 (0.13)

MDM n/a 65 3 0.15 (0.25)

MDC n/a 94 3 0.25 (0.50)

LDO n/a 43 3 0.08 (0.09)

LDM n/a 55 3 0.15 (0.19)

LDC n/a 74 3 0.25 (0.38)

RFR_U n/a 120 n/a n/a n/a

ED n/a 117 3 1.00

MDO n/a 50 3 0.10 (0.09)

MDM n/a 69 3 0.15 (0.17)

MDC n/a 110 3 0.25 (0.35)

LDO n/a 46 3 0.08 (0.05)

LDM n/a 63 3 0.15 (0.16)

LDC n/a 104 3 0.25 (0.41)

(Table B8.5 continued) 
214 USDA Forest Service RMRS-GTR-385. 2018

Table B8.6—Disturbance rules for RFR and RFR_ASP cover types governing seral stage transitions following a 

low-mortality wildfire. Note that conditions in which low-mortality wildfire has no effect are not listed.

Cover type From seral stagea

 To seral stagea Probability of transition

RFR_M MDC MDM 0.09

MDC MDO 0.09

MDM MDO 0.13

LDC LDM 0.08

LDC LDO 0.08

LDM LDO 0.10

RFR_X MDC MDM 0.09

MDC MDO 0.09

MDM MDO 0.13

LDC LDM 0.08

LDC LDO 0.08

LDM LDO 0.10

RFR_U MDC MDM 0.09

MDC MDO 0.09

MDM MDO 0.13

LDC LDM 0.08

LDC LDO 0.08

LDM LDO 0.10

RFR_ASP LDC LD-CA 0.15

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; LD-CA = Late - Conifer and Aspen. 
USDA Forest Service RMRS-GTR-385. 2018 215

Table B8.7—Disturbance rules for RFR and RFR_ASP cover types governing seral stage transitions following 

vegetation treatments. Note that treatments not affecting seral stage transitions (e.g., mastication) are not included 

here.

Cover type Treatment type

From seral

stagea To seralstagea Probability oftransition

RFR_M Clearcut and Group cuts Any ED 1 

Thinning, including cells thinned in: 

(1) matrix thin and group cut; (2) 

thin and burn; (3) thin, hand cut, 

pile, and burn; (4) thin, masticate, 

and burn; and (5) matrix thin, group 

cut, and burn treatments

MDC MDM 1 

MDM MDO 1 

LDC LDM 1 

LDM LDO 1 

Prescribed fire, including cells 

burned as part of a prescribed fireonly treatment; "cool" burn/"hot" 

burn transition probabilities

MDC MDM 0.03/0.05

MDC MDO 0/0.03

MDC ED 0/0.01

MDM MDO 0.05/0.14

MDM ED 0/0.01

LDC LDM 0.02/0.03

LDC LDO 0/0.02

LDC ED 0/0.01

LDM LDO 0.04/0.11

LDM ED 0/0.01

Thin and burn, including cells 

burned only as part of: (1) thin and

burn; and (2) hand cut, pile and

burn treatments

MDC MDM 0.03

MDM MDO 0.05

LDC LDM 0.02

LDM LDO 0.04

RFR_X; 

RFR_U

Clearcut and Group cuts Any ED 1 

Thinning, including cells thinned in: 

(1) matrix thin and group cut; (2) 

thin and burn; (3) thin, hand cut, 

pile, and burn; (4) thin, masticate, 

and burn; and (5) matrix thin, group 

cut, and burn treatments

MDC MDM 1 

MDM MDO 1 

LDC LDM 1 

LDM LDO 1 

(Table B8.7 continued on next page.)
216 USDA Forest Service RMRS-GTR-385. 2018

(Table B8.7 continued) 

Cover type Treatment type

From seral

stagea To seralstagea Probability oftransition

Prescribed fire, including cells 

burned as part of a prescribed fireonly treatment) "cool" burn/"hot" 

burn transition probabilities

MDC MDM 0.05/0.15

MDC MDO 0.03/0.09

MDC ED 0.01/0.03

MDM MDO 0.05/0.15

MDM ED 0.01/0.03

LDC LDM 0.04/0.12

LDC LDO 0.02/0.06

LDC ED 0.01/0.03

LDM LDO 0.04/0.12

LDM ED 0.01/0.03

Thin and burn, including cells 

burned only as part of: (1) thin and

burn; and (2) hand cut, pile, and

burn treatments

MDC MDM 0.05

MDC MDO 0.03

MDC ED 0.01

MDM MDO 0.05

MDM ED 0.01

LDC LDM 0.04

LDC LDO 0.02

LDC ED 0.01

LDM LDO 0.04

LDM ED 0.01

RFR_ASP Thinning MD_AC MD_A 1 

LDC MD_A 1 

LD_CA MD_A 1 

Prescribed fire LDC LD_CA 0.1

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; 

LD-CA = Late - Conifer and Aspen. 
USDA Forest Service RMRS-GTR-385. 2018 217

9. Sierran Mixed Conifer (SMC)

Reviewed by: (1) Hugh Safford, Regional Ecologist, USDA Forest Service; and (2) Becky 

Estes, Central Sierra Province Ecologist, USDA Forest Service.

Cover Type Classification and Crosswalks

Sierran Mixed Conifer (SMC) Variant

▪ EVeg: Regional Dominance Type 1:

◦ Mixed Conifer – Fir

◦ Mixed Conifer – Pine

• Mesic Modifier (SMC_M)

▪ Presettlement Fire Regime Type: 

◦ Moist Mixed Conifer

▪ LandFire BpS model: 

◦ 0610280: Mediterranean California Mesic Mixed Conifer Forest and Woodland

 This type was created by intersecting a binary xeric/mesic layer with the EVeg layer. 

SMC cells that intersected with mesic cells were assigned to the mesic modifier.

• Xeric Modifier (SMC_X)

▪ Presettlement Fire Regime Type:

◦ Dry Mixed Conifer

▪ LandFire BpS model: 

◦ 0610270 Mediterranean California Dry-Mesic Mixed Conifer Forest and Woodland

 This type was created by intersecting a binary xeric/mesic layer with the EVeg layer. 

SMC cells that intersected with xeric cells were assigned to the xeric modifier.

• Ultramafic Modifier (SMC_U)

▪ LandFire BpS model:

◦ 0710220 Klamath-Siskiyou Upper Montane Serpentine Mixed Conifer Woodland

 This type was created by intersecting an ultramafic soils/geology layer with the EVeg 

layer. Where ultramafic cells intersected with SMC, they were assigned to the ultramafic 

modifier.

Sierran Mixed Conifer with Aspen (SMC_ASP) Variant

This type was created by overlaying the NRIS TERRA Inventory of Aspen on the EVeg 

layer. Where it intersected with SMC, it was assigned to SMC_ASP.

Vegetation Description

Sierran Mixed Conifer (SMC) Variant

The Sierran Mixed Conifer cover type is typically composed of three or more conifer 

species, sometimes mixed with hardwoods. In forests experiencing the natural fire regime, 

stand and landscape structure are both highly heterogeneous and age structure is usually 

uneven. Past management (e.g., logging and fire suppression) and its effects on forest 

succession have resulted in greater structural homogeneity and a dramatic increase in the 

presence of shade-tolerant and fire-intolerant tree species. Old-growth stands where fire 

has been excluded are often multistoried, with the overstory composed of various species 

(often dominated by pines) and the understory dominated by Abies concolor and Calocedrus

decurrens. In the absence of fire, forested stands can form closed, multilayered canopies with 

over 100 percent overlapping cover. Such dense stands were probably relatively uncommon 
218 USDA Forest Service RMRS-GTR-385. 2018

before settlement and found in moist microsites, on north slopes, and at higher elevations. 

When openings occur, shrubs are common in the understory. Before Euro-American 

settlement, this cover type was dominated by open stand conditions and old forest, but today 

closed canopy conditions dominated by middle-aged trees are more common, and even-aged 

stands are widespread (Allen 2005). 

Five conifers and one hardwood typify this cover type: A. concolor, Pseudotsuga 

menziesii, Pinus ponderosa, Pinus lambertiana, C. decurrens, and Quercus kelloggii. Abies 

concolor tends to be the most ubiquitous species because it is the competitive dominant in 

this cover type. It tolerates shade, reproduces prolifically in the absence of fire, and has the 

ability to survive long periods of overtopping in brush fields. Pseudotsuga menziesii replaces 

A. concolor as the competitive dominant at lower elevations. Pinus ponderosa, which was 

historically the dominant species in SMC forest, still dominates at lower elevations and on 

south slopes, but like P. lambertiana, its densities have been much reduced by logging. Pinus 

jeffreyi commonly replaces P. ponderosa at higher elevations, on cold sites, or on ultramafic 

soils. Abies magnifica is a minor associate at the highest elevations, as are Pinus monticola 

and Pinus contorta ssp. murrayana. Pinus lambertiana is found throughout the cover type, 

but its densities have been much reduced by selective logging and white pine blister rust 

(Cronartium ribicola). Quercus kelloggii is a common component in stands on warm, dry 

sites. This species sprouts prolifically after fire, and although it does best on open sites, it is 

maintained under adverse conditions, such as overtopping by conifers and thin soils (Allen 

2005). In some locations, Populus tremuloides is also a component of the stand and, when 

present, typically dominates during the early seral stages after disturbance (see following).

Ceanothus, Arctostaphylos, Chrysolepis, Prunus, Ribes, Rosa, and Chamaebatia are 

common shrub genera in the understory (Allen 2005). Grasses and forbs are diverse but rarely 

contribute much cover, except where the stand structure is open. 

• Mesic Modifier

The primary species associated with mesic sites are A. concolor, P. menziesii, C. 

decurrens, and P. lambertiana. Pinus contorta ssp. murrayana may also be associated 

with mesic forests at higher elevations. As elevations begin to increase, A. magnifica

becomes more prominent. Notholithocarpus densiflora is an indicator of lower elevation 

sites with high water availability, either from meteoric or surface water. Understory 

diversity is often low on these sites as high canopy cover and tree density reduce solar 

incidence at the soil surface. Very often the ground is covered in thick litter and duff. 

Some shade-tolerant shrub and herb species occur.

• Xeric Modifier

Xeric sites are characterized by the presence of shade-intolerant and fire-tolerant conifer 

species such as P. ponderosa, P. jeffreyi, and P. lambertiana, as well as varying amounts 

of more shade-tolerant species such as A. concolor and C. decurrens. Quercus kelloggii 

is locally common. The pines normally are prominent on south- and west-facing slopes, 

A. concolor and sometimes P. menziesii are prominent on north- and east-facing slopes, 

and C. decurrens is present as a secondary component on all slopes. At lower elevations, 

Pinus sabiniana, and Q. chrysolepis may become common associates. Understory shrubs 

include Ceanothus, Arctostaphylos, and Chamaebatia, and Artemisia and Purshia on dry, 

eastern sites.

• Ultramafic Modifier

Ultramafic soils support many endemic plant species. Slow-growing and often stunted 

P. contorta ssp. murrayana and P. jeffreyi occur in combinations or in nearly pure open 

stands. Other tree associates on ultramafic soils include P. menziesii, C. decurrens, and 
USDA Forest Service RMRS-GTR-385. 2018 219

Pinus attenuata. Hardwoods are usually sparse, but shrubs such as Arctostaphylos, 

Quercus, Rhamnus, Notholithocarpus, Rhododendron, and Ceanothus may occur on 

these sites. Often a dramatic landscape shift occurs across abrupt discontinuities between 

ultramafics and other rock types. For example, regional stands of dense conifer forests are 

replaced by stunted and open stands of other conifers, by chaparral, or even by barrens on 

which woody vegetation is absent (USDA Forest Service 2008).

Sierran Mixed Conifer with Aspen (SMC_ASP) Variant 

When P. tremuloides co-occurs with SMC on the west side of the Sierran crest, it is 

typically found in smaller patches, often less than 2 hectares in size. This variant is not subject 

to the modifiers described earlier because it is found only on mesic sites. Mature stands in 

which P. tremuloides are still dominant are usually relatively open. Average canopy closures 

of stands in eastern California range from 60 to 100 percent in young and intermediate-aged 

stands and from 25 to 60 percent in mature stands. The open nature of the stands results in 

substantial light penetration to the ground (Verner 1988a).

Distribution

Sierran Mixed Conifer (SMC) Variant

SMC generally forms a vegetation band ranging from 500 to 2,000 meters (1,500–6,500 

feet). It dominates the western mid-elevation slopes of the Sierra Nevada. Soils supporting 

SMC are varied in depth and composition, and are derived primarily from Mesozoic granitic, 

Paleozoic metamorphic rocks, and Cenozoic volcanic rocks (Allen 2005). 

We derived a xeric-mesic gradient based on four variables: (1) aspect, (2) potential 

evapotranspiration, (3) topographic wetness index, and (4) soil water storage. We standardized 

the variables by z-scores such that higher values corresponded to more mesic environments. 

Thus, potential evapotranspiration was inverted to maintain this balance. We combined the four 

variables with equal weights into a topographic position index (TPI), which we split into xeric 

vs. mesic, with xeric occupying the negative end of the range up to one-fourth of a standard 

deviation below the mean (zero) and mesic occupying the remaining portion of the spectrum.

• Mesic Modifier

This type is generally found on favorable slopes, primarily north and east aspects 

throughout the geographic range, as well as along streams in drier areas. It is more 

common at higher elevations compared to the xeric type (USDA Forest Service 2008).

• Xeric Modifier

This type occurs on south- and west-facing aspects (LandFire 2007c). At lower elevations 

patches may be found on north slopes. At higher elevations this cover type typically 

occurs on south-, east-, and west-facing aspects. 

• Ultramafic Modifier

Ultramafics have been mapped at various spatial densities throughout the elevational 

range of the SMC cover type. Low to moderate elevations in ultramafic and serpentinized 

areas often produce soils low in essential minerals such as calcium, potassium, and 

nitrogen, and have excessive accumulations of heavy metals such as nickel and 

chromium. These sites vary widely in the degree of serpentinization and effects on 

their overlying plant communities (USDA Forest Service 2008). Note that the terms 

“ultramafic rock” and “serpentine” are broad terms used to describe a number of different 

but related rock types: serpentinite, peridotite, dunite, pyroxenite, talc, and soapstone, 

among others (O’Geen et al. 2007). 
220 USDA Forest Service RMRS-GTR-385. 2018

Sierran Mixed Conifer with Aspen (SMC_ASP) Variant 

Sites supporting P. tremuloides are usually associated with added soil moisture, that is, 

azonal wet sites. These sites are found throughout the SMC zone, often close to streams and 

lakes. Other sites include meadow edges, rock reservoirs, springs, and seeps. Terrain can be 

simple to complex. At lower elevations, topographic conditions for this type tend toward 

positions resulting in relatively colder, wetter conditions within the prevailing climate (e.g., 

ravines, north slopes, wet depressions) (LandFire 2007m).

Disturbances

Wildfire

Sierran Mixed Conifer (SMC) Variant

Wildfires are common and frequent in SMC; vegetation mortality caused by wildfire 

depends on vegetation (i.e., fuel) characteristics and wildfire intensity. Low-mortality fires 

(≤75 percent overstory mortality) kill small trees and may consume aboveground portions 

of small oaks, shrubs, and herbs, but do not often kill large trees or belowground organs of 

most oaks, shrubs, and herbs, which promptly resprout. High-mortality fires (>75 percent 

overstory mortality) kill trees of all sizes and may kill many of the shrubs and herbs as well. 

However, high-mortality fires typically kill only the aboveground portions of the oaks, shrubs, 

and herbs; consequently, most oaks, shrubs, and herbs promptly resprout from surviving 

belowground organs.

Fire severity in SMC is typically positively correlated with slope position, with higher 

mortality occurring on upper slopes and ridgetops, especially on southwest-facing aspects.

• Mesic Modifier

Fire is relatively less common on mesic sites than xeric sites, resulting in a relatively 

longer fire rotation period; however, when fires do occur they tend to result in higher 

severity. 

• Xeric Modifier

Fire (of any mortality level) is relatively more common on xeric sites than mesic sites, 

resulting in a relatively shorter fire rotation period. 

• Ultramafic Modifier

This type has a very limited distribution and consequently limited information for fire 

occurrence history.

Sierran Mixed Conifer with Aspen (SMC_ASP) Variant 

Sites supporting P. tremuloides are maintained by both low- and high-mortality wildfire 

that promote regeneration of P. tremuloides from belowground suckers. Upland clones are 

impaired or suppressed by conifer ingrowth and overtopping and intensive grazing that 

inhibits growth. In a reference condition scenario, a few stands will advance toward conifer 

dominance, but in most stands fire disturbance is frequent enough to maintain P. tremuloides 

as a dominant or codominant component of the stand. In the current landscape, where fire has 

been reduced from reference conditions, there are many more conifer-dominated mixed aspen 

stands than was typical of the reference period conditions (LandFire 2007m; Verner 1988a).

Other Disturbances

Other disturbances are not currently being modeled, but may, depending on the seral stage 

and mortality levels, reset patches to early development, maintain existing seral stages, or shift 

or accelerate succession to a more open condition. All of the tree species associated with this 

vegetation type are susceptible to a wide variety of pathogens and insects.
USDA Forest Service RMRS-GTR-385. 2018 221

Seral Stages

The classification of seral stages originated from the corresponding LandFire biophysical 

setting models, but with some modifications (e.g., the addition of a moderate canopy cover 

stage in the mid- and late-seral stages) based on expert input, as follows and as summarized in 

table B9.1. The seral stage map corresponding to this classification for the current landscape 

was derived from the EVeg dataset and the rules in table B9.2 for the SMC cover types; SMC_

ASP seral stages were mapped manually by using NAIP 2010 Color IR imagery.

Sierran Mixed Conifer (SMC) Variant

• Early Development (ED)

This seral stage is characterized by the recruitment of a new cohort of early-successional 

tree species into an open area created by a stand-replacing disturbance. Note that there can 

be a residual or legacy of overstory trees from the predisturbance stand making up less 

than 25 percent canopy cover. After disturbance, succession proceeds from an ephemeral 

herb to a perennial grass-herb community. This grass-herb condition generally lasts only 

a few years before shifting to a shrub-seedling-sapling condition dominated by any of 

the following genera: Arctostaphylos, Ceanothus, Prunus, Ribes, and Chamaebatia, as 

well as Q. vaccinifolia. Tree seedlings and saplings typical of the cover type can occur 

in either low or high density (i.e., 0–100 percent understory cover) depending on local 

environmental conditions and climate conditions following the disturbance. In some cases 

(e.g., favorable climate conditions after the stand-replacing disturbance, coupled with a 

good seed source), tree seedlings may develop a nearly continuous canopy and succeed 

relatively quickly to mid-development seral stages. In other cases, and more commonly 

on xeric or ultramafic sites, chaparral conditions may dominate and persist for long 

periods of time (LandFire 2007c,d).

• Mid-Development – Open Canopy Cover (MDO)

This seral stage is characterized by a heterogeneous ground cover of grasses, forbs, and 

shrubs, with a low canopy cover (<40 percent) of pole- to medium-sized (5–20 inches 

d.b.h.) conifers (LandFire 2007d). Conifer species likely to be present include A. concolor, 

C. decurrens, P. ponderosa, P. menziesii, and P. lambertiana. Pines predominate on xeric 

sites, and firs predominate on mesic sites. Quercus kelloggii may occur as well, mostly on 

warmer slopes and where soils are less productive (LandFire 2007c,d). Ultramafic sites 

will have similar species composition, especially at edges, but P. jeffreyi and C. decurrens 

are relatively more common (O’Geen et al. 2007).

• Mid-Development – Moderate Canopy Cover (MDM)

This seral stage is characterized by a moderate canopy cover (40–70 percent) of pole- to 

medium-sized conifers, and is otherwise similar to MDO.

• Mid-Development – Closed Canopy Cover (MDC)

This seral stage is characterized by a dense canopy cover (>70 percent) of pole- to 

medium-sized conifers, and is otherwise similar to MDO. 

• Late Development – Open Canopy Cover (LDO)

This seral stage is characterized by a heterogeneous ground cover of grasses, forbs, and 

low shrubs, with a low canopy cover (<40 percent) of large trees (>20 inches d.b.h.). 

This stage often occurs in small to moderate-sized patches on south-facing aspects and 

ridgetops. Upper canopy trees may be very large, but overall size classes vary with a 

patchy distribution and open canopy. This seral stage develops when low-mortality 

disturbance is fairly frequent; it persists as long as low-mortality fires continue to occur 

periodically. Conifer species likely to be present include A. concolor, C. decurrens, P. 

ponderosa, P. menziesii, and P. lambertiana. Quercus kelloggii may occur as well, mostly 

on warmer slopes and where soils are less productive (LandFire 2007c,d). Ultramafic sites 
222 USDA Forest Service RMRS-GTR-385. 2018

will have similar species composition, especially at edges, but P. jeffreyi and C. decurrens 

are relatively more common (O’Geen et al. 2007).

• Late Development – Moderate Canopy Cover (LDM)

This seral stage is characterized by an overstory of large trees with canopy cover 40–70 

percent, and is otherwise similar to LDO.

• Late Development – Closed Canopy Cover (LDC)

This seral stage is characterized by an overstory of large trees with canopy cover greater 

than 70 percent, and is otherwise similar to LDO.

Sierran Mixed Conifer with Aspen (SMC_ASP) Variant

• Early Development – Aspen (ED–A)

This seral stage is characterized by the recruitment of a new cohort of early-successional, 

shade-intolerant tree species (primarily P. tremuloides) into an open area created by a 

stand-replacing disturbance. Note that there can be a residual or legacy of overstory trees 

from the predisturbance stand making up less than 25 percent canopy cover. Following 

disturbance, succession proceeds rapidly from an herbaceous layer to shrubs and trees, 

which invade together (Verner 1988a). Populus tremuloides suckers more than 2 meters 

tall develop within about 10 years (LandFire 2007m).

• Mid-Development – Aspen (MD–A)

This seral stage is characterized by P. tremuloides trees 5–16 inches d.b.h. Canopy cover is 

highly variable, and can range from 40 to 100 percent. Some understory conifers, including 

P. ponderosa, P. lambertiana, and A. concolor are encroaching, but P. tremuloides is still the 

dominant component of the stand (LandFire 2007m).

• Mid-Development – Aspen with Conifer (MD–AC)

This seral stage is characteristic of stands that have been protected from fire since the 

last stand-replacing disturbance. Populus tremuloides trees are predominantly more than 

16 inches d.b.h. Conifers are present and overtopping the P. tremuloides. Abies concolor 

is a typical conifer that is successional to P. tremuloides, but other conifers including P. 

ponderosa and P. lambertiana are also possible. Conifers are pole- to medium-sized (5–20 

inches d.b.h.) with canopy cover greater than 40 percent (LandFire 2007m).

• Late Development – Conifer with Aspen (LD–CA)

If stands are sufficiently protected from fire such that conifer species overtop P. 

tremuloides, become large (>20 inches d.b.h.) and compose more than 70 percent canopy 

cover, they may be able to withstand some fire that more sensitive P. tremuloides cannot. 

When this occurs, it creates a stand characterized by late-development conifers, such as A. 

concolor, P. ponderosa, or P. lambertiana, but with P. tremuloides present in the midstory 

and understory at varying densities depending on the disturbance history.

• Late Development – Closed (LDC)

In this seral stage, some P. tremuloides continue to be present in the understory largely as 

a legacy, but large (>20 inches d.b.h.) conifers are now the dominant tree species, having 

overtopped the P. tremuloides, and compose more than 70 percent canopy cover. Smaller 

conifers are present in the midstory as well. Conifer species likely to be present include 

A. concolor, C. decurrens, P. ponderosa, P. menziesii, and P. lambertiana (LandFire 

2007c,d,m,r). Note that this seral stage is analogous to the LDC condition for the SMC 

variant.

Model Parameterization

This section includes a listing of the model parameters that are cover type-specific. Note 

that there are additional model parameters not specific to a cover type (e.g., climate modifier) 
USDA Forest Service RMRS-GTR-385. 2018 223

that ultimately affect the model processes and outcomes, and these are discussed under the 

Methods section in McGarigal et al. 2018.

Succession

The rules (i.e., parameters) governing succession for the SMC and SMC_ASP cover types 

are listed in table B9.3. These rules were initially based on the corresponding LandFire BpS 

descriptions (LandFire 2007c,d,m,r) and associated models created by using the Vegetation 

Dynamics Development Tool (VDDT), as modified by Safford and Estes. They were 

subsequently modified based on expert input to include probabilistic rather than deterministic 

seral stage transitions. Specifically, we modified the rules so that stands would gradually, 

instead of abruptly, transition from one seral stage to the next to reflect stochasticity in the 

real-world processes governing succession. For example, the first three rules dictate that a cell 

in the SMC_M cover type, which has been in the ED seral stage for 15–35 years, will have a 

40-percent chance of transitioning to MDC, a 25-percent chance of transitioning to MDM, and 

a 15-percent chance of transitioning to MDO at the beginning of each timestep. Thus, stands 

will randomly begin transitioning to one of the MD stages after 15 years in the ED stage, 

but some stands could remain in the ED stage for as much as 40 years to reflect delayed tree 

establishment. Note that for stands currently in the ED stage and between 15 and 35 years 

in this stage, the combined chance of transitioning to MD at each timestep is 80 percent; 

therefore, there is a 20-percent chance of remaining in the ED seral stage at each timestep. The 

next three rules together dictate that a cell that has been in the ED seral stage for 40 years will 

have a 100-percent chance of transitioning to one of the MD seral stages; thus, all stands will 

have transitioned to the MD stage after 40 years since establishment. Note that on xeric and 

ultramafic sites stands can be delayed in the ED stage for up to 70 or 120 years, respectively, 

and they always transition to MDO.

Applying the succession rules listed in table B9.3 results in stands transitioning between 

seral stages in a probabilistic rather than deterministic manner, such that we can compute the 

average stand age (years) for the transition to the next seral stage, as shown in table B9.4. 

For example, the first row in table B9.4 indicates that for a cell in the SMC_M cover type in 

the ED seral stage, the earliest stand age (i.e., number of years since the last stand-replacing 

disturbance) for transitioning to one of the MD seral stages is 15 years, the latest stand age 

is 40 years, and the average stand age at the time of the transition is 16 years. Note that the 

average stand age at transition (16 years) is close to the specified earliest stand age (15 years) 

because of the relatively high rate (0.8) of transitioning beginning at the specified earliest 

stand age. Also, the third row in table B9.4 indicates that a cell in the SMC_M cover type in 

the MDO seral stage will, on average, take 16 years without a low-mortality fire disturbance 

to transition to the MDM seral stage (i.e., transition from an open-canopy cover, <40 percent, 

to a moderate-canopy cover, 40–70 percent, condition). Note that a low-mortality fire every 15 

years will maintain the stand in the open-canopy condition.

Wildfire Disturbance

Rotation Period

Wildfire rotation period (equivalent to the point-specific mean return interval) is not 

formally a model parameter, but rather is specified as a target value to be achieved through 

model calibration. Target fire rotation periods (FRPs) were specified by cover type (table 

B9.5). FRPs for SMC_M and SMC_X were based on Mallek et al. (2013). FRP for SMC_U 

was set to be double that of SMC_M, and FRP for SMC_ASP was set to be equal to SMC_M, 

based on the corresponding LandFire BpS models (LandFire 2007m,r) and expert input from 

Safford and Estes.
224 USDA Forest Service RMRS-GTR-385. 2018

Susceptibility

The cover type-specific factors affecting susceptibility of a cell to wildfire were: (1) 

topographic position, and (2) fuel characteristics, as represented by vegetation cover type, 

seral stage, and time since the last wildfire. Each of these two factors is represented as a 

probability.

Topographic position—Topographic position, as represented by the topographic position 

index (TPI) described under the Methods section in McGarigal et al. 2018, was treated as 

having a static (i.e., constant over time) and universal effect on the relative susceptibility of 

a cell to wildfire regardless of seral stage or disturbance history. We allowed topographic 

position to affect susceptibility for the SMC cover types (SMC_M, S MC_X, and SMC_U), 

but not SMC_ASP. Specifically, all other things being equal, for the SMC cover types, 

susceptibility decreased by 30 percent as the TPI decreased over its full range according to 

the four-parameter logistic function depicted in figure B.2. However, because the bulk of 

the landscape varies over a much smaller range of TPI values, the effect on susceptibility is 

typically much less than 30 percent.

The specified logistic parameters were based on consensus expert opinion about the 

strength and nature of the topographic influence on wildfire susceptibility, and reflect general 

support for such an effect in the scientific literature (North 2012; Taylor and Skinner 2003).

Fuels (vegetation and disturbance history)—Fuels, as represented by vegetation cover 

type, seral stage, and recent disturbance history, were treated as having a dynamic (i.e., 

changing over time) effect on the relative susceptibility of a cell to wildfire. Specifically, 

susceptibility varied among cover types and seral stages in relation to the time (number of 

years) since the last fire according to the cumulative Weibull function and the parameters 

listed in table B9.5 (e.g., as illustrated in figure B.1). Note that here we use the cumulative 

form of the Weibull distribution, which gives the cumulative probability of a disturbance 

for any number of years since the last disturbance. Thus, the probability increases from 0 

immediately following a fire to approaching 1 after a certain number of years since the last 

fire, depending on the specified mean return interval (MRI) and shape parameters of the 

Weibull function. Holding Shape constant, and all other things being equal, as MRI increases 

the curve shifts to the right, resulting in a lower probability for any given number of years 

since the last disturbance. In this manner, varying the MRI among cover types and seral stages 

affects the relative susceptibility to wildfire.

The specified Weibull MRI parameters were based on the corresponding LandFire BpS 

descriptions (LandFire 2007c,d,m,r) and associated VDDT models, as modified by Safford 

and Estes. 

Importantly, although susceptibility of the various seral stages is determined by MRI 

(holding Shape constant), these return intervals should not be interpreted literally, as the 

concept of a return interval does not meaningfully apply to a dynamic seral stage. Moreover, 

these MRIs were derived from the LandFire BpS descriptions and associated VDDT models, 

as modified by Safford and Estes; taken collectively, these values do not necessarily agree 

with the target FRPs for the cover types. Thus, the MRIs assigned to each cover type and 

seral stage should be interpreted as relative values that affect the relative susceptibility of the 

various vegetation states. 

Mortality

The cover type-specific factors affecting overstory mortality following wildfire (i.e., 

fire severity) were: (1) topographic position, and (2) fuel characteristics, as represented by 

vegetation cover type and seral stage. Each of these two factors is represented as a probability.

Topographic position—The effect of topographic position on mortality was treated 

identically to its effect on susceptibility (see previous description). Again, the specified 
USDA Forest Service RMRS-GTR-385. 2018 225

logistic parameters were based on consensus expert opinion about the strength and nature of 

the topographic influence on wildfire severity, and reflect general support for such an effect in 

the scientific literature (North 2012; Taylor and Skinner 2003).

Fuels (vegetation)—Fuels, as represented by vegetation cover type and seral stage, were 

treated as having a dynamic (i.e., changing over time) effect on the relative probability of a 

high-mortality response to wildfire. Specifically, we assigned a probability of high-mortality 

response to wildfire to each cover type and seral stage (table B9.5); values were originally 

based on the corresponding LandFire BpS descriptions (LandFire 2007c,d,m,r) and associated 

VDDT models, but were subsequently modified based on expert input from Safford and Estes.

Disturbance Transitions

The rules (i.e., parameters) governing seral stage transitions following low-mortality 

wildfire disturbance for the SMC and SMC_ASP cover types are listed in table B9.6. 

These rules were initially based on the corresponding LandFire BpS descriptions (LandFire 

2007c,d,m,r) and associated models created by using the Vegetation Dynamics Development 

Tool (VDDT), as modified by Safford and Estes, but were subsequently modified to include 

the moderate canopy cover seral stages not present in the VDDT models. Note that rules 

governing transitions following high-mortality wildfire are not listed in table B9.6 because 

high-mortality wildfires always result in transition to the ED seral stage. In addition, 

conditions in which low-mortality wildfire has no effect on the seral stage (i.e., does not 

cause a transition) are not listed. For example, the first two rules dictate that a low-mortality 

wildfire in a cell of SMC_M in the MDC seral stage has a 9-percent chance of transitioning to 

the MDM stage, a 9-percent chance of transitioning to the MDO stage, and (by implication) 

an 82-percent chance of remaining in the MDC stage. In addition, by implication (given the 

absence of a rule), a low-mortality wildfire in the ED, MDO, or LDO seral stage has no effect 

other than to maintain the cell in that seral stage.

Vegetation Treatments

Dynamic spatial constraints and priorities affecting individual cover types were described 

under the Methods section in McGarigal et al. 2018; here we describe the rules governing 

seral stage transitions following each unique vegetation treatment (table B9.7). Note that these 

rules were created by the principals involved in this project and reflect expectations based on 

the common prescriptions applied today.
226 USDA Forest Service RMRS-GTR-385. 2018

Table B9.1—Summary of SMC and SMC_ASP seral stage characteristics: average overstory tree diameter at 

breast height (d.b.h.) of the dominant and codominant trees, overstory tree percent cover from above (CFA), 

assigned average CFA value for classifying the landscape by percent canopy cover, and range of stand ages 

(number of years since the last stand-replacing disturbance) possible for the corresponding seral stage. Note that

overstory tree d.b.h. and CFA for the ED/ED-A seral stages refers to the residual or legacy overstory from the predisturbance stand.

Cover type

Seral

stagea Overstory treed.b.h. (inches)

Overstory

tree CFA (%)

Assigned

average CFA (%)

Stand age

range (years)

SMC_M ED <5 (13 cm) <25 10 0–35

MDO 5–19.9 (13–50.5 cm) <40 30 15–175

MDM 5–19.9 40–70 55 15–175

MDC 5–19.9 >70 85 15–175

LDO ≥20 (51 cm) <40 30 ≥75

LDM ≥20 40–70 55 ≥75

LDC ≥20 >70 85 ≥75

SMC_X ED <5 <25 10 0–65

MDO 5–19.9 <40 30 30–245

MDM 5–19.9 40–70 55 30–245

MDC 5–19.9 >70 85 30–245

LDO ≥20" <40 30 ≥120

LDM ≥20 40–70 55 ≥120

LDC ≥20 >70 85 ≥120

SMC_U ED <5 <25 10 0–115

MDO 5–19.9 <40 30 60–335

MDM 5–19.9 40–70 55 60–335

MDC 5–19.9 >70 85 60–335

LDO ≥20 <40 30 ≥180

LDM ≥20 40–70 55 ≥180

LDC ≥20 >70 85 ≥180

SMC_ASP ED-A <5 <25 10 0–5 

MD-A 5–15.9 >40 60 10–105

MD-AC 5–19.9 >40 70 60–205

LD-CA ≥20 >70 85 160–275

LDC ≥20 >70 85 ≥230

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; 

LD-CA = Late - Conifer and Aspen. 
USDA Forest Service RMRS-GTR-385. 2018 227

Table B9.2—Mapping rules for SMC seral stages. Diameter at breast height (d.b.h.) and cover from above (CFA) 

values were taken from EVeg polygons. Categories for d.b.h. are: null, 0–0.9, 1–4.9, 5–9.9, 10–19.9, 20–29.9, ≥30. 

CFA categories (%) are: null, 0–10, 10–20, ... , 90–100. Each row should be read with a Boolean AND across each 

column. Within each seral stage the rows should be read with a Boolean OR across rows.

Seral stagea Overstory tree

d.b.h. 1 (inches)

Overstory tree

d.b.h. 2 (inches)

Total tree

CFA (%)

Conifer

CFA (%)

Hardwood

CFA (%)

ED null any any any any

ED 0–4.9 (0–12.4 cm) any any any any

MDO 5–19.9 (13–50.5 cm) any null null null

MDO 5–19.9 any 0–40 any any

MDO 5–19.9 any null 0–40 null

MDM 5–19.9 any 40–70 any any

MDM 5–19.9 any null 40–70 null

MDC 5–19.9 any 70–100 any any

MDC 5–19.9 any null 70–100 any

LDO ≥20 (51 cm) any null null null

LDO ≥20 any 0–40 any any

LDO ≥20 any null 0–40 null

LDM ≥20 any 40–70 any any

LDM ≥20 any null 40–70 any

LDC ≥20 any 70–100 any any

LDC ≥20 any null 70–100 any

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
228 USDA Forest Service RMRS-GTR-385. 2018

Table B9.3—Succession rules for SMC and SMC_ASP seral stages. Note that for SMC cover types, number of

years in current successional stage refers to the number of years in either early-development (ED), mid-development 

(MD), or late-development (LD) stage independent of canopy cover class, whereas for SMC_ASP it refers to the 

number of years in the corresponding seral stage. 

Cover type

From seral

stagea To seralstagea

Number of

years in current

successional

stage

Number of

years since

low-mortality

fire

Probability of

transition

SMC_M ED MDC 15–35 any 0.4

ED MDM 15–35 any 0.25

ED MDO 15–35 any 0.15

ED MDC 40 any 0.5

ED MDM 40 any 0.3

ED MDO 40 any 0.2

MDC LDC 60–135 any 0.4

MDC LDC 140 any 1.0

MDM LDM 60–135 any 0.4

MDM LDM 140 any 1.0

MDM MDC ≥15 ≥15 0.9

MDO LDO 60–135 any 0.4

MDO LDO 140 any 1.0

MDO MDM ≥15 ≥15 0.9

LDM LDC ≥15 ≥15 0.9

LDO LDM ≥15 ≥15 0.9

SMC_X ED MDO 30–65 any 0.4

ED MDO 70 any 1.0

MDC LDC 90–175 any 0.3

MDC LDC 180 any 1.0

MDM LDM 90–175 any 0.3

MDM LDM 180 any 1.0

MDM MDC ≥20 ≥20 0.3

MDO LDO 90–175 any 0.3

MDO LDO 180 any 1.0

MDO MDM ≥20 ≥20 0.3

LDM LDC ≥20 ≥20 0.3

LDO LDM ≥20 ≥20 0.3

(Table B9.3 continued on next page.)
USDA Forest Service RMRS-GTR-385. 2018 229

Table B9.3—Succession rules for SMC and SMC_ASP seral stages. Note that for SMC cover types, number of

years in current successional stage refers to the number of years in either early-development (ED), mid-development 

(MD), or late-development (LD) stage independent of canopy cover class, whereas for SMC_ASP it refers to the 

number of years in the corresponding seral stage. 

Cover type

From seral

stagea To seralstagea

Number of

years in current

successional

stage

Number of

years since

low-mortality

fire

Probability of

transition

SMC_M ED MDC 15–35 any 0.4

ED MDM 15–35 any 0.25

ED MDO 15–35 any 0.15

ED MDC 40 any 0.5

ED MDM 40 any 0.3

ED MDO 40 any 0.2

MDC LDC 60–135 any 0.4

MDC LDC 140 any 1.0

MDM LDM 60–135 any 0.4

MDM LDM 140 any 1.0

MDM MDC ≥15 ≥15 0.9

MDO LDO 60–135 any 0.4

MDO LDO 140 any 1.0

MDO MDM ≥15 ≥15 0.9

LDM LDC ≥15 ≥15 0.9

LDO LDM ≥15 ≥15 0.9

SMC_X ED MDO 30–65 any 0.4

ED MDO 70 any 1.0

MDC LDC 90–175 any 0.3

MDC LDC 180 any 1.0

MDM LDM 90–175 any 0.3

MDM LDM 180 any 1.0

MDM MDC ≥20 ≥20 0.3

MDO LDO 90–175 any 0.3

MDO LDO 180 any 1.0

MDO MDM ≥20 ≥20 0.3

LDM LDC ≥20 ≥20 0.3

LDO LDM ≥20 ≥20 0.3

SMC_U ED MDO 60–115 any 0.2

ED MDO 120 any 1.0

MDC LDC 120–215 any 0.2

MDC LDC 220 any 1.0

MDM LDM 120–215 any 0.2

MDM LDM 220 any 1.0

MDM MDC ≥30 ≥30 0.1

MDO LDO 120–215 any 0.2

MDO LDO 220 any 1.0

MDO MDM ≥30 ≥30 0.1

LDM LDC ≥30 ≥30 0.1

LDO LDM ≥30 ≥30 0.1

SMC_ASP ED-A MD-A 10 any 1.0

MD-A MD-AC 50–95 any 0.6

MD-A MD-AC 100 any 1.0

MD-AC LD-CA 100 any 1.0

LD-CA LDC 70 any 1.0

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; 

LD-CA = Late - Conifer and Aspen. 

(Table B9.3 continued) 
230 USDA Forest Service RMRS-GTR-385. 2018

Table B9.4—Summary of SMC and SMC_ASP seral stage transitions: earliest, latest, and average stand age 

(number of years since the last stand-replacing disturbance) for the transition to the next seral stage; and average 

number of years without low-mortality fire to transition to the next canopy cover class.

Cover type

From

seral

stagea To seralstagea

Earliest

stand age

(years) at

transition

Latest

stand age

(years) at

transition

Average

stand age

(years) at

transition

Average no. of

years without

low-mortality

fire to

transition

SMC_M ED MD 15 40 16 n/a

MD LD 75 180 83 n/a

MDO MDM n/a n/a n/a 16

MDM MDC n/a n/a n/a 16

LDO LDM n/a n/a n/a 16

LDM LDC n/a n/a n/a 16

SMC_X ED MD 30 70 37 n/a

MD LD 120 250 139 n/a

MDO MDM n/a n/a n/a 32

MDM MDC n/a n/a n/a 32

LDO LDM n/a n/a n/a 32

LDM LDC n/a n/a n/a 32

SMC_U ED MD 60 120 79 n/a

MD LD 180 340 219 n/a

MDO MDM n/a n/a n/a 70

MDM MDC n/a n/a n/a 70

LDO LDM n/a n/a n/a 70

LDM LDC n/a n/a n/a 70

SMC_ASP ED-A MD-A 10 10 10 n/a

MD-A MD-AC 60 110 63 n/a

MD-AC LD-CA 160 210 163 n/a

LD-CA LDC 230 280 233 n/a

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; 

LD-CA = Late - Conifer and Aspen. 
USDA Forest Service RMRS-GTR-385. 2018 231

Table B9.5—Weibull function parameters associated with the susceptibility of a cell to wildfire based on fuels (i.e., 

vegetation cover type, seral stage, and number of years since the last fire) and the probability of a high-mortality 

wildfire by cover type and seral stage for the SMC and SMC_ASP cover types (original values in parentheses). 

Weibull parameters

Cover type

Seral

stagea

Target fire

rotation period

(years)

Mean return

interval (years) Shape

Probability of

high-mortality fire

SMC_M n/a 29 n/a n/a n/a

ED n/a 44 3 0.67 (1.00)

MDO n/a 10 3 0.06 (0.14)

MDM n/a 13 3 0.09 (0.17)

MDC n/a 19 3 0.16 (0.23)

LDO n/a 8 3 0.03 (0.08)

LDM n/a 13 3 0.06 (0.14)

LDC n/a 34 3 0.19 (0.37)

SMC_X n/a 22 n/a n/a n/a

ED n/a 32 3 0.80 (1.00)

MDO n/a 9 3 0.03 (0.09)

MDM n/a 10 3 0.06 (0.26)

MDC n/a 11 3 0.15 (0.48)

LDO n/a 8 3 0.01 (0.05)

LDM n/a 10 3 0.03 (0.11)

LDC n/a 16 3 0.10 (0.25)

SMC_U n/a 60 n/a n/a n/a

ED n/a 89 3 0.67 (1.00)

MDO n/a 21 3 0.06 (0.14)

MDM n/a 27 3 0.09 (0.17)

MDC n/a 39 3 0.16 (0.23)

LDO n/a 16 3 0.03 (0.08)

LDM n/a 27 3 0.06 (0.14)

LDC n/a 69 3 0.19 (0.37)

SMC_ASP n/a 29 n/a n/a n/a

ED-A n/a 44 3 1 

(Table B9.5 continued on next page.)
232 USDA Forest Service RMRS-GTR-385. 2018

MD-A n/a 19 3 0.26

MD-AC n/a 13 3 0.18

LD-CA n/a 13 3 0.14

LDC n/a 34 3 0.37

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; 

LD-CA = Late - Conifer and Aspen. 

Table B9.5—Weibull function parameters associated with the susceptibility of a cell to wildfire based on fuels (i.e., 

vegetation cover type, seral stage, and number of years since the last fire) and the probability of a high-mortality 

wildfire by cover type and seral stage for the SMC and SMC_ASP cover types (original values in parentheses). 

Weibull parameters

Cover type

Seral

stagea

Target fire

rotation period

(years)

Mean return

interval (years) Shape

Probability of

high-mortality fire

SMC_M n/a 29 n/a n/a n/a

ED n/a 44 3 0.67 (1.00)

MDO n/a 10 3 0.06 (0.14)

MDM n/a 13 3 0.09 (0.17)

MDC n/a 19 3 0.16 (0.23)

LDO n/a 8 3 0.03 (0.08)

LDM n/a 13 3 0.06 (0.14)

LDC n/a 34 3 0.19 (0.37)

SMC_X n/a 22 n/a n/a n/a

ED n/a 32 3 0.80 (1.00)

MDO n/a 9 3 0.03 (0.09)

MDM n/a 10 3 0.06 (0.26)

MDC n/a 11 3 0.15 (0.48)

LDO n/a 8 3 0.01 (0.05)

LDM n/a 10 3 0.03 (0.11)

LDC n/a 16 3 0.10 (0.25)

SMC_U n/a 60 n/a n/a n/a

ED n/a 89 3 0.67 (1.00)

MDO n/a 21 3 0.06 (0.14)

MDM n/a 27 3 0.09 (0.17)

MDC n/a 39 3 0.16 (0.23)

LDO n/a 16 3 0.03 (0.08)

LDM n/a 27 3 0.06 (0.14)

LDC n/a 69 3 0.19 (0.37)

SMC_ASP n/a 29 n/a n/a n/a

ED-A n/a 44 3 1 

(Table B9.5 continued) 
USDA Forest Service RMRS-GTR-385. 2018 233

Table B9.6—Disturbance rules for SMC and SMC_ASP cover types governing seral stage transitions following a 

low-mortality wildfire. Note that conditions in which low-mortality wildfire has no effect are not listed.

Cover type From seral stagea

 To seral stagea Probability of transition

SMC_M MDC MDM 0.09

MDC MDO 0.09

MDM MDO 0.24

LDC LDM 0.27

LDC LDO 0.27

LDM LDO 0.24

SMC_X MDC MDM 0.21

MDC MDO 0.21

MDM MDO 0.32

LDC LDM 0.28

LDC LDO 0.28

LDM LDO 0.30

SMC_U MDC MDM 0.09

MDC MDO 0.09

MDM MDO 0.24

LDC LDM 0.27

LDC LDO 0.27

LDM LDO 0.24

SMC_ASP LDC LD-CA 0.54

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; LD-CA = Late - Conifer and Aspen. 
234 USDA Forest Service RMRS-GTR-385. 2018

Table B9.7—Disturbance rules for SMC and SMC_ASP cover types governing seral stage transitions following 

vegetation treatments. Note that treatments not affecting seral-stage transitions (e.g., mastication) are not included 

here.

Cover type Treatment type

From seral

stagea To seralstagea Probability oftransition

SMC_M Clearcut and Group cuts Any ED 1 

Thinning, including cells thinned in: 

(1) matrix thin and group cut; (2) 

thin and burn; (3) thin, hand cut, 

pile, and burn; (4) thin, masticate, 

and burn; and (5) matrix thin, group 

cut, and burn treatments

MDC MDM 1 

MDM MDO 1 

LDC LDM 1 

LDM LDO 1 

Prescribed fire (including cells 

burned as part of a prescribed fireonly treatment); "cool" burns/"hot" 

burn transition probabilities

MDC MDM 0.03/0.05

MDC MDO 0/0.03

MDC ED 0/0.01

MDM MDO 0.05/0.14

MDM ED 0/0.01

LDC LDM 0.02/0.03

LDC LDO 0/0.02

LDC ED 0/0.01

LDM LDO 0.04/0.11

LDM ED 0/0.01

Thin and burn, including cells 

burned only as part of: (1) thin and

burn; and (2) hand cut, pile, and

burn treatments

MDC MDM 0.03

MDM MDO 0.05

LDC LDM 0.02

LDM LDO 0.04

SMC_X; 

SMC_U

Clearcut and Group cuts Any ED 1 

Thinning, including cells thinned in: 

(1) matrix thin and group cut; (2) 

thin and burn; (3) thin, hand cut, 

pile, and burn; (4) thin, masticate, 

and burn; and (5) matrix thin, group 

cut, and burn treatments

MDC MDM 1 

MDM MDO 1 

LDC LDM 1 

LDM LDO 1 

(Table B9.7 continued on next page.)
USDA Forest Service RMRS-GTR-385. 2018 235

Cover type Treatment type

From seral

stagea To seralstagea Probability oftransition

Prescribed fire (including cells 

burned as part of a prescribed fireonly treatment) "cool" burns/"hot" 

burn transition probabilities

MDC MDM 0.05/0.15

MDC MDO 0.03/0.09

MDC ED 0.01/0.03

MDM MDO 0.05/0.15

MDM ED 0.01/0.03

LDC LDM 0.04/0.12

LDC LDO 0.02/0.06

LDC ED 0.01/0.03

LDM LDO 0.04/0.12

LDM ED 0.01/0.03

Thin and burn, including cells 

burned only as part of: (1) thin and 

burn; and (2) hand cut, pile, and

burn treatments

MDC MDM 0.05

MDC MDO 0.03

MDC ED 0.01

MDM MDO 0.05

MDM ED 0.01

LDC LDM 0.04

LDC LDO 0.02

LDC ED 0.01

LDM LDO 0.04

LDM ED 0.01

SMC_ASP Thinning MD_AC MD_A 1 

LDC MD_A 1 

LD_CA MD_A 1 

Prescribed fire LDC LD_CA 0.54

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; 

LD-CA = Late - Conifer and Aspen. 

(Table B9.7 continued) 
236 USDA Forest Service RMRS-GTR-385. 2018

10. Subalpine Conifer (SCN) 

Reviewed by: (1) Hugh Safford, Regional Ecologist, USDA Forest Service; (2) Becky Estes, 

Central Sierra Province Ecologist, USDA Forest Service; and (3) Marc Meyer, Southern Sierra 

Province Ecologist, USDA Forest Service.

Cover Type Classification and Crosswalks

Subalpine Conifer (SCN) Variant

▪ EVeg: Regional Dominance Type 1:

◦ Alpine Mixed Scrub

◦ Mountain Hemlock

◦ Subalpine Conifers

◦ Whitebark Pine

▪ Presettlement Fire Regime Type:

◦ Subalpine Conifer

▪ LandFire BpS models: 

◦ 0610330: Mediterranean California Subalpine Woodland

◦ 0610440: Northern California Mesic Subalpine Woodland

◦ 0610710: Sierra Nevada Alpine Dwarf-Shrubland

Subalpine Conifer with Aspen (SCN_ASP) Variant

This type was created by overlaying the NRIS TERRA Inventory of Aspen on top of the 

EVeg layer. Where it intersected with SCN, it was assigned to SCN_ASP.

Vegetation Description

Subalpine Conifer (SCN) Variant

The Subalpine Conifer landscape is composed of a mosaic of subalpine forests and 

woodlands, meadows, rock outcrops, and scrub vegetation types. These forests are open stands 

of conifers occurring on generally sandy soils or rocky slopes at elevations above the upper 

montane forest stands of Abies magnifica. Stand densities are low. Many, but not all, species 

form shrubby krummholz forms of growth near their upper elevational limits (Fites-Kaufman 

et al. 2007). 

Tsuga mertensiana is often the most common tree species and mixes with P. contorta 

ssp. murrayana, A. magnifica, Pinus monticola, and P. albicaulis. In some areas, P. contorta 

ssp. murrayana dominates postdisturbance stands. Tsuga mertensiana seedlings are relatively 

shade-tolerant compared to other subalpine conifers and do well under closed-canopy 

conditions. Pinus albicaulis presence increases in the southern portion of the project area 

(Fites-Kaufman et al. 2007; LandFire 2007j).

Treeline growth of multistemmed trees and shrubby krummholz growth of conifers varies 

with latitude in the Sierra Nevada. Treeline in the northern Sierra Nevada is dominated by P. 

albicaulis, which frequently occurs with a krummholz form of growth near its upper limit. 

Several other species may also form krummholz growth forms, including Sierra juniper, T. 

mertensiana, P. contorta ssp. murrayana, and occasionally P. jeffreyi (Fites-Kaufman et al. 

2007). 

Although typically of minor importance, a shrub understory may include Arctostaphylos, 

Ribes, Phyllodoce, and Vaccinium, and Kalmia can occur on moist sites. Herbs present may 

include Lupinus, Hieracium, Arabis, Aster, and Erigeron. Carex and various grasses are also 

common (LandFire 2007j; Verner and Purcell 1988).
USDA Forest Service RMRS-GTR-385. 2018 237

Subalpine Conifer With Aspen (SCN_ASP) Variant 

This SCN variant occurs when these upland forests and woodlands are dominated by 

Populus tremuloides without a significant conifer component. Conifers may be present in 

these systems; however, these patches of P. tremuloides are not typically successional to 

conifers. The understory structure may be complex with multiple shrub and herbaceous 

layers, or simple with just an herbaceous layer. The herbaceous layer may be dense or sparse, 

dominated by graminoids or forbs. Common shrubs include Acer, Amelanchier, Artemisia, 

Juniperus, Prunus, Rosa, Shepherdia, Symphoricarpos, and the dwarf-shrubs Mahonia

and Vaccinium. Common graminoids may include Bromus, Calamagrostis, Carex, Elymus, 

Festuca, and Hesperostipa. Associated forbs may be Achillea, Eucephalus, Delphinium, 

Geranium, Heracleum, Ligusticum, Lupinus, Osmorhiza, Pteridium, Rudbeckia, Thalictrum, 

Valeriana, Wyethia, and many others (LandFire 2007m).

Distribution

Subalpine Conifer (SCN) Variant

The elevational distribution of subalpine forest communities varies with latitude. In the 

northern Sierra Nevada, such stands begin around 2,450 meters (8,000 feet) and extend up to 

treeline at 2,750–3,100 meters (9,000–11,000 feet). Both upper and lower limits of subalpine 

species distributions are driven by a variety of factors, including soil resources, water 

availability, and climatic limiting factors (Fites-Kaufman et al. 2007).

These forests are characterized by a relatively short growing season with cool 

temperatures. With the exception of occasional summer thunderstorms, most precipitation 

falls as snow. Wet years with abundant snowfall can limit growth as these may produce 

late-lying snowfields that reduce the length of the growing season. Winds can be severe, 

particularly around exposed ridges. Such wind conditions may produce snow-free winter 

areas that lower soil temperatures and increase plant water stress (Fites-Kaufman et al. 

2007). Because of the solid granite parent material, areas with deeper soil accumulation can 

become waterlogged for much of the year. For these reasons, the length of the growing season 

is a function of not only early-season limitation due to low temperatures and snowfields, 

but also late-season limitations due to drought. Studies of the dynamics of alterations of 

treeline elevation over the past several millennia have reinforced the significance of complex 

interactions of both temperature and seasonal water availability in determining such changes 

(Fites-Kaufman et al. 2007). 

Subalpine Conifer with Aspen (SCN_ASP) Variant 

Sites supporting P. tremuloides are usually associated with added soil moisture, that 

is, azonal wet sites. These sites are found throughout the SCN zone, often close to streams, 

lakes, and meadows. Other sites include rock reservoirs, springs, and seeps. Terrain can be 

simple to complex. At lower elevations, topographic conditions for this type tend toward 

positions resulting in relatively colder, wetter conditions within the prevailing climate (e.g., 

ravines, north slopes, wet depressions) (LandFire 2007m). Populus tremuloides stands may 

also be associated with lateral or terminal moraine boulder material, talus-colluvium, rock 

falls, or lava flows. In addition, pure stands may be found in topographic positions where 

snow accumulates, mostly at higher north-facing elevations, where snow presence means the 

growing season is too short to support conifers (Shepperd et al. 2006).
238 USDA Forest Service RMRS-GTR-385. 2018

Disturbances

Wildfire

Subalpine Conifer (SCN) Variant

Most of the subalpine areas of the Sierra Nevada were subjected to repeated glaciation 

during the Pleistocene, and thus have thin and poorly developed soils with little organic 

matter. The small amounts of litter accumulation and open stand structure of subalpine forests 

mean that fire is rare (Fites-Kaufman et al. 2007). It is, however, the major disturbance event 

of this type (LandFire 2007j). Meyer’s (2013) review suggests that historical and current fire 

regimes in subalpine forests are normally climate-limited and dominated by surface fires with 

crown fires occurring occasionally. 

Subalpine Conifer with Aspen (SCN_ASP) Variant

Sites supporting P. tremuloides are maintained by stand-replacing disturbances that allow 

regeneration from belowground suckers. Replacement fire and ground fire are thought to have 

been common in stable P. tremuloides stands historically. Because P. tremuloides is associated 

with mesic conditions, it rarely burns during the normal lightning season. However, during 

years with little precipitation, stands may be more susceptible to burning. Evidence from fire 

scars and historical studies show that past fires occurred mostly during the spring and fall. 

These are typically self-perpetuating stands (LandFire 2007b).

Other Disturbances

Other disturbances are not currently being modeled, but may, depending on the seral stage 

and mortality levels, reset patches to early development, maintain existing seral stages, or shift 

or accelerate succession to a more open condition. All of the tree species associated with this 

vegetation type are susceptible to a wide variety of pathogens and insects.

Seral Stages

The classification of seral stages originated from the corresponding LandFire biophysical 

setting models, but with some modifications (e.g., the addition of a moderate canopy cover 

stage in the mid- and late-seral stages) based on expert input, as follows and as summarized in 

table B10.1. The seral stage map corresponding to this classification for the current landscape 

was derived from the EVeg dataset and the rules in table B10.2 for the SCN cover types; 

SCN_ASP seral stages were mapped manually by using NAIP 2010 Color IR imagery.

Subalpine Conifer (SCN) Variant

• Early Development (ED)

This seral stage is characterized by bare ground, herbs, shrubs, and varying densities 

of tree seedlings (presumably dependent on seed sources). Dominant species include 

coniferous tree seedlings, resprouting grasses and shrubs, and invading herbs. Shrubs 

include Ribes species. Herbs and grasses include Aster, Pedicularis, Hieracium, Arabis, 

Erigeron, Carex, Luzula, and Poa (LandFire 2007j). Note that there can be a residual or 

legacy of overstory trees from the predisturbance stand making up less than 25 percent 

canopy cover.

• Mid-Development – Open Canopy Cover (MDO)

This seral stage is characterized by a heterogeneous ground cover of grasses, forbs, and 

shrubs, with a low canopy cover (<40 percent) of pole- to medium-sized (5–20 inches 

d.b.h.) conifers. This seral stage results from delayed tree regeneration and long-term 
USDA Forest Service RMRS-GTR-385. 2018 239

domination by shrubs and herbs. Shrubs include Ribes species. Forbs and grasses include 

Aster, Pedicularis, Hieracium, Arabis, Erigeron, Carex, Luzula, and Poa. Trees are 

represented by seedlings and saplings of P. contorta ssp. murrayana, T. mertensiana, A. 

magnifica, and P. monticola (LandFire 2007j).

• Mid-Development – Moderate Canopy Cover (MDM)

This seral stage is characterized by a moderate canopy cover (40–70 percent) of pole- to 

medium-sized conifers, resulting from rapid regeneration of conifers following standreplacing disturbance, and is otherwise similar to MDO.

• Mid-Development – Closed Canopy Cover (MDC)

This seral stage is characterized by a dense canopy cover (>70 percent) of pole- to 

medium-sized conifers, resulting from rapid regeneration of conifers following standreplacing disturbance, and is otherwise similar to MDO. 

• Late Development – Open Canopy Cover (LDO)

This seral stage is characterized by a heterogeneous ground cover of grasses, forbs, and 

low shrubs, with a low canopy cover (<40 percent) of large trees (>20 inches d.b.h.). 

The open stand structure is maintained by mixed-severity fire and insect-caused tree 

mortality (the latter not modeled at this time). Shrubs include Ribes species. Forbs and 

grasses include Aster, Pedicularis, Hieracium, Arabis, Erigeron, Carex, Luzula, and Poa

(LandFire 2007j).

• Late Development – Moderate Canopy Cover (LDM)

This seral stage is characterized by an overstory of large trees with canopy cover 40–70 

percent, and is otherwise similar to LDO.

• Late Development – Closed Canopy Cover (LDC)

This seral stage is characterized by an overstory of large trees with canopy cover greater 

than 70 percent, and is otherwise similar to LDO.

Subalpine Conifer with Aspen (SCN_ASP) Variant 

• Early Development – Aspen (ED–A)

This seral stage is characterized by the recruitment of a new cohort of early-successional, 

shade-intolerant tree species (primarily P. tremuloides) into an open area created by a 

stand-replacing disturbance. Note that there can be a residual or legacy of overstory trees 

from the predisturbance stand making up less than 25 percent canopy cover. Following 

disturbance, succession proceeds rapidly from an herbaceous layer to shrubs and trees, 

which invade together (Verner 1988a). Populus tremuloides suckers over 2 meters tall 

develop within about 10 years (LandFire 2007b).

• Mid-Development – Aspen (MD–A)

This seral stage is characterized by P. tremuloides trees 5–16 inches d.b.h. Canopy cover 

is highly variable, and can range from 40 to 100 percent. Some understory conifers are 

encroaching, but P. tremuloides is still the dominant component of the stand (LandFire 

2007b).

• Late Development – Conifer with Aspen (LD–CA)

If stands are sufficiently protected from fire such that conifer species encroach, they may 

eventually overtop the P. tremuloides, which are predominantly larger than 16 inches d.b.h. 

Total canopy cover is high variable, but generally more than 50 percent. The conifers in 

this stage may be able to withstand some fire that the more sensitive P. tremuloides cannot. 

When this occurs, it creates a stand characterized by late-development conifers, but with

P. tremuloides present in the midstory and understory at varying densities depending on the 

disturbance history.
240 USDA Forest Service RMRS-GTR-385. 2018

Model Parameterization

This section includes a listing of the model parameters that are cover type-specific. Note 

that there are additional model parameters not specific to a cover type (e.g., climate modifier) 

that ultimately affect the model processes and outcomes, and these are discussed under the 

Methods section in McGarigal et al. 2018.

Succession

The rules (i.e., parameters) governing succession for the SCN and SCN_ASP cover types 

are listed in table B10.3. These rules were initially based on the corresponding LandFire 

BpS descriptions (LandFire 2007j,m) and associated models created by using the Vegetation 

Dynamics Development Tool (VDDT), as modified by Safford and Estes. They were 

subsequently modified based on expert input to include probabilistic rather than deterministic 

seral stage transitions. Specifically, we modified the rules so that stands would gradually, 

instead of abruptly, transition from one seral stage to the next to reflect stochasticity in the 

real-world processes governing succession. For example, the first three rules dictate that a 

cell in the SCN cover type, which has been in the ED seral stage for 20–75 years, will have a 

20-percent chance of transitioning to MDC, a 15-percent chance of transitioning to MDM, and 

a 5-percent chance of transitioning to MDO at the beginning of each timestep. Thus, stands 

will randomly begin transitioning to one of the MD stages after 20 years in the ED stage, 

but some stands could remain in the ED stage for as much as 80 years to reflect delayed tree 

establishment. Note that for stands currently in the ED stage and between 20 and 75 years 

in this stage, the combined chance of transitioning to MD at each timestep is 40 percent; 

therefore, there is a 60-percent chance of remaining in the ED seral stage at each timestep. The 

next three rules together dictate that a cell that has been in the ED seral stage for 80 years will 

have a 100-percent chance of transitioning to one of the MD seral stages; thus, all stands will 

have transitioned to the MD stage after 80 years since establishment.

Applying the succession rules listed in table B10.3 results in stands transitioning between 

seral stages in a probabilistic rather than deterministic manner, such that we can compute the 

average stand age (years) for the transition to the next seral stage, as shown in table B10.4. 

For example, the first row in table B10.4 indicates that for a cell in the SCN cover type in 

the ED seral stage, the earliest stand age (i.e., number of years since the last stand-replacing 

disturbance) for transitioning to one of the MD seral stages is 20 years, the latest stand age 

is 80 years, and the average stand age at the time of the transition is 27 years. Also, the third 

row in table B10.4 indicates that a cell in the SCN cover type in the MDO seral stage will, on 

average, take 52 years without a low-mortality fire disturbance to transition to the MDM seral 

stage (i.e., transition from an open-canopy cover, <40 percent, to a moderate-canopy cover, 

40–70 percent, condition). Note that a low-mortality fire every 40 years will maintain the 

stand in the open-canopy condition.

Wildfire Disturbance

Rotation Period

Wildfire rotation period (equivalent to the point-specific mean return interval) is not 

formally a model parameter, but rather is specified as a target value to be achieved through 

model calibration. Target fire rotation periods (FRPs) were specified by cover type (table 

B10.5). FRP for the SCN cover type was based on Mallek et al. (2013), whereas the FRP for 

the SCN_ASP cover type was based on the corresponding LandFire BpS model (LandFire 

2007m) and expert input from Safford and Estes.

Susceptibility—The only cover type-specific factor affecting susceptibility of a cell to 

wildfire was fuel characteristics, as represented by vegetation cover type, seral stage, and time 

since the last wildfire, which was represented as a relative probability.
USDA Forest Service RMRS-GTR-385. 2018 241

Fuels (vegetation and disturbance history)—Fuels, as represented by vegetation cover 

type, seral stage, and recent disturbance history, were treated as having a dynamic (i.e., 

changing over time) effect on the relative susceptibility of a cell to wildfire. Specifically, 

susceptibility varied among cover types and seral stages in relation to the time (number of 

years) since the last fire according to the cumulative Weibull function and the parameters 

listed in table B10.5 (e.g., as illustrated in figure B.1). Note that here we use the cumulative 

form of the Weibull distribution, which gives the cumulative probability of a disturbance 

for any number of years since the last disturbance. Thus, the probability increases from 0 

immediately following a fire to approaching 1 after a certain number of years since the last 

fire, depending on the specified mean return interval (MRI) and shape parameters of the 

Weibull function. Holding Shape constant, and all other things being equal, as MRI increases 

the curve shifts to the right, resulting in a lower probability for any given number of years 

since the last disturbance. In this manner, varying the MRI among cover types and seral stages 

affects the relative susceptibility to wildfire.

The specified Weibull MRI parameters were based on the corresponding LandFire BpS 

descriptions (LandFire 2007j,m) and associated VDDT models, as modified by Safford and 

Estes. 

Importantly, although susceptibility of the various seral stages is determined by MRI 

(holding Shape constant), these return intervals should not be interpreted literally, as the 

concept of a return interval does not meaningfully apply to a dynamic seral stage. Moreover, 

these MRIs were derived from the LandFire BpS descriptions and associated VDDT models, 

as modified by Safford and Estes; taken collectively, these values do not necessarily agree 

with the target FRPs for the cover types. Thus, the MRIs assigned to each cover type and 

seral stage should be interpreted as relative values that affect the relative susceptibility of the 

various vegetation states. 

Mortality

The only cover type-specific factor affecting overstory mortality following wildfire (i.e., 

fire severity) was fuel characteristics, as represented by vegetation cover type and seral stage, 

which was represented as a relative probability.

Fuels (vegetation)—Fuels, as represented by vegetation cover type and seral stage, were 

treated as having a dynamic (i.e., changing over time) effect on the relative probability of a 

high-mortality response to wildfire. Specifically, we assigned a probability of high-mortality 

response to wildfire to each cover type and seral stage (table B10.5); values were based on the 

corresponding LandFire BpS descriptions (LandFire 2007j,m) and associated VDDT models, 

as modified by Safford and Estes.

Disturbance Transitions

The rules (i.e., parameters) governing seral stage transitions following low-mortality 

wildfire disturbance for the SCN cover type are listed in table B10.6. These rules were initially 

based on the corresponding LandFire BpS descriptions (LandFire 2007j) and associated 

models created by using the Vegetation Dynamics Development Tool (VDDT), as modified by 

Safford and Estes, but were subsequently modified to include the moderate canopy cover seral 

stages not present in the VDDT models. Note that rules governing transitions following highmortality wildfire are not listed in table B10.6 because high-mortality wildfires always result 

in transition to the ED seral stage. In addition, conditions in which low-mortality wildfire has 

no effect on the seral stage (i.e., does not cause a transition) are not listed. For example, the 

first two rules dictate that a low-mortality wildfire in a cell of SCN in the MDC seral stage has 

a 50-percent chance of transitioning to the MDM stage, a 50-percent chance of transitioning 

to the MDO stage, and (by implication) a 0-percent chance of remaining in the MDC stage. 
242 USDA Forest Service RMRS-GTR-385. 2018

In addition, by implication (given the absence of a rule), a low-mortality wildfire in the ED, 

MDO, or LDO seral stage has no effect other than to maintain the cell in that seral stage. 

Similarly, for cells in the SCN_ASP cover type, all low-mortality wildfires maintain the stand 

in the current seral stage and thus are not listed.

Table B10.1—Summary of SCN and SCN_ASP seral stage characteristics: average overstory tree diameter at 

breast height (d.b.h.) of the dominant and codominant trees, overstory tree percent cover from above (CFA), 

assigned average CFA value for classifying the landscape by percent canopy cover, and range of stand ages 

(number of years since the last stand-replacing disturbance) possible for the corresponding seral stage. Note that

overstory tree d.b.h. and CFA for the ED/ED-A seral stages refer to the residual or legacy overstory from the predisturbance stand.

Cover type

Seral

stagea Overstory treed.b.h. (inches)

Overstory tree

CFA (%)

Assigned

average CFA (%)

Stand age

range (years)

SCN ED <5 (13 cm) <25 10 0–75

MDO 5–19.9 (13–50.5 

cm)

<40 30 20–215

MDM 5–19.9 40–70 55 20–215

MDC 5–19.9 >70 85 20–215

LDO ≥20 (51 cm) <40 30 ≥80

LDM ≥20 40–70 55 ≥80

LDC ≥20 >70 85 ≥80

SCN_ASP ED-A <5 <25 10 0–5 

MD-A 5–15.9 (13–40.4 

cm)

>40 60 10–205

LD-CA ≥16 (40.6 cm) >50 70 ≥90

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; LD-CA = Late - Conifer and Aspen. 
USDA Forest Service RMRS-GTR-385. 2018 243

Table B10.2—Mapping rules for SCN seral stages. Diameter at breast height (d.b.h.) and cover from above (CFA) 

values were taken from EVeg polygons. Categories for d.b.h. are: null, 0–0.9, 1–4.9, 5–9.9, 10–19.9, 20–29.9, ≥30. 

CFA categories (%) are: null, 0–10, 10–20, ... , 90–100. Each row should be read with a Boolean AND across each 

column. Within each seral stage the rows should be read with a Boolean OR across rows.

Seral stagea Overstory tree

d.b.h. 1 (inches)

Overstory tree

d.b.h. 2 (inches)

Total tree

CFA (%)

Conifer

CFA (%)

Hardwood

CFA (%)

ED null any any any any

ED 0–4.9 (0–12.4 cm) any any any any

MDO 5–19.9 (13–50.5 

cm)

any null null null

MDO 5–19.9 any 0–40 any any

MDO 5–19.9 any null 0–40 null

MDM 5–19.9 any 40–70 any any

MDM 5–19.9 any null 40–70 null

MDC 5–19.9 any 70–100 any any

MDC 5–19.9 any null 70–100 any

LDO ≥20 (51 cm) any null null null

LDO ≥20 any 0–40 any any

LDO ≥20 any null 0–40 null

LDM ≥20 any 40–70 any any

LDM ≥20 any null 40–70 any

LDC ≥20 any 70–100 any any

LDC ≥20 any null 70–100 any

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
244 USDA Forest Service RMRS-GTR-385. 2018

Table B10.3—Succession rules for SCN and SCN_ASP seral stages. Note that for SCN cover types, number of

years in current successional stage refers to the number of years in either early-development (ED), mid-development 

(MD), or late-development (LD) stage independent of canopy cover class, whereas for SCN_ASP it refers to the 

number of years in the corresponding seral stage. 

Cover type

From seral

stagea To seralstagea

Number of years

in current

successional

stage

Number of

years since

low-mortality

fire

Probability of

transition

SCN ED MDC 20-75 any 0.2

ED MDM 20-75 any 0.15

ED MDO 20-75 any 0.05

ED MDC 80 any 0.6

ED MDM 80 any 0.3

ED MDO 80 any 0.1

MDC LDC 60-135 any 0.4

MDC LDC 140 any 1.0

MDM LDM 60-135 any 0.4

MDM LDM 140 any 1.0

MDM MDC ≥40 ≥40 0.3

MDO LDO 60-135 any 0.4

MDO LDO 140 any 1.0

MDO MDM ≥40 ≥40 0.3

LDM LDC ≥40 ≥40 0.3

LDO LDM ≥40 ≥40 0.3

SCN_ASP ED-A MD-A 10 any 1.0

MD-A LD-CA 80-195 any 0.3

MD-A LD-CA 200 any 1.0

a ED = Early - all structures; MDC = Mid-closed; MDM = Mid-moderate; MDO = Mid-open; LDC = Late-closed; LDM = 

Late-moderate; LDO = Late-open; ED-A = Early – Aspen; MD-A = Mid – Aspen; LD-CA = Late - Conifer and Aspen. 
USDA Forest Service RMRS-GTR-385. 2018 245

Table B10.4— Summary of SCN and SCN_ASP seral stage transitions: earliest, latest, and average stand age 

(number of years since the last stand-replacing disturbance) for the transition to the next seral stage; and average 

number of years without low-mortality fire to transition to the next canopy cover class.

Cover type

From seral

stagea To seralstagea

Earliest

stand age

(years) at

transition

Latest

stand age

(years) at

transition

Average

stand age

(years) at

transition

Average no.

ofyears without

low-mortality

fire to

transition

SCN ED MD 20 80 27 n/a

MD LD 80 220 94 n/a

MDO MDM n/a n/a n/a 52

MDM MDC n/a n/a n/a 52

LDO LDM n/a n/a n/a 52

LDM LDC n/a n/a n/a 52

SCN_ASP ED-A MD-A 10 10 10 n/a

MD-A LD-CA 90 210 102 n/a

wildfire by cover type and seral stage for the SCN and SCN_ASP cover types.

Weibull parameters

Cover type

Seral 

stagea

Target fire

rotation period

(years)

Mean return

interval (years) Shape

Probability of

high-mortality fire

SCN n/a 296 n/a n/a n/a

ED n/a 500 3 1.00

MDO n/a 303 3 0.61

MDM n/a 317 3 0.63

MDC n/a 333 3 0.67

LDO n/a 303 3 0.61

LDM n/a 317 3 0.63

LDC n/a 333 3 0.67

SCN_ASP n/a 296 n/a n/a n/a

ED-A n/a 200 3 1 

MD-A n/a 333 3 0.67

LD-CA n/a 317 3 0.63

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; LD-CA = Late - Conifer and Aspen. 
246 USDA Forest Service RMRS-GTR-385. 2018

Table B10.6—Disturbance rules for SCN cover types governing seral stage transitions following a low-mortality 

wildfire. Note that conditions in which low-mortality wildfire has no effect are not listed. 

Cover type

From seral

stagea To seral stagea Probability of transition

SCN MDC MDM 0.5

MDC MDO 0.5

MDM MDO 1.0

LDC LDM 0.5

LDC LDO 0.5

LDM LDO 1.0

a MDC = Mid-closed; MDM = Mid-moderate; MDO = Mid-open; LDC = Late-closed; LDM = Late-moderate; LDO = 

Late-open. 

Table B10.5—Weibull function parameters associated with the susceptibility of a cell to wildfire based on fuels (i.e., 

vegetation cover type, seral stage, and number of years since the last fire) and the probability of a high-mortality 

wildfire by cover type and seral stage for the SCN and SCN_ASP cover types.

Weibull parameters

Cover type

Seral

stagea

Target fire

rotation period

(years)

Mean return

interval (years) Shape

Probability of

high-mortality fire

SCN n/a 296 n/a n/a n/a

ED n/a 500 3 1.00

MDO n/a 303 3 0.61

MDM n/a 317 3 0.63

MDC n/a 333 3 0.67

LDO n/a 303 3 0.61

LDM n/a 317 3 0.63

LDC n/a 333 3 0.67

SCN_ASP n/a 296 n/a n/a n/a

ED-A n/a 200 3 1 

MD-A n/a 333 3 0.67

LD-CA n/a 317 3 0.63

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; LD-CA = Late - Conifer and Aspen. 
USDA Forest Service RMRS-GTR-385. 2018 247

11. Western White Pine (WWP)

Reviewed by: (1) Hugh Safford, Regional Ecologist, USDA Forest Service; and (2) Becky 

Estes, Central Sierra Province Ecologist, USDA Forest Service.

Cover Type Classification and Crosswalks

▪ EVeg: Regional Dominance Type 1:

◦ Western White Pines

▪ Presettlement Fire Regime Type: 

◦ Western White Pine

▪ LandFire BpS model: 

◦ 0711720: Sierran-Intermontane Desert Western White Pine-White Fir Woodland 

Vegetation Description

Pinus monticola is locally abundant in subalpine habitats along the west slope of the Sierra 

Nevada, where it may occur in small pure stands. More commonly, it mixes with P. contorta 

ssp. murrayana, P. jeffreyi, Tsuga mertensiana, and Abies magnifica (particularly on the west 

side of the Sierra crest) and A. concolor or P. ponderosa (particularly on the east side) (FitesKaufman et al. 2007; LandFire 2007t).

This system tends to be more woodland than forest in character, and the undergrowth is 

more open and drier, with little shrub or herbaceous cover. Tree regeneration is less prolific 

than in other mixed-montane conifer systems of the Cascades, Sierras, and California Coast 

Ranges (LandFire 2007t). Pinus monticola generally maintains a tree form of growth up 

nearly to treeline, where it is commonly replaced by other subalpine species on rocky ridges 

(Fites-Kaufman et al. 2007).

Understories are typically open, with moderately low shrub cover and diversity, and 

include Arctostaphylos, Chrysolepis, Ceanothus, and Ribes. Common herbaceous taxa include 

Arnica, Festuca, Poa, Carex, Pyrola, and Hieracium. In openings, Wyethia can be abundant 

(LandFire 2007t).

Distribution

In the northern Sierra Nevada, these forests and woodlands are found in the upper 

montane to subalpine zones, at elevations generally over 2,000 meters (6,600 feet). This cover 

type is found on all slopes and aspects, although it occurs more frequently on drier areas. 

This cover type generally occurs on basalts, andesite, glacial till, basaltic rubble, colluvium, 

or volcanic ash-derived soils. These soils have characteristic features of good aeration and 

drainage, coarse textures, circumneutral to slightly acidic pH, an abundance of mineral 

material, rockiness, and periods of drought during the growing season. This system occurs 

somewhat in the rainshadow of the Sierras and has a more continental climate, similar to the 

northern Great Basin (LandFire 2007t). 

Disturbances

Wildfire

Most fires in this cover type are low-mortality fires that allow large areas of the landscape 

to develop mature characteristics. Occasional severe fires are driven by weather extremes 

(LandFire 2007t). Young trees are very susceptible to mortality from fire, but mature P. monticola

is moderately fire resistant. After a stand-replacing fire, P. monticola will seed in from adjacent 
248 USDA Forest Service RMRS-GTR-385. 2018

areas. After a cool to moderate fire that leaves a mosaic of mineral soil and duff, it will reoccupy 

the site from seed stored in the seedbank. Overall, P. monticola is a fire-dependent, seral species. 

Fire suppression has resulted in decreased stocking levels, mostly due to the increase in white pine 

blister rust (Cronartium ribicola). Periodic, stand-replacing fire or other disturbance is needed to 

remove competing conifers and allow P. monticola to develop (Griffith 1992). 

Other Disturbances

Other disturbances are not currently being modeled, but may, depending on the seral stage 

and mortality levels, reset patches to early development, maintain existing seral stages, or shift 

or accelerate succession to a more open condition. All of the tree species associated with this 

vegetation type are susceptible to a wide variety of pathogens and insects.

Seral Stages

The classification of seral stages originated from the corresponding LandFire biophysical 

setting model, but with some modifications (e.g., the addition of a moderate canopy cover 

stage in the mid- and late-seral stages) based on expert input, as follows and as summarized in 

table B11.1. The seral stage map corresponding to this classification for the current landscape 

was derived from the EVeg dataset and the rules in table B11.2 for the WWP cover type.

• Early Development (ED)

This seral stage is characterized by an open stand of P. monticola and A. magnifica, as 

well as other tree seedlings, mixed with grasses and shrubs. Early-seral dominant species 

include Ceanothus and various grasses. A portion of these stands enter a shrub-dominated 

stage that can persist for a few decades (LandFire 2007t). Note that there can be a residual 

or legacy of overstory trees from the predisturbance stand making up less than 25 percent 

canopy cover.

• Mid-Development – Open Canopy Cover (MDO)

This seral stage is characterized by a heterogeneous ground cover of grasses, forbs, and 

shrubs, with a low canopy cover (<40 percent) of pole- to medium-sized (5–20 inches 

d.b.h.) conifers. Conifer species likely to be present include P. monticola, A. magnifica, 

and P. jeffreyi (LandFire 2007t).

• Mid-Development – Moderate Canopy Cover (MDM)

This seral stage is characterized by a moderate canopy cover (40–70 percent) of pole- to 

medium-sized conifers, and is otherwise similar to MDO.

• Mid-Development – Closed Canopy Cover (MDC)

This seral stage is characterized by a dense canopy cover (>70 percent, although rarely, if 

ever, exceeding 80 percent) of pole- to medium-sized conifers, and is otherwise similar to 

MDO. 

• Late Development – Open Canopy Cover (LDO)

This seral stage is characterized by a heterogeneous ground cover of grasses, forbs, and 

low shrubs, with a low canopy cover (<40 percent) of large trees (>20 inches d.b.h.). The 

open stand structure is maintained by mixed-severity fire and insect-caused tree mortality 

(the latter not modeled at this time) (LandFire 2007t).

• Late Development – Moderate Canopy Cover (LDM)

This seral stage is characterized by an overstory of large trees with canopy cover 40–70 

percent, and is otherwise similar to LDO.

• Late Development – Closed Canopy Cover (LDC)

This seral stage is characterized by an overstory of large trees with canopy cover greater 

than 70 percent (although rarely, if ever, exceeding 80 percent), and is otherwise similar 

to LDO.
USDA Forest Service RMRS-GTR-385. 2018 249

Model Parameterization

This section includes a listing of the model parameters that are cover type-specific. Note 

that there are additional model parameters not specific to a cover type (e.g., climate modifier) 

that ultimately affect the model processes and outcomes, and these are discussed under the 

Methods section in McGarigal et al. 2018.

Succession

The rules (i.e., parameters) governing succession for the WWP cover type are listed in 

table B11.3. These rules were initially based on the corresponding LandFire BpS description 

(LandFire 2007t) and associated model created by using the Vegetation Dynamics Development 

Tool (VDDT). They were subsequently modified based on expert input to include probabilistic 

rather than deterministic seral stage transitions. Specifically, we modified the rules so that 

stands would gradually, instead of abruptly, transition from one seral stage to the next to reflect 

stochasticity in the real-world processes governing succession. For example, the first three rules 

dictate that a cell in the WWP cover type, which has been in the ED seral stage for 30–65 years, 

will have a 40-percent chance of transitioning to MDO, a 20-percent chance of transitioning 

to MDM, and a 10-percent chance of transitioning to MDC at the beginning of each timestep. 

Thus, stands will randomly begin transitioning to one of the MD stages after 30 years in the ED 

stage, but some stands could remain in the ED stage for as much as 70 years to reflect delayed 

tree establishment. Note that for stands currently in the ED stage and between 30 and 65 

years in this stage, the combined chance of transitioning to MD at each timestep is 70 percent; 

therefore, there is a 30-percent chance of remaining in the ED seral stage at each timestep. The 

next three rules together dictate that a cell that has been in the ED seral stage for 70 years will 

have a 100-percent chance of transitioning to one of the MD seral stages; thus, all stands will 

have transitioned to the MD stage after 70 years since establishment.

Applying the succession rules listed in table B11.3 results in stands transitioning between 

seral stages in a probabilistic rather than deterministic manner, such that we can compute the 

average stand age (years) for the transition to the next seral stage, as shown in table B11.4. 

For example, the first row in table B11.4 indicates that for a cell in the WWP cover type in 

the ED seral stage, the earliest stand age (i.e., number of years since the last stand-replacing 

disturbance) for transitioning to one of the MD seral stages is 30 years, the latest stand age 

is 70 years, and the average stand age at the time of the transition is 32 years. Also, the third 

row in table B11.4 indicates that a cell in the WWP cover type in the MDO seral stage will, on 

average, take 16 years without a low-mortality fire disturbance to transition to the MDM seral 

stage (i.e., transition from an open-canopy cover, <40 percent, to a moderate-canopy cover, 

40–70 percent, condition). Note that a low-mortality fire every 40 years will maintain the 

stand in the open-canopy condition.

Wildfire Disturbance

Rotation Period

Wildfire rotation period (equivalent to the point-specific mean return interval) is not 

formally a model parameter, but rather is specified as a target value to be achieved through 

model calibration. Target fire rotation periods (FRPs) were specified by cover type (table 

B11.5). FRP for the WWP cover type was based on Van de Water and Safford (2011) and 

expert input from Safford and Estes.

Susceptibility

The cover type-specific factors affecting susceptibility of a cell to wildfire were: (1) 

topographic position, and (2) fuel characteristics, as represented by vegetation cover type, 
250 USDA Forest Service RMRS-GTR-385. 2018

seral stage, and time since the last wildfire. Each of these two factors is represented as a 

probability.

Topographic position—Topographic position, as represented by the topographic position 

index (TPI) described under the Methods section in McGarigal et al. 2018, was treated as 

having a static (i.e., constant over time) and universal effect on the relative susceptibility of 

a cell to wildfire regardless of seral stage or disturbance history. Specifically, all other things 

being equal, for the WWP cover type, susceptibility decreased by 30 percent as the TPI index 

decreased over its full range according to the four-parameter logistic function depicted in 

figure B.2. However, because the bulk of the landscape varies over a much smaller range of 

TPI values, the effect on susceptibility is typically much less than 30 percent.

The specified logistic parameters were based on consensus expert opinion about the 

strength and nature of the topographic influence on wildfire susceptibility, and reflect general 

support for such an effect in the scientific literature (North 2012; Taylor and Skinner 2003).

Fuels (vegetation and disturbance history)—Fuels, as represented by vegetation cover 

type, seral stage, and recent disturbance history, were treated as having a dynamic (i.e., 

changing over time) effect on the relative susceptibility of a cell to wildfire. Specifically, 

susceptibility varied among cover types and seral stages in relation to the time (number of 

years) since the last fire according to the cumulative Weibull function and the parameters 

listed in table B11.5 (e.g., as illustrated in figure B.1). Note that here we use the cumulative 

form of the Weibull distribution, which gives the cumulative probability of a disturbance 

for any number of years since the last disturbance. Thus, the probability increases from 0 

immediately following a fire to approaching 1 after a certain number of years since the last 

fire, depending on the specified mean return interval (MRI) and shape parameters of the 

Weibull function. Holding Shape constant, and all other things being equal, as MRI increases 

the curve shifts to the right, resulting in a lower probability for any given number of years 

since the last disturbance. In this manner, varying the MRI among cover types and seral stages 

affects the relative susceptibility to wildfire.

The specified Weibull MRI parameters were based on the corresponding LandFire BpS 

description (LandFire 2007t) and associated VDDT model. 

Importantly, although susceptibility of the various seral stages is determined by MRI 

(holding Shape constant), these return intervals should not be interpreted literally, as the 

concept of a return interval does not meaningfully apply to a dynamic seral stage. Moreover, 

these MRIs were derived from the LandFire BpS description and associated VDDT model; 

taken collectively, these values do not necessarily agree with the target FRP for the cover type. 

Thus, the MRIs assigned to each cover type and seral stage should be interpreted as relative 

values that affect the relative susceptibility of the various vegetation states. 

Mortality

The cover type-specific factors affecting overstory mortality following wildfire (i.e., 

fire severity) were: (1) topographic position, and (2) fuel characteristics, as represented by 

vegetation cover type and seral stage. Each of these two factors is represented as a probability.

Topographic position—The effect of topographic position on mortality was treated 

identically to its effect on susceptibility (see previous description). Again, the specified 

logistic parameters were based on consensus expert opinion about the strength and nature of 

the topographic influence on wildfire severity, and reflect general support for such an effect in 

the scientific literature (North 2012; Taylor and Skinner 2003).

Fuels (vegetation)—Fuels, as represented by vegetation cover type and seral stage, were 

treated as having a dynamic (i.e., changing over time) effect on the relative probability of a 

high-mortality response to wildfire. Specifically, we assigned a probability of high-mortality 
USDA Forest Service RMRS-GTR-385. 2018 251

response to wildfire to each seral stage (table B11.5); values were based on the corresponding 

LandFire BpS description (LandFire 2007t) and associated VDDT model.

Disturbance Transitions

The rules (i.e., parameters) governing seral stage transitions following low-mortality 

wildfire disturbance for the WWP cover type are listed in table B11.6. These rules were 

initially based on the corresponding LandFire BpS description (LandFire 2007t) and 

associated model created by using the VDDT, but were subsequently modified to include the 

moderate canopy cover seral stages not present in the VDDT model. Note that rules governing 

transitions following high-mortality wildfire are not listed in table B11.6 because highmortality wildfires always result in transition to the ED seral stage. In addition, conditions in 

which low-mortality wildfire has no effect on the seral stage (i.e., does not cause a transition) 

are not listed. For example, the first two rules dictate that a low-mortality wildfire in a cell 

of WWP in the MDC seral stage has a 40-percent chance of transitioning to the MDM stage, 

a 40-percent chance of transitioning to the MDO stage, and (by implication) a 20-percent 

chance of remaining in the MDC stage. In addition, by implication (given the absence of a 

rule), a low-mortality wildfire in the ED, MDO, or LDO seral stage has no effect other than to 

maintain the cell in that seral stage.

Vegetation Treatments

Dynamic spatial constraints and priorities affecting individual cover types were described 

under the Methods section in McGarigal et al. 2018; here we describe the rules governing 

seral stage transitions following each unique vegetation treatment (table B11.7). Note that 

these rules were created by the principals involved in this project and reflect expectations 

based on the common prescriptions applied today.

Table B11.1—Summary of WWP seral stage characteristics: average overstory tree diameter at breast height 

(d.b.h.) of the dominant and codominant trees, overstory tree percent cover from above (CFA), assigned average 

CFA value for classifying the landscape by percent canopy cover, and range of stand ages (number of years since 

the last stand-replacing disturbance) possible for the corresponding seral stage. Note that overstory tree d.b.h. and 

CFA for the ED seral stages refer to the residual or legacy overstory from the predisturbance stand.

Cover type

Seral

stagea Overstory treed.b.h. (inches)

Overstory tree

CFA (%)

Assigned

average CFA (%)

Stand age

range (years)

WWP ED <5 (13 cm) <25 10 0–65

MDO 5–19.9 (13–50.5 

cm)

<40 30 30–185

MDM 5–19.9 40–70 55 30–185

MDC 5–19.9 >70 75 30–185

LDO ≥20 (51 cm) <40 30 ≥100

LDM ≥20 40–70 55 ≥100

LDC ≥20 >70 75 ≥100

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
252 USDA Forest Service RMRS-GTR-385. 2018

Table B11.2—Mapping rules for WWP seral stages. Diameter at breast height (d.b.h.) and cover from above (CFA) 

values were taken from EVeg polygons. Categories for d.b.h. are: null, 0–0.9, 1–4.9, 5–9.9, 10–19.9, 20–29.9, ≥30. 

CFA categories (%) are: null, 0–10, 10–20, ... , 90–100. Each row should be read with a Boolean AND across each 

column. Within each seral stage the rows should be read with a Boolean OR across rows.

Seral stagea Overstory tree

d.b.h. 1 (inches)

Overstory tree

d.b.h. 2 (inches)

Total tree

CFA (%)

Conifer

CFA (%)

Hardwood

CFA (%)

ED 0–4.9 (0–12.4 cm) any any any any

MDO 5–19.9 (13–50.5 cm) any 0–40 any any

MDM 5–19.9 any 40–70 any any

MDC 5–19.9 any 70–100 any any

MDC 5–19.9 any null 70–100 any

LDO ≥20 (51 cm) any 0–40 any any

LDM ≥20 any 40–70 any any

LDC ≥20 any 70–100 any any

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 

Table B11.3—Succession rules for WWP seral stages. Note that number of years in current successional stage 

refers to the number of years in either early-development (ED), mid-development (MD), or late-development (LD) 

stage independent of canopy cover class. 

Cover type

From seral

stagea To seralstagea

Number of years

in current

successional

stage

Number of

years since

low-mortality

fire

Probability of

transition

WWP ED MDO 30–65 any 0.4

ED MDM 30–65 any 0.2

ED MDC 30–65 any 0.1

ED MDO 70 any 0.6

ED MDM 70 any 0.25

ED MDC 70 any 0.15

MDC LDC 70–115 any 0.4

MDC LDC 120 any 1.0

MDM LDM 70–115 any 0.4

MDM LDM 120 any 1.0

MDM MDC ≥15 ≥15 0.8

MDO LDO 70–115 any 0.4

MDO LDO 120 any 1.0

MDO MDM ≥15 ≥15 0.8

LDM LDC ≥15 ≥15 0.8

LDO LDM ≥15 ≥15 0.8

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
USDA Forest Service RMRS-GTR-385. 2018 253

Table B11.4—Summary of WWP seral stage transitions: earliest, latest, and average stand age (number of years 

since the last stand-replacing disturbance) for the transition to the next seral stage; and average number of years 

without low-mortality fire to transition to the next canopy cover class.

Cover type

From seral

stagea

To

seral

stagea

Earliest

stand age

(years) at

transition

Latest

stand age

(years) at

transition

Average

stand age

(years) at

transition

Average no. of

years without

low-mortality

fire to transition

WWP ED MD 30 70 32 n/a

MD LD 100 190 109 n/a

MDO MDM n/a n/a n/a 16

MDM MDC n/a n/a n/a 16

LDO LDM n/a n/a n/a 16

LDM LDC n/a n/a n/a 16

a ED = Early - all structures; MD = mid development; LD = late development; MDO = Mid-open; MDM = Midmoderate; MDC = Mid-closed; LDO = Late-open; LDM = Late-moderate; LDC = Late-closed; 

Table B11.5—Weibull function parameters associated with the susceptibility of a cell to wildfire based on fuels (i.e., 

vegetation cover type, seral stage, and number of years since the last fire) and the probability of a high-mortality 

wildfire by cover type and seral stage for the WWP cover type.

Weibull parameters

Cover type

Seral

stagea

Target fire

rotation period

(years)

Mean return

interval (years) Shape

Probability of

high-mortality fire

WWP n/a 88 n/a n/a n/a

ED n/a 33 3 0.17

MDO n/a 18 3 0.09

MDM n/a 24 3 0.12

MDC n/a 33 3 0.17

LDO n/a 18 3 0.09

LDM n/a 24 3 0.12

LDC n/a 33 3 0.17

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
254 USDA Forest Service RMRS-GTR-385. 2018

Table B11.6—Disturbance rules for WWP cover type governing seral stage transitions following a low-mortality 

wildfire. Note that conditions in which low-mortality wildfire has no effect are not listed.

Cover type

From seral

stagea

 To seral stagea Probability of transition

WWP MDC MDM 0.4

MDC MDO 0.4

MDM MDO 0.4

LDC LDM 0.4

LDC LDO 0.4

LDM LDO 0.4

a MDC = Mid-closed; MDM = Mid-moderate; MDO = Mid-open; LDC = Late-closed; LDM = Late-moderate; LDO = 

Late-open. 
USDA Forest Service RMRS-GTR-385. 2018 255

Table B11.7—Disturbance rules for the WWP cover type governing seral stage transitions following vegetation 

treatments. Note that treatments not affecting seral stage transitions (e.g., mastication) are not included here.

Cover type Treatment type

From seral

stagea To seralstagea Probability oftransition

WWP Clearcut and Group cuts Any ED 1 

Thinning, including cells thinned in: 

(1) matrix thin and group cut; (2) 

thin and burn; (3) thin, hand cut, 

pile, and burn; (4) thin, masticate 

and burn; and (5) matrix thin, group 

cut, and burn treatments

MDC MDM 1 

MDM MDO 1 

LDC LDM 1 

LDM LDO 1 

Prescribed fire, including cells 

burned as part of a prescribed fireonly treatment); "cool" burn/"hot" 

burn transition probabilities

MDC MDM 0.03/0.05

MDC MDO 0/0.03

MDC ED 0/0.01

MDM MDO 0.05/0.14

MDM ED 0/0.01

LDC LDM 0.02/0.03

LDC LDO 0/0.02

LDC ED 0/0.01

LDM LDO 0.04/0.11

LDM ED 0/0.01

Thin and burn, including cells 

burned only as part of: (1) thin and

burn; and (2) hand cut, pile, and

burn treatments

MDC MDM 0.03

MDM MDO 0.05

LDC LDM 0.02

LDM LDO 0.04

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; 

LD-CA = Late - Conifer and Aspen. 
256 USDA Forest Service RMRS-GTR-385. 2018

12. Yellow Pine (YPN)

Reviewed by: (1) Hugh Safford, Regional Ecologist, USDA Forest Service; and (2) Becky 

Estes, Central Sierra Province Ecologist, USDA Forest Service.

Cover Type Classification and Crosswalks

Yellow Pine (YPN) Variant

▪ EVeg: Regional Dominance Type 1:

◦ Eastside Pine

◦ Jeffrey Pine

◦ Ponderosa Pine

▪ Presettlement Fire Regime Type: 

◦ Yellow Pine

▪ LandFire BpS model: 

◦ 0610310: California Montane Jeffrey Pine (–Ponderosa Pine) Woodland 

Yellow Pine With Aspen (YPN_ASP) Variant

This type was created by overlaying the NRIS TERRA Inventory of Aspen on the EVeg 

layer. Where it intersected with YPN, it was assigned to YPN_ASP.

Vegetation Description

Yellow Pine (YPN) Variant

The Yellow Pine cover type is characterized by yellow pine species such as Pinus 

ponderosa or P. jeffreyi that occur on the east side of the Sierra crest (LandFire 2007f). 

Relatively pure stands of yellow pine may occur, or they may mix with other tree species, 

including Abies concolor, Juniperus occidentalis, P. contorta ssp. murrayana, and Quercus 

kelloggii (Fites-Kaufman et al. 2007; Fitzhugh 1988a). The understory may include both 

montane forest and Great Basin shrubs, such as Ceanothus, Arctostaphylos, Symphoricarpos, 

Artemisia tridentata, Purshia tridentata, Ericameria nauseosa, Cercocarpus, and Holodiscus. 

Herbaceous plants may include Wyethia, Balsamorhiza sagittata, Festuca, Calamagrostis, and 

Elymus (Fitzhugh 1988a; LandFire 2007f).

Where fire occurs naturally, a mosaic of uneven-aged patches develops, with open spaces 

and dense sapling stands. Quercus kelloggii or J. occidentalis may form an understory, but 

pure stands of pine also are found. An open stand of low shrubs and a grassy herb layer are 

typical. Crowns of pines are open, allowing light, wind, and rain to penetrate, whereas other 

associated trees provide more dense foliage (Fitzhugh 1988a).

Yellow Pine With Aspen (YPN_ASP) Variant 

This YPN variant occurs when these upland forests and woodlands are dominated by 

Populus tremuloides without a significant conifer component, and are often termed “stable 

aspen.” The understory structure may be complex with multiple shrub and herbaceous 

layers, or simple with just an herbaceous layer. The herbaceous layer may be dense or sparse, 

dominated by graminoids or forbs. Common shrubs include Acer, Amelanchier, Artemisia, 

Juniperus, Prunus, Rosa, Shepherdia, Symphoricarpos, and the dwarf-shrubs Mahonia

and Vaccinium. Common graminoids may include Bromus, Calamagrostis, Carex, Elymus, 

Festuca, and Hesperostipa. Associated forbs may be Achillea, Eucephalus, Delphinium, 

Geranium, Heracleum, Ligusticum, Lupinus, Osmorhiza, Pteridium, Rudbeckia, Thalictrum, 

Valeriana, Wyethia, and many others (LandFire 2007a).
USDA Forest Service RMRS-GTR-385. 2018 257

Distribution

Yellow Pine (YPN) Variant

This cover type occurs on all aspects from about 1,200 to 2,000 meters (4,000–6,500 

feet) in elevation, east of the Sierra Nevada crest (Fitzhugh 1988a). It is usually found 

on volcanic and granitic substrates, in shallow soils with a frigid soil temperature regime 

(LandFire 2007f).

Yellow Pine With Aspen (YPN_ASP) Variant 

Sites supporting P. tremuloides are usually associated with added soil moisture, that 

is, azonal wet sites. These sites are found throughout the YPN zone, often close to streams, 

lakes, and meadows. Other sites include rock reservoirs, springs, and seeps. Terrain can be 

simple to complex. At lower elevations, topographic conditions for this type tend toward 

positions resulting in relatively colder, wetter conditions within the prevailing climate (e.g., 

ravines, north slopes, wet depressions) (LandFire 2007a). Populus tremuloides stands may 

also be associated with lateral or terminal moraine boulder material, talus-colluvium, rock 

falls, or lava flows. In addition, pure stands may be found in topographic positions where 

snow accumulates, mostly at higher north-facing elevations, where snow presence means the 

growing season is too short to support conifers (Shepperd et al. 2006).

Disturbances

Wildfire

Yellow Pine (YPN) Variant

Wildfires are common and frequent; mortality depends on vegetation vulnerability and 

wildfire intensity. Low-mortality fires kill small trees and consume aboveground portions of 

shrubs and herbs, but do not kill large trees or belowground organs of most shrubs and herbs, 

which promptly resprout. High-mortality fires kill large as well as small trees, and may kill 

many of the shrubs and herbs as well. Fire kills the aboveground portions of the shrubs and 

herbs, but most shrubs and herbs resprout from surviving belowground organs.

The relatively long needles of yellow pines and relatively open structure of these stands 

make for dry surface and ground fuels that burn readily. Thus, fires in these stands burn 

more frequently than those in adjacent forests (Fites-Kaufman et al. 2007). In fact, fire is an 

integral part of the ecology of yellow pines. Fire has allowed yellow pines to dominate sites 

where they are the potential climax species, as well as sites where seral to more shade-tolerant 

tree species would occur otherwise. Pinus ponderosa and P. jeffreyi have evolved with thick 

bark and an open crown structure that allow them to survive most fires. Mature trees will 

self-prune, leaving a smooth bole that reduces aerial fire spread. Fire also creates favorable 

seedbeds for seedling establishment (Habeck 1992). 

Yellow Pine With Aspen (YPN_ASP) Variant

Sites supporting P. tremuloides are maintained by stand-replacing disturbances that allow 

regeneration from belowground suckers. Replacement fire and ground fire are thought to have 

been common in stable P. tremuloides stands historically. Because P. tremuloides is associated 

with mesic conditions, it rarely burns during the normal lightning season. However, during 

years with little precipitation, stands may be more susceptible to burning. Evidence from fire 

scars and historical studies show that past fires occurred mostly during the spring and fall. 

These are typically self-perpetuating stands (LandFire 2007a).

Van de Water and Safford (2011) found a mean fire return interval (FRI) of 19 years, 
258 USDA Forest Service RMRS-GTR-385. 2018

median of 20 years, mean minimum interval of 10 years, and mean maximum interval of 90 

years for aspen. The LandFire model for northern Sierra Nevada “stable aspen” predicts a 

mean FRI of 31 years. Replacement FRI has a mean of 68 years with a range of 50–300 years, 

mixed-severity FRI has a mean of 57 years with a range of 20–60 years, and low-severity fire 

is not modeled (LandFire 2007a). 

Other Disturbances

Other disturbances are not currently being modeled, but may, depending on the seral stage 

and mortality levels, reset patches to early development, maintain existing seral stages, or shift 

or accelerate succession to a more open condition. All of the tree species associated with this 

vegetation type are susceptible to a wide variety of pathogens and insects.

Seral Stages

The classification of seral stages originated from the corresponding LandFire biophysical 

setting models, but with some modifications (e.g., the addition of a moderate canopy cover 

stage in the mid- and late-seral stages) based on expert input, as follows and as summarized in 

table B12.1. The seral stage map corresponding to this classification for the current landscape 

was derived from the EVeg dataset and the rules in table B12.2 for the YPN cover types; 

YPN_ASP seral stages were mapped manually by using NAIP 2010 Color IR imagery.

Yellow Pine (YPN) Variant

• Early Development (ED)

This seral stage is characterized by the recruitment of a new cohort of early-successional, 

shade-intolerant tree species (primarily P. ponderosa or P. jeffreyi) into an open area 

created by a stand-replacing disturbance. Following such disturbance, some sites 

are dominated by dense shrub stands composed of P. tridentata, Arctostaphylos, or 

Ceanothus, or a combination thereof, depending on location. Other postfire sites are more 

open and dominated by dense pine seedlings, bunchgrasses, and forbs (LandFire 2007f). 

Note that there can be a residual or legacy of overstory trees from the predisturbance 

stand making up less than 25 percent canopy cover.

• Mid-Development – Open Canopy Cover (MDO)

This seral stage is characterized by a heterogeneous ground cover of grasses, forbs, and 

shrubs, with a low canopy cover (<40 percent) of pole- to medium-sized (5–20 inches 

d.b.h.) conifers. This seral stage results from delayed tree regeneration or frequent burning 

(LandFire 2007f).

• Mid-Development – Moderate Canopy Cover (MDM)

This seral stage is characterized by a moderate canopy cover (40–70 percent) of pole- to 

medium-sized conifers, resulting from rapid regeneration of conifers following standreplacing disturbance, and is otherwise similar to MDO. 

• Mid-Development – Closed Canopy Cover (MDC)

This seral stage is characterized by a dense canopy cover (>70 percent) of pole- to 

medium-sized conifers, resulting from rapid regeneration of conifers following standreplacing disturbance, and is otherwise similar to MDO. These stands are susceptible to 

stagnation and often have a marginal understory associated with limited site resources. 

This condition develops where fire frequency is too low to thin small trees (LandFire 

2007f).

• Late Development – Open Canopy Cover (LDO)

This seral stage is characterized by a heterogeneous ground cover of grasses, forbs, and 

low shrubs, with a low canopy cover (<40 percent) of large trees (>20 inches d.b.h.). The 

open stand structure is maintained by frequent low- or mixed-severity fire and insect-
USDA Forest Service RMRS-GTR-385. 2018 259

caused tree mortality (the latter not modeled at this time) (LandFire 2007f).

• Late Development – Moderate Canopy Cover (LDM)

This seral stage is characterized by an overstory of large trees with canopy cover 40–70 

percent, and is otherwise similar to LDO.

• Late Development – Closed Canopy Cover (LDC)

This seral stage is characterized by an overstory of large trees with canopy cover greater 

than 70 percent, and is otherwise similar to LDO. There can exist substantial surface fuel 

accumulation and ladder fuels (LandFire 2007f).

Yellow Pine with Aspen (YPN_ASP) Variant 

• Early Development – Aspen (ED–A)

This seral stage is characterized by the recruitment of a new cohort of early-successional, 

shade-intolerant tree species (primarily P. tremuloides) into an open area created by a 

stand-replacing disturbance. Note that there can be a residual or legacy of overstory trees 

from the predisturbance stand making up less than 25 percent canopy cover. Following 

disturbance, succession proceeds rapidly from an herbaceous layer to shrubs and trees, 

which invade together (Verner 1988a). Populus tremuloides suckers more than 2 meters 

tall develop within about 10 years (LandFire 2007a).

• Mid-Development – Aspen (MD–A)

This seral stage is characterized by P. tremuloides trees 5–16 inches d.b.h. Canopy cover 

is highly variable, and can range from 40 to 100 percent. Some understory conifers are 

encroaching, but P. tremuloides is still the dominant component of the stand (LandFire 

2007a).

• Late Development – Conifer with Aspen (LD–CA)

If stands are sufficiently protected from fire such that conifer species encroach and 

eventually overtop the P. tremuloides, which are predominantly larger than 16 inches 

d.b.h., but it is unlikely that conifers will be the dominant portion of the stand. Total 

canopy cover is high variable, but generally greater than 50 percent. 

Model Parameterization

This section includes a listing of the model parameters that are cover type-specific. Note 

that there are additional model parameters not specific to a cover type (e.g., climate modifier) 

that ultimately affect the model processes and outcomes, and these are discussed under the 

Methods section in McGarigal et al. 2018.

Succession

The rules (i.e., parameters) governing succession for the YPN and YPN_ASP cover types 

are listed in table B12.3. These rules were initially based on the corresponding LandFire 

BpS descriptions (LandFire 2007a,f) and associated models created by using the Vegetation 

Dynamics Development Tool (VDDT), as modified by Safford and Estes. They were 

subsequently modified based on expert input to include probabilistic rather than deterministic 

seral stage transitions. Specifically, we modified the rules so that stands would gradually, 

instead of abruptly, transition from one seral stage to the next to reflect stochasticity in the 

real-world processes governing succession. For example, the first three rules dictate that a 

cell in the YPN cover type which has been in the ED seral stage for 40–75 years, will have a 

40-percent chance of transitioning to MDC, a 20-percent chance of transitioning to MDM, and 

a 10-percent chance of transitioning to MDO at the beginning of each timestep. Thus, stands 

will randomly begin transitioning to one of the MD stages after 40 years in the ED stage, 

but some stands could remain in the ED stage for as much as 80 years to reflect delayed tree 

establishment. Note that for stands currently in the ED stage and between 40 and 75 years 
260 USDA Forest Service RMRS-GTR-385. 2018

in this stage, the combined chance of transitioning to MD at each timestep is 70 percent; 

therefore, there is a 30-percent chance of remaining in the ED seral stage at each timestep. The 

next three rules together dictate that a cell that has been in the ED seral stage for 80 years will 

have a 100-percent chance of transitioning to one of the MD seral stages; thus, all stands will 

have transitioned to the MD stage after 80 years since establishment. 

Applying the succession rules listed in table B12.3 results in stands transitioning between 

seral stages in a probabilistic rather than deterministic manner, such that we can compute the 

average stand age (years) for the transition to the next seral stage, as shown in table B12.4. 

For example, the first row in table B12.4 indicates that for a cell in the YPN cover type in 

the ED seral stage, the earliest stand age (i.e., number of years since the last stand-replacing 

disturbance) for transitioning to one of the MD seral stages is 40 years, the latest stand age 

is 80 years, and the average stand age at the time of the transition is 42 years. Also, the fifth 

row in table B12.4 indicates that a cell in the YPN cover type in the MDO seral stage will, on 

average, take 21 years without a low-mortality fire disturbance to transition to the MDM seral 

stage (i.e., transition from an open-canopy cover, <40 percent, to a moderate-canopy cover, 

40–70 percent, condition). Note that a low-mortality fire every 20 years will maintain the 

stand in the open-canopy condition.

Wildfire Disturbance

Rotation Period

Wildfire rotation period (equivalent to the point-specific mean return interval) is not 

formally a model parameter, but rather is specified as a target value to be achieved through 

model calibration. Target FRPs were specified by cover type (table B12.5). FRP for the YPN 

cover type was based on Mallek et al. (2013), whereas the FRP for the YPN_ASP cover type 

was based on Van de Water and Safford (2011) and expert input from Safford and Estes.

Susceptibility

The cover type-specific factors affecting susceptibility of a cell to wildfire were: (1) 

topographic position, and (2) fuel characteristics, as represented by vegetation cover type, 

seral stage, and time since the last wildfire. Each of these two factors is represented as a 

probability.

Topographic position—Topographic position, as represented by the topographic position 

index (TPI) described under the Methods section in McGarigal et al. 2018, was treated as 

having a static (i.e., constant over time) and universal effect on the relative susceptibility of 

a cell to wildfire regardless of seral stage or disturbance history. We allowed topographic 

position to affect susceptibility for the YPN cover type, but not YPN_ASP. Specifically, all 

other things being equal, for the YPN cover type, susceptibility decreased by 30 percent as 

the TPI index decreased over its full range according to the four-parameter logistic function 

depicted in figure B.2. However, because the bulk of the landscape varies over a much smaller 

range of TPI values, the effect on susceptibility is typically much less than 30 percent.

The specified logistic parameters were based on consensus expert opinion about the 

strength and nature of the topographic influence on wildfire susceptibility, and reflect general 

support for such an effect in the scientific literature (North 2012; Taylor and Skinner 2003).

Fuels (vegetation and disturbance history)—Fuels, as represented by vegetation cover 

type, seral stage, and recent disturbance history, were treated as having a dynamic (i.e., 

changing over time) effect on the relative susceptibility of a cell to wildfire. Specifically, 

susceptibility varied among cover types and seral stages in relation to the time (number years) 

since the last fire according to the cumulative Weibull function and the parameters listed in 

table B12.5 (e.g., as illustrated in figure B.1). Note that here we use the cumulative form 

of the Weibull distribution, which gives the cumulative probability of a disturbance for any 

number of years since the last disturbance. Thus, the probability increases from 0 immediately 
USDA Forest Service RMRS-GTR-385. 2018 261

following a fire to approaching 1 after a certain number of years since the last fire, depending 

on the specified mean return interval (MRI) and shape parameters of the Weibull function. 

Holding Shape constant, and all other things being equal, as MRI increases the curve shifts 

to the right, resulting in a lower probability for any given number of years since the last 

disturbance. In this manner, varying the MRI among cover types and seral stages affects the 

relative susceptibility to wildfire.

The specified Weibull MRI parameters were based on the corresponding LandFire BpS 

descriptions (LandFire 2007a,f) and associated VDDT models, as modified by Safford and 

Estes. 

Importantly, although susceptibility of the various seral stages is determined by MRI 

(holding Shape constant), these return intervals should not be interpreted literally, as the 

concept of a return interval does not meaningfully apply to a dynamic seral stage. Moreover, 

these MRIs were derived from the LandFire BpS descriptions and associated VDDT models, 

as modified by Safford and Estes; taken collectively, these values do not necessarily agree 

with the target FRPs for the cover types. Thus, the MRIs assigned to each cover type and 

seral stage should be interpreted as relative values that affect the relative susceptibility of the 

various vegetation states. 

Mortality

The cover type-specific factors affecting overstory mortality following wildfire (i.e., 

fire severity) were: (1) topographic position, and (2) fuel characteristics, as represented by 

vegetation cover type and seral stage. Each of these two factors is represented as a probability.

Topographic position—The effect of topographic position on mortality was treated 

identically to its effect on susceptibility (see previous description). Again, the specified 

logistic parameters were based on consensus expert opinion about the strength and nature of 

the topographic influence on wildfire severity, and reflect general support for such an effect in 

the scientific literature (North 2012; Taylor and Skinner 2003).

Fuels (vegetation)—Fuels, as represented by vegetation cover type and seral stage, were 

treated as having a dynamic (i.e., changing over time) effect on the relative probability of a 

high-mortality response to wildfire. Specifically, we assigned a probability of high-mortality 

response to wildfire to each cover type and seral stage (table B12.5); values were based on the 

corresponding LandFire BpS descriptions (LandFire 2007a,f) and associated VDDT models, 

as modified by Safford and Estes.

Disturbance Transitions

The rules (i.e., parameters) governing seral stage transitions following low-mortality 

wildfire disturbance for the YPN and YPN_ASP cover types are listed in table B12.6. These 

rules were initially based on the corresponding LandFire BpS descriptions (LandFire 2007a,f) 

and associated models created by using the VDDT as modified by Safford and Estes, but were 

subsequently modified to include the moderate canopy cover seral stages not present in the 

VDDT models. Note that rules governing transitions following high-mortality wildfire are not 

listed in table B12.6 because high-mortality wildfires always result in transition to the earlydevelopment (ED) seral stage. In addition, conditions in which low-mortality wildfire has no 

effect on the seral stage (i.e., does not cause a transition) are not listed. For example, the first 

two rules dictate that a low-mortality wildfire in a cell of YPN in the MDC seral stage has a 

30-percent chance of transitioning to the MDM stage, a 30-percent chance of transitioning 

to the MDO stage, and (by implication) a 40-percent chance of remaining in the MDC stage. 

In addition, by implication (given the absence of a rule), a low-mortality wildfire in the ED, 

MDO, or LDO seral stage has no effect other than to maintain the cell in that seral stage. 

Similarly, for cells in the YPN_ASP cover type, all low-mortality wildfires maintain the stand 

in the current seral stage and thus are not listed.
262 USDA Forest Service RMRS-GTR-385. 2018

Table B12.1—Summary of YPN and YPN_ASP seral stage characteristics: average overstory tree diameter at 

breast height (d.b.h.) of the dominant and codominant trees, overstory tree percent cover from above (CFA), 

assigned average CFA value for classifying the landscape by percent canopy cover, and range of stand ages 

(number of years since the last stand-replacing disturbance) possible for the corresponding seral stage. Note that

overstory tree d.b.h. and CFA for the ED/ED-A seral stages refer to the residual or legacy overstory from the 

predisturbance stand.

Cover type

Seral

stage

Overstory tree

d.b.h. (inches)

Overstory tree

CFA (%)

Assigned

average CFA (%)

Stand age

range (years)

YPN ED <5 (13 cm) <25 10 0–75

MDO 5–19.9 (13–50.5 cm) <40 30 40–305

MDM 5–19.9 40–70 55 40–285

MDC 5–19.9 >70 85 40–275

LDO ≥20 (51 cm) <40 30 ≥210

LDM ≥20 40–70 55 ≥170

LDC ≥20 >70 85 ≥140

YPN_ASP ED-A <5 <25 10 0–5 

MD-A 5–15.9 (13–40.4 cm) >40 60 10–135

LD-CA ≥16 (40.6 cm) >50 70 ≥90

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; 

LD-CA = Late - Conifer and Aspen. 

Table B12.2—Mapping rules for YPN seral stages. Diameter at breast height (d.b.h.) and cover from above (CFA) 

values were taken from EVeg polygons. Categories for d.b.h. are: null, 0–0.9, 1–4.9, 5–9.9, 10–19.9, 20–29.9, ≥30. 

CFA categories (%) are: null, 0–10, 10–20, ... , 90–100. Each row should be read with a Boolean AND across each 

column. Within each seral stage the rows should be read with a Boolean OR across rows.

Seral stagea Overstory tree

d.b.h. 1 (inches)

Overstory tree

d.b.h. 2 (inches)

Total tree

CFA (%)

Conifer

CFA (%)

Hardwood

CFA (%)

ED 0–4.9 (0–12.4 cm) any any any any

MDO 5–19.9 (13–50.5 cm) any 0–40 any any

MDM 5–19.9 any 40–70 any any

MDC 5–19.9 any 70–100 any any

LDO ≥20 (51 cm) any 0–40 any any

LDM ≥20 any 40–70 any any

LDC ≥20 any 70–100 any any

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
USDA Forest Service RMRS-GTR-385. 2018 263

Table B12.3—Succession rules for YPN and YPN_ASP seral stages. Note that for YPN cover types, number of 

years in current successional stage refers to the number of years in either early-development (ED), middevelopment (MD), or late-development (LD) stage independent of canopy cover class, whereas for YPN_ASP it 

refers to the number of years in the corresponding seral stage. 

Cover

type

From seral

stagea To seralstagea

Number of years

in current

successional

stage

Number of

years since

low-mortality

fire

Probability of

transition

YPN ED MDC 40–75 any 0.4

ED MDM 40–75 any 0.2

ED MDO 40–75 any 0.1

ED MDC 80 any 0.5

ED MDM 80 any 0.3

ED MDO 80 any 0.2

MDC LDC 100–195 any 0.2

MDC LDC 200 any 1.0

MDM LDM 130–205 any 0.3

MDM LDM 210 any 1.0

MDM MDC ≥20 ≥20 0.8

MDO LDO 170–225 any 0.4

MDO LDO 230 any 1.0

MDO MDM ≥20 ≥20 0.8

LDM LDC ≥20 ≥25 0.7

LDO LDM ≥20 ≥25 0.7

YPN_ASP ED-A MD-A 10 any 1.0

MD-A LD-CA 80–125 any 0.6

MD-A LD-CA 130 any 1.0

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and 

Conifer; LD-CA = Late - Conifer and Aspen. 
264 USDA Forest Service RMRS-GTR-385. 2018

Table B12.4—Summary of YPN and YPN_ASP seral stage transitions: earliest, latest, and average stand age 

(number of years since the last stand-replacing disturbance) for the transition to the next seral stage; and average 

number of years without low-mortality fire to transition to the next canopy cover class.

Cover type

From seral

stagea To seralstagea

Earliest

stand age

(years) at

transition

Latest

stand age

(years) at

transition

Average

stand age

(years) at

transition

Average no. of

years without

low-mortality

fire to

transition

YPN ED MD 40 80 42 n/a

MDO LDO 210 310 219 n/a

MDM LDM 170 290 184 n/a

MDC LDC 140 280 162 n/a

MDO MDM n/a n/a n/a 21

MDM MDC n/a n/a n/a 21

LDO LDM n/a n/a n/a 27

LDM LDC n/a n/a n/a 27

YPN_ASP ED-A MD-A 10 10 10 n/a

MD-A LD-CA 90 140 93 n/a

a ED = Early - all structures; MD = Mid development; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; 

LDO = Late-open; LDM = Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = 

Mid - Aspen and Conifer; LD-CA = Late - Conifer and Aspen. 
USDA Forest Service RMRS-GTR-385. 2018 265

Table B12.5—Weibull function parameters associated with the susceptibility of a cell to wildfire based on fuels (i.e., 

vegetation cover type, seral stage, and number of years since the last fire) and the probability of a high-mortality 

wildfire by cover type and seral stage for the YPN and YPN_ASP cover types.

Weibull parameters

Cover type

Seral

stagea

Target fire

rotation period

(years)

Mean return

interval (years) Shape

Probability of

high-mortality fire

YPN n/a 21 n/a n/a n/a

ED n/a 30 3 1.00

MDO n/a 8 3 0.05

MDM n/a 9 3 0.14

MDC n/a 11 3 0.26

LDO n/a 8 3 0.01

LDM n/a 10 3 0.08

LDC n/a 15 3 0.20

YPN_ASP n/a 21 n/a n/a n/a

ED-A n/a 30 3 1 

MD-A n/a 11 3 0.26

LD-CA n/a 10 3 0.08

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; 

LD-CA = Late - Conifer and Aspen. 

Table B12.6—Disturbance rules for YPN and YPN_ASP cover types governing seral-stage transitions following a 

low-mortality wildfire. Note that conditions in which low-mortality wildfire has no effect are not listed.

Cover type From seral

stagea To seral stagea Probability of transition

YPN MDC MDM 0.3

MDC MDO 0.3

MDM MDO 0.32

LDC LDM 0.29

LDC LDO 0.29

LDM LDO 0.18

a MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = Late-moderate; LDC = Lateclosed; 
266 USDA Forest Service RMRS-GTR-385. 2018

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M.; Honkala, Barbara H., tech. coords. Silvics of North America: vol. 2. Hardwoods. Agric. 

Handb. 654. Washington, DC: U.S. Department of Agriculture, Forest Service: 417–425.

Taylor, Alan H.; Halpern, Charles B. 1991. The structure and dynamics of Abies magnifica 

forests in the southern Cascade Range, USA. Journal of Vegetation Science. 2: 189–200.

Taylor, A.H.; Skinner, Carl N. 2003. Spatial patterns and controls on historical fire regimes 

and forest structure in the Klamath Mountains. Ecological Applications. 13: 704–719.

Uchytil, Ronald J. 1991. Pseudotsuga menziesii var. menziesii. In: Fire Effects Information 

System. Missoula, MT: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station, Fire Sciences Laboratory. http://www.fs.fed.us/database/feis/plants/tree/

quekel/all.html [Accessed 21 December 2012].

USDA Forest Service 2008. North Sierran Ecological Province, Calveg Zone 3, vegetation 

descriptions. U.S. Department of Agriculture, Forest Service. http://www.fs.usda.gov/

Internet/FSE DOCUMENTS/fsbdev3 046269.pdf [Accessed 2 April 2013].

Van de Water, Kip M.; Safford, Hugh D. 2011. A summary of fire frequency estimates for 

California vegetation before Euro-American settlement. Fire Ecology. 7(3): 26–57. https://

doi.org/10.4996/fireecology.0703026.

Verner, Jared. 1988a. Aspen (ASP). In: Mayer, Kenneth A.; Laudenslayer, William F., eds. A 

guide to wildlife habitats of California. Sacramento, CA: California Department of Fish and 

Game. http://www.dfg.ca.gov/biogeodata/cwhr/pdfs/ASP.pdf [Accessed 4 December 2012].

Verner, Jared. 1988b. Blue oak-foothill pine (BOP). In: Mayer, Kenneth A.; Laudenslayer, 

William F., eds. A guide to wildlife habitats of California. Sacramento, CA: California 

Department of Fish and Game. http://www.dfg.ca.gov/biogeodata/cwhr/pdfs/ASP.pdf 

[Accessed 4 December 2012].

Verner, Jared. 1988c. Low sage (LSG). In: Mayer, Kenneth A.; Laudenslayer, William F., eds. A 

guide to wildlife habitats of California. Sacramento, CA: California Department of Fish and 

Game. http://www.dfg.ca.gov/biogeodata/cwhr/pdfs/SGB.pdf [Accessed 4 December 2012].

Verner J; Purcell KL. 1988. Subalpine conifer (SCN). In: Mayer, Kenneth A.; Laudenslayer, 

William F., eds. A guide to wildlife habitats of California. Sacramento, CA: California 

Department of Fish and Game. http://www.dfg.ca.gov/biogeodata/cwhr/pdfs/SCN.pdf 

[Accessed 4 December 2012].
USDA Forest Service RMRS-GTR-385. 2018 271

APPENDIX C: Parameterization of Selected Landscape 

Metrics

Core Area Metrics

Tables C.1, C.2, and C.3 list the specified edge depths (meters and feet) between adjacent 

patches defined by developmental stage, canopy cover class, and seral stage, respectively, as 

used to quantify range of variability (ROV) in landscape structure in the upper Yuba River 

watershed. Edge depths are required for quantifying several landscape metrics pertaining 

to core area (DCORE_MN, DCORE_AM, CAI_MN, and CAI_AM), where “core area” is 

defined as the patch area excluding the area within the specified distance of the patch edge. 

Core area metrics require the specification of edge depths, that is, the distance from the patch 

perimeter, to exclude when computing the interior or core area of a patch. Although depth-ofedge effects can vary widely depending on the process or organism under consideration, here 

we did not have any focal process or species under consideration. Therefore, we specified 

edge depths based on the general magnitude of ecological differences between patch types, 

based loosely on the range of depth-of-edge effects reported in the literature for various 

biophysical attributes and focal species. Each table is read as follows. Each row refers to 

the focal patch and each column the adjacent patch. Thus, a patch of late-development stage 

adjacent to an early-development stage has a 60-meter (200-foot) edge effect penetrating into 

the focal patch of late development (table C.1).

The table of edge depths for the 151 unique combinations of cover type and seral stage, 

the most finely resolved thematic classification we considered, is too large to present here, 

so a brief explanation will have to suffice. For each edge between two abutting patches, we 

considered three ecological attributes: (1) difference in vegetation canopy cover between 

the two patches, with edge depths varying up to a maximum of 120 meters (400 feet) for a 

nonvegetated patch adjacent to a closed-canopy forest; (2) differences in vegetation vertical 

structure between the two patches, with edge depths varying up to a maximum of 120 meters 

for a nonvegetated patch adjacent to a late-development forest; and (3) an edge distance of 

240 meters (800 feet) for natural cover adjacent to Developed (i.e., Urban or Agriculture). 

For each edge we applied the maximum edge depth from these three criteria. Thus, all patch 

edges involving Developed received an edge effect distance of 240 meters, whereas as all 

other edges between natural cover types and seral stages received edge effect distances 

increasing with the difference in canopy cover and developmental stage and ranging from 0 

to 120 meters.

Edge Contrast Metrics

Tables C.4, C.5, and C.6 list the specified edge contrast weights (0–1) between adjacent 

patches defined on the basis of developmental stage, canopy cover class, and seral stage, 

respectively, as used to quantify ROV in landscape structure in the project area. Contrast 

weights the area required for quantifying the edge contrast landscape metrics (TECI, CWED), 

where edge contrast is defined as the magnitude of ecological differences between the adjacent 

patch types forming the edge, with 1 equal to the maximum contrast. Although edge contrast 

can vary widely depending on the process or organism under consideration, here we did not 

have any focal process or species under consideration. Therefore, we specified edge contrast 

weights based on the general magnitude of ecological differences between patch types.

The table of edge contrast weights for the 151 unique combinations of cover type and 

seral stage is too large to present here. Briefly, for each edge between two abutting patches, 
272 USDA Forest Service RMRS-GTR-385. 2018

we considered four ecological attributes: (1) difference in floristic composition, with the edge 

contrast between conifer and mixed conifer-aspen variants of the same cover type equal to 

0.1, and all other edges equal to 0.2; (2) difference in vegetation canopy cover between the 

two patches, with edge contrasts varying up to a maximum of 0.3 for a nonvegetated patch 

adjacent to a closed-canopy forest; (3) differences in vegetation vertical structure between 

the two patches, with edge contrasts varying up to a maximum of 0.4 for a nonvegetated 

patch adjacent to a late-development forest; and (4) an edge contrast of 1.0 for natural cover 

adjacent to Developed (i.e., Urban or Agriculture). For each edge we applied either the 

maximum of the sum of the edge contrasts across the first three criteria, or the fourth criterion. 

Thus, all patch edges involving Developed received an edge contrast weight of 1.0, whereas 

all other edges between natural cover types and seral stages received edge weights increasing 

with the difference in cover type, canopy cover, and developmental stage and ranging from 

0 to 0.9.

Table C1—Edge depths (m) between adjacent patches defined by vegetation 

developmental stage.

Developmental stage None Early Mid Late

None 0 60 (200 ft) 90 (300 ft) 120 (400 ft)

Early 60 0 30 (100 ft) 60

Mid 90 30 0 30

Late 120 60 30 0 

Table C2—Edge depths (m) between adjacent patches defined by canopy cover class.

Canopy cover class None Open Moderate Closed

None 0 30 (100 ft) 60 (200 ft) 120 (400 ft)

Open 30 0 30 60

Moderate 60 30 0 30

Closed 120 60 30 0 
USDA Forest Service RMRS-GTR-385. 2018 273

Table C3—Edge depths (m) between adjacent patches defined on the basis of seral stage.

Seral stage Nonseral Early – All

Structures

Mid –

Closed

Mid –

Moderate

Mid –

Open

Late –

Closed

Late –

Moderate

Late –

Open

Nonseral 0 30 

(100 ft)

150 

(500 ft)

120 

(400 ft)

90

(300 ft)

180

(600 ft)

150 120

Early – All 

Structures

30 0 120 90 60

(200 ft)

150 120 90

Mid – Closed 150 120 0 30 60 30 60 90

Mid –

Moderate

120 90 30 0 30 60 30 60

Mid – Open 90 60 60 30 0 90 60 30

Late – Closed 180 150 30 60 90 0 30 60

Late –

Moderate

150 120 60 30 60 30 0 30

Late – Open 120 90 90 60 30 60 30 0 

Early – Aspen 60 30 90 60 30 120 90 60

Mid – Aspen 90 60 30 45 60 60 60 90

Mid – Aspen 

and Conifer

90 60 30 45 60 30 45 

(150 ft)

60

Late – Conifer 

and Aspen

120 90 30 60 90 0 30 45

(Table C3 continued) 

Table C.3. continued.

Seral stage

Early –

Aspen

Mid – Aspen

Mid – Aspen and

Conifer

Late – Conifer

and Aspen

Nonseral 60 90 90 120

Early – All Structures 30 60 60 90

Mid – Closed 90 30 30 30

Mid – Moderate 60 45 45 60

Mid – Open 30 60 60 90

Late – Closed 120 60 30 0 

Late – Moderate 90 60 45 30

Late – Open 60 90 60 45

Early – Aspen 0 0 60 60

Mid – Aspen 0 0 0 0 

Mid – Aspen and Conifer 60 0 0 0 

Late – Conifer and Aspen 60 0 0 0 
274 USDA Forest Service RMRS-GTR-385. 2018

Table C5—Edge contrast weights (0–1) between adjacent patches defined by canopy 

cover class.

Canopy cover class None Open Moderate Closed

None 0 0.33 0.66 1 

Open 0.33 0 0.33 0.66

Moderate 0.66 0.33 0 0.33

Closed 1 0.66 0.33 0 

Table C4—Edge contrast weights (0–1) between adjacent patches defined by vegetation 

developmental stage.

Developmental stage None Early Mid Late

None 0 0.33 0.66 1 

Early 0.33 0 0.33 0.66

Mid 0.66 0.33 0 0.33

Late 1 0.66 0.33 0 
USDA Forest Service RMRS-GTR-385. 2018 275

Table C.6. continued. 

Seral stage

Early –

Aspen

Mid – Aspen

Mid – Aspen and

Conifer

Late – Conifer

and Aspen

Nonseral 0.6 0.8 1 1 

Early – All Structures 0.4 0.6 0.8 0.8

Mid – Closed 0.2 0.1 0.2 0.2

Mid – Moderate 0.3 0.1 0.3 0.3

Mid – Open 0.4 0.2 0.4 0.4

Late – Closed 0.6 0.4 0.2 0.2

Late – Moderate 0.5 0.3 0.1 0.1

Late – Open 0.6 0.4 0.2 0.2

Early – Aspen 0 0.2 0.4 0.4

Mid – Aspen 0.2 0 0.2 0.2

Mid – Aspen and Conifer 0.4 0.2 0 0.1

Late – Conifer and Aspen 0.4 0.2 0.1 0 

Table C6—Edge contrast weights (0–1) between adjacent patches defined by seral stage.

Seral stage Nonseral Early – All

Structures

Mid –

Closed

Mid –

Moderate

Mid–

Open

Late –

Closed

Late –

Moderate

Late –

Open

Nonseral 0 0.3 0.8 0.7 0.6 1 0.9 0.8

Early – All 

Structures

0.3 0 0.6 0.5 0.4 0.8 0.7 0.6

Mid – Closed 0.8 0.6 0 0.1 0.2 0.2 0.3 0.4

Mid – Moderate 0.7 0.5 0.1 0 0.1 0.3 0.2 0.3

Mid – Open 0.6 0.4 0.2 0.1 0 0.4 0.3 0.2

Late – Closed 1 0.8 0.2 0.3 0.4 0 0.1 0.2

Late –

Moderate

0.9 0.7 0.3 0.2 0.3 0.1 0 0.1

Late – Open 0.8 0.6 0.4 0.3 0.2 0.2 0.1 0 

Early – Aspen 0.6 0.4 0.2 0.3 0.4 0.6 0.5 0.6

Mid – Aspen 0.8 0.6 0.1 0.1 0.2 0.4 0.3 0.4

Mid – Aspen 

and Conifer

1 0.8 0.2 0.3 0.4 0.2 0.1 0.2

Late – Conifer 

and Aspen

1 0.8 0.2 0.3 0.4 0.2 0.1 0.2

(Table C6 continued)
276 USDA Forest Service RMRS-GTR-385. 2018

APPENDIX D: Detailed Supplemental Results

This appendix provides detailed supplemental results organized into several tables (tables 

D.1–D.10).

Table D1—Historical range of variability (HRV) in the percentage of each major cover type (i.e., those with ≥1,000 

ha [2,500 ac] extent) in each vegetation developmental stage for ca. 1550–1850 in the upper Yuba River watershed. 

Select percentiles of the simulated HRV are given, as well as the current landscape condition and its corresponding 

percentile of the simulated HRV.

Cover

type/

Developmental

stage

Percentile of historical range of variability Current

0th 5th 25th 50th 75th 95th 100th 

%

eligible % HRV

Mixed Evergreen – Mesic 

Early 1.34 2.73 4.81 6.65 9.22 13.05 19.14 8.21 65

Mid 1.17 2.67 4.28 5.89 8.32 11.95 17.88 52.66 100

Late 76.18 80.09 83.91 86.42 89.03 91.72 95.69 39.12 0 

Mixed Evergreen – Xeric 

Early 2.62 3.71 5.5 7.56 9.7 13.03 15.8 10.88 85

Mid 3.32 5.38 7.33 8.71 10.66 13.82 16.99 71.06 100

Late 72.42 77.35 80.57 83.28 85.64 87.8 89.65 18.06 0 

Oak-Conifer Forest and Woodland 

Early 6.03 8.91 12.25 15.04 18.24 23.32 29.64 19.97 84

Mid 24.12 28.46 32.84 35.73 38.45 42.29 47.93 76.32 100

Late 37.14 42.15 46.35 48.69 51.55 55.81 59.99 3.72 0 

Oak-Conifer Forest and Woodland – Ultramafic 

Early 0.54 0.99 2.41 3.7 6.27 12.77 15.65 17.76 100

Mid 1.19 1.97 3.11 5.06 8.63 15.12 18.87 74.35 100

Late 69.73 75.97 84.09 90.67 92.66 95.37 96.84 7.89 0 

Red Fir – Mesic 

Early 1.16 2.42 5.04 7.78 11.1 19.68 27.64 24.21 99

Mid 9.25 13.41 17.64 20.98 24.63 31.52 35.86 39.00 100

Late 56.2 59.06 65.85 70.41 74.3 78.71 83.45 36.79 0 

(Table D1 continued on next page.)
USDA Forest Service RMRS-GTR-385. 2018 277

(Table D1 continued) 

Cover

type/

Developmental

stage

Percentile of historical range of variability Current

0th 5th 25th 50th 75th 95th 100th 

%

eligible % HRV

Red Fir – Xeric 

Early 11.54 16.13 21.32 25.57 30.87 39.46 46.21 32.39 82

Mid 14.47 17.88 22.88 27.2 31.12 36.43 42.19 39.49 98

Late 29.55 35.38 41.83 46.69 51.11 56.64 61.17 28.12 0 

Sierran Mixed Conifer – Mesic 

Early 2.26 3.57 5.2 7.25 9.64 13.55 15.68 14.98 100

Mid 16.25 19.59 22.62 24.63 27.19 29.88 32.27 44.00 100

Late 58.06 62.03 65.05 67.48 70.09 73.44 76.66 41.02 0 

Sierran Mixed Conifer – Ultramafic 

Early 7.32 9.07 11.12 12.61 14.47 17.09 20.71 48.70 100

Mid 8.37 9.64 12.57 14.18 16.3 18.98 20.02 15.09 62

Late 66.73 68.15 70.66 72.99 74.3 77.04 82.53 36.21 0 

Sierran Mixed Conifer – Xeric 

Early 4.88 7.17 8.43 9.54 10.65 12.46 13.8 19.48 100

Mid 8.25 10.35 12.14 12.96 13.89 15.23 17.13 38.36 100

Late 74.73 75.42 76.35 77.29 78.41 79.81 80.64 42.16 0 
278 USDA Forest Service RMRS-GTR-385. 2018

Table D2—Historical range of variability (HRV) in the percentage of each vegetation developmental stage in each 

canopy cover class by major cover type (i.e., those with ≥1,000 ha [2,500 ac] extent) for ca. 1550–1850 in the upper 

Yuba River watershed. Select percentiles of the simulated HRV are given, as well as the current landscape condition 

and its corresponding percentile of the simulated HRV. Note that percentages reflect the percentage of the 

developmental stage (mid or late) in each canopy cover class (open, moderate, or closed) for the corresponding 

cover type.

Cover type/

Developmental

stagea

/ Canopy

cover class

Percentile of historical range of variability Current

0th 5th 25th 50th 75th 95th 100th % eligible

%

HRV

Mixed Evergreen – Mesic 

MDO 0.57 1.64 3.51 5.75 8.46 13.08 17.29 12.09 93

MDM 35.23 52.13 69.52 79.2 86.55 93.09 97.05 18.54 0 

MDC 0.73 2.17 7.51 14.07 24.25 42.37 60.23 69.37 100

LDO 2.12 3.43 5.44 7.28 10.64 15.04 20.35 6.39 38

LDM 6.13 8.33 10.6 13.04 15.15 18.27 22.21 18.69 97

LDC 62.73 69.18 74.91 79.44 82.99 86.47 90.68 74.92 26

Mixed Evergreen – Xeric

MDO 1.72 3.06 4.78 6.71 9.08 12.68 19.85 18.1 100

MDM 32.74 43.33 55.66 64.11 71.78 79.35 90.23 13.22 0 

MDC 5.39 13.39 20.9 28.41 37.49 50.16 60.87 68.68 100

LDO 3.8 4.85 6.83 8.79 11.53 15.73 20.07 7.64 34

LDM 8.99 10.68 12.88 14.91 16.82 20.7 23.74 21.27 97

LDC 57.94 65.29 71.68 76.48 79.84 83.39 86.64 71.08 24

Oak-Conifer Forest and Woodland

MDO 11.13 16.83 21.35 26.69 33.9 44.27 53.39 31.9 71

MDM 21.39 24.36 27.71 30.02 32.57 36.32 43.35 19.15 0 

MDC 19.14 24.53 34.88 42.89 48.9 56.46 62.74 48.95 76

LDO 8.63 10.87 14.94 19.11 25.63 33.41 42.76 30.23 89

LDM 20.38 22.78 27.27 30.74 33.89 39.17 43.99 27.37 27

(Table D2 continued on next page.)
USDA Forest Service RMRS-GTR-385. 2018 279

Cover type/

Developmental

stagea

/ Canopy

cover class

Percentile of historical range of variability Current

0th 5th 25th 50th 75th 95th 100th % eligible

%

HRV

LDC 23.2 28.84 41.6 49.28 56.97 63.65 69.51 42.4 27

Oak-Conifer Forest and Woodland – Ultramafic 

MDO 23.88 59.22 79.03 87.95 93.52 97.61 99.08 45.05 3 

MDM 0.86 2.19 6.15 11.32 19.3 38.06 76.12 15.52 63

MDC 0 0 0.1 0.39 1.05 4.93 10.35 39.43 100

LDO 74.34 88.29 96.25 97.88 98.71 99.06 99.29 4.31 0 

LDM 0.71 0.92 1.26 1.99 3.68 10.68 17.92 27.88 100

LDC 0 0 0.01 0.04 0.15 1.02 7.74 67.81 100

Red Fir – Mesic 

MDO 1.01 1.94 3.06 4.25 6.27 10 12.75 42.82 100

MDM 1.25 2.32 3.32 4.28 5.75 7.66 9.86 47.87 100

MDC 77.39 82.68 88.1 91.44 93.56 95.65 97.75 9.31 0 

LDO 2.35 3.16 4.41 5.57 6.89 10.17 12.07 11.23 100

LDM 5.8 6.95 8.29 9.38 10.65 12.35 13.62 59.69 100

LDC 74.41 78.06 82.61 85.12 87.1 89.75 91.28 29.08 0 

Red Fir – Xeric 

MDO 35.33 44.56 53.58 60.11 66.97 73.71 82.22 31.85 0 

MDM 14.53 19.07 25.81 31.48 36.04 43.72 50.87 47.24 99

MDC 1.99 3.93 6.26 8.43 11.27 16.45 21.19 20.91 100

LDO 7.86 10.32 12.55 14.75 18.15 21.77 26.77 11.02 10

LDM 17.3 19.4 22.24 24.34 26.77 30 33.57 51.81 100

LDC 44.6 50.59 56.03 60.18 64.83 69.41 72.47 37.17 0 

(Table D2 continued on next page.)

(Table D2 continued) 
280 USDA Forest Service RMRS-GTR-385. 2018

(Table D2 continued) 

Cover type/

Developmental

stagea

/ Canopy

cover class

Percentile of historical range of variability Current

0th 5th 25th 50th 75th 95th 100th % eligible

%

HRV

Sierran Mixed Conifer – Mesic 

MDO 13.37 16.37 20.89 26.5 32.58 40.22 48.66 37.01 88

MDM 18.89 22.38 25.9 28.05 30.7 34.76 39.36 40.84 100

MDC 25.61 31.15 38.93 44.48 50.02 57.76 65.32 22.14 0 

LDO 7.59 11.32 16 21.09 26.92 35.37 44.35 8.78 1 

LDM 15.89 21.91 24.64 27.45 30.99 36.6 43.63 34.58 91

LDC 22.46 32.58 43.57 51.31 57.04 63.9 72.04 56.64 73

Sierran Mixed Conifer – Ultramafic 

MDO 54.4 66.06 73.08 77.15 80.9 84.87 89.24 35.32 0 

MDM 9.67 13.44 17.02 20.18 24.01 30.01 37.24 44.84 100

MDC 0.72 1.15 1.83 2.4 3.08 5.15 11.18 19.84 100

LDO 33.66 38.09 42.89 46.42 50.48 57.54 64.23 9.04 0 

LDM 24.69 26.55 29.05 31.18 33.61 37.1 41.31 23.51 0 

LDC 9.95 13.86 18.6 21.82 25.03 28.65 33.49 67.45 100

Sierran Mixed Conifer – Xeric 

MDO 54.58 59.76 64.73 69.78 74.73 79.94 85.03 29.93 0 

MDM 12.98 17.22 20.64 24.38 28.09 32.27 36.06 38.9 100

MDC 1.7 2.55 4.05 5.32 6.97 9.67 11 31.17 100

LDO 31.7 36.73 43.63 48.98 55.07 65.3 73.83 9.79 0 

LDM 20.45 25.78 31.18 33.37 35.7 39.34 44.54 31.57 30

LDC 5.72 8.61 12.81 17.09 20.96 26.77 32.37 58.63 100

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
USDA Forest Service RMRS-GTR-385. 2018 281

Table D3—Historical range of variability (HRV) in the percentage of each major cover type (i.e., those with ≥1,000 

ha [2,500 ac] extent) in each vegetation seral stage for ca. 1550–1850 in the upper Yuba River watershed. Select 

percentiles of the simulated HRV are given, as well as the current landscape condition and its corresponding 

percentile of the simulated HRV.

Cover

type/

Seral

stagea

Percentile of historical range of variability Current

0th 5th 25th 50th 75th 95th 100th % eligible % HRV

Mixed Evergreen – Mesic

ED 1.34 2.72 4.81 6.65 9.22 13.05 19.14 8.21 65

MDC 0.04 0.14 0.37 0.79 1.32 3.04 7.96 36.53 100

MDM 0.89 1.80 3.15 4.41 6.41 9.98 16.42 9.76 95

MDO 0.02 0.10 0.19 0.30 0.51 0.96 1.40 6.37 100

LDC 50.40 56.99 63.11 68.29 73.45 78.63 86.77 29.31 0 

LDM 5.84 7.56 9.34 11.05 12.86 15.39 18.43 7.31 4 

LDO 1.68 3.06 4.83 6.39 9.11 12.53 17.10 2.50 2 

Mixed Evergreen – Xeric

ED 2.62 3.71 5.50 7.56 9.70 13.03 15.80 10.88 85

MDC 0.49 0.96 1.73 2.43 3.41 5.08 8.84 48.80 100

MDM 1.67 2.80 4.41 5.48 7.16 9.57 12.89 9.39 95

MDO 0.13 0.26 0.40 0.59 0.81 1.26 1.78 12.87 100

LDC 44.43 51.06 58.6 63.39 67.84 72.35 76.86 12.84 0 

LDM 7.46 9.24 10.87 12.35 13.62 16.24 18.41 3.84 0 

LDO 3.23 4.2 5.78 7.48 9.45 12.47 15.39 1.38 0 

Oak-Conifer Forest and Woodland

ED 6.03 8.91 12.25 15.05 18.24 23.33 29.64 19.97 84

MDC 4.96 7.65 11.84 15.05 18.24 22.12 28.16 37.36 100

MDM 6.77 8.33 9.42 10.5 11.8 14.24 16.27 14.61 98

MDO 4.09 6.27 7.63 9.47 11.82 14.68 16.48 24.34 100

LDC 10.11 12.63 19.29 24.36 28.57 33.09 38.54 1.58 0 

LDM 10.41 11.8 13.37 14.81 16.41 18.69 22.92 1.02 0 

LDO 4.19 5.47 7.4 9.4 12.07 15.96 22.35 1.12 0 

(Table D3 continued on next page.)
282 USDA Forest Service RMRS-GTR-385. 2018

(Table D3 continued) 

Cover

type/

Seral

stagea

Percentile of historical range of variability Current

0th 5th 25th 50th 75th 95th 100th % eligible % HRV

Oak-Conifer Forest and Woodland – Ultramafic

ED 0.54 0.99 2.41 3.7 6.27 12.78 15.65 17.76 100

MDC 0.01 0.01 0.02 0.03 0.08 0.19 0.54 29.32 100

MDM 0.05 0.12 0.31 0.56 0.96 2.51 7.16 11.54 100

MDO 0.72 1.45 2.5 4.35 7.94 14.33 17.57 33.49 100

LDC 0.01 0.01 0.02 0.06 0.15 0.79 5.49 5.35 100

LDM 0.61 0.81 1.13 1.69 3.25 8.09 12.7 2.2 61

LDO 52.7 66.87 81.64 88.27 90.11 94.01 95.58 0.34 0 

Red Fir – Mesic

ED 1.16 2.42 5.04 7.77 11.1 19.68 27.64 24.21 99

MDC 7.49 11.69 15.88 18.81 22.82 28.87 33.75 3.63 0 

MDM 0.34 0.47 0.7 0.9 1.17 1.64 2.18 18.67 100

MDO 0.27 0.43 0.64 0.89 1.24 1.96 3.05 16.7 100

LDC 43.55 47.21 54.02 59.83 63.99 69.91 74.23 10.7 0 

LDM 4.32 5.21 5.9 6.51 7.12 8.03 8.6 21.96 100

LDO 1.56 2.31 3.19 3.85 4.68 6.66 7.48 4.13 59

Red Fir – Xeric

ED 11.54 16.13 21.32 25.57 30.87 39.46 46.21 32.39 82

MDC 0.39 0.94 1.62 2.23 3.06 4.79 8.94 8.26 100

MDM 2.83 4.63 6.55 8.08 10.37 13.05 17.44 18.66 100

MDO 6.56 9.67 13.01 16.08 19.51 23.44 26.51 12.58 22

LDC 14.23 18.13 23.94 27.84 32.62 38.15 42.7 10.45 0 

LDM 7.32 8.82 10.2 11.21 12.48 13.9 16.12 14.57 99

LDO 3.65 4.78 6.1 6.91 7.79 9.51 11.23 3.1 0 

Sierran Mixed Conifer – Mesic

ED 2.26 3.57 5.2 7.25 9.64 13.55 15.68 14.98 100

(Table D3 continued on next page.)
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(Table D3 continued) 

Cover

type/

Seral

stagea

Percentile of historical range of variability Current

0th 5th 25th 50th 75th 95th 100th % eligible % HRV

MDC 5.23 6.99 9.28 10.93 12.94 15.42 19.25 9.74 32

MDM 3.89 4.87 6.08 6.94 8 9.67 12.29 17.97 100

MDO 2.95 4.07 5.27 6.47 7.91 9.71 12.91 16.29 100

LDC 13.96 21.15 29.26 34.46 39.32 44.86 52.89 23.23 10

LDM 10.72 14.82 16.8 18.43 20.69 24.29 27.81 14.18 3 

LDO 5.16 7.87 11.15 14.22 18.09 23.38 29.2 3.6 0 

Sierran Mixed Conifer – Ultramafic

ED 7.32 9.07 11.12 12.61 14.48 17.09 20.71 48.7 100

MDC 0.07 0.13 0.24 0.35 0.45 0.85 1.86 2.99 100

MDM 1.09 1.52 2.25 2.92 3.55 5.1 6.52 6.77 100

MDO 7.03 7.75 9.45 10.77 12.15 14.42 15.75 5.33 0 

LDC 7.37 9.68 13.33 15.87 18.22 21.41 27.64 24.43 99

LDM 17.09 19.06 20.96 22.81 24.59 26.94 28.9 8.51 0 

LDO 24.27 27.89 31.36 33.58 36.44 41.57 47.62 3.27 0 

Sierran Mixed Conifer – Xeric

ED 4.88 7.17 8.43 9.54 10.65 12.46 13.8 19.48 100

MDC 0.16 0.3 0.51 0.69 0.91 1.33 1.88 11.96 100

MDM 1.27 1.9 2.61 3.16 3.74 4.64 5.9 14.92 100

MDO 6.14 7.24 8.32 9.08 9.69 10.74 11.62 11.48 100

LDC 4.45 6.58 9.91 13.29 16.35 20.98 24.74 24.72 100

LDM 15.91 19.63 24.05 25.85 27.69 30.53 34.69 13.31 0 

LDO 24.37 28.56 33.64 37.83 42.57 49.66 57.44 4.13 0 

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed. 
284 USDA Forest Service RMRS-GTR-385. 2018

Table D4—Range of variability (ROV) in the percentage of each major cover type (i.e., those with >1,000 ha extent) 

in each vegetation developmental stage (early, mid, and late) for the simulated historical range of variability (circa 

1550–1850) (HRV) and management scenarios with a modified fire regime (MS1) and varying intensities and types of 

vegetation treatments (MS2-7) in the Upper Yuba River watershed. Select percentiles of the simulated ROV are given 

as well as the current landscape condition.

Cover

type Development stage

 Percentile of range of variability 

Scenario 5th 50th 95th Current

Mixed Evergreen – Mesic 

 Early HRV 2.73 6.65 13.05 8.21

 MS1 0.14 0.77 6.47 8.21

 MS2 4.54 6.96 14.34 8.21

 MS3a 4.21 6.80 9.29 8.21

 MS3b 4.50 6.52 8.42 8.21

 MS4 5.19 7.45 10.41 8.21

 MS5 6.58 7.70 10.88 8.21

 MS6 7.40 10.16 12.77 8.21

 MS7 6.58 9.29 11.69 8.21

 Mid HRV 2.67 5.89 11.95 52.66

 MS1 0.04 0.69 3.84 52.66

 MS2 3.58 6.12 7.81 52.66

 MS3a 3.19 5.58 7.15 52.66

 MS3b 4.89 7.02 8.41 52.66

 MS4 4.17 5.94 7.16 52.66

 MS5 5.98 7.17 8.38 52.66

 MS6 6.63 8.71 10.61 52.66

 MS7 6.85 8.72 10.52 52.66

 Late HRV 80.09 86.42 91.72 39.12

 MS1 90.35 98.08 99.81 39.12

 MS2 80.67 86.97 88.93 39.12

 MS3a 85.54 87.68 89.48 39.12

 MS3b 83.17 87.00 89.65 39.12

 MS4 83.34 86.84 88.95 39.12

 MS5 82.79 84.81 86.89 39.12

(Table D4 continued on next page.)
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(Table D4 continued) 

Cover

type Development stage

 Percentile of range of variability 

Scenario 5th 50th 95th Current

 MS6 78.88 81.02 83.09 39.12

 MS7 79.62 82.80 83.70 39.12

Mixed Evergreen – Xeric 

 Early HRV 3.71 7.56 13.03 10.88

 MS1 0.27 1.34 5.42 10.88

 MS2 3.48 5.12 10.17 10.88

 MS3a 2.96 4.32 6.26 10.88

 MS3b 3.77 4.75 6.12 10.88

 MS4 3.99 4.59 8.82 10.88

 MS5 5.07 6.10 8.60 10.88

 MS6 6.78 8.91 10.81 10.88

 MS7 6.25 7.56 9.34 10.88

 Mid HRV 5.38 8.71 13.82 71.06

 MS1 0.16 1.11 5.06 71.06

 MS2 3.84 5.10 7.44 71.06

 MS3a 4.94 6.21 8.12 71.06

 MS3b 7.18 8.86 9.99 71.06

 MS4 3.83 5.80 7.86 71.06

 MS5 6.95 7.62 9.23 71.06

 MS6 9.55 11.21 12.21 71.06

 MS7 9.24 10.25 11.36 71.06

 Late HRV 77.35 83.28 87.80 18.06

 MS1 90.47 96.27 99.12 18.06

 MS2 85.19 89.25 91.22 18.06

 MS3a 87.04 89.33 90.33 18.06

 MS3b 84.49 86.65 88.13 18.06

 MS4 83.70 89.25 91.91 18.06

 MS5 83.62 85.77 86.99 18.06

 MS6 78.35 80.23 81.93 18.06

(Table D4 continued on next page.)
286 USDA Forest Service RMRS-GTR-385. 2018

(Table D4 continued) 

Cover

type Development stage

 Percentile of range of variability 

Scenario 5th 50th 95th Current

 MS7 79.51 82.17 83.52 18.06

Oak-Conifer Forest and Woodland 

 Early HRV 8.91 15.04 23.32 19.97

 MS1 0.58 1.64 11.82 19.97

 MS2 5.53 8.29 17.40 19.97

 MS3a 5.44 6.76 9.62 19.97

 MS3b 5.85 6.67 9.45 19.97

 MS4 5.51 7.02 14.67 19.97

 MS5 6.92 8.16 12.94 19.97

 MS6 9.67 10.70 13.21 19.97

 MS7 7.68 9.99 11.84 19.97

 Mid HRV 28.46 35.73 42.29 76.32

 MS1 9.12 14.32 32.11 76.32

 MS2 19.07 22.44 28.91 76.32

 MS3a 17.91 22.35 28.45 76.32

 MS3b 21.89 24.32 31.64 76.32

 MS4 19.98 22.50 28.68 76.32

 MS5 21.85 25.98 28.87 76.32

 MS6 29.96 31.58 36.07 76.32

 MS7 28.22 30.17 35.24 76.32

 Late HRV 42.15 48.69 55.81 3.72

 MS1 60.28 81.06 88.91 3.72

 MS2 61.67 68.41 72.40 3.72

 MS3a 66.25 70.96 73.45 3.72

 MS3b 62.06 67.66 71.54 3.72

 MS4 62.00 68.41 72.69 3.72

 MS5 60.08 65.32 67.60 3.72

 MS6 53.33 57.25 59.43 3.72

 MS7 54.72 59.69 61.92 3.72

(Table D4 continued on next page.)
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(Table D4 continued) 

Cover

type Development stage

 Percentile of range of variability 

Scenario 5th 50th 95th Current

 MS7 79.51 82.17 83.52 18.06

Oak-Conifer Forest and Woodland 

 Early HRV 8.91 15.04 23.32 19.97

 MS1 0.58 1.64 11.82 19.97

 MS2 5.53 8.29 17.40 19.97

 MS3a 5.44 6.76 9.62 19.97

 MS3b 5.85 6.67 9.45 19.97

 MS4 5.51 7.02 14.67 19.97

 MS5 6.92 8.16 12.94 19.97

 MS6 9.67 10.70 13.21 19.97

 MS7 7.68 9.99 11.84 19.97

 Mid HRV 28.46 35.73 42.29 76.32

 MS1 9.12 14.32 32.11 76.32

 MS2 19.07 22.44 28.91 76.32

 MS3a 17.91 22.35 28.45 76.32

 MS3b 21.89 24.32 31.64 76.32

 MS4 19.98 22.50 28.68 76.32

 MS5 21.85 25.98 28.87 76.32

 MS6 29.96 31.58 36.07 76.32

 MS7 28.22 30.17 35.24 76.32

 Late HRV 42.15 48.69 55.81 3.72

 MS1 60.28 81.06 88.91 3.72

 MS2 61.67 68.41 72.40 3.72

 MS3a 66.25 70.96 73.45 3.72

 MS3b 62.06 67.66 71.54 3.72

 MS4 62.00 68.41 72.69 3.72

 MS5 60.08 65.32 67.60 3.72

 MS6 53.33 57.25 59.43 3.72

 MS7 54.72 59.69 61.92 3.72

(Table D4 continued on next page.)
288 USDA Forest Service RMRS-GTR-385. 2018

(Table D4 continued) 

Cover

type Development stage

 Percentile of range of variability 

Scenario 5th 50th 95th Current

Oak-Conifer Forest and Woodland – Ultramafic 

 Early HRV 0.99 3.70 12.77 17.76

 MS1 0.88 3.38 9.79 17.76

 MS2 1.18 2.61 6.15 17.76

 MS3a 0.55 1.48 3.52 17.76

 MS3b 0.86 1.58 3.54 17.76

 MS4 0.65 2.38 7.96 17.76

 MS5 2.26 3.53 7.77 17.76

 MS6 3.36 4.59 6.77 17.76

 MS7 3.88 5.05 7.03 17.76

 Mid HRV 1.97 5.06 15.12 74.35

 MS1 17.38 20.18 23.12 74.35

 MS2 18.34 20.80 22.90 74.35

 MS3a 19.98 22.55 26.78 74.35

 MS3b 23.40 25.48 27.48 74.35

 MS4 18.28 20.81 23.81 74.35

 MS5 18.58 22.23 24.20 74.35

 MS6 20.66 23.77 28.30 74.35

 MS7 21.63 23.66 26.04 74.35

 Late HRV 75.97 90.67 95.37 7.89

 MS1 68.40 76.82 79.32 7.89

 MS2 73.05 76.14 80.36 7.89

 MS3a 72.39 75.56 77.97 7.89

 MS3b 70.11 72.97 74.42 7.89

 MS4 73.40 75.94 80.12 7.89

 MS5 71.75 73.92 76.52 7.89

 MS6 66.49 71.22 74.30 7.89

 MS7 68.61 71.16 73.24 7.89

Red Fir – Mesic 

(Table D4 continued on next page.)
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(Table D4 continued) 

Cover

type Development stage

 Percentile of range of variability 

Scenario 5th 50th 95th Current

 Early HRV 2.42 7.78 19.68 24.21

 MS1 0.11 1.02 10.32 24.21

 MS2 4.90 6.93 17.36 24.21

 MS3a 4.94 5.76 7.14 24.21

 MS3b 4.65 5.43 6.74 24.21

 MS4 4.43 6.29 12.95 24.21

 MS5 7.03 7.72 12.46 24.21

 MS6 8.50 10.12 13.56 24.21

 MS7 7.75 9.29 13.42 24.21

 Mid HRV 13.41 20.98 31.52 39.00

 MS1 1.91 5.31 12.40 39.00

 MS2 7.43 10.23 16.08 39.00

 MS3a 6.93 8.43 10.03 39.00

 MS3b 9.50 11.82 12.91 39.00

 MS4 7.58 10.36 17.18 39.00

 MS5 10.10 12.55 13.87 39.00

 MS6 17.02 18.75 20.60 39.00

 MS7 13.71 16.18 19.76 39.00

 Late HRV 59.06 70.41 78.71 36.79

 MS1 78.01 90.70 97.41 36.79

 MS2 72.30 81.81 85.76 36.79

 MS3a 83.70 85.78 87.01 36.79

 MS3b 81.03 82.91 84.70 36.79

 MS4 73.98 83.27 86.90 36.79

 MS5 76.38 79.57 80.87 36.79

 MS6 66.90 71.33 72.75 36.79

 MS7 69.24 74.43 76.78 36.79

Red Fir – Xeric 

 Early HRV 16.13 25.57 39.46 32.39

(Table D4 continued on next page.)
290 USDA Forest Service RMRS-GTR-385. 2018

(Table D4 continued) 

Cover

type Development stage

 Percentile of range of variability 

Scenario 5th 50th 95th Current

 MS1 1.28 4.98 19.70 32.39

 MS2 3.69 6.57 30.11 32.39

 MS3a 3.73 4.88 9.25 32.39

 MS3b 4.17 5.12 7.56 32.39

 MS4 4.69 7.23 17.97 32.39

 MS5 6.54 8.41 14.16 32.39

 MS6 8.34 10.32 16.96 32.39

 MS7 8.11 10.52 21.82 32.39

 Mid HRV 17.88 27.20 36.43 39.49

 MS1 20.34 24.53 36.48 39.49

 MS2 21.95 27.51 30.89 39.49

 MS3a 26.56 28.91 31.41 39.49

 MS3b 29.70 32.88 36.16 39.49

 MS4 24.10 29.41 34.08 39.49

 MS5 25.45 29.32 33.56 39.49

 MS6 29.51 31.95 34.34 39.49

 MS7 25.42 29.65 34.17 39.49

 Late HRV 35.38 46.69 56.64 28.12

 MS1 59.63 65.84 73.72 28.12

 MS2 50.08 65.32 69.04 28.12

 MS3a 61.08 65.08 68.96 28.12

 MS3b 58.74 61.57 63.98 28.12

 MS4 52.49 64.06 70.44 28.12

 MS5 56.13 61.63 63.73 28.12

 MS6 51.38 56.75 60.22 28.12

 MS7 52.77 57.33 61.67 28.12

Sierran Mixed Conifer – Mesic 

 Early HRV 3.57 7.25 13.55 14.98

 MS1 0.19 0.89 5.26 14.98

(Table D4 continued on next page.)
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(Table D4 continued) 

Cover

type Development stage

 Percentile of range of variability 

Scenario 5th 50th 95th Current

 MS2 4.77 5.90 11.73 14.98

 MS3a 4.68 5.30 7.65 14.98

 MS3b 5.11 5.56 9.23 14.98

 MS4 5.16 5.78 10.37 14.98

 MS5 5.75 6.39 10.32 14.98

 MS6 7.85 8.52 9.09 14.98

 MS7 6.69 7.42 9.99 14.98

 Mid HRV 19.59 24.63 29.88 44.00

 MS1 2.28 6.43 14.37 44.00

 MS2 15.02 18.28 24.41 44.00

 MS3a 13.92 15.77 18.90 44.00

 MS3b 16.71 18.59 23.05 44.00

 MS4 14.37 16.91 21.04 44.00

 MS5 17.40 19.45 22.09 44.00

 MS6 24.14 25.47 27.96 44.00

 MS7 22.40 24.04 27.92 44.00

 Late HRV 62.03 67.48 73.44 41.02

 MS1 82.59 91.85 96.71 41.02

 MS2 66.18 72.71 80.22 41.02

 MS3a 75.46 78.34 80.65 41.02

 MS3b 67.88 75.59 78.03 41.02

 Late MS4 70.76 75.92 79.96 41.02

 MS5 71.39 73.17 75.63 41.02

 MS6 63.59 66.13 67.37 41.02

 MS7 63.45 68.14 69.79 41.02

Sierran Mixed Conifer – Ultramafic 

 Early HRV 9.07 12.61 17.09 48.70

 MS1 1.12 2.12 10.75 48.70

 MS2 2.25 4.34 5.83 48.70

(Table D4 continued on next page.)
292 USDA Forest Service RMRS-GTR-385. 2018

(Table D4 continued) 

Cover

type Development stage

 Percentile of range of variability 

Scenario 5th 50th 95th Current

 MS3a 1.96 3.66 4.59 48.70

 MS3b 2.02 3.23 5.56 48.70

 MS4 2.01 2.97 6.05 48.70

 MS5 3.81 5.41 7.14 48.70

 MS6 5.43 7.00 8.03 48.70

 MS7 5.59 6.64 8.95 48.70

 Mid HRV 9.64 14.18 18.98 15.09

 MS1 46.59 48.27 49.13 15.09

 MS2 47.62 48.92 50.57 15.09

 MS3a 49.96 51.02 52.75 15.09

 MS3b 52.57 53.39 54.35 15.09

 MS4 47.22 49.46 50.38 15.09

 MS5 48.58 50.02 51.19 15.09

 MS6 48.93 50.00 52.09 15.09

 MS7 48.53 50.23 51.33 15.09

 Late HRV 68.15 72.99 77.04 36.21

 MS1 43.19 49.81 50.56 36.21

 MS2 45.24 46.48 48.88 36.21

 MS3a 44.00 45.62 46.43 36.21

 MS3b 42.06 43.36 44.34 36.21

 MS4 45.52 47.31 48.66 36.21

 MS5 43.03 44.52 45.71 36.21

 MS6 41.42 43.07 44.38 36.21

 MS7 40.74 43.16 44.39 36.21

Sierran Mixed Conifer – Xeric 

 Early HRV 7.17 9.54 12.46 19.48

 MS1 0.57 2.35 7.82 19.48

 MS2 4.83 6.84 10.06 19.48

 MS3a 4.52 5.12 6.07 19.48

(Table D4 continued on next page.)
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(Table D4 continued) 

Cover

type Development stage

 Percentile of range of variability 

Scenario 5th 50th 95th Current

 MS3b 4.75 5.47 7.42 19.48

 MS4 4.92 5.47 8.25 19.48

 MS5 6.52 7.29 9.31 19.48

 MS6 8.95 9.46 10.97 19.48

 MS7 8.01 9.11 10.93 19.48

 Mid HRV 10.35 12.96 15.23 38.36

 MS1 15.29 17.05 19.81 38.36

 MS2 20.83 22.19 23.64 38.36

 MS3a 23.98 24.87 26.52 38.36

 MS3b 26.80 28.59 29.37 38.36

 MS4 21.05 22.88 24.38 38.36

 MS5 24.39 25.20 26.32 38.36

 MS6 26.83 28.04 29.31 38.36

 MS7 24.97 27.08 28.47 38.36

 Late HRV 75.42 77.29 79.81 42.16

 MS1 75.11 80.71 82.25 42.16

 MS2 69.05 70.74 73.08 42.16

 MS3a 68.60 69.76 70.63 42.16

 MS3b 65.18 65.98 66.67 42.16

 MS4 67.94 71.41 72.89 42.16

 MS5 65.90 67.23 68.01 42.16

 MS6 61.36 62.56 63.16 42.16

 MS7 62.61 63.72 64.43 42.16
294 USDA Forest Service RMRS-GTR-385. 2018

Table D5—Range of variability (ROV) in the percentage of each major cover type (i.e., those with >1,000 ha extent) 

in each canopy cover class (open, moderate, closed) for the simulated historical range of variability (circa 1550–

1850) (HRV) and management scenarios with a modified fire regime (MS1) and varying intensities and types of 

vegetation treatments (MS2-7) in the Upper Yuba River watershed. Select percentiles of the simulated ROV are given 

as well as the current landscape condition.

Cover type

Canopy

cover

class

 Percentile of range of variability 

Scenario 5th 50th 95th Current

Mixed Evergreen – Mesic 

 Open HRV 6.14 13.29 26.01 17.08

 MS1 0.35 1.58 12.31 17.08

 MS2 5.78 9.07 22.64 17.08

 MS3a 5.48 8.73 11.94 17.08

 MS3b 7.79 11.13 15.56 17.08

 MS4 13.64 15.93 20.69 17.08

 MS5 15.90 17.28 21.59 17.08

 MS6 23.01 26.09 30.07 17.08

 MS7 19.34 22.24 27.09 17.08

 Moderate HRV 10.47 15.71 23.74 17.07

 MS1 0.44 2.86 8.68 17.07

 MS2 12.80 15.16 21.98 17.07

 MS3a 10.14 12.30 13.89 17.07

 MS3b 14.07 15.83 18.89 17.07

 MS4 24.71 26.03 28.19 17.07

 MS5 21.09 22.78 25.21 17.07

 MS6 21.52 22.62 24.38 17.07

 MS7 22.69 25.80 29.30 17.07

 Closed HRV 57.71 69.28 80.12 65.85

 MS1 80.34 94.89 99.21 65.85

 MS2 57.48 74.79 80.42 65.85

 MS3a 76.06 79.00 82.60 65.85

 MS3b 66.13 73.32 77.94 65.85

 MS4 51.98 58.30 60.65 65.85

 MS5 55.16 59.18 63.36 65.85

 MS6 46.65 50.95 55.22 65.85

 MS7 47.00 51.87 55.89 65.85

(Table D5 continued on next page.)
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Cover type

Canopy

cover

class

 Percentile of range of variability 

Scenario 5th 50th 95th Current

Mixed Evergreen – Xeric 

 Open HRV 8.57 15.70 25.15 25.12

 MS1 0.59 2.90 10.71 25.12

 MS2 4.95 7.50 17.56 25.12

 MS3a 6.87 8.62 11.03 25.12

 MS3b 13.49 15.82 20.30 25.12

 MS4 12.02 13.23 20.08 25.12

 MS5 18.24 20.52 22.82 25.12

 MS6 26.03 28.62 31.38 25.12

 MS7 21.16 23.45 27.77 25.12

 Moderate HRV 13.27 17.98 23.75 13.23

 MS1 0.99 3.25 9.98 13.23

 MS2 11.06 13.65 19.02 13.23

 MS3a 13.40 15.46 18.08 13.23

 MS3b 23.06 24.80 26.77 13.23

 MS4 19.92 23.68 27.53 13.23

 MS5 22.55 24.71 25.82 13.23

 MS6 21.11 22.44 24.64 13.23

 MS7 24.92 26.49 29.01 13.23

 Closed HRV 53.90 66.06 75.51 61.64

 MS1 79.38 93.66 98.43 61.64

 MS2 65.31 77.19 83.93 61.64

 MS3a 73.10 75.94 78.27 61.64

 MS3b 54.60 59.56 63.04 61.64

 MS4 55.18 61.84 67.00 61.64

 MS5 52.15 54.95 56.84 61.64

 MS6 46.34 48.64 51.34 61.64

 MS7 45.64 49.59 52.92 61.64

Oak-Conifer Forest and Woodland 

 Open HRV 21.69 34.38 51.94 45.43

 MS1 1.69 4.83 26.95 45.43

(Table D5 continued) 

(Table D5 continued on next page.)
296 USDA Forest Service RMRS-GTR-385. 2018

Cover type

Canopy

cover

class

 Percentile of range of variability 

Scenario 5th 50th 95th Current

 MS2 7.95 13.41 30.17 45.43

 MS3a 7.15 10.18 15.51 45.43

 MS3b 10.95 12.54 23.39 45.43

 MS4 12.39 14.56 29.78 45.43

 MS5 17.04 19.23 25.58 45.43

 MS6 25.65 26.52 30.19 45.43

 MS7 25.71 29.26 33.36 45.43

 Moderate HRV 20.86 25.61 31.05 15.63

 MS1 2.43 6.05 20.90 15.63

 MS2 12.65 17.34 23.00 15.63

 MS3a 10.51 12.60 17.62 15.63

 MS3b 13.50 15.21 20.21 15.63

 MS4 21.37 23.62 27.82 15.63

 MS5 21.00 23.10 24.87 15.63

 MS6 20.20 21.88 25.00 15.63

 MS7 20.17 22.03 28.62 15.63

 Closed HRV 20.98 39.78 53.92 38.93

 MS1 55.87 88.34 96.12 38.93

 MS2 47.14 69.32 77.82 38.93

 MS3a 68.33 76.38 81.79 38.93

 MS3b 54.43 71.83 75.53 38.93

 MS4 45.18 61.09 66.11 38.93

 MS5 50.07 57.67 62.11 38.93

 MS6 46.53 51.22 53.41 38.93

 MS7 42.15 48.23 52.93 38.93

Oak-Conifer Forest and Woodland – Ultramafic 

 Open HRV 89.90 97.31 98.85 51.59

 MS1 34.87 41.80 65.59 51.59

 MS2 41.72 49.16 58.00 51.59

 MS3a 47.80 53.89 57.07 51.59

 MS3b 62.46 64.86 70.59 51.59

(Table D5 continued) 

(Table D5 continued on next page.)
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Cover type

Canopy

cover

class

 Percentile of range of variability 

Scenario 5th 50th 95th Current

 MS4 57.57 62.81 70.36 51.59

 MS5 61.73 64.92 67.26 51.59

 MS6 62.39 67.58 71.17 51.59

 MS7 69.59 71.67 75.79 51.59

 Moderate HRV 1.13 2.52 9.47 13.74

 MS1 19.82 24.68 31.14 13.74

 MS2 22.50 27.12 31.09 13.74

 MS3a 19.27 23.60 28.87 13.74

 MS3b 18.60 21.05 23.52 13.74

 MS4 17.38 20.59 25.25 13.74

 MS5 17.42 19.61 22.17 13.74

 MS6 13.67 17.55 22.22 13.74

 MS7 13.67 17.16 20.05 13.74

 Closed HRV 0.01 0.08 0.89 34.67

 MS1 14.92 31.06 36.68 34.67

 MS2 16.62 24.52 28.45 34.67

 MS3a 20.17 23.53 25.55 34.67

 MS3b 10.82 13.60 14.73 34.67

 MS4 10.60 15.75 19.60 34.67

 MS5 13.27 14.96 16.83 34.67

 MS6 11.89 14.42 16.70 34.67

 MS7 8.94 10.77 13.00 34.67

Red Fir – Mesic 

 Open HRV 5.34 12.70 25.74 45.04

 MS1 0.49 2.38 14.79 45.04

 MS2 6.32 9.11 25.01 45.04

 MS3a 8.29 9.80 12.53 45.04

 MS3b 13.16 15.82 21.16 45.04

 MS4 13.89 16.82 25.61 45.04

 MS5 24.03 25.50 30.32 45.04

 MS6 30.98 32.81 37.91 45.04

(Table D5 continued) 

(Table D5 continued on next page.)
298 USDA Forest Service RMRS-GTR-385. 2018

Cover type

Canopy

cover

class

 Percentile of range of variability 

Scenario 5th 50th 95th Current

 MS7 24.46 25.82 31.78 45.04

 Moderate HRV 5.81 7.50 9.27 40.63

 MS1 1.16 3.12 5.85 40.63

 MS2 9.46 13.30 20.38 40.63

 MS3a 13.26 15.49 17.93 40.63

 MS3b 16.52 18.27 20.63 40.63

 MS4 21.89 23.71 26.98 40.63

 MS5 21.39 23.55 26.04 40.63

 MS6 20.40 21.82 23.57 40.63

 MS7 23.28 24.80 26.09 40.63

 Closed HRV 65.44 79.77 88.39 14.33

 MS1 79.82 94.45 98.33 14.33

 MS2 61.95 75.99 82.23 14.33

 MS3a 69.79 73.91 78.06 14.33

 MS3b 59.60 66.16 69.69 14.33

 MS4 47.61 59.44 62.68 14.33

 MS5 46.18 50.72 53.45 14.33

 MS6 41.29 45.31 47.72 14.33

 MS7 44.94 49.51 50.57 14.33

Red Fir – Xeric 

 Open HRV 37.50 49.48 63.70 48.07

 MS1 5.42 16.64 31.14 48.07

 MS2 14.13 21.67 46.19 48.07

 MS3a 27.96 33.32 41.51 48.07

 MS3b 39.12 46.33 57.60 48.07

 MS4 23.82 29.98 44.97 48.07

 MS5 40.48 44.47 52.06 48.07

 MS6 42.91 45.25 56.11 48.07

 MS7 39.43 43.94 52.38 48.07

 Moderate HRV 15.21 19.68 24.50 33.22

 MS1 14.00 16.67 22.66 33.22

(Table D5 continued) 

(Table D5 continued on next page.)
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Cover type

Canopy

cover

class

 Percentile of range of variability 

Scenario 5th 50th 95th Current

 MS2 19.25 24.01 28.64 33.22

 MS3a 30.58 32.51 35.00 33.22

 MS3b 26.41 31.34 35.17 33.22

 MS4 24.03 28.00 32.51 33.22

 MS5 25.14 27.05 29.00 33.22

 MS6 22.44 23.78 27.36 33.22

 MS7 23.29 26.65 30.42 33.22

 Closed HRV 19.78 30.15 41.11 18.71

 MS1 51.96 62.94 79.35 18.71

 MS2 32.16 52.84 65.89 18.71

 MS3a 28.66 33.36 38.42 18.71

 MS3b 16.28 21.52 25.38 18.71

 MS4 29.54 40.55 48.58 18.71

 MS5 21.46 28.20 31.53 18.71

 MS6 19.56 30.39 33.16 18.71

 MS7 20.20 29.41 32.73 18.71

Sierran Mixed Conifer – Mesic 

 Open HRV 16.39 28.61 44.10 34.87

 MS1 0.99 2.62 15.40 34.87

 MS2 7.39 10.66 24.72 34.87

 MS3a 6.91 9.32 12.62 34.87

 MS3b 10.51 12.45 20.14 34.87

 MS4 12.48 14.32 28.37 34.87

 MS5 15.48 17.71 25.64 34.87

 MS6 24.68 25.75 28.15 34.87

 MS7 23.34 25.27 30.59 34.87

 Moderate HRV 20.60 25.26 33.23 32.16

 MS1 1.38 4.24 16.78 32.16

 MS2 12.01 15.10 26.68 32.16

 MS3a 10.81 12.67 16.66 32.16

 MS3b 13.91 15.35 22.59 32.16

(Table D5 continued) 

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300 USDA Forest Service RMRS-GTR-385. 2018

Cover type

Canopy

cover

class

 Percentile of range of variability 

Scenario 5th 50th 95th Current

 MS4 22.02 23.24 26.86 32.16

 MS5 21.39 22.38 27.26 32.16

 MS6 21.21 22.67 25.11 32.16

 MS7 22.89 23.95 29.25 32.16

 Closed HRV 29.20 45.65 58.50 32.98

 MS1 72.81 92.76 96.66 32.98

 MS2 55.19 70.72 80.27 32.98

 MS3a 72.00 76.96 82.16 32.98

 MS3b 55.84 71.78 75.41 32.98

 MS4 43.72 61.95 65.07 32.98

 MS5 52.42 58.99 62.70 32.98

 MS6 46.86 51.78 53.10 32.98

 MS7 42.02 50.10 53.48 32.98

Sierran Mixed Conifer – Ultramafic 

 Open HRV 49.54 57.59 67.55 57.30

 MS1 15.36 22.70 40.90 57.30

 MS2 20.76 27.15 38.26 57.30

 MS3a 43.27 46.72 52.75 57.30

 MS3b 52.18 57.44 64.76 57.30

 MS4 34.15 41.84 55.19 57.30

 MS5 46.25 50.84 57.11 57.30

 MS6 47.05 52.45 57.18 57.30

 MS7 52.50 55.31 61.07 57.30

 Moderate HRV 21.32 25.84 30.67 15.28

 MS1 24.00 31.73 39.84 15.28

 MS2 29.70 38.91 45.23 15.28

 MS3a 25.93 31.95 35.59 15.28

 MS3b 23.38 29.34 34.60 15.28

 MS4 27.17 36.34 43.10 15.28

 MS5 24.14 30.62 35.93 15.28

 MS6 25.53 29.62 34.15 15.28

(Table D5 continued) 

(Table D5 continued on next page.)
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Cover type

Canopy

cover

class

 Percentile of range of variability 

Scenario 5th 50th 95th Current

 MS7 24.79 29.61 32.42 15.28

 Closed HRV 9.89 16.22 21.96 27.42

 MS1 24.88 45.86 55.68 27.42

 MS2 26.48 31.43 45.75 27.42

 MS3a 19.06 21.38 23.77 27.42

 MS3b 10.47 12.74 15.94 27.42

 MS4 16.00 20.82 27.19 27.42

 MS5 16.45 18.94 21.06 27.42

 MS6 15.66 17.71 21.68 27.42

 MS7 13.01 15.23 16.76 27.42

Sierran Mixed Conifer – Xeric 

 Open HRV 44.95 56.55 71.14 35.09

 MS1 5.55 10.54 28.63 35.09

 MS2 16.88 23.11 40.81 35.09

 MS3a 28.26 32.75 37.86 35.09

 MS3b 40.83 44.50 53.94 35.09

 MS4 30.39 33.80 44.76 35.09

 MS5 42.02 44.97 53.13 35.09

 MS6 42.84 44.83 48.18 35.09

 MS7 43.07 46.30 51.64 35.09

 Moderate HRV 21.39 28.98 34.89 28.23

 MS1 10.21 15.71 26.45 28.23

 MS2 20.15 23.58 27.67 28.23

 MS3a 28.49 29.90 31.99 28.23

 MS3b 27.67 30.08 30.93 28.23

 MS4 26.17 28.88 31.93 28.23

 MS5 23.36 25.05 27.66 28.23

 MS6 21.07 22.64 25.68 28.23

 MS7 22.58 24.19 26.67 28.23

 Closed HRV 6.92 14.01 22.01 36.68

 MS1 39.97 71.72 82.43 36.68

(Table D5 continued) 

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Cover type

Canopy

cover

class

 Percentile of range of variability 

Scenario 5th 50th 95th Current

 MS2 36.65 53.56 63.01 36.68

 MS3a 31.87 37.58 42.14 36.68

 MS3b 17.62 25.59 28.95 36.68

 MS4 25.79 36.47 43.49 36.68

 MS5 23.42 29.21 33.16 36.68

 MS6 27.71 32.63 35.53 36.68

 MS7 23.89 28.58 33.29 36.68

(Table D5 continued) 
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Table D6—Range of variability (ROV) in the percentage of each major cover type (i.e., those with >1,000 ha extent) 

in each vegetation seral stage (i.e., combinations of developmental stage and canopy cover class) for the simulated 

historical range of variability (circa 1550–1850)(HRV) and management scenarios with a modified fire regime (MS1) 

and varying intensities and types of vegetation treatments (MS2-7) in the Upper Yuba River watershed. Select 

percentiles of the simulated ROV are given as well as the current landscape condition.

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

Mixed Evergreen – Mesic 

 Early – All Structures HRV 2.72 6.65 13.05 8.21

 MS1 0.13 0.77 6.47 8.21

 MS2 4.53 6.96 14.33 8.21

 MS3a 4.21 6.80 9.29 8.21

 MS3b 4.50 6.51 8.42 8.21

 MS4 5.19 7.44 10.41 8.21

 MS5 6.58 7.69 10.89 8.21

 MS6 7.40 10.16 12.77 8.21

 MS7 6.59 9.29 11.69 8.21

 Mid – Open HRV 0.10 0.30 0.96 6.37

 MS1 0.00 0.01 0.20 6.37

 MS2 0.17 0.49 1.28 6.37

 MS3a 0.23 0.58 0.76 6.37

 MS3b 0.14 0.62 1.10 6.37

 MS4 0.24 0.60 0.87 6.37

 MS5 0.58 0.99 1.40 6.37

 MS6 1.59 1.99 2.55 6.37

 MS7 1.31 1.65 1.99 6.37

 Mid – Moderate HRV 1.80 4.41 9.98 9.76

 MS1 0.03 0.58 2.73 9.76

 MS2 2.63 4.82 6.05 9.76

 MS3a 1.84 4.47 5.50 9.76

 MS3b 3.52 5.35 6.44 9.76

 MS4 2.87 4.44 5.81 9.76

 MS5 3.91 5.25 6.89 9.76

 MS6 4.16 5.83 7.52 9.76

(Table D6 continued on next page.)
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(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 MS7 4.38 5.76 8.45 9.76

 Mid – Closed HRV 0.14 0.79 3.04 36.53

 MS1 0.00 0.04 0.93 36.53

 MS2 0.27 0.71 2.02 36.53

 MS3a 0.30 0.76 1.58 36.53

 MS3b 0.40 0.92 2.09 36.53

 MS4 0.33 0.96 1.44 36.53

 MS5 0.39 0.95 1.54 36.53

 MS6 0.35 0.76 1.87 36.53

 MS7 0.49 0.78 1.57 36.53

 Late – Open HRV 3.06 6.39 12.53 2.50

 MS1 0.25 0.59 6.09 2.50

 MS2 1.07 1.82 6.92 2.50

 MS3a 0.76 1.61 2.55 2.50

 MS3b 3.12 4.11 6.99 2.50

 MS4 6.48 8.14 9.72 2.50

 MS5 7.73 8.68 10.43 2.50

 MS6 12.81 14.13 15.39 2.50

 MS7 10.36 11.45 13.25 2.50

 Late – Moderate HRV 7.56 11.05 15.39 7.31

 MS1 0.41 2.01 7.74 7.31

 MS2 7.89 10.36 17.03 7.31

 MS3a 6.68 7.77 9.73 7.31

 MS3b 8.77 10.64 13.98 7.31

 MS4 20.13 21.67 23.72 7.31

 MS5 16.26 17.54 19.38 7.31

 MS6 15.21 17.04 18.43 7.31

 MS7 17.91 19.62 21.67 7.31

 Late – Closed HRV 56.99 68.29 78.63 29.31

 MS1 79.45 94.82 99.21 29.31

(Table D6 continued on next page.)
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(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 MS2 57.20 73.93 78.24 29.31

 MS3a 75.41 78.05 81.64 29.31

 MS3b 64.85 71.75 77.01 29.31

 MS4 51.66 57.28 59.66 29.31

 MS5 54.74 58.55 61.82 29.31

 MS6 45.97 50.25 53.79 29.31

 MS7 46.43 51.19 54.98 29.31

Mixed Evergreen – Xeric 

 Early – All Structures HRV 3.71 7.56 13.03 10.88

 MS1 0.26 1.34 5.42 10.88

 MS2 3.49 5.12 10.17 10.88

 MS3a 2.95 4.32 6.26 10.88

 MS3b 3.77 4.75 6.11 10.88

 MS4 3.99 4.59 8.82 10.88

 MS5 5.06 6.10 8.60 10.88

 MS6 6.78 8.91 10.81 10.88

 MS7 6.25 7.57 9.34 10.88

 Mid – Open HRV 0.26 0.59 1.26 12.87

 MS1 0.00 0.01 0.20 12.87

 MS2 0.26 0.43 0.76 12.87

 MS3a 0.22 0.37 0.62 12.87

 MS3b 0.60 0.80 1.27 12.87

 MS4 0.19 0.49 0.86 12.87

 MS5 1.00 1.25 1.50 12.87

 MS6 2.61 3.30 3.52 12.87

 MS7 1.96 2.11 2.59 12.87

 Mid – Moderate HRV 2.80 5.48 9.57 9.39

 MS1 0.08 0.62 3.63 9.39

 MS2 2.42 3.26 5.39 9.39

 MS3a 2.97 4.17 5.51 9.39

(Table D6 continued on next page.)
306 USDA Forest Service RMRS-GTR-385. 2018

(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 MS3b 4.77 5.64 6.93 9.39

 MS4 2.34 3.40 4.62 9.39

 MS5 4.30 4.86 5.76 9.39

 MS6 4.58 5.84 6.99 9.39

 MS7 5.04 6.13 7.69 9.39

 Mid – Closed HRV 0.96 2.43 5.08 48.80

 MS1 0.01 0.44 1.62 48.80

 MS2 0.70 1.23 2.51 48.80

 MS3a 1.16 1.76 3.07 48.80

 MS3b 1.31 2.03 2.77 48.80

 MS4 0.96 1.63 3.09 48.80

 MS5 1.11 1.69 2.51 48.80

 MS6 1.33 2.01 2.58 48.80

 MS7 1.30 1.89 2.49 48.80

 Late – Open HRV 4.20 7.48 12.47 1.38

 MS1 0.26 1.10 5.72 1.38

 MS2 0.89 2.04 6.67 1.38

 MS3a 3.34 3.88 4.95 1.38

 MS3b 8.06 9.82 12.34 1.38

 MS4 6.75 8.43 10.53 1.38

 MS5 11.48 12.76 14.13 1.38

 MS6 15.41 16.90 17.89 1.38

 MS7 12.46 13.60 15.36 1.38

 Late – Moderate HRV 9.24 12.35 16.24 3.84

 MS1 0.77 2.22 6.89 3.84

 MS2 7.49 10.23 15.13 3.84

 MS3a 10.26 11.04 12.58 3.84

 MS3b 17.88 19.15 20.09 3.84

 MS4 17.42 20.40 23.33 3.84

 MS5 18.06 19.72 20.76 3.84

(Table D6 continued on next page.)
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(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 MS6 15.48 16.73 18.37 3.84

 MS7 19.62 20.47 22.07 3.84

 Late – Closed HRV 51.06 63.39 72.35 12.84

 MS1 77.86 93.28 97.84 12.84

 MS2 64.66 75.72 82.68 12.84

 MS3a 70.75 73.72 76.08 12.84

 MS3b 53.00 57.58 61.29 12.84

 MS4 53.05 60.40 65.36 12.84

 MS5 50.81 53.25 55.09 12.84

 MS6 44.36 46.62 49.40 12.84

 MS7 43.60 47.98 50.45 12.84

Oak-Conifer Forest and Woodland 

 Early – All Structures HRV 8.91 15.05 23.33 19.97

 MS1 0.58 1.64 11.83 19.97

 MS2 5.53 8.29 17.39 19.97

 MS3a 5.44 6.76 9.62 19.97

 MS3b 5.84 6.67 9.45 19.97

 MS4 5.51 7.01 14.67 19.97

 MS5 6.92 8.17 12.95 19.97

 MS6 9.67 10.71 13.22 19.97

 MS7 7.68 9.99 11.84 19.97

 Mid – Open HRV 6.27 9.47 14.68 24.34

 MS1 0.35 0.84 6.71 24.34

 MS2 1.40 1.84 4.94 24.34

 MS3a 1.11 1.59 2.45 24.34

 MS3b 2.21 2.86 6.28 24.34

 MS4 2.24 2.80 6.26 24.34

 MS5 3.67 4.40 5.20 24.34

 MS6 7.42 8.08 9.00 24.34

 MS7 7.68 8.79 10.04 24.34

(Table D6 continued on next page.)
308 USDA Forest Service RMRS-GTR-385. 2018

(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 Mid – Moderate HRV 8.33 10.50 14.24 14.61

 MS1 0.47 1.23 9.62 14.61

 MS2 5.59 6.55 8.62 14.61

 MS3a 4.68 5.84 7.25 14.61

 MS3b 6.06 6.90 8.74 14.61

 MS4 5.73 7.95 9.35 14.61

 MS5 7.95 8.54 10.87 14.61

 MS6 9.92 10.51 11.47 14.61

 MS7 8.59 9.43 11.90 14.61

 Mid – Closed HRV 7.65 15.05 22.12 37.36

 MS1 7.25 11.17 22.24 37.36

 MS2 8.90 13.81 17.92 37.36

 MS3a 11.16 14.57 19.72 37.36

 MS3b 10.14 14.63 19.20 37.36

 MS4 7.99 12.18 16.30 37.36

 MS5 8.73 12.86 16.21 37.36

 MS6 10.89 12.78 16.65 37.36

 MS7 10.74 11.49 15.65 37.36

 Late – Open HRV 5.47 9.40 15.96 1.12

 MS1 0.57 1.83 9.05 1.12

 MS2 0.63 2.64 8.67 1.12

 MS3a 0.50 1.86 3.79 1.12

 MS3b 2.61 3.18 8.54 1.12

 MS4 3.63 4.77 10.71 1.12

 MS5 5.53 6.80 8.85 1.12

 MS6 7.65 8.05 9.14 1.12

 MS7 9.26 10.27 11.07 1.12

 Late – Moderate HRV 11.80 14.81 18.69 1.02

 MS1 1.92 4.78 11.64 1.02

 MS2 6.50 10.07 16.04 1.02

(Table D6 continued on next page.)
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(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 MS3a 5.03 7.35 10.15 1.02

 MS3b 7.10 7.84 12.22 1.02

 MS4 14.20 15.78 21.05 1.02

 MS5 12.76 14.41 15.54 1.02

 MS6 10.13 11.35 13.34 1.02

 MS7 10.89 13.04 15.77 1.02

 Late – Closed HRV 12.63 24.36 33.09 1.58

 MS1 49.43 74.27 85.58 1.58

 MS2 40.03 55.61 64.62 1.58

 MS3a 56.25 60.94 66.67 1.58

 MS3b 44.80 56.77 61.23 1.58

 MS4 37.19 46.24 54.58 1.58

 MS5 38.57 43.51 48.11 1.58

 MS6 31.85 37.57 40.49 1.58

 MS7 30.61 36.40 38.88 1.58

Oak-Conifer Forest and Woodland – Ultramafic 

 Early – All Structures HRV 0.99 3.70 12.78 17.76

 MS1 0.88 3.38 9.79 17.76

 MS2 1.18 2.61 6.15 17.76

 MS3a 0.55 1.48 3.52 17.76

 MS3b 0.86 1.58 3.55 17.76

 MS4 0.65 2.38 7.95 17.76

 MS5 2.25 3.53 7.76 17.76

 MS6 3.36 4.59 6.76 17.76

 MS7 3.88 5.06 7.03 17.76

 Mid – Open HRV 1.45 4.35 14.33 33.49

 MS1 8.54 12.58 15.58 33.49

 MS2 11.44 14.14 17.45 33.49

 MS3a 16.38 17.75 22.17 33.49

 MS3b 18.02 20.64 22.82 33.49

(Table D6 continued on next page.)
310 USDA Forest Service RMRS-GTR-385. 2018

(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 MS4 11.13 14.10 16.07 33.49

 MS5 13.71 17.97 20.60 33.49

 MS6 15.70 18.52 24.57 33.49

 MS7 17.44 19.36 22.10 33.49

 Mid – Moderate HRV 0.12 0.56 2.51 11.54

 MS1 4.00 5.34 9.80 11.54

 MS2 2.57 5.29 8.99 11.54

 MS3a 1.51 3.42 5.49 11.54

 MS3b 2.67 4.11 5.74 11.54

 MS4 3.62 5.06 9.91 11.54

 MS5 2.20 3.38 5.52 11.54

 MS6 2.82 3.97 6.82 11.54

 MS7 2.47 3.61 5.33 11.54

 Mid – Closed HRV 0.01 0.03 0.19 29.32

 MS1 0.28 1.60 4.17 29.32

 MS2 0.22 0.81 2.23 29.32

 MS3a 0.16 0.80 2.15 29.32

 MS3b 0.11 0.71 1.52 29.32

 MS4 0.19 0.53 2.23 29.32

 MS5 0.09 0.81 1.52 29.32

 MS6 0.16 0.55 2.18 29.32

 MS7 0.19 0.44 0.81 29.32

 Late – Open HRV 66.87 88.27 94.01 0.34

 MS1 22.06 28.22 38.70 0.34

 MS2 26.76 31.23 37.40 0.34

 MS3a 28.97 32.96 36.74 0.34

 MS3b 40.29 42.96 45.12 0.34

 MS4 43.47 46.43 51.33 0.34

 MS5 41.44 43.56 45.42 0.34

 MS6 39.53 43.43 48.08 0.34

(Table D6 continued on next page.)
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(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 MS7 45.09 47.26 50.59 0.34

 Late – Moderate HRV 0.81 1.69 8.09 2.20

 MS1 15.05 19.09 24.54 2.20

 MS2 18.00 21.92 24.17 2.20

 MS3a 15.84 20.38 24.80 2.20

 MS3b 15.29 17.10 19.18 2.20

 MS4 11.84 15.14 19.13 2.20

 MS5 14.37 16.29 18.23 2.20

 MS6 10.04 13.47 17.67 2.20

 MS7 10.33 13.42 15.58 2.20

 Late – Closed HRV 0.01 0.06 0.79 5.35

 MS1 14.72 29.13 34.73 5.35

 MS2 16.22 23.95 27.76 5.35

 MS3a 19.71 22.48 24.74 5.35

 MS3b 10.53 13.02 13.97 5.35

 MS4 10.22 14.54 19.08 5.35

 MS5 12.73 14.17 15.52 5.35

 MS6 11.35 13.57 15.77 5.35

 MS7 8.30 10.25 12.63 5.35

Red Fir – Mesic 

 Early – All Structures HRV 2.42 7.77 19.68 24.21

 MS1 0.10 1.03 10.32 24.21

 MS2 4.89 6.93 17.36 24.21

 MS3a 4.94 5.77 7.14 24.21

 MS3b 4.65 5.43 6.74 24.21

 MS4 4.43 6.28 12.96 24.21

 MS5 7.02 7.72 12.46 24.21

 MS6 8.49 10.12 13.56 24.21

 MS7 7.76 9.28 13.42 24.21

 Mid – Open HRV 0.43 0.89 1.96 16.70

(Table D6 continued on next page.)
312 USDA Forest Service RMRS-GTR-385. 2018

(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 MS1 0.01 0.06 0.23 16.70

 MS2 0.08 0.30 0.88 16.70

 MS3a 0.07 0.23 0.42 16.70

 MS3b 0.31 0.48 0.91 16.70

 MS4 0.12 0.43 0.94 16.70

 MS5 0.83 1.12 1.54 16.70

 MS6 3.20 3.44 3.73 16.70

 MS7 2.03 2.40 2.99 16.70

 Mid – Moderate HRV 0.47 0.90 1.64 18.67

 MS1 0.01 0.05 0.22 18.67

 MS2 1.54 2.75 3.76 18.67

 MS3a 1.44 2.46 3.18 18.67

 MS3b 1.48 2.71 3.40 18.67

 MS4 2.12 3.07 4.14 18.67

 MS5 3.01 3.98 5.24 18.67

 MS6 5.83 6.52 7.85 18.67

 MS7 4.40 5.39 6.24 18.67

 Mid – Closed HRV 11.69 18.81 28.87 3.63

 MS1 1.90 5.12 12.32 3.63

 MS2 4.63 7.08 12.42 3.63

 MS3a 4.34 5.99 6.91 3.63

 MS3b 6.79 8.45 10.28 3.63

 MS4 4.23 7.29 12.49 3.63

 MS5 5.45 7.51 9.18 3.63

 MS6 7.01 8.22 11.28 3.63

 MS7 6.76 8.35 11.52 3.63

 Late – Open HRV 2.31 3.85 6.66 4.13

 MS1 0.26 1.13 3.94 4.13

 MS2 1.32 2.19 6.27 4.13

 MS3a 2.89 3.75 5.45 4.13

(Table D6 continued on next page.)
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(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 MS3b 7.88 9.69 14.29 4.13

 MS4 8.62 10.06 12.18 4.13

 MS5 15.57 16.43 17.57 4.13

 MS6 18.40 19.39 20.45 4.13

 MS7 13.57 14.44 15.72 4.13

 Late – Moderate HRV 5.21 6.51 8.03 21.96

 MS1 1.15 3.01 5.69 21.96

 MS2 7.08 10.58 16.63 21.96

 MS3a 11.15 12.75 15.75 21.96

 MS3b 13.47 15.66 17.10 21.96

 MS4 18.85 20.51 23.47 21.96

 MS5 17.98 19.83 21.98 21.96

 MS6 14.02 15.12 16.18 21.96

 MS7 18.41 19.35 20.60 21.96

 Late – Closed HRV 47.21 59.83 69.91 10.70

 MS1 69.59 85.27 96.00 10.70

 MS2 56.60 68.04 76.19 10.70

 MS3a 63.69 68.24 71.91 10.70

 MS3b 51.21 57.43 61.98 10.70

 MS4 43.32 52.76 56.97 10.70

 MS5 40.09 43.01 45.32 10.70

 MS6 33.03 36.74 38.90 10.70

 MS7 34.96 40.72 42.55 10.70

Red Fir – Xeric 

 Early – All Structures HRV 16.13 25.57 39.46 32.39

 MS1 1.28 4.99 19.70 32.39

 MS2 3.69 6.57 30.12 32.39

 MS3a 3.73 4.88 9.25 32.39

 MS3b 4.18 5.12 7.56 32.39

 MS4 4.68 7.23 17.97 32.39

(Table D6 continued on next page.)
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(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 MS5 6.53 8.40 14.16 32.39

 MS6 8.34 10.32 16.96 32.39

 MS7 8.11 10.51 21.81 32.39

 Mid – Open HRV 9.67 16.08 23.44 12.58

 MS1 2.54 4.76 12.03 12.58

 MS2 5.57 7.21 13.35 12.58

 MS3a 12.74 13.92 17.63 12.58

 MS3b 17.31 19.68 23.40 12.58

 MS4 7.49 10.66 19.36 12.58

 MS5 12.76 14.31 17.81 12.58

 MS6 14.31 16.41 19.56 12.58

 MS7 13.34 16.51 19.74 12.58

 Mid – Moderate HRV 4.63 8.08 13.05 18.66

 MS1 5.76 7.96 12.45 18.66

 MS2 7.66 8.74 11.04 18.66

 MS3a 8.48 10.18 11.97 18.66

 MS3b 7.37 10.25 12.36 18.66

 MS4 6.85 9.36 11.94 18.66

 MS5 7.48 9.07 10.56 18.66

 MS6 8.68 9.82 10.87 18.66

 MS7 6.16 8.06 11.15 18.66

 Mid – Closed HRV 0.94 2.23 4.79 8.26

 MS1 7.17 11.41 14.65 8.26

 MS2 4.20 9.72 12.56 8.26

 MS3a 2.72 4.01 5.06 8.26

 MS3b 2.17 2.82 4.12 8.26

 MS4 4.09 7.41 10.17 8.26

 MS5 4.01 5.81 7.22 8.26

 MS6 3.80 6.12 7.33 8.26

 MS7 3.58 5.66 6.92 8.26

(Table D6 continued on next page.)
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(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 Late – Open HRV 4.78 6.91 9.51 3.10

 MS1 0.91 2.28 5.82 3.10

 MS2 3.20 5.12 8.53 3.10

 MS3a 10.37 13.32 15.74 3.10

 MS3b 17.41 21.04 29.15 3.10

 MS4 9.31 11.65 14.23 3.10

 MS5 19.34 21.12 23.53 3.10

 MS6 17.97 19.26 21.81 3.10

 MS7 14.13 16.15 18.23 3.10

 Late – Moderate HRV 8.82 11.21 13.90 14.57

 MS1 6.49 8.89 12.11 14.57

 MS2 12.60 15.29 18.93 14.57

 MS3a 19.60 22.52 24.45 14.57

 MS3b 18.70 20.87 23.71 14.57

 MS4 16.42 18.67 20.70 14.57

 MS5 15.94 17.66 19.46 14.57

 MS6 13.48 14.10 16.55 14.57

 MS7 16.68 18.30 21.71 14.57

 Late – Closed HRV 18.13 27.84 38.15 10.45

 MS1 41.82 51.74 65.10 10.45

 MS2 27.95 43.12 54.07 10.45

 MS3a 24.56 29.68 34.15 10.45

 MS3b 14.11 18.74 21.83 10.45

 MS4 24.13 33.21 39.74 10.45

 MS5 17.10 22.17 25.14 10.45

 MS6 16.69 24.03 26.27 10.45

 MS7 16.46 23.70 26.84 10.45

Sierran Mixed Conifer – Mesic 

 Early – All Structures HRV 3.57 7.25 13.55 14.98

 MS1 0.19 0.89 5.26 14.98

(Table D6 continued on next page.)
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(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 MS2 4.77 5.90 11.73 14.98

 MS3a 4.68 5.30 7.65 14.98

 MS3b 5.11 5.57 9.23 14.98

 MS4 5.15 5.78 10.37 14.98

 MS5 5.75 6.39 10.32 14.98

 MS6 7.85 8.52 9.08 14.98

 MS7 6.69 7.42 10.00 14.98

 Mid – Open HRV 4.07 6.47 9.71 16.29

 MS1 0.05 0.15 1.47 16.29

 MS2 1.40 1.95 3.75 16.29

 MS3a 1.28 1.63 2.27 16.29

 MS3b 1.69 2.21 4.82 16.29

 MS4 1.35 1.89 2.85 16.29

 MS5 2.49 2.93 3.83 16.29

 MS6 5.73 6.23 6.61 16.29

 MS7 4.97 5.50 6.23 16.29

 Mid – Moderate HRV 4.87 6.94 9.67 17.97

 MS1 0.13 0.60 3.13 17.97

 MS2 4.82 6.07 10.77 17.97

 MS3a 4.96 5.61 6.97 17.97

 MS3b 5.57 6.27 9.62 17.97

 MS4 5.14 6.01 7.65 17.97

 MS5 6.45 7.39 8.08 17.97

 MS6 8.92 9.50 10.56 17.97

 MS7 8.33 8.82 10.64 17.97

 Mid – Closed HRV 6.99 10.93 15.42 9.74

 MS1 2.09 4.84 12.35 9.74

 MS2 7.54 9.23 14.33 9.74

 MS3a 7.06 8.37 10.93 9.74

 MS3b 8.08 9.82 11.42 9.74

(Table D6 continued on next page.)
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(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 MS4 6.77 8.54 13.03 9.74

 MS5 6.84 8.96 10.67 9.74

 MS6 8.41 9.72 11.40 9.74

 MS7 8.73 9.62 11.87 9.74

 Late – Open HRV 7.87 14.22 23.38 3.60

 MS1 0.49 1.51 9.85 3.60

 MS2 0.77 2.23 8.27 3.60

 MS3a 0.76 2.23 4.61 3.60

 MS3b 3.64 4.26 10.09 3.60

 MS4 5.80 6.76 14.77 3.60

 MS5 6.75 7.90 11.78 3.60

 MS6 10.74 11.15 12.47 3.60

 MS7 11.49 11.99 14.77 3.60

 Late – Moderate HRV 14.82 18.43 24.29 14.18

 MS1 1.26 3.00 15.39 14.18

 MS2 6.69 9.29 17.43 14.18

 MS3a 5.33 6.97 9.69 14.18

 MS3b 8.14 9.30 14.28 14.18

 MS4 15.89 17.50 20.34 14.18

 MS5 14.01 15.12 19.22 14.18

 MS6 11.94 13.09 15.22 14.18

 MS7 14.12 14.93 19.04 14.18

 Late – Closed HRV 21.15 34.46 44.86 23.23

 MS1 58.78 85.36 92.78 23.23

 MS2 46.26 60.43 71.46 23.23

 MS3a 63.51 69.02 73.82 23.23

 MS3b 47.76 61.14 65.95 23.23

 MS4 36.96 52.16 56.94 23.23

 MS5 44.05 50.05 53.39 23.23

 MS6 37.35 41.46 44.15 23.23

(Table D6 continued on next page.)
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(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 MS7 33.12 40.32 43.44 23.23

Sierran Mixed Conifer – Ultramafic 

 Early – All Structures HRV 9.07 12.61 17.09 48.70

 MS1 1.12 2.12 10.75 48.70

 MS2 2.25 4.34 5.84 48.70

 MS3a 1.96 3.67 4.59 48.70

 MS3b 2.02 3.23 5.56 48.70

 MS4 2.00 2.98 6.05 48.70

 MS5 3.81 5.41 7.14 48.70

 MS6 5.43 7.00 8.04 48.70

 MS7 5.59 6.65 8.95 48.70

 Mid – Open HRV 7.75 10.77 14.42 5.33

 MS1 9.89 15.45 24.86 5.33

 MS2 12.02 15.95 24.40 5.33

 MS3a 27.83 31.66 36.91 5.33

 MS3b 29.50 34.24 41.79 5.33

 MS4 18.19 22.62 29.36 5.33

 MS5 24.60 29.48 36.59 5.33

 MS6 24.98 28.46 34.43 5.33

 MS7 28.65 31.33 36.94 5.33

 Mid – Moderate HRV 1.52 2.92 5.10 6.77

 MS1 13.80 24.01 31.63 6.77

 MS2 16.93 26.73 31.10 6.77

 MS3a 12.54 17.18 20.91 6.77

 MS3b 10.12 15.96 20.79 6.77

 MS4 14.34 21.21 26.82 6.77

 MS5 11.66 16.37 22.41 6.77

 MS6 13.25 17.60 22.34 6.77

 MS7 10.25 15.69 18.36 6.77

 Mid – Closed HRV 0.13 0.35 0.85 2.99

(Table D6 continued on next page.)
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(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 MS1 3.05 7.37 18.07 2.99

 MS2 3.27 5.37 13.69 2.99

 MS3a 1.05 2.05 4.73 2.99

 MS3b 1.28 2.38 5.51 2.99

 MS4 2.40 4.68 8.18 2.99

 MS5 1.28 3.75 5.85 2.99

 MS6 1.49 3.45 6.22 2.99

 MS7 1.06 2.80 4.19 2.99

 Late – Open HRV 27.89 33.58 41.57 3.27

 MS1 3.20 4.54 10.31 3.27

 MS2 4.58 6.85 10.21 3.27

 MS3a 10.82 11.55 13.02 3.27

 MS3b 18.00 19.36 20.96 3.27

 MS4 12.40 15.70 18.92 3.27

 MS5 14.57 15.77 16.80 3.27

 MS6 14.38 16.12 17.12 3.27

 MS7 14.68 16.37 18.27 3.27

 Late – Moderate HRV 19.06 22.81 26.94 8.51

 MS1 6.78 8.56 11.58 8.51

 MS2 9.60 13.06 16.83 8.51

 MS3a 13.34 14.21 16.56 8.51

 MS3b 12.52 13.40 16.17 8.51

 MS4 12.31 14.76 17.22 8.51

 MS5 11.71 13.75 15.06 8.51

 MS6 11.13 12.31 13.83 8.51

 MS7 11.76 14.17 15.06 8.51

 Late – Closed HRV 9.68 15.87 21.41 24.43

 MS1 20.67 36.18 39.83 24.43

 MS2 22.25 26.22 32.10 24.43

 MS3a 17.99 18.98 20.77 24.43

(Table D6 continued on next page.)
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(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 MS3b 8.85 9.98 11.43 24.43

 MS4 13.37 16.63 20.27 24.43

 MS5 13.80 15.26 16.05 24.43

 MS6 13.23 14.39 16.04 24.43

 Late – Closed MS7 10.58 12.44 13.60 24.43

Sierran Mixed Conifer – Xeric 

 Early – All Structures HRV 7.17 9.54 12.46 19.48

 MS1 0.57 2.35 7.82 19.48

 MS2 4.82 6.84 10.06 19.48

 MS3a 4.52 5.12 6.07 19.48

 MS3b 4.75 5.47 7.42 19.48

 MS4 4.92 5.47 8.25 19.48

 MS5 6.51 7.29 9.31 19.48

 MS6 8.94 9.45 10.97 19.48

 MS7 8.01 9.10 10.93 19.48

 Mid – Open HRV 7.24 9.08 10.74 11.48

 MS1 1.44 2.59 5.86 11.48

 MS2 6.31 7.54 9.74 11.48

 MS3a 11.61 12.62 14.79 11.48

 MS3b 16.05 17.44 18.70 11.48

 MS4 9.04 10.07 13.01 11.48

 MS5 14.28 15.05 16.04 11.48

 MS6 16.13 16.73 17.30 11.48

 MS7 15.39 16.89 17.93 11.48

 Mid – Moderate HRV 1.90 3.16 4.64 14.92

 MS1 3.51 4.92 7.31 14.92

 MS2 6.46 7.50 8.84 14.92

 MS3a 8.49 9.08 9.96 14.92

 MS3b 7.22 8.69 9.39 14.92

 MS4 6.04 7.96 9.35 14.92

(Table D6 continued on next page.)
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(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 MS5 6.33 7.21 7.94 14.92

 MS6 6.97 7.66 7.97 14.92

 MS7 6.33 7.07 7.66 14.92

 Mid – Closed HRV 0.30 0.69 1.33 11.96

 MS1 4.52 9.43 11.35 11.96

 MS2 5.43 7.19 8.74 11.96

 MS3a 2.35 2.98 3.88 11.96

 MS3b 1.51 2.67 3.25 11.96

 MS4 3.29 4.34 5.61 11.96

 MS5 2.22 2.93 3.94 11.96

 MS6 3.13 3.74 4.50 11.96

 MS7 2.61 3.18 3.73 11.96

 Late – Open HRV 28.56 37.83 49.66 4.13

 MS1 2.49 5.49 15.08 4.13

 MS2 4.70 8.81 21.00 4.13

 MS3a 11.53 14.50 18.17 4.13

 MS3b 19.34 21.77 28.64 4.13

 MS4 15.53 17.82 25.55 4.13

 MS5 20.75 22.94 28.18 4.13

 MS6 17.37 18.68 20.46 4.13

 MS7 17.76 20.39 23.82 4.13

 Late – Moderate HRV 19.63 25.85 30.53 13.31

 MS1 6.48 10.28 20.56 13.31

 MS2 13.13 15.42 20.54 13.31

 MS3a 19.45 20.80 23.14 13.31

 MS3b 19.89 21.19 22.09 13.31

 MS4 18.15 21.24 23.58 13.31

 MS5 17.11 18.08 19.76 13.31

 MS6 13.79 14.88 18.17 13.31

 MS7 15.43 17.32 19.24 13.31

(Table D6 continued on next page.)
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(Table D6 continued) 

 Percentile of range of

variability 

Cover type Seral stage Scenario 5th 50th 95th Current

 Late – Closed HRV 6.58 13.29 20.98 24.72

 MS1 35.66 63.01 71.93 24.72

 MS2 31.23 46.25 54.31 24.72

 MS3a 29.63 34.64 38.12 24.72

 MS3b 16.11 22.95 25.94 24.72

 MS4 22.37 32.21 37.89 24.72

 MS5 21.20 26.28 29.32 24.72

 MS6 24.58 28.94 31.14 24.72

 MS7 21.10 25.35 29.59 24.72

(Table D6 continued on next page.)
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Table D7—Range of variability (ROV) in landscape metrics (see Landscape Configuration under Methods 

for description of each landscape metric) computed on the basis of the landscape classified into vegetation 

developmental stages (none, early, mid, late) for the simulated historical range of variability (HRV) (ca. 1550–1850) 

and management scenarios with a modified fire regime (MS1) and varying intensities and types of vegetation 

treatments (MS2–MS7) in the upper Yuba River watershed. Select percentiles of the simulated ROV are given, as 

well as the current landscape condition.

Table D7—Range of variability (ROV) in landscape metrics (see Landscape Configuration under Methods for

description of each landscape metric) computed on the basis of the landscape classified into vegetation 

developmental stages (none, early, mid, late) for the simulated historical range of variability (HRV) (ca. 1550–1850)

and management scenarios with a modified fire regime (MS1) and varying intensities and types of vegetation 

treatments (MS2–MS7) in the upper Yuba River watershed. Select percentiles of the simulated ROV are given, as 

well as the current landscape condition. 

 Percentile of range of variability 

Landscape metric Scenario 5th 50th 95th Current

LPI HRV 17.94 29.87 38.72 5.72

 MS1 26.42 44.70 53.61 5.72

 MS2 17.19 31.74 36.64 5.72

 MS3a 24.33 35.26 36.73 5.72

 MS3b 21.08 32.30 35.22 5.72

 MS4 19.99 33.13 37.48 5.72

 MS5 20.16 30.93 35.10 5.72

 MS6 19.06 27.99 29.19 5.72

 MS7 18.64 21.97 31.28 5.72

AREA_AM HRV 13,564 22,738 34,699 1,808 

 MS1 29,224 51,199 68,238 1,808 

 MS2 12,253 26,237 33,069 1,808 

 MS3a 20,795 31,971 34,384 1,808 

 MS3b 16,472 25,696 33,842 1,808 

 MS4 14,742 30,223 35,542 1,808 

 MS5 16,256 25,921 32,276 1,808 

 MS6 13,963 21,237 24,836 1,808 

 MS7 14,469 18,135 25,533 1,808 

GYRATE_AM HRV 5,955 8,009 9,826 2,397 

 MS1 8,803 11,565 13,046 2,397 

 MS2 5,826 8,214 9,037 2,397 

 MS3a 6,788 8,922 9,373 2,397 

 MS3b 6,199 8,044 9,330 2,397 

 MS4 6,099 8,819 9,806 2,397 

 MS5 6,068 7,818 8,939 2,397 

 MS6 5,873 7,324 8,174 2,397 

(Table D7 continued on next page.)
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(Table D7 continued) 

 Percentile of range of variability 

Landscape metric Scenario 5th 50th 95th Current

 MS7 5,943 6,846 7,918 2,397 

SHAPE_AM HRV 42.82 54.07 69.39 8.65

 MS1 23.36 26.13 30.74 8.65

 MS2 18.46 22.12 25.19 8.65

 MS3a 24.06 29.88 31.91 8.65

 MS3b 37.38 42.92 49.01 8.65

 MS4 20.14 23.92 27.42 8.65

 MS5 34.86 42.91 47.03 8.65

 MS6 44.91 55.48 59.64 8.65

 MS7 43.17 49.23 57.25 8.65

DCORE_AM HRV 380 1150 2511 682

 MS1 9777 29954 44088 682

 MS2 6069 9009 15566 682

 MS3a 9004 12221 14675 682

 MS3b 2522 4991 7795 682

 MS4 5637 11705 15862 682

 MS5 1661 3444 4797 682

 MS6 791 1313 1710 682

 MS7 657 1138 1756 682

CAI_AM HRV 33.53 39.69 44.44 62.03

 MS1 63.57 71.68 74.17 62.03

 MS2 60.00 64.81 67.61 62.03

 MS3a 60.59 62.27 63.73 62.03

 MS3b 47.79 52.01 53.30 62.03

 MS4 59.70 64.80 67.54 62.03

 MS5 50.03 52.04 53.71 62.03

 MS6 40.99 42.76 43.85 62.03

 MS7 41.81 43.38 44.42 62.03

TECI HRV 44.80 46.50 48.79 44.43

 MS1 49.92 53.78 56.76 44.43

(Table D7 continued on next page.)
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(Table D7 continued) 

 Percentile of range of variability 

Landscape metric Scenario 5th 50th 95th Current

 MS2 48.44 49.81 50.87 44.43

 MS3a 46.01 47.18 47.96 44.43

 MS3b 42.74 43.06 43.97 44.43

 MS4 47.08 49.28 51.15 44.43

 MS5 45.24 45.79 46.72 44.43

 MS6 44.29 44.49 44.80 44.43

 MS7 44.02 44.49 44.97 44.43

IJI HRV 60.00 65.61 69.64 84.95

 MS1 53.44 60.87 73.94 84.95

 MS2 70.32 74.70 79.37 84.95

 MS3a 62.25 66.23 69.39 84.95

 MS3b 53.34 54.71 60.99 84.95

 MS4 67.64 72.34 79.28 84.95

 MS5 65.11 66.57 69.71 84.95

 MS6 66.16 66.74 67.77 84.95

 MS7 63.42 65.05 67.46 84.95

AREA_MN HRV 2.77 3.30 3.83 21.00

 MS1 11.97 21.35 27.65 21.00

 MS2 9.87 13.37 15.63 21.00

 MS3a 6.07 6.44 6.80 21.00

 MS3b 2.71 2.91 2.98 21.00

 MS4 9.21 12.57 15.27 21.00

 MS5 3.55 3.70 3.81 21.00

 MS6 2.51 2.57 2.63 21.00

 MS7 2.57 2.62 2.67 21.00

SHAPE_MN HRV 1.32 1.33 1.33 1.63

 MS1 1.36 1.40 1.42 1.63

 MS2 1.37 1.38 1.39 1.63

 MS3a 1.18 1.19 1.21 1.63

 MS3b 1.13 1.14 1.16 1.63

(Table D7 continued on next page.)
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(Table D7 continued) 

 Percentile of range of variability 

Landscape metric Scenario 5th 50th 95th Current

 MS4 1.36 1.36 1.37 1.63

 MS5 1.23 1.23 1.24 1.63

 MS6 1.26 1.27 1.27 1.63

 MS7 1.23 1.24 1.25 1.63

DCORE_MN HRV 3.41 4.68 5.73 12.30

 MS1 18.15 28.90 40.31 12.30

 MS2 12.12 16.12 19.03 12.30

 MS3a 13.11 15.26 17.61 12.30

 MS3b 7.30 9.56 10.87 12.30

 MS4 11.74 15.67 19.47 12.30

 MS5 7.43 8.07 8.96 12.30

 MS6 4.68 5.05 5.46 12.30

 MS7 4.66 5.20 5.56 12.30

CAI_MN HRV 0.83 0.98 1.11 17.92

 MS1 4.44 6.87 8.52 17.92

 MS2 5.68 7.31 8.39 17.92

 MS3a 3.13 3.27 3.47 17.92

 MS3b 1.31 1.42 1.50 17.92

 MS4 5.15 6.96 8.20 17.92

 MS5 1.76 1.82 1.93 17.92

 MS6 1.15 1.19 1.23 17.92

 MS7 1.21 1.24 1.30 17.92

ED HRV 135.17 148.59 168.54 70.95

 MS1 38.13 47.10 65.41 70.95

 MS2 55.44 63.08 76.35 70.95

 MS3a 67.16 72.08 78.31 70.95

 MS3b 101.75 106.32 118.00 70.95

 MS4 55.78 64.57 76.72 70.95

 MS5 103.17 108.54 112.24 70.95

 MS6 148.39 151.63 156.02 70.95

(Table D7 continued on next page.)
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(Table D7 continued) 

 Percentile of range of variability 

Landscape metric Scenario 5th 50th 95th Current

 MS7 139.31 142.16 147.19 70.95

CWED HRV 62.16 70.04 81.29 32.25

 MS1 22.67 25.21 34.98 32.25

 MS2 28.86 31.95 38.58 32.25

 MS3a 32.54 34.30 36.70 32.25

 MS3b 44.53 46.40 52.70 32.25

 MS4 28.67 31.86 40.08 32.25

 MS5 48.06 50.19 52.98 32.25

 MS6 66.68 68.03 70.73 32.25

 MS7 62.22 63.94 66.77 32.25

AI HRV 74.69 77.68 79.70 89.35

 MS1 90.17 92.91 94.26 89.35

 MS2 88.53 90.52 91.67 89.35

 MS3a 88.24 89.17 89.91 89.35

 MS3b 82.28 84.03 84.72 89.35

 MS4 88.48 90.30 91.62 89.35

 MS5 83.14 83.70 84.50 89.35

 MS6 76.57 77.23 77.71 89.35

 MS7 77.90 78.65 79.08 89.35

CONTAG HRV 36.91 41.30 45.24 41.51

 MS1 56.19 66.25 70.10 41.51

 MS2 49.47 54.30 57.67 41.51

 MS3a 53.09 54.22 56.23 41.51

 MS3b 45.55 48.92 50.62 41.51

 MS4 49.96 54.67 57.77 41.51

 MS5 44.88 46.66 48.68 41.51

 MS6 37.76 39.07 40.14 41.51

 MS7 38.94 40.95 42.29 41.51
328 USDA Forest Service RMRS-GTR-385. 2018

Table D8—Range of variability (ROV) in landscape metrics (see Landscape Configuration under Methods for 

description of each landscape metric) computed on the basis of the landscape classified into vegetation canopy 

cover classes (none, open, moderate, closed) for the simulated historical range of variability (HRV) (ca. 1550–1850) 

and management scenarios with a modified fire regime (MS1) and varying intensities and types of vegetation 

treatments (MS2–MS7) in the upper Yuba River watershed. Select percentiles of the simulated ROV are given, as 

well as the current landscape condition.

Table D8—Range of variability (ROV) in landscape metrics (see Landscape Configuration under Methods for

description of each landscape metric) computed on the basis of the landscape classified into vegetation canopy cover 

classes (none, open, moderate, closed) for the simulated historical range of variability (HRV) (ca. 1550–1850) and

management scenarios with a modified fire regime (MS1) and varying intensities and types of vegetation treatments 

(MS2–MS7) in the upper Yuba River watershed. Select percentiles of the simulated ROV are given, as well as the 

current landscape condition. 

 Percentile of range of variability 

Landscape metric Scenario 5th 50th 95th Current

LPI HRV 4.06 8.57 16.39 5.97

 MS1 16.99 45.89 56.97 5.97

 MS2 10.72 18.98 30.71 5.97

 MS3a 11.31 16.22 27.62 5.97

 MS3b 5.76 7.74 16.44 5.97

 MS4 5.24 9.06 14.73 5.97

 MS5 3.20 5.69 7.63 5.97

 MS6 4.31 5.68 6.31 5.97

 MS7 3.47 5.42 6.43 5.97

AREA_AM HRV 1,459 2,957 7,559 1,720 

 MS1 9,169 49,577 75,291 1,720 

 MS2 5,305 12,435 25,888 1,720 

 MS3a 7,057 9,488 17,776 1,720 

 MS3b 2,131 3,587 7,167 1,720 

 MS4 1,737 3,972 6,245 1,720 

 MS5 948 1,615 2,122 1,720 

 MS6 1,190 1,591 1,981 1,720 

 MS7 1,043 1,506 1,799 1,720 

GYRATE_AM HRV 2,041 2,655 4,036 2,302 

 MS1 4,764 11,199 12,863 2,302 

 MS2 3,239 5,351 7,571 2,302 

 MS3a 4,170 5,420 7,058 2,302 

 MS3b 2,461 3,091 4,501 2,302 

 MS4 2,123 3,085 3,895 2,302 

 MS5 1,762 1,997 2,331 2,302 

 MS6 1,868 2,026 2,251 2,302 

 MS7 1,751 1,996 2,154 2,302 

(Table D8 continued on next page.)
USDA Forest Service RMRS-GTR-385. 2018 329

(Table D8 continued) 

 Percentile of range of variability 

Landscape metric Scenario 5th 50th 95th Current

SHAPE_AM HRV 21.95 29.27 48.37 9.05 

 MS1 31.76 38.93 51.61 9.05 

 MS2 18.07 22.90 30.13 9.05 

 MS3a 29.97 33.11 44.38 9.05 

 MS3b 22.06 27.31 35.32 9.05 

 MS4 12.94 14.62 20.12 9.05 

 MS5 12.27 13.72 16.84 9.05 

 MS6 12.18 13.50 15.62 9.05 

 MS7 12.94 14.46 17.55 9.05 

DCORE_AM HRV 136 266 625 449 

 MS1 2,554 22,745 41,568 449 

 MS2 1,530 4,864 10,666 449 

 MS3a 350 516 856 449 

 MS3b 114 214 266 449 

 MS4 252 563 1,590 449 

 MS5 196 456 691 449 

 MS6 176 724 1,099 449 

 MS7 127 358 1,284 449 

CAI_AM HRV 16.47 20.22 23.97 60.70 

 MS1 32.52 59.58 68.18 60.70 

 MS2 36.40 51.03 60.21 60.70 

 MS3a 32.22 35.66 39.41 60.70 

 MS3b 19.94 25.07 26.83 60.70 

 MS4 35.21 45.28 51.82 60.70 

 MS5 28.65 33.30 36.80 60.70 

 MS6 33.71 36.89 39.46 60.70 

 MS7 29.08 33.25 34.66 60.70 

TECI HRV 42.74 43.99 44.99 46.07 

 MS1 46.44 50.54 54.44 46.07 

 MS2 45.61 47.72 50.39 46.07 

 MS3a 45.63 46.55 46.81 46.07 

(Table D8 continued on next page.)
330 USDA Forest Service RMRS-GTR-385. 2018

(Table D8 continued) 

 Percentile of range of variability 

Landscape metric Scenario 5th 50th 95th Current

 MS3b 45.14 46.26 46.65 46.07 

 MS4 44.52 45.37 46.72 46.07 

 MS5 43.82 44.26 44.92 46.07 

 MS6 43.78 44.29 44.53 46.07 

 MS7 42.86 43.88 44.44 46.07 

IJI HRV 70.30 72.27 73.02 87.14 

 MS1 67.77 73.59 78.08 87.14 

 MS2 74.33 78.25 80.87 87.14 

 MS3a 73.73 74.29 74.82 87.14 

 MS3b 73.05 73.52 73.82 87.14 

 MS4 75.57 77.67 80.10 87.14 

 MS5 74.08 74.87 75.82 87.14 

 MS6 74.63 75.37 75.81 87.14 

 MS7 73.42 74.61 75.17 87.14 

AREA_MN HRV 1.18 1.38 1.62 21.53 

 MS1 2.37 4.86 8.84 21.53 

 MS2 2.28 4.00 6.89 21.53 

 MS3a 1.78 1.90 2.09 21.53 

 MS3b 1.30 1.44 1.46 21.53 

 MS4 2.26 3.87 5.28 21.53 

 MS5 1.87 2.10 2.26 21.53 

 MS6 2.05 2.28 2.43 21.53 

 MS7 1.77 2.04 2.16 21.53 

SHAPE_MN HRV 1.29 1.32 1.34 1.68 

 MS1 1.31 1.33 1.36 1.68 

 MS2 1.32 1.33 1.36 1.68 

 MS3a 1.21 1.23 1.26 1.68 

 MS3b 1.24 1.26 1.28 1.68 

 MS4 1.33 1.34 1.35 1.68 

 MS5 1.29 1.30 1.32 1.68 

 MS6 1.28 1.29 1.30 1.68 

(Table D8 continued on next page.)
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(Table D8 continued) 

 Percentile of range of variability 

Landscape metric Scenario 5th 50th 95th Current

 MS7 1.29 1.30 1.32 1.68 

DCORE_MN HRV 1.17 1.55 1.98 10.17 

 MS1 3.28 16.69 30.50 10.17 

 MS2 3.14 8.10 12.75 10.17 

 MS3a 2.94 3.84 4.39 10.17 

 MS3b 1.59 2.14 2.47 10.17 

 MS4 3.23 4.90 6.79 10.17 

 MS5 2.06 2.50 2.97 10.17 

 MS6 2.57 3.10 3.47 10.17 

 MS7 1.96 2.50 2.77 10.17 

CAI_MN HRV 0.22 0.32 0.43 21.31 

 MS1 0.41 1.02 1.70 21.31 

 MS2 0.89 1.82 3.21 21.31 

 MS3a 0.79 0.90 1.01 21.31 

 MS3b 0.44 0.58 0.61 21.31 

 MS4 1.30 2.60 3.87 21.31 

 MS5 0.93 1.21 1.34 21.31 

 MS6 1.08 1.26 1.38 21.31 

 MS7 0.82 1.06 1.16 21.31 

ED HRV 237.86 257.78 280.16 74.08 

 MS1 60.92 92.80 196.09 74.08 

 MS2 79.25 118.16 181.73 74.08 

 MS3a 149.08 167.48 183.42 74.08 

 MS3b 204.46 214.18 246.47 74.08 

 MS4 104.33 133.14 184.01 74.08 

 MS5 164.72 181.03 204.93 74.08 

 MS6 154.92 166.60 182.47 74.08 

 MS7 175.01 181.94 208.14 74.08 

CWED HRV 105.09 113.59 124.83 34.88 

 MS1 33.29 47.46 86.80 34.88 

 MS2 40.77 57.59 84.29 34.88 

(Table D8 continued on next page.)
332 USDA Forest Service RMRS-GTR-385. 2018

(Table D8 continued) 

 Percentile of range of variability 

Landscape metric Scenario 5th 50th 95th Current

 MS3a 70.26 78.91 84.76 34.88 

 MS3b 96.52 99.83 111.62 34.88 

 MS4 49.66 60.42 84.01 34.88 

 MS5 73.95 80.64 91.11 34.88 

 MS6 69.45 74.39 81.44 34.88 

 MS7 78.18 80.75 93.85 34.88 

AI HRV 57.93 61.29 64.28 88.88 

 MS1 70.55 86.05 90.83 88.88 

 MS2 72.71 82.26 88.10 88.88 

 MS3a 72.46 74.85 77.61 88.88 

 MS3b 62.99 67.84 69.30 88.88 

 MS4 72.37 80.01 84.34 88.88 

 MS5 69.23 72.82 75.27 88.88 

 MS6 72.60 74.99 76.74 88.88 

 MS7 68.75 72.68 73.72 88.88 

CONTAG HRV 20.26 21.75 23.92 39.11 

 MS1 33.66 56.09 66.38 39.11 

 MS2 28.67 40.32 49.48 39.11 

 MS3a 30.87 33.63 37.54 39.11 

 MS3b 22.07 26.91 28.42 39.11 

 MS4 26.55 33.86 38.66 39.11 

 MS5 25.09 27.80 30.17 39.11 

 MS6 27.30 29.17 30.72 39.11 

 MS7 24.67 27.30 28.26 39.11 
USDA Forest Service RMRS-GTR-385. 2018 333

Table D9—Range of variability (ROV) in landscape metrics computed on the basis of the landscape classified into 

vegetation seral stagesa for the simulated historical range of variability (HRV) (ca. 1550–1850) and management

scenarios with a modified fire regime (MS1) and varying intensities and types of vegetation treatments (MS2–MS7) in 

the upper Yuba River watershed. Select percentiles of the simulated ROV are given, as well as the current landscape 

condition.

 Percentile of Range of Variability 

Landscape metric Scenario 5th 50th 95th Current

LPI HRV 2.50 2.58 5.06 2.50

 MS1 9.59 35.93 45.28 2.50

 MS2 8.57 12.48 18.53 2.50

 MS3a 7.69 11.37 16.46 2.50

 MS3b 3.87 5.69 8.74 2.50

 MS4 3.54 6.08 8.54 2.50

 MS5 2.64 5.11 6.37 2.50

 MS6 3.60 4.88 5.61 2.50

 MS7 2.50 4.91 5.64 2.50

AREA_AM HRV 290 500 916 303 

 MS1 4,029 33,653 45,698 303 

 MS2 2,666 5,583 11,704 303 

 MS3a 3,079 5,877 10,409 303 

 MS3b 971 1,588 3,450 303 

 MS4 665 1,458 2,453 303 

 MS5 535 996 1,190 303 

 MS6 492 829 978 303 

 MS7 290 773 896 303 

GYRATE_AM HRV 942 1,138 1,447 1,055 

 MS1 3,642 9,308 10,626 1,055 

 MS2 2,174 3,693 5,320 1,055 

 MS3a 2,767 3,755 4,948 1,055 

 MS3b 1,608 1,978 3,131 1,055 

 MS4 1,268 1,788 2,275 1,055 

 MS5 1,203 1,435 1,602 1,055 

 MS6 1,045 1,257 1,431 1,055 

 MS7 939 1,245 1,344 1,055 

SHAPE_AM HRV 8.92 10.81 13.66 4.52

(Table D9 continued on next page.)
334 USDA Forest Service RMRS-GTR-385. 2018

(Table D9 continued) 

 Percentile of Range of Variability 

Landscape metric Scenario 5th 50th 95th Current

 MS1 26.22 32.20 41.16 4.52

 MS2 11.36 15.55 19.42 4.52

 MS3a 19.17 25.20 32.75 4.52

 MS3b 13.55 17.34 25.45 4.52

 MS4 7.19 9.35 11.05 4.52

 MS5 8.14 9.01 9.79 4.52

 MS6 6.81 7.31 8.10 4.52

 MS7 7.14 7.74 9.08 4.52

DCORE_AM HRV 90 121 192 93 

 MS1 1,810 8,345 15,901 93 

 MS2 556 1,586 4,258 93 

 MS3a 183 231 324 93 

 MS3b 81 94 138 93 

 MS4 120 265 938 93 

 MS5 121 283 436 93 

 MS6 114 489 860 93 

 MS7 98 238 1,153 93 

CAI_AM HRV 4.29 7.04 10.36 33.16 

 MS1 21.69 46.80 55.31 33.16 

 MS2 22.53 35.17 43.93 33.16 

 MS3a 19.54 22.30 25.73 33.16 

 MS3b 8.79 12.91 14.59 33.16 

 MS4 21.37 30.50 36.90 33.16 

 MS5 13.59 17.52 20.74 33.16 

 MS6 14.70 17.63 19.68 33.16 

 MS7 11.27 15.17 16.57 33.16 

TECI HRV 26.37 27.72 29.70 37.84 

 MS1 24.59 28.50 33.30 37.84 

 MS2 26.13 29.76 34.10 37.84 

 MS3a 25.21 26.27 26.81 37.84 

 MS3b 24.84 25.03 25.37 37.84 

(Table D9 continued on next page.)
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(Table D9 continued) 

 Percentile of Range of Variability 

Landscape metric Scenario 5th 50th 95th Current

 MS4 26.28 27.22 29.21 37.84 

 MS5 27.12 27.98 28.25 37.84 

 MS6 30.79 31.65 32.03 37.84 

 MS7 28.60 29.58 30.03 37.84 

IJI HRV 64.81 66.96 69.26 75.31 

 MS1 53.91 57.60 60.31 75.31 

 MS2 61.80 66.72 70.46 75.31 

 MS3a 62.67 65.40 68.55 75.31 

 MS3b 64.02 67.09 69.90 75.31 

 MS4 64.07 67.89 69.21 75.31 

 MS5 67.70 70.45 71.60 75.31 

 MS6 71.60 72.26 75.50 75.31 

 MS7 70.37 73.62 74.50 75.31 

AREA_MN HRV 0.63 0.73 0.84 10.83 

 MS1 1.71 3.64 6.33 10.83 

 MS2 1.57 2.78 4.50 10.83 

 MS3a 1.27 1.37 1.52 10.83 

 MS3b 0.76 0.85 0.88 10.83 

 MS4 1.52 2.55 3.59 10.83 

 MS5 1.03 1.15 1.23 10.83 

 MS6 0.89 0.95 0.99 10.83 

 MS7 0.84 0.94 0.98 10.83 

SHAPE_MN HRV 1.25 1.27 1.28 1.63 

 MS1 1.31 1.32 1.35 1.63 

 MS2 1.31 1.32 1.35 1.63 

 MS3a 1.18 1.20 1.24 1.63 

 MS3b 1.18 1.19 1.23 1.63 

 MS4 1.32 1.33 1.34 1.63 

 MS5 1.24 1.25 1.27 1.63 

 MS6 1.24 1.25 1.25 1.63 

 MS7 1.24 1.24 1.26 1.63 
336 USDA Forest Service RMRS-GTR-385. 2018

(Table D9 continued) 

 Percentile of Range of Variability 

Landscape metric Scenario 5th 50th 95th Current

DCORE_MN HRV 0.78 1.19 1.57 4.59 

 MS1 3.37 12.30 20.94 4.59 

 MS2 2.74 6.19 9.45 4.59 

 MS3a 2.42 3.08 3.54 4.59 

 MS3b 1.22 1.63 1.98 4.59 

 MS4 2.53 3.58 4.87 4.59 

 MS5 1.63 2.11 2.59 4.59 

 MS6 2.23 2.80 3.16 4.59 

 MS7 1.55 2.03 2.33 4.59 

CAI_MN HRV 0.05 0.09 0.13 9.95 

 MS1 0.36 1.00 1.82 9.95 

 MS2 0.53 1.23 2.18 9.95 

 MS3a 0.43 0.50 0.60 9.95 

 MS3b 0.18 0.26 0.27 9.95 

 MS4 0.84 1.72 2.65 9.95 

 MS5 0.36 0.47 0.53 9.95 

 MS6 0.30 0.35 0.39 9.95 

 MS7 0.25 0.31 0.36 9.95 

ED HRV 293.88 318.64 344.07 96.30 

 MS1 76.10 110.69 217.97 96.30 

 MS2 96.90 137.83 205.06 96.30 

 MS3a 165.33 184.95 203.28 96.30 

 MS3b 237.67 249.21 288.09 96.30 

 MS4 120.59 153.16 207.52 96.30 

 MS5 204.66 223.42 248.00 96.30 

 MS6 224.24 236.49 254.16 96.30 

 MS7 235.63 243.95 269.90 96.30 

CWED HRV 79.61 88.68 100.90 37.06 

 MS1 25.85 32.00 51.49 37.06 

 MS2 33.60 40.70 55.65 37.06 

 MS3a 44.72 48.69 52.46 37.06 

(Table D9 continued on next page.)
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(Table D9 continued) 

 Percentile of Range of Variability 

Landscape metric Scenario 5th 50th 95th Current

 MS3b 60.39 62.93 72.12 37.06 

 MS4 35.63 42.06 55.55 37.06 

 MS5 58.20 61.93 68.37 37.06 

 MS6 72.42 75.26 80.28 37.06 

 MS7 70.54 73.03 79.77 37.06 

AI HRV 48.36 52.18 55.90 85.60 

 MS1 67.30 83.39 88.58 85.60 

 MS2 69.25 79.34 85.48 85.60 

 MS3a 69.51 72.26 75.20 85.60 

 MS3b 56.77 62.61 64.34 85.60 

 MS4 68.88 77.04 81.93 85.60 

 MS5 62.80 66.49 69.30 85.60 

 MS6 61.87 64.52 66.36 85.60 

 MS7 59.51 63.40 64.65 85.60 

CONTAG HRV 30.91 32.88 35.43 46.63 

 MS1 47.72 63.65 71.28 46.63 

 MS2 43.04 51.78 57.26 46.63 

 MS3a 43.39 45.79 50.12 46.63 

 MS3b 33.53 37.41 41.64 46.63 

 MS4 40.23 46.39 49.67 46.63 

 MS5 35.39 38.48 40.45 46.63 

 MS6 33.33 36.44 38.04 46.63 

 MS7 31.13 34.73 36.24 46.63 

a ED = Early - all structures; MDO = Mid-open; MDM = Mid-moderate; MDC = Mid-closed; LDO = Late-open; LDM = 

Late-moderate; LDC = Late-closed; ED-A = Early – Aspen; MD-A = Mid – Aspen; MD-AC = Mid - Aspen and Conifer; 

LD-CA = Late - Conifer and Aspen.
338 USDA Forest Service RMRS-GTR-385. 2018

Table D10—Range of variability (ROV) in landscape metrics (see Landscape Configuration under Methods for 

description and units for each landscape metric) computed on the basis of the landscape classified into combinations 

of cover type and seral stage classes (see Appendix B for the classification) for the simulated historical range of 

variability (HRV) (ca. 1550–1850) and management scenarios with a modified fire regime (MS1) and varying 

intensities and types of vegetation treatments (MS2-MS7) in the upper Yuba River watershed. Select percentiles of 

the simulated ROV are given, as well as the current landscape condition.

 Percentile of range of variability 

Landscape metric Scenario 5th 50th 95th Current

LPI HRV 2.08 2.08 2.08 2.08

 MS1 2.08 4.32 4.60 2.08

 MS2 2.08 2.08 3.08 2.08

 MS3a 2.08 2.32 2.91 2.08

 MS3b 2.08 2.08 2.80 2.08

 MS4 2.08 2.08 2.08 2.08

 MS5 2.08 2.08 2.08 2.08

 MS6 2.08 2.08 2.08 2.08

 MS7 2.08 2.08 2.08 2.08

AREA_AM HRV 126 151 196 119 

 MS1 322 908 1,167 119 

 MS2 185 292 411 119 

 MS3a 260 312 416 119 

 MS3b 157 237 312 119 

 MS4 133 161 177 119 

 MS5 133 151 183 119 

 MS6 122 133 149 119 

 MS7 118 132 145 119 

GYRATE_AM HRV 588 636 694 616 

 MS1 914 1,344 1,550 616 

 MS2 706 881 981 616 

 MS3a 802 852 943 616 

 MS3b 657 767 830 616 

 MS4 622 695 727 616 

 MS5 618 652 683 616 

 MS6 584 614 638 616 

 MS7 572 612 630 616 

SHAPE_AM HRV 5.17 5.63 6.19 3.27 

 MS1 6.78 7.31 7.77 3.27 

(Table D10 continued on next page.)
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(Table D10 continued) 

 Percentile of range of variability 

Landscape metric Scenario 5th 50th 95th Current

 MS2 4.51 4.84 5.19 3.27 

 MS3a 5.34 5.51 5.79 3.27 

 MS3b 5.35 6.02 6.47 3.27 

 MS4 3.93 4.10 4.26 3.27 

 MS5 4.27 4.35 4.48 3.27 

 MS6 3.93 3.98 4.15 3.27 

 MS7 4.12 4.20 4.33 3.27 

DCORE_AM HRV 96.68 121.21 152.13 45.27 

 MS1 213.30 366.76 457.35 45.27 

 MS2 91.71 138.96 180.21 45.27 

 MS3a 71.06 84.49 169.31 45.27 

 MS3b 72.57 81.90 108.16 45.27 

 MS4 58.02 64.28 91.28 45.27 

 MS5 67.15 76.61 89.81 45.27 

 MS6 67.58 78.89 91.39 45.27 

 MS7 69.11 83.26 104.01 45.27 

CAI_AM HRV 5.22 7.42 10.28 38.78 

 MS1 20.98 47.75 58.61 38.78 

 MS2 24.21 37.09 46.37 38.78 

 MS3a 17.13 19.95 23.02 38.78 

 MS3b 8.52 12.00 13.45 38.78 

 MS4 21.08 29.54 36.16 38.78 

 MS5 13.96 17.19 20.49 38.78 

 MS6 15.92 18.79 21.07 38.78 

 MS7 12.60 15.90 17.73 38.78 

TECI HRV 21.72 22.22 22.98 26.14 

 MS1 21.96 23.47 24.70 26.14 

 MS2 22.52 24.30 25.61 26.14 

 MS3a 22.32 22.81 23.14 26.14 

 MS3b 21.42 21.72 21.84 26.14 

 MS4 22.72 23.53 24.51 26.14 

 MS5 22.33 22.65 22.87 26.14 

(Table D10 continued on next page.)
340 USDA Forest Service RMRS-GTR-385. 2018

(Table D10 continued) 

 Percentile of range of variability 

Landscape metric Scenario 5th 50th 95th Current

 MS6 23.08 23.37 23.55 26.14 

 MS7 22.27 22.77 22.91 26.14 

IJI HRV 54.90 55.81 56.78 63.80 

 MS1 49.61 51.89 54.23 63.80 

 MS2 55.96 58.41 59.06 63.80 

 MS3a 55.67 56.22 56.42 63.80 

 MS3b 56.03 56.26 56.76 63.80 

 MS4 58.03 59.50 60.46 63.80 

 MS5 58.44 59.77 60.24 63.80 

 MS6 61.02 61.46 61.86 63.80 

 MS7 59.33 60.64 61.06 63.80 

AREA_MN HRV 0.51 0.58 0.67 3.60 

 MS1 1.23 2.34 3.56 3.60 

 MS2 1.14 1.85 2.78 3.60 

 MS3a 1.00 1.08 1.21 3.60 

 MS3b 0.61 0.69 0.72 3.60 

 MS4 1.10 1.73 2.28 3.60 

 MS5 0.81 0.90 0.96 3.60 

 MS6 0.70 0.76 0.79 3.60 

 MS7 0.66 0.74 0.78 3.60 

SHAPE_MN HRV 1.23 1.24 1.26 1.42 

 MS1 1.30 1.34 1.36 1.42 

 MS2 1.30 1.32 1.35 1.42 

 MS3a 1.22 1.23 1.25 1.42 

 MS3b 1.19 1.20 1.21 1.42 

 MS4 1.29 1.31 1.32 1.42 

 MS5 1.24 1.24 1.25 1.42 

 MS6 1.23 1.23 1.24 1.42 

 MS7 1.23 1.23 1.24 1.42 

DCORE_MN HRV 0.89 1.17 1.46 2.26 

 MS1 3.55 5.32 6.30 2.26 

 MS2 2.60 3.36 3.95 2.26 

(Table D10 continued on next page.)
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(Table D10 continued) 

 Percentile of range of variability 

Landscape metric Scenario 5th 50th 95th Current

 MS3a 1.61 1.77 1.94 2.26 

 MS3b 1.08 1.35 1.49 2.26 

 MS4 1.76 2.29 2.70 2.26 

 MS5 1.34 1.49 1.74 2.26 

 MS6 1.48 1.68 1.82 2.26 

 MS7 1.25 1.45 1.60 2.26 

CAI_MN HRV 0.16 0.28 0.48 18.78 

 MS1 2.35 9.04 16.50 18.78 

 MS2 2.46 6.53 11.66 18.78 

 MS3a 1.46 1.90 2.36 18.78 

 MS3b 0.53 0.87 0.97 18.78 

 MS4 2.27 4.95 8.16 18.78 

 MS5 1.16 1.56 1.99 18.78 

 MS6 1.19 1.52 1.78 18.78 

 MS7 0.94 1.27 1.42 18.78 

ED HRV 312.13 335.58 360.03 139.71 

 MS1 124.93 153.54 245.37 139.71 

 MS2 139.31 174.03 233.70 139.71 

 MS3a 198.11 215.58 231.39 139.71 

 MS3b 263.59 273.58 309.62 139.71 

 MS4 155.52 183.39 232.52 139.71 

 MS5 233.05 248.85 271.45 139.71 

 MS6 253.69 264.21 280.09 139.71 

 MS7 261.73 269.77 293.58 139.71 

CWED HRV 69.20 75.04 81.42 36.96 

 MS1 31.20 36.41 52.12 36.96 

 MS2 36.18 42.27 53.78 36.96 

 MS3a 46.21 49.47 52.28 36.96 

 MS3b 58.03 59.80 66.18 36.96 

 MS4 38.54 43.52 53.22 36.96 

 MS5 53.51 56.48 61.19 36.96 

 MS6 59.93 62.12 65.66 36.96 

(Table D10 continued on next page.)
342 USDA Forest Service RMRS-GTR-385. 2018

(Table D10 continued) 

 Percentile of range of variability 

Landscape metric Scenario 5th 50th 95th Current

 MS7 60.32 61.93 67.28 36.96 

AI HRV 46.11 49.79 53.32 79.33 

 MS1 63.36 77.14 81.43 79.33 

 MS2 65.15 74.12 79.34 79.33 

 MS3a 65.48 67.85 70.48 79.33 

 MS3b 53.71 59.12 60.62 79.33 

 MS4 65.34 72.73 76.92 79.33 

 MS5 59.47 62.86 65.24 79.33 

 MS6 58.17 60.56 62.14 79.33 

 MS7 56.14 59.72 60.93 79.33 

CONTAG HRV 44.65 45.54 46.92 50.89 

 MS1 52.65 60.19 63.54 50.89 

 MS2 49.33 53.56 56.60 50.89 

 MS3a 51.27 52.41 53.90 50.89 

 MS3b 46.29 48.94 49.81 50.89 

 MS4 47.83 51.04 52.95 50.89 

 MS5 46.64 47.49 48.41 50.89 

 MS6 44.50 45.71 46.18 50.89 

 MS7 44.53 45.51 46.06 50.89 

In accordance with Federal civil rights law and U.S. Department of Agriculture (USDA) civil rights 

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To file a program discrimination complaint, complete the USDA Program Discrimination Complaint 

Form, AD-3027, found online at http://www.ascr.usda.gov/complaint_filing_cust.html and at any 

USDA office or write a letter addressed to USDA and provide in the letter all of the information 

requested in the form. To request a copy of the complaint form, call (866) 632-9992. Submit your 

completed form or letter to USDA by: (1) mail: U.S. Department of Agriculture, Office of the Assistant 

Secretary for Civil Rights, 1400 Independence Avenue, SW, Washington, D.C. 20250-9410; (2) fax: 

(202) 690-7442; or (3) email: program.intake@usda.gov.

In accordance with Federal civil rights law and U.S. Department of Agriculture (USDA) 

civil rights regulations and policies, the USDA, its Agencies, offices, and employees, and 

institutions participating in or administering USDA programs are prohibited from discriminating based on race, color, national origin, religion, sex, gender identity (including gender 

expression), sexual orientation, disability, age, marital status, family/parental status, income 

derived from a public assistance program, political beliefs, or reprisal or retaliation for prior 

civil rights activity, in any program or activity conducted or funded by USDA (not all bases 

apply to all programs). Remedies and complaint filing deadlines vary by program or incident.

Persons with disabilities who require alternative means of communication for program information (e.g., Braille, large print, audiotape, American Sign Language, etc.) should contact 

the responsible Agency or USDA’s TARGET Center at (202) 720-2600 (voice and TTY) or 

contact USDA through the Federal Relay Service at (800) 877-8339. Additionally, program 

information may be made available in languages other than English.

To file a program discrimination complaint, complete the USDA Program Discrimination 

Complaint Form, AD-3027, found online at http://www.ascr.usda.gov/complaint_filing_

cust.html and at any USDA office or write a letter addressed to USDA and provide in the 

letter all of the information requested in the form. To request a copy of the complaint form, 

call (866) 632-9992. Submit your completed form or letter to USDA by: (1) mail: U.S. 

Department of Agriculture, Office of the Assistant Secretary for Civil Rights, 1400 Independence Avenue, SW, Washington, D.C. 20250-9410; (2) fax: (202) 690-7442; or (3) email: 

program.intake@usda.gov. 

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