United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical 

Report RMRS-GTR-310

September 2013

Restoring Composition and 

Structure in Southwestern 

Frequent-Fire Forests:

A science-based framework for 

improving ecosystem resiliency

Richard T. Reynolds, Andrew J. Sánchez Meador, James A. Youtz, 

Tessa Nicolet, Megan S. Matonis, Patrick L. Jackson, 

Donald G. DeLorenzo, Andrew D. Graves
Reynolds, Richard T.; Sánchez Meador, Andrew J.; Youtz, James A.; Nicolet, Tessa; 

Matonis, Megan S.; Jackson, Patrick L.; DeLorenzo, Donald G.; Graves, Andrew D. 

2013. Restoring composition and structure in Southwestern frequent-fire forests: A 

science-based framework for improving ecosystem resiliency. Gen. Tech. Rep. RMRSGTR-310. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 76 p.

Abstract

Ponderosa pine and dry mixed-conifer forests in the Southwest United States are 

experiencing, or have become increasingly susceptible to, large-scale severe wildfire, 

insect, and disease episodes resulting in altered plant and animal demographics, reduced 

productivity and biodiversity, and impaired ecosystem processes and functions. We present a 

management framework based on a synthesis of science on forest ecology and management, 

reference conditions, and lessons learned during implementations of our restoration 

framework. Our framework focuses on the restoration of key elements similar to the 

historical composition and structure of vegetation in these forests: (1) species composition; 

(2) groups of trees; (3) scattered individual trees; (4) grass-forb-shrub interspaces; (5) snags, 

logs, and woody debris; and (6) variation in the arrangements of these elements in space 

and time. Our framework informs management strategies that can improve the resiliency 

of frequent-fire forests and facilitate the resumption of characteristic ecosystem processes 

and functions by restoring the composition, structure, and spatial patterns of vegetation. 

We believe restoration of key compositional and structural elements on a per-site basis will 

restore resiliency of frequent-fire forests in the Southwest, and thereby position them to better 

resist, and adapt to, future disturbances and climates.

Keywords: dry-mixed conifer, ecosystem services, ecosystem processes and functions, 

frequent-fire forests, forest structure, ponderosa pine, restoration, species composition, 

spatial patterns

Authors

Richard T. Reynolds is Research Wildlife Biologist with the U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station, in Fort Collins, Colorado. He 

has investigated the relationship between the composition and structure of habitats and the 

demographic performance of apex avian predators in forested ecosystems for over 40 years. 

His recent research focuses on relationships between individual and population demographics 

of northern goshawks and the structure and composition of their habitats in ponderosa pine 

and dry mixed-conifer forests on the Kaibab Plateau, Arizona. He and his colleagues have 

written management recommendations for restoring the native biodiversity, food webs, 

and goshawk habitat in ponderosa pine and dry mixed-conifer forests. He received his B.S. 

degree in Life Science and M.S. degree and Ph.D. in Wildlife Ecology from Oregon State 

University.

Andrew J. Sánchez Meador is the Program Director of Biometrics and Forest Management 

with the Ecological Restoration Institute and Assistant Professor of Biostatistics and 

Quantitative Ecology in the School of Forestry at Northern Arizona University in Flagstaff, 

Arizona. Andrew’s most recent research focused on: 

(1) quantifying how natural and anthropogenic disturbances shape forested ecosystems; 

(2) application of spatial statistics to characterize individual tree-, stand-, and landscapescale patterns; (3) using state-transition-modeling to examine factors influencing treatment 

effectiveness and longevity; and (4) applications of remote sensing and computer vision 

techniques to quantify vegetation structure. He received his B.S. and M.S. degrees in 

Forestry from Mississippi State University and his Ph.D. in Forest Science from Northern 

Arizona University.
James A. Youtz is Regional Silviculturist with the U.S. Forest Service, Southwestern 

Region, in Albuquerque, New Mexico. He has been involved with the practice of silviculture 

and fire management in Southwestern forests since 1983. His program management 

responsibilities include training and certification of silviculturists in forest ecology and 

silvicultural practices and transfer of best available science to other managers to develop 

and incorporate strategies for ecological restoration of forest resiliency into program and 

project plans for National Forests in the Southwestern United States. He also has extensive 

experience serving with the U.S. Department of Interior in post-fire emergency stabilization 

and rehabilitation throughout the continental United States. He received a B.S. degree in 

Forest Management from Northern Arizona University.

Tessa Nicolet is the Regional Fire Ecologist with the U.S. Forest Service, Southwestern 

Region, in Payson, Arizona. She has been involved with fire ecology and fire management 

in the Southwestern Region since 2005. Her responsibilities include training, analysis, 

and transfer of best available science relating to fire and fuels management, fire ecology, 

fire regimes, fire analysis modeling, and restoring fire to fire adapted ecosystems for fire 

professionals across the Southwest. She received an M.F. degree in Forestry with emphasis 

in Fire Ecology from Northern Arizona University.

Megan S. Matonis is a Ph.D. student with the Graduate Degree Program in Ecology at 

Colorado State University. She is also a Graduate Student Cooperator with the Science 

Integration and Application program at the U.S. Forest Service, Rocky Mountain Research 

Station, in Fort Collins, Colorado. Her dissertation research focuses on collaborative 

landscape-scale restoration of ponderosa pine and dry mixed-conifer forests along the 

Colorado Front Range and Uncompahgre Plateau. She received a B.S. degree in Natural 

Resource Management from Colorado State University and an M.S. degree in Forestry from 

Michigan State University.

Patrick L. Jackson is Chief of Staff with the U.S. Forest Service, Southwestern Region, in 

Albuquerque, New Mexico. He led a Forest Service Team in the development of Desired 

Conditions for frequent-fire forests in the Southwestern United States that is being utilized 

in the Forest Plan Revision process. He also led the Regional Landscape-Scale Restoration 

efforts resulting in an ecological restoration strategy and program based on Desired 

Condition. He received a B.S. degree in Forestry and Range Management from Colorado 

State University and an M.S. degree in Watershed Management from the University of 

Arizona.

Donald G. DeLorenzo is Director of the Wildlife, Fish, and Rare Plant program with the 

U.S. Forest Service, Southwestern Region, in Albuquerque, New Mexico. Don has over 

35 years of natural resource management experience with the U.S. Forest Service. He is a 

certified Wildlife Biologist and has held the positions of Research Associate at New Mexico 

State University and Assistant Professor at Oregon State University. He received a B.S. 

degree in Agriculture (Wildlife Science emphasis), and an M.S. degree in Wildlife Science 

from New Mexico State University.

Andrew D. Graves is a Forest Entomologist with the U.S. Forest Service, Forest Health, 

New Mexico Zone, Southwestern Region, in Albuquerque, New Mexico. He has worked 

in the forests of the upper Midwest, Alaska, and California primarily focusing on bark 

beetles in these systems. He has worked in the region as an Entomologist since 2010. 

His responsibilities include providing expertise for the Forest Health, Prevention, and 

Suppression Program; providing hazard tree training; and assisting public land managers 

with forest insect detection, identification, and disturbance mitigation. He received a B.S. 

degree in Forest Management and a Ph.D. in Forest Entomology from the University of 

Minnesota.

i
Executive Summary

Many forest landscapes in the Southwestern United States (Arizona, New Mexico, southwest 

Colorado, and southern Utah) have become increasingly susceptible to large-scale, severe wildfire, 

insect, and disease episodes. As a result, these areas are experiencing altered plant and animal 

demographics, reduced structural and spatial heterogeneity of vegetation, reduced productivity and 

biodiversity, and impaired ecosystem processes, functions, and services. Increased susceptibilities 

are most evident in frequent-fire forests—forests that historically experienced frequent, lowseverity fire, which in the Southwest include ponderosa pine and dry mixed-conifer forests. 

Changes to these frequent-fire forests largely resulted from unregulated livestock grazing around 

the turn of the 20th Century, logging, and human activities such as fire suppression, resource use, 

and infrastructure development.

We present a management framework for improving the resistance and resiliency of frequent-fire 

forest ecosystems to severe disturbances. This is accomplished by restoring the characteristic 

vegetation composition and structure in these forests. Frequent-fire forests had a characteristic 

uneven-aged structure consisting of a temporally shifting mosaic of different aged tree groups 

and scattered individual trees in an open grass-forb-shrub matrix—a spatial and temporal pattern 

that provided and sustained plant and animal habitat adjacency, local biodiversity, and food webs. 

Hence, the key compositional and structural elements of our restoration framework are: (1) species 

composition (tree and understory vegetation); (2) groups of trees; (3) scattered individual trees; 

(4) open grass-forb-shrub interspaces between tree groups and individual trees; (5) snags, logs, 

and woody debris; and (6) variation in arrangements of these elements in space and time. Our 

framework is informed by:

• reference conditions (conditions of ecosystems before significant industrial human disturbance),

• natural ranges of variability (ranges of reference conditions for a specific ecosystem and time 

period),

• observed changes in disturbance regimes, and

• lessons learned during applications of our framework in frequent-fire forests in the Southwest.

The types, frequencies, and severities of disturbances (e.g., fires, insects, and diseases) played an 

important role in shaping the historical composition, structure, and function of frequent-fire forests. 

Therefore, where forest composition and its structure allow, the framework recommends that fire, 

the primary historical disturbance agent in these forests, play a prominent role in their restoration. 

The framework also emphasizes that mechanical treatments may be necessary to initiate suitable 

compositions and structures before reintroducing fire. Where use of fire is limited, mechanical 

treatments may be the only available tool to create and maintain restored forests. Conversely, fire 

may be the only tool in some areas. Restoration provides opportunities for the re-establishment 

of the characteristic disturbance regimes as well as the spatial and temporal links between pattern 

and process (e.g., the feedback relationship between forest structure and fire) that sustained the 

characteristic composition and structure of these forests. Implementation of our framework should 

improve overall ecosystem productivity and function and enhance ecosystem services such as soil 

productivity, biodiversity, wildlife habitat, clean air, water quality and quantity, wood products, and 

recreation.

Natural ranges of variability are considered a “best” estimate of a resilient and functioning 

ecosystem because they reflect the evolutionary and historical ecology of forests. Natural ranges 

of variability are thereby a powerful template for improving the resiliency of frequent-fire forests. 

Natural variability in the composition and structure across sites in these forests results from and 

drives spatial differences in fire effects, plant species compositions, tree establishment patterns 

and densities, and numbers and distribution of snags, logs, and woody debris. Managers are 

encouraged to recognize the natural variability in ponderosa pine and dry mixed-conifer forests 

and to use historical evidence, such as old trees, stumps, and logs, and biophysical site attributes 

(e.g., soils, slopes, aspects, and climate) to guide the restoration of variability in these forests. 

Studies of reference conditions in Southwestern ponderosa pine and dry mixed-conifer showed that 

trees occurred in a range of spatial patterns, most often aggregated but with a random distribution 

on certain soils. Tree groups were separated by open grass-forb-shrub interspaces of variable 

sizes and shapes that often contained scattered individual trees. In areas exhibiting strong tree 

aggregation, openness was typically higher, but on sites with less tree aggregation, openness may 

have been lower depending on the arrangement of trees, their sizes, and crown widths (Table 1). 

The distribution and abundance of snags and logs varied with site and likely coincided with the 

type, severity, and scale of historical disturbance (Table 1). While reference condition literature 

on the fine-scale (<10 acres) composition and structure in dry mixed-conifer is more limited than 

ii
scales showed that mean tree densities and basal areas were slightly greater in dry mixed-conifer 

forests than ponderosa pine, and snag and log abundances appeared similar to or slightly greater in 

dry mixed-conifer than in ponderosa pine forests. Compared to today’s forests, characteristic dry 

mixed-conifer forests had higher proportions of fire-resistant/shade-intolerant tree species; lower 

tree densities; a more open structure comprised of higher proportions of large, old trees; and more 

spatial heterogeneity (groups and patches of trees).

To illustrate implementation of our framework, we describe a restoration treatment in a ponderosa 

pine stand in New Mexico that had experienced incidental tree cutting and no fire since the 1880s. 

While the stand had a characteristic component of old trees, there was a preponderance of midaged trees. Fire behavior modeling of pre-treatment conditions showed that 11 percent of the stand 

could support torching and active crown fire under dry conditions and moderate wind speeds. 

Our restoration treatment moved the composition and structure of the stand towards characteristic 

conditions—distinct tree groups, scattered single trees, and open interspaces between tree groups. 

Implementation of the framework resulted in predicted crown fire behavior on only 1 percent of the 

stand. Post-treatment abundance of logs and snags was lower than desired, but these elements will 

accumulate over time.

Our framework incorporates knowledge of the historical compositions, structures, functions, and 

processes in Southwest frequent-fire forests and how these operated through feedback mechanisms 

to sustain their characteristic compositions and structures. Current forest conditions are reviewed 

in light of historical conditions and how human-caused changes to these forests lowered their 

resistance and resilience to disturbance agents, which have become more intense and frequent. Our 

framework is based on the assumption that managing these forest ecosystems towards references 

conditions and ranges of natural variation should allow the reestablishment of characteristic 

processes, thereby increasing ecosystem survival probabilities in the face of current disturbances, 

as well as any uncertain changes in disturbance types frequencies, and intensities due to climate 

change. Whereas, reference conditions and ranges of natural variability may not be sustainable 

in future climates, we believe their use in informing and guiding the restoration of frequent-fire 

forests is the most feasible means of increasing the probability for ecosystem survival which should 

lower uncertainties with respect to sustaining these forests through the near-term. We recognize 

that reference conditions in frequent-fire forest may become less relevant in changing climates, 

but believe that restoring their composition, structure, and characteristic processes today should 

aid the retention of ecosystem components while research and management develop options 

for whatever the future might bring. Our framework offers management recommendations for 

achieving the key compositional and structural elements for restoring frequent-fire forests. Once 

restored, these forests comprise a temporally shifting mosaic of groups of trees with interlocking 

crowns; scattered single trees; open grass-forb-shrub interspaces between tree groups; and 

dispersed snags, logs, and woody debris. It may not always be feasible or even desirable to restore 

exact reference compositions and structures. Instead, our framework’s objective is to increase 

forest resiliency by managing forest composition and structure toward reference conditions. We 

believe restoration of key compositional and structural elements on a per-site basis will enhance 

the resiliency of frequent-fire forests in the Southwest, thereby positioning them to better adapt to 

future disturbances and climates. It is our intent that application of this framework be flexible and 

adaptive (i.e., learn-as-you-go), that it will evolve with accumulation of knowledge, and that its 

conceptual approach will provide a blueprint against which management plans and practices can be 

evaluated.

Table 1. Ranges of reference conditions for ponderosa pine and dry mixed-conifer forests in the Southwestern United 

States from studies detailed in Tables 3, 6, 7, and 9.

Reference conditions by forest type

Forest attribute Ponderosa pine Dry mixed-conifer

Trees / acre 11.7-124 20.9-99.4

Basal area (ft2 / acre) 22.1-89.3 39.6-124

Openness (%)a 52-90 78.5-87.1

Openness on sites with strong tree aggregation (%)a 70-90 79-87

Spatial patterns Grouped or random Grouped or random

Number of trees / group 2-72 Insufficient data

Size of groups (acres) 0.003-0.72 Insufficient data

Number of groups / acre 6-7 Insufficient data

Snags / acre 1-10 ≥ Ponderosa pine forests

Logs / acre 2-20 ≥ Ponderosa pine forests aOpenness is the proportion of area not covered by tree crowns, estimated as the inverse of canopy cover. Openness data for dry mixedconifer is limited; range of reference condition openness will likely change with additional studies.

iii
iv

Contents

Executive Summary.......................................................................................... ii

Introduction....................................................................................................... 1

Science Review: Forest Ecology..................................................................... 4

Mechanisms Influencing Forest Composition............................................ 4

Mechanisms Influencing Forest Structure................................................. 5

Spatial Patterns: Formation and Maintenance.............................................. 9

Southwestern Frequent-Fire Forests........................................................11

Ponderosa Pine....................................................................................... 12

Dry Mixed-Conifer ................................................................................... 21

The Restoration Framework .......................................................................... 29

Spatial and Temporal Scales................................................................... 29

Key Elements by Forest Type: Ponderosa Pine...................................... 30

Ponderosa Pine: Fine-Scale Elements (<10 acres).................................... 30

Ponderosa Pine: Mid-Scale Elements (10-1000 acres).............................. 31

Ponderosa Pine: Landscape-Scale Elements (1000-10,000+ acres) ......... 31

Key Elements by Forest Type: Dry Mixed-Conifer .................................. 32

Dry Mixed-Conifer: Fine-Scale Elements (<10 acres)................................. 32

Dry Mixed-Conifer: Mid-Scale Elements (10-1000 acres)........................... 33

Dry Mixed-Conifer: Landscape-Scale Elements (1000-10,000+ acres)...... 33

Implementation Recommendations .............................................................. 35

Classification of Site Variability................................................................ 35

Recommendations by Key Elements ...................................................... 35

Species Composition .................................................................................. 35

Tree Groups and Individual Trees............................................................... 35

Grass-Forb-Shrub Interspaces.................................................................... 36

Snags, Logs, and Woody Debris................................................................. 36

Arrangement of Key Elements in Space and Time ..................................... 36

Management Feasibility .......................................................................... 36

Implementation of the Framework: Bluewater Demonstration Site........... 38

Pre-Treatment Conditions ....................................................................... 38

Prescription Description .......................................................................... 38

Post-Treatment Conditions...................................................................... 42

Future Management................................................................................ 42

Expected Outcomes of Framework Implementation ................................... 44

Ecosystem Resilience to Climate Change .............................................. 44

Disturbance Regimes.............................................................................. 45

Nutrient Cycling....................................................................................... 46

Biodiversity and Food Webs.................................................................... 46

Old-Growth.............................................................................................. 47

Hydrologic Function................................................................................. 48

Wood Products........................................................................................ 48

Aesthetics and Recreation ...................................................................... 49

Monitoring, Adaptive Management, and Research Needs.......................... 50

Summary.......................................................................................................... 52

Acknowledgments .......................................................................................... 52

Literature Cited ............................................................................................... 52

Glossary........................................................................................................... 70

APPENDIX 1. Scientific Names...................................................................... 74

APPENDIX 2. Ponderosa Pine Forests Subtypes ........................................ 76
Restoring Composition and 

Structure in Southwestern 

Frequent-Fire Forests: A ScienceBased Framework for Improving 

Ecosystem Resiliency

Richard T. Reynolds1, Andrew J. Sánchez Meador2, James A. Youtz3, 

Tessa Nicolet4, Megan S. Matonis1,5, Patrick L. Jackson3, 

Donald G. DeLorenzo3, Andrew D. Graves3

1Rocky Mountain Research Station, USDA Forest Service, 240 Prospect St., Fort Collins, Colorado 

80526, USA

2Ecological Restoration Institute and School of Forestry, Northern Arizona University, P.O. Box 

15017, Flagstaff, Arizona 86011, USA

3Southwestern Regional Office, USDA Forest Service, 333 Broadway SE, Albuquerque, New Mexico 

87102, USA

4Southwestern Regional Office, USDA Forest Service, 1009 East Highway 260, Payson, Arizona 

85541, USA

5Colorado State University, Graduate Degree Program in Ecology, Room 237 Natural Resources, Fort 

Collins, Colorado 80523, USA
 Table 2. Characteristic fire regimes of Southwestern forest types. Fire frequency refers to the mean number of years between fires, and fire severity relates to the effect of the 

fire on dominant overstory vegetation. Infrequent-fire forests (wet mixed-conifer and spruce-fir) are included for comparison to frequent-fire forests.

Forest type

(subtype)

Fire regimea

Fire typeb Forest structure Seral speciesc Climax species Fire frequency Fire severity

Ponderosa pine(all subtypes)

Regime I Surface Uneven-aged, grouped, open Dominant: ponderosa pine Dominant: ponderosa pine Shade-intolerant species. 0-35 years Low

Dry mixed-conifer Regime I (common)

Surface

Uneven-aged, 

grouped, open

Dominant: ponderosa pineSubdominant: aspen, oak, Douglas-fir, Southwestern white pine, and limber pine

Dominant: ponderosa pineSubdominant: Douglas-fir and Southwestern white pine or limber pineShade-intolerant species.

0-35 years Low

Regime III (rare) Mixed Uneven-aged, patched, open 35-100+ years Mixed

Wet mixed-conifer Regime III (common)

Mixed

Uneven-aged, 

patched, closed

Dominant (depending on plant association): aspen or Douglas-fir 

Dominant (depending on plant association): white fir and/or blue spruceShade-tolerant species. 

35-100+ years Mixed

Regime IV (rare)

Stand-replacing

Even-aged, closed

35-100+ years High

Spruce-fir(mixed, lower subalpine)

Regime III and/or IV

Mixed/

stand-replacing

Even-aged, closed Dominant (depending on 

plant association): aspen or Douglas-fir

Dominant (depending on plant association): Engelmann spruce and/or white firShade-tolerant species.

35-100+ years Mixed / High

Spruce-fir(upper subalpine)

Regime V

Stand-replacing

Even-aged, closed Dominant (depending on 

plant association): aspen, Douglas-fir, or Engelmann spruce

Dominant: Engelmann spruce and corkbark fir or subalpine firShade-tolerant species. 200+ years High

 aSchmidt and others (2002)

bSmith (2006a, 2006b, 2006c)cUSDA Forest Service (1997)
USDA Forest Service RMRS-GTR-310. 2013. 1

There is increasing recognition that frequent-fire 

forests, defined as forests with fire return intervals <35 

years (Table 2), have become progressively more susceptible to large-scale, severe wildfire (Covington and 

Moore 1994b; Steele and others 1986; Westerling and 

others 2006). These forests, which in the Southwestern 

United States include ponderosa pine and dry mixedconifer forests (see Appendix 1 for scientific names 

of species referred to herein), are also increasingly 

prone to insect and disease epidemics and altered plant 

and animal habitats, all leading to reduced biodiversity, ecological function, resilience, and sustainability (Allen and others 2002; Benayas and others 2009; 

Carey and others 1992; Carey and others 1999; Colgan 

and others 1999; Covington and Moore 1994a; Kalies 

and others 2012; Lynch and others 2010). Reduced 

ecosystem resilience to disturbances is more evident in 

frequent-fire forests where the composition, structure 

(age, size, density, and spatial patterns of vegetation), 

processes (e.g., disturbances), and functions (e.g., food 

webs) have changed to a greater degree due to reductions in fire frequency than in forest types where fire 

was historically less frequent (Agee 2003; Covington 

and Moore 1994a; Crist and others 2009; Hessburg 

and others 1999). This reduction in fire frequency is, 

in part, a result of more than a century of intensive human activities, including fire suppression, livestock 

grazing, and logging. Important compositional and 

structural changes in these forests resulting from human activities, especially those that changed historical 

fire regimes, include:

• increased tree densities,

• reduced structural and spatial heterogeneity of 

vegetation,

• declines in grass-forb-shrub vegetation,

• loss of old trees, and

• reductions in the diversity and quality of plant and 

animal habitats and food webs (Abella 2009; Arnold 

1950; Covington and others 1997; Kalies and others 

2012; Larson and Churchill 2012).

In addition to increasingly frequent and uncharacteristic disturbances such as large-scale severe fire events 

(Allen 2007; Covington and Moore 1994b; Fitzgerald 

2005; Graham and others 2004; Swetnam and others 

1999) and insect epidemics (Ferry and others 1995; 

Hessburg and others 2005; Kolb and others 1998; 

Negrón 1997), these changes resulted in environments 

that differed from those in which the native fauna and 

flora evolved (Carey 2003; Carey and others 1992, 

1999; Colgan and others 1999; Covington and Moore 

1994b; Kalies and others 2012; Reynolds and others 

1992, 2006a). Furthermore, ecosystem services such as 

clean air and water, water yield, wood products, recreation, aesthetic and spiritual experiences, old-growth, 

nutrient cycling, pollination, and carbon sequestration have been altered and are now more vulnerable 

to rapid degradation by uncharacteristic fire and insect 

epidemics (Benayas and others 2009; Ferry and others 1995; Finkral and Evans 2008; Hessburg and others 

2005; Kolb and others 1998; Negrón 1997; reviewed in 

Evans and others 2011 and Hunter and others 2007).

Prior to human-influenced changes to the characteristic fire regime, the composition, structure, and 

spatial pattern in frequent-fire forests were maintained 

by frequent, low-severity fire through a functional 

relationship between pattern and process; that is, frequent low-severity fires resulted in forest structures 

that facilitated continued low-severity fire (Fitzgerald 

2005; Graham and others 2004; Hiers and others 2009; 

Mitchell and others 2009; Thaxton and Platt 2006). 

Over time, shifting mosaics of tree groups and individual trees of varying ages were maintained within 

a grass-forb-shrub matrix by relationships among the 

severity and frequency of fire, presence of surface fuels (fuels on or near the surface of the ground), and 

tree regeneration sites that escaped fire (Larson and 

Churchill 2012). Some dry mixed-conifer forests 

and ponderosa pine-shrub communities experienced 

mixed-severity fires, which included combinations 

of surface and crown fires (see Table 2), sometimes 

resulting in larger patches of tree aggregation (Agee 

1993; Arno and others 1995; Kaufmann and others 

2007; Larson and Churchill 2012).

Forest restoration guided by reference conditions 

(conditions that characterized the status of ecosystems before significant industrial human disturbance; 

sensu Kaufmann and others 1994) provides for the approximation of the historical (i.e., natural) effects of 

characteristic disturbances. Restoration is the process 

of assisting the recovery of degraded, damaged, or 

destroyed ecosystems (SER 2004). Restoration initiates or accelerates ecosystem recovery with respect 

to ecological health (productivity), integrity (species 

composition, community and ecosystem structure), and 

sustainability (resistance and resilience to disturbance) 

Introduction
2 USDA Forest Service RMRS-GTR-310. 2013.

(SER 2004). Ecosystem resiliency is the ability of an 

ecosystem to absorb and recover from disturbances 

without altering its inherent function (SER 2004). 

A functioning ecosystem provides opportunities for 

sustaining plant and animal habitats and populations, 

increased biodiversity, nutrient cycling, carbon sequestration, air quality, water quality and quantity, wood 

products, forage, recreation, and aesthetic and spiritual experiences (Aronson and others 2007; Benayas 

and others 2009). Restoring forest composition and 

structure improves ecosystem function and resiliency 

(Bradshaw 1984; Cortina and others 2006).

A holistic approach to forest restoration based 

on appropriate science can also help meet multiple 

management objectives, including fuels reduction; 

reintroduction of characteristic disturbances; and the 

return of wildlife habitats, native biodiversity, and food 

webs (Covington and Moore 1994b; Kalies and others 

2012; Reynolds and others 1992, 2006a). Management 

informed by reference conditions and natural ranges of 

variability (the range of ecological and evolutionary 

conditions appropriate for an area; sensu Landres and 

others 1999) allow for the restoration of the characteristic composition, structure, spatial pattern, processes, 

and functions of ecosystems. Managing forests guided 

by historical conditions also restores the evolutionary 

environment (Kalies and others 2012; Moore and others 1999), enhancing the capacity of organisms in 

ecosystems to adapt to stressors such as fire, insects, 

disease, and climatic variability and change.

We describe a framework, including assumptions, 

principles, values, concepts, and procedures, for restoring the composition, structure, and spatial pattern 

of ponderosa pine and dry mixed-conifer forests in the 

Southwest. Our framework is a science-based approach 

to restoration that will prove useful for developing 

strategic plans and management applications. The 

framework emphasizes vegetation composition and 

structure, describes expected outcomes, and presents 

management recommendations for implementation. 

Expected outcomes include: increased biodiversity, 

plant and animal habitats, and ecosystem services; 

increased resilience to insects, disease, and climate 

change; and reduced fuel loads and fire hazards. Key 

compositional and structural elements of our restoration framework are:

(1) species composition (tree and understory 

vegetation);

(2) groups of trees;

(3) scattered individual trees;

(4) open grass-forb-shrub interspaces;

Figure 1. Characteristic vegetation patterns at 

three spatial scales for frequent-fire forests in 

the Southwest. The landscape-scale illustrates 

the importance of multiple stands (patches), 

meadows, and grasslands. The mid- and finescales illustrate grass-forb-shrub interspaces 

and uneven-aged stand conditions consisting of 

single, random, and grouped trees of different 

vegetation structural stages (from young to old) 

represented by different shades and sizes at the 

fine-scale. Also depicted are two different tree 

spatial patterns at the mid-scale (separated by 

the dashed line): trees are randomly spaced 

on the left side of the dashed line and are 

aggregated on the right (given the definition 

of stand as a homogenous area, both patterns 

could not actually be present).
USDA Forest Service RMRS-GTR-310. 2013. 3

(5) snags, logs, and woody debris; and

(6) variation in arrangements of these elements in 

space and time (Fig. 1).

Ecosystems are structured hierarchically and their 

composition, structure, processes, and functions are 

temporally and spatially dynamic. Therefore, we characterize the key compositional and structural elements 

at three spatial scales: fine (<10 acres), mid (10-1000 

acres), and landscape (1000-10,000+ acres) (Fig. 1). 

These scales generally correspond with structural features in frequent-fire forests. The fine scale is an area 

in which the species composition—age, structure, and 

spatial distribution of trees (single and grouped)—and 

grass-forb-shrub interspaces are expressed. Aggregates 

of fine-scale units comprise mid-scale patches or 

stands, which are relatively homogeneous in vegetation composition and structure. The landscape scale is 

composed of aggregates of mid-scale units and usually has variable elevations, slopes, aspects, soil types, 

plant associations, disturbance processes, and land 

uses. Understanding and incorporating temporal scales 

(e.g., seasonal, annual, decadal, and centuries) in a restoration framework is required to sustain vegetation 

dynamics of forests that result from growth, succession, senescence, and the historical and anthropogenic 

disturbances that periodically reset the dynamics.

Management recommendations for implementing our framework are tempered by our management 

and research experience in frequent-fire forests, as 

well as by lessons learned during implementations 

of the framework in the Southwest. The intent of our 

framework is to inform management strategies that 

will facilitate the resumption of historical processes 

and functions. Managing for the framework’s key elements should increase the resilience of the forests and 

facilitate opportunities for the resumption of characteristic function and disturbance regimes. The spatial and 

temporal aspects of these elements reflect the reciprocal interactions between pattern and process in these 

forests and are an ecological basis (Turner 1989) for 

incorporating spatial information in forest restoration 

(Larson and Churchill 2012).
4 USDA Forest Service RMRS-GTR-310. 2013.

Mechanisms Influencing 

Forest Composition

Plant species composition of a forest ecosystem is 

influenced by both deterministic and stochastic factors, including complex interactions among species’ 

life histories, disturbance regimes, and chance events. 

The establishment, growth, and survival of under- and 

over-story species are affected by competition for space, 

light, nutrients, and moisture. For example, tree regeneration and growth is affected by species-specific shade 

tolerance (Fig. 2); open stand conditions favor the regeneration of shade-intolerant species while closed 

stands favor shade-tolerant species (Langsaeter 1944; 

Long 1985; USDA Forest Service 1990). Biophysical 

conditions, such as soils, temperature, and moisture regimes, also influence the establishment, development, 

and abundance of under- and over-story plant species. 

Disturbances (e.g., fire, insects, pathogens, drought, and 

wind) often interact with biophysical site characteristics 

to further influence composition and structure of forest 

ecosystems. Such disturbances have variable temporal 

and spatial effects on vegetation depending on their 

type, frequency, intensity, seasonality, and spatial scale, 

which collectively define a characteristic disturbance 

regime of an ecosystem. Species in a forest ecosystem 

evolved under its characteristic disturbance regime, resulting in a natural range of variability or the range of 

ecological and evolutionary conditions appropriate to an 

ecosystem (Landres and others 1999).

Fire is the primary disturbance agent in many 

Southwestern forests, and fire regimes are central to 

understanding an ecosystem’s reference conditions 

and natural range of variability (Fig. 3; Table 2) (Fulé 

and others 2003). The species composition, as well 

as the structure and spatial pattern of vegetation in 

Southwestern frequent-fire forests developed in a feedback relationship with fire. Ponderosa pine and dry 

mixed-conifer forests are characterized by a frequent 

low-severity fire regime (Swetnam and Baisan 1996; 

Swetnam and Betancourt 1990) with historic mean fire 

return intervals ranging from 2-24 years (Brown and 

others 2001; Brown and Wu 2005; Evans and others 

2011; Hunter and others 2007; Swetnam and Baisan 

1996). Frequent low-severity fire favors shade intolerant 

and fire-resistant tree species (Fig. 2) and open forest 

conditions with discontinuous crowns and minimal fuels 

build-up, often with tree groups separated by open interspaces with grass-forb-shrub communities. In contrast, 

longer fire return intervals permit seedling development 

to larger, more fire-resistant tree sizes and favor survival 

of less fire-resistant species (Fig. 2) (Fulé and Laughlin 

2007; Laacke 1990; Taylor and Skinner 2003).

Endemic forest insects and pathogens are important 

disturbance agents that do not threaten long-term stability and productivity of forests under endemic conditions 

Science Review: Forest Ecology

Figure 2. Dry mixed-conifer forests occupy 

the ecological gradient from warm/dry to 

cool/wet biophysical site conditions. Dry 

mixed-conifer is not a homogenous type, 

intergrading with ponderosa pine forest 

on warm/dry sites and wet mixed-conifer 

forests on cool/wet sites. Its structure and 

composition become more similar as it 

intergrades with adjacent forest. Common 

tree species in ponderosa pine and mixedconifer forests also vary in their relative 

shade and fire tolerance.
USDA Forest Service RMRS-GTR-310. 2013. 5

due to moderation by millions of years of evolution 

(Goheen and Hansen 1993). When large or uncharacteristic insect and disease outbreaks occur, profound changes 

to the composition, structure, processes, and functions 

of forests often take place. Insects and diseases affect 

nearly all aspects of forest stand dynamics, from seed 

viability to seedling survival, from bud, shoot, and leaf 

production to growth and maintenance, and, ultimately, 

the survival and distribution of mature trees (Castello 

and others 1995; Tainter and Baker 1996). Bark beetles, 

in particular, are considered primary sources of mortality in Southwestern ponderosa pine forests. In 2011 

alone, bark beetles caused varying rates of ponderosa 

pine mortality on more than 144,000 acres in Arizona 

and New Mexico (USDA Forest Service 2012). Unlike 

bark beetles in ponderosa pine, the primary sources of 

mortality attributed to insects in mixed-conifer forests 

are typically defoliating insects. Damage from defoliators can range from large areas of widespread growth 

losses and infrequent mortality, as with the spruce budworm, to more localized, high levels of mortality caused 

by the Douglas-fir tussock moth (Wickman 1963).

While numerous species of dwarf mistletoe occur 

in frequent-fire forests, Southwestern (ponderosa pine) 

dwarf mistletoe and Douglas-fir dwarf mistletoe are 

the most prevalent. Dwarf mistletoes may be the most 

damaging of pathogens in Southwest forests with estimates of current infection being 30 percent or greater 

in ponderosa pine forests (Andrews and Daniels 1960; 

Maffei and Beatty 1988) and around 50 percent in 

mixed-conifer forests (Conklin and Fairweather 2010; 

Drummond 1982). Additionally, the presence and intensity of Southwestern dwarf mistletoe infection in 

ponderosa pine stands has been implicated as a source of 

mortality or as an exacerbating factor in bark beetle outbreaks (Negrón 1997; Stevens and Hawksworth 1984). 

Endemic soil fungi that cause root disease (e.g., armillaria and black-stain root diseases) also influence forest 

composition and structure (Rippy and others 2005). 

Root diseases are known to affect the ponderosa pine 

forests of the Southwest, with observations of mortality 

associated with root disease, mistletoe, and bark beetles 

as high as 25 percent (Wood 1983). In some locations, 

conifers killed by root disease are replaced by less susceptible conifers, hardwood species, or grass-forb-shrub 

interspaces. In the case of armillaria and related wood 

decay fungi, this shift in species composition can be 

maintained for decades due to remnant fungi in stumps 

and root systems (Roth and others 1980). In most situations, native root diseases do not cause irreplaceable loss 

of entire stands over large areas, nor do they threaten the 

existence of any host species. However, shifts in stand 

composition and other natural and human-caused disturbances have frequently resulted in increased damage 

from root diseases (Edmonds and others 2000).

Mechanisms Influencing 

Forest Structure

Frequent-fire forests typically comprise a mosaic 

pattern of groups of trees, scattered single trees, grassforb-shrub interspaces, snags, logs, and woody debris 

(Cooper 1960; Larson and Churchill 2012; Pearson 

1950; White 1985). Structural heterogeneity in these forests is a consequence of interactions among biophysical 

site conditions (e.g., topography, soils, climate); disturbance types, frequencies, intensities, and extent; levels 

of competition among species; and tree demographic 

Figure 3. Prescribed, low-severity 

surface fire carried by needles, 

cones, dried grass, and forbs 

on the Lincoln National Forest, 

2010.
6 USDA Forest Service RMRS-GTR-310. 2013. Table 3. Historical spatial patterns and tree group characteristics in frequent-fire forests of the Southwest, arranged by forest type (PP: ponderosa pine, PO: pine-oak, DMC: dry mixed-conifer). Location Parent material Elevation (ft) Forest type Reference date Tree sizes (dbh in inch.) Group density (groups/ acre) Group size (acres) Trees per groupa Percent basal area in groups Citation Malay Gap, Arizona Basalt 7200 PP 1952 ≥ 4.0 0.16-0.32 Cooper 1960 Gus Pearson Natural Area, Arizona Basalt 7398 PP 1875 Unknown 0.05-0.72 3-44 White 1985 Flagstaff, Arizona Varying 7800 PP 1880 Unknown 1-33 2-25 28%-74% Abella and Denton 2009 Woolsey Plots, Arizona Basalt 7052 PP 1874 ≥ 3.5 25-67 0.003-0.09 3-24 62%-75% Sánchez Meador and others 2011 Coulter Ranch, Arizona Basalt 7520 PO 1913 ≥ 3.5 0.01-0.1 Sánchez Meador and Moore 2010 Uncompahgre Plateau, Colorado Shale 8000 PP / DMC 1875 Unknown 0.1-0.25 Binkley and others 2008 Numerous national forests in Arizona and New Mexico Varying 8650 PP / PO / DMC 1910 ≥ 3.5 24-80 0.01-0.32 2-72 51%-85% Sánchez Meador and others unpublished datab aValues should be not interpreted as “strict” densities of trees within groups as authors used different definitions and methods to define and characterize “groups.” We suspect that as the number of species and site productivity increase the metric of “tree group” becomes less useful than this metric at the mid- to landscape-scale. For example, when tree density is fixed and numbers of tree species varies (i.e., compare ponderosa pine vs. ponderosa-pine Gambel oak vs. dry-mixed conifer forests), the area available to a “tree group” will likely decrease. bData based on 2.47-acre plots reconstructed prior to Euro-American settlement (1876-1890) in Arizona (n = 17 plots) and New Mexico (n = 7 plots) using the same methods as Sánchez Meador and others (2010, 2011). Historical and contemporary field methods, as well as contemporary conditions, are detailed in Sánchez Meador and others (2010) and Moore and others (2004) who reported forest structural reference conditions (size distributions, tree density ranges, spatial patterns, etc.) on a subset of these same plots. In brief, all live and dead tree structures were measured, including stumps, snags, and wind-fallen trees, that grew to at least breast height (4.5 ft). All tree structures were located using historical stem-maps and measured spatial coordinates, and dendrochronological reconstructions were used to quantify structural and spatial reference conditions (Baker and others 2008; Sánchez Meador and others 2010). Spatial attributes (e.g., group size and density) were quantified using methods described in Sánchez Meador and others (2011).
USDA Forest Service RMRS-GTR-310. 2013. 7

rates. Variability in biophysical site conditions is a 

primary source of spatial and temporal variation in vegetation structure. Of studies that investigated the origin, 

distribution, and mortality of ponderosa pine forests, 

most reported uneven-aged reference conditions at the 

stand scale (Sánchez Meador and others 2010), but 

three different within-group age structures were identified. Cooper (1960) reported relatively even-aged tree 

groups, White (1985) and Abella (2008) reported groups 

of multi-aged trees, and Sánchez Meador and others (unpublished data; see Table 3 footnote) found mixtures of 

both types. Variation of tree ages within groups likely reflects the establishment and growth of a single, grouped 

cohort of trees and perhaps seedling establishment and 

growth of trees under, or adjacent to, tree groups (see 

Spatial Patterns: Formation and Maintenance) (Sánchez 

Meador and others 2009).

Heterogeneity of within-group tree sizes can generate from processes related to growth, competition, and 

disturbances and may result in a range of tree sizes irrespective of age (Mast and Veblen 1999; Pearson 1950; 

Sánchez Meador and others 2011; Taylor 2010; Woodall 

2000). Trees on the perimeter of groups tend to have 

higher growth rates, attain larger sizes, lean away from 

the group center, and have asymmetrical crowns with 

larger lower limbs than interior trees (Pearson 1950). 

Heterogeneity in tree sizes and spacing within groups 

may decline over time due to mortality resulting in a 

gradual transition from dense to more uniform spacing 

of trees (Cooper 1961; Mast and Veblen 1999; Mast and 

Wolf 2004, 2006; Pielou 1960). However, tight clumps 

of trees sharing the same root ball often persist within 

groups (Fig. 4) (Larson and Churchill 2012). Mortality 

over time may also gradually reduce within-group tree 

density, resulting in increased variation in tree densities 

and ages within and among groups.

Like composition, the structure of forest vegetation 

is also affected by disturbances such as fire, insects, disease, wind, and drought (Brown and others 2001; Ehle 

and Baker 2003; Mast and others 1998, 1999). Numerous 

abiotic and biotic disturbances affect the composition, 

amount, arrangement, spatial continuity, and volatility 

of surface and canopy fuels (Franklin and others 2012), 

which in turn effects fire behavior (Van Wagner 1977). 

Dense forest structures can facilitate crown fire by providing a potential path for fire through tree crowns (Cruz 

and others 2003; Fulé and others 2001; Graham and 

others 2004; Stratton 2004; Van Wagner 1977, 1993). 

Forest density further influences surface and canopy fuels through interactions with insects and diseases. The 

effects of bark beetles in ponderosa pine stands are more 

pronounced during and following extended droughts 

and under dense stand conditions; both of which are 

conducive to the survival and reproduction of beetle 

populations. Negrón (1997) showed a link between 

roundheaded pine beetle attacks and higher densities 

of smaller, pole-sized trees in relatively homogenous 

stands of ponderosa pine in the Sacramento Mountains 

of New Mexico. Additionally, trees with heavy mistletoe 

infection are more susceptible to severe crown scorch 

and death from fires (Harrington and Hawksworth 1990; 

Hoffman and others 2007). Hawksworth and Wiens 

(1996) suggested that mistletoes have been important 

species in frequent-fire forests since fire first appeared 

on these landscapes.

The density and arrangement of forest canopies affects the penetration of sunlight, precipitation, humidity, 

and wind. In fact, dense forest structures can maintain 

relatively high fuel moistures and ameliorate wind 

Figure 4. A group of ponderosa pine trees 

comprised of two clumps of trees.
8 USDA Forest Service RMRS-GTR-310. 2013.

effects. Forest canopies also influence the composition 

and abundance of surface fuels, which are essential to 

facilitate fire as a disturbance agent. Surface fuels also 

offer nutrients to soils, help reduce erosion, and influence understory vegetation productivity, density, and 

diversity (Kalies and others 2012; Kerns and others 

2003; Moore and others 1999). In general, more fuel 

accumulates and persists in forests with longer fire return intervals than in those with more frequent surface 

fire (Brewer 2008; Minnich and others 2000). Fine fuels (grass, needles, cones, and woody material less than 

0.25 inches in diameter) and small branches accumulate 

more rapidly under tree groups than in interspaces between tree groups (Fig. 5). This accumulation facilitates 

fire, in turn restricting the establishment and persistence of trees and shrubs under tree groups. The amount 

and composition of surface fuels interact with weather 

conditions to influence fire behavior. Herbaceous fuels 

respond quickly to relative humidity and thus carry fire 

less readily when humidity is high, whereas pine needles 

will readily carry fire under these conditions (see moisture of extinction in Anderson 1982; Scott and Burgan 

2005). Furthermore, needle and twig litter will burn 

with higher intensity than herbaceous fuel under similar 

weather conditions.

Forest structure affects the distribution, density, and 

composition of surface and canopy fuels, which affects the behavior of fire and, ultimately, post-fire forest 

structure. Historically, seedling establishment was more 

frequent in fire-created areas of bare mineral soil where 

competition with other vegetation and the abundance of 

surface fuels were reduced (Agee 1993; Cooper 1960; 

Stephens and others 2008). However, regeneration is 

less affected by the availability of bare mineral soil in 

some plant associations and soil types (Hanks and others 1983; USDA Forest Service 1997). A study in the 

Southwest showed a high density of tree regeneration on 

sites with one or more of the following: low clay soils, 

understories where screwleaf muhly was the dominant 

graminoid, and sites with high annual precipitation 

(Puhlick and others 2012). Depending on seed availability, some individuals and small groups of seedlings 

may establish throughout the stand, including under tree 

groups (Abella 2008; Sánchez Meador and others 2009; 

White 1985).

(a)

(b)

Figure 5. (a) Fine fuels (grasses, forbs, needles, 

branches, cones) beneath the crown of an 

individual tree and (b) under the canopy of a 

tree group.
USDA Forest Service RMRS-GTR-310. 2013. 9

Figure 6. A group of ponderosa pine saplings 

in a grass-forb interspace between mature 

tree groups that experienced faster 

growth and survived a prescribed fire. 

Shade-suppressed saplings in heavier fine 

fuel loadings under a mature group of 

pine did not survive the fire.

Tree seedlings that established in small forest openings are subsequently thinned by later fires and/or 

other sources of mortality (Fig. 6) (Cooper 1960, 1961; 

Sánchez Meador and others 2010; Stephens and Fry 

2005; White 1985). Young tree groups in open areas 

reach fire-resistant sizes more rapidly than those beneath closed canopies (Fitzgerald 2005; Sackett and 

Hasse 1998; York and others 2004). Fire-caused thinning of young tree groups was more substantial if the 

group was overtopped by older trees due to suppressed 

seedling growth and increased litter accumulation 

(Agee 1993; Cooper 1960). Fire-spread through young 

tree groups may also be influenced by microclimate 

and fuel moisture in these groups (Harrington 1982). 

As trees grow, increasing needle and twig accumulations facilitate the spread of surface fire. Seedlings that 

establish some distance away from mature older trees 

are also more likely to survive fires due to less rapid 

accumulation of fine fuels and small branches from 

overstory trees (Fig. 5, 6), likely leading to less intense 

and severe fire (Cooper 1960) and variable spacing of 

tree groups. The seasonality and burning conditions of 

fire occurrence also result in variable outcomes.

Spatial Patterns: Formation and Maintenance

Spatial patterns of vegetation are a component of 

forest structure. The historical spatial mosaic of tree 

groups, scattered individual trees, and openings in 

frequent-fire forests was maintained by interactions 

among the locations and types of fuels, the frequency 

and severity of fire, and tree regeneration and mortality patterns. A landscape mosaic of tree groups and 

scattered individual trees within an open grass-forbshrub matrix, along with snags, logs, and woody 

debris, provides for the predominance of surface fire 

mixed with small-scale, variable fire behavior (e.g., 

torching). An open or grouped spatial structure reduces 

canopy continuity, decreasing a stand’s vulnerability 

to active crown fire (Fitzgerald 2005; Fulé and others 2004; Roccaforte and others 2008; Stephens and 

others 2009). These interactions were mediated by 

small-scale variability in fire behavior and effects and 

often resulted in sites with aggregated tree regeneration that were temporarily “free” or “safe” from fire 

(Larson and Churchill 2012). The location of some 

safe-sites for tree regeneration appeared to be related 

to local areas of previously more intense fire associated with accumulations of coarse woody debris (logs 

and other dead woody material greater than 3 inches 

in diameter) originating from the death of individual 

trees (Sánchez Meador and Moore 2010; West 1969; 

White 1985) or tree groups (Cooper 1960; Stephens 

and Fry 2005; Taylor 2010; West 1969). Death of individuals or groups of old trees create new snags and 

logs that, when consumed by fire, result in “safe” sites 

for tree regeneration. Extended fire-free periods may 

allow tree regeneration in areas not typically fire “safe” 

(Fig. 7) (Fulé and others 2009), resulting in temporal 

shifting of tree locations where new cohorts develop 

to fire-resistant sizes. The cyclic repetition of forest 

vegetation dynamics stemming from disturbances and 

tree regeneration perpetuates a shifting mosaic of tree 

groups and individual trees in different stages of development in a grass-forb-shrub matrix (Fig. 8).
10 USDA Forest Service RMRS-GTR-310. 2013.

Figure 8. Tree groups and a single individual tree on the right 

in a grass-forb-shrub interspace.

Figure 7. Ponderosa pine regeneration under a 

group of snags. This site is not currently fire 

“safe” due to the accumulation of surface 

fuels over an extended fire-free period. In the 

absence of fire, these seedlings could grow to 

fire-resistant sizes. If fire occurs prior to the 

trees attaining fire-resistant size, the seedlings 

would likely not survive. However, the 

reduction of surface fuels post-fire may create a 

temporary fire-safe site for future regeneration.

Insects and diseases also shape spatial patterns of 

forested landscapes. Due to the slow spread of infection, it has been suggested that the current distribution 

of mistletoe throughout the Southwest is likely similar 

to its historical distribution, although spatial continuity and levels of infection may have changed (Conklin 

and Fairweather 2010). Under historical forest conditions, it is likely that large-scale, contiguous insect 

and disease outbreaks would have been rare. It is more 

likely that mistletoe would have thrived in denser multistoried portions of stands that escaped fire pruning and 

thinning (see Conklin and Geils 2008 for additional discussion). In such portions, periodic tree deaths would 

have occurred directly from mistletoe, or infected trees 

would have had increased the likelyhood of succumbing to bark beetles or root disease. Localized mistletoe 

infections would have created pockets of tree death 

that could eventually serve as regeneration sites. In 

cases where regeneration occurred in larger openings 

between trees, trees may have escaped mistletoe infection altogether. Other scenarios can be envisioned. For 

instance, in cases of stands with relatively homogenous 

age and spacing, bark beetles may have had periodic population increases, causing high rates of local 

mortality. Localized beetle outbreaks likely occurred 

in stands with severe crown damage following fire 

(Breece and others 2008), and these infestations may 

have spilled over into undamaged trees nearby, creating larger openings. Root diseases also create scattered 

mortality, small openings, and increased volume of 

snags and downed large woody debris (Rippy and others 2005).

An understanding of forest processes and their effects at different spatial scales is important because 

landscapes are spatially dependent (Turner 1989). 

Inferences about patterns and processes in forests are 

contingent upon the scale at which they are investigated. For example, a fine-scale model for ponderosa pine 

regeneration showed that the majority of the variance 

(76 percent) in seedling density was explained by properties such as soil texture and pH, precipitation, seed 

tree proximity, and composition of the plant community (Puhlick and others 2012). However, at the mid- to 

landscape-scale, models including abiotic conditions 

and tree density at this broader scale accounted for less 
USDA Forest Service RMRS-GTR-310. 2013. 11

of the variability in observed seedling densities (only 

13 percent) (Puhlick and others 2012). Fire further 

shapes tree spatial patterns at varying scales through 

its influence on seedling survival, with variability in 

the severity, seasonality, and frequency of fire (Cooper 

1960; Pearson 1950; Stephens and Fry 2005; Taylor 

2010; West 1969; White 1985). An overall aggregated 

(grouped) historical tree pattern separated by openings 

has been frequently reported in Southwestern frequentfire forests (Fig. 8) (Larson and Churchill 2012). 

However, Abella (2008), Binkley and others (2008), 

and Sánchez Meador and others (unpublished data, see 

Table 3 footnote) observed grouped and random (no 

aggregation) historical tree spatial patterns (Fig. 9). 

Schneider (2012) observed only random historical tree 

spatial patterns in Southwestern ponderosa pine.

Spatial heterogeneity can exist at any scale, and the 

value of metrics used to assess forest conditions varies 

in usefulness with scale. At mid- and landscape scales, 

elements such as single tree and group density become 

less useful as a metric and elements such as patches, 

the grass-forb-shrub matrix, stand density, canopy cover, and basal area become more appropriate. Patches 

are roughly synonymous with stands, being defined 

as an area of relatively homogeneous vegetation composition and structure differing from its surroundings 

(Forman 1995). Patches are the basic unit of the landscape, and their sources of variability are attributed to 

scale-appropriate factors such as elevation, topography, climate, and land use. Our restoration framework 

describes forest composition, structure, and spatial patterns at fine-, mid-, and landscape-scales (Fig. 1).

Southwestern Frequent-Fire Forests

The natural range of variability is a “best” estimate 

of a resilient and functioning ecosystem because it 

reflects the evolutionary ecology of these forests. 

Natural range of variability is therefore a powerful 

science-based foundation for developing a framework 

for restoring the composition and structure of forests 

(Kaufmann and others 1994; Keane and others 2009; 

Moore and others 1999). However, the relevance of 

reference conditions and natural ranges of variability as references against which to evaluate changes 

in ecosystems has been questioned on the basis of 

uncertainties in future ecological conditions due to 

climate change (Burkett and others 2005; Harris and 

others 2006; Millar and others 2007; Choi and others 

2008; Bolt and others 2009; Wagner and others 2000). 

Two primary challenges to restoring and sustaining 

frequent-fire forests in the face of projected climate 

change are (1) uncharacteristically rapid alterations 

of environments and combinations of disturbances, 

and (2) non-native biotic factors resulting in unprecedented environmental conditions (Fulé 2008). Future 

climates and disturbances are unknown; therefore, 

historical reference conditions may not be sustainable. 

However, it is clear that frequent-fire forest ecosystems are being degraded or lost at a growing rate due 

to increasingly atypical disturbances. Concepts such 

as reference conditions and natural ranges of variability can inform plans and actions for restoring these 

ecosystems and for research needed to effectively respond to a changing future.

The natural range of variability can be estimated 

by pooling reference conditions across sites within 

a forest type. Reference conditions for a forest type 

typically vary from site to site due to differences 

in factors such as soil, elevation, slope, aspect, and 

micro-climate and manifests as differences in fire effects, tree densities, patterns of tree establishment and 

persistence, and numbers and dispersion of snags and 

logs. When pooled, these sources of variability comprise the natural range of variability of a site or forest 

type. Figure 9. Random (i.e., not aggregated) distribution of 

ponderosa pine trees in a patch of old trees. Also displayed 

are snags, logs, and coarse woody debris.
12 USDA Forest Service RMRS-GTR-310. 2013.

Our estimates of natural ranges of variability are 

derived from multiple lines of evidence based on historical ecology techniques (Egan and Howell 2001) 

such as written and oral historical records, historical 

photographs, early forest inventories, and dendrochronological studies (Table 4). While cultural accounts 

and early inventories provide a general context of historical conditions, they do not fully characterize forest 

structure by today’s statistical standards. More recently, dendrochronological techniques for quantifying 

historical conditions, including spatial and temporal 

variation, have been developed (e.g., Covington and 

Moore 1994a; Covington and others 1997; Fulé and 

others 1997; Mast and others 1999; Sánchez Meador 

and others 2010; White 1985). Nonetheless, there is a 

clear need for additional reference condition data sets, 

including sites from a wider spectrum across environmental gradients (e.g., soils, moisture, elevations, 

slopes, aspects) occupied by frequent-fire forests in 

the Southwest, especially in dry mixed-conifer. While 

the quantity of reference data sets is increasing, existing data represent a largely unbalanced sampling 

across gradients (e.g., most data sets are from basaltic 

soils and on dry to typic plant associations), and there 

have been few studies quantitatively examining and 

reporting spatial patterns of trees and the sizes and 

shapes of grass-forb-shrub interspaces.

Ponderosa Pine

Woolsey (1911) described Southwestern ponderosa 

pine forests as having “...pure park-like stand(s) made 

up of scattered groups of 2-20 trees, usually connected 

by scattering individuals. Openings are frequent and 

vary in size. Because of the open character of the stand 

and the fire-resisting bark, often 3 inches thick, the actual loss in yellow (ponderosa) pine by fire is less than 

with other more gregarious species.” Others also described historical ponderosa pine forests as having low 

density, open stands consisting of groups of pine trees 

interspersed with grassy or shrubby openings (Dutton 

1882; Lang and Stewart 1910; Pearson 1923; White 

1985).

Tree density, structure, spatial pattern, and ecological functions in today’s ponderosa pine forests of the 

Southwest are greatly altered from their historical conditions. Most Southwest ponderosa pine forests are at 

much greater risk of high-intensity, severe fire than they 

were prior to Euro-American settlement (Covington 

1993; Fulé and others 2004; Moore and others 1999; 

Roccaforte and others 2008). Historical ponderosa pine 

Table 4. Citations informing our restoration framework for frequent-fire forests arranged by information type.

Information type Citations (arranged alphabetically)

Reference conditions from oldgrowth, natural areas, and other 

restoration studies

Abella (2008); Abella and Denton (2009); Abella and others (2011); Agee (2003); 

Binkley and others (2008); Biondi (1996); Biondi and others (1994); Boyden and 

others (2005); Cocke and others (2005); Cooper (1960, 1961); Covington and 

Moore (1994a, 1994b); Covington and Sackett (1986); Covington and others 

(1997); Fornwalt and others (2002); Friederici (2004); Fulé and others (1997, 

2002a, 2003, 2009); Harrod and others (1999); Heinlein and others (1999, 2005); 

Hessburg and others (1999); Johnson (1994); Larson and Churchill (2012); Madany 

and West (1983); Mast and others (1999); Menzel and Covington (1997); Moore 

and others (2002, 2004); Pearson (1950); Roccaforte and others (2010); Romme 

and others (2009); Sánchez Meador and Moore (2010); Sánchez Meador and 

others (2009, 2010, 2011); Schneider (2012); Smith (2006a, 2006b, 2006c); Taylor 

(2010); Waltz and Fulé (1998); West (1969); White (1985); White and Vankat 

(1993); Williams and Baker (2011, 2012); Youngblood and others (2004)

Reference conditions from 

observations of early explorers, 

scientists, and managers

Beale (1858); Dutton (1882); Greenamyre (1913); Lang and Stewart (1910); 

Leopold (1924); Liebeg and others (1904); Meyer (1934); Pearson (1923); Plummer 

(1904); Rasmussen (1941); Wheeler (1875); Woolsey (1911)

Disturbance histories Agee (1993); Allen (2007); Andrews and Daniels (1960); Brown and others (2001); 

Brown and Wu (2005); Dieterich (1980); Ehle and Baker (2003); Ferry and others 

(1995); Fulé and others (2003); Fulé and others (2004); Grissino-Mayer and others 

(1995, 2004); Hart and others (2005); Heinlein and others (2005); Hessburg and 

others (1994); Hessburg and others (2005); Kaye and Swetnam (1999); Korb and 

others (2013); Littell and others (2009); Lynch and others (2010); Maffei and Beatty 

(1988); Minnich and others (2000); Scholl and Taylor (2010); Stephens and others 

(2008); Swetnam and Baisan (1996); Swetnam and Bentacourt (1990); Swetnam 

and Dieterich (1985); Taylor (2010); Taylor and Skinner (2003); Touchan and others 

(1996); Weaver (1951); Williams and Baker (2012)
USDA Forest Service RMRS-GTR-310. 2013. 13

Disturbance effects (fires, insects, 

and diseases)

Arno and others (1995); Barton (2002); Bentz and others (2009); Conklin and Geils 

(2008); Castello and others (1995); DeLuca and Sala (2006); Dhillon and Anderson 

(1993); Drummond (1982); Edmonds and others (2000); Fettig (2012); Fitzgerald 

(2005); Franklin and others (2012); Fulé and Laughlin (2007); Goheen and Hansen 

(1993); Harrington and Hawksworth (1980); Hawksworth and Wiens (1996); 

Hessburg and others (1994); Hoffman and others (2007); Jenkins and others (2008); 

Lundquist (1995); Madany and West (1983); Miller and Keen (1960); Miller (2000); 

Moeck and others (1981); Naficy and others (2010); Negrón (1997); Negrón and 

others (2009); Parsons and DeBenedetti (1979); Rippy and others (2005); Savage 

and Mast (2005); Stevens and Hawksworth (1984); Tainter and Baker (1996); Von 

Schrenck (1903); Wickman (1963); Wood (1983)

Effects of forest management on 

ecosystem functions and processes

Arnold (1950); Baker (1986, 2003); Benayas and others (2009); Beier and others 

(2008); Boerner and others (2009); Breece and others (2008); Carey (2003); Carey 

and others (1999); Cocke and others (2005); Colgan and others (1999); Conklin 

and Geils (2008); Cortina and others (2006); Covington and others (1997); 

Covington and Sackett (1986, 1992); Cram and others (2007); Dodd and others 

(2006); Douglass (1983); Feeney and others (1998); Fettig and others (2007); 

Ffolliott and others (1989); Finkral and Evans (2008); Fulé and others (2001); Harr 

(1983); Honig and Fulé (2012); Kolb and others (1998); Koonce and Roth (1980); 

Korb and others (2003); Long and Smith (2000); Mitchell and others (2009); 

Moore and others (2006); Pilliod and others (2006); Roccaforte and others (2008); 

Stephens and others (2009); Stratton (2004); Strom and Fulé (2007); Troendle 

(1983); Waltz and Covington (2003); Wightman and Germaine (2006)

Climate change projections and 

impacts

Bentz and others (2010); Breshears and others (2005); Brown and others (2004); 

Harris and others (2006); Honig and Fulé (2012); Karl and others (2009); McKenzie 

and others (2004); Millar and others (2007); Miller and others (2009); Overpeck 

and others (2012); Parker and others (2000); Price and Neville (2003); Seager and 

others (2007); Shafer and others (2001); Smith and others (2008); Spittlehouse and 

Stewart (2004); Spracklen and others (2009); Westerling and others (2006)

Approaches to restoration and/or 

monitoring

Allen and others (2002); Aronson and others (2007); Block and others (2001); 

Bradshaw (1984); Busch and Trexler (2003); Clewell and others (2005); Covington 

(1993, 2003); Covington and others (1997); Crist and others (2009); Egan and 

Howell (2001); Falk (2006); Fiedler and others (1996); Fitzgerald (2005); Fulé 

and others (2002b); Graham and others (2004); Kaufmann and others (1994); 

Keane and others (2009); Landres and others (1999); Laughlin and others (2006); 

Lindenmayer and Likens (2010); Long and others (2004); Moore and others (1999); 

Morgan and others (1994); Mulder and others (1999); Murray and Marmorek 

(2003); Noon (2003); Palmer and Mulder (1999); Reynolds and others (1992, 

2006a); Roccaforte and others (2010); SER (2004); Sitko and Hurteau (2010); 

Swetnam and others (1999); Wagner and others (2000); Walters (1986); Williams 

and others (2009)

Science syntheses and tools for 

forest management

Abella (2008); Abella and others (2006); Anderson (1982); Brewer (2008); Brown 

and others (2003); Clary (1975); Conklin and Fairweather (2010); Cruz and others 

(2003); Evans and others (2011); Graham and others (1994); Hunter and others 

(2007); Long (1985); Noss and others (2006); Patton and Severson (1989); Pearson 

(1950); Schmidt and others (2002); Schubert (1974); Scott and Burgan (2005); 

Triepke and others (2011); USDA Forest Service (1990)

Vegetation classifications Comer and others (2003); DeVelice and others (1986); Hanks and others (1983); 

USDA Forest Service (1997); Winthers and others (2005)

Table 4. Continued.

Information type Citations (arranged alphabetically)
14 USDA Forest Service RMRS-GTR-310. 2013.

forests had widely spaced, large trees, typically occurring in small groups with scattered single trees, and 

open forest conditions with a productive grass-forbshrub understory (Cooper 1960; Dutton 1882; Lang and 

Stewart 1910; Pearson 1923, 1950; Sánchez Meador and 

others 2009, 2011; White 1985). The grass-forb-shrub 

vegetation and other fine fuels and branches carried 

fires started by lightning and, to an uncertain extent, by 

Native Americans (Allen and others 2002; Kaye and 

Swetnam 1999). Forest composition, structure, and spatial patterns were maintained by low-severity surface 

fires that occurred every 2-26 years (Fig. 3), rarely killing large trees, thinning regeneration, and maintaining 

an open forest structure (Dieterich 1980; Fiedler and 

others 1996; Fitzgerald 2005; Pearson 1950; Swetnam 

and Dieterich 1985; Weaver 1951; Woolsey 1911). Fire 

chronologies in Western U.S. frequent-fire forests are 

reviewed in Evans and others (2011), Hunter and others 

(2007), Smith (2006b), and Swetnam and Baisan (1996).

Bark beetles also influenced pre-Euro-American ponderosa pine structure. Various sources indicate that bark 

beetle outbreaks occurred periodically in the Western 

United States since at least the 1750s (Bentz and others 2009) and likely much longer. Current forested 

landscapes are experiencing outbreaks that are larger 

and more frequent than previously recorded (Lynch and 

others 2010). For example, bark beetles caused variable amounts of mortality on more than 700,000 acres 

in Arizona and New Mexico in 2003 (Fettig and others 2007; USDA Forest Service 2004). Although there 

is no direct evidence linking the effects of bark beetles 

to the structure of pre-Euro-American frequent-fire forests, evidence from today’s beetle population dynamics 

suggests that homogenous, dense, even-aged stands are 

highly susceptible to beetle outbreaks (Fettig and others 

2007; Negrón 1997). However, historical observations 

suggest that high-density, even-aged stand structures 

were infrequent or rare in frequent-fire forests (Woolsey 

1911; reviewed in Covington and Moore 1994a, 1994b). 

Alternatively, spatial heterogeneity would have been 

promoted and maintained at the fine scale by bark beetle 

attacks on single or small groups of trees, or perhaps in 

high density groups or patches, which would have created growing space for regeneration or surviving trees 

(Fettig 2012; Lundquist 1995; Von Schrenck 1903). 

During droughts, it was likely that many more trees 

would have succumbed to bark beetles (Bentz and others 2010; Negrón and others 2009). Bark beetles evolved 

under the range of natural variability where there would 

have been sufficient hosts (e.g., fire-stressed, lightning 

struck, and broken top trees) to maintain endemic beetle 

populations (reviewed in Jenkins and others 2008 and 

Moeck and others 1981).

Ponderosa Pine: Species Composition: Ponderosa 

pine is the dominant seral and climax tree species in 

Southwest ponderosa pine forests. Depending on locale, ponderosa pine forests may also have a mix of 

Gambel oak, evergreen oaks, junipers, pinyon pines 

(DeVelice and others 1986), with occasional presence 

of quaking aspen, New Mexico locust, Douglas-fir, or 

southwestern white pine. Ponderosa pine is one of the 

most fire-adapted conifer species in the West, and its 

resistance to surface fire increases as trees age (Miller 

2000).

Composition of the grass-forb-shrub community 

in ponderosa pine forests is typically diverse, especially in open interspaces between trees (e.g., Fig. 8) 

(Abella and others 2011; Laughlin and others 2006; 

Moir 1966; Naumburg and DeWald 1999). Ponderosa 

pine plant associations (classified by understory plant 

assemblages, plant succession, and co-dominant tree 

species) are variable and are reflective of local biophysical site and climate conditions that both influence 

the type of disturbances and vegetation responses to 

disturbances (Table 5) (USDA Forest Service 1997). 

Southwestern ponderosa pine plant associations range 

from pure ponderosa pine to mixed tree species overstories with understories ranging from bunchgrass/

forb to shrub-dominated types, and these can be 

broadly grouped into four forest subtypes: (1) ponderosa pine-bunchgrass, (2) ponderosa pine-Gambel oak, 

(3) ponderosa pine-evergreen oak, and (4) ponderosa 

pine-shrub (Appendix 2). The most mesic sites are the 

ponderosa pine-Gambel oak and some ponderosa pinebunchgrass plant associations; the most xeric sites are 

the ponderosa pine-evergreen oak and some ponderosa 

pine-shrub plant associations. Bunchgrass plant associations generally occupy the mid-range of the moisture 

gradient for ponderosa pine forests in the Southwest.

Understory composition includes various combinations of grasses, forbs, shrubs, ferns, and cacti depending 

upon plant associations (Korb and Springer 2003; USDA 

Forest Service 1997), all of which contribute to the biodiversity found in frequent-fire forests (Laughlin and 

others 2006). The growth habit (e.g., bunchgrass, sod, or 

shrub) and spatial patterns of the understory influence the 

establishment and growth of trees (Biondi 1996; Boyden 

and others 2005; Sánchez Meador and others 2009; 

Youngblood and others 2004) and provide wildlife habitats (Dodd and others 2006; Reynolds and others 1992; 

Waltz and Covington 2003; Wightman and Germaine 

2006; USDA Forest Service 1997). Variation in species 
USDA Forest Service RMRS-GTR-310. 2013. 15 Table 5. Plant associations for the ponderosa pine series in the Southwestern United States sorted by ponderosa pine subtype, temperature-moisture gradient, dominant season of precipitation, and parent material type (USDA Forest Service 1997). Plant association (common name) Ponderosa pine subtype Temperature- moisture gradienta Climateb Dominant season of precipitationc Parent material typed Ponderosa pine plant association series Ponderosa pine/screwleaf muhly-Arizona fescue Bunchgrass Cool-wet Cold Summer Variable Ponderosa pine/screwleaf muhly-Arizona fescue/blue grama Bunchgrass Cool-wet Cold Summer Variable Ponderosa pine/screwleaf muhly-Arizona fescue/Gambel oak Bunchgrass Cool-wet Cold Summer Variable Ponderosa pine/screwleaf muhly Bunchgrass Cool-wet Cold Winter Sed./rhy./tuff Ponderosa pine/screwleaf muhly/Gambel oak Bunchgrass Cool-wet Cold Winter Variable Ponderosa pine/Arizona fescue Bunchgrass Typic Cold Winter Variable Ponderosa pine/Arizona fescue/Parry’s oatgrass Bunchgrass Typic Cold Winter Volcanic Ponderosa pine/Arizona fescue/blue grama Bunchgrass Typic Cold Winter Variable Ponderosa pine/Arizona fescue/Gambel oak Bunchgrass Typic Cold Winter Variable Ponderosa pine/blue grama Bunchgrass Warm-dry Cold Summer Variable Ponderosa pine/blue grama/gray oak Bunchgrass Warm-dry Cold Summer Variable Ponderosa pine/blue grama/Gambel oak Bunchgrass Warm-dry Cold Summer Variable Ponderosa pine/mountain muhly Bunchgrass Warm-dry Cold Summer Variable Ponderosa pine/Arizona walnut Bunchgrass Warm-dry Cold Summer Alluvium Ponderosa pine/Indian ricegrass Bunchgrass Warm-dry Cold Winter Aeolian Ponderosa pine/rockland Bunchgrass Variable Variable Variable Variable Ponderosa pine/Gambel oak Gambel oak Cool-wet Cold Winter Variable Ponderosa pine/Gambel oak/Arizona fescue Gambel oak Cool-wet Cold Winter Variable Ponderosa pine/Gambel oak/mountain muhly Gambel oak Cool-wet Cold Winter Variable Ponderosa pine/Gambel oak/New Mexico locust Gambel oak Cool-wet Cold Winter Variable Ponderosa pine/Gambel oak/longtongue muhly Gambel oak Warm-dry Cold Winter Sed./gran. Ponderosa pine/Gambel oak/two needle pinyon Gambel oak Warm-dry Cold Winter Variable Ponderosa pine/Gambel oak/blue grama Gambel oak Warm-dry Cold Winter Variable
16 USDA Forest Service RMRS-GTR-310. 2013. Table 5. Continued. Plant association (common name) Ponderosa pine subtype Temperature- moisture gradienta Climateb Dominant season of precipitationc Parent material typed Ponderosa pine plant association series Ponderosa pine/netleaf oak Evergreen oak Cool-wet Mild Summer Volcanic Ponderosa pine/silverleaf oak Evergreen oak Typic Mild Summer Volcanic Ponderosa pine/gray oak/mountain muhly Evergreen oak Warm-dry Mild Summer Variable Ponderosa pine/gray oak/longtongue muhly Evergreen oak Warm-dry Mild Summer Variable Ponderosa pine/Arizona white oak Evergreen oak Warm-dry Mild Summer Variable Ponderosa pine/Arizona white oak/blue grama Evergreen oak Warm-dry Mild Summer Variable Ponderosa pine/Emory oak Evergreen oak Warm-dry Mild Winter Alluvium Ponderosa pine/blue grama/big sagebrush Shrub Cool-wet Cold Winter Sed. Ponderosa pine/kinnikinnik Shrub Typic Cold Winter Rhy./tuff/gran. Ponderosa pine/black sagebrush Shrub Warm-dry Cold Winter Sed. Ponderosa pine/pointleaf manzanita Shrub Warm-dry Mild Summer Variable Ponderosa pine/wavyleaf oak Shrub Warm-dry Cold Summer Variable Ponderosa pine/Stansbury cliffrose Shrub Variable Cold Winter Limestone aTypic refers to modal, mid-gradient temperature-moisture types. bClimate refers to mean annual soil temperatures, with cold climates having frigid soils (mean annual soil temperatures <8  ̊C) and mild climates having mesic soils (mean annual soil temperatures >8  ̊C). cDominant season of precipitation refers to the 6-month period (winter = October-March, summer = April-September) that typically has higher average precipitation levels. Most ponderosa pine and dry mixed-conifer sites in the Southwestern United States receive bimodal precipitation, but the season listed in the table experiences higher average precipitation levels. dVariable = multiple parent materials; sed. = sedimentary; rhy. = rhyolites; gran. = granites
USDA Forest Service RMRS-GTR-310. 2013. 17

composition among plant associations within forest subtypes influences forest dynamics. For example, within 

the ponderosa pine bunchgrass subtype, tree regeneration establishes rapidly following disturbance on sites 

with screwleaf muhly plant associations (the most mesic 

associations in the bunchgrass subtype), episodically on 

Arizona fescue plant associations (the typic associations in the bunchgrass subtype), and sparsely on blue 

grama plant associations (the most xeric associations in 

the bunchgrass subtype) (USDA Forest Service 1997). 

Tree establishment often occurs differently in shrubdominated plant associations than in bunchgrass types, 

where rapid re-sprouting of shrub species (e.g., shrub 

live oak) following disturbances may inhibit pine regeneration. Other re-sprouting shrubs (e.g., New Mexico 

locust) are nitrogen-fixers and have been shown to facilitate pine seedling establishment (Fisher and Fulé 

2004; USDA Forest Service 1997). Fire may also 

facilitate establishment of tree regeneration on sites with 

non-sprouting shrub species (e.g., black or big sagebrush species) by removing competition. Together, trees 

and the grass-forb-shrub community affect below- and 

aboveground microclimates (i.e., soil moisture, nutrients, etc.) as well as ecological processes and functions 

such as biodiversity, trophic interactions, food webs, 

disturbances, and hydrology (Abella 2009; Arnold 

1950; Barth 1980; Covington and others 1997; Kalies 

and others 2012; Moir 1966; Parker and Muller 1982; 

Scholes and Archer 1997) (see Expected Outcomes of 

Framework Implementation). Environmental variables 

such as light intensity, soil pH, soil and litter depth, and 

percent litter cover are directly influenced by the presence of tree canopy cover (Evenson and others 1980). 

For example, Abella (2009) reported that understory 

species richness was greater and plant cover was up to 

eight times greater in openings than under tree canopies 

in a ponderosa pine/Gambel oak forest.

Mycorrhizal fungi are important species in ponderosa pine and play an important role in plant nutrition, 

nutrient cycling, soil structure, and food webs (Carey 

2003; Johnson and others 1997). Two Arizona studies 

reported higher densities of mycorrhizal propagules in 

areas where grass cover was greater and tree cover was 

less, such as in areas following mechanical treatments 

and burning, and that increased light and soil moisture 

in restored stands likely increased photosynthesis and 

mycorrhizal infection (Korb and others 2003; Korb and 

Springer 2003). Other studies show that abundant arbuscular mycorrhizae can increase plant diversity and 

overall community structure (Klironomos and others 

2000; van der Heijden and others 1998). Arbuscular 

mycorrhizae are particularly important in grassdominated ecosystems (Dhillion and Anderson 1993; 

Koske and Gemma 1997), but little is known of their 

status in the grass-forb-shrub community in ponderosa 

forests (Korb and Springer 2003).

Ponderosa Pine: Forest Structure: Structure in ponderosa pine forests emanates from the vertical and 

horizontal arrangement of trees and grass-forb-shrub 

species. Specifically, the vertical and horizontal architecture of a forest arises from variations in tree and 

grass-forb-shrub species and their ages, heights, crown 

spreads, densities, and spatial heterogeneity. Human activities since the late 19th Century resulted in changes to 

forest structure due to a reduction in fire frequency causing tree density and surface fuel load increases (Moore 

and others 2004; Naficy and others 2010; Parsons and 

DeBenedetti 1979; Scholl and Taylor 2010). For example, Moore and others (2004) reported a mean tree 

density increase by a factor of almost 7 (32-208 trees 

per acre) between 1909 and the 1990s. Tree encroachment into grass-forb-shrub forest openings has resulted 

in a decline in percent cover, abundance, and biodiversity of open grass-forb-shrub communities (Abella 

2009; Bogan and others 1998; Clary 1975; Covington 

and Moore 1994b; Moore and others 2006; Moore and 

Deiter 1992; Swetnam and others 1999).

Differences in reference conditions for tree densities 

have been reported for fine- versus coarse-textured soils 

(Abella and Denton 2009; Puhlick 2011). Average plotlevel reference conditions in ponderosa pine on basalt 

soils ranged between 0-220 trees per acre and 33-83 

square ft per acre of basal area while sites on coarse-textured soils (primarily limestone) ranged between 8 and 

262 trees per acre and 22 and 89 square ft per acre of 

basal area (Table 6; Fig. 10). In general, ranges reported for reference tree densities on coarse-textured soils 

were higher than those reported on fine-textured soils 

(Table 6). The minimum diameters reported in Table 6 

may also result in a source of error that can lead to small 

underestimates of historical tree densities reported in 

studies. Additional error may result from missing fully decomposed structures at time of measurement and 

reconstruction (Fulé and others 1997; Mast and others 

1999; Moore and others 2004).

To date, only six studies report tree spatial reference 

conditions in the Southwestern ponderosa pine forests. 

Based on these studies, the historical conditions in ponderosa pine exhibited as many as 67 tree groups per acre. 

Tree groups ranged between 0.003 and 0.72 acres in size 

and were composed of 2-72 trees (Table 3; Fig. 4). Tree 

groups were separated by grass-forb-shrub openings 

of variable sizes and shapes that contained scattered 
18 USDA Forest Service RMRS-GTR-310. 2013. Table 6. Historical forest structure of ponderosa pine (pine-oak shaded) forests of the Southwest, arranged by parent material and mean tree density. Location Parent material Elevation (ft) Size/age reported Reference date Trees per acre Basal area (ft2/acre) Citation Range Mean Std Err Range Mean Std Err Gus Pearson Natural Area, Arizona Basalt 7398 Age 1875 15.0 White 1985 Coconino National Forest, Arizona (avg)a Basalt 6907 Size 1910 16.0 38.1 Woolsey 1911 Gus Pearson Natural Area, Arizonab Basalt 7400 Size 1925 21.8 56.6 Pearson 1950 Gus Pearson Natural Area, Arizona Basalt 7300 No 1876 22.8 46.2 Covington and others 1997 Bar M Canyon, Arizona Basalt 7000 No 1867 21-24 23.0 65.0 Covington and Moore 1994b Flagstaff, Arizonac Basalt 7355 No 1880 1-58 23.7 4.0 Abella and Denton 2009 Gus Pearson Natural Area, Arizona Basalt 7300 Age 1876 24.0 Mast and others 1999 San Francisco Peaks, Arizona Basalt 8594 Age 1876 24.8 2.6 33.0 4.9 Cocke and others 2005 Mt. Trumbull, Arizona Basalt 7740 Age/Size 1870 25.2 3.5 38.8 6.1 Heinlein and others 1999 Coconino National Forest, Arizona (max)a Basalt 6907 Size 1910 34.5 81.2 Woolsey 1911 Mt. Logan, Arizonab Basalt 7483 Age/Size 1870 38.3 5.8 46.2 7.8 Waltz and Fulé 1998 Mt. Trumbull, Arizona Basalt 6970 Size 1870 0-220 39.2 3.9 0-143 41.6 4.1 Roccaforte and others 2010 Chimney Spring, Arizonaa Basalt 7380 Size 1920 42.8 Biondi and others 1994 Coulter Ranch, Arizonaa Basalt 7520 Size 1913 30-66 51.5 10.8 67-120 83.0 19.5 Sánchez Meador and Moore 2010 Camp Navajo, Arizona Basalt 7592 Age/Size 1883 59.9 5.8 56.2 6.1 Fulé and others 1997 Malay Gap, Arizonab Basalt 7200 Age/Size 1952 124.0 70.1 Cooper 1960 Woolsey Plots, Arizonaa Basalt 7052 Size 1874 18-51 33.1 4.6 40-79 61.5 5.6 Sánchez Meador and others 2010 Flagstaff, Arizonac Cinders 7355 No 1880 7-74 22.5 6.2 Abella and others 2011 Mt. Logan, Arizonac Cinders 7115 Age/Size 1870 34-38 29.9 6.4 60-64 60.3 9.1 Waltz and Fulé 1998 Red Cinder, Arizona Cinders 7631 Age/Size 1885 74.1 65.3 Abella 2008 Prescott National Forest, 

Arizona (avg)a Granitic 5320 Size 1910 27.7 25.5 Woolsey 1911
USDA Forest Service RMRS-GTR-310. 2013. 19 Location Parent material Elevation (ft) Size/age reported Reference date Trees per acre Basal area (ft2/acre) Citation Range Mean Std Err Range Mean Std Err Tusayan, Arizona (avg)a Limestone 7075 Size 1910 10.7 22.1 Woolsey 1911 Zion National Park, Utah Limestone 7096 Age 1881 3-25 14.0 Madany and West 1983 Flagstaff, Arizonaa Limestone 7355 No 1880 14-34 22.0 2.2 Abella and others 2011 Walnut Canyon National Monument, Arizonad Limestone 6808 Size 1876 29.1 39.2 Menzel and Covington 1997 North Kaibab, Arizona Limestone 7300 No 1881 55.9 Covington and Moore 1994a Kaibab Plateau, Arizonac Limestone 7500 No 1929 40-55 Rasmussen 1941 Grandview, Arizona Limestone 7422 Age 1887 4-247 64.6 10.4 19-281 74.1 12.6 Fulé and others 2002 Fire Point, Arizona Limestone 7671 Age 1887 16-126 61.8 61.8 28-132 89.3 9.1 Fulé and others 2002 Rainbow Plateau, Arizona Limestone 7612 Age 1879 8-228 56.7 5.7 1-99 39.6 2.6 Fulé and others 2002 Powell Plateau, Arizona Limestone 7533 Age 1879 8-262 63.6 9.4 20-337 78.0 10.9 Fulé and others 2002 Zuni, New Mexico (max)a Rhyolite 6557 Size 1910 22.6 52.8 Woolsey 1911 Cibola National Forest, New Mexicoa Rhyolite 8382 Age/Size 1890 47-61 54.2 6.9 Moore and others 2004 Carson, New Mexico (max)a Shale 6983 Size 1910 38.4 79.9 Woolsey 1911 Mogollon Plateau, Arizonac Mixed Mixed No 1890 33-76 57.3 30.7 48-79 Williams and Baker 2011, 2012 Uncompahgre Plateau, Colorado Shale 7500 Size 1875 30-90 55 20-90 55 Binkley and others 2008 aMinimum tree DBH recorded = 3.5 inches bMinimum tree DBH recorded = 6 inches cMinimum tree DBH recorded = 4 inches dMinimum tree DBH recorded = 10 inches
20 USDA Forest Service RMRS-GTR-310. 2013.

Figure 10. Theoretical diameter distributions representing 

reference conditions illustrating a superimposed basal 

area-diameter distribution (BDq) (where q = 1.2); (a) pure 

ponderosa pine present on basalt soils, (b) dry mixedconifer on limestone soils. Seedling and sapling-sized tree 

distribution (i.e., trees in the 2-inch DBH class) on both 

sites may not be fully represented.

individual trees (Fig. 8). The proportion of the stand 

or mid-scale area not covered by vertical projections 

of tree crowns (referred to as “openness”) has received 

little attention. However, several studies have reported 

the inverse of openness—canopy cover (Table 7); White 

(1985), Covington and Sackett (1986), and Covington 

and others (1997) reported 21.9, 19.0, and 17.3 percent 

canopy cover for ponderosa pine stand reference condi-

tions on the Fort Valley Experimental Forest, Arizona, 

respectively. A nearby study of a reconstructed pon-

derosa pine/Gambel oak site on the Coconino National 

Forest, Arizona, reported a range of 10.2-18.8 percent 

canopy cover (Sánchez Meador and others 2011). Fulé 

and others (2002) reported an average canopy cover of 

48.3 percent for the Rainbow Plateau, an area in the 

Grand Canyon National Park-North Rim where the 

authors suggested that contemporary conditions were 

statistically similar to historical reference conditions 

as determined by basal area comparisons. A reference 

condition study conducted in ponderosa pine near 

Cheeseman Lake, Colorado, reported a range of 12.9-

21.5 percent canopy cover (Fornwalt and others 2002). 

Table 7. 

Overall, the range of canopy cover for ponderosa pine 

Historical canopy cover and openness estimates of frequent-fire forests of the Southwest, arranged by forest type (PP: ponderosa pine, PO: pine-oak, DMC: dry mixedconifer). 

Location

Parent

material

Forest 

type Method

Reference

date

Canopy cover 

(%)

Opennessa

(%) Citation

Gus Pearson Natural Area, Arizona Basalt PP Standing age class 1875 21.9 78.1 White 1985 Gus Pearson Natural Area, Arizona Basalt PP Dendro-reconstruction 1876 19.0 81.0 Covington and others 1997Chimney Springs, Arizona Basalt PP Standing size class 1876 17.3 82.7 Covington and Sackett 1986Woolsey Plots, Arizona Basalt PP/PO Dendro-reconstruction 1874 10.2-18.8 81.2-89.8 Sánchez Meador and others 2011Rainbow Plateau, Arizona Limestone PP/PO Dendro-reconstruction 1879 48.3 51.7 Fulé and others 2002aCheesman Lake, Colorado Granitic PP/DMC FVSb-reconstruction 1900 12.9-21.5 78.5-87.1 Fornwalt and others 2002

aOpenness is the proportion of area not covered by tree crowns, estimated as the inverse of canopy cover.bForest Vegetation Simulator
USDA Forest Service RMRS-GTR-310. 2013. 21

for these studies was about 10-50 percent, giving reference conditions for openness (i.e., inverse of canopy 

cover) of 50-90 percent. If areas with strong tree aggregation (i.e., with interlocking crowns; Fig. 11) exhibit 

lower mid-scale canopy cover (10.2-21.9 percent; Table 

7), then it stands to reason that sites with less tree aggregation would have higher mid-scale canopy cover 

due to tree arrangement and reduced crown overlap 

(Christopher and Goodburn 2008).

Snags, logs, and woody debris are important structural and functional elements in frequent-fire forests (Figs. 

Figure 13. Litter, logs, and coarse woody debris contribute 

to fire spread and intensity. Old logs also provide local 

evidence of historical forest composition and structure. 

The excessive quantity of litter is a result of the lack of fire 

in this frequent-fire forest.

Figure 11. Interlocking or nearly interlocking crowns are 

components of groups of mid-aged to old trees.

12 and 13), yet little is known about volumes of coarse 

woody debris under historical fire regimes. Nonetheless, 

studies using extensive, historical stem-maps and/or locations of historical evidences (e.g., logs, stumps, and 

snags) reported a mean of 2.3 snags and 2.7 logs per acre 

(Moore and others 2004), 1-8 snags and 3-23 logs per 

acre (Sánchez Meador and others 2010), and 10 snags 

and 20 logs per acre as reference conditions for southwestern ponderosa pine (Abella 2008). These densities 

suggest that the distribution and abundance of snags and 

logs varied with site and likely coincided with the type, 

severity, and scale of historical disturbance.

Dry Mixed-Conifer

Mixed conifer forests can be divided into two subtypes: a warm-dry (dry mixed-conifer) type and a 

cool-moist (wet mixed-conifer) type. Dry mixed-conifer forests are similar to ponderosa pine forests in 

general stand structure, but Douglas fir, white fir, white 

pines, and, occasionally, blue spruce are also important 

components of these forests (Fig. 14). Wet mixedconifer forests typically lack ponderosa pine, have a 

greater abundance of Douglas-fir and white fir, and, 

on some sites, include other fire-intolerant and shadetolerant species such as blue spruce, subalpine/corkbark 

fir, and Engelmann spruce (Fig. 2). Dry mixed-conifer 

forests typically occupy the lower, warmer, and drier 

end of the elevation zone occupied by mixed-conifer 

forests. They intergrade with the cool/moist ponderosa 

pine types on warmer/drier sites at the lower end of the 

mixed-conifer zone and with wet mixed-conifer forests on cooler/moister sites at the upper end of the zone 

(Korb and others 2013; Romme and others 2009; Smith 

and others 2008).

Figure 12. Snags, logs, and woody debris are important 

components of frequent-fire forests. They provide structural 

diversity, nutrient cycling, and wildlife habitat.
22 USDA Forest Service RMRS-GTR-310. 2013.

Figure 14. Groups of dry mixed-conifer are similar to groups 

in ponderosa pine forests but often have more diverse 

assemblages of species and higher tree densities.

Dry mixed-conifer forests intergrade with or are adjacent to pure ponderosa pine forests and experience 

similar site conditions and ecological disturbances (types 

and frequencies) (Grissino-Mayer and others 1995). 

Romme and others (2009) suggested that the stand 

structure of dry mixed-conifer was maintained in part 

by recurrent fires of relatively low to moderate severity, although small areas of higher-severity crown fire 

were likely. While only a few studies report the extent of 

mixed-severity fires (Romme and others 2009), Fulé and 

others (2009) found no areas of high-severity fire larger 

than 158 acres as inferred by the current extent and presence of even-aged structures or early seral species.

Dry mixed-conifer forests occur on relatively warm 

sites at lower elevations or on southerly aspects at higher 

elevations and are characterized by historical frequent 

surface-fires synchronized by climate (approximately 

9-30 years) (Brown and others 2001; Brown and Wu 

2005; Fulé and others 2003, 2009; Grissino-Mayer and 

others 2004; Heinlein and others 2005). In contrast, wet 

mixed-conifer is typified by mixed-severity fire regime 

(Fulé and others 2003). Many studies based on firescarred trees show that dry mixed-conifer forests had 

frequent but variable fire return intervals. Some studies 

report fire return intervals that were similar to ponderosa 

pine, as frequently as every 4-14 years (Brown and others 

2001; Touchan and others 1996; reviewed in Evans and 

others 2011), whereas other dry mixed-conifer forests 

experienced fires as infrequently as every 18-32 years 

(Fulé and others 2003; Korb and others 2013; Touchan 

and others 1996; reviewed in Evans and others 2011). A 

recent study in Southwestern Colorado warm/dry mixed 

conifer forests found a mean fire return interval ranging 

from 9-30 years on three different sites at similar latitude and elevation. Korb and others (2013) also showed 

significant influence of local site factors (e.g., topography, forest structure, and species composition) on 

fire frequency and severity. Departures from historical 

compositions, structures, and spatial patterns are likely 

greater on the warmer/drier than the cooler/wetter portion of the mixed-conifer environmental gradient due to 

a more severe disruption of the characteristic fire regime 

(Fulé and others 2002).

When direct evidence of historical fire regime is lacking (i.e., fire scars not present), plant associations that 

classify seral and climax species composition relative 

to the shade and fire tolerance of tree species and biophysical site conditions may assist in making inferences 

regarding fire regimes (see Tables 2 and 8). Openings in 

dry mixed-conifer include grasses, forbs, shrubs, ferns, 

and cacti (Korb and Springer 2003), but the specific assemblage of understory plants varies greatly by plant 

association, being broadly grouped as dominated by 

bunchgrasses or by forbs/shrubs (Table 8). Bunchgrassdominated plant associations in dry mixed-conifer 

forests generally occur in warmer/drier conditions than 

sites dominated by forbs and shrub understories (e.g. 

white fir/Arizona fescue [warm/dry] compared to white 

fir/forest fleabane [cool/moist]; Table 8). For example, 

the U.S. Forest Service, Southwestern Region utilizes 

plant association classifications for mapping the spatial 

extent of dry and wet mixed-conifer forests on National 

Forest Lands.

Dry Mixed-Conifer: Species Composition: Due to a 

predominance of frequent, low-severity fire, historical 

species composition in dry mixed-conifer forests was 

dominated by fire-resistant, shade-intolerant conifers 

such as ponderosa pine, Southwestern white pine, and 

Douglas-fir (Fig. 2) (Evans and others 2011; Fulé and 

others 2003). Dry mixed-conifer forests occur in environments that are wet enough to support trees such as 

white fir and aspen. However, these species are also more 

susceptible to death from fire than fire-resistant pines 

and Douglas-fir (Fig. 2) (Evans and others 2011; Fulé 

and others 2003). Consequently, species composition 
USDA Forest Service RMRS-GTR-310. 2013. 23

in dry mixed-conifer forests was historically regulated 

by the balance between climate and disturbance agents 

such as fire. Periods of frequent fire in mixed-conifer 

gave fire-resistant species a competitive advantage, allowing them to establish dominance. During “fire-free” 

or less frequent-fire periods, ponderosa pine persisted 

due to its dominant positions in the forest canopy (Fulé 

and others 2009). As a result, shade-tolerant, less fireresistant species were historically minor components 

on drier sites, such as ridge tops and southwest-facing 

slopes, and likely more frequent on cooler and/or more 

mesic sites in frequent-fire forests, such as drainages and 

north-facing slopes (Fig. 15) (Romme and others 2009).

Compared to early 1900s Southwestern forest inventories, the current species composition of dry 

mixed-conifer forests has shifted toward more shadetolerant, less fire-resistant species (Fulé and others 

2009; Johnson 1994; Romme and others 2009). For 

example, one study in northern Arizona found that 

ponderosa pine represented an average 64 percent of 

basal area in the 1870 forest (range 54-69 percent) but 

only 36 percent in the same forest in 2003 (range 27-

46 percent) (Fulé and others 2009). A recent study in 

Southwestern Colorado found that species composition prior to the last fire record on two different sites 

(1861 and 1878) was dominated by ponderosa pine, 

but white fir and Douglas-fir increased in dominance 

since the cessation of fire (Korb and others 2013). Other 

studies similarly concluded that extended fire exclusion in dry mixed-conifer forests resulted in substantial 

increases in stand-level tree density, especially by shadetolerant white fir and Douglas-fir (Fulé and others 2004; 

Heinlein and others 2005). These increases resulted in 

forests with greater homogeneity in species composition 

across landscapes (Cocke and others 2005; White and 

Vankat 1993). Furthermore, early selective logging of 

ponderosa pine and intensive grazing exacerbated the 

compositional shift toward mesic species (Cocke and 

Figure 15. Illustration of changes in forest type by elevation and aspect (adapted from LANL 2011).
24 USDA Forest Service RMRS-GTR-310. 2013. Table 8. Plant associations for dry mixed-conifer forests in the Southwestern United States sorted by plant association series, dry mixed-conifer subtype, temperature-moisture gradient, dominant season of precipitation, and parent material type (USDA Forest Service 1997). Plant association (common name) Dry mixed-conifer subtype Temperature- moisture gradienta Climateb Dominant season of precipitationc Parent material typed Douglas-fir plant association series Douglas-fir/Arizona fescue Bunchgrass Warm-dry Cold Winter Variable Douglas-fir/Arizona fescue/bristlecone pine Bunchgrass Warm-dry Cold Winter Variable Douglas-fir/Arizona fescue/limber pine Bunchgrass Warm-dry Cold Winter Variable Douglas-fir/Arizona fescue/quaking aspen Bunchgrass Warm-dry Cold Winter Variable Douglas-fir/fringed brome Bunchgrass Cool-wet Cold Winter Variable Douglas-fir/Gambel oak/Arizona fescue Bunchgrass Warm-dry Cold Winter Variable Douglas-fir/Gambel oak/screwleaf muhly Bunchgrass Warm-dry Cold Winter Variable Douglas-fir/mountain muhly/limber pine Bunchgrass Warm-dry Cold Winter Variable Douglas-fir/mountain muhly/two needle pinyon Bunchgrass Warm-dry Cold Winter Variable Douglas-fir/screwleaf muhly Bunchgrass Warm-dry Cold Winter Variable Douglas-fir (scree) Forb-shrub Variable Variable Summer or winter Igneous Douglas-fir/creeping barberry Forb-shrub Warm-dry Cold Winter Variable Douglas-fir/Gambel oak Forb-shrub Warm-dry Cold Winter Variable Douglas-fir/Gambel oak/rockspirea Forb-shrub Warm-dry Cold Winter Variable Douglas-fir/kinnikinnik Forb-shrub Warm-dry Cold Winter Rhy./tuff Douglas-fir/mountain ninebark Forb-shrub Warm-dry Cold Winter Variable Douglas-fir/silverleaf oak/Chihuahua pine Forb-shrub Warm-dry Mild Summer Variable Douglas-fir/silverleaf oak/netleaf oak Forb-shrub Warm-dry Mild Summer Rhy./tuff Douglas-fir/silverleaf oak/ponderosa pine Forb-shrub Warm-dry Mild Summer Variable Douglas-fir/Arizona white oak Forb-shrub Warm-dry Mild Summer Variable Douglas-fir/bigtooth maple Forb-shrub Typic Cold Summer Sed. Douglas-fir/rockspirea Forb-shrub Warm-dry Cold Winter Variable Douglas-fir/wavyleak oak Forb-shrub Warm-dry Cold Summer Sed.
USDA Forest Service RMRS-GTR-310. 2013. 25 Table 8. Continued. Plant association (common name) Dry mixed-conifer subtype Temperature- moisture gradienta Climateb Dominant season of precipitationc Parent material typed White fir plant association series White fir/Arizona fescue Bunchgrass Cool-wet Cold Winter Variable White fir/Arizona fescue/Gambel oak Bunchgrass Cool-wet Cold Winter Variable White fir/Arizona fescue/muttongrass Bunchgrass Cool-wet Cold Winter Variable White fir/screwleaf muhly Bunchgrass Cool-wet Cold Summer Variable White fir/Arizona walnut Forb-shrub Cool-wet Cold Winter Alluvium White fir/creeping barberry Forb-shrub Typic Cold Winter Variable White fir/creeping barberry/common juniper Forb-shrub Typic Cold Winter Variable White fir/creeping barberry/New Mexico locust Forb-shrub Typic Cold Winter Variable White fir/Gambel oak Forb-shrub Typic Cold Winter Variable White fir/Gambel oak/Arizona fescue Forb-shrub Typic Cold Winter Variable White fir/Gambel oak/pine muhly Forb-shrub Typic Cold Winter Cong./tuff/and. White fir/Gambel oak/rockspirea Forb-shrub Typic Cold Winter Variable White fir/Gambel oak/screwleaf muhly Forb-shrub Typic Cold Winter Variable White fir/kinnikinnik Forb-shrub Typic Cold Winter Rhyolite White fir/mountain snowberry/limber pine Forb-shrub Cool-wet Cold Winter Variable White fir/mountain snowberry/ponderosa pine Forb-shrub Typic Cold Winter Variable White fir/Nevada pea Forb-shrub Typic Cold Winter Variable White fir/New Mexico locust Forb-shrub Typic Cold Winter Variable White fir/New Mexico locust/dryspike sedge Forb-shrub Typic Cold Winter Variable Blue spruce plant association series Blue spruce/Arizona fescue Bunchgrass Typic Cold Winter Rhy./basalt Blue spruce/dryspike sedge Bunchgrass Typic Cold Winter Rhy./basalt aGradient based on all mixed conifer forests (dry and wet mixed-conifer types). Typic refers to modal, mid-gradient temperature-moisture types. bClimate refers to mean annual soil temperatures, with cold climates having frigid soils (mean annual soil temperatures <8  ̊C) and mild climates having mesic soils (mean annual soil temperatures >8  ̊C). cDominant season of precipitation refers to the 6-month period (winter = October-March, summer = April-September) that typically has higher average precipitation levels. Most ponderosa pine and dry mixed-conifer sites in the Southwestern United States receive bimodal precipitation, but the season listed in the table experiences higher average precipitation levels. dVariable = multiple parent materials; sed. = sedimentary; rhy.= rhyolites; cong. = conglomerate; and. = andesite
26 USDA Forest Service RMRS-GTR-310. 2013.

Figure 16. Aerial photo of a dry mixed-conifer forest on a 

north-facing slope in the Cibola National Forest. In this 

stand, about 60-70 percent of the area is under mid- to 

old-age tree cover and 30-40 percent is in grass-forb-shrub 

interspaces.

others 2005). The combination of fire exclusion, grazing, selective logging, and favorable climatic conditions 

for young tree establishment in the early 20th Century 

has created atypical stand compositions and structures in 

many of today’s dry mixed-conifer forests (Moore and 

others 2004). In many locations, large, dominant ponderosa pine and Douglas-fir trees have been reduced to 

few or none, leaving today’s stands dominated by young 

ponderosa pine, Douglas-fir, and white fir (Fulé and others 2003).

Dry mixed-conifer plant associations are highly variable and reflective of local biophysical site conditions 

that influence the type of disturbances and vegetation 

responses to disturbances (Table 8) (USDA Forest 

Service 1997). These plant associations are classified by 

forest series representing the most shade-tolerant conifer species that can establish and grow on a given site, 

absent disturbance. However, ponderosa pine typically 

dominates the species mix in dry mixed-conifer forest 

series under the characteristic fire regime. Dry mixedconifer forest series include: (1) Douglas-fir, (2) white 

fir, and (3) those blue spruce plant associations that do 

not classify as wet mixed-conifer. These series can be 

subdivided by understory plant composition into the 

general subtypes of bunchgrass and forb-shrub. The 

most mesic dry mixed-conifer sites are the forb-shrub 

plant associations, and the most xeric are the bunchgrass 

plant associations. These subtypes differ in their relative fire frequencies; bunchgrass understories support 

more frequent surface fire, while forb-shrub understories facilitate less frequent surface fire and greater fuel 

accumulation (Anderson 1982; LANDFIRE 2007; Scott 

and Burgan 2005; USDA Forest Service 1997).

Dry Mixed-Conifer: Forest Structure: Compared to 

ponderosa pine, there is considerably less literature on 

fine-scale forest structure and spatial pattern reference 

conditions in dry mixed-conifer forests. However, there 

are some historical references to similarities between 

structure and spatial pattern of these two forest types. 

Due to its frequent fire regime, the historical fine-scale 

structure and spatial pattern of dry mixed-conifer forests were similar to ponderosa pine in having a more 

open structure (Muldavin and Tonne 2003; Swetnam 

and Baisan 1996) and a similar aggregated arrangement of trees in some stands (Binkley and others 2008; 

Sánchez Meador and others unpublished data, see Table 

3 footnote). Lang and Stewart (1910; p. 19) noted that 

“evidence indicates light ground fires over practically 

the whole forest, some of the finest stands of yellow 

pine show only slight charring of the bark and very little 

damage to poles and undergrowth.” Dutton (1882) observed that within both the ponderosa pine and mixed 

ponderosa pine/Douglas-fir forest types “the trees are 

large and noble in aspect and stand widely apart, except 

in the highest parts of the [Kaibab] plateau where the 

spruces predominate. Instead of dense thickets where 

we are shut in by impenetrable foliage, we can look far 

beyond and see the tree trunks vanishing away like an 

infinite colonnade.” These observations are consistent 

with statements that “pure ponderosa pine forests and 

warm-dry mixed conifer forests were affected primarily 

by frequent, low-severity fires that maintained an open 

stand structure with a broad range of tree sizes and ages” 

(Romme and others 2009).

Empirical evidence also indicates that historical dry 

mixed-conifer forests had lower tree densities and a 

more open structure comprised of a higher proportion of 

old and large trees, were more spatially heterogeneous 

(having groups and patches of trees), and were more 

uneven-aged compared to current conditions (Fig.16) 

(Binkley and others 2008; Fulé and others 2002a, 

2003, 2009; Heinlein and others 2005; Moore and others 2004). However, as mixed conifer forests transition 

toward cooler and wetter site conditions, less frequent 

and more severe fires result in mixtures of even- and uneven-aged forest structures. At the landscape scale, wet 

mixed-conifer forests were historically more spatially 

heterogeneous than ponderosa pine forests because of 

a mixed-severity fire regime affected by topography, 

soils, land use, and vegetation (Binkley and others 2008; 

Fulé and others 2002a, 2003, 2009; Muldavin and Tonne 
USDA Forest Service RMRS-GTR-310. 2013. 27

2003; Smith 2006a; Romme and others 2009; Touchan 

and others 1996). Variable forest structures and spatial 

patterns across landscapes resulted, in part, from variation among sites on the temperature/moisture continuum 

and their species compositions, successional status, and 

disturbance regimes. Warm, dry mixed-conifer sites 

likely experienced more frequent and less severe surface fire, resulting in more open forests with a mixture 

of small tree groups and areas with random tree spatial 

patterns. In contrast, cool, moist sites experienced mixed 

or high-severity fires at longer fire return intervals, resulting in relatively closed forests with tree cohorts 

distributed in larger patches (Fig. 14) (Fulé and others 

2003; Romme and others 2009).

Studies of reference conditions for dry mixedconifer forests reported mean tree densities and basal 

areas similar to those in ponderosa pine stands but with 

slight increases at the fine scale (Table 9; Fig. 17). For 

example, pre-Euro-American settlement dry mixed-conifer forests on limestone soils ranged between 36 and 

100 trees per acre and 34 and 124 square ft of basal area 

per acre on sites in Arizona and New Mexico, respectively (Table 9; Fig. 10). For dry mixed-conifer forests 

on the Uncompahgre Plateau in Colorado, Binkley and 

others (2008) reported reference conditions for canopy 

cover ranging from 12.0-21.5 percent in areas that exhibit fine-scale aggregation; openness was therefore 

78.5-88.0 percent in these areas. Fornwalt and others (2002) modeled reference canopy cover conditions 

of 13-22 percent (78-87 percent openness) for forests 

with fine-scale tree aggregation on the Colorado Front 

Range (Table 7). Based on reported studies, historical 

dry mixed-conifer forests were structurally similar to 

ponderosa pine with respect to tree groups with small 

meadows between them (Binkley and others 2008).

Abundance of snags, logs, and woody debris in 

dry mixed-conifer was likely similar to or slightly 

greater than that of ponderosa pine. Moore and others (2004) reported 4.9-34.9 snags per acre for dry 

mixed-conifer reference conditions as determined from 

extensive, historical stem-maps and relocation of historical evidences (e.g., logs, stumps, and snags). While the 

historical amount of these structural elements in dry mixedconifer has received little attention, contemporary studies suggest that more productive dry mixed-conifer sites 

had higher fuel loads than ponderosa pine sites (Brown 

and others 2003; Graham and others 1994).

Despite the above similarities, dry mixed-conifer 

forests occur on a diverse range of sites and have more 

diversity in species composition, structure (Fig. 17), 

spatial pattern, processes (i.e., fire regimes and other 

disturbances), and functions than ponderosa pine forests. While studies demonstrate considerable similarity 

between dry mixed-conifer and ponderosa pine disturbance processes and forest structures, we point again 

to the limited numbers and geographical locations of 

studies of historic structural conditions in dry mixedconifer and call for additional research to increase our 

understanding of historical ranges of conditions for 

these forests (see Monitoring, Adaptive Management, 

and Research Needs).

Figure 17. Distribution of 

reference conditions 

reported in Tables 6 and 

9 for basal area and trees 

per acre in ponderosa 

pine and dry mixedconifer forests. Lines 

bisecting boxes represent 

median values; lower 

and upper borders of 

boxes represent first and 

third quartile values; and 

whiskers (i.e., endpoints 

of dashed lines) 

represent maximum 

and minimum reported 

values.
28 USDA Forest Service RMRS-GTR-310. 2013. Table 9. Historical forest structure of dry mixed-conifer forests of the Southwest, arranged by parent material and average tree density. Location Parent material Elevation (ft) Size/age reported Reference date Trees per acre Basal area (ft2/acre) Citation Range Mean Std Err Range Mean Std Err San Francisco Peaks-East, Arizona Basalt 8318 Age 1892 20.9 3.4 39.6 3.9 Heinlein and others 2005 San Francisco Peaks-West, Arizona Basalt 8318 Age 1876 21.0 1.7 54.0 6.1 Heinlein and others 2005 Sitgreaves National Forest, Arizona (max)a Basalt 6300 Size 1910 31.0 66.9 Woolsey 1911 San Francisco Peaks, Arizona Basalt 9200 Age 1876 65.1 6.8 77.9 12.8 Cocke and others 2005 Blue and White Mountains, Arizonab Basalt 8950 Size 1912 68.7 84.4 Greenamyre 1913 Middle Mountain, Colorado Granitic 8520 Size 1870 51-59 57.3 4.0 43-60 47.9 4.6 Fulé and others 2009 Jemez, New Mexico (max)a Limestone 7013 Size 1910 35.6 91.2 Woolsey 1911 Kaibab Plateau, New Mexicoc Limestone 7500 Size 1909 45.3 60.7 Lang and Stewart 1910 Alamo, New Mexico (max)a Limestone 8650 Size 1910 46.5 97.9 Woolsey 1911 Gila, New Mexicoa Limestone 9055 Age/Size 1890 65.6 Moore and others 2004 Jemez, New Mexicoa Limestone 7825 Age/Size 1890 66-112 88.8 23.2 Moore and others 2004 Little Park, Arizona Limestone 8640 Age 1880 98.3 5.8 76.7 9.1 Fulé and others 2003 Swamp Ridge, Arizona Limestone 8143 Age 1879 36-151 99.4 5.2 65-235 124.1 7.8 Fulé and others 2002a Black Mesa, Arizonac Sedimentary Mixed No 1890 58.4 27.3 Williams and Baker 2011; 2012 Uncompahgre Plateau, Colorado Shale 8000 Size 1875 30-110 60 25-130 70 Binkley and others 2008 aMinimum tree DBH recorded = 3.5in. bMinimum tree DBH recorded = 4 in. cMinimum tree DBH recorded = 6 in 
USDA Forest Service RMRS-GTR-310. 2013. 29

Here, we describe our framework for restoring resiliency to frequent-fire forests in the Southwest. We 

first provide an overview of our framework, including its ecological foundation, its key elements, and the 

sources of its science base. We then discuss the spatial 

and temporal scales at which forest structures are described, and follow this with a description of the desired key compositional and structural elements of a 

restored forest at those scales for ponderosa pine and 

then dry mixed-conifer forests. Finally, we provide 

recommendations for implementing the framework in 

these forests and finish with brief before and after descriptions of the composition and structure in a ponderosa pine area in New Mexico where we implemented 

our framework.

The framework is organized around key compositional and structural elements at three spatial scales and 

is based on a synthesis of reference conditions, literature on the ecology of frequent-fire forests (Table 4) 

(see Science Review: Forest Ecology), our understanding of the ecology of these forests, decades of collective 

experience of forest managers and researchers (e.g., 

Schubert 1974), and lessons learned during applications of our framework in Southwestern frequent-fire 

forests. Our framework is informed by the ranges of 

mean forest characteristics from reference conditions 

research plots, which were typically <10 acres and 

therefore best describe variability at the fine scale 

(Tables 3, 6, 7, and 9). Means across plots are more 

representative of mid-scale conditions than means reported for individual sample plots. Therefore, we point 

out that any point estimates with a range of mean values 

may not be appropriate for a given site and we recommend using local, site-specific biophysical conditions 

and historical evidences to inform specific treatments.

Forest ecology, historical (reference) conditions, 

and the natural range of variability are frequently used 

to define restoration goals, to estimate the restoration 

potential of sites, and to evaluate the success of restoration efforts. Natural range of variability is useful for 

understanding the natural variability in composition, 

structure, processes, and functions among sites and for 

understanding the dynamic nature of ecosystems. It is 

also a useful reference for establishing limits of acceptable change for ecosystem components and processes 

(Morgan and others 1994). Our framework is not intended to re-create specific reference conditions. Rather, the 

framework identifies key elements that characterized 

frequent-fire forests before industrial logging and the 

disruption of historical disturbance regimes. These key 

compositional and structural elements are:

(1) species composition (tree and understory 

vegetation);

(2) groups of trees;

(3) scattered individual trees;

(4) open grass-forb-shrub interspaces;

(5) snags, logs, and woody debris; and

(6) variation in arrangements of these elements in 

space and time.

The key elements provide inferences about species 

compositions, structural conditions, and the cumulative effects of disturbances on processes and functions 

that provide frequent-fire forests with resistance and 

resilience to disturbance.

Citations supporting our restoration framework appear mostly in the Science Review: Forest Ecology

section but in other sections as needed. We recognize 

the limited number and geographic extent of scientific studies of reference conditions for ponderosa pine 

and especially for dry mixed-conifer, not only in the 

Southwest but throughout the western United States. 

Nonetheless, our framework is timely because of the 

growth in knowledge over the past decades regarding current and historical ecology of these forests. It 

is also timely because of increased frequencies, intensities, and extents of uncharacteristic disturbances, 

which may worsen under climate change (Littell and 

others 2009; Millar and others 2007; Miller and others 2009; Westerling and others 2006). We believe that 

moving current forest conditions toward their characteristic compositions, structures, and spatial patterns 

will increase their resistance and resilience to future 

disturbances and will result in outcomes as varied as 

fire fuels reduction, restoration of wildlife habitats, 

biodiversity, diverse food webs, and increased ability 

of these forests to provide ecosystem services.

Spatial and Temporal Scales

Ecosystems are structured hierarchically and their 

composition, structure, process, and function are 

temporally and spatially dynamic. Therefore, we 

characterize the key compositional and structural elements at three spatial scales: the fine-scale (<10 acres), 

The Restoration Framework
30 USDA Forest Service RMRS-GTR-310. 2013.

mid-scale (10-1000 acres), and landscape-scale 

(1000-10,000+ acres) (Fig. 1). These scales generally 

correspond with structural features in frequent-fire forests. For example, the fine scale is an area in which 

the species composition, age, structure, and spatial 

distribution of trees (single and grouped), and open 

grass-forb-shrub interspaces are expressed. Aggregates 

of fine-scale units comprise mid-scale units, which are 

referred to as patches (i.e., stands) and are relatively 

homogeneous in vegetation composition and structure that differ from their surroundings. The landscape 

scale is composed of aggregates of mid-scale units and 

usually has variable elevations, slopes, aspects, soil 

types, plant associations, disturbance processes, and 

land uses. Understanding and incorporating temporal 

scales (seasonal, annual, decadal, and centuries) in a 

restoration framework is required to sustain vegetation 

dynamics of a forest that result from growth, succession, senescence, and the natural and anthropogenic 

disturbances that periodically reset the dynamics.

Key Elements by Forest Type: Ponderosa Pine

Southwest ponderosa pine forests occur at elevations ranging from approximately 5000-9000 ft and 

typically intergrade with woodland types on warm/dry 

sites (typically at lower elevations) and mixed-conifer 

types on cool/moist sites (typically at higher elevations). The characteristic fire regime for ponderosa pine 

is frequent, low-severity fires (Fire Regime 1; Table 2). 

Surface fuels (fine fuels, branches, and coarse woody 

debris) and small trees facilitate this fire regime. Fires 

burn primarily on the forest floor and rarely spread 

to tree crowns and canopies. Individual trees or tree 

groups may occasionally torch during fires. Based on 

plant associations, a system for classifying plant communities on their potential climax species compositions 

(Table 5) (USDA Forest Service 1997), we differentiated four Southwestern ponderosa pine forests subtypes: 

(1) ponderosa pine-bunchgrass, (2) ponderosa pineGambel oak, (3) ponderosa pine-evergreen oak, and 

(4) ponderosa pine-shrub (Appendix 2).

Ponderosa Pine: Fine-Scale Elements (<10 acres):

Species composition: Overstories are dominated 

by ponderosa pine but may occasionally contain other 

conifer or hardwood species. Herbaceous understories 

are typically grasses and forbs at the mid-point within 

the temperature/moisture gradient over which ponderosa pine occurs. At the warm/dry end of the gradient, 

ponderosa pine forest intergrades with pinyon-juniper 

or evergreen oak woodlands (e.g., juniper, pinyon, 

Emory oak, Arizona white oak, silverleaf oak, and grey 

oak) with a shrub component (e.g., manzanita, shrub 

live oak, sumac, or mountain mahogany). In the cool/

moist portion of the gradient, Gambel oak is often a 

component of ponderosa pine forests, and grass and 

forb understories may include shrubs (e.g., ceanothus, 

and currants) (Table 5). At the cool/moist end of the 

gradient, ponderosa pine intergrades with dry mixedconifer forests where there may be a minor presence 

of quaking aspen, Douglas-fir, Southwestern white 

pine, white fir, and blue spruce. Variation in overstory 

species composition influences forest structure, disturbance types and intensities, tree mortality rates, and 

the composition and structure of the grass-forb-shrub 

community.

• Trees typically occur in irregularly shaped, small 

groups with interlocking or nearly interlocking 

crowns when in the mid- to old-aged structures 

(Fig. 11), range in size from 2-72 trees, and occupy 

between 0.003 and 0.72 acres each (Table 3; Fig. 4). 

Groups can be even- or uneven-aged. Size, shape, 

number of trees per group, and number of groups 

per area are variable (see Science Review: Forest 

Ecology). If trees are aggregated (i.e., grouped), 

more productive sites will have more trees per 

group, and if not aggregated, will support more individual trees per acre. Where groups are even-aged, 

a high level of interspersion of groups of differing 

ages constitutes the desired uneven-aged structure 

at the fine- and mid-scale. Trees within groups are 

variably spaced with some tight clumps.

• Where reference conditions show the presence of 

scattered individual trees, their ages are variable 

(young to old) and they can comprise 15-70 percent 

of total stand basal area, with the remaining stand 

basal area being trees in groups (Table 3). Variability 

in number of individual trees is associated with various factors, such as soils, plant associations, climate, 

and disturbances.

• Grass-forb-shrub interspaces surround tree groups 

and individual trees (Fig. 8) and are variably shaped 

and sized.

• Snags, top-killed, lightning- and fire-scarred trees, 

and coarse woody debris (logs and other dead 

woody material greater than 3 inches in diameter) 

are generally large in diameter and height, scattered 

throughout the mid-scale, and concentrated in past 

disturbance sites in abundances of 1-10 snags and 

3-10 tons per acre (Figs. 12 and 13). Overall, snags, 

logs, and coarse woody debris are spatially and temporally variable.
USDA Forest Service RMRS-GTR-310. 2013. 31

Ponderosa Pine: Mid-Scale Elements 

(10-1000 acres):

The mid-scale is an aggregate of fine-scale units 

(i.e., tree groups, scattered individual trees, and grassforb-shrub interspaces) and is collectively referred to 

as a patch or stand. Mid-scale patches are relatively 

homogeneous in vegetation composition and structure 

and differ from surrounding patches.

• Tree species composition is relatively homogenous 

within patches and is a function of disturbance, time 

since disturbance, tree density and size/age structure, topography, soils, local climate, site history, 

ecological legacy, and stochasticity (e.g., mass seeding and weather events).

• Average total tree densities and basal areas generally 

range from 11-124 trees per acre and 22-90 square ft 

of basal area per acre (Table 6).

• More productive sites may have more trees per area. 

Aggregates of many randomly distributed trees (areas >10 acres) function as patches.

• For sustainability and biodiversity purposes, it is desirable that patches comprise uneven-aged forests 

with an approximate balance of age classes ranging 

from young to old (Fig. 18). Infrequently, patches of 

even-aged forest structure may be present.

• All age classes of appropriate hardwood species (e.g., 

Gambel and evergreen oaks and other hardwoods) 

are present depending on a site’s plant association 

(Table 5).

• “Openness” (estimated as the inverse of canopy cover) ranges from 52-90 percent. In areas exhibiting 

fine-scale aggregation of trees, mid-scale openness 

is typically high (78-90 percent; Table 7), and on 

more productive sites, especially where tree arrangement is random, openness may be less (see 

discussion of openness in Science Review: Forest 

Ecology).

Ponderosa Pine: Landscape-Scale Elements 

(1000-10,000+ acres):

• The landscape scale is an aggregate of mid-scale 

units and includes areas with variable topography 

(i.e., elevation, slope, and aspect), soils, plant associations, disturbance types, and land use legacies. 

The landscape is a functioning ecosystem that contains all components, processes, and functions that 

result from characteristic disturbances, including 

snags, downed logs, and old trees.

• Old-growth structural features occur throughout 

the landscape as tree groups or single trees within 

uneven-aged patches (stands) or occassionally as 

small even-aged patches. Old-growth structural features include old trees, snags, downed wood (coarse 

woody debris), and horizontal and vertical structural diversity in a grass-forb-shrub matrix (Table 10; 

Fig. 9). The location of old-growth structural features may shift on the landscape over time as a result 

of succession and disturbance.

Figure 18. Uneven-aged forest 

comprised of an interspersion of 

tree groups of different ages.
32 USDA Forest Service RMRS-GTR-310. 2013.

quaking aspen, and other hardwoods, with a lesser 

presence of shade-tolerant species such as white fir and 

blue spruce depending on biophysical site conditions 

and the frequency of low-severity fire. Aspen may occur individually or in groups of variable size. While 

less is known about historical conditions in dry mixedconifer than in ponderosa pine, available information 

shows a similarity in the structure and spatial pattern 

of these two forest types.

Characteristic fire regimes for Southwestern dry 

mixed-conifer are frequent low-severity fires (Fire 

Regime 1) with infrequent mixed-severity fires (Fire 

Regime 3; Table 2) operating at all spatial scales. 

Surface fuels and small trees facilitate this fire regime. 

While fires burn primarily on the forest floor, occasionally individual trees or tree groups may torch. Crown 

fires rarely spread from tree group to tree group.

Dry Mixed-Conifer: Fine-Scale Elements 

(<10 acres)

• Species composition: Overstories are dominated by 

fire-resistant, shade-intolerant trees such as ponderosa pine, Douglas-fir, Southwestern white pine, 

and limber pine, with occasional inclusion of aspen 

and other hardwoods. Shade-tolerant conifers, such 

as white fir and blue spruce, may be present but are 

subdominant in abundance. At the warm/dry end of 

the temperature/moisture gradient occupied by dry 

Table 10. Essential structural features of old growth in frequent-fire forests. Note that whether or not a feature is essential may 

depend on scale—fine-, mid-, and landscape-scale. For example, age variability is possible at all scales, but snags and 

large dead and downed fuels may not exist in some groups and patches (adapted from Kaufmann and others 2007). 

Structural feature

Essential 

structural 

feature? Comment

Large trees No Tree size depends on species and site characteristics (moisture, soils, and 

competition). Young trees may be large, and old trees may be small.

Old trees Yes Trees develop unique structural characteristics when old (e.g., dead tops, 

flattened crowns, branching characteristics, bark color and texture).

Age variability No

An important feature in some old-growth forest types. Some forests regenerate 

episodically (even-aged) with most trees establishing in a few years to a decade, 

probably in conjunction with wet years and large seed crops and in concurrence 

with relatively long intervals between fires. Others may regenerate over decades 

(uneven-aged).

Snags and large dead 

and downed fuels Yes

Snags and large logs are essential for old growth, but forests with more frequent 

fires may have fewer logs. Densities and sizes of snags and logs vary depending 

on forest type, precipitation, and other factors. Snags, logs, and woody debris 

typically distributed unevenly in landscapes.

Between-patch 

structural variability Yes

High variability is a critical feature. Within-patch variability may be low, but 

variation among patches may be high. Proportions of patches with different 

developmental stages vary depending on forest type, climate, etc.

• Plant associations vary across environmental gradients (e.g., changes in slope, aspect, climate, and soil 

type) and reflect their historical species composition, structure, and spatial aggregations.

• Denser tree conditions may exist as patches in locations such as north-facing slopes and canyon 

bottoms.

• Natural and anthropogenic disturbances such as fire 

or tree thinning treatments are sufficient to maintain 

desired overall species composition, tree density, 

age structures, snags, coarse woody debris, and nutrient cycling.

Key Elements by Forest Type: 

Dry Mixed-Conifer

Southwest dry mixed-conifer forests generally occur at elevations ranging from 5500-9500 ft. At lower 

elevations within this range, dry mixed-conifer forests 

commonly occur on north-facing slopes or canyon bottoms and ponderosa pine forests on south-facing slopes 

and ridgetops. At the upper elevation range, dry mixedconifer forests typically occupy south and west slopes, 

with wetter forest types (e.g., wet mixed-conifer) on 

north aspects. Dry mixed-conifer forests are dominated by shade-intolerant trees such as ponderosa pine, 

Douglas-fir, Southwestern white pine, limber pine, 
USDA Forest Service RMRS-GTR-310. 2013. 33

mixed-conifer types, this forest type intergrades 

with ponderosa pine-bunchgrass and ponderosa 

pine-Gambel oak subtypes. At the cool/moist end 

of the gradient, dry mixed-conifer intergrades with 

the wet mixed-conifer type typified by a mixed-severity fire regime. Differences in overstory species 

composition influences structure (tree density, tree 

group size, number of individuals, regeneration), 

disturbance events (species-specific insect and diseases, fuel type and quantity), distribution of snags 

and coarse woody debris, and species composition 

of the grass-forb-shrub community.

• Where dry mixed-conifer forests occur at the warmer/drier end of the environmental gradient (Fig. 2), 

trees typically occur in irregularly shaped groups, 

trees within groups are variably spaced, and group 

sizes generally range from a few trees up to an acre 

(Fig. 14), similar to ponderosa pine forest types. 

Reference conditions show tree group sizes ranging 

from 0.01-0.33 acres (Table 3) (see Science Review: 

Forest Ecology). Trees within groups are of similar 

or variable ages and groups are composed of one or 

more species. Crowns of trees within the mid-aged 

to old groups are interlocking or nearly interlocking 

(Fig. 11). Size, shape, number of trees per group, and 

numbers of groups per area are variable (see Science 

Review: Forest Ecology). If aggregated, more productive sites will have more trees per group, or if not 

aggregated will support more trees per acre.

• No data are available on the proportion of stand 

basal area in individual trees verses tree groups. 

More research is needed (see Monitoring, Adaptive 

Management, and Research Needs).

• Grass-forb-shrub interspaces surround tree groups 

and individual trees (Figs. 14 and 16) and are variably shaped and sized.

• Snags, top-killed, lightning- and fire-scarred trees, 

logs, and coarse woody debris (>3 inches diameter) 

are generally large in height and diameter, scattered 

throughout, and concentrated at past disturbance 

events in abundances of 5-35 snags and 8-16 tons 

per acre (see Science Review: Forest Ecology). 

Overall, snags, logs, and coarse woody debris are 

spatially and temporally variable.

Dry Mixed-Conifer: Mid-Scale Elements 

(10-1000 acres)

• The mid-scale is an aggregate of fine-scale units (i.e., 

tree groups, scattered individual trees, and grassforb-shrub interspaces) and is collectively referred to 

as a patch or stand. At the mid-scale, patches can be 

relatively homogeneous in vegetation composition 

and structure and differ from surrounding patches. 

Vegetation is typically characterized by variation in 

the sizes and numbers of tree groups and the density 

and extent of patches of trees, each typically varying 

by elevation, soil type, aspect, and site productivity. 

Occasionally, patches may be composed of randomly arranged trees.

• In general, tree densities range from 20-100 trees 

per acre and 40-125 square ft basal area per acre 

(Table 9) (see Science Review: Forest Ecology). 

Stand density is likely to increase as site conditions 

transition toward the cooler/moister end of the environmental gradient for dry mixed-conifer forests 

and on more productive soil types.

• For sustainability and biodiversity purposes, it is 

desirable that patches have an uneven-aged forest 

structure with an approximate balance of age classes 

ranging from young to old. Infrequently, patches of 

even-aged forest structure may be present.

• Species composition may be variable within patches and is a function of disturbance, tree density, 

tree size and age structure, topography, soil, local 

climate, site history, ecological legacy, and stochasticity (e.g., weather events, mass seeding).

• It is desirable that all age classes of appropriate hardwood species (e.g., aspen, Gambel oak, and maple) 

are present depending on a site’s plant association 

(Table 8).

• “Openness” is similar to ponderosa pine at the warmer/drier end of the environmental gradient occupied 

by dry mixed-conifer forests (Table 7) but is likely 

to decrease from the warmer/drier site conditions to 

the cooler/wetter end of the environmental gradient 

due to moister conditions, higher productivity, and 

less frequent low-severity fire.

Dry Mixed-Conifer: Landscape-Scale Elements 

(1000-10,000+ acres)

• The landscape scale is an aggregate of mid-scale 

units and includes areas with variable topography, 

soils, plant associations, disturbance types, and land 

use legacies. The landscape is a functioning ecosystem that contains all its components, processes, and 

functions that result from characteristic disturbances, including snags, downed logs, and old trees.

• Old-growth structural features occur throughout the 

landscape as tree groups or single trees within uneven-aged patches (stands) or occassionally as small 

even-aged patches. Old-growth structural features
34 USDA Forest Service RMRS-GTR-310. 2013.

include old trees, dead trees (snags), downed wood 

(coarse woody debris), and horizontal and vertical structural diversity in a grass-forb-shrub matrix 

(Table 10). The location of old-growth may shift on 

the landscape over time as a result of succession and 

disturbance (tree growth and mortality).

• Plant associations vary across environmental gradients (e.g., changes in slope, aspect, climate, and soil 

type) and reflect their historical species composition, structure, and spatial aggregations.

• Denser tree conditions may exist as patches in some 

locations such as north-facing slopes and canyon 

bottoms.

• Natural and anthropogenic disturbances such as fire 

or tree thinning treatments are sufficient to maintain 

desired overall species composition, tree density, 

age structures, snags, coarse woody debris, and nutrient cycling.
USDA Forest Service RMRS-GTR-310. 2013. 35

Here, we offer recommendations for implementing 

our framework. These were developed from our understanding of the body of forest ecology and management literature (see Science Review: Forest Ecology), 

our research and management experience, and lessons 

learned during implementations of our restoration 

framework. At the end of this section we present an 

overview of a case study on the implementation of our 

framework that illustrates its success in moving current 

forest conditions toward uneven-aged forest mosaics 

comprised mostly of fire-adapted species; tree groups; 

scattered individual trees; grass-forb-shrub interspaces; snags, logs, woody debris; and the spatial arrangement of these elements.

Classification of Site Variability

Ecological classification of a site indicates its 

biological capabilities regarding species composition, structure, processes, and functions. Ecological 

classification is useful for implementing our restoration framework because classification depends on 

variability of local climate, soil, vegetation, geology 

and geomorphology, and a site’s characteristic disturbances and vegetation responses (USDA Forest 

Service 1997). The variability within and among sites 

across landscapes is the basis for describing the range 

of variation in forest conditions in our restoration 

framework. Recognition of within- and among-site 

variability is paramount for developing localized restoration objectives. Example classification systems 

include the U.S. Forest Service Terrestrial Ecosystem 

Unit Inventory (Winthers and others 2005), which 

classifies land units by soil, climate, slope, geology, geomorphology, and plant associations, and 

NatureServe’s Ecological Systems (Comer and others 

2003). The biotic and abiotic variables used in these 

classification systems describe a site’s biophysical 

characteristics.

Recommendations by Key Elements

Species Composition

• Manage for percent species composition as indicated 

by local historical evidence (live trees and snags 

and logs from trees that originated prior to 1880), 

biophysical site conditions, and other management 

objectives (e.g., favoring scarce species; preserving genetic diversity; enhancing wildlife habitat; 

resilience to climate change; or achieving other 

resource objectives, social values, and regulatory 

requirements).

Tree Groups and Individual Trees

• Use a site’s historical spatial patterns to inform restoration targets and treatments. Where information on 

reference conditions is not available, fine-scale spatial patterns may be informed by reference data in 

Table 3, 6, 7 and 9 and combined with local historical evidence (see Friederici 2004) such as grouped 

and individual old trees, large logs, and stumps, and 

a site’s biophysical conditions.

• Evaluate current conditions in relation to desired 

conditions to develop management prescriptions. 

Avoid arbitrary constraints such as diameter limits 

for tree cutting (see Abella and others 2006; Triepke 

and others 2011).

• Where spatial heterogeneity is desired, consider 

combinations of burns, intermediate and free thinning, and individual tree or small group selection 

cutting methods to create a heterogeneous structure 

of groups, single trees, and grass-forb-shrub interspaces. Once heterogeneity is established, consider 

maintaining the desired structure and spatial pattern 

with fire and/or single tree and small group selection.

• Where trees are spatially aggregated, maintain interlocking or nearly interlocking crowns in mature 

and old groups and provide for variable tree spacing 

within groups; avoid thinning old tree groups.

• Manage young tree groups to create future variable 

tree spacing and interlocking crowns. Thin young 

tree-groups to facilitate development of desired 

within-group characteristics (e.g., variable tree spacing and interlocking or nearly interlocking crowns) 

in mid- to old-aged tree groups.

• Tree groups generally are small (2-72 trees per group, 

see Science Review: Forest Ecology) (Fig. 4). Use 

historical evidence and biophysical capabilities to 

determine a site’s mean and range (minimum, maximum) of trees per group and numbers and spacing 

of tree groups per area.

• Mid-scale patches (stands) of less-aggregated or 

randomly arranged trees may be appropriate where 

Implementation Recommendations
36 USDA Forest Service RMRS-GTR-310. 2013.

historical evidences do not exhibit spatial aggregation or for achieving other resource objectives.

• Where appropriate, retain or regenerate scattered individual trees between groups.

• Use historical evidence, biophysical site conditions, 

plant associations, and current conditions (e.g., 

competition from brush on certain plant association 

types, degree of disease or insect infestation) to inform regeneration treatments.

• Where management objectives are to maintain conifer dominance and where post-treatment dominance 

by shrub understories is undesired (e.g., in some ponderosa pine-evergreen oak, ponderosa pine-shrub, 

and dry mixed conifer-forb/shrub forest types), consider smaller interspaces to avoid excessive shrub 

response and increased ladder fuel accumulation.

• Consider temporary deviations from uneven-aged 

management to even-aged cutting methods to initiate recovery on sites damaged by epidemic (severe) 

insect or disease infestation or other disturbances.

• Manage fire (wildfire or prescribed) frequency and 

severity towards achieve desired forest structures, 

spatial arrangements, regeneration patterns, and fuel 

consumption objectives.

• Design and place regeneration treatments to favor recruitment of shade-intolerant, fire-resistant species.

• Vary treatment prescriptions (cutting and/or fire) to 

create a mosaic of groups of trees, scattered single 

trees, and grass-forb-shrub interspaces.

Grass-Forb-Shrub Interspaces

• The grass-forb-shrub community is the matrix in 

which tree groups and scattered individual trees are 

arranged (Fig. 8).

• The size and arrangement of grass-forb-shrub interspaces reflect local site conditions and historical 

evidence. Where trees are grouped, interspaces may 

be as wide as 1-2 mature tree heights from nearest 

drip lines of adjacent tree groups. Binkley and others (2008) reported approximately 150 ft between 

historic groups of trees in dry mixed-conifer in 

Southwest Colorado; Pearson (1923) reported 100-

150 ft diameter openings (interspaces) between 

historic tree groups in ponderosa pine forests in 

northern Arizona.

• Sizes of grass-forb-shrub interspaces are a less useful metric for tree spacing in areas where trees are 

more randomly spaced (i.e., not aggregated). Use a 

site’s historical vegetation spatial patterns as a guide 

for restoration.

• Grass-forb-shrub interspaces are generally larger on 

dry sites. Interspaces provide rooting space to support grouped trees.

• Meadows, grasslands, and other non-forested areas 

may be present as inclusions in forested landscapes; 

these areas are not considered interspaces.

Snags, Logs, and Woody Debris

• Manage for the continuous presence of snags, logs, 

and woody debris, especially large snags in various 

stages of decay throughout the landscape (Figs. 12 

and 13). Frequent fires both recruit and consume 

these elements.

Arrangement of Key Elements in Space and Time

• Recognize the importance of spatial and temporal 

heterogeneity in forest composition and structure to 

ecological processes and functions.

• Where objectives include sustainability of wildlife 

habitat, biodiversity, and wood products, manage 

for a balance of age classes from cohort establishment (seedling/saplings) to old forest structure, and 

for grass-forb-shrub interspaces (Figs. 18 and 19).

• Where threatened, endangered, or other rare species 

are a concern, alternative composition and structures may be needed.

Management Feasibility

Our key elements focus on the compositional and 

structural features of frequent-fire forests with the goal 

of creating opportunities for the resumption of characteristic ecological processes and functions and to 

re-establish the pattern-process link. In some cases, 

fire can be used to develop the desired composition and 

structure, while in other cases, it may be more effective 

when it follows the restoration of forest composition 

and structure through mechanical treatments. Some of 

the recent wildfire events in the Southwest may present 

opportunities to initiate the post-fire “reset” of composition and structure toward desired conditions through 

broad-scale application of managed fires. In many 

Southwestern areas, restoration of frequent-fire forests will be labor intensive and costly. In other areas, 

implementation, or certain implementation tools, may 

be constrained by logistic, economic, social considerations, and special land designations (e.g., wilderness 

and protected areas). For example, degraded conditions in current forests may limit the use of fire. In such 

areas, mechanical treatments may be necessary before 

introducing fire. In areas where silvicultural treatments 
USDA Forest Service RMRS-GTR-310. 2013. 37

are constrained by operational feasibility (e.g., access, 

slope, or economics) or in wilderness areas, fire may be 

the only management tool.

• It may not be feasible for management to approximate historical composition and structure patterns 

and/or fully restore characteristic ecological processes and functions everywhere.

o Socio-economic considerations (e.g., smoke, operational capacity, and public safety) may limit 

the use of fire and prescribed cutting. Some areas 

may require combinations of treatments to create 

and maintain desired compositions, structures, 

processes, and functions.

• Existing conditions influence treatment prescription 

and choice of tools.

o Fire alone can be used where there may be less 

need for precise outcomes. Fire may result in 

more variable forest density, numbers, and sizes 

of groups, and greater distribution of age classes.

o Where sustained production of ecosystem services is desired, managing at the extremes of the 

natural range of variability may be desired. For 

example:

 Higher forest density and a balance of forest 

structural stages may be desirable to ensure 

economic sustainability (i.e., to maintain some 

level of sustained wood products) and for 

maintaining denser tree habitat conditions for 

some wildlife species.

 Lower forest density and open forest structure 

may be desirable to facilitate additional reductions in fire hazard and for maintenance of 

more open habitat for some wildlife species.

o Depending on existing conditions, achieving the 

key elements may require multiple treatments 

(e.g., prescribed cutting and fire) over long time 

periods.

• Past disturbances, such as those resulting from fire 

and insects, may provide early management opportunities (i.e., reforestation and fire management) 

to put recovering forests on trajectories toward 

development of key compositional and structural 

elements.

• Consider strategic placement of restoration treatments to capitalize on the use of wildfire, under 

appropriate conditions, across broad landscapes.

Figure 19. Illustration of the development of tree groups from seedlings to old forest at the fine 

scale.
38 USDA Forest Service RMRS-GTR-310. 2013.

One of several implementations of our restoration framework was on the Cibola National Forest 

(Bluewater demonstration project) in New Mexico in 

2010. Objectives of this project were to:

(1) create resilient forest composition and structure;

(2) move a predominately mid-aged forest toward 

uneven-aged conditions with an approximate 

balance of tree age classes;

(3) restore grass-forb-shrub interspaces;

(4) reduce fuels and fire hazard; and

(5) promote wildlife habitat, biodiversity, and wood 

products.

Our attempt to achieve the key compositional and 

structural elements in one treatment on the Bluewater 

site was limited by existing conditions; a portion of the 

mature and old trees had been harvested in prior treatments, there was little existing regeneration, and the 

site had a preponderance of mid-aged ponderosa pine 

trees. A comparison of pre- and post-treatment conditions (Figs. 20 and 21; unit 5A) attests to on-the-ground 

feasibility and utility of our framework recommendations for restoring the key elements in Southwestern 

ponderosa pine forests. Details for this project are 

available from the Forestry Staff with the USDA Forest 

Service Southwestern Region in Albuquerque, New 

Mexico.

Pre-Treatment Conditions

The Bluewater demonstration site is a 73-acre 

ponderosa pine stand (Fig. 22) that contained three 

different plant associations: ponderosa pine/mountain 

muhly, ponderosa pine/Arizona fescue, and ponderosa pine/blue grama, all of which are characterized as 

bunchgrass plant associations. The ponderosa pine site 

index is 72 for a base age of 100 years (Minor 1964). 

Soils are moderately productive and variable throughout the unit, comprised of alluvium and residuum 

from granite, and residuum derived from sandstone 

and claystone. The climate is temperate, with an average 180-day frost-free growing season from mid-May 

through mid-September and annual precipitation ranging from 17-25 inches, with greater than half occurring 

during the growing season.

Sanitation and improvement harvests occurred in 

the stand in the mid-1980s to remove diseased, dying, and poorly formed trees and, with the exception 

of piled slash burning in that treatment, the site had not 

experienced fire since the early 1900s. Prior to treatment, stand density averaged 216 trees and 125 square 

ft of basal area per acre. The stand was uneven-aged 

but had a predominance of mid-aged trees (Table 11). 

Fire behavior modeling demonstrated that 11 percent 

of the area had potential to support torching and active 

crown fire under dry conditions (i.e., completely dried 

fuel) and 15-mile/hr unobstructed wind speed.

Prescription Description

Tree marking occurred in spring 2010, tree cutting 

occurred in summer 2010, and prescribed burning is 

scheduled for fall and winter 2013. Treatment prescriptions were developed to produce the composition, 

structure, and spatial pattern identified in our framework for ponderosa pine: a predominant composition 

of ponderosa pine; re-establishment of a grass-forbshrub community in interspaces between trees; groups 

of trees with interlocking or nearly interlocking crowns 

in the older age-classes; scattered individual trees; and 

retention of snags, logs, and woody debris.

The objective was to adjust stocking and spatial arrangement of residual trees (i.e., leave trees) to create or 

move the forest toward an uneven-aged and aggregated 

stand structure with a balance of age classes. Treatment 

prescriptions allowed within-site flexibility in numbers 

of trees per group and numbers and dispersions of 

groups per area as informed by historical evidence (i.e., 

old trees, logs, stumps with establishment date <1880) 

and existing forest structure. Treatment prescriptions 

used group selection to create grass-forb-shrub interspaces and regeneration sites and free thinning in 

immature leave tree groups to develop/retain interspersion of tree groups of different age classes and group 

sizes. Tree marking crews were instructed not to thin 

mature and old groups of trees except to remove young 

trees within these groups to reduce ladder fuel. Our intent was to have about 40 percent of the forested area 

occupied by mature-to-old tree groups, both of which 

meet old-growth objectives.

Implementation of the Framework: Bluewater 

Demonstration Site
USDA Forest Service RMRS-GTR-310. 2013. 39

Objectives were to favor retention of Southwestern 

white pine, ponderosa pine, and Douglas-fir; maintain 

minor components of pinyon pine and some juniper 

species; and favor Gambel oak and Rocky Mountain 

juniper trees for wildlife habitat. Leave-tree marking 

identified tree groups and single trees for retention. 

Leave trees were selected based on tree vigor and ages, 

with the objective of retaining an approximate balance 

of age classes. Special emphasis was also placed on 

within-group structure, including the retention of subdominant, dead-topped, and lightning-struck trees for 

wildlife habitat. Because no snags were present on the 

site, trees with declining vigor were retained for snag 

recruitment. Leave tree groups were either a single 

size or a blend of variably-sized trees. Trees within 

young groups were selected to encourage the development of future interlocking crowns. Overly dense 

young tree groups were thinned to facilitate vigor and 

future crown growth. Leave tree groups were generally 0.25-0.75 acres, but groups as small as a few trees 

and as large as 2 acres were also desired. After an 

initial training period, the marking crew successfully 

created the desired pattern of groups, scattered single 

trees, and grass-forb-shrub interspaces. However, they 

tended to mark numerous small-sized groups instead 

of a range of group sizes. To establish group size variability, we revisited the treatment area and added trees 

to some groups.

Figure 20. Aerial views of unit 5a on the Bluewater demonstration site in the Cibola National Forest, New Mexico. Prior 

to treatment (top image), forest density was substantially greater and more spatially homogenous than after the 2010 

restoration treatment (bottom image) that applied the principles of our restoration framework.
40 USDA Forest Service RMRS-GTR-310. 2013.

Interspaces between tree groups were created to provide for grass-forb-shrub vegetation and areas for root 

development. Desired interspace distances between 

leave groups ranged from 20-100 ft (drip line to drip 

line), with most distances ranging from 50-70 ft. To 

remedy a deficit of seedlings and saplings, regeneration sites ranging from 0.33-1.0 acre were created.

Treatment prescriptions specified the desired abundance of snags, logs, and woody debris: averages of 

2 snags per acre with diameter at breast height (dbh) 

>12 inches and 3 downed logs per acre with dbh >12 

inches. Where existing snag density was less than 2-3 

per acre, live trees with broken tops or defects or fading green trees were retained for future snag and log 

recruitment.

The northern goshawk, tassel-eared squirrel, and 

Merriam’s turkey were given special consideration. 

The treatment prescription was consistent with the 

restoration of habitats of plants and animals in the 

northern goshawk’s food web (Reynolds and others 

Figure 21. Paired photos from the same point before (left) and after (right) treatment in the Bluewater 

demonstration site, Cibola National Forest, New Mexico, USA. Colored boxes identify the same trees, 

cut stumps, or logs in before and after photos.
USDA Forest Service RMRS-GTR-310. 2013. 41

Figure 22. Location of the Bluewater demonstration project (108.45555o W, 38.45461o N) on the Cibola National 

Forest (green outline) in New Mexico, USA.
42 USDA Forest Service RMRS-GTR-310. 2013.

1992, 2006a, 2006b), including older tree groups with 

interlocking crowns for tassel-eared squirrels (Dodd 

and others 2003, 2006; Reynolds and others 1992) and 

high interspersion of grass-forb-shrub interspaces (foraging and brood habitat), closed-canopied tree groups 

(nesting and hiding cover), and large, old trees (roosting habitat) for Merriam’s turkey (Hoffman and others 

1993; Porter 1992).

Post-Treatment Conditions

This restoration treatment succeeded in creating the 

key compositional and structural elements identified in 

our framework (Figs. 21 and 23). The treatment retained 

the uneven-aged structure in the stand, increased the 

degree of interspersion of age classes, and is on a trajectory toward an approximate balance of age classes. The 

stand still had fewer seedling-saplings and mature and 

old trees than desired due to deficits in pre-treatment 

conditions (Tables 11 and 12). Approximately 28 percent of the area in the post-treatment stand was under 

the crowns of mid- to old-aged trees and 72 percent 

was open with no tree cover (Fig. 20). Approximately 

20 percent of the post-treatment open area is designated for future tree recruitment, which will result in 

a desired 52 percent openness and 48 percent under 

tree cover. Open interspaces between tree groups were 

created for grass-forb-shrub communities and fire-safe 

sites were created for tree regeneration (Fig. 23). Posttreatment stand densities averaged 57 trees and about 

40-80 square ft of basal area per acre. Most leave trees 

were arranged in groups with interlocking crowns, but 

scattered individual trees were retained across the site. 

Tree group sizes ranged from a few trees to 0.47 acres 

based on the area covered by tree crowns estimated 

from aerial photographs.

The post-treatment composition, structure, and 

spatial pattern of the stand reduced the risk of crown 

fire from pre-treatment conditions. Post-treatment 

FlamMap simulations predicted surface fires across 99 

percent of the area and passive crown fire on 1 percent. 

Post-treatment abundance of small diameter woody 

debris was higher than intended, but prescribed burning will consume much of this material. Post-treatment 

abundance of logs and snags was lower than desired; 

however, these key structural features are expected to 

accumulate over time and with maintenance treatments. 

Mechanical treatments moved this forest stand toward 

restored conditions, but many years and multiple follow-up treatments (fire, mechanical, or combinations 

of these) will be needed to produce and maintain the 

desired key elements.

Future Management

Future plans are to broadcast burn the Bluewater site 

in the fall and winter of 2013 in order to initiate nutrient 

cycling and maintain fuels at desired levels. Subsequent 

entries will involve either tree felling, fire, or combinations of these to maintain or enhance the restoration 

treatment and manage for the desired mix and balance 

of tree age structures. Post-treatment conditions are being monitored at fixed photo-plots (Fig. 21) to determine 

whether compositional and structural objectives are being met and to inform future management.

Table 11. Estimated proportion of stand area represented by different tree ages and sizes pre- and 

post-treatment on the Bluewater demonstration site.

Tree structural classes Proportion of stand area under tree canopy

Tree agea dbhb range (inches)

Pre-treatment 

conditions

Post-treatment 

conditions

Seedling/sapling 0-4.9 5% 22%

Young 5-11.9 35% 26%

Mid-aged 12-17.9 40% 32%

Mature 18-23.9 10% 10%

Old >24 10% 10%

aTree ages are assumed to be related to sizes of dominant /co-dominant trees

bdbh = diameter at breast height
USDA Forest Service RMRS-GTR-310. 2013. 43

Figure 23. Implementation of our 

framework in a ponderosa 

pine forest on the Bluewater 

demonstration site created groups 

of trees of a variety of vegetation 

structural stages (Table 11). The 

mechanical treatment also created 

open areas that will support grassforb-shrub communities and tree 

regeneration.

Table 12. Post-treatment stocking level for the Bluewater 

demonstration site. All tree species are included in these 

estimates.

dbha range 

(inches) Trees/acre Basal area (ft2/acre)

1-4.9 3 0.4

5-8.9 17 4.6

9-12.9 23 16.2

13-16.9 5 6.1

17-20.9 5 10.2

21-24.9 2 4.3

>25 2 6.1

 Total 57 47.9

adbh = diameter at breast height
44 USDA Forest Service RMRS-GTR-310. 2013.

Our restoration framework is intended to promote 

ecosystem resilience by using fire and prescribed cutting treatments to restore the species compositions, 

structures, and spatial patterns of Southwestern frequent-fire forests. Restoring these features should allow re-establishment of characteristic processes such 

as disturbance regimes, nutrient cycling, food webs, 

hydrologic function, and ecosystem services such as 

biodiversity, old-growth, wood products, aesthetics, 

and recreation. Restoring characteristic compositions, 

structures, processes, and functions should also re-establish the evolutionary environment to which plants 

and animals native to these forests were adapted. 

Having intact, self-regulating, productive, and adaptive ecosystems is a compelling strategy for allowing 

species in the ecosystem to adapt to changing environments and facilitate their migration in the face of 

uncertain climate changes and disturbances. The following description of expected outcomes from restoring forest composition, structure, and spatial pattern 

in Southwestern frequent-fire forests is intended as an 

overview of some important outcomes from the restoration of these forests; this overview is not a comprehensive review of the literature. Improved understandings of these and other outcomes will require additional 

research (see Monitoring, Adaptive Management, and 

Research Needs).

Ecosystem Resilience to Climate Change

Restoring ecosystem resilience based on historical 

conditions has been a central concept in ecosystem 

management (Covington 2003; Folke and others 2004; 

Scheffer and others 2001). However, the relevance of 

historical conditions as reference points and targets 

for restoration has been questioned on the basis of uncertainty of future ecological conditions due to global 

climate change (Harris and others 2006; Millar and 

others 2007; Wagner and others 2000). Specific challenges for restoring and sustaining frequent-fire forests 

in the face of climate change are uncharacteristically 

rapid alterations of environments and combinations of 

disturbances and non-native biotic factors producing 

conditions never before documented in evolutionary 

time—conditions that may overwhelm characteristic ecological processes (Fulé 2008). In light of these 

challenges, we review the evolutionary history of these 

forests.

Over the past several million years, forests and 

woodlands in the Southwest, including their associated microbial, plant, and animal communities, have 

tracked favorable habitats and climates whose migrations across geographical and elevational ranges were 

driven by major climate fluctuations (Bonnicksen 

2000; Covington 2003; Delcourt and Delcourt 1988). 

Since the end of the last major glacial period (14,000 

years ago), ponderosa pine returned to the high elevation plateaus and mountains of Arizona about 10,000 

years ago and to the central Rocky Mountains only 

about 5000 years ago (Baker 1986; Covington 2003; 

Latta and Milton 1999; Millar 1998). In the last 50 million years, frequent-fire forests survived wide swings 

in environmental conditions (Moore and others 1999). 

Component species of frequent-fire forests adapted 

over evolutionary time to arid environments that have 

been characterized by variable wet and dry periods, 

including prolonged droughts, and disturbances such 

as fire, insects, and diseases. These disturbances varied in frequency, intensity, and extent (Covington and 

Moore 1994b); served as checks on the demographic 

rates of component species; and resulted in selfregulating processes of nutrient cycling, productivity, 

and regeneration (Allen and others 2002; Cooper 1960; 

Covington and others 1997; Covington and Moore 

1994b; Falk 2006).

The highest confidence in future climates is associated with projections that are consistent among 

climate change models and observed climate changes. 

Surface temperatures in the Southwest are predicted to 

increase substantially, with more warming in the summer and fall than in winter and spring; summer heat 

waves will become longer and hotter, with reductions 

of late winter/spring mountain snowpack due mostly 

to warmer temperatures (Overpeck and others 2012). 

Observed Southwest droughts have been exacerbated 

by warmer summer temperatures and are projected 

to become hotter, more severe, and more frequent, 

suggesting an increased drying in the Southwestern 

United States and that historical drought levels may 

become the norm (Overpeck and others 2012; Seager 

and others 2007). Such droughts will directly increase tree mortality and vulnerability to pathogen 

attacks (Breshears and others 2005) and enhance 

the size and severity of wildfires (Fulé 2008). Thus, 

current conditions in frequent-fire forests (i.e., high 

stand densities, accumulations of fuels on the forest 

Expected Outcomes of Framework Implementation
USDA Forest Service RMRS-GTR-310. 2013. 45

floor, and encroachment of fire-susceptible species; 

Cocke and others 2005; Cooper 1960) will increase 

the susceptibility to stand-killing fire (Fulé 2008). It 

is also likely that on some sites, fire-caused changes 

in vegetation (e.g., forest to grasslands or shrublands) 

may not at all resemble those of historical forests 

(Barton 2002; Savage and Mast 2005; Strom and Fulé 

2007). Predicted changes to warmer climates in the 

American Southwest are expected to affect forests via 

geographical shifts in suitable environments for the 

dominate forest species. Shifts are expected to be to 

higher elevations and northward (Fulé 2008; Shafer 

and others 2001).

Uncertainties associated with future climate 

changes make the development of restoration strategies increasingly complex and challenging. The 

scenario of future hotter, more severe, and more frequent droughts in the Southwest (see Karl and others 

2009) includes increased competition for water and 

increased frequency and extent of high-severity fire, 

insect, and disease disturbances. Restoring the characteristic composition, structure, and spatial pattern 

in frequent-fire forests would thereby:

• reduce tree densities and canopy continuity;

• recreate grass-forb-shrub plant communities;

• reduce competition for space, water, and nutrients 

(Covington and others 1997); and

• provide for the re-establishment of characteristic 

disturbance regimes (Covington and others 1997; 

Fulé and others 2002b; Kolb and others 1998).

Nonetheless, restoration strategies should account 

for an ecosystem’s current condition as they may 

influence an ecosystem’s development under future 

climate. Alternative successional pathways under 

future climactic variability may invalidate reference 

conditions as baselines for restoration (Clewell and 

others 2005; Pilliod and others 2006).

While climate forecasting remains imperfect, 

fire predictions for Western North America suggest 

substantial increases in occurrences, spread, and intensity (Brown and others 2004; Honig and Fulé 

2012; McKenzie and others 2004; Spracklen and 

others 2009). Thus, managing frequent-fire forests toward the historical composition, structure, and spatial 

pattern is consistent with a reduced vulnerability to 

catastrophic loss (Allen and others 2002; Falk 2006; 

Honig and Fulé 2012). While we recognize that uncertainties in how species and communities can and 

will respond to rapid climate change, we agree with 

Fulé (2008) that it makes sense to restore fire and firerelated composition, structures, and spatial patterns to 

enhance resistance to catastrophic loss. Restoring the 

composition, structure, and spatial pattern of these 

forests should increase their resistance and resilience 

to climate changes, thereby providing opportunities 

for species to migrate or develop local adaptations. In 

fact, Fulé (2008) suggested a restoration strategy that 

focuses on mesic areas at higher latitudes and elevations (i.e., upper portions of the ponderosa zone and 

the transitional dry mixed-conifer zone) where forests are more likely to survive climate change. Fulé 

(2008) recommended using reference conditions from 

low and southerly areas to guide management in higher-elevation ecosystems to provide for the migration 

of species as climate warms.

In summary, both reference conditions and natural 

range of variability are useful guides for management because Southwest frequent-fire forests were 

historically resilient to drought, insect pathogens, and 

severe wildfire. Our restoration framework should 

therefore increase the resistance (by forestalling impacts), resilience (through improved recovery after 

disturbance), and response (allowing transitions or 

migrations to new conditions) of frequent-fire forests 

to climate change (Millar and others 2007; Parker and 

others 2000; Price and Neville 2003; Spittlehouse and 

Stewart 2003).

Disturbance Regimes

Restoring the composition, structure, and spatial 

patterns of frequent-fire forests will provide for the 

re-establishment of feedback relationships between 

pattern and disturbance processes in these forests 

(Larson and Churchill 2012). Disturbances are temporary changes in environmental conditions that 

cause changes in ecosystem composition and structure. Restoring the composition and structure of 

frequent-fire forests will result in a more open forest structure and decrease the potential for epidemic 

outbreaks of insects and diseases and stand-replacing 

fire (Fitzgerald 2005; Fulé and others 2002, 2004; 

Graham and others 2004; Roccaforte and others 

2008; Strom and Fulé 2007). The restoration of grassforb-shrub interspaces and resultant separation of tree 

canopies will increase herbaceous plant development 

and provide fuels to carry frequent surface fires. In 

turn, restoration of characteristic fire regimes should 

sustain forest composition, structure, processes, and 

functions. Reduced tree densities result in reduced 

competition for resources, increased tree vigor, and 

reduced insect and disease infestations (Hessburg and 

others 1994; Kolb and others 1998).
46 USDA Forest Service RMRS-GTR-310. 2013.

The intent of our framework is not to eliminate 

insects and diseases but to return populations and 

their effects to an endemic, low background level of 

tree mortality (Miller and Keen 1960). In areas with 

higher tree densities that may have escaped repeated 

surface fire, bark beetles can be a significant agent 

for shaping forest structure and fine-scale spatial heterogeneity. Increasing the spacing between groups of 

trees can reduce the continuity of mistletoe occurrence 

across the landscape and reduce mistletoe spread between groups, creating the opportunity for groups of 

trees that are free of mistletoe (Hawksworth 1961). 

Frequent surface fires can elevate tree crown bases 

and increase tree spatial heterogeneity, both of which 

can slow mistletoe spread (Conklin and Geils 2008). 

Frequent surface fire can also reduce the severity of 

mistletoe infection by killing heavily infected trees 

(Conklin and Geils 2008; Koonce and Roth 1980).

Nutrient Cycling

A restored fire regime can also improve soil nutrient 

conditions. Intense heat from fire volatilizes nitrogen 

from the soil and surface fuels, often causing the total nitrogen concentration of forest soils to decline 

(Boerner and others 2009; DeLuca and Sala 2006). 

However, nitrogen concentrations tend to recover and 

even increase two to four years following fire as soil 

microbes decompose ash and plant litter (Boerner and 

others 2009). Fire can also cause an immediate pulse 

of inorganic nitrogen due to the combustion of organic 

matter and mortality of soil microbes (DeLuca and 

Sala 2006). Soil ammonium concentrations in ponderosa pine forests may increase as much as 20-fold 

following fire followed by dramatic increases in nitrate 

levels after the first year (Covington and Sackett 1992). 

Frequent burning can maintain elevated levels of inorganic nitrogen in forest soils by depositing charcoal, 

which binds to inorganic nitrogen and slows its leaching, and by promoting the establishment of grasses and 

herbaceous vegetation (DeLuca and Sala 2006; Hart 

and others 2005). Grasses and herbaceous vegetation 

produce litter with higher nitrogen-to-carbon ratios 

than conifer vegetation; thus, the presence of herbaceous vegetation may stimulate decomposition and 

enhance the availability of inorganic nitrogen in forest soils (Hart and others 2005). Fires also kill large 

trees, creating snags that ultimately become coarse 

woody debris that plays an important role in nutrient cycling (Brown and others 2003; Cram and others 

2007; Graham and others 1994; Harvey and others 

1988; Lowe 2006).

Biodiversity and Food Webs

Many ecosystem processes influence plant productivity, soil fertility, water availability, and other local 

and global environmental conditions. These processes 

are often controlled by the diversity and composition 

of plant, animal, and microbial species native to an 

ecosystem, and recent studies suggest that losses in 

biodiversity can alter the magnitude and stability of 

ecosystem processes (Naeem and others 1999). As a 

dominant species in frequent-fire forests, ponderosa 

pine influences the understory vegetation, soils, and 

plant and animal habitats and communities (Moore and 

others 1999). A community is a group of organisms 

that interact and share an environment. Organisms in 

a community may compete for resources, profit from 

presence of other organisms, or use other organisms as 

a food source. In the Southwest, ponderosa pine forests 

are occupied by over 250 species of vertebrates, invertebrates, soil organisms, and plant species (Allen and 

others 2002; Patton and Severson 1989), many of which 

adapted to high levels of the spatial heterogeneity and 

biodiversity that characterized historical frequent-fire 

forests. A compositionally and structurally diverse understory provides food and cover for many species of 

vertebrates and invertebrates, each contributing to ecological functioning and food webs. For example, the 

dispersion of mycorrhizal fungi, a root symbiont critical to the growth and health of trees, is likely reliant on 

small mammal transfer via feces (Johnson 1996).

Current frequent-fire forests are uncharacteristically homogeneous in composition and structure with 

reduced plant and animal habitats and lowered biodiversity (Allen and others 2002; Kalies and others 2012; 

Laughlin and others 2006; Patton and Severson 1989; 

Villa-Castillo and Wagner 2002; Waltz and Covington 

2003). Achieving our restoration framework’s key 

elements restores habitats at multiple spatial scales, 

especially through the re-establishment of species-rich 

grass-forb-shrub communities and the productivity, biodiversity, and trophic interactions they support 

(Abella 2009; Clary 1975; Kalies and others 2012; 

Oliver and others 1998; Reynolds and others 1992, 

2006a; Rieman and Clayton 1997). Dense tree conditions in current frequent-fire forests favor plants and 

animals that do better in more close-canopied forests. 

Restoration to more open forest conditions may result in the decline of these species but should increase 

abundance of more open forest species (Kalies and others 2012). Nonetheless, because our framework creates 

a variety of forest age and structural stages, including groups and patches with dense forest structures, 
USDA Forest Service RMRS-GTR-310. 2013. 47

declines of denser-forest obligates may be minimized 

(e.g., tassel-eared squirrel; Dodd and others 2003, 

2006; Kalies and others 2012), resulting in higher 

overall species diversity (Noss and others 2006).

Another concern is that the fine-scale structural 

heterogeneity of forests resulting from restoration of 

frequent-fire forests may lower the abundance and viability of large-area-dependent species (e.g., spotted 

owl; Holthausen and others 1999; Prather and others 

2008). These concerns might be ameliorated by developing specific desired conditions for breeding sites 

(e.g., on denser north slopes) and feeding sites with 

prey habitats (Prather and others 2008; Reynolds and 

others 1992). It is worth noting that breeding sites or 

entire refugia for imperiled species may receive protection from loss by encircling them with restored 

forests, lowering risk of catastrophic loss through fire 

or insects (Prather and others 2008). This indicates 

that restoration of these forests and the habitats they 

contain may provide for the historical distribution and 

abundance of plants and animals in Southwestern frequent-fire forests.

Restoration of frequent-fire forests should lead to 

more robust food webs by re-creating diverse habitats across landscapes. Species diverse and productive 

grass-forb-shrub communities in interspaces between 

tree groups support broad-based food webs that many 

invertebrates, birds, mammals, and their predators depend upon (Abella 2009; Dodd and others 2003; Ganey 

and others 1992; Kalies and others 2012; Linkhart 

and others 1998; Reynolds and others 1992, 2006a; 

Rosenstock 1998). The importance of diverse tree 

and grass-forb-shrub habitats and robust food webs 

at multiple spatial scales was demonstrated by temporal variations in the vital rates of northern goshawk 

(Reynolds and others 1992, 2005, 2006a, 2006b), a 

sensitive species that has been the subject of extensive research in the Southwest (Beier and Drennan 

1997; Beier and others 2008; Boal and Mannan 1994; 

Ingraldi 2005). In the Southwest, goshawk reproduction typically varied extensively year-to-year and was 

strongly associated with the abundance and availability of food; in years when prey numbers were low, 

goshawk population reproduction was a fraction of 

reproduction in years when prey was abundant (Beier 

and others 2008; Reynolds and others 2005; Salafsky 

and others 2005, 2007). Goshawks typically feed on a 

broad suite of prey—from robins, jays, woodpeckers, 

doves, and grouse to tree squirrels, ground squirrels, 

rabbits, and hares, each occupying different habitats 

(Reynolds and others 1992, 2006a). Annual population 

highs and lows of each prey species are not always in 

phase; a year’s population low of one or more prey is 

often compensated by higher abundances of other species (Salafsky and others 2005). Due to compensation, 

forest management strategies that provide a fine- to 

mid-scale interspersion of habitats are more likely to 

successfully maintain an entire suite of prey at higher total abundance through both good and poor prey 

years in individual goshawk home ranges (Reynolds 

and others 1992, 2006a). For the goshawk and the 

many other avian and mammalian predators (e.g., raptors, weasels, bobcats, and coyotes) in Southwestern 

frequent-fire forests, the grass-forb-shrub prey community is particularly important because it is occupied 

by a large proportion of the birds and mammals native to these forests as well as many important prey 

species, including rabbits, grouse, ground squirrels, 

mice, and voles. Prey species in this vegetation layer 

had larger body masses than most other species occurring in frequent-fire forests (Reynolds and others 

1994; Salafsky and others 2005). Furthermore, several 

of these species are known to attain high population 

abundance in response to grass-forb-shrub productivity and biodiversity (Ernest and others 2000; Gross 

and others 1974; Hernández and others 2011; Hostetler 

and others 2012; McKay 1974). Others of our framework’s key elements also create important habitats in 

Southwestern frequent-fire forests, including:

• dense groups and patches of older-aged trees with 

interlocking crowns for tree squirrels and species 

requiring denser forest conditions;

• snags for woodpecker foraging and nesting;

• snags for secondary-cavity nesters, bark gleaning 

birds, and hunting and sallying perches;

• logs for many invertebrate species (spiders, ants), 

woodpeckers, mice, rabbits, ground squirrels, 

grouse, and wild turkey; and

• woody debris for many small mammals.

Old-Growth

The key elements described in the restoration framework provide and sustain old-growth tree components 

at all spatial scales. Old-growth components provide 

a number of ecosystem services—plant and animal 

habitat, biodiversity, carbon sequestration, hydrologic 

function, high-quality wood products, aesthetics, and 

spiritual values. Old-growth structure includes old 

trees, dead trees (snags), downed wood (coarse woody 

debris), and structural diversity (Figs. 9, 12, and 13) 

(Franklin and Spies 1991; Helms 1998; Kaufmann 
48 USDA Forest Service RMRS-GTR-310. 2013.

and others 2007). The concept of old-growth includes 

multiple spatial and temporal scales, ranging from individual trees to tree groups and patches to landscapes 

and their development overtime. Definitive characteristics of old growth in the Southwest vary by forest type 

as a consequence of differences in species composition, 

tree longevities and sizes, and the characteristic types, 

frequencies, and severities of disturbances (Harmon 

and others 1986). Old-growth forests in the Southwest 

have been partitioned into three groups based on different fire regimes and resultant compositional and 

structural features (Table 10): frequent, low-severity 

fire; mixed-severity fire; and infrequent, high-severity 

fire (Table 2).

Old-growth in frequent-fire forests occurs as old 

trees in groups and as scattered individuals within 

uneven-aged forests. These forests are less dense and 

have fewer logs and woody debris than high-severity 

infrequent-fire forests. Old-growth structural features 

typically occur at the fine scale (Meyer 1934; Weaver 

1951) and are composed of small, old tree groups interspersed with similarly sized groups of younger 

trees, seedlings to mid-aged (Table 10) (Cooper 1961; 

Harrod and others 1999; Morgan and others 2002; 

Pearson 1950; Woolsey 1911). The fine-scale age diversity through growth and development sustained the 

old-growth tree components. Our framework’s key restoration elements in frequent-fire forests include all the 

essential structural features of old growth distributed 

throughout the uneven-aged forest (Kaufmann and others 2007).

In contrast to frequent-fire forests, old-growth in 

forests with a mixed-severity fire regime (Table 2) is 

characterized by adjacent forest patches burned by either low- or high-severity fire (Fulé and others 2003; 

Grissino-Mayer and others 1995). This results in landscapes with patches of old-growth intermixed with 

patches of different forest ages. Under an infrequent, 

high-severity regime (Table 2), old-growth forests are 

driven by mid- to landscape-scale, high-severity fire 

followed by vegetation recovery and succession occurring over long periods between fires. Infrequent, 

high-severity fire regimes typically have large (>100 

acres) patches of forests dominated by large, old trees 

with multiple canopy layers with similar times since 

disturbance and vegetation origin dates.

Hydrologic Function

We found no published studies that evaluated the 

long-term effects of restoration on hydrologic function and water yield in Southwestern frequent-fire 

forests (see Monitoring, Adaptive Management, 

and Research Needs). However, studies on the effects of different tree harvest prescriptions on 

hydrologic function and water yield offer insights 

into the probable effects of reducing current high 

tree densities through restoration of frequent-fire 

forests in the Southwest. Hydrologic function and 

water yield in forests are greatly influenced by the 

amount and distribution of vegetation, precipitation, snow melt, basin physiography, and soil type. 

In dense (92-140 ft2/acre) ponderosa pine forests, 

reduction of residual basal area to less than 100 ft2

per acre resulted in increased water yield, although 

large variations in yield are typical. In addition, initial mean increases in water yield of 15-45 percent 

can be realized in ponderosa pine forests on basaltderived soils when high basal area in current forests is reduced. However, increases can be expected 

to decline with time as vegetation establishes and 

develops (Baker 1986; Douglas 1983; Harr 1983; 

MacDonald and Stednick 2003; Troendle 1983). 

Removal or reduction of forest cover can increase 

soil water storage, which then becomes available for 

groundwater recharge (Baker and others 2003). Soil 

water content was reported to be higher in thinned 

and thinned-and-burned areas than in untreatedcontrol areas on basalt soils in northern Arizona. 

However, observed annual variation in water yield 

showed that the amount and timing of precipitation 

had a greater overall effect on water yield than did 

the removal of trees (Feeney and others 1998).

From the above it seems reasonable that restoring 

our framework’s key elements will benefit hydrologic 

function by reducing stand density and creating open 

grass-forb-shrub interspaces, decreasing canopy transpiration and interception losses, concentrating snow 

in interspaces, and increasing soil infiltration, water 

storage, and stream and spring flow (Baker 1986; 

Ffolliott and others 1989). While an objective of 

increasing water yield may not be a sufficient justification for forest restoration, increases in water yield 

are a significant incidental benefit (Baker 2003).

Wood Products

The re-establishment of frequent, low-severity fire is critical to the success of our restoration 

framework. However, because of limitations such 

as proximity to human developments, air quality restrictions, and workforce capacity, the use of fire will 

probably continue to be limited. Therefore, mechanical-only treatments, or perhaps combinations of fire and 
USDA Forest Service RMRS-GTR-310. 2013. 49

mechanical treatments, are likely to be the restoration 

tools of choice in much of the Southwestern landscape. 

Another limitation to restoration is the economic viability of treatments; can treatments generate revenue 

to fund restoration or must they be subsidized? In the 

initial stages of forest restoration, an abundant supply 

of lower-valued wood products could help create local products, industries, and enterprises and generate 

some revenue. Establishment of small-diameter tree 

markets, followed by shifts to markets targeting the use 

of restoration by-products (e.g. traditional and emerging products utilizing a wider range of tree sizes), will 

be essential to long-term restoration and stable local industries. Yields between 400 and 700 cubic ft per acre 

seem reasonable from a cutting cycle of 25 to 30 years 

once restoration achieves an approximate balance of 

structural stages in frequent-fire forests (Youtz and 

Vandendrieshe 2012). Such yields would help offset 

costs of achieving multiple objectives.

Aesthetics and Recreation

The public often judges the ecological health 

of a forest by appearance. Hill and Daniel (2008) 

found that acceptance of restoration activities may 

be contingent on public perceptions of aesthetics 

and knowledge of ecological benefits. People prefer landscapes with large trees, openings, and varied 

spatial distribution of vegetation that provide views 

through the site and into the landscape (Brush 1979). 

Recreational campers preferred camp-sites that were 

about 60 percent shaded (James and Cordell 1970), 

while others preferred uneven-aged forest landscapes 

over even-aged, dense stands (Brown and Daniel 

1984, 1986, 1987; Ryan 2005). Restored forests meet 

these scenery preferences, suggesting greater public 

acceptance and support.
50 USDA Forest Service RMRS-GTR-310. 2013.

Frequent-fire forests in the Southwest are complex 

and dynamic, and our understanding of how they function and respond to disturbances is limited by available 

data. Knowledge gaps and unexpected events inevitably make forest management and restoration inherently 

challenging. Key to meeting restoration challenges are 

the conduct of ecological monitoring, adaptive management, and additional research. This framework and 

its application are intended to be dynamic and adaptive 

and will evolve with accumulations of new monitoring 

and research information.

Ecological monitoring is the means by which 

managers evaluate whether the current conditions 

of an ecological system match, or are on a trajectory to match, some desired condition (Noon 2003). 

Monitoring provides feedback on the impacts of management treatments (Lindenmayer and Likens 2010; 

Palmer and Mulder 1999) and is typically divided into 

three categories: implementation, effectiveness, and 

validation (Busch and Trexler 2003). Implementation 

monitoring occurs during implementation and determines whether treatments were carried out as intended. 

Effectiveness monitoring determines the extent to 

which treatments achieved their ultimate objectives. 

Validation monitoring assesses the degree to which 

underlying assumptions about ecosystem relationships are supported (Block and others 2001; Busch 

and Trexler 2003) and functions to identify knowledge 

gaps or research needs.

Adaptive management requires feedback obtained 

from monitoring regarding the success or failure of 

treatments (Walters 1986). Adaptive management is 

the “rigorous approach for learning through deliberately designing and applying management actions 

as experiments” (Murray and Marmorek 2003). In 

contrast to simply measuring treatment effects and 

making slight adjustments to future treatments, adaptive management depends on structured, adaptive 

decision making (Williams and others 2009). It is 

most useful when managers and scientists identify 

threshold values for triggering management actions 

(Noon 2003). A clear description in a plan of how 

monitoring will be used in decision-making is essential (Noon 2003; Williams and others 2009). This 

could be achieved administratively (Mulder and others 1999; Sitko and Hurteau 2010), legally via the 

National Environmental Policy Act process (Buckley 

and others 2001), or through collaborative agreements 

(Gori and Schussman 2005). Monitoring data should 

be compiled, analyzed, and reported in a timely manner so that managers are provided information to 

improve decision-making (Mulder and others 1999) 

and to identify knowledge gaps.

Although much is known about historical forest 

composition, structure, and disturbance in frequent-fire 

forests, our knowledge of the mechanisms of spatial 

pattern formation and maintenance is limited, indicating a research need (Larson and Churchill 2012). A 

limited understanding of reference conditions on different parent material, especially in dry mixed-conifer, 

is an important data limitation for designing and implementing appropriate resource management. While the 

number of reference data sets is increasing, existing 

data have focused largely on tree density. There is a 

clear need for studies on spatial patterns and the sizes 

and shapes of grass-forb-shrub interspaces, as well as 

the mechanisms for the formation and maintenance of 

spatial patterns. Additional research needs are:

• Increased understanding of reference conditions 

and the natural range of variation across ecological gradients such as latitude and longitude, soils, 

topography, and climate in Southwest frequent-fire 

forests, especially in dry mixed-conifer.

• Increased understanding of differences between 

ponderosa pine and dry mixed-conifer forests in 

reference conditions and the historical types, frequencies, severities of disturbances, and responses 

of vegetation. Of particular need are:

(1) A greater understanding of variation of 

reference conditions (composition, structure, and 

spatial pattern) in forest subtypes and different 

plant associations.

(2) How reference conditions influenced the 

effects of fire on tree regeneration and mortality 

in forest subtypes and in the transition zones 

between subtypes.

(3) The effectiveness of restoration treatments 

at achieving desired objectives, especially on 

avoiding the conversion of these subtypes to 

alternative plant associations.

• Increased understanding of ecosystem processes and 

functions as they respond to restoration of the composition and structure of frequent-fire forests.

Monitoring, Adaptive Management, and Research Needs
USDA Forest Service RMRS-GTR-310. 2013. 51

• Increased understanding of the mechanisms of spatial pattern formation (e.g., aggregated and random 

tree distributions) within- and among-groups, including the presence, abundance, and dispersion of 

individual trees.

• An understanding of historical roles of insect and 

disease in shaping forest composition, structure, and 

spatial pattern, and the effects of restoration on the 

frequency and severity of insect and disease disturbances at all scales.

• An understanding of the effects of exotic insect, disease, plant, and animal species, and how these may 

alter forest composition, structure, processes, and 

functions.

• Increased understanding of the efficacy of fire versus 

tree cutting only and cutting combined with fire at 

achieving the desired composition, structure, processes, and functions in frequent-fire forests at all 

scales.

• Identification of management strategies for restoring composition and structure in transitional zones 

between forest types and future directions given climate change.

• Development and refinement of new and existing 

tools and metrics for measuring spatial heterogeneity at ecologically meaningful scales.

• Improved understanding of wildlife habitat and 

wildlife uses of restored composition and structure 

of frequent-fire forests.

• Improved understanding of long-term effects of restoration and maintenance treatments (mechanical, 

fire, and a combination of the two) on water yield 

and quality.

• Assessment of ecological, economic, and social 

benefits and costs (e.g., invasive species) of different restoration methodologies and implementation 

practices, such as methods for treating slash, tree 

marking approaches, spatial scales of treatment, and 

frequency of maintenance treatments.

• Exploration of management applications to implement our framework on broad landscapes in an 

economically efficient manner.
52 USDA Forest Service RMRS-GTR-310. 2013.

Our forest restoration framework provides managers and researchers a review of existing knowledge 

regarding the historical compositions and structures 

in Southwest frequent-fire forests and how these operated through feedback mechanisms that sustained 

their characteristic compositions, structures, and functions. Current forest conditions, the cumulative consequences of various human activities that altered historical conditions, are reviewed in light of historical 

conditions with a focus on how human-caused changes 

lowered the resistance and resilience of these forests to 

historical disturbance agents that themselves have become more intense and frequent. Guided by our understanding of how the composition, structure, and spatial 

pattern of historical frequent-fire forests affected their 

resistance, resilience, and responses to disturbances, 

our restoration framework identifies desired key compositional and structural elements of these forests and 

provides management recommendations for restoring 

those key elements. We believe implementation of our 

framework provides opportunities for re-establishing 

characteristic processes such as frequent, low-severity 

fire and ecological functions such as habitat, biodiversity, and food webs.

The key compositional and structural elements of 

historical frequent-fire ponderosa pine and dry mixedconifer forests in the Southwest can be envisioned over 

time as a shifting mosaic of groups of trees with interlocking crowns; single trees; open grass-forb-shrub 

interspaces; and dispersed snags, logs, woody debris 

(Larson and Churchill 2012; Long and Smith 2000; 

Reynolds and others 1992). Research shows that the 

degrees of tree aggregation; sizes and numbers of tree 

groups; numbers and dispersion of single trees; sizes 

and shapes of grass-forb-shrub interspaces; and numbers, sizes, and dispersions of snags, logs, and woody 

debris in reference conditions varied among sites by 

soil, topography, climate, disturbance regime, and 

past stochastic events. Our restoration framework recognizes this site-to-site variability and articulates the 

importance of restoring that variability by using existing evidence (e.g., old trees, snags, stumps, and logs) 

and biophysical site indicators as guides for restoring 

local variability. In our view, restoration of spatial and 

non-spatial elements of forest structure on a per-site 

basis is the most practical, science-based strategy to 

return frequent-fire forest ecosystems in the Southwest 

to resistant, resilient, and responsive conditions that 

will best position them to adapt to future disturbance 

regimes and climates (Larson and Churchill 2012; 

Millar and others 2007). We intend this framework 

and its application to be flexible and adaptive (i.e., 

learn-as-you-go) and to evolve with accumulation of 

knowledge, and for its conceptual approach to provide 

a blueprint against which management plans and practices can be evaluated.

Acknowledgments

We acknowledge the many valuable comments from 

the following reviewers: S. Baggett (USDA Rocky 

Mountain Research Station), M. Battaglia (USDA 

Rocky Mountain Research Station), M. Buford (USDA 

Forest Service) C. Fiedler (University of Montana), D. 

Huffman (Ecological Restoration Institute, Northern 

Arizona University), M. Kaib (USDI Fish and Wildlife 

Service), and three anonymous reviewers. We thank 

Forest Service colleagues C. Dawe, B. Higgins, E. Irwin, 

R. Maes, M. Manthei, and J. Stringer for participating 

in discussions during development of early desired condition drafts of this framework. We thank R. Davis, R. 

Periman, W. Robbie, J. Triepke, and M. Turner of the 

Southwestern Regional Office for helpful comments 

on an early draft, and comments and discussions of the 

many scientists, managers, and interested publics who 

attended our multiple restoration workshops. All of the 

above helped improve our understanding of the ecology 

and management challenges in frequent-fire forests and 

all contributed to the improvement of the manuscript. 

We thank Rocky Mountain Research Station, the Forest 

Service Southwestern Region, and the Ecological 

Restoration Institute and Northern Arizona University 

for allowing the authors’ time and travel during the development of the framework.

Literature Cited

Abella, S.R. 2008. Managing oak in southwestern ponderosa pine forests: The status of our knowledge. 

General Technical Report RMRS-GTR-218. U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station, Fort Collins, CO. 27 pp.

Abella, S.R. 2009. Tree canopy types constrain plant 

distributions in ponderosa pine-Gambel oak forests, 

northern Arizona. Research Note RMRS-RN-39. 

Summary
USDA Forest Service RMRS-GTR-310. 2013. 53

U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station, Fort Collins, 

CO. 7 pp.

Abella, S.R.; Denton, C.W. 2009. Spatial variation in 

reference conditions: Historical tree density and 

pattern on a Pinus ponderosa landscape. Canadian 

Journal of Forestry 39:2391-2403.

 Abella, S.R.; Fulé, P.Z.; Covington, W.W. 2006. 

Diameter caps for thinning southwestern ponderosa pine forests: Viewpoints, effects, and tradeoffs. 

Journal of Forestry 104(8):407-414.

Abella, S.R.; Denton, C.W.; Brewer, D.G.; Robbie, 

W.A.; Steinke, R.W.; Covington, W.W. 2011. Using 

a terrestrial ecosystem survey to estimate the historical density of ponderosa pine trees. Research Note 

RMRS-RN-45. U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station, 

Fort Collins, CO. 9 pp.

Agee, J.K. 1993. Fire Ecology of Pacific Northwest 

Forests. Washington, DC: Island Press.

Agee, J.K. 2003. Historical range of variability in eastern Cascade forests, Washington, USA. Landscape 

Ecology 18:725-740.

Allen, C.D. 2007. Interactions across spatial scales 

among forest dieback, fire, and erosion in Northern 

New Mexico landscapes. Ecosystems 10:797-808.

Allen, S.R.; Savage, M.; Falk, D.A.; [and others]. 2002. 

Ecological restoration of southwestern ponderosa 

pine ecosystems: A broad perspective. Ecological 

Applications 12(5):1418-1433.

American Heritage Dictionary. 2011. American 

Heritage Dictionary of the English Language, Fifth 

Edition. New York, NY: Houghton Mifflin Harcourt 

Publishing Co.

Anderson, H.E. 1982. Aids to determining fuel models for estimating fire behavior. General Technical 

Report INT-GTR-122. U.S. Department of 

Agriculture, Forest Service, Intermountain Forest 

and Range Experiment Station, Ogden, UT. 22 pp.

Andrews, S.R.; Daniels, J.P. 1960. A survey of dwarf 

mistletoes in Arizona and New Mexico. Station 

Paper RM-SP-49. U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Forest and Range 

Experiment Station, Fort Collins, CO. 17 pp.

Arno, S.F.; Scott, J.H.; Hartwell, M.G. 1995. Age-class 

structure of old growth ponderosa pine/Douglas-fir 

stands and its relationship to fire history. Research 

Paper INT-RP-481. U.S. Department of Agriculture, 

Forest Service, Intermountain Research Station, 

Ogden, UT. 25 pp.

Arnold, J.F. 1950. Changes in ponderosa pine bunchgrass ranges in northern Arizona resulting from 

pine regeneration and grazing. Journal of Forestry 

48:118-126.

Aronson, J.; Milton, S.J.; Blignaut, J.N. (eds.). 2007. 

Restoring Natural Capital: Science, Business, and 

Practice. Washington, DC: Island Press.

Baker, M.B., Jr. 1986. Effects of ponderosa pine treatments on water yield in Arizona. Water Resources 

Research 22(1):67-73.

Baker, M.B., Jr. 2003. Hydrology. Pp. 161-174 in 

P. Friederici (ed.). Ecological Restoration of 

Southwestern Ponderosa Pine Forests. Washington, 

DC: Island Press.

Barth, R.C. 1980. Influence of pinyon pine trees on 

soil chemical and physical properties. Soil Science 

Society of America Journal 44:112-114.

Barton, A.M. 2002. Intense wildfire in southeastern 

Arizona: Transformation of a Madrean oak-pine 

forest to an oak woodland. Forest Ecology and 

Management 165:205-212.

Beale, E.F. 1858. Wagon Road from Fort Defiance to 

the Colorado River. Senate Executive Documents 

No. 124. 35th Congress, 1st Session, Washington, 

DC. 87 pp.

Beier, P.; Drennan, J.E. 1997. Forest structure and prey 

abundance in foraging areas of northern goshawks. 

Ecological Applications 7(2):564-571.

Beier, P.; Rogan, E.C.; Ingraldi, M.F.; Rosenstock, 

S.S. 2008. Does forest structure affect reproduction 

of northern goshawks in ponderosa pine forests? 

Journal of Applied Ecology 45(1):342-350.

Benayas, J.M.R.; Newton, A.C.; Diaz, A.; Bullock, J.M. 

2009. Enhancement of biodiversity and ecosystem 

services by ecological restoration: A meta-analysis. 

Science 325:1121-1124.

Bentz, B.; Logan, J.; MacMahon, J.; Allen, C. 2009. 

Bark Beetle Outbreaks in Western North America: 

Causes and Consequences. Chicago, IL: University 

of Utah Press. 42 p.

Bentz, B.J.; Régnière, J.; Fettig, C.J.; Hansen, E.M.; 

Hayes, J.L.; Hicke, J.A.; Kelsey, R.G.; Negrón, J.F.; 

Seybold, S.J. 2010. Climate change and bark beetles 

of the western United States and Canada: Direct and 

indirect effects. BioScience 60:602-613.
54 USDA Forest Service RMRS-GTR-310. 2013.

Binkley, D.; Romme, B.; Cheng, T. 2008. Historical 

forest structure on the Uncompahgre Plateau: 

Informing restoration prescriptions for mountainside stewardship. Colorado State University, 

Colorado Forest Restoration Institute, Fort Collins, 

CO. 27 pp.

Biondi, F. 1996. Decadal-scale dynamics at the Gus 

Pearson Natural Area: Evidence for inverse (a)symmetric competition? Canadian Journal of Forest 

Research 26:1397-1406.

Biondi, F.; Myers, D.E.; Avery, C.C. 1994. Geostatically 

modeling stem size and increment in an oldgrowth forest. Canadian Journal of Forest Research 

24:1354-1368.

Block, W.A.; Franklin, A.B.; Ward, J.P.; Ganey, J.L.; 

White, G.C. 2001. Design and implementation of 

monitoring studies to evaluate the success of ecological restoration on wildlife. Restoration Ecology 

9:293-303.

Boal, C.W.; Mannan, R.W. 1994. Northern goshawk diets in ponderosa pine forests on the Kaibab Plateau. 

Studies in Avian Biology 16:97-102.

Boerner, R.E.J.; Huang, J.; Hart, S.C. 2009. Impacts 

of fire and fire surrogate treatments on ecosystem 

nitrogen storage patterns: Similarities and differences between forests of eastern and western North 

America. Canadian Journal of Forest Research 

38:3056-3070.

Bogan, M.A.; Allen, C.D.; Muldavin, E.H.; Platania, 

S.P.; Stuart, J.N.; Farley, G.H.; Melhop, P.; Belnap, 

J. 1998. Regional trends of biological resources: 

Southwest. Pp. 543-592 in M.J. Mac, P.A. Opler, 

C.E. Puckett Haecker, P.D. Doran (eds.). Status 

and trends of the nation’s biological resources. U.S. 

Department of the Interior, U.S. Geological Survey, 

Reston, VA. 528 pp.

Bolte, A.; Ammer, C.; Löf, M.; Madsen, P.; Nabuurs, 

G.; Schall, P.; Spathelf, P.; Rock, J. 2009. Adaptive 

forest management in central Europe: climate 

change impacts, strategies and integrative concept. Scandinavian Journal of Forest Research 

24:473-482.

Bonnicksen, T.M. 2000. America’s Ancient Forests: 

From the Ice Age to the Age of Discovery. New 

York, New York: Wiley.

Bosakowski, T.; Smith, D.G. 1992. Comparative diets 

of sympatric nesting raptors in the eastern deciduous forest biome. Canadian Journal of Zoology 

70:984-992.

Boyden, S.; Binkley, D.; Shepperd, W. 2005. Spatial 

and temporal patterns in structure, regeneration, 

and mortality of an old-growth ponderosa pine forest in the Colorado Front Range. Forest Ecology & 

Management 219(1):43-55.

Bradshaw, A.D. 1984. Ecological principles and land 

reclamation practice. Landscape Planning 11:35-48.

Breece, C.R.; Kolb, T.R.; Dikson, B.G.; McMillin, J.D.; 

Clancy, K.M. 2008. Prescribed fire effects on bark 

beetle activity and tree mortality in southwestern 

ponderosa pine. Forest Ecology and Management 

255:119-128.

Breshears, D.D.; Cobb, N.S.; Rich, P.M.; Price, K.P.; 

Allen, C.D.; Balice, R.G. 2005. Regional vegetation 

die-off in response to global-change-type drought. 

Proceedings of the National Academy of Science 

102:15144-15148.

Brewer, D. 2008. Managing coarse woody debris in 

fire-adapted Southwestern forests. Working papers 

in Southwestern ponderosa pine forest restoration. 

Ecological Restoration Institute, Flagstaff, AZ. 

12 pp.

Brown, J.K.; Reinhardt, E.D.; Kramer, K.A. 2003. 

Coarse woody debris: Managing benefits and fire 

hazard in the recovering forest. General Technical 

Report RMRS-GTR-105. U.S. Department of 

Agriculture, Forest Service, Rocky Mountain 

Research Station, Ogden, UT. 16 pp.

Brown, P.M.; Kaye, M.W.; Huckaby, L.; Baisan, C. 

2001. Fire history along environmental gradients in 

the Sacramento Mountains, New Mexico: Influences 

of local patterns and regional processes. Ecoscience 

8:115-126.

Brown, P.M.; Wu, R. 2005. Climate and disturbance forcing of episodic tree recruitment in a 

Southwestern ponderosa pine landscape. Ecology 

86(11):3030-3038.

Brown, T.C.; Daniel, T.C. 1984. Modeling forest scenic beauty: Concepts and application to ponderosa 

pine. Research Paper RM-RP-256. U.S. Department 

of Agriculture, Forest Service, Rocky Mountain 

Forest and Range Experiment Station, Fort Collins, 

CO. 35 pp.

Brown, T.C.; Daniel, T.C. 1986. Predicting scenic beauty of timber stands. Forest Science 32(2):471-487.

Brown, T.C.; Daniel, T.C. 1987. Context effects in perceived environmental quality assessment: Science 

selection and landscape quality ratings. Journal of 

Environmental Psychology 7:233-250.
USDA Forest Service RMRS-GTR-310. 2013. 55

Brown, T.J.; Hall, B.L.; Westerling, A.L. 2004. The 

impact of twenty-first century climate change on 

wildland fire danger in the western United States: 

An application perspective. Climate Change 62: 

365-388.

Brush, R.O. 1979. The attractiveness of woodlands: 

Perceptions of forest landowners in Massachusetts. 

Forest Science 25:495-506.

Buckley, B.; Cloyd, C.; Mummey, D.; Obermeyer, E.; 

Sleeper, J.; Thomas, P. 2001. Five Rivers Landscape 

Management Project, Waldport Ranger District, 

Siuslaw National Forest: Final Environmental Impact 

Statement. U.S. Department of Agriculture, Forest 

Service, Siuslaw National Forest, Corvallis, OR.

Burkett, V.R.; Wilcox, D.A.; Stottlemeyer, R.; Barrow, 

W.; Fagre, D.; Baron, J.; Price, J.; Nielson, J.L.; 

Allen, C.D.; Peterson, D.L.; Ruggerone, G.; Doyle, 

T. 2005. Nonlinear dynamics in ecosystem response 

to climate change: case studies and policy implications. Ecological Complexity 2:357-394.

Busch, D.E.; Trexler, J.C. 2003. The importance of monitoring in regional ecosystem initiatives. Pp. 1-23 

in D.E. Busch and J.C. Trexler (eds.). Monitoring 

Ecosystems: Interdisciplinary Approaches for 

Evaluating Ecoregional Objectives. Washington, 

DC: Island Press.

Carey, A.B. 2003. Biocomplexity and restoration 

of biodiversity in temperate coniferous forests: 

Inducing spatial heterogeneity with variable-density 

thinning. Forestry 76:127-136.

Carey, A.B.; Horton, S.P.; Biswell, B.L. 1992. Northern 

spotted owls: Influence of prey base and landscape 

character. Ecological Monographs 62:223-250.

Carey, A.B.; Kershner, J.; Biswell, B.; de Toledo, L.D. 

1999. Ecological scale and forest development: 

Squirrels, dietary fungi, and vascular plants in managed and unmanaged forests. Wildlife Monographs 

142:1-71.

Castello, J.D.; Leopold, D.J.; Smallidge, P.J. 1995. 

Pathogens, patterns, and processes in forest ecosystems. BioScience 45:16-24.

Choi, Y.D.; Temperton, V.M.; Allen, E.B.; Grootjans, 

A.P.; Halassy, M.; Hobbs, R.J.; Naeth, M.A.; Torok, 

K. 2008. Ecological restoration for future sustainability in a changing environment. Ecoscience 

15:53-64.

Christopher, T.A.; Goodburn, J.M. 2008. The effects of 

spatial patterns on the accuracy of forest vegetation 

simulator (FVS) estimates of forest canopy cover. 

Western Journal of Applied Forestry 23(1):5-11.

Clary, W.P. 1975. Range management and its ecological 

basis in the ponderosa pine type of Arizona: The status of our knowledge. Research Paper RM-RP-157. 

U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Forest and Range Experiment 

Station, Fort Collins, CO. 35 pp.

Clewell, A.; Rieger, J.; Munro, J. 2005. Guidelines 

for developing and managing ecological restoration projects. Society for Ecological Restoration 

International, Tucson, AZ.

Cocke, A.E.; Fulé, P.Z.; Crouse, J.E. 2005. Forest 

change on a steep mountain gradient after extended 

fire exclusion: San Francisco Peaks, Arizona, USA. 

Journal of Applied Ecology 42:814-823.

Colgan, W., III.; Carey, A.B.; Trappe, J.M.; Molina, 

R.; Thysell, D. 1999. Diversity and productivity of 

hypogeous fungal sporocarps in a variously thinned 

Douglas-fir forest. Canadian Journal of Forest 

Research 29:1259-1268.

Comer, P.; Faber-Langendoen, D.; Evans, R.; [and others]. 2003. Ecological systems of the United States: 

A working classification of U.S. terrestrial systems. 

NatureServe, Arlington, VA.

Conklin, D.A.; Fairweather, M.L. 2010. Dwarf mistletoes and their management in the Southwest. 

R3-FH-10-01. U.S. Department of Agriculture, 

Forest Service, Southwestern Region, Albuquerque, 

NM. 23 pp.

Conklin, D.A.; Geils. B.W. 2008. Survival and sanitation of dwarf-mistletoe-infected ponderosa pine 

following prescribed underburning. Western Journal 

of Applied Forestry 23:216-222

Cooper, C.F. 1960. Changes in vegetation, structure, 

and growth of southwestern pine forests since white 

settlement. Ecological Monographs 30:129-164.

Cooper, C.F. 1961. Patterns in ponderosa pine forests. 

Ecology 42:493-499.

Cortina, J.; Maestre, F.T.; Vallejo, R.; Baeza, M.J.; 

Valdecantos, A.; Pérez-Devesa, M. 2006. Ecosystem 

structure, function, and restoration success: Are 

they related? Journal for Nature Conservation 

14:152-160.

Covington, W.W. 1993. Conservation biology and 

restoration ecology: Implications for ponderosa 

pine/bunchgrass ecological systems. Pp. 92-97 in 

W.W. Covington and L.F. DeBano (tech. coords.). 

Sustainable ecological systems: Implementing an 

ecological approach to land management. General 

Technical Report RM-GTR-247. U.S. Department 

of Agriculture, Forest Service, Rocky Mountain 
56 USDA Forest Service RMRS-GTR-310. 2013.

Forest and Range Experiment Station, Fort Collins, 

CO. 363 pp.

Covington, W.W. 2000. Helping western forests heal: 

The prognosis is poor for U.S. forest ecosystems. 

Nature 408:135-136.

Covington, W.W. 2003. The evolutionary and historical context. Pp. 26-47 in P. Friederici (ed.). 

Ecological Restoration of Southwestern Ponderosa 

Pine Forests. Washington, DC: Island Press.

Covington, W.W.; Fulé, P.Z.; Moore, M.M.; Hart, S.C.; 

Kolb, T.E.; Mast, N.J.; Sackett, S.S.; Wagner, M.R. 

1997. Restoring ecological health in ponderosa 

pine forests of the Southwest. Journal of Forestry 

95:23-29.

Covington, W.W.; Moore, M.M. 1994a. Postsettlement 

changes in natural fire regimes and forest 

structure: Ecological restoration of old-growth ponderosa pine forests. Journal of Sustainable Forestry 

2(1/2):153-181.

Covington, W.W.; Moore, M.M. 1994b. Southwestern 

ponderosa forest structure: Changes since 

Euro-American settlement. Journal of Forestry 

92(1):39-47.

Covington,W.W.; Sackett, S.S. 1986. Effect of periodic 

burning on soil nitrogen concentrations in ponderosa pine. Soil Science Society of America Journal 

50:452-457.

Covington, W.W.; Sackett, S.S. 1992. Soil mineral 

nitrogen changes following prescribed burning in 

ponderosa pine. Forest Ecology and Management 

54:175-191.

Cram, D.S.; Baker, T.T.; Fernald, A.G.; Madrid, A.; 

Rummer, B. 2007. Mechanical thinning impacts 

on runoff, infiltration, and sediment yield following fuel reduction treatments in a southwestern dry 

mixed-conifer forest. Journal of Soil and Water 

Conservation 65(2):359-366.

Crist, M.R.; DeLuca, T.H.; Wilmer, B.; Aplet, G.H. 

2009. Restoration of low-elevation dry forests of 

the northern Rocky Mountains: A holistic approach. 

The Wilderness Society, Washington, DC. 39 pp.

Cruz, M.G.; Alexander, M.E.; Wakimoto, R.H. 2003. 

Assessing canopy fuel stratum characteristics 

in crown fire prone fuel types of western North 

America. International Journal of Wildland Fire 

12:39-50.

Delcourt, H.R.; Delcourt, P.A. 1988. Quaternary landscape ecology: Relevant scales in space and time. 

Landscape Ecology 2:23-44.

DeLuca, T.H.; Sala, A. 2006. Frequent fire alters nitrogen transformations in ponderosa pine stands of the 

inland Northwest. Ecology 87(10):2511-2522.

DeVelice, R.L.; Ludwig, J.A.; Moir, W.H.; Ronco, 

F., Jr. 1986. A classification of forest habitat types 

of northern New Mexico and southern Colorado. 

General Technical Report RM-GTR-131. U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Forest and Range Experiment Station, 

Fort Collins, CO. 59 pp.

Dhillon, S.S.; Anderson, R.C. 1993. Below-ground 

dynamics and associated mycorrhizal fungi of little bluestem grass (Schizachyrium scoparium) on 

burned and unburned sand prairies. New Phytologist 

123:77-91. 

Dieterich, J.H. 1980. Chimney Spring fire history. 

Research Paper RM-RP-220. U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Forest 

and Range Experiment Station, Fort Collins, CO. 

8 pp.

Dodd, N.L.; Schweinsburg, R.E.; Boe, S. 2006. 

Landscape-scale forest habitat relationships to 

tassel-eared squirrel populations: Implications 

for ponderosa pine forest restoration. Restoration 

Ecology 14:537-547.

Dodd, N.L.; States, J.S.; Rosenstock, S.S. 2003. 

Tassel-eared squirrel population, habitat condition, 

and dietary relationships in north-central Arizona. 

Journal of Wildlife Management 67:622-633.

Douglass, J.E. 1983. The potential for water yield augmentation from forest management in the eastern 

United States. Water Resource Bulletin 19:351-358.

Drummond, D.B. 1982. Timber loss estimates for 

the coniferous forests in the United States due to 

dwarf mistletoes. Report 83-2. U.S. Department 

of Agriculture, Forest Service, Forest Pest 

Management, Methods Application Group, Fort 

Collins, CO. 24 pp.

Dutton, C. 1882. Tertiary history of the Grand Cañon 

District. Monograph 2. U.S. Department of the 

Interior, U.S. Geological Survey, Washington, DC. 

264 pp.

Edmonds, R.L.; Agee, J.K.; Gara, R.I. 2000. Root diseases. Pp. 275-308 in R.L. Edmonds, J.K. Agee, and 

R.I. Gara (eds.). Forest Health and Protection. New 

York, NY: McGraw-Hill Companies, Inc.

Egan, D.; Howell, E.A. (eds.). 2001. The Historical 

Ecology Handbook: A Restorationist’s Guide to 

Reference Ecosystems. Washington, DC: Island 

Press.
USDA Forest Service RMRS-GTR-310. 2013. 57

Ehle, D.S.; Baker, W.L. 2003. Disturbance and 

stand dynamics in ponderosa pine forests in 

Rocky Mountain National Park, USA. Ecological 

Monographs 73:543-566.

Ernest, S.K.; Brown, J.H.; Prementer, R.R. 2000. 

Rodents, plants, and precipitation: Spatial and temporal dynamics of consumers and resources. Oikos 

88:470-482.

Evans, A.M.; Everett, R.G.; Stephens, S.L.; Youtz, 

J.A. 2011. Comprehensive fuels treatment practices guide for mixed conifer forests: California, 

Central and Southern Rockies, and the Southwest. 

U.S. Department of Agriculture, Forest Service, 

Southwestern Region and The Forest Guild, 

Albuquerque, NM. 106 pp.

Evenson, W.E.; Brotherson, J.D.; Wilcox, R.B. 1980. 

Relationship between environmental and vegetational parameters for understory and open-area 

communities. Great Basin Naturalist 40:167-174.

Falk, D.A. 2006. Process-centered restoration in a fireadapted ponderosa pine forest. Journal for Nature 

Conservation 14: 140-151.

Feeney, S.R.; Kolb, T.E.; Wagner, M.R.; Covington, 

W.W. 1998. Influence of thinning and burning restoration treatments on presettlement ponderosa pines 

at the Gus Pearson Natural Area. Canadian Journal 

of Forest Research 28:1295-1306.

Ferry, G.W.; Clark, R.G.; Montgomery, R.E.; Mutch, 

R.W.; Leenhouts, W.P.; Zimmerman, G.T. 1995. 

Altered fire regimes within fire-adapted ecosystems. Pp. 222-224 in E.T. LaRoe, G.S. Farris, C.E. 

Puckett, P.D. Doran, and M.J. Mac (eds.). Our living 

resource: A report to the nation on the distribution, abundance and health of U.S. plants, animals 

and ecosystems. United States Department of the 

Interior, National Biological Service, Washington, 

DC. 530 pp.

Fettig, C.J. 2012. Forest health and bark beetles. Pp. 

13-22 in M. North (ed.). Managing Sierra Nevada 

Forests. General Technical Report PSW-GTR-237. 

U.S. Department of Agriculture, Forest Service, 

Pacific Southwest Research Station, Albany, CA. 

184 p.

Fettig, C.J.; Klepzig, K.D.; Billings, R.F.; Munson, 

A.S.; Nebeker, T.E.; Negrón, J.F.; Nowak, J.T. 

2007. The effectiveness of vegetation management practices for prevention and control of bark 

beetle outbreaks in coniferous forests of the western and southern United States. Forest Ecology and 

Management 238:24-53.

Ffolliott, P.F.; Gottfried, G.J.; Baker, M.B. 1989. Water 

yield from forest snowpack management: Research 

findings in Arizona and New Mexico. Water 

Resources Research 25(9):1999-2007.

Fiedler, C.E.; Arno, S.F.; Harrington, M.G. 1996. 

Flexible silvicultural and prescribed burning approaches for improving health of ponderosa pine 

forests. Pp. 69-74 in W.W. Covington and P.K. 

Wagner (eds.). Conference on Adaptive Ecosystem 

Restoration and Management: Restoration of 

Cordilleran Conifer Landscapes of North America. 

General Technical Report RM-GTR-278. U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Forest and Range Experiment Station, 

Fort Collins, CO. 91 pp.

Finkral, A.J.; Evans, A.M. 2008. Effects of thinning treatment on carbon stocks in a northern 

Arizona ponderosa pine forest. Forest Ecology and 

Management 255:2743-2750.

Fisher, M.A.; Fulé, P.Z. 2004. Changes in forest vegetation and arbuscular mycorrhizae along a steep 

elevation gradient in Arizona. Forest Ecology and 

Management 200:293-311.

Fitzgerald, S.A. 2005. Fire ecology of ponderosa pine 

and the rebuilding of fire-resilient ponderosa pine 

ecosystems. Pp. 197-225 in Ritchie, M.W.; Maguire, 

D.A.; Youngblood, A. (tech. coords.). Proceedings 

of the Symposium on Ponderosa Pine: Issues, 

Trends, and Management. General Technical Report 

PSW-GTR-198. U.S. Department of Agriculture, 

Forest Service, Pacific Southwest Research Station, 

Albany, CA. 281 pp.

Folke, C.; Carpenter, S.; Walker, B.; Scheffer, M.; 

Elmqvist, T.; Gunderson, L.; Holling, C.S. 2004. 

Regime shifts, resilience, and biodiversity in ecosystem management. Annual Review of Ecology 

and Systematics 35:557-581.

Forman, R.T.T. 1995. Some general principles of landscape and regional ecology. Landscape Ecology 

10(3):133-142.

Fornwalt, P.J.; Kaufmann, M.R.; Stoker, J.M.; 

Huckaby, L.S. 2002. Using the Forest Vegetation 

Simulator to reconstruct historical stand conditions in the Colorado Front Range. Pp. 108-115 in 

N.L. Crookston, N.L. and R.N. Havis (comps.). 

Second Forest Vegetation Simulator Conference. 

Proceedings RMRS-P-25. U.S. Department of 

Agriculture, Forest Service, Rocky Mountain 

Research Station, Fort Collins, CO. 208 pp.
58 USDA Forest Service RMRS-GTR-310. 2013.

Franklin, J.F.; Spies, T.A. 1991. Ecological definitions of old-growth Douglas-fir forests. Pp. 61-69 

in L.F. Ruggiero, K.B. Aubry, A.B. Carey, and M.H. 

Huff (tech. coords.). Wildlife and Vegetation of 

Unmanaged Douglas-fir Forests. General Technical 

Report PNW-GTR-285. U.S. Department of 

Agriculture, Forest Service, Pacific Northwest 

Research Station, Portland, OR. 533 pp.

Franklin, J.F.; Spies, T.A.; Van Pelt, R.; Carey, A.B.; 

Thornburgh, D.A.; Berb, D.R.; Lindenmayer, D.B.; 

Harmon, M.E.; Keeton, W.S.; Shaw, D.C.; Bible, K.; 

Chen, J. 2012. Disturbances and structural development of natural forest ecosystems with silvicultural 

implications, using Douglas-fir forests as an example. Forest Ecology and Management 155:399-423.

Friederici, P. 2004. Establishing reference conditions 

for southwestern ponderosa pine forests. Working 

Papers in Southwestern Ponderosa Pine Forest 

Restoration No. 7. Northern Arizona University, 

Ecological Restoration Institute, Flagstaff, AZ. 

12 pp.

Fulé, P.Z. 2008. Does it make sense to restore wildland fire in changing climate? Restoration Ecology 

16:526-531.

Fulé, P.Z.; Covington, W.W.; Moore, M.M. 1997. 

Determining reference conditions for ecosystem 

management of southwestern ponderosa pine forests. Ecological Applications 7:895-908.

Fulé, P.Z.; Covington, W.W.; Moore, M.M.; Heinlein, 

T.A.; Waltz, A.E.M. 2002a. Natural variability 

in forests of the Grand Canyon, USA. Journal of 

Biogeography 29:31-47.

Fulé, P.Z.; Covington, W.W.; Smith, H.B.; Springer, 

J.D.; Heinlein, T.A.; Huisinga, K.D.; Moore, M.M. 

2002b. Comparing ecological restoration alternatives: Grand Canyon, Arizona. Forest Ecology and 

Management 170(1-3):19-41.

Fulé, P.Z; Crouse, J.E.; Cocke, A.E.; Moore, M.M.; 

Covington, W.W. 2004. Changes in canopy fuels and 

potential fire behavior 1880-2040: Grand Canyon, 

Arizona. Ecological Modelling 175(3):231-248.

Fulé, P.Z.; Crouse, J.E.; Heinlein, T.A.; Moore, M.M.; 

Covington, W.W.; Vankamp, G. 2003. Mixedseverity fire regime in high-elevation forest of the 

Grand Canyon, Arizona, USA. Landscape Ecology 

18:465-486.

Fulé, P.Z.; Korb, J.E.; Wu, R. 2009. Changes in forest structure of a mixed-conifer forest, southwestern 

Colorado, USA. Forest Ecology and Management 

258(7):1200-1210.

Fulé, P.Z.; Laughlin, D.C. 2007.Wildland fire effects on 

forest structure over an altitudinal gradient, Grand 

Canyon National Park, USA. Journal of Applied 

Ecology 44:136-146.

Fulé, P.Z.; McHugh, C.; Heinlein, T.A.; Covington, 

W.W. 2001. Potential fire behavior is reduced following forest restoration treatments. Pp. 28-35 

in R.K. Vance, C.B. Edminster, W.W. Covington, 

and J.A. Blake (tech. coords.). Ponderosa Pine 

Ecosystem Restoration and Conservation: Steps 

Toward Stewardship. Proceedings RMRS-P-22. 

U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station, Ogden, UT. 

188 pp.

Ganey, J.L.; Duncan, R.B.; Block, W.M. 1992. Use 

of oak and associated woodlands by Mexican spotted owls in Arizona. Pp. 125-128 in P.F. Ffolliott, 

G.J. Gottfried, D.A. Bennett, C.V.M. Hernandez, 

A. Ortega-Rubio, and H.R. Hamre (tech. coords.). 

Ecology and Management of Oak and Associated 

Woodlands: Perspectives in the Southwestern 

United States and Northern Mexico. General 

Technical Report RM-GTR-218. U.S. Department 

of Agriculture, Forest Service, Rocky Mountain 

Forest and Range Experiment Station, Fort Collins, 

CO. 224 pp.

Goheen, D.; Hansen, E. 1993. Effects of pathogens 

and bark beetles on forests. Pp. 175-196 in T.D. 

Schowalter and G.M. Filip (eds.). Beetle-Pathogen 

Interactions in Conifer Forests. London, England: 

Academic Press.

Gori, D.; Schussman, H. 2005. State of the Las Cienegas 

National Conservation Area. Part I: Condition and 

trend of the desert grassland and watershed. The 

Nature Conservancy of Arizona, Tucson, AZ. 63 pp.

Graham, R.T.; Harvey, A.E.; Martin, M.F.; Jain, 

T.B.; Tonn, J.R.; Page-Dumroese, D.S. 1994. 

Managing coarse woody debris in forests of the 

Rocky Mountains. Research Paper INT-RP-477. 

U.S. Department of Agriculture, Forest Service, 

Intermountain Research Station, Ogden, UT. 12 pp.

Graham, R.T.; McCaffrey, S.; Jain, T.B. (tech. eds.). 

2004. Science basis for changing forest structure to modify wildfire behavior and severity. 

General Technical Report RMRS-GTR-120. U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station, Fort Collins, CO. 43 pp.

Greenamyre, H.H. 1913. The composite type on 

the Apache National Forest. Bulletin 125. U.S. 

Department of Agriculture, Forest Service, 

Government Printing Office, Washington, DC.
USDA Forest Service RMRS-GTR-310. 2013. 59

Grissino-Mayer, H.D.; Baisan, C.H.; Swetnam, T.W. 

1995. Fire history in the Pinaleño Mountains of 

southeastern Arizona: Effects of human-related 

disturbances. Pp. 399-407 in L.F. Debano, G.J. 

Gottfried, R.H. Hamre, C.B. Edminster, P.F. 

Ffolliott, and A. Ortega-Rubio (eds.). Biodiversity 

and Management of the Madrean Archipelago: The 

Sky Islands of Southwestern United States and 

Northwestern Mexico. General Technical Report 

GTR-RM-264. U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Forest and Range 

Experiment Station, Fort Collins, CO. 669 pp.

Grissino-Mayer, H.D.; Romme, W.H.; Floyd, M.L.; 

Hanna, D. 2004. Climatic and human influences on 

fire regimes in the southern San Juan Mountains, 

Colorado, USA. Ecology 85: 1708-1724.

Gross, J.E.; Stoddart, L.C.; Wagner, F.H. 1974. 

Demographic analysis of a northern Utah jackrabbit 

population. Wildlife Monographs 40:3-68.

Hanks, J.P.; Fitzhugh, E.L.; Hanks, S.R. 1983. A habitat 

type classification system for ponderosa pine forests 

of northern Arizona. Technical Report RM-GTR-97. 

U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Forest and Range Experiment 

Station, Fort Collins, CO. 22 pp.

Harmon, M.E.; Franklin, J.F.; Swanson, E.J.; [and 

others]. 1986. Ecology of coarse wood debris in 

temperate ecosystems. Advances in Ecological 

Research 15:133-302.

Harr, R.D. 1983. Potential for augmenting water yield 

through forest practices in western Washington 

and western Oregon. Water Resource Bulletin 

19:383-393.

Harrington, M.G. 1982. Stand, fuel, and potential 

fire behavior characteristics in an irregular southeastern Arizona ponderosa pine stand. Research 

Note RM-RN-418. U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Forest and Range 

Experiment Station. 6 pp.

Harrington, M.G.; Hawksworth, F.G. 1990. 

Interactions of fire and dwarf mistletoe on mortality of southwestern ponderosa pine. Pp. 234-240 

in J.S. Krammes (tech. coord.). Effects of Fire 

Management of Southwestern Natural Resources: 

Proceedings. Technical Report RM-GTR-191. U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Forest and Range Experiment Station, 

Fort Collins, CO. 293 pp.

Harris, J.A.; Hobbs, R.J.; Higgs, E.; Aronson, J. 2006. 

Ecological restoration and global climate change. 

Restoration Ecology 14:170-176.

Harrod, R.J.; McRae, B.H.; Hartl, W.E. 1999. Historical 

stand reconstruction in ponderosa pine forests to 

guide silvicultural prescriptions. Forest Ecology and 

Management 114:433-446.

Hart, S.C.; DeLuca, T.H.; Newman, G.S.; MacKenzie, 

D.M.; Boyle, S.I. 2005. Post-fire vegetative dynamics as drivers of microbial community structure 

and function in forest soils. Forest Ecology and 

Management 220:166-184.

Harvey, A.E.; Jurgensen, M.F.; Graham, R.T. 1988. 

The role of residues in soils of ponderosa pine forests. Pp. 141-147 in D.M. Baumgartner and J.E. 

Lotans (eds.). Ponderosa Pine: The species and 

Its Management. Proceedings. Washington State 

University Cooperative Extension, Pullman, WA. 

284 pp.

Hawksworth, F.G. 1961. Dwarf mistletoe of ponderosa 

pine in the Southwest. Technical Bulletin No. 1246. 

U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Forest and Range Experiment 

Station, Fort Collins, CO. 112 pp.

Hawksworth, F.G.; Wiens, D. 1996. Dwarf mistletoes: 

Biology, pathology, and systematics. Agricultural 

Handbook 709. U.S. Department of Agriculture, 

Forest Service, Washington, DC. 410 pp.

Heinlein, T.A.; Fulé, P.Z.; Waltz, A.E.M.; Springer, 

J.D. 1999. Changes in ponderosa pine forest of the 

Mt. Trumbull Wilderness. Unpublished report on 

file at Northern Arizona University, Flagstaff, AZ. 

36 pp.

Heinlein, T.A.; Moore, M.M.; Fulé, P.Z.; Covington, 

W.W. 2005. Fire history and stand structure of two 

ponderosa pine-mixed-conifer sites: San Francisco 

Peaks, Arizona, USA. International Journal of 

Wildland Fire 14:307-320.

Helms, J.A. 1998. The Dictionary of Forestry. Bethesda, 

MD: The Society of American Foresters.

Hernández, L.; Laundré, J.W.; Grajales, K.M.; Portales, 

G.L.; López-Portillo, J.; Gonzáles-Romero, A.; 

Garcia, A.; Martínez, J.M. 2011. Plant productivity, 

predation, and the abundance of black-tailed jackrabbits in the Chihuahuan Desert of Mexico. Journal 

of Arid Environments 75:1043-1049.

Hessburg, P.F.; Agee, J.K.; Franklin, J.F. 2005. Dry 

mixed conifer forests of the Inland Northwest, 

USA: Contrasting the landscape ecology of the 
60 USDA Forest Service RMRS-GTR-310. 2013.

pre-settlement and modern eras. Forest Ecology and 

Management 211(1):117-139.

Hessburg, P.F.; Mitchell, R.G.; Filip, G.M. 1994. 

Historical and current roles of insects and pathogens 

in eastern Oregon and Washington forested landscapes. General Technical Report PNW-GTR-327. 

U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Research Station, Portland, OR. 

72 pp.

Hessburg, P.F.; Smith, B.G.; Salter, R.B. 1999. Detecting 

change in forest spatial patterns from reference conditions. Ecological Applications 9(4):1232-1252.

Hiers, J.K.; O’Brien, J.J.; Mitchell, R.J.; Grego, J.M.; 

Loudermilk, E.L. 2009. The wildland fuel cell concept: An approach to characterize fine-scale variation 

in fuels and fire in frequently burned longleaf pine 

forests. International Journal of Wildland Fire 

18(3):315-325.

Hill, D.; Daniel, T. 2008. Foundations for an ecological 

aesthetic: Can information alter landscape preferences? Society and Natural Resources 21:34-49.

Hoffman, C.M.; Mathiasen, R.; Sieg, C.H. 2007. Dwarf 

mistletoe effects on fuel loadings in ponderosa pine 

forests in northern Arizona. Canadian Journal of 

Forest Research 37(3):662-670.

Hoffman, R.W.; Shaw, H. G.; Rumble, M.A.; Wakeling, 

B.F.; Mollohan, C.M.; Schemnitz, S.D.; EngleWilson, R.; Hengel, D.A. 1993. Management 

guidelines for Merriam’s wild turkeys. Report No. 

18. Colorado Division of Wildlife, Denver, CO, and 

USDA Forest Service, Rocky Mountain Forest and 

Range Experiment Station, Fort Collins, CO. 24 pp.

Holthausen, R.S.; Raphael, M.G.; Sampson, F.B.; Elbert, 

D.; Hiebert, R.; Manasco, K. 1999. Population viability in ecosystem management. Pp. 135-156 in N. 

Johnson, R.C. Szaro, W.T. Sexton (eds.). Ecological 

Stewardship: A Common Reference for Ecosystem 

Management. Oxford, United Kingdoms: Elsevier 

Science.

Honig, K.A.; Fulé, P.Z. 2012. Simulating effects 

of climate change and ecological restoration on 

fire behavior in a south-western USA ponderosa 

pine forest. International Journal of Wildland Fire 

21(6):731-742.

Hostetler, J.A.; Kneip, E.; Van Vuren, D.H.; Oli, M.K. 

2012. Stochastic population dynamics of a montane 

ground-dwelling squirrel. PLoS ONE 7:1-10.

Hunter, M.E.; Shepperd, W.D.; Lentile, L.B.; Lundquist, 

J.E.; Andreu, M.G.; Butler, J.L.; Smith, F.W. 2007. A 

comprehensive guide to fuels treatment practices for 

ponderosa pine in the Black Hills, Colorado Front 

Range, and Southwest. General Technical Report 

RMRS-GTR-198. U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station, 

Fort Collins, CO. 99 pp.

Ingraldi, M.F. 2005. A skewed sex ratio in northern 

goshawks: Is it a sign of a stressed population? 

Journal of Raptor Research 39:247-253.

James, G.A.; Cordell, H.K. 1970. Importance of 

shading to visitors selecting a campsite at Indian 

Boundary Campground in Tennessee. Research 

Note SE-RN-130. U.S. Department of Agriculture, 

Forest Service, Southeast Forest Experiment Station, 

Asheville, NC. 5 pp.

Jenkins, M.J.; Hebertson, E.; Page, W.; Jorgenson, 

C.A. 2008. Bark beetles, fuels, fires and implications for forest management in the Intermountain 

West. Forest Ecology and Management 254:16-34.

Johnson, C.N. 1996. Interactions between mammals 

and ectomycorrhizal fungi. Trends in Ecology & 

Evolution 11(12):503-507.

Johnson, M. 1994. Changes in Southwestern forests: Stewardship implications. Journal of Forestry 

92(12):16-19.

Johnson, N.C.; Graham, J.H.; Smith, F.A. 1997. 

Functioning of mycorrhizal associations along the 

mutualism-parasitism continuum. New Phytologist 

135(4):575-586.

Kalies, E.L.; Dickson, B.G.; Chambers, C.L.; 

Covington, W.W. 2012. Community occupancy responses of small mammals to restoration treatments 

in ponderosa pine forests, northern Arizona, USA. 

Ecological Applications 22:204-217.

Karl, T.R.; Melillo, J.M.; Peterson, T.C. (eds.). 2009. 

Global Climate Change Impacts in the United 

States. Cambridge, United Kingdom: Cambridge 

University Press.

Kaufmann, M.R.; Binkley, D.; Fulé, P.Z.; Johnson, M.; 

Stephens, S.L.; Swetnam, T.W. 2007. Defining old 

growth for fire-adapted forests of the western United 

States. Ecology and Society 12(2):15.

Kaufmann, M.R.; Graham, R.T.; Boyce, D.A., Jr.; 

[and others]. 1994. An ecological basis for ecosystem management. General Technical Report 

RM-GTR-246. U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Forest and Range 

Experiment Station, Fort Collins, CO. 22 pp.
USDA Forest Service RMRS-GTR-310. 2013. 61

Kaye, M.W.; Swetnam, T.W. 1999. An assessment of 

fire, climate, and Apache history in the Sacramento 

Mountains, New Mexico, USA. Physical Geography 

20:305-330.

Keane, R.E.; Hessburg, P.F.; Landres, P.B.; Swanson, 

F.J. 2009. The use of historical range and variability 

(HRV) in landscape management. Forest Ecology 

and Management 258(7):1025-1037.

Kennedy, P.L. 1991. Reproductive strategies of northern goshawks and Cooper’s hawks in north-central 

New Mexico. Dissertation. Utah State University, 

Logan, UT.

Kerns, B.K.; Moore, M.M.; Timpson, M.E.; Hart, 

S.C. 2003. Soil properties associated with vegetation patches in Pinus ponderosa bunchgrass mosaic. 

Western North American Naturalist 63(4):452-462.

Klironomos, J.N.; McCune, J.; Hart, M.; Neville, J. 

2000. The influence of arbuscular mycorrhizae on 

the relationship between plant diversity and productivity. Ecology Letters 3:137-141.

Kolb, T.E.; Holmberg, K.M.; Wagner, M.R.; Stone, J.E. 

1998. Regulation of ponderosa pine foliar physiology and insect resistance mechanisms by basal area 

treatments. Tree Physiology 18:375-381.

Koonce, A.L.; Roth, L.F. 1980. The effects of prescribed 

burning on dwarf mistletoe in ponderosa pine. Pp. 

197-203 in R.E. Martin, R.L. Edmonds, and D.A. 

Faulkner (eds.). Proceedings, Sixth Conference on 

Fire and Forest Meteorology. Society of American 

Foresters, Washington, DC. 304 pp.

Korb, J.E.; Johnson, N.C.; Covington, W.W. 2003. 

Arbuscular mycorrhizal propagule densities respond 

rapidly to ponderosa pine restoration treatments. 

Journal of Applied Ecology 40:101-110.

Korb, J.E.; Springer, J.D. 2003. Understory vegetation. Pp. 233-250 in P. Friederici (ed.). Ecological 

Restoration of Southwestern Ponderosa Pine 

Forests. Washington, DC: Island Press.

Korb, J.E.; Fule, P.Z.; Wu, R. 2013. Variability of 

warm/dry mixed conifer forests in southwestern 

Colorado, USA: Implications for ecological restoration. Forest Ecology and Management 304:182-191.

Koske, R.E.; Gemma, J.N. 1997. Mycorrhizae and 

succession in plantings of beachgrass in sand dunes. 

American Journal of Botany 84:118-130.

Laacke, R.J. 1990. Abies concolor (Gord. & Glend.) 

Lindl. ex Hildebr. (White fir). Pp. 36-46 in Burns, 

R.M.; Honkala, B.H (tech. coords.). Silvics of 

North America: Volume 1 (Conifers). Agriculture 

Handbook 654. U.S. Department of Agriculture, 

Forest Service, Washington, DC. 877 pp.

LANDFIRE. 2007. LANDFIRE rapid assessment 

reference condition models. U.S. Department of 

Agriculture Service and U.S. Department of Interior, 

Washington, DC. Online at: http://www.landfire.

gov/models_EW.php; last accessed May 2013.

Landres, P.B.; Morgan, P.; Swanson, F.J. 1999. 

Overview of the use of natural variability concepts in managing ecological systems. Ecological 

Applications 9(4):1179-1188.

Lang, D.M.; Stewart, S.S. 1910. Reconnaissance of the 

Kaibab National Forest, 1909. Unpublished document on file at the North Kaibab Ranger District. 

U.S. Department of Agriculture, Forest Service, 

Kaibab National Forest, Fredonia, AZ. 58 pp.

Langsaeter, A. 1944. Om tynning i enaldret gran-og 

furuskog. Referat: Produktionsuntersuchungen von 

Fichtenwald. Medd. Det norske Skogforsoksvesen 

VIII:131-216.

Larson, A.J.; Churchill, D. 2012. Tree spatial patterns 

in fire-frequent forests of western North America, 

including mechanisms of pattern formation and 

implications for designing fuel reduction and restoration treatments. Forest Ecology and Management 

267:74-92.

Latta, R.G.; Mitton, J.B. 1999. Historical separation 

and present gene flow through a zone of secondary 

contact in ponderosa pine. Evolution 53:769-776.

Laughlin, D.C.; Moore, M.M.; Bakker, J.D.; Casey, 

C.A.; Springer, J.D.; Fulé, P.Z.; Covington, W.W. 

2006. Assessing targets for the restoration of herbaceous vegetation in ponderosa pine forests. 

Restoration Ecology 14:548-560.

Leopold, A. 1924. Grass, brush, timber, and fire in 

southern Arizona. Journal of Forestry 22:2-8.

Lieberg, J.B.; Rixon, T.F.; Dodwell, A. 1904. Forest 

conditions in the San Francisco Mountain Reserve, 

Arizona. Professional Paper No. 22. U.S. Department 

of the Interior, U.S. Geological Survey, Washington, 

DC. 96 pp.

Lindenmayer, D.B.; Likens, G.E. 2010. The science 

and application of ecological monitoring. Biological 

Conservation 143:1317-1328.

Linkhart, B.D.; Reynolds, R.T.; Ryder, R.A. 1998. 

Home range and habitat of breeding flammulated owls in Colorado. The Wilson Bulletin 

110(3):342-351.
62 USDA Forest Service RMRS-GTR-310. 2013.

Littell, J.S.; McKenzie, D.; Peterson, D.L.; Westerling, 

A.L. 2009. Climate and wildfire area burned in 

western US ecoprovinces, 1916-2003. Ecological 

Applications 19(4):1003-1021.

Long, J.N. 1985. A practical approach to density management. Forestry Chronicle 61:23-27.

Long, J.N.; Dean, T.J.; Roberts, S.D. 2004. Linkages 

between silviculture and ecology: Examination 

of several important conceptual models. Forest 

Ecology and Management 200:249-261.

Long, J.N.; Smith, F.W. 2000. Restructuring the forest: 

Goshawks and the restoration of Southwestern ponderosa pine. Journal of Forestry 98:25-30.

Los Alamos National Laboratory [LANL]. 2011. 

Road geology & ecology of the Pajarito Plateau & 

Jemez Mountains: Selection Portions, North Central 

New Mexico. Los Alamos National Laboratory, 

Los Alamos, NM. Online at: http://www.lanl.gov/

source/orgs/ees/ees14/fieldtrip.shtml; last accessed 

13 April 2011.

Lowe, K. 2006. Snags and forest restoration. Working 

Papers in Ecological Restoration No. 16. Ecological 

Restoration Institute, Flagstaff, AZ.

Lundquist, J.E. 1995. Pest interactions and canopy gaps 

in ponderosa pine stands in the Black Hills, South 

Dakota, USA. Forest Ecology and Management 

74:37-48.

Lynch, A.M.; Anhold, J.A.; McMillin, J.D.; Dudley, 

S.M.; Fitzgibbon, R.A.; Fairweather, M.L. 2010. 

Forest insect and disease activity on the ApacheSitgreaves National Forest and Fort Apache 

Indian Reservation, 1918-2009. U.S. Department 

of Agriculture, Forest Service, Forest Health 

Protection, Arizona Zone Office, Flagstaff, AZ.

MacDonald, L.H.; Stednick, J.D. 2003. Forests and water: A state-of-the art review for Colorado. Colorado 

Water Resources Research Institute Completion 

Report No. 196. Colorado State University, Fort 

Collins, CO. 65 pp.

Madany, M.H.; West, N.E. 1983. Livestock grazingfire regime interactions within montane forests of 

Zion National Park, Utah. Ecology 64(4):661-667.

Maffei, H.; Beatty, J. 1988. Changes in the incidence of 

dwarf mistletoe over 30 years in the Southwest. Pp. 

88-90 in B.J. van der Kamp (ed.). Proceedings of the 

Thirty-sixth Western International Forest Disease 

Work Conference, Park City, Utah. University of 

British Columbia, Department of Forest Sciences, 

Vancouver, BC, Canada. 153 pp.

Mast, J.N.; Fulé, P.Z.; Moore, M.M.; Covington, W.W.; 

Waltz, A.E.M. 1999. Restoration of presettlement 

age structure of an Arizona ponderosa pine forest. 

Ecological Applications 9:228-239.

Mast, J.N.; Veblen, T.T. 1999. Tree spatial patterns and 

stand development along the pine-grassland ecotone 

in the Colorado Front Range. Canadian Journal of 

Forest Research 29:575-584.

Mast, J.N.; Veblen, T.T.; Linhart, Y.B. 1998. 

Disturbance and climatic influences on age structure 

of ponderosa pine at the pine/grassland ecotone, 

Colorado Front Range. Journal of Biogeography 

25:743-767.

Mast, J.N.; Wolf, J.J. 2004. Ecotonal changes and altered tree spatial patterns in lower mixed-conifer 

forests, Grand Canyon National Park, Arizona, 

USA. Landscape Ecology 19(2):167-180.

Mast, J.N.; Wolf, J.J. 2006. Spatial patch patterns and 

altered forest structure in middle elevation versus 

upper ecotonal mixed-conifer forests, Grand Canyon 

National Park, Arizona, USA. Forest Ecology and 

Management 236(2-3):241-250.

McKay, D.O. 1974. Dynamics of a population of mountain cottontail rabbits in central Oregon. Thesis. 

Oregon State University, Department of Fisheries 

and Wildlife, Corvallis, OR. 63 pp.

McKenzie, D.; Gedalof, Z.A.; Peterson, D.L.; Mote, P. 

2004. Climate change, wildfire, and conservation. 

Conservation Biology 18: 890-902.

McPherson, G.; Wade, E.; Phillips, C.B. 1990. Glossary 

of Wildland Fire Management Terms. Bethesda, 

MD: Society of American Foresters.

Menzel, J.P.; Covington, W.W. 1997. Changes from 

1876 to 1994 in a forest ecosystem near Walnut 

Canyon, northern Arizona. Pp. 151-172 in C. van 

Riper III and E.T. Deshler (eds.). Proceedings of 

the Third Biennial Conference of Research on the 

Colorado Plateau. Transactions and Proceedings 

Series NPS/NRNAU/NRTP-97/12. US. Department 

of the Interior, National Park Service, Washington, 

DC. 256 pp.

Meyer, W.H. 1934. Growth in selectively cut ponderosa pine forests of the Pacific Northwest. Technical 

Bulletin 407. U.S. Department of Agriculture, 

Washington, DC. 64 pp.

Millar, C.I.; Stephenson, N.L.; Stephens, S.L. 2007. 

Climate change and forests of the future: Managing 

in the face of uncertainty. Ecological Applications 

17:2145-2151.
USDA Forest Service RMRS-GTR-310. 2013. 63

Millennium Ecosystem Assessment. 2005. Ecosystems 

and Human Well-being: Synthesis. Washington, 

DC: Island Press.

Miller, J.D.; Safford, H.D.; Crimmins, M.; Thode, 

A.E. 2009. Quantitative evidence for increasing forest fire severity in the Sierra Nevada and Southern 

Cascade Mountains, California and Nevada, USA. 

Ecosystems 12:16-32.

Miller, J.M.; Keen, F.P. 1960. Biology and control of 

the western pine beetle: A summary of the first fifty 

years of research. Miscellaneous Publication 800. 

U.S. Department of Agriculture, Forest Service, 

Washington, DC. 381 p.

Miller, M. 2000. Chapter 2: Fire autecology. Pp. 9-34 

in J.K. Brown and J.K. Smith (eds.). Wildland 

Fire in Ecosystems: Effects of Fire on Flora. Vol. 

2. General Technical Report RMRS-GTR-42. U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station, Ogden, UT. 257 pp.

Minnich, R.A.; Barbour, M.G.; Burk, J.H.; SosaRamírez, J. 2000. Californian mixed-conifer forests 

under unmanaged fire regimes in the Sierra San 

Pedro Mártir, Caja California, Mexico. Journal of 

Biogeography 27(1):105-129.

Minor, C.O. 1964. Site-index curves for young-growth 

ponderosa pine in Northern Arizona. Research Note 

RM-RN-37. USDA Forest Service, Rocky Mountain 

Forest and Range Experiment Station, Fort Collins, 

CO. 8 pp.

Mitchell, S.R.; Harmon, M.E.; O’Connell, K.E. 2009. 

Forest fuel reduction alters fire severity and longterm carbon storage in three Pacific Northwest 

ecosystems. Ecological Applications 19(3):643-655.

Moeck, H.A.; Wood, D.L.; Lindahl, K.Q, Jr. 1981. 

Host selection behavior of bark beetles (Coleoptera: 

Scolytidae) attacking Pinus ponderosa, with special 

emphasis on the western pine beetle, Dendroctonus 

brevicomis. Journal of Chemical Ecology 7(1):49-83.

Moir, W.H. 1966. Influence of ponderosa pine on herbaceous vegetation. Ecology 47:1045-1048.

Moore, M.M.; Casey, C.A.; Bakker, J.D.; Springer, 

J.D.; Fulé, P.Z.; Covington, W.W.; Laughlin, D.D. 

2006. Herbaceous vegetation responses (1992-2004) 

to restoration treatments in a ponderosa pine forest. 

Rangeland Ecology and Management 59:135-144.

Moore, M.M.; Covington, W.W.; Fulé, P.Z. 1999. 

Evolutionary environment, reference conditions, 

and ecological restoration: A southwestern ponderosa pine perspective. Ecological Applications 

9:1266-1277.

Moore, M.M.; Deiter, D.A. 1992. Stand density index as a predictor of forage production in northern 

Arizona pine forests. Journal of Range Management 

45:267-271.

Moore, M.M.; Huffman, D.W.; Fulé, P.Z.; Covington, 

W.W.; Crouse, J.E. 2004. Comparison of historical 

and contemporary forest structure and composition 

on permanent plots in southwestern ponderosa pine 

forests. Forest Science 50:162-176.

Morgan, P.; Aplet, G.H.; Haufler, J.B.; Humphries, 

H.C.; Moore, M.M.; Wilson, W.D. 1994. Historical 

range of variability: A useful tool for evaluating 

ecosystem change. Journal of Sustainable Forestry 

2:87-111.

Morgan, T.A.; Fiedler, C.E.; Woodall, C.W. 2002. 

Characteristics of dry site old-growth ponderosa 

pine in the Bull Mountains of Montana, USA. 

Natural Areas Journal 22:11-19.

Muldavin, E.; Tonne, P. 2003. A vegetation survey 

and preliminary ecological assessment of Valles 

Caldera National Preserve, New Mexico. Report 

for Cooperative Agreement No. 01CRAG0014. 

University of New Mexico, Albuquerque, NM.

Mulder, B.S.; Noon, B.R.; Spies, T.A.; Raphael, M.G.; 

Palmer, C.J.; Olsen, A.R.; Reeves, G.H.; Welsh, 

H.H. (eds.). 1999. The strategy and design of the effectiveness monitoring program for the Northwest 

Forest Plan. U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station, 

Portland, OR. 138 pp.

Murray, C.; Marmorek, D. 2003. Adaptive management and ecological restoration. Pp. 417-428 

in P. Friederici (ed.). Ecological Restoration of 

Southwestern Ponderosa Pine Forests. Washington, 

D.C.: Island Press.

Naeem, S.; Chapin, F.S., III; Costanza, R.; [and others]. 1999. Biodiversity and ecosystem functioning: 

Maintaining natural life support processes. Issues 

in Ecology No. 4. Ecological Society of America, 

Washington, DC.

Naficy, C.; Sala, A.; Keeling, E.G.; Graham, J.; DeLuca, 

T.H. 2010. Interactive effects of historical logging 

and fire exclusion on ponderosa pine structure in 

the northern Rockies. Ecological Applications 

20:1851-1864.

Naumburg, E.; DeWald, L.E. 1999. Relationships 

between Pinus ponderosa forest structure, light 

characteristics, and understory graminoid species presence and abundance. Forest Ecology and 

Management 124:205-215.
64 USDA Forest Service RMRS-GTR-310. 2013.

Negrón, J. 1997. Estimating probabilities of infestation and extent of damage by the roundheaded 

pine beetle in ponderosa pine in the Sacramento 

Mountains, New Mexico. Canadian Journal of 

Forest Research 27:1634-1645.

Negrón, J.F.; McMillin, J.D.; Anhold, J.A.; Coulson, 

D. 2009. Bark beetle-caused mortality in a 

drought-affected ponderosa pine landscape in 

Arizona, USA. Forest Ecology and Management 

257(4):1353-1362.

Noon, B.R. 2003. Conceptual issues in monitoring 

ecological resources. Pp. 27-71 in D.E. Busch 

and J.C. Trexler (eds.). Monitoring Ecosystems: 

Interdisciplinary Approaches for Evaluating 

Ecoregional Initiatives. Washington, DC: Island 

Press.

Noss, R.F.; Franklin, J.F.; Baker, W.L.; Schoennagel, 

T.; Moyle, P.B. 2006. Managing fire-prone forests 

in the western United States. Frontiers in Ecology 

and the Environment 4(9):481-487.

National Wildfire Coordination Group [NWCG]. 2012. 

Glossary of wildland fire terminology. Product 

Management System PMS-205. National Wildfire 

Coordination Group, Program Management Unit, 

Boise, ID. 190 pp.

Oliver, C.D.; Osawa, A.; Camp, A. 1998. Forest dynamics and resulting animal and plant population 

changes at the stand and landscape levels. Journal 

of Sustainable Forestry 6:281-312.

Overpeck, J.; Garfin, G.; Jardine, A.; [and others]. 

2012. Chapter 1: Summary for decisions makers. 

In G. Garfin, A. Jardine, R. Merideth, M. Black, 

and J. Overpeck (eds.). Assessment of Climate 

Change in the Southwestern United States: A technical Report Prepared for the U.S. National Climate 

Assessment. June 2012 Southwest Climate Summit 

Draft. Southwest Climate Alliance, Tucson, AZ.

Palmer, C. J.; Mulder, B.S. 1999. Components of 

the effectiveness monitoring program. Pp. 69-

98 in B.S. Mulder, B.R. Noon, T.A. Spies, M.G. 

Raphael, C.J. Palmer, A.R. Olsen, G.H. Reeves, 

and H.H. Welsh (eds.). The Strategy and Design 

of the Effectiveness Monitoring Program for 

the Northwest Forest Plan. U.S. Department of 

Agriculture, Forest Service, Pacific Northwest 

Research Station, Portland, OR. 138 pp.

Parker, V.T.; Muller, C.H. 1982. Vegetational and environmental changes beneath isolated live oak trees 

(Quercus agrifolia) in a California annual grassland. American Midland Naturalist 107:69-81.

Parker, W.C.; Colombo, S.J.; Cherry, M.L.; Flannigan, 

M.D.; Greifen, S.; McAlpine, R.S.; Papadopol, C.; 

Scarr, T. 2000. Third millennium forestry: What 

climate change might mean to forests and forest management in Ontario. Forestry Chronicle 

76:445-463.

Parsons, D.J.; DeBenedetti, S.H. 1979. Impact of 

fire suppression on a mixed-conifer forest. Forest 

Ecology and Management 2:21-23.

Patton, D.R.; Severson, K.E. 1989. WILDSHARE: A 

wildlife habitat relationship data model for southwestern ponderosa pine. Pp. 268-276 in A. Techle, 

W.W. Covington, and R.H. Hamre (tech. coords.). 

Multiresource Management of Ponderosa Pine 

Forests. General Technical Report RM-GTR-185. 

U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Forest and Range Experiment 

Station, Fort Collins, CO. 282 pp.

Pearson, G.A. 1923. Natural reproduction of western 

yellow pine in the Southwest. Bulletin No. 1105. 

U.S. Department of Agriculture, Forest Service, 

Washington, DC. 144 pp.

Pearson, G.A. 1950. Management of ponderosa pine 

in the Southwest: As developed by research and 

experimental practice. Agriculture Monograph No. 

6. U.S. Department of Agriculture, Forest Service, 

Fort Collins, CO. 34 pp.

Pielou, E.C. 1960. A single mechanism to account for 

regular, random and aggregated populations. Journal 

of Ecology 48(3):575-584.

Pilliod, D.S.; Bull, E.L.; Hayes, J.L.; Wales, B.C. 2006. 

Wildlife and invertebrate response to fuel reduction 

treatments in dry coniferous forests of the Western 

United States: A synthesis. General Technical Report 

RMRS-GTR-173. U.S. Department of Agriculture, 

Rocky Mountain Research Station, Fort Collins, 

CO. 34 pp.

Plummer, F.G. 1904. Forest conditions in the Black 

Mesa Forest Reserve, Arizona. Professional Paper 

No. 23. U.S. Department of the Interior, U.S. 

Geological Survey, Washington, DC. 62 pp.

Porter, W.F. 1992. Habitat requirements. Pp. 202-213 

in J.G. Dickson, (ed.). The Wild Turkey: Biology 

and Management. Stackpole Books: Harrisburg, PA.

Prather, J.W.; Noss, R.F.; Sisk, T.D. 2008. Real versus 

perceived conflicts between restoration of ponderosa 

pine forests and conservation of the Mexican spotted owl. Forest Policy and Economics 10:140-150.

Price, M.F.; Neville, G.R. 2003. Designing strategies 

to increase the resilience of alpine/montane systems 
USDA Forest Service RMRS-GTR-310. 2013. 65

to climate changes. Pp. 73-94 in L.J. Hansen, 

J.L. Biringer, and J.R. Hoffman (eds.). Buying 

Time: A User’s Manual for Building Resistance 

and Resilience to Climate Change in Natural 

Systems. World Wildlife Fund International, Gland, 

Switzerland. 246 pp.

Puhlick, J.J. 2011. Regional- and local-scale modeling of ponderosa pine seedling densities in the 

Southwest. Thesis. Northern Arizona University, 

School of Forestry, Flagstaff, AZ. 80 pp.

Puhlick, J.J.; Laughlin, D.C.; Moore, M.M. 2012. 

Factors influencing ponderosa pine regeneration 

in the southwestern USA. Forest Ecology and 

Management 264:10-19.

Rasmussen, D.I. 1941. Biotic communities of the 

Kaibab Plateau, Arizona. Ecological Monographs 

11:229-275.

Reynolds, R.T.; Graham, R.T.; Boyce, D.A., Jr. 2006a. 

An ecosystem-based conservation strategy for 

the northern goshawk. Studies in Avian Biology 

31:299-311.

Reynolds, R.T.; Graham, R.T.; Reiser, M.H.; Bassett, 

R.L.; Kennedy, P.L.; Boyce, D.A., Jr.; Goodwin, 

G.; Smith, R.; Fisher, E.L. 1992. Management 

recommendations for the northern goshawk in the 

Southwestern United States. General Technical 

Report RMRS-GTR-217. U.S. Department of 

Agriculture, Forest Service, Rocky Mountain 

Research Station, Fort Collins, CO. 90 pp.

Reynolds, R.T.; Joy, S.M.; Leslie, D.G. 1994. Nest productivity, fidelity, and spacing of northern goshawks 

in northern Arizona. Studies in Avian Biology 

16:106-113.

Reynolds, R.T.; Wiens, J.D.; Joy, S.M.; Salafsky, S.R. 

2005. Sampling considerations for demographic 

and habitat studies of northern goshawks. Journal of 

Raptor Research 39:274-285.

Reynolds, R.T.; Wiens, J.D.; Salafsky, S.R. 2006b. A 

review and evaluation of factors limiting northern 

goshawk populations. Studies in Avian Biology 

31:260-273.

Rieman, B.; Clayton, J. 1997. Wildlife and native fish: 

Issues of forest health of sensitive species. Fisheries 

22:6-15.

Rippy, R.C.; Stewart, J.E.; Zambino, P.J.; Klopfenstein, 

N.B.; Tirocke, J.M.; Kim, M.S.; Thies, W.G. 2005. 

Root diseases in coniferous forests of the Inland 

West: Potential implications of fuels treatments. 

General Technical Report RMRS-GTR-141. U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station, Fort Collins, CO. 32 p.

Roccaforte, J.P.; Fulé, P.Z.; Covington, W.W. 2008. 

Landscape-scale changes in canopy fuels and potential fire behavior following ponderosa pine 

restoration treatments. International Journal of 

Wildland Fire 17(2):293-303.

Roccaforte, J.P.; Fulé, P.Z.; Covington, W.W. 2010. 

Monitoring landscape-scale ponderosa pine restoration treatment implementation and effectiveness. 

Restoration Ecology 18:820-833.

Romme, W.H.; Floyd, M.L.; Hanna, D. 2009. 

Historical range of variability and current landscape condition analysis: South Central Highlands 

Section, Southwestern Colorado & Northwestern 

New Mexico. Colorado State University, Colorado 

Forest Restoration Institute, Fort Collins, CO, and 

USDA Forest Service, Rocky Mountain Region, 

Golden, CO.

Rosenstock, S.S. 1998. Influence of Gambel oak 

on breeding birds in Northern Arizona. Condor 

100:485-492.

Roth, L.F.; Rolph, L.; Cooley, S. 1980. Identifying 

infected ponderosa pine stumps to reduce costs of 

controlling Armillaria root rot. Journal of Forestry 

78(3):145-151.

Ryan, R.L. 2005. Social science to improve fuels management: A synthesis of research an aesthetics and 

fuels management. General Technical Report NCGTR-261. U.S. Department of Agriculture, Forest 

Service, North Central Research Station, St. Paul, 

MN. 58 pp.

Sackett, S.S.; Haase, S.M. 1998. Two case histories for using prescribed fire to restore ponderosa 

pine ecosystems in northern Arizona. Pp. 380-389 

in T.L. Pruden and L.A. Brennan (eds.). Fire in 

Ecosystem Management: Shifting the Paradigm 

from Suppression to Prescription. Proceedings of 

the 20th Tall Timbers Fire Ecology Conference, 

7-10 May 1996, Boise, ID. Tall Timbers Research 

Station, Tallahassee, FL.

Salafsky, S.R.; Reynolds, R.T.; Noon, B.R. 2005. 

Patterns of temporal variation in goshawks reproduction and prey resources. Journal of Raptor 

Research 39:237-246.

Salafsky, S.R.; Reynolds, R.T.; Noon, B.R.; Wiens, 

J.A. 2007. Reproductive responses of northern 

goshawks to variable prey populations. Journal of 

Wildlife Management 71:2274-2283.
66 USDA Forest Service RMRS-GTR-310. 2013.

Sánchez Meador, A.J.; Moore, M.M. 2010. Lessons 

from long-term studies of harvest methods in 

southwestern ponderosa pine-Gambel oak forests 

on the Fort Valley Experimental Forest. Forest 

Ecology and Management 260:193-206.

Sánchez Meador, A.J.; Moore, M.M.; Bakker, J.D.; 

Parysow, P.F. 2009. 108 years of change in spatial 

pattern following selective harvest of a Pinus ponderosa stand in northern Arizona, USA. Journal of 

Vegetation Science 20:79-90.

Sánchez Meador, A.J.; Parysow, P.F.; Moore, M.M. 

2010. Historical stem-mapped permanent plots 

increase precision of reconstructed reference data 

in ponderosa pine forests of northern Arizona. 

Restoration Ecology 18:224-234.

Sánchez Meador, A.J.; Parysow, P.F.; Moore, M.M. 

2011. A new method for delineating tree patches 

and assessing spatial reference conditions of ponderosa pine forest in northern Arizona. Restoration 

Ecology 19(4):490-499.

Savage, M.; Mast, J.N. 2005. How resilient are southwestern ponderosa pine forests after crown fire? 

Canadian Journal of Forest Research 35: 967-977.

Scheffer, M.; Carpenter, S.; Foley, J.A.; Folke, C.; 

Walker, B. 2001. Catastrophic shifts in ecosystems. Nature 413:591-596.

Schmidt, K.M.; Menakis, J.P.; Hardy, C.C.; Wendel, J.; 

Bunnell, D.L. 2002. Development of coarse-scale 

spatial data for wildland fire and fuel management. 

General Technical Report RMRS-GTR-87. U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station, Fort Collins, CO. 41 

pp. + CD.

Schneider, E. 2012. Reference conditions and historical changes in an unharvested ponderosa 

pine stand: Implications for forest health. Thesis. 

Northern Arizona University, School of Forestry, 

Flagstaff, AZ.

Scholes, R.J.; Archer, S.R. 1997. Tree-grass interactions in savannas. Annual Review of Ecology and 

Systematics 28:517-544.

Scholl, A.E.; Taylor, A.H. 2010. Fire regimes, forest change, and self-organization in an old-growth 

mixed-conifer forest, Yosemite National Park, 

USA. Ecological Applications 20:362-380.

Schubert, G.H. 1974. Silviculture of southwestern 

ponderosa pine: The status of our knowledge. 

U.S. Department of Agriculture, Forest Service. 

Research Paper RM-213. U.S. Department of 

Agriculture, Forest Service, Rocky Mountain 

Forest and Range Experiment Station, Fort Collins, 

CO. 71 p.

Scott, J.H.; Burgan, R.E. 2005. Standard fire behavior fuel models: A comprehensive set for 

use with Rothermel’s surface fire spread model. 

General Technical Report RMRS-GTR-153. U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station, Fort Collins, CO. 

72 pp.

Seager, R.M.: Ting, M.F.; Held, I.M.; [and others]. 

2007. Model projections of an imminent transition to a more arid climate in southwestern North 

America. Science 316:1181-1184.

Shafer, S.L.; Bartlein, P.J.; Thompson, R.S. 2001. 

Potential changes in the distributions of western 

North America tree and shrub taxa under future 

climate scenarios. Ecosystems 4:200-215.

Sitko, S.; Hurteau, S. 2010. Evaluating the impacts 

of forest treatments: The first five years of the 

White Mountain Stewardship Project. The Nature 

Conservancy, Phoenix, AZ.

Smith, C.K.; Youtz, J.; Bradley, A.; Allen-Reid, D.; 

Evans, A.; Johnson, M.; Bird, B.; Bada, C.; Fúle, 

P. 2008. Climate change and mixed-conifer/aspen systems in New Mexico: Consideration for 

managers. New Mexico Forest Restoration Series 

Working Paper No. 7. New Mexico Highlands 

University, New Mexico Forest and Watershed 

Restoration Institute, Las Vegas, NM.

Smith, E. 2006a. Historical range of variation and 

state and transition modeling of historical and current landscape conditions for mixed-conifer of 

the Southwestern U.S. The Nature Conservancy, 

Tucson, AZ. 31 pp.

Smith, E. 2006b. Historical range of variation and 

state and transition modeling of historical and current landscape conditions for ponderosa pine of 

the Southwestern U.S. The Nature Conservancy, 

Tucson, AZ. 43 pp.

Smith, E. 2006c. Historical range of variation and 

state and transition modeling of historical and 

current landscape conditions for spruce-fir of 

the Southwestern U.S. The Nature Conservancy, 

Tucson, AZ. 37 pp.

Society for Ecological Restoration [SER]. 2004. The 

SER international primer on ecological restoration. 

Society for Ecological Restoration International, 

Science & Policy Working Group, Washington, 

DC.
USDA Forest Service RMRS-GTR-310. 2013. 67

Spittlehouse, D.L.; Stewart, R.B. 2003. Adaptation to 

climate change in forest management. Journal of 

Ecosystems and Management 4(1).

Spracklen, D.V.; Mickley, L.J.; Logan, J. A.; Hudman, 

R.C.; Yevich, R.; Flannigan, M.D.; Westerling, A.L. 

2009. Impacts of climate change from 2000 to 2050 

on wildfire activity and carbonaceous aerosol concentrations in the western United States. Journal of 

Geophysical Research 114:D20301. 

Steele, R.; Arno, S.F.; Geier-Hayes, K. 1986. Wildfire 

patterns change in central Idaho’s ponderosa pineDouglas-fir forest. Western Journal of Applied 

Forestry 1(1):16-18.

Stephens, S.L.; Fry, D.L. 2005. Spatial distribution of 

regeneration patches in an old-growth Pinus jeffrey-mixed-conifer forest in northwestern Mexico. 

Journal of Vegetation Science 16(6):693-702.

Stephens, S.L.; Fry, D.L.; Franco-VizcaÍno, E. 2008. 

Wildfire and spatial patterns in forests in northwestern Mexico: The United States wishes it had similar 

fire problems. Ecology and Society 13: 10.

Stephens, S.L.; Moghaddas, J.J.; Edminster, C.; [and 

others]. 2009. Fire treatment effects on vegetation 

structure, fuels, and potential fire severity in western 

U.S. forests. Ecological Applications 19(2):305-320.

Stevens, R.E.; Hawksworth, F.G. 1984. Insectdwarf mistletoe associations: An update. General 

Technical Report RM-GTR-111. U.S. Department 

of Agriculture, Forest Service, Rocky Mountain 

Forest and Range Experiment Station, Fort Collins, 

CO:94-101.

Stratton, R.D. 2004. Assessing the effectiveness of 

landscape fuel treatments on fire growth and behavior. Journal of Forestry 102:32-40.

Strom, B.A.; Fulé, P.Z. 2007. Pre-wildfire fuel treatments affect long-term ponderosa pine forest 

dynamics. International Journal of Wildland Fire 

16:128-138.

Swetnam, T.W.; Allen, C.D.; Betancourt, J.L. 1999. 

Applied historical ecology: Using the past to manage 

for the future. Ecological Applications 9:1189-1206.

Swetnam, T.W.; Baisan, C.H. 1996. Historical fire 

regime patterns in the southwestern United States 

since AD 1700. Pp. 11-32 in C.D. Allen (ed.). 2nd La 

Mesa Fire Symposium; Los Alamos, NM. General 

Technical Report RM-GTR-286. U.S. Department 

of Agriculture, Forest Service, Rocky Mountain 

Forest and Range Experiment Station, Fort Collins, 

CO. 216 pp.

Swetnam, T.W.; Betancourt, J.L. 1990. Fire-southern 

oscillation relations in the southwestern United 

States. Science 249:1017-1020.

Swetnam, T.W.; Dieterich, J.H. 1985. Fire history 

of ponderosa pine forests in the Gila Wilderness, 

New Mexico. Pp. 390-397 in J. Lotan, B.M. 

Kilgore, W.C. Fischer, and R.W. Mutch (tech. coords.). Proceedings, Symposium and Workshop on 

Wilderness Fire, Missoula, MT. General Technical 

Report INT-GTR-182. U.S. Department of 

Agriculture, Forest Service, Intermountain Research 

Station, Ogden, UT. 434 pp.

Tainter, F.H.; Baker, F.A. 1996. Principles of Forest 

Pathology. New York, NY: John Wiley & Sons, Inc.

Taylor, A.H. 2010. Fire disturbance and forest structure 

in an old-growth Pinus ponderosa forest, southern Cascades, USA. Journal of Vegetation Science 

21(3):561-572.

Taylor, A.H.; Skinner, C.N. 2003. Spatial patterns 

and controls on historical fire regimes and forest 

structure in the Klamath Mountains. Ecological 

Application 13(3):704-719.

Thaxton, J.M.; Platt, W.J. 2006. Small-scale fuel variation alters fire intensity and shrub abundance in a 

pine savanna. Ecology 87(5):1331-1337.

Touchan, R.; Allen, C.D.; Swetnam, T.W. 1996. Fire 

history and climatic patterns in ponderosa pine 

and mixed-conifer forests of the Jemez Mountains, 

northern New Mexico. Pp. 179-195 C.D. Allen 

(tech. ed.). Fire Effects in Southwestern Forests: 

Proceedings of the Second La Mesa Fire Symposium. 

General Technical Report RM-GTR-286. U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Forest and Range Experiment Station, 

Fort Collins, CO. 216 pp.

Triepke, F.J.; Higgins, B.J.; Weisz, R.N.; Youtz, 

J.A.; Nicolet, T. 2011. Diameter caps and forest 

restoration: Evaluation of a 16-inch cut limit on 

achieving desired conditions. Forestry Report FRR3-16-3. U.S. Department of Agriculture, Forest 

Service, Southwestern Region, Regional Office, 

Albuquerque, NM. 31 pp.

Troendle, C.A. 1983. The potential for water yield 

augmentation from forest management in the 

Rocky Mountain region. Water Resource Bulletin 

19:359-373.

Turner, M.G. 1989. Landscape ecology: The effect of 

pattern on process. Annual Review of Ecology and 

Systematics 20:171-197.
68 USDA Forest Service RMRS-GTR-310. 2013.

USDA Forest Service. 1990. Silvics of North America, 

Volume 1, Conifers. Agriculture Handbook 654. 

Washington D.C. 675 pp.

USDA Forest Service. 1997. Plant associations of 

Arizona and New Mexico. Vol. 1, 3rd ed. U.S. 

Department of Agriculture, Forest Service, 

Southwestern Region, Albuquerque, NM. 291 pp.

USDA Forest Service. 2004. Forest insect and disease conditions in the Southwestern Region, 2003. 

Report R3-04-02. U.S. Department of Agriculture, 

Forest Service, Southwestern Region, Albuquerque, 

NM. 34 pp.

USDA Forest Service. 2012. Forest insect and disease conditions in the Southwestern Region, 2011. 

Report R3-16-8. U.S. Department of Agriculture, 

Forest Service, Southwestern Region, Albuquerque, 

NM. 43 pp.

van der Heijden, M.G.A.; Klironomos, J.N.; Ursic, 

M.; Moutoglis, P.; Streitwolf-Engel, R.; Boller, 

T.; Wiemken, A.; Sanders, I.R. 1998. Mycorrhizal 

fungal diversity determines plant biodiversity, 

ecosystem variability and productivity. Nature 

396:69-72.

Van Wagner, C.E. 1977. Conditions for the start and 

spread of crown fire. Canadian Journal of Forest 

Research 7:23-34.

Van Wagner, C.E. 1993. Prediction of crown fire behavior in two stands of jack pine. Canadian Journal 

of Forest Research 23:442-449.

Villa-Castillo, J.; Wagner, M.R. 2002. Ground beetle 

(Coleoptera: Carabidae) species assemblage as an 

indicator of forest condition in northern Arizona 

ponderosa pine forests. Environmental Entomology 

31(2):242-252.

Von Schrenck, H. 1903. The “bluing” and the “red 

rot” of the western yellow pine, with special reference to the Black Hills Forest Reserve. Bulletin 36. 

U.S. Department of Agriculture, Bureau of Plant 

Industry, Washington, DC. 40 pp.

Wagner, M.R.; Block, W.M.; Geils, B.W.; Wenger, K.F. 

2000. Restoration ecology: A new forest management paradigm, or another merit badge for foresters? 

Journal of Forestry 98(10):22-27.

Walters, C.J. 1986. Adaptive Management of 

Renewable Resources. New York, NY: Macmillan 

Publishers.

Waltz, A.E.M.; Covington, W.W. 2003. Ecological restoration treatments increase butterfly richness and 

abundance: Mechanisms of response. Restoration 

Ecology 12:85-96.

Waltz, A.E.M.; Fulé, P.Z. 1998. Changes in ponderosa pine forests of the Mt. Logan Wilderness. Paper 

on file at Northern Arizona University. Northern 

Arizona University, Flagstaff, AZ.

Watson, J.W.; Hayes, D.W.; Finn, S.P.; Meehan-Martin, 

P. 1998. Prey of breeding goshawks in Washington. 

Journal of Raptor Research 32:297-305.

Weaver, H. 1951. Fire as an ecological factor in southwestern ponderosa pine forests. Journal of Forestry 

49:93-98.

West, N.E. 1969. Tree patterns in central Oregon ponderosa pine forests. American Midland Naturalist 

81:584-590.

Westerling, A.L.; Hidalgo, H.G.; Cayan, D.R.; 

Swetnam, T.W. 2006. Warming and earlier spring 

increase western US forest wildfire activity. Science 

313(5789):940-943.

Wheeler, G.M. (ed.). 1875. Annual report upon the 

geographical explorations and surveys west of the 

one hundredth meridian in California, Nevada, 

Nebraska, Utah, Arizona, Colorado, New Mexico, 

Wyoming, and Montana: Appendix LL of the annual 

report of the Chief of Engineers for 1875. Washington 

Government Printing Office, Washington, DC. 

196 pp.

White, A.S. 1985. Presettlement regeneration patterns 

in a Southwestern ponderosa pine stand. Ecology 

66:589-594.

White, M.A.; Vankat, J.L. 1993. Middle and high elevation coniferous forest communities of the North 

Rim region of the Grand Canyon National Park, 

Arizona, USA. Vegetatio 109:161-174.

Wickman, B.E. 1963. Mortality and growth reduction 

of white fir following defoliation by the Douglasfir tussock moth. Research Paper PSW-RP-7. U.S. 

Department of Agriculture, Forest Service, Pacific 

Southwest Forest & Range Experimental Station, 

Berkeley, CA. 15 pp.

Wightman, C.S.; Germaine, S.S. 2006. Forest stand 

characteristics altered by restoration affect western bluebird habitat quality. Restoration Ecology 

14(4):653-661.

Williams, B.K.; Szaro, R.C.; Shapiro, C.D. 2009. 

Adaptive management: The U.S. Department of 

the Interior technical guide. U.S. Department of the 

Interior, Adaptive Management Working Group, 

Washington, DC. 72 pp.

Williams, M.A.; Baker, W.L. 2011. Testing the accuracy of new methods for reconstructing historical 
USDA Forest Service RMRS-GTR-310. 2013. 69

structure of forest landscapes using GLO survey 

data. Ecological Monographs 81:63-88.

Williams, M.A.; Baker, W.L. 2012. Spatially extensive reconstructions show variable-severity fire 

and heterogeneous structure in historical western United States dry forests. Global Ecology and 

Biogeography 21(10):1042-1052.

Winthers, E.; Fallon, D.; Haglund, J.; [and others]. 

2005.Terrestrial ecological unit inventory technical guide. General Technical Report WO-68. 

U.S. Department of Agriculture, Forest Service, 

Ecosystem Management Coordination, Washington, 

DC. 245 pp.

Wood, R.E. 1983. Mortality caused by root diseases and associated pests on six National Forests 

in Arizona and New Mexico. Report R3-83-13. 

U.S. Department of Agriculture, Forest Service, 

Southwestern Region, Forest Pest Management, 

Albuquerque, NM. 31 p.

Woodall, C.W. 2000. The growth and structural dynamics of uneven-aged ponderosa pine stands 

in eastern Montana. Dissertation, University of 

Montana, College of Forestry and Conservation, 

Missoula, MT.

Woolsey, T.S., Jr. 1911. Western yellow pine in Arizona 

and New Mexico. U.S. Department of Agriculture, 

Forest Service Bulletin 101. Government Printing 

Office, Washington, DC. 64 pp.

York, R.A.; Heald, R.C.; Battles, J.J.; York, J.D. 2004. 

Group selection management in conifer forests: 

Relationship between opening size and tree growth. 

Canadian Journal of Forest Research 34:630-641.

Youngblood, A.; Max, T.; Coe, K. 2004. Stand structure in eastside old-growth ponderosa pine forests of 

Oregon and northern California. Forest Ecology and 

Management 119:191-217.

Youtz, J.A.; Vandendrieshe, D. 2012. Long-term sustained yield projections for Southwestern Region 

national forests planning. Unpublished report on 

file with the Southwestern Region USDA Forest 

Service, Albuquerque, NM.
70 USDA Forest Service RMRS-GTR-310. 2013.

Glossary

Age class is defined as trees that originated within a 

relatively distinct range of years. Typically, the 

range of years is considered to fall within 20 percent 

of the average maturity (e.g., if 100 years is required 

to reach maturity, then there would be five 20-year 

age classes) (Helms 1998).

Basal area is the cross-sectional area of all stems of 

a species or all stems in a stand measured at breast 

height (4.5 ft above the ground) and expressed per 

unit of land area.

Biodiversity is the variety and abundance of life 

forms, processes, functions, and structures of plants, 

animals, and other living organisms, including the 

relative complexity of species in communities, gene 

pools, and ecosystems at spatial scales from local to 

regional to global (Helms 1998).

Canopy cover (see forest canopy cover)

Canopy fuels are all burnable materials, including live 

and dead foliage, lichen, stems, and branch wood 

located in the forest canopy.

Characteristic (natural) conditions (e.g., vegetation composition and structure), processes (e.g., 

disturbance regimes), and functions (e.g., habitat, 

biodiversity, and food webs) of a forest type that are 

present under the natural range of variability.

Clump refers to (1) the aggregate of stems issuing 

from the same root, rhizome system, or stool; or (2) 

an isolated generally dense group of trees (Helms 

1998). A clump is relatively isolated from other 

clumps or trees within a group of trees, but a standalone clump of trees can function as a tree group or 

a single structure (Fig. 4).

Coarse woody debris is dead woody material on the 

ground greater than 3 inches in diameter, including 

logs (Figs. 12 and 13).

Composition is the array of species present in an 

ecosystem. In forestry, this term often refers to the 

proportion of each tree species in a stand expressed 

as a percentage of the total number, basal area, or 

volume of all tree species in the stand (Helms 1998).

Diameter at breast height (DBH) is the diameter of a 

tree typically measured at 4.5 ft above ground level.

Disturbance (characteristic and uncharacteristic):

Any relatively discrete event in time that disrupts 

ecosystems, communities, or population structure 

and changes resources, substrate availability, or the 

physical environment (Helms 1998). Characteristic 

disturbances are those whose extent, frequency, and 

severity fall within the natural range of variability. 

Uncharacteristic disturbances are outside the natural 

range of variability and interrupt characteristic processes and functions.

Dry mixed-conifer forests occupy the warmer and 

drier sites between elevations of 5000 and 10,000 ft 

and are characterized by a relatively frequent historic 

fire regime (<35 years fire return interval), resulting in surface fire and infrequently, mixed-severity 

fire effects. This forest type is typically dominated 

by shade-intolerant species such as ponderosa pine, 

with minor association of aspen, Douglas-fir, and 

Southwestern white pine during early seral stages. 

More shade-tolerant conifers such as Douglas-fir, 

white fir, and blue spruce are dominant at climax 

stages. In the Southwestern United States, this type 

is primarily described by the Society of American 

Foresters cover types interior Douglas-fir and white 

fir.

Ecological (ecosystem) health (see forest health)

Ecological restoration is the process of assisting the 

recovery of an ecosystem that has been degraded, 

damaged, or destroyed. Restoration initiates or accelerates ecosystem recovery with respect to its 

health (productivity), processes, and functions (biodiversity, food webs, and sustainability) (adapted 

from SER 2004).

Ecosystem integrity is the state or condition of an ecosystem that displays the biodiversity characteristic 

of the reference, such as species composition and 

community structure, and is fully capable of sustaining normal ecosystem functioning (SER 2004).

Ecosystem resiliency is the ability of an ecosystem to 

absorb and recover from disturbances without altering its inherent functions (SER 2004).

Ecosystem services are the benefits people obtain from 

ecosystems, including provisioning services such as 

food and water; regulating services such as flood and 

disease control; cultural services such as spiritual, 

recreational, and cultural benefits; and supporting 

services such as nutrient cycling, that maintain the 

conditions for life on Earth (Millennium Ecosystem 

Assessment 2005).
USDA Forest Service RMRS-GTR-310. 2013. 71

Ecosystem stability is the ability of an ecosystem to 

maintain its given trajectory (SER 2004).

Ecosystem sustainability is the capacity of ecosystems to maintain ecosystem services in perpetuity 

without degradation of its productivity and function at all scales. For example, in the context of 

our restoration framework, sustainability results in 

maintaining the key elements in space and time.

Even-aged forests are forests that are comprised of 

one or two distinct age classes of trees.

Evolutionary environment refers to the range of abiotic and biotic conditions that have exerted selection 

pressure on and are critical to the survival of species 

or groups of species (Kalies and others 2012; Moore 

and others 1999).

Fine fuels are fast-drying dead or live fuels, generally characterized by a comparatively high surface 

area-to-volume ratio, that are less than 0.25 inch in 

diameter and have a time-lag of one hour or less. 

These fuels (grass, leaves, needles, etc.) ignite 

readily and are consumed rapidly by fire when dry 

(NWCG 2012).

Fire regime refers to the patterns of fire occurrences, 

frequency, size, severity, and sometimes vegetation 

and fire effects in a given area or ecosystem. A fire 

regime is a generalization based on fire histories at 

individual sites (McPherson and others 1990).

Fire return interval is the number of years between 

two successive fires in a specified area (McPherson 

and others 1990).

Forest canopy cover is the proportion of ground or 

water covered by a vertical projection of the outermost perimeter of tree canopies, regardless of tree 

spatial arrangement.

Forest health is the state or condition of forest ecosystems in which its attributes (i.e., productivity) 

are expressed within ”normal” ranges of activity 

relative to its ecological stage of development. A 

restored ecosystem expresses health if it functions 

normally relative to its reference ecosystem (adapted from SER 2004).

Frameworks provide a set of assumptions, concepts, 

values, and practices that constitute a way of viewing reality (American Heritage Dictionary 2011).

Free thinning is the removal of trees to control stand 

spacing and favor desired trees using a combination 

of thinning criteria without regard to crown position 

(Helms 1998).

Frequent-fire forests are forests with fire regime 1, 

those forests with fire frequency <35 years (Schmidt 

and others 2002).

Functions (ecological functions) are the outcomes of 

ecosystem components and processes (e.g., interactions within and among species). Examples include 

primary and secondary production and mutualistic 

relationships. Ecosystem functions are broadly categorized as regulation functions, habitat functions, 

production functions (e.g., genetic and medicinal 

resources), and information functions (e.g., spiritual and historic information) (De Groot and others 

2002).

Group refers to a cluster of two or more trees with 

interlocking or nearly interlocking crowns (Fig. 4 

and 12) at maturity surrounded by grass-forb-shrub 

interspaces (Fig. 8). Size of tree groups is typically 

variable depending on forest type and site conditions and can range from fractions of an acre (i.e., 

a two-tree group), such as in ponderosa pine or dry 

mixed-conifer forests, to many acres, as is common 

in wet mixed-conifer and spruce fir forests. Trees 

within groups are typically non-uniformly spaced, 

some of which may be tightly clumped.

Group cutting (selection) is the removed of small 

groups of trees to establish of new age classes 

(Helms 1998).

Improvement harvests involve the removal of poorly 

formed or low-vigor trees to improve stand productivity and/or quality (Helms 1998).

Interspaces are areas not currently under the vertical projection of the outermost perimeter of tree 

canopies (Fig. 8). They are generally composed of 

grass-forb-shrub communities but could also be 

areas with scattered rock or exposed mineral soil. 

Interspaces do not include meadows, grasslands, 

rock outcroppings, and wetlands (i.e., exclusions adjacent to and sometimes within forested landscapes).

Leave trees or snags (see residual (leave) trees or 

snags)

Matrix refers to the background cover type of an area. 

In frequent-fire forests, grass-forb-shrub communities form the background matrix upon which tree 

groups and individual trees are spatially arranged. 

It is the most extensive and connected landscape 

element that plays the dominant role in landscape 
72 USDA Forest Service RMRS-GTR-310. 2013.

functioning. The expression of this matrix between 

tree groups and individual trees is referred to as interspace. The location of tree groups and individual 

trees on the matrix and the proportion of patches 

represented by the matrix will change over time due 

to disturbance.

Mixed-severity fire regimes are characterized by 

closely juxtaposed forest patches affected by lowand high-severity burning (Fulé and others 2003).

Natural (historical, characteristic) range of variation describes the variability of ecological conditions 

(e.g., reference compositional and structural conditions) and the spatial and temporal variation in 

these conditions during a period of time specified to 

represent characteristic conditions (i.e., conditions 

relatively unaffected by people) for an ecosystem in 

a specific geographical area (Kaufmann and others 

1994; Landres and others 1999).

Old growth in Southwestern forested ecosystems is defined differently than the traditional definition based 

on Northwestern infrequent-fire forests. Due to 

large differences among Southwest forest types and 

their characteristic disturbances, old growth forests 

vary extensively in tree size, age classes, presence 

and abundance of structural elements, stability, and 

presence of understory. Important structural features of old growth in frequent-fire forests are large 

trees, old trees, age variability, snags, large dead and 

downed fuels, and between-patch structural variability (Fig. 9 and Table 10) (Kaufmann and others 

2007).

Openness is estimated as the inverse of forest canopy 

cover for a given area. For example, a forest with 

70 percent canopy cover would have openness of 

30 percent.

Patches are areas larger than tree groups in which the 

vegetation composition and structure are relatively 

homogeneous (sensu Forman 1995). Patches can be 

composed of randomly arranged trees or multiple 

tree groups, and they can be even-aged or unevenaged. Patches comprise the mid-scale, ranging in 

size from 10-1000 acres. Patches and stands are 

roughly synonymous.

Pattern (see spatial pattern)

Plant associations are plant community types based 

on land management potential, successional patterns, and species composition (Helms 1998).

Ponderosa pine forests are widespread in the 

Southwest occurring at elevations ranging from 

6000-7500 ft and occupying warmer and drier 

sites within the montane forest life zone. These 

forests are characterized by a relatively frequent 

historic fire regime resulting in surface fire effects. 

Ponderosa pine is the dominant tree species in this 

forest type, but other tree species may be present, 

including Gambel oak, pinyon pine, and juniper species. This forest type often has a shrubby understory 

mixed with grasses and forbs but sometimes occurs 

as savannah with extensive grasslands interspersed 

between widely spaced clumps or individual trees. 

The ponderosa pine type is distinguished from dry 

mixed-conifer types by the plant community successional stages. The ponderosa pine forest type is 

dominated at all successional stages from seral to 

climax by ponderosa pine. Ponderosa pine often 

dominates early seral stages of dry mixed-conifer 

forests also, but these types are not considered to 

be ponderosa pine forest types because the climax 

species composition is dominated by other conifer 

species or ponderosa pine in mixtures with other conifer species.

Processes (ecological processes) are the dynamic attributes of ecosystems in terms of matter and energy, 

including interactions among organisms and interactions between organisms and their environment (De 

Groot and others 2002; SER 2004). Examples of 

processes are: evolution, fire and insect disturbances, photosynthesis, seed dispersal, decomposition, 

and soil formation.

Reference conditions are conditions existing prior 

to the suppression or exclusion of the primary processes and mechanisms influencing a system along 

a natural trajectory (sensu Kaufmann and others 

1994). The reference can consist of one or several 

specified locations that contain model ecosystems, 

a written description, or a combination of both. 

Information collected on the reference includes both 

biotic and abiotic components (SER 2004)

Regeneration sites are tree-free areas created by 

group cutting for the purpose of establishing tree 

regeneration.

Residual (leave) trees or snags are those remaining 

after an intermediate or partial cutting of a stand 

(Helms 1998).

Resilience (see ecological resiliency)

Resiliency (see ecological resiliency)
USDA Forest Service RMRS-GTR-310. 2013. 73

Restoration (see ecological restoration)

Sanitation harvests involve the removal of trees to 

improve stand health by stopping or reducing the 

actual or anticipated spread of insects and disease 

(Helms 1998).

Safe zones (fire-free zones) are microsites where 

seedlings can establish and grow above the lethal 

flaming zone. Safe zones can be created by fire, such 

as the ash bed of a consumed log.

Single tree selection cutting is removal of individual trees of all size classes more or less uniformly 

throughout the stand to promote growth of remaining trees and to provide space for regeneration 

(Helms 1998).

Site index is an indicator of site quality expressed in 

terms of the average height of trees (defined as a 

certain number of dominants, codominants, or the 

largest and tallest trees per unit area) of a given species at a specified index or base age (Helms 1998).

Snags are standing dead or partially dead trees (snagtopped), often missing many or all limbs. They 

provide essential wildlife habitat for many species 

and are important for forest ecosystem function 

(Fig. 12).

Spatial pattern is the spatial arrangement of elements 

at the fine-, mid-, and landscape-scales that determine the function of a landscape as an ecological 

system (adapted from Helms 1998).

Stand density index is a widely used measure that 

expresses relative stand density based on some standard condition such as the relationship of number of 

trees to the stand quadratic mean diameter (Helms 

1998) or the biological maximum density for a specific species (Long 1985).

Stands are areas in which the biophysical site conditions and the vegetation composition and structure 

are relatively homogeneous. Stands comprise the 

mid-scale, thus ranging in size from 100-1000 acres. 

Stands and patches are roughly synonymous

Structure is the physiognomy or architecture of an 

ecosystem with respect to the density, horizontal 

stratification, spatial pattern, and frequency distribution of vegetation (i.e., overstory, understory, etc.) 

size, age, and/or life form (adapted from SER 2004).

Surface fuel includes all fuels lying on or near the 

surface of the ground, consisting of leaf and needle 

litter, dead branch material, downed logs, bark, tree 

cones, and low stature living and dead plants (adapted from NWCG 2012).

Sustainability (see ecosystem sustainability)

Uneven-aged forests are forests that are comprised of 

three or more distinct age classes of trees, either intimately mixed or in small groups (Fig. 18) (Helms 

1998).
74 USDA Forest Service RMRS-GTR-310. 2013.

Common name Scientific name

Tree species

Arizona walnut Juglans major

Arizona white oak Quercus arizonica

Bigtooth maple Acer grandidentatum

Blue spruce Picea pungens

Bristlecone pine Pinus aristata

Chihuahua pine Pinus leiophylla

Corkbark fir Abies lasiocarpa var. arizonica

Douglas-fir Pseudotsuga menziesii var. glauca

Emory oak Quercus emoryi

Evergreen oaks Quercus spp.

Gambel oak Quercus gambelii

Grey oak Quercus grisea

Junipers Juniperus spp.

Limber pine Pinus flexilis

Pinyon pines Pinus spp.

Ponderosa pine Pinus ponderosa

Quaking aspen Populus tremuloides

Silverleaf oak Quercus hypoleucoides

Southwest white pine Pinus strobiformis

Subalpine fir Abies lasiocarpa

Two-needle pinyon Pinus edulis

White fir Abies concolor

Shrub species

Big sagebrush Artemisia tridentata

Black sagebrush Artemisia nova

Ceanothus Ceanothus spp.

Common juniper Juniperus communis

Creeping barberry Mahonia repens

Currant Ribes spp.

Kinnikinnik Arctostaphylos uva-ursi

Manzanita Arctostaphylos spp.

Mountain mahogany Cercocarpus montanus

Mountain ninebark Physocarpus monogynus

Mountain snowberry Symphoricarpos oreophilus

Netleaf oak Quercus rugosa

New Mexico locust Robinia neomexicana

Pointleaf manzanita Arctostaphylos pungens

Rockspirea Holodiscus dumosus

Shrub live oak Quercus turbinella

Appendix 1. Common and Scientific Names for Species Referenced in This 

Document.
USDA Forest Service RMRS-GTR-310. 2013. 75

Stansbury cliffrose Purshia stansburiana

Sumac Rhus spp.

Wavyleaf oak Quercus undulata

Grass and sedge species

Arizona fescue Festuca arizonica

Blue grama Bouteloua gracilis

Dryspike sedge Carex siccata

Fringed brome Bromus ciliatus

Indian ricegrass Achnatherum hymenoides

Longtongue muhly Muhlenbergia longiligula

Mountain muhly Muhlenbergia montana

Muttongrass Poa fendleriana

Parry’s oatgrass Danthonia parryi

Screwleaf muhly Muhlenbergia virescens

Forb species

Forest fleabane Erigeron eximius

Nevada pea Lathyrus lanszwertii

Parasitic plant species

Douglas-fir dwarf mistletoe Arceuthobium douglasii

Southwestern (Ponderosa pine) dwarf mistletoe Arceuthobium vaginatum subsp. cryptopodum

Fungus species

Armillaria root disease Armillaria spp.

Black stain root disease Leptographium spp.

Insect species

Bark beetles Dendroctonus spp. and Ips spp.

Douglas-fir tussock moth Orgyia pseudotsugata

Roundheaded pine beetle Dendroctonus adjunctus

Spruce budworm Choristoneura occidentalis

Mammal species

Ground squirrels Callospermophilus spp. 

Coyote Canis latrans

Tassel-eared squirrel Sciurus aberti

Hares Lepus spp.

Bobcat Lynx rufus

Rabbits Sylvilagus spp.

Bird species

Northern goshawk Accipiter gentilis

Merriam’s turkey Meleagris gallopavo var. merriami
76 USDA Forest Service RMRS-GTR-310. 2013.

Appendix 2. Major Ponderosa Pine Forest Subtypes: (a) Ponderosa Pine/

Bunchgrass, (b) Ponderosa Pine/Gambel Oak, (c) Ponderosa Pine/Evergreen 

Oak, and (d) Ponderosa Pine/Evergreen Shrub.

APPENDIX 2. Major ponderosa pine forest subtypes: (a) ponderosa pine/bunchgrass, (b) 

ponderosa pine/Gambel oak, (c) ponderosa pine/evergreen oak, and (d) ponderosa pine/evergreen 

shrub. 

(a) (b)

(c) (d)
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