Pacific Southwest 

Research Station

Research Paper

PSW-RP-266

January 2014

United States Department of Agriculture 

Forest Service

Using Fire Return Interval 

Departure (FRID) Analysis to Map 

Spatial and Temporal Changes in 

Fire Frequency on National Forest 

Lands in California

Hugh D. Safford and Kip M. Van de Water
The U.S. Department of Agriculture (USDA) prohibits discrimination against its customers, 

employees, and applicants for employment on the bases of race, color, national origin, age, 

disability, sex, gender identity, religion, reprisal, and where applicable, political beliefs, marital 

status, familial or parental status, sexual orientation, or all or part of an individual’s income 

is derived from any public assistance program, or protected genetic information in employment or in any program or activity conducted or funded by the Department. (Not all prohibited 

bases will apply to all programs and/or employment activities.) 

If you wish to file an employment complaint, you must contact your agency’s EEO Counselor 

(PDF) within 45 days of the date of the alleged discriminatory act, event, or in the case of 

a personnel action. Additional information can be found online at http://www.ascr.usda.gov/

complaint_filing_file.html. 

If you wish to file a Civil Rights program complaint of discrimination, complete the USDA

Program Discrimination Complaint Form (PDF), found online at http://www.ascr.usda.gov/

complaint_filing_cust.html, or at any USDA office, or call (866) 632-9992 to request the form. 

You may also write a letter containing all of the information requested in the form. Send your 

completed complaint form or letter to us by mail at U.S. Department of Agriculture, Director, 

Office of Adjudication, 1400 Independence Avenue, S.W., Washington, D.C. 20250-9410, by 

fax (202) 690-7442 or email at program.intake@usda.gov. 

Individuals who are deaf, hard of hearing or have speech disabilities and you wish to file 

either an EEO or program complaint please contact USDA through the Federal Relay Service 

at (800) 877-8339 or (800) 845-6136 (in Spanish). 

Persons with disabilities, who wish to file a program complaint, please see information above 

on how to contact us by mail directly or by email. If you require alternative means of communication for program information (e.g., Braille, large print, audiotape, etc.) please contact 

USDA’s TARGET Center at (202) 720-2600 (voice and TDD). 

For any other information dealing with Supplemental Nutrition Assistance Program (SNAP) 

issues, persons should either contact the USDA SNAP Hotline Number at (800) 221-5689, 

which is also in Spanish or call the State Information/Hotline Numbers. 

For any other information not pertaining to civil rights, please refer to the listing of the USDA 

Agencies and Offices for specific agency information. 

Authors

Hugh D. Safford is a regional ecologist, Pacific Southwest Region, 1323 

Club Drive, Vallejo, CA 94592; Kip M. Van de Water is a fire planner, 

Klamath National Forest, 1711 South Main Street, Yreka, CA 96097.
i

Summary

In the state of California, fire regimes and related ecosystem processes have been 

altered by land use practices associated with Euro-American settlement, and climate warming is exacerbating the magnitude and effects of these changes. Because 

of changing environmental baselines, restoration of narrowly defined historical conditions may no longer be an attainable or sustainable long-term management goal, 

but comparisons between historical and current fire regimes can assist managers 

in prioritizing areas for ecological restoration and other management actions. Fire 

return interval departure (FRID) analysis quantifies the difference between current 

and presettlement fire frequencies, allowing managers to target areas at high risk of 

threshold-type responses owing to altered fire regimes and interactions with other 

factors. We assessed FRID variability along geographic, climatic, and vegetation 

gradients in California on lands managed by the U.S. Department of Agriculture 

Forest Service and three forest-dominated national parks, using two types of FRID 

metrics: percent FRID, and the NPS-FRID index. Percent FRID (PFRID) quantifies 

the extent in percentage to which contemporary fires (i.e., since 1908) are burning 

at frequencies similar to those that occurred prior to Euro-American settlement. 

The NPS-FRID index represents the number of intervals missed since the last fire 

relative to the central tendency of presettlement fire return interval (FRI) distributions. Much of northwestern (NW) California and the Sierra Nevada sensu lato 

(including the southern Cascades, Modoc Plateau, and White and Inyo Mountains) 

has missed multiple fire cycles owing to fire suppression, while southern California 

is characterized by large areas burning at higher frequencies than under presettlement conditions. Ecologically speaking, fire suppression is a management necessity 

in much of southern California, but it is a major contributor to the growing forest 

fuels problem in NW California and the Sierra Nevada region. The PFRID exhibited a unimodal (hump-shaped) relationship to elevation in all three regions. The 

PFRID showed little relationship to precipitation in NW California or the Sierra 

Nevada region, but it decreased with precipitation in southern California. PFRID 

trends with temperature were unimodal, reaching a maximum at temperatures that 

approximate the elevation of the mean freezing line in winter storms, which is also 
ii

the transition between moist mixed conifer and red fir in most of northern California. Low- and middle-elevation vegetation types supported the greatest departures 

from presettlement fire frequencies, with oak woodlands, yellow pine, and mixedconifer forests missing the most fire cycles, and coastal fir, coastal sage scrub, and 

chaparral tending to experience shorter FRIs than under presettlement conditions. 

We provide examples of how FRID data may be used in resource management in an 

age of global change. Our results help refine our understanding of departures from 

presettlement fire regimes across California, and provide a spatial basis for resource 

management and planning focused on ecological restoration and adaptation to 

climate change in a fire-prone region. 
iii

Abstract

Safford, Hugh D.; Van de Water, Kip M. 2014. Using fire return interval departure (FRID) analysis to map spatial and temporal changes in fire frequency 

on national forest lands in California. Res. Pap. PSW-RP-266. Albany, CA: U.S. 

Department of Agriculture, Forest Service, Pacific Southwest Research Station. 

59 p.

In California, fire regimes and related ecosystem processes have been altered by 

land use practices associated with Euro-American settlement, and climate warming 

is exacerbating the magnitude and effects of these changes. Because of changing 

environmental baselines, restoration of narrowly defined historical conditions may 

no longer be an attainable or sustainable long-term management goal, but comparisons between historical and current fire regimes can assist managers in prioritizing 

areas for ecological restoration and other management actions. Fire return interval 

departure (FRID) analysis quantifies the difference between current and presettlement fire frequencies. We assessed FRID variability along geographic, climatic, 

and vegetation gradients in California on lands managed by the U.S. Department 

of Agriculture Forest Service and three forest-dominated national parks, using two 

types of FRID metrics: percent FRID (PFRID), and the NPS-FRID index. Much 

of northern California north of the Tehachapi Mountains has missed multiple fire 

cycles owing to fire suppression, while southern California is characterized by 

large areas burning at higher frequencies than under presettlement conditions. 

PFRID exhibited a unimodal (hump-shaped) relationship to elevation across our 

study area. PFRID showed little relationship to precipitation in northwest California or the Sierra Nevada region, but it decreased with precipitation in southern 

California. PFRID trends with temperature were unimodal, reaching a maximum 

at temperatures that approximate the elevation of the mean freezing line in winter 

storms, which also marks the transition between moist mixed conifer and red fir in 

most of northern California. Low- and middle-elevation vegetation types supported 

the greatest departures from presettlement fire frequencies, with oak woodlands, 

yellow pine, and mixed-conifer forests missing the most fire cycles, and coastal fir, 

coastal sage scrub, and chaparral tending to experience shorter FRIs than under 

presettlement conditions. Our results help refine our understanding of departures 

from presettlement fire regimes across California, and provide a spatial basis for 

resource management and planning focused on ecological restoration and adaptation to climate change in a fire-prone region. 

Keywords: Ecological restoration, fire history, presettlement fire regime, Sierra 

Nevada, time since last fire.
iv

Contents

1 Introduction

3 Methods

3 Study Area

5 Fire Regime Typing

7 FRID Mapping

15 Results

15 Geographic Patterns

20 Differences Among Management Units

24 Elevational Trends

26 Precipitation Trends

26 Temperature Trends

31 Differences Among Presettlement Fire Regime (PFR) Types 

31 Discussion

33 Patterns in Southern California

35 Great Basin

36 Northwestern California and the Sierra Nevada Regions

38 FRID Trends Along Environmental Gradients

41 Using FRID Data in Resource Management

49 Acknowledgments

50 English Equivalents

50 Literature Cited
1

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Introduction

Fire is a keystone ecological process in most of the world’s Mediterranean-climate 

regions (Keeley et al. 2011). In the state of California, which comprises most of 

the North American Mediterranean-climate region, fire regimes (including fire 

frequency, severity, extent, spatial patterning, etc.) and related ecosystem processes 

have been profoundly altered by land use practices associated with Euro-American 

settlement, especially since American settlement began in earnest after 1849. 

These changes have in turn led to major modifications in vegetation distribution, 

structure, and composition (Agee 1993, Barbour et al. 2007, Skinner and Chang 

1996, Sugihara et al. 2006). Climate variability at different temporal scales has 

also been shown to be associated with past, current, and projected changes in fire 

regimes, vegetation, and other ecosystem properties across California (Lenihan et 

al. 2003; Miller et al. 2009, 2012; National Research Council 2011; Swetnam 1993; 

Westerling et al. 2006, 2011). In a drought- and fire-prone region like California, 

ecological restoration efforts intended to increase ecosystem resilience to global 

change should consider fire and climate as central organizing principles (Keeley et 

al. 2011, North et al. 2009). 

Practices of ecological restoration traditionally depend on the characterization 

of reference conditions, which can provide management targets and a means to 

measure management success (Egan and Howell 2001). Because modern human 

alteration of many ecosystems has been so profound, reference states must often be 

derived from historical information from before the onset of anthropogenic change. 

Maintaining managed ecosystems within the bounds of the “historical range of 

variation” (HRV) for key ecosystem patterns or processes has traditionally been 

seen as the best hope for preserving species and landscapes and ensuring long-term 

ecological sustainability (Egan and Howell 2001, Landres et al. 1999). Our trust 

in history as a dependable guide to the future has been tempered, however, by the 

revelation that many key ecosystem processes are not stationary, and that historical 

environmental baselines may or may not represent conditions that are particularly 

germane to either contemporary or future circumstances (Millar et al. 2007, Safford 

et al. 2012, Stephenson et al. 2010). At the same time, there is widespread recognition that the real problem lies not in history itself, which provides our only window 

into ecological processes operating at longer temporal scales, but rather in how 

historical information is applied to resource management (Stephenson et al. 2010, 

Wiens et al. 2012). 

Because of the uncertainty surrounding future effects of climate and land

use change on ecological processes such as fire, myopic focus on restoration of 

narrowly defined and static snapshots of historical conditions is probably not a 
2

research paper psw-rp-266

sustainable management option in most California ecosystems (Millar et al. 2007, 

Safford et al. 2012). Nevertheless, information on fire regimes and ecosystem 

response to fire before Euro-American settlement is of elementary importance to 

current and future resource management (Fulé 2008, Millar et al. 2007, North et al. 

2009, Van de Water and Safford 2011, Wiens et al. 2012). Such historical information can provide a foundation for understanding status of and trends in fire activity 

and its ecological effects over time; improve our understanding of the mechanisms 

that drive ecosystem response to climate and fire, their variability and their interactions with the landscape; furnish data upon which models of “properly functioning” 

or “resilient” ecosystems might be built; and determine to what extent current 

conditions may be historically anomalous and worthy of management intervention 

(Safford et al. 2012, Wiens et al. 2012).

The most commonly used fire regime attribute in reconstructions of historical 

fire regimes is fire frequency (Agee 1993, Johnson and Gutsell 1994). Disturbance 

frequency is a major driver of ecological and evolutionary response (Connell 

1978, Huston 1994, Pickett and White 1985), and although frequency is only one 

component of the fire regime, the dependence of fire occurrence and behavior 

on the growth of vegetation produces a broadly inverse relationship between fire 

frequency and intensity (within a given ecosystem type, and assuming a constant 

climate) (Huston 2003, Turner et al. 1989). This relationship has permitted the 

development of simplistic but useful frequency-based and severity-based (a measure 

of the effect of fire intensity on the ecosystem) fire regime classifications that 

underlie mapping and management of wildland fire and fuels in the United States 

(e.g., Hardy et al. 1998, Heinselman 1978, Kilgore 1981). Within ecosystem types, 

this relationship also allows some (cautious) inference to be made about the effects 

of changing fire frequency on other fire regime attributes. 

Drawing comparisons between past and current fire frequencies can assist 

resource managers in prioritizing areas for ecological restoration, fuels reduction, 

certain fire or habitat management practices, and other activities. Fire return interval departure (FRID) analysis is a method for quantifying the difference between 

current and presettlement fire frequencies on a management landscape (Caprio 

et al. 1997, Caprio and Graber 2000, van Wagtendonk et al. 2002). By comparing 

current fire return intervals (FRIs) with the range of reported pre-Euro-American 

settlement FRIs from the literature, a sort of rudimentary HRV analysis can be 

conducted. In such an analysis, quantified current departures from the reference 

conditions provide a basis to identify areas on the management landscape that are 

at high risk of type conversion or threshold-type responses owing to either greatly 

accelerated or greatly decelerated fire frequencies; areas that are within HRV can 

also be identified and targeted for maintenance management or study. 
3

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Several FRID assessments have been conducted in California, focused on 

individual national parks in the Sierra Nevada (Caprio et al. 1997, Caprio and 

Graber 2000, van Wagtendonk et al. 2002). These studies are enlightening, but they 

are of limited use in regional-scale restoration planning applications. In addition, 

the national park FRID analyses have been focused only on the time since the most 

recent fire, rather than on the complete record of fire location and size that has been 

collected since the early 20th century. Restoration of fire as an ecological process, 

and restoration of other ecosystem properties dependent on fire, will require more 

than a single application of prescribed fire or the occurrence of a single wildland 

fire. We therefore developed a complementary set of different FRID measures 

based on spatial fire records from throughout the last century. These percentagebased measures are less sensitive to the incidence of a single fire, and are more 

suitable to comparisons of fire frequencies over time between the current and 

pre-Euro-American settlement periods.

In this study, we determined time since last fire (TSLF) and calculated two sets

of FRID measures (percent-based and National Park Service [NPS] calculations) 

for the approximately 8.1 million ha of land managed by the U.S. Forest Service in 

California. Except in some wilderness areas, most fires on Forest Service lands

in California are subject to fire-suppression efforts, although indirect attack techniques can allow for significant fire spread under some circumstances. For comparison, we also include analyses of three of the largest national parks in California 

(Sequoia, Kings Canyon, and Yosemite national parks (NPs), 1.6 million ha in 

total), which encompass large areas where naturally ignited fires are allowed to 

burn under certain conditions for ecological benefit (van Wagtendonk 2007). In our 

analysis, we divided the state into three large geographic regions, which exhibit 

notable differences in climate, geography, and human land use that we expected to 

affect FRID: northwestern (NW) California; the Sierra Nevada, southern Cascades, 

Modoc Plateau, and White and Inyo Mountains (the combination of which we refer 

to as the “Sierra Nevada”); and southern California. Our focus was on identifying 

and interpreting major patterns in FRID across gradients of geography, climate, 

vegetation, and management, so as to provide a basis for broad-scale decisionmaking in resource management and planning on Forest Service and neighboring lands 

across California.

Methods

Study Area

The analysis area for this study, which consisted of the 19 national forests (NFs) 

and three NPs in California, was divided into three geographic regions: (1) NW 
4

research paper psw-rp-266

California (Klamath, Mendocino, Shasta-Trinity, and Six Rivers NFs); (2) the Sierra 

Nevada, including the southern Cascades, Modoc Plateau, and White and Inyo 

Mountains (El Dorado, Humboldt-Toiyabe [California portion only], Inyo, Lassen,

Modoc, Plumas, Sequoia, Sierra, Tahoe, and Stanislaus NFs); the Lake Tahoe 

Basin Management Unit; and Yosemite, Sequoia, and Kings Canyon NPs; and (3) 

Southern California (Angeles, Cleveland, Los Padres, and San Bernardino NFs) 

(fig. 1). Most of California and almost all Forest Service lands in the state (except 

the eastern Inyo and Modoc NFs) are found in the Mediterranean-climate zone. In 

this paper, we refer collectively to the NW California and Sierra Nevada regions as 

“northern California.” In northern California, winters are cool and wet, and summers are warm and dry. In most of southern California, winters are cool but not 

as wet (except for the northern and central maritime portions of ecological section 

261A (Miles and Goudey 1997) (fig. 1), and summers are warmer and drier than 

in the north. See Major (1988) and Minnich (2007) for information on temperature 

and precipitation in each region. Elevations in the NW California region range from 

near sea level to 4320 m; in the Sierra Nevada region, elevations range from 50 to 

Figure 1—The study area. (A) Federal land management units included in the study area, segregated by geographic region (see text): NW 

= northwest California region; SN = Sierra Nevada region; SC = southern California region. National forests, clockwise from NW to SC: 

MNF = Mendocino, SRF = Six Rivers, SHF = Shasta-Trinity, KNF = Klamath; MDF = Modoc, LNF = Lassen, PNF = Plumas, TNF = 

Tahoe, TMU = Lake Tahoe Basin Management Unit, ENF = Eldorado, STF = Stanislaus, HTF = Humboldt-Toiyabe, SNF = Sierra, INF 

= Inyo, SQF = Sequoia; BDF = San Bernardino, CNF = Cleveland, ANF = Angeles, LPF = Los Padres. National Parks, in the Sierra 

Nevada: YNP = Yosemite, KNP = Sequoia-Kings Canyon. (B) Ecological sections in California, from Miles and Goudey (1997), and 

defined in table 2 on p. 12. 
5

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

4420 m; in the southern California region, elevations range from near sea level to 

3500 m. Vegetation in the analysis area in northern California is dominated by conifer forest, with substantial hardwood presence in lower and middle elevation areas 

with sufficient precipitation. Vegetation in the analysis area in southern California 

is dominated by hardwood forests/woodlands and shrublands (Barbour et al. 2007) 

with conifer forests of relatively small areal extent at high elevations.

Fire Regime Typing

Current existing vegetation types within the analysis area (as identified by the

Forest Service’s CALVEG classification) (Franklin et al. 2000; see http://www.

fs.usda.gov/detail/r5/landmanagement/resourcemanagement/?cid=stelprdb5347192)

were organized into 28 presettlement fire regime (PFR) groups according to the 

similarity of their historical relationships with fire (Van de Water and Safford 2011). 

The PFRs were developed only for vegetation types dominated by woody plants, as 

our understanding of historical fire regimes in herbaceous vegetation is poor. For 

each PFR, we conducted an exhaustive review of the published and unpublished 

literature pertaining to mean, median, minimum, and maximum FRIs that occurred 

prior to significant Euro-American settlement (i.e., the middle of the 19th century), 

and the average was taken of all mean, median, minimum, and maximum FRI 

values to yield a single mean, median, mean minimum, and mean maximum FRI 

estimate for each PFR (Van de Water and Safford 2011) (table 1). Most of the data 

used came from composite dendrochronological fire histories including records 

from multiple trees in a defined area. Our reference period is thus primarily the c. 

200 to 500 years before 1850, although some of the records we accessed go back 

over 2000 years (Van de Water and Safford 2011).

Polygons were created for each area dominated by a given PFR and its associated presettlement mean, median, minimum, and maximum FRIs using ArcGIS 9.31

(ESRI 2008). The mapping base was the Forest Service existing vegetation (EVEG) 

geodatabase (available at http://www.fs.usda.gov/detail/r5/landmanagement/

gis/?cid=STELPRDB5327836), which uses the CALVEG classification. We chose 

to use existing-vegetation polygons instead of potential natural vegetation (PNV) 

types because Forest Service PNV mapping was never completed in California and 

completed PNV mapping (mostly) disregarded disturbances like fire. The LANDFIRE Biophysical Settings (BpS) data are PNV types that incorporate disturbance 

(Rollins 2009), but the mapped accuracy of this modeled data layer varies across

1

 Use of trade or firm names in this publication is for reader information and does not 

imply endorsement by the U.S. Department of Agriculture of any product or service.
6

research paper psw-rp-266

Table 1—Mean, mean minimum (min) and mean maximum (max) pre-Euro-American 

settlement fire return intervals (FRIs) for the presettlement fire regime (PFR) groups,

and the current FRIs associated with the boundaries between condition classes (CCs) 

PFR

Mean 

min FRI

CCs-2 

and-3

CCs-1 

and-2

Mean 

FRI

CCs 1 

and 2

CCs 2 

and 3

Mean 

max FRI

- - - - - - - - - - - - - - - - - - - - - Years - - - - - - - - - - - - - - - - - - - - - 

Aspen 10 6.3 12.7 19 28.4 57.6 90

Big sagebrush 15 11.6 23.5 35 52.2 106.1 85

Bigcone Douglas-fir 5 10.2 20.8 31 46.3 93.9 95

Black and low sagebrush 35 21.8 44.2 66 98.5 200.0 115

California juniper 5 27.4 55.6 83 123.9 251.5 335

Chaparral and serotinous conifers 30 18.2 36.9 55 82.1 166.7 90

Coastal fir 90 32.7 66.3 99 147.8 300.0 435

Coastal sage scrub 20 25.1 50.9 76 113.4 230.3 120

Curl-leaf mountain mahogany 30 17.2 34.8 52 77.6 157.6 130

Desert mixed shrub 120 201.3 408.7 610 910.4 1,848.5 1,440

Dry mixed conifer 5 3.6 7.4 11 16.4 33.3 50

Fire-sensitive spruce or fir 90 38.6 78.4 117 174.6 354.5 250

Lodgepole pine 15 12.2 24.8 37 55.2 112.1 290

Mixed evergreen 15 9.6 19.4 29 43.3 87.9 80

Moist mixed conifer 5 5.3 10.7 16 23.9 48.5 80

Montane chaparral 15 8.9 18.1 27 40.3 81.8 50

Oak woodland 5 4.0 8.0 12 17.9 36.4 45

Pinyon-juniper 50 49.8 101.2 151 225.4 457.6 250

Port Orford cedar 10 9.9 20.1 30 44.8 90.9 160

Red fir 15 13.2 26.8 40 59.7 121.2 130

Redwood 10 7.6 15.4 23 34.3 69.7 170

Semidesert chaparral 50 21.5 43.6 65 97.0 197.0 115

Shore pine 190 82.5 167.5 250 373.1 757.6 1,025

Silver sagebrush 15 11.6 23.5 35 52.2 106.1 65

Spruce-hemlock 180 90.8 184.3 275 410.4 833.3 550

Subalpine forest 100 43.9 89.1 133 198.5 403.0 420

Western white pine 15 16.5 33.5 50 74.6 151.5 370

Yellow pine 5 3.6 7.4 11 16.4 33.3 40

Note: Mean, mean minimum, and mean maximum FRIs are from Van de Water and Safford (2011).

Forest Service lands in California. We believe the BpS maps are of reasonable 

accuracy at the regional (e.g., NW California, Sierra Nevada, southern California) 

or statewide scale, especially when similar vegetation types are pooled (see, e.g., 

Miller and Safford 2012), but our FRID geodatabase is intended to support planning 

and management at all spatial scales. Local inaccuracies in the BpS maps make use 

at or below the scale of a national forest or national park unit challenging. Note that 

our use of an existing-vegetation map from the 2000s as our base layer means that 
7

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

major changes in vegetation that have occurred since Euro-American settlement 

will affect the accuracy of our results. We elaborate on this issue in the “Discussion” section. 

We carried out a union between the PFR geodatabase and CalFire’s Fire 

Perimeters database (FRAP 2011), which tracks California fire history. The Fire 

Perimeters database is considered more or less complete for fires larger than 40 ha 

after 1908, and over 4 ha after 1950 (Miller et al. 2009); many smaller fires are also 

reported. The resulting geodatabase split the PFR polygons into smaller polygons 

based on the number of fires that had occurred in each PFR polygon since 1908.

FRID Mapping

Time since last fire was calculated by subtracting the year of the last fire in each 

polygon from 2010 (the most recent year included in the Fire Perimeters database 

when we conducted our analysis). Any polygon that had not had a fire since prior 

to 1908 was assigned a default TSLF value of 103 years; in many cases, TSLF 

will thus be a conservative measure of the time since last burn. Current FRI was 

calculated by dividing the number of years in the fire record (i.e., 2010–1908 = 103 

years inclusive) by the number of fires occurring in each polygon (according to the 

Fire Perimeters database) plus one (current FRI = 103/number of times burned + 1). 

This calculation of current FRI is generally conservative because small fires (<40 

ha prior to 1950, <4 ha after 1950) are not included in the Fire Perimeters database 

(FRAP 2011), prescribed fires are rarely included, and some parts of California 

have poor fire records for the period before World War II. 

Five FRID metrics were calculated for each polygon. Four of them—mean, 

median, mininum, and maximum percent FRID (PFRID)—were calculated using 

the following equation when current FRI is longer than presettlement FRI (Hann 

and Strohm 2003): 

PFRID = [1 – (presettlement FRI/purrent FRI)] × 100,

or, when current FRI is shorter than presettlement FRI: 

PFRID = – [1 – (current FRI/presettlement FRI)] × 100

where presettlement mean, median, mean minimum, and mean maximum FRI

are each substituted for their respective PFRID metrics. These PFRID metrics 

quantify the extent in percent to which contemporary fires (i.e., since 1908) are 

burning at frequencies similar to the frequencies that occurred prior to EuroAmerican settlement. To use an example: assuming a presettlement mean FRI of 10 

years for some theoretical PFR, a current FRI of 30 years would be a +67 percent 

departure ([1 – (10/30)] × 100); for the same PFR, a current FRI of 5 years would be 

a -50 percent departure (-[1 – (5/10)] × 100). PFRID considers the cumulative fire 
8

research paper psw-rp-266

history of each polygon since 1908 and does not return to zero when a fire occurs. 

For areas dominated by PFRs with a presettlement FRI greater than 103 years and 

that have not burned in the period of historical record considered in this analysis 

(i.e., since 1908), PFRID is assumed to equal zero. 

Plotted against current FRI, the underlying distribution of the PFRID metric 

appears approximately linear between -99.9 percent and about +40 percent departure, but above +40 percent departure, increasingly larger leaps in current FRI are 

required to move the departure statistic (fig. 2), This is because the metric is actually asymptotic (see plot of PFRID against a logarithmic scale of current FRI, fig. 

2 inset), owing to the behavior of the PFRID formulas, which divide presettlement 

FRI by current FRI in cases of positive PFRID and divide current FRI by presettlement FRI in cases of negative PFRID (Hann 2004, Hann and Strohm 2003). This 

property of the PFRID equations makes them particularly well suited to the analysis 

of fire frequency distributions where the mean and median FRIs are similar but a 

small number of high values results in a long tail to the right. This is not an uncommon situation with discrete data sets (Von Hippel 2005) and is the case with many 

of our nonmaritime forested PFRs (see fig. 1 in Van de Water and Safford 2011). 

Mean and median PFRID measure the departure of current FRIs from the 

central tendency of presettlement FRIs, with median PFRID perhaps better representing the skewed nature of FRI distributions in some vegetation types (i.e., more 

short or long intervals, depending on the fire regime). Mean PFRID is the standard 

measure used by the Forest Service in California to spatially map contemporary 

departure from presettlement fire frequencies (Safford et al. 2011). Mean minimum 

and mean maximum (hereafter min and max) PFRIDs represent liberal and conservative estimates, respectively, of the departure of current from presettlement FRIs. 

Min and max PFRIDs are important variables, as they (hypothetically) provide 

us with approximate lower and upper bounds of sustainability/resilience for the 

ecosystems represented by our PFRs. Landscapes that are characterized by negative 

min PFRID values are burning much more frequently today than under presettlement conditions; these landscapes may have undergone or may be undergoing 

vegetation type conversion and should be focus areas for enhanced fire suppression and public education efforts. In contrast, landscapes that are characterized by 

positive max PFRID values are usually those that were historically characterized 

by frequent fire but have not experienced fire for a half century or more. These 

areas may have experienced major changes in vegetation composition and fuels and 

should be focus areas for fuel reduction by fire restoration or fire surrogates. 

We categorized mean PFRID values into “condition classes” (CCs) (see Safford 

et al. 2011), where values from 0 to 33 percent and 0 to -33 percent are classified as 
9

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Figure 2—Percent departure versus current fire return interval (FRI) for a presettlement fire 

regime with a mean presettlement reference FRI of 100 years. The inset plots the departure from 

-99.9 percent to +99.9 percent against a Log FRI scale.

CC 1 and CC -1 (“low departure”), respectively, while values from 33 to 67 percent 

and -33 to -67 percent are CC 2 and CC -2 (“moderate departure”), and values 

greater than 67 percent and more negative than -67 percent are CC 3 and CC -3 

(“high departure”). This CC categorization, which simply divides the 0 to 100-percent departure scale into thirds, was developed by the interagency Fire Regime 

Condition Class (FRCC) program (Hann 2004, Hann and Strom 2003). Positive CCs 

apply where contemporary fire frequencies are less than presettlement frequencies 

(Hann and Strohm 2003); negative CCs apply where contemporary fire frequencies 

are greater than presettlement frequencies (Safford et al. 2011). 

Table 1 provides background information to help in the interpretation of the 

PFRID statistics. Table 1 expands on Van de Water and Safford (2011) to provide 

the current FRIs associated with the boundaries between the CCs for each PFR. 

It can be appreciated from table 1 that CC 1 and CC -1 occupy the area between 

current FRIs that are one-half the frequency of the presettlement FRI and 1.5 times 

the presettlement FRI. Condition class 2 is found in the area where current FRI 

is between 1.5 and three times the presettlement FRI; CC 3 is thus defined as any 

current FRI that is more than three times the presettlement FRI. The boundary 

between CCs -2 and -3 is found where current FRIs are one-third the presettlement 

FRIs (i.e., current fires are three times as frequent as the presettlement mean). 
10

research paper psw-rp-266

The fifth FRID metric we calculated was the NPS FRID index, which represents the number of intervals missed since the last fire relative to the central 

tendency of presettlement FRI distributions. The NPS-FRID index was calculated 

using the following equation: 

NPS-FRID index = -[(presettlement mean FRI-TSLF)/presettlement mean FRI].

Note that the sign of the equation used in this analysis has been reversed from 

the original formula (see van Wagtendonk et al. 2002) to facilitate interpretation 

of FRID trends that is consistent with the sign of our other PFRID metrics. The 

NPS-FRID metrics were developed by the NPS for the southern Sierra Nevada and 

do not consider the cumulative fire history of each polygon, but only the time since 

the last fire (Caprio et al. 1997, Caprio and Graber 2000, Keifer et al. 2000, van 

Wagtendonk et al. 2002). The NPS-FRID metrics measure the number of presettlement FRIs missed since the first missed cycle, and are thus not helpful in identifying areas where current FRI is shorter than presettlement FRI. The NPS-FRID 

index values less than 0 are classified as low departure, while values from 0 to 2 

are moderate, values from 2 to 5 are high, and values greater than 5 are extreme 

(van Wagtendonk et al. 2002). Note that the interpretive differences between the 

two metrics notwithstanding, the numerical boundary between moderate and high 

NPS-FRID (three missed fires) is equivalent to the boundary between moderate 

(CC 2) and high (CC 3) departure in the PFRID measure (current FRI = three times 

the reference FRI). 

Fire return interval departure mapping products described above (including 

PFR, number of fires since 1908, TSLF, and all FRID values) were developed 

by the Forest Service’s Pacific Southwest Region Ecology Program and Remote 

Sensing Lab (Safford et al. 2011) and are available online at http://www.fs.usda.gov/

main/r5/landmanagement/gis.

To provide some geoecological context for our spatial patterns of departure, 

FRID GIS layers were intersected with a layer consisting of the ecological sections 

and subsections in California (figs. 1 and 3) (Miles and Goudey 1997). The subsection descriptions in Miles and Goudey (1997) provide information on prevalent 

environmental conditions in each subsection, including elevations, temperature 

and precipitation, soils, geology, vegetation, and human uses. The area-weighted 

average of TSLF and FRID values within each subsection was calculated, using 

only those Forest Service or analyzed NPS lands that occurred within each subsection, and then TSLF and FRID values were mapped by subsection; the section 

summaries in table 2 are the summed results from the subsection area-weighting 

and likewise represent only analyzed federal lands. We report results only for 

subsections that contained at least 5 percent Forest Service or NPS lands. Figure 3 
11

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Figure 3—Ecological subsections intersecting with the national forests and national 

parks analyzed in this study. Dark gray subsections are those where >25 percent of the

land base is found within analyzed management units; light gray = 10 to 25 percent

of the land base is within analyzed management units; black = ≤ 10 percent of the

land base is within analyzed management units. USFS = United States Forest Service, 

NPS = Natioinal Park Service.
12

research paper psw-rp-266 Table 2—Differences in time since last fire (TSLF) and fire return interval departure (FRID)a among ecological sectionsa, averaged from subsections within the section that contains lands under Forest Service or National Park Service (NPS) (Yosemite or Sequoia-Kings Canyon National Parks) administration 

Ecological section Code n

Analyzed 

area

Section 

area

Percentage 

of section 

analyzed TSLF

Min.

PFRID

MeanPFRID Median PFRID Max. PFRID

NPSFRID 

Indexb

Hectares Years

Central Coast 261A 2 137 055 1 375 722 10.0 16 25 -19 -7 -57 -0.6

Southern Coast 261B 5 75 765 1 449 424 5.2 33 30 -20 -25 -49 -0.4Northern Coast 263A 3 11 637 1 722 125 0.7 101 85 71 83 18 3.3Mojave Desert 322A 3 26 074 6 683 237 0.4 79 16 -1 -4 -17 -0.3Colorado Desert 322C 1 229 1 185 242 0.0 19 -65 -91 -92 -95 -1.0Mono 341D 14 402 040 798 345 50.4 98 65 39 39 8 1.0Southeastern Great Basin 341F 4 83 116 1 103 764 7.5 103 38 21 21 5 0.2Northwestern Basin and Range 342B 5 3 776 522 403 0.7 94 78 53 51 6 1.8Klamath Mountains M261A 16 1 755 085 2 256 796 77.8 77 84 67 79 10 3.3Northern Coast Ranges M261B 3 614 783 1 552 449 39.6 77 82 64 70 9 3.3Northern Interior Coast Ranges M261C 2 4267 749 443 0.6 69 74 52 60 3 2.3Southern Cascades M261D 9 958 842 1 702 525 56.3 90 86 70 73 18 4.6Sierra Nevada M261E 21 4 116 267 5 159 316 79.8 84 78 61 66 11 3.3Sierra Nevada Foothills M261F 5 152 857 1 819 108 8.4 58 62 31 39 -18 1.8Modoc Plateau M261G 11 711 801 1 430 882 49.8 90 75 51 56 13 2.9Central Coast Ranges M262A 3 190 822 2 485 244 7.7 22 0 -41 -45 -63 -0.6Southern Mountains and Valleys M262B 16 1 193 734 2 755 093 43.3 43 34 -6 -5 -40 0.3

n = Number of analyzed ecological subsections within each section. a Percentage fire return interval departure (PFRID) data are read as percentage departure. b Sign of NPS-FRID Index values reversed to allow consistent interpretation with other FRID measures.
13

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Figure 4—Time since last fire and the five fire return interval departure metrics for federal lands analyzed in this study. Negative 

percent FRID (PFRID) measures (warm colors) identifies places where current fire return interval (FRI) is shorter than the presettlement 

FRI; positive PFRID (cool colors) identifies places where current FRI is longer than the presettlement FRI. The PFRID measures are 

grouped into categories that approximately correspond to the standard condition classes from Hann and Strohm (2003) (see “Methods”). 

For the National Park Service FRID Index, see “Methods” for interpretation of the index values. 
14

research paper psw-rp-266

identifies those ecological subsections where these federal lands account for < 25 

percent and < 10 percent of subsection area. Be careful to avoid overinterpretation 

of our results in these subsections. 

We selected those ecological subsections with the highest positive departure 

(greater than 17 percent departure, n = 25; the “P” group) as determined by max 

PFRID (see fig. 4), and those subsections with the most negative departure (more 

than 33 percent negative departure, n = 17; the “N” group), and used data from 

Miles and Goudey (1997) to characterize each of the 42 subsections with respect 

to lowest elevation, mean annual precipitation (from the range given); low mean 

annual temperature; high mean annual temperature; mean annual frost-free period 

in days (from the range given); dominant precipitation type (rain = 1, snow = 2, 

mix = 1.5); soil temperature (thermic = 4, mesic = 3, frigid = 2, cryic = 1; intermediate values given for mixes); and soil moisture (udic = 3, xeric = 2, aridic = 1; 

intermediate values given for mixes). Each of the 42 subsections was assigned a 

predominant pre-Euro-American settlement FRI using the predominant vegetation 

types described by Miles and Goudey (1997) and crosswalking these to the PFRs 

in Van de Water and Safford (2011). We also assigned each subsection a human 

Figure 4—Continued.
15

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

population density from the California county that the subsection was found in; in 

cases of multiple counties, we averaged the densities (data from http://www.csac.

counties.org/). The above data were entered into a Principal Components Analysis 

(PCA) ordination using PC-ORD v. 5 (McCune and Mefford 2006). Before analysis, 

the input data were relativized by their maximum values. Monte Carlo permutations 

of the data were carried out 1,000 times in order to calculate correlations of the 

environmental data with the ordination axes. We also statistically compared the P 

and N group means for the environmental and population data listed above using 

the univariate nonparametric Mann-Whitney U test. 

In the southern Sierra Nevada, we compared differences in FRID between three 

large national parks (Yosemite, Sequoia, and Kings Canyon, all of which include 

large areas where naturally ignited fires are allowed to burn for ecological benefit) 

and the five adjacent national forests by calculating the area-weighted average of 

TSLF and FRID values in the two jurisdictions. 

Elevational trends in departure were explored using the zonal statistics function 

of ArcGIS 9.3 (ESRI 2008), which averaged the TSLF and FRID values of all cells 

of identical elevation in a 100-m digital elevation model. Trends in departure along 

precipitation, mean, minimum, and maximum temperature gradients were also 

explored using zonal statistics on averaged annual PRISM climate normal grids, 

1971–2000 (PRISM Group 2004).

Results

Geographic Patterns

Geographic patterns of FRI departure on the analyzed Forest Service and NPS 

lands generally showed positive FRID and longer TSLF in NW California and 

Sierra Nevada, and negative FRID and shorter TSLF in southern California; trends 

were relatively consistent across all metrics (table 2, figs. 3 through 6). TSLF 

strongly reflected the fire seasons of 2003 and 2005–2008 and resulted in very 

low departure according to the NPS-FRID Index (which is based only on the most 

recent fire) for much of southern California and scattered portions of the North 

Coast Ranges, Klamath Mountains, and Sierra Nevada (figs. 4a–4f). 

The PFRID measures, which consider fire history across the entire 103-year 

study period, were much less affected by recent fire seasons (figs. 4b–4e). Min 

PFRID, which is based on the mean minimum presettlement FRI for the mapped 

PFRs (Van de Water and Safford 2011), classified most of the study area at high 

positive departure (fig. 4b). Lower elevation PFRs in the southern California 

national forests were an exception: some areas in the southern California foothills 
16

research paper psw-rp-266

mapped as high negative departure even using the min PFRID measure, which 

means they have burned much more frequently over the last century than during 

any comparable (average) presettlement period in our reference period. Mean and 

median PFRID were nearly indistinguishable: both showed very high positive 

departure from presettlement fire frequencies throughout most of NW California 

and the Sierra Nevada (with some exceptions of low to moderate departure in the 

central Klamath Mountains, parts of the Modoc Plateau, and the southeastern Sierra 

Nevada), and a belt of moderate to high negative departures through most of lower 

and middle elevation southern California (figs. 4c–4d). Max PFRID, based on the 

mean maximum presettlement FRI, is a more conservative measure of departure 

for places that are lacking fire and a more liberal measure for places that are seeing 

much more fire (fig. 4e). Places identified as high positive departure using max 

PFRID have missed a greater than average number of fire cycles; these tended to 

be centered in the eastern Klamath Mountains, the southern Cascades, and middle 

elevations in the main Sierra Nevada. As would be expected, most of southern

California is mapped as very large negative departure using the max PFRID 

measure (fig. 4e).

Figure 5 compares the three major geographic regions by the proportion of area 

in Forest Service and (in the Sierra Nevada) NPS jurisdiction that falls in each of 

the CCs, based on the standard mean PFRID measure. It can be readily seen how 

different southern California is from the northern California regions. Forty-three 

percent of Forest Service lands in southern California are burning more (CC -2) or 

much more (CC -3) frequently currently than under the pre-Euro-American settlement fire regime; only 2 percent of the Sierra Nevada and 1 percent of NW California lands fall into this category (fig. 5). About one-third of southern California falls 

into CCs 1 and -1, within +/- 33 percent of the presettlement mean fire frequency, 

versus 16 percent in the Sierra Nevada and 9 percent in NW California. More 

than 85 percent of Forest Service lands in NW California is burning either less 

frequently (CC 2) or much less frequently (CC 3) currently than under presettlement 

conditions, compared with 67 percent of Forest Service and NPS lands in the Sierra 

Nevada and 19 percent in southern California.

Table 2 averages results of the area-weighting procedure among the ecological 

sections (see fig. 1 for map); figure 6 portrays the geographic results by ecological 

subsection for mean and max PFRID. After rounding, only seven ecological sections contained more than 40 percent Forest Service or NPS lands (table 2). It is 

important to remember that our results summarized by ecological units (sections 

and subsections) are only valid for the Forest Service and NPS lands within those 

units. As noted above, the highest positive FRID values were consistently on Forest 
17

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Figure 5—Proportion of total analyzed area in the three geographic regions falling into each of the 

six condition classes (CCs). Negative CCs represent places where the current fire return interval 

(FRI) is shorter than the presettlement FRI; positive CCs identify places where current FRI is longer 

than the presettlement FRI. SC = Southern California, SN = Sierra Nevada, NW = Northwestern 

California.

Figure 6—Mean and max percent fire return interval departure (PFRID), with the results of figure 4 extrapolated to the ecological 

subsection boundaries (Miles and Goudey 1997). See figure 3 for percentages of subsections analyzed; in this figure, subsections with

< 5 percent analyzed land have been removed.
18

research paper psw-rp-266

Service lands in the eastern Klamath Mountains and the southern Cascades (fig. 6). 

Mean TSLF averaged 68 years, ranging from 16 years in the Central Coast Ecological Section to 103+ years in the Southeastern Great Basin Section (table 2). The min 

PFRID measure averaged 50 and ranged from -65 in the very small area of Forest 

Service land in the Colorado Desert Section to 86 in the Southern Cascades Section 

(table 2). Mean PFRID averaged 24 across the analyzed subsections, which is high 

CC 1. Mean PFRID varied from -91 in the Colorado Desert Section to 70 in the 

Southern Cascades and 71 in the North Coast Section (table 2, fig. 6a). The median 

PFRID measure averaged 27 and varied from -92 in the Colorado Desert Section 

to 83 in the North Coast Section (table 2). Max PFRID averaged -14, and ranged 

from -95 in the Colorado Desert Section to 18 in the Northern Coast and Southern 

Cascades Sections (table 2, fig. 6b). The NPS-FRID index averaged 1 (moderate 

departure), ranging from -1.0 in the Colorado Desert Section to 4.6 in the Southern 

Cascades Section; many northern California Sections fell between 2.9 and 3.3, 

which fall in the “high” departure category (table 2).

The results of the Principal Components Analysis (PCA) on the subsection 

environmental data are shown in figure 7. Axis 1 explained 51.3 percent of the 

variance in the data matrix, axis 2 explained 17.8 percent, and axis 3 explained 11 

percent. The subsections supporting strongly positive FRIDs (as determined using 

max PFRID) were clearly segregated from the subsections supporting strongly 

negative FRIDs. Of the 25 subsections in the positive FRID (P) group, all but three 

were from NW California and the Sierra Nevada (the exceptions being P23–25, 

which are the Upper San Gabriel, Upper San Gorgonio, and San Jacinto Mountains 

in southern California). Of the 17 subsections in the negative FRID (N) group, all 

but one (N17—Tuscan Flows [Miles and Goudey 1997]) were from southern California. Along axis 1, all but one of the subsections in the N group were clustered 

to the right of the ordination center; all but three of the subsections in the P group 

were grouped to the left of center (fig. 7). Axis 1 was most strongly correlated with 

environmental variables related to temperature and elevation, with the warmest and 

lowest subsections (excepting P6) all in the N group. The most strongly correlated 

variables with axis 1 were: high mean temperature (r = 0.924); lowest elevation (r

= -0.919); and mean frost-free period (r = 0.894). Soil temperature was a categorical variable and an r value could not be determined, but it was the most closely 

associated variable with axis 1. Axis 2 was most closely correlated with moisture 

(mean annual precipitation [r = -0.745] and soil moisture [categorical variable]) 

and presettlement FRI (r = 0.542). Along axis 2, the P subsections were evenly 

distributed above and below the ordination center, but most of the N sites were near 

or below the center (i.e., N sites tend to be drier and support longer presettlement 
19

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Figure 7—Principal Components Analysis ordination of highly positively (“P,” triangles) and 

highly negatively departed (“N,” squares) subsections (see “Methods”). The gray triangles are three 

southern California sites belonging to the P group.

FRIs). Population density was correlated with axis 3 at r = 0.960, which was the 

strongest correlation overall between the environmental variables and any axis; the 

arrow representing population density in the ordination diagram seems very short 

because the effect of population density is nearly orthogonal to axes 1 and 2. Along 

axis 3, the N sites were characterized by high population densities (and are in the 

distance along axis 3) and the P sites (which are nearer to the observer along axis 3) 

by low population densities (fig. 7). 

We compared the means for the environmental data entered into the PCA

for the N and P groups using Mann-Whitney U tests. The results are shown in 

figure 8. Based on figures 7 and 8 and Miles and Goudey (1997), we can describe the P group as predominantly northern California (plus a few small areas 
20

research paper psw-rp-266

of high-elevation southern California); higher elevation, wetter and cooler, with 

most precipitation arriving as snow; growing season of 3 to 4 months; conifer 

forest-dominated vegetation with short presettlement FRIs; mesic to frigid soil 

temperature regime; and in areas of low population density. The N group can be 

characterized as predominantly southern California; lower elevation, warmer 

and drier, with precipitation arriving as rain; growing season of 8 to 10+ months; 

shrub-dominated vegetation with longer presettlement FRIs; thermic to mesic soil 

temperature regime; and in areas of high population density. 

Differences Among Management Units

Using mean PFRID and considering only those lands for which FRID was calculated (i.e., ignoring barren and rocky areas, lakes, herbaceous vegetation, etc.), the 

percentage of lands with negative FRI departures (CC -2 and -3) vs. lands within

33 percent of the mean presettlement FRI (CC -1 and 1) vs. lands with positive

FRI departures (CC 2 and 3) is different in the three Sierra Nevada national parks 

(2, 30, and 68 percent) compared to Forest Service-managed lands in the Sierra 

Nevada (2, 18, and 80 percent). If only the five national forests adjacent to the parks 

Figure 8—Means comparisons for six environmental variables and human population density 

between the positive (P) and negative (N) subsection groups from the Principal Components 

Analysis ordination. LowElev = lowest listed elevation; MeanPpt = mean annual precipitation; 

LowMeanTemp = lowest listed mean annual temperature; HiMeanTemp = highest listed mean annual 

temperature; MeanFrostFree = mean length of annual frost free period, in days; PredomFRI = 

mean presettlement fire return interval (FRI) for the predominant vegetation type in the subsection; 

PopDens = mean human population density. See “Methods” for data sources. Comparison made 

with nonparametric Mann-Whitney U-test; * = significantly different at P < 0.05; *** = significantly 

different at P < 0.001.
21

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

are compared, however (Humboldt-Toiyabe, Inyo, Stanislaus, Sierra, and Sequoia 

NFs), the national forest percentages are very similar to the national parks (3, 27, 

and, 70 percent). Most other measures of fire frequency departure in the neighboring national forests are also relatively similar (table 3). Average TSLF is lower in 

the national parks, although Sequoia NF has the lowest overall TSLF (table 3). The 

NPS-FRID index is in the “moderate” range (0-2) in the national parks, while the 

national forests average in the “high” range of departure. Two national forests with 

large areas of high-elevation wilderness (Inyo and Humboldt-Toiyabe) also fall in 

the low range (table 3).

Table 3 also highlights the strong differences between NW California plus 

Sierra Nevada and southern California. The average TSLF in southern California is 

less than half of the value in the other two regions, and the average PFRID values 

are all negative in southern California, compared to highly positive numbers in NW 

California and the Sierra Nevada. The extensive 2008 fires result in a lower TSLF 

in NW California than the Sierra Nevada, but Min, Mean, and Median PFRID 

and the NPS-FRID index are all lower in the Sierra Nevada. Averaged across all 

national forest land, NW California is the region with the greatest FRI departures, 

but the individual national forest units with the greatest departures are found in the 

central and northern Sierra Nevada (table 3). 

We can also compare management units on the basis of the CC measures 

derived from mean PFRID. On three national forests in the Sierra Nevada (Lassen, 

Plumas, and Tahoe), over 70 percent of the landscape falls in CC 3, which is to say 

that on 7/10 of these lands there has been a greater than 67 percent decrease in fire 

frequency (i.e., at least three FRIs have been missed) over the last century as compared to the pre-Euro-American settlement period (fig. 9a). The Shasta-Trinity NF 

in NW California is nearly as extreme. In terms of overall area, the Shasta-Trinity 

is the only management unit with over 500 000 ha of CC 3 lands; the Klamath, Lassen, Plumas, and Six Rivers NFs all manage more than 300 000 ha of CC 3 lands 

(fig. 9b).

The lowest percentage and area of CC 3 lands are found in the shrub and 

hardwood-dominated southern California national forests. At the same time, these 

four national forests (Angeles, Cleveland, Los Padres, and San Bernardino) contain 

the only substantial areas of negative CCs (where fire frequencies are currently 

greater than in presettlement conditions) in California (figs. 4, 6, and 9). In management units dominated by conifer forests, only Yosemite and Sequoia-Kings 

Canyon National Parks and the Inyo NF have less than one-third of their area in 

CC 3. Three other national forests in the Sierra Nevada region (Sequoia, Sierra, and 

Modoc) are 40 to 44 percent CC 3 (fig. 9).
22

research paper psw-rp-266

Table 3—Differences in time since last fire (TSLF) and fire return interval departure (FRID) measures between 

national park (NP) and adjacent national forest (NF) lands in the southern Sierra Nevada, followed by TSLF 

and FRID measures for the remaining NFs analyzed

Unit TSLF 

Min 

PFRID

Mean 

PFRID

Median 

PFRID

Max 

PFRID

NPS-FRID 

Indexa Analyzed areab

Years Hectares

National parks:

Sequoia-Kings Canyon NPs 80 60 43 48 -2 1.3 222 045

Yosemite NP 77 70 51 57 1 1.7 237 318

National park mean 78 65 47 52 -1 1.5 459 363 Total

Adjacent national forests:

Humboldt-Toiyabe NF 91 73 50 50 7 1.9 339 646

Inyo NF 98 55 35 35 7 1.0 766 030

Sequoia NF 71 71 46 51 -1 2.3 538 316

Sierra NF 89 71 56 63 10 3.4 529 756

Stanislaus NF 81 83 68 74 17 4.2 395 477

Adjacent national forest mean 87 68 49 52 8 2.4 2 569 225 Total

Northwest (NW) California:

Klamath NF 82 83 65 74 11 3.6 803 959

Mendocino NF 73 79 60 64 6 3.3 435 526

Shasta-Trinity NF 81 87 73 80 17 4.3 1 219 109

Six Rivers NF 79 82 63 79 4 2.4 546 924

NW California mean 80 84 67 76 11 3.6 3 005 517 Total

Southern California:

Angeles NF 34 27 -16 -14 -50 0.0 264 271

Cleveland NF 31 23 -21 -22 -52 -0.2 221 536

Los Padres NF 35 33 -13 -11 -45 -0.2 753 572

San Bernardino NF 56 46 13 15 -24 1.1 310 261

Southern California mean 39 33 -9 -8 -43 0.1 1 549 640 Total

Sierra Nevada:

El Dorado NF 91 90 76 82 22 4.8 325 558

Modoc NF 89 75 50 55 11 2.9 835 343

Lassen NF 86 87 72 76 17 4.4 609 661

Plumas NF 79 91 77 82 16 4.4 559 398

Tahoe NF 86 89 75 80 16 4.4 447 880

Lake Tahoe Basin management unit 99 83 71 74 23 4.0 53 218

Sierra Nevada meanc 87 77 59 63 12 3.2 5 400 282 Total

PFRID = percentage of FRID.

a

 Sign of NPS FRID values reversed for consistent interpretation. 

b

 Areas refer only to lands for which FRID was calculated, i.e., they exclude rocky and barren areas, herbaceous vegetation, lakes, etc.

c

 Sierra Nevada means values and local area calculated from 11 Sierra Nevada region forests.
23

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Figure 9—Comparison of condition class (CC) measures (from the mean percent fire return interval departure [PFRID]

metric) for the 18 California national forests managed by the Pacific Southwest Region, and Yosemite and SequoiaKings Canyon National Parks (NPs); (a) percentage of area; (b) total area. Total area of each management unit includes 

lands not in woody vegetation (grassland and meadows, barren lands, rocks, etc.), so totals do not add up to 100 percent. 

Colors are reversed from figures 3 and 5 to correspond to the nationally standard color scheme for CCs 1 through 3 

(green-yellow-red).
24

research paper psw-rp-266

Elevational Trends

Above 700 m, TSLF rose in all three regions when plotted against elevation (fig. 

10). Fire return interval departure measures were mostly unimodally related to 

elevation, but they rose over most of the elevational gradient in southern California, and dropped over the upper half of the gradient in NW California and the 

Sierra Nevada. In NW California, the 100-m moving average of TSLF decreased 

as elevation increased from sea level to 400 m, then increased again at higher 

elevations (fig. 10a). The 100-m moving averages of mean, median, min, and max 

PFRID and the NPS-FRID index rose slightly between sea level and 1500–1700 

m, then decreased to a minimum at 2900 m. Mean, median, and min PFRID, and 

NPS-FRID index remained largely positive over the entire elevational range, while 

max PFRID transitioned from positive to negative at approximately 2400 m. In 

the Sierra Nevada, TSLF decreased between sea level and 700 m, then increased 

again at higher elevations (fig. 10b). Mean, median, min, and max PFRID, and the 

NPS-FRID index decreased between sea level and 700 m, increased until 1750 m, 

then decreased to a minimum at 3700 m. Mean, median, and min PFRID remained 

positive over the entire elevation range, while max PFRID was negative from 450 

m to 1000 m, and the NPS-FRID index was negative from 3050 m to 3700 m. 

As elevation increased in southern California, TSLF decreased from sea level to 

approximately 700 m, then increased again at higher elevations (fig. 10c). Mean, 

median, min, and max PFRID, and the NPS-FRID index also decreased between 

sea level and approximately 750 m, then increased to a maximum at around 2500 

m before decreasing again. Mean and min PFRID remained largely positive over 

most of the elevational range. Median PFRID transitioned from negative to positive 

at approximately 1400 m, max PFRID transitioned at 1800 m, and the NPS-FRID 

index transitioned at 1600 m. 

Using the standard mean PFRID measure as an index of central tendency, the 

southern California landscape is mostly in CC 2 from about 1500 m to 2900 m elevation (fig. 10c). Averaged across the NW California area, mean PFRID remained 

above CC 1 throughout the elevational profile until about 2200 m; mean PFRID 

was in CC 3 between 1200 and 1800 m elevation (fig. 10a). In the Sierra Nevada, 

mean PFRID showed similar patterns to NW California: it was greater than CC 1 

throughout the elevational gradient and in CC 3 between 1600 and 2000 m. Mean 

PFRID fell to CC 1 values above about 2700 m (fig. 10b). The NPS-FRID index 

never reached “high” (>2) values in southern California, was >2 between 1000 and 

1600 in NW California, and >2 between 900 and 2200 m in the Sierra Nevada. 
25

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Figure 10—100-m moving averages of time since last fire (TSLF) and fire return interval departure (FRID) versus elevation in (A) 

northwest California region, (B) Sierra Nevada region, and (C) southern California region. Median percent FRID (PFRID) closely 

corresponds to mean PFRID and was removed for clarity. For figures 10 through 14, solid horizontal reference lines are provided for 

PFRID values of -67 (transition from CC -2 to CC -3), -33 (CC -1 to CC -2), +33 (CC 1 to CC 2), and +67 (CC 2 to CC 3). Dotted reference line refers to the National Park Service (NPS) FRID Index value of 2, which is the transition from “moderate” departure to “high” 

departure.
26

research paper psw-rp-266

Precipitation Trends

The TSLF in NW California fluctuated considerably but generally increased with 

increasing annual precipitation, from 55 years at 25 cm to 103+ years at 420 cm 

(fig. 11a). As precipitation increased in the Sierra Nevada, TSLF decreased between 

10 and 85 cm, increased until 130 cm, decreased until 250 cm, and then increased 

to a maximum at 300 cm (fig. 11b). Annual precipitation had little relationship to 

the different FRID measures in either NW California or the Sierra Nevada. In both 

regions, most FRID measures rose almost imperceptibly across the precipitation 

gradient (except below 50 cm), although max PFRID decreased gradually with precipitation in NW California. The TSLF and the FRID measures in southern California decreased with precipitation, except between 50 and 100 cm, where they leveled 

off (fig. 11c). Mean PFRID decreased as precipitation increased from 20 to 50 cm, 

rose slightly to 100 cm, and then dropped again; the NPS-FRID index stayed near 

zero, except at the highest precipitation values. Min PFRID remained positive over 

the entire precipitation gradient, whereas max PFRID remained negative.

In both NW California and the Sierra Nevada, mean PFRID tracked the boundary between CC 2 and 3 across most precipitation values. In southern California, 

areas with precipitation over 110 cm generally fell in the CC 1 to CC -1 belt (within 

+/-33 percent of presettlement fire frequency) (fig. 11). 

Temperature Trends

Fire return interval departure trends with temperature in NW California and the 

Sierra Nevada were broadly similar. In both regions, FRID measures were (generally) low at low temperatures, gradually rose to a maximum, then gently declined 

(figs. 12 and 13). In both regions, the maximum departure between contemporary 

and presettlement fire frequency was generally reached between -4 and -2 °C 

mean minimum temperatures, 9 to 11 °C mean temperatures, and 25 to 27 °C 

mean maximum temperatures. In the Sierra Nevada, a rise in all of the FRID 

values also occurred again at the highest temperatures (fig. 13). In NW California, 

mean PFRID exceeded CC 1 along most of the temperature gradient, beginning 

at about -7 °C mean minimum temperature, 4 °C mean temperature, and 19 °C 

mean maximum temperature (fig. 12). In the Sierra Nevada, mean PFRID exceeded 

CC 1 above -10 °C mean minimum temperature, 3 °C mean temperature, and 19° 

mean maximum temperature; it fell back within CC 1 above 4 °C mean minimum 

temperature.

In southern California, FRID measures rose with increasing temperature until 

approximately -6 °C mean minimum temperature, 7 °C mean temperature, and 25 

°C mean maximum temperature (fig. 14). Thereafter all measures dropped strongly 
27

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Figure 11—Time since last fire (TSLF) and fire return interval departure (FRID) versus precipitation in (A) northwest California 

region, (B) Sierra Nevada region, and (C) southern California region. Median percent FRID (PFRID) closely corresponds to mean 

PFRID and was removed for clarity. See figure 10 for explanation of horizontal reference lines. NPS = National Park Service.
28

research paper psw-rp-266

Figure 12—For the northwest California region, time since last fire (TSLF) and fire return interval departure (FRID) versus (A) mean 

minimum annual temperature, (B) mean annual temperature, and (C) mean maximum annual temperature. Median percent FRID 

(PFRID) closely corresponds to mean PFRID and was removed for clarity. See figure 10 for explanation of horizontal reference lines. 

NPS = National Park Service.
29

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Figure 13—For the Sierra Nevada region, time since last fire (TSLF) and fire return interval departure (FRID) versus (A) mean minimum annual temperature, (B) mean annual temperature, and (C) mean maximum annual temperature. Median percent FRID (PFRID) 

closely corresponds to mean PFRID and was removed for clarity. See figure 10 for explanation of horizontal reference lines. NPS = 

National Park Service.
30

research paper psw-rp-266

Figure 14—For the southern California region, time since last fire (TSLF) and fire return interval (FRID versus (A) mean minimum 

annual temperature, (B) mean annual temperature, and (C) mean maximum annual temperature. Median percent FRID (PFRID) closely 

corresponds to mean PFRID and was removed for clarity. See figure 10 for explanation of horizontal reference lines. NPS = National 

Park Service.
31

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

with increasing temperature. In southern California, mean PFRID (and most other 

indices) fell in the CC 1 to CC -1 belt above -1 °C mean minimum temperature, 11 

°C mean temperature and 27 °C mean maximum temperature (fig. 14).

Differences Among Presettlement Fire Regime (PFR) Types

There were notable differences in departure statistics among the 28 major PFR 

types (table 4). With respect to PFRs experiencing missed fire events, the most 

extreme departures (CC 3) were in lower montane and montane forest and woodland types, with the degree of departure decreasing broadly with increasing elevation, precipitation and the snow:rain ratio, and decreasing temperature: yellow pine 

(Pinus ponderosa Dougl. ex Loud. and P. jeffreyi Balf. ) and dry mixed conifer > 

moist mixed conifer (all CC 3) > lodgepole pine (P contorta Douglas ex Louden) 

> red fir (Abies magnifica A. Murry bis) > western white pine (P. monticola ex D. 

Don) (all CC 2) > subalpine (CC 1) (table 4). Aspen (Populus tremuloides Michx.) 

was another PFR type with mean PFRID in CC 3. With respect to PFRs experiencing enhanced fire activity, there were two groups with high departures: coastal 

fir and fire-sensitive spruce/fir (both CC -2), which are primarily ± maritime or 

in areas of higher precipitation; and coastal sage scrub (also CC -2 on average), a 

shrub type in coastal and near coastal southern California. Much chaparral in

southern California is also in CC -2 or -3, but northern California stands are not 

as prone to anthropogenic fire, and statewide averaging results in an overall mean 

PFRID of only -19 (CC -1). With respect to Great Basin PFRs, the three sagebrush types were ranked thus: silver sagebrush (Artemisia cana; almost CC 3) > 

big sagebrush (A. tridentata; high CC 2) > black and low sagebrush (A. nova, A. 

arbuscula; CC 1). The pinyon-juniper PFR was ranked CC -1 (table 4). Both desert 

shrubland PFRs (desert mixed shrub and semidesert chaparral) were CC -1, with 

some geographic areas experiencing much higher current frequencies of fire than 

under presettlement conditions (e.g., the western Colorado desert), and others not. 

Discussion

Our analysis highlights some broad dichotomies and notable gradients in the 

contemporary occurrence of wildland fire when viewed in the context of the probable “natural” fire frequencies experienced by the ecosystems over the centuries 

preceding Euro-American settlement. Geographically speaking, there are major 

differences between northern California and southern California, continentalclimate California (Great Basin and desert) and Mediterranean-climate California, 

and wildland and suburban California. Elevation is unimodally related to most 

FRID measures, as are mean minimum, mean, and mean maximum temperatures. 
32

research paper psw-rp-266

Table 4—Total area and spatial averages of time since last fire (TSLF) and fire return interval departure (FRID) 

measures for the 28 presettlement fire regime (PFR)a

 groups analyzed in this study 

PFR Area TSLF 

Mean 

PFRID

Median 

PFRID

Min 

PFRID

Max 

PFRID

NPS-FRID 

Index

 Hectares Years 

Aspen 24 533 96 80 79 89 7 4.0

Big sagebrush 1 105 857 89 57 50 82 5 1.5

Bigcone Douglas-fir 31 939 31 20 23 87 -51 0.0

Black and low sagebrush 336 888 99 32 45 63 -4 0.5

California juniper 7084 71 -4 1 93 -32 -0.2

Chaparral and serotinous conifers 1 575 973 38 -19 -23 24 -47 -0.3

Coastal fir 18 036 20 -57 -67 23 -72 -0.7

Coastal sage scrub 108 323 40 -33 -48 51 -53 -0.5

Curl-leaf mountain mahogany 64 907 96 43 32 67 -8 0.9

Desert mixed shrub 308 386 99 -4 -4 -3 -4 -0.9

Dry mixed conifer 1 398 610 85 85 88 93 35 6.7

Fire-sensitive spruce or fir 159 16 -63 -47 -38 -75 -0.8

Lodgepole pine 186 298 99 62 63 84 -5 1.7

Mixed evergreen 1 302 184 67 51 78 74 -11 1.3

Moist mixed conifer 2 090 128 85 80 85 94 7 4.3

Montane chaparral 519 106 73 61 64 77 27 1.8

Oak woodland 184 673 73 79 79 91 25 5.1

Pinyon juniper 650 199 95 -7 3 46 -8 -0.5

Port Orford cedar 8373 91 65 81 88 -12 2.0

Red fir 522 511 93 55 63 83 -8 1.4

Redwood 7585 60 51 68 78 -38 1.6

Semidesert chaparral 56 233 58 -2 -2 15 -35 -0.1

Silver sagebrush 7921 98 64 69 84 33 1.8

Spruce-hemlock 636 103 0 0 0 0 -0.6

Subalpine forest 335 339 100 -2 -2 1 -3 -0.5

Western white pine 35 066 99 48 57 84 -5 1.0

Yellow pine 1 279 302 80 84 90 93 43 6.3

Note: Results for the shore pine PFR are not reported as it does not occur in the analysis area. Percent fire return interval departure PFRID data are read as 

percentage departure.

a

 From Van de Water and Safford (2011).

In southern California, precipitation is negatively related to FRID, but elsewhere 

(both NW California and the Sierra Nevada) it shows little relationship above 50 

cm. There are differences among management units and vegetation types as well. 

We discuss these patterns below.

Vegetation in “northern California” (NW California and Sierra Nevada regions) 

is dominated primarily by conifer forest and woodland, while shrublands and 

hardwoods dominate coastal southern California with dryland and desert vegetation covering interior southern California. For any given longitude, precipitation 
33

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

in northern California is generally higher than in southern California, and areas of 

similar elevation and topographic position are usually warmer and drier in southern 

California. The fire season in southern California is nearly year-round (compared 

to 4 to 6 months in most of northern California), and half of the year is characterized by periodic strong, dry easterly winds (e.g., “Santa Ana winds”) that are rare 

north of latitude 35°. About two-thirds of California’s human population lives in 

the southern one-fifth of the state, and fire ignition patterns strongly follow this 

pattern. The proportion of lightning to human ignitions (on Forest Service lands) is 

about 1:5 in southern California, but closer to 1:1 in the rest of the state (and 1.3:1 

if the Lake Tahoe Basin is excluded) (Keeley 1982). These major ecogeographic 

differences between southern and “northern” California are key to understanding 

many of the geographic gradients we see in the FRID data. 

Patterns in Southern California

Forest Service lands in 6 of 18 of the ecological sections had average mean and 

median PFRID values less than 0, and all of these are in southern California. Most 

of these averaged in the CC -1 range (0 to 33 percent departure), with the exception 

of Forest Service lands in the Colorado Desert section (a very small area in only 

one subsection analyzed), which were CC -3 or borderline by all of the PFRID 

measures, and the central Coast Ranges section, which were CC -2 (almost all Forest Service land in the section is in one subsection, the southern part of the interior 

Santa Lucia Range). In our analysis, the Colorado Desert section was represented 

by 521 ha of Forest Service land in the Coachella Valley subsection. Much of the 

valley is populated or converted to agriculture, and human ignitions are affecting 

Forest Service lands in areas that are (or were) dominated by the desert mixed 

shrub PFR, which has the longest reference mean FRI (610 years) of the 28 PFR 

types in our study (Van de Water and Safford 2011). The Anza-Borrego desert to 

its south (the Borrego-West Mesa subsection, not in our study area) is also subject 

to a regime of numerous ignitions by humans. In both subsections, invasion of 

drylands by exotic grasses and forbs (e.g., red brome [Bromus madritensis L. ssp. 

rubens], Mediterranean grass [Schismus spp.], Sahara mustard [Brassica tournefortii Gouan]) is widespread and leads to fuel continuity that abets fire spread (Brooks 

and Minnich 2006). Similar invasion by highly flammable exotic species is also 

occurring in areas of the Mojave Desert Section (which is also mapped predominantly in the mixed desert shrub PFR), but Forest Service lands in the Mojave 

Desert are only a very small proportion of the section area (fig. 3, table 2) and

they are dominated by stands of California juniper (Juniperus californica),

whose mean presettlement FRI (77 years) is very close to the average TSLF for

the section (table 3). 
34

research paper psw-rp-266

Many areas in the Southern Coast and Southern Mountains and Valleys ecological sections have seen fire frequencies rise dramatically over the last century. Most 

of this increase in fire activity has occurred since the end of World War II, and 

temporal and spatial patterns in increasing fire frequency in southern California are 

strongly correlated with human population growth (Keeley and Fotheringham 2001, 

Syphard et al. 2007). Interactions between human populations and highly flammable vegetation types like coastal sage scrub and chaparral have led to major changes 

in fire regimes in and around southern California’s urban areas. The ecological 

subsections surrounding the San Diego, Los Angeles, and Santa Barbara metropolitan areas are among the most negatively departed in the state (figs. 4 and 6; 

because they did not include Forest Service lands, the coastal subsections including 

the San Diego and Los Angeles metropolitan areas, Oxnard, and the Santa Monica 

Mountains were not analyzed in our study, but they have some of the highest fire 

frequencies in California). In these areas, extensive landscapes characterized originally by dense native shrublands have been converted to degraded, open stands of 

native shrubs and exotic annual grasses and forbs, which are easily reignited. These 

fire-mediated changes in vegetation lead to higher rates of erosion, increased exotic 

species invasion, and higher fire hazard as grass fuels replace shrubs (Merriam et 

al. 2006, Wells 1987, Zedler et al. 1983). 

For its size, southern California between Santa Barbara, San Diego, and San 

Bernardino is the national leader in average annual wildfire frequency and area, as 

well as fire-caused human mortality, home loss, and economic damages (Halsey 

2004, Hammer et al. 2007, Safford 2007). Although the ecological consequences of 

the contemporary anthropogenic fire regime in southern California are significant, 

they receive comparatively little coverage in the popular press, and further degradation of the remaining natural landscapes in southern California will feed back into 

yet greater human exposure to natural hazards like debris flows, flash floods, and 

wildfires in suburban settings (Cannon and Gartner 2005, Halsey 2004). Continued 

high fire frequencies in southern California also threaten the viability of plant and 

animal species that require longer fire-free periods. High-profile examples of such 

species include the federally listed California gnatcatcher (Polioptila californica) 

and Tecate and Cuyamaca cypress (Hesperocyparis forbesii, H. stephensonii) 

(Bontrager et al. 1995, Gouvenain and Ansary 2006). 

Three small areas of higher mountains (up to 3500 m) rise above southern 

California south of 35° N latitude. These are found within the Upper San Gabriel, 

Upper San Gorgonio, and San Jacinto Ecological Subsections. These three subsections can be clearly seen in the mean PFRID map in fig. 6a, where they appear 

as positive (blue) inclusions in the sea of negatively departed landscapes. These 
35

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

mountain “sky islands” support coniferous PFRs like dry mixed conifer, yellow 

pine, and lodgepole pine, as well as mixed evergreen forest and montane chaparral. To a great extent, plant communities inhabiting these higher mountains are 

southern extensions of montane communities in the Sierra Nevada, and their 

historical relationship with fire is similar (Sugihara et al. 2006). Lightning-ignited 

fires once burned frequently in these high-elevation forests, but—unlike in lowland 

chaparral—fire suppression policies have been successful in nearly eliminating 

wildfire as an ecological force (Keeley et al. 2009). This has resulted in a curious 

elevational schizophrenia in contemporary southern California fire regimes, where 

high-elevation forests that once experienced frequent, low- to moderate-severity 

fire now rarely experience it (and when they do, it is often high severity), while 

many areas of lower elevation shrublands that experienced relatively infrequent fire 

before Euro-American settlement (on average maybe every 50 to 80 years) (Van de 

Water and Safford 2011) are now seeing fire return intervals of 10 to 20 years or 

less (Safford 2007).

Although it is also part of our southern California block, Forest Service lands 

in the Central Coast Section are mostly within the probable HRV for fire frequency 

or are only moderately departed (table 2, figs. 4 and 6) (Moritz 1997). Although a 

spate of recent large fires in the northern Santa Lucia Range has brought attention 

to the area, presettlement mean FRIs for the dominant vegetation types in the area 

were between 23 years (redwood; but this was strongly anthropogenic [Greenlee 

and Langenheim 1990]) and 76 years (coastal sage scrub) (Van de Water and 

Safford 2011). Most Forest Service lands in the north and south Santa Lucia Range 

subsections have burned between two and four times since 1908, so FRIs over the 

last century are mostly between 25 and 50 years. 

Great Basin

In similar fashion to the southern California deserts but to an even greater degree, 

the Great Basin is experiencing a rash of large fires in lower elevation ecosystems 

that are driven largely by invasive species (chief among them cheatgrass [Bromus 

tectorum L.] and red brome), which have altered fire regimes by increasing fine 

fuels, fuel connectivity, and the rate of fire spread (Link et al. 2006). Most PFRs 

mapped in the small portion of eastern California belonging to the Great Basin 

sensu lato (ecological sections include Mono, the Southeastern Great Basin, and 

the Northwestern Basin and Range) (table 2) had presettlement FRIs between 35 

and 150+ years (Van de Water and Safford 2011). Some areas in the Great Basin of 

Nevada and southern Idaho are now burning at intervals of 3 to 5 years (Whisenant 

1990). Such extremely high fire frequencies are not yet common in the Great Basin 
36

research paper psw-rp-266

portion of our study area, because the wave of cheatgrass invasion only recently 

arrived in eastern California, but also because Great Basin lands contained in the 

California national forests tend to be higher elevation and are less subject to invasion by annual grasses (D’Antonio et al. 2004). Fire frequencies are very high in the 

Nevada counties east of Lake Tahoe and northeast of Mono Lake (between 38 and 

40° N latitude, in Washoe, Douglas, and Lyon Counties and Carson City, just to the 

east of our study area), and many former stands of single-leaf pinyon pine (Pinus 

monophylla Torr. & Frém. ), juniper (Juniperus spp.), sagebrush (especially subspecies of Artemisia tridentate Nutt.), and even Jeffrey pine have been eliminated and 

replaced by degraded landscapes of exotic grasses and scattered shrubs. Cheatgrass 

has recently become an issue in the Mediterranean part of California as well, and it 

is now a frequent invader of burned areas along the west slope of the Sierra Nevada, 

especially where the regenerating shrub layer is sparse (McGinnis et al. 2010).

Northwestern California and Sierra Nevada Regions

Unlike most of southern California, the NW California and Sierra Nevada Regions 

(“northern California”) are experiencing major ecosystem impacts from a century 

of fire suppression. The effects of fire exclusion on fire frequencies in northern 

California are the most obvious large-scale pattern in figures 4 to 6 and table 2. 

Densities of ignitions by humans are much lower in northern California; northern 

California is less subject to extreme thermal winds than southern California; the 

northern California fire season is shorter; and fires in contemporary forests in 

northern California burn largely through woody coniferous fuels, in which relatively low vertical and horizontal continuity in fuel structure makes the occurrence 

and sustenance of crown fire much less likely than in the more homogeneous and 

continuous fuels found in southern California chaparral. As a result, the fire suppression policy has been effective in much of northern California, although recent 

trends in fire activity, burned area, and fire severity suggest that the situation is 

rapidly changing as climate warms and fuels continue to accumulate (Miller and 

Safford 2012, Miller et al. 2009, Westerling et al. 2011). 

The ecological sections with Forest Service lands having the greatest FRI 

departures in California are the Southern Cascades, the North Coast (but only a 

very small part of the section analyzed), the Klamath Mountains, the Northern 

Coast Ranges and the Sierra Nevada (table 2). The NPS-FRID index, which focuses 

on the time since last fire, identifies the Southern Cascades as the section with the 

greatest departure—its 4.6 score is almost in the “extreme” range—while the other 

sections listed above all scored as 3.3, or “high” departure. There have been very 
37

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

few wildfires in the Southern Cascades section during the contemporary period, 

largely because—between the volcanoes—much of the landscape is comprised of 

forests on rolling lava beds and the road density is high, which combine to permit 

rapid firefighter access to and containment of fire starts (Skinner and Taylor 2006). 

Another factor contributing to high FRID in certain NW California and Sierra 

Nevada ecological sections is the high level of fragmentation of federal land ownership. The checkerboard ownership pattern across much of eastern NW California 

and the northern Sierra Nevada leaves little opportunity for creative fire management to serve ecological purposes, as private land is usually within a burning 

period of any ignition point.

The only large area of low CC 2 (in some cases CC 1) lands in the Sierra 

Nevada and NW California regions is found in the southern Sierra Nevada (fig. 6). 

Here, checkerboard ownership is rare, and Forest Service and NPS lands combine 

to form one of the largest contiguous blocks of federally managed forest lands in the 

lower 48 States. The core of this block is formed by Yosemite, Sequoia, and Kings 

Canyon NPs, all of which include large areas of wilderness managed to promote 

the occurrence of naturally ignited wildfires. Neighboring national forests (e.g., 

Sequoia, Sierra, Stanislaus, Inyo, and Humboldt-Toiyabe) also include large tracts 

of high-elevation wilderness, and wildfires are much more likely to be managed for 

ecological benefits here than on any other national forests in California. That said, 

it is noteworthy that the mean PFRID averaged across these southern Sierra Nevada 

management units still falls into CC 2 (in the case of the Stanislaus NF, CC 3) (table 

3). The similarities in PFRID metrics between the adjacent national forest and NPS 

units are due to the fact that fire was suppressed in both land ownerships for the 

majority of the time period considered in our analysis (until the early 1970s, when 

the NPS embarked on a more aggressive wildland fire-use program [note that the 

term “wildland fire use” is no longer in use]), and relatively few areas have burned 

a sufficient number of times since 1908 to make up the long-term deficit in fire. If 

we had based our PFRID comparisons on current FRIs beginning in 1970 instead of 

1908, the differences between the NPS units and the national forests would doubtless be much greater. A further consideration is that naturally ignited fires managed 

for ecological benefit tend to occur in higher elevation forests (e.g., high-elevation 

mixed-conifer, red fir, subalpine), where fire is more easily controlled owing to 

lower tree densities, low fuel loadings, and higher fuel moistures, but where FRIs 

are longer and FRID is generally lower than at lower elevations that are (or were 

once) dominated by yellow pine, oak, and dry mixed-conifer forests. Fire managers 

are much less comfortable allowing fires in lower elevation forests to burn, as high 
38

research paper psw-rp-266

fuel loads, drier conditions, and the presence of human communities magnify the 

consequences of a fire escape. Such lower elevation forests are the core of the fuels 

problem however, and—at the landscape scale—the use of managed fire in highelevation forests, while commendable, does nothing to resolve the growing potential 

for high-severity fire in the yellow pine and mixed-conifer belt (Miller et al. 2009, 

Miller and Safford 2012). Even under a much expanded managed fire program, it 

will take many decades of progressive wildfire use in these landscapes to restore 

them to a compositional and structural state that is reasonably resilient to the probably accelerated disturbance regimes of a warmer future (Overpeck et al. 1990). 

The only ecological subsections in NW California and the Sierra Nevada with 

contemporary fire frequencies approaching presettlement frequencies are in the 

northern and southern ends of the Sierra Nevada Foothills, the mountain ranges and 

dry valleys of the Mojave/Southeastern Great Basin, and portions of the Modoc Plateau in the northeastern corner of the state (fig. 6). In the case of the Sierra Nevada 

Foothills, these are largely landscapes of grasslands and oak woodlands, where 

contemporary fire frequencies are high owing to intensive human land use and relatively high population densities. The Great Basin and Modoc Plateau subsections 

support dryland ecosystems (sagebrush, pinyon-juniper, etc.) with longer natural 

FRIs (35 to 151+ years), and fire frequencies have been relatively low over the last 

century. The ongoing invasion of cheatgrass into the Great Basin borderlands seems 

likely to increase fire activity in these subsections as the climate continues to warm 

(Billings 1994, Brooks and Minnich 2006).

FRID Trends Along Environmental Gradients

Fire return interval departure trends along the analyzed environmental gradients 

(elevation, precipitation, and temperature) underline the similarities between NW 

California and the Sierra Nevada, and the different nature of the fire situation in 

southern California. Along the west coast of North America at 90° W longitude and 

between 20° and 60° N latitude, the latitudinal gradient in monthly mean temperature averages about 5.6 °C per 1000 km (ranging from 8.7° January to 2.5° July; 

calculated from isotherms in FAA [1975]). Thus, on average, mean annual temperatures in interior NW California at latitude 41° N are about 1.8 °C cooler than at the 

same elevation in the central Sierra Nevada (latitude 38° N) and about 4.3 °C cooler 

than the same elevation in interior southern California (latitude 34° N). The latitudinal increase in warmth to the south results in an upward shift of the major forest 

types. The elevations and temperatures of the maximum mean PFRID values in 

NW California and the Sierra Nevada (elevations of 1500 to 1700 m in the former, 

1700 to 1900 m in the latter; corresponding temperatures of -2 °C mean minimum, 
39

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

10 °C mean, 27 °C mean maximum; figs. 10 through 13) correspond broadly to the 

average elevation of the transition from moist mixed-conifer (“lower montane”) to 

red fir (“upper montane”) forest. These elevations are slightly above the level of 

maximum annual precipitation (Armstrong and Stidd 1967, Barbour et al. 2007), 

and correspond approximately to the elevations at which the mean freezing level 

occurs during mid-winter storms (Barbour et al. 2002). Red fir-dominated forests 

above this transition receive the highest snowfall totals of any vegetation type in 

California, the growing season is short, and productivity is markedly lower than 

in lower montane forests (Barbour et al. 2002, 2007). This leads to lower levels of 

fuel accumulation, moister fuel beds in the early fire season, and lower fire hazard 

than in the mixed-conifer belt (Cope 1993, Kilgore 1981, Sugihara et al. 2006); the 

latter is also partly due to the enhanced component of fir needles in surface litter in 

red fir forest, as fir litter is substantially less flammable than pine litter (Fonda et 

al. 1998). Presettlement FRIs in the red fir belt averaged 40 years (range of means = 

15 to 130), while moist mixed-conifer forests in the lower montane zone supported 

much more frequent fire (mean = 16 years, range = 5 to 80) (Van de Water and 

Safford 2011).

In southern California, the behavior of FRID metrics along the environmental 

gradients was substantially different than in the two northern California regions. 

The elevations of the maximum mean PFRID values were much higher than in 

northern California (2500 to 2700 m), and the temperatures much lower (-7 °C mean 

minimum, 7 °C mean, 25 °C mean maximum; these correspond to about 2600 m). 

Above these elevations, FRID dropped rapidly (fig. 10), suggesting a threshold type 

of response. We believe this is because there is no red fir in southern California, so 

there is no transitional fire regime between the mixed-conifer and subalpine forests, 

which begin above 2400 to 2700 m in southern California. According to Minnich 

(2007), the mixed-conifer/subalpine ecotone in southern California represents a 

shift from frequent surface burns to infrequent stand-replacing burns, and our data 

support this generalization. As in NW California and the Sierra Nevada, the elevation of the highest mean PFRID values in southern California corresponded broadly 

to the elevation of the mean freezing level during winter storms, which ranges from 

2300 to 2500 m (Minnich 1986) in the San Bernardino Mountains.

The NPS-FRID index is almost invariant along the analyzed environmental 

gradients in southern California, remaining very near zero in almost all cases. The 

only slight rises in the NPS-FRID index in southern California are at about those 

temperatures or elevations that correspond to the maximum mean PFRID values. 

Because it was developed for assessing departure in fire-suppressed forests, the 

NPS-FRID index is insensitive to fire frequencies that are occurring at shorter 
40

research paper psw-rp-266

intervals than under reference conditions, which is the case in most of the southern 

California lowlands that we analyzed. The fact that the NPS-FRID index remains 

near zero even in fire-suppressed montane forests underlines the recent increase 

in fire activity that has characterized southern California in general. The PFRID 

metrics, which depend on FRI information from the entire 103-year record of fire 

perimeters, show major departures from presettlement conditions and dilute the 

signal of recent rises in burned area and the incidence of large fires in southern 

California, which have been ascribed to the effects of increasing drought severities 

on fuel conditions (Keeley and Zedler 2009).

On its own, precipitation shows no obvious relationship to any of the FRID 

metrics or TSLF in northern California, but most of the FRID metrics and TSLF 

appear to decline with rising precipitation in southern California. The range of 

precipitation in NW California and the Sierra Nevada is at least twice as broad 

as the range in southern California (fig. 11). All of the regions include some areas 

with annual precipitation < 50 cm, but none of our southern California analysis 

area receives more than 150 cm annually, while many areas in the Sierra Nevada 

and NW California do (fig. 11) (Minnich 2007, Potter 1998). Working in the Sierra 

Nevada and NW California, Miller et al. (2009, 2012) found that for the period 

1908–1910 to 2006–2008 (depending on the region), annual precipitation had no 

relationship to annual fire number, mean or maximum fire size, or total annual 

burned area in either region (only forest fires analyzed). However, different seasonal precipitation totals explained substantial variation in the response variables, 

with the season in question changing over the course of the study period; summer (June through August) or spring (March through May) precipitation had the 

strongest relationship over the last 20 to 25 years (Miller et al. 2009, 2012). We did 

not relate seasonal precipitation totals to FRID or TSLF, but Miller et al.’s results, 

where no single season of precipitation was related to fire number, size, or area 

measures through the entire study period, make us confident that we would have 

found no simple relationship. The lower PFRID and NPS-FRID index measures 

below 50 cm precipitation in NW California and the Sierra Nevada are driven 

partly by the longer reference FRIs in sagebrush-dominated habitats in the northeastern and southeastern Sierra Nevada regions, and partly by recurrent fires in 

drier lowland habitats around the Central Valley and elsewhere (fig. 6). 

In southern California, the strong drop in TSLF and the PFRID metrics (and 

the slight dip in the NPS-FRID index) above 100 cm precipitation (fig. 11) is due to 

the very large 2008 and 2009 southern and central California fires, which burned 

over 160 000 ha in areas of relatively high precipitation, including the Monterey 

District of the Los Padres NF (which includes the only part of southern California 
41

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

with mean annual precipitation > 120 cm), and the San Gabriel Mountains around 

Mount Wilson. Higher PFRID values below 50 cm precipitation (fig. 11) are due 

primarily to the northeastern, transmontane part of the main body of the Los Padres 

NF (see figs. 1 and 4), which supports >100 000 ha of pinyon-juniper (presettlement 

FRI mean = 151 years, range 50 to 250), California juniper (mean = 83 years, range 

= 5 to 335), and related dryland vegetation and has largely escaped fire for the last 

century.

Using FRID Data in Resource Management

Using historical data to inform resource management is not simple, and a number 

of critical limitations must be surmounted. Important limitations include how 

to account for the roles of humans in reference ecosystems, mismatches in scale 

within the data and between the data and their application, the quality and quantity 

of available data, and the lack of stationarity in environmental baselines (Wiens et 

al. 2012). We discuss these complications below, and then finish with a few examples of how our FRID data can be used in resource management and restoration.

The role of human ignitions prior to Euro-American settlement—

A question that always arises when restoration of fire is discussed is, What role did 

humans have in the pre-Euro-American settlement regime, and should we be targeting that regime or some version of a fire regime driven only by “natural” (lightning, etc.) ignition sources? The presettlement fire record is derived primarily from 

fire-caused injury lesions in tree stems or charcoal in layers of sediment or peat, 

and we are mostly unable to discern lightning-ignited fires from anthropogenic 

fires. Although lightning occurrence differs temporally at multiple scales, contemporary lightning strike densities (LSDs) can provide some idea as to where on the 

landscape the ratio of lightning to anthropogenic ignitions was skewed one way or 

another. California is one of the least lightning-prone states in the United States, 

with most of the Mediterranean part of the state averaging only 0 to 0.25 strikes 

per square kilometer per year (compare to the Gulf Coast, with over six strikes per 

square kilometer per year, or the southeastern and Midwestern United States, with 

more than three strikes per square kilometer per year [Orville 2008]). The highest 

LSDs in California are in the deserts of southeastern California and the eastern and 

higher western slopes of the Sierra Nevada, where average annual LSDs range from 

0.3 to 0.55 strikes per square kilometer per year. Highlands in the eastern Klamath 

Mountains, the southern Cascades, and the interior ranges of southern California 

average about 0.25 to 0.3 strikes per square kilometer per year (van Wagtendonk 

and Cayan 2008). Pre-Euro-American settlement fire frequencies in the California 

Coast Ranges were clearly due primarily to human use of fire, with redwood and 
42

research paper psw-rp-266

coastal oak woodlands being two of the best examples of ecosystems strongly 

shaped by human fire management. The relative magnitude of human inputs to the 

fire regime in the Sierra Nevada before Euro-American settlement is much less 

certain, although anthropogenic fire was certainly a significant factor within some 

radius of many Native American cultural sites and as California probably supported 

more Native Americans than any other Western State (Anderson 2005, Starr 2005), 

giving consideration to their role in molding California ecosystems seems both 

wise and justified. (Anderson 2005, Sugihara et al. 2006). Depending on desired 

conditions and the results of collaborative planning, restoration target conditions 

in California landscapes might focus on the cultural presettlement landscape, or 

on some vision of an ecosystem resilient to warming temperatures and higher fire 

hazard in the future, or even on some conception of how the landscape might have 

looked in the absence of anthropogenic fire. Whatever the location and the management situation, humans have been in California for more than 10,000 years.

Issues of scale—

We conducted our assessment primarily at the state and regional level, which necessarily hides substantial variability at finer scales. For example, the Sierra Nevada 

sensu stricto is nearly 700 km long, and precipitation drops and temperature rises 

from north to south. Forest structure and composition also change. Tree densities and canopy cover decrease to the south, pine dominance increases, and shade 

tolerant tree species like Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), 

tanoak (Notholithocarpus densiflorus Hook. & Arn.) Manos, CH. Cannon & S. 

oh), and madrone (Arbutus menziesii Pursh) are rare or altogether absent south 

of 37° 30’ N latitude (Barbour et al. 2007). Other tree species, like giant sequoia 

(Sequoiadendron giganteum (Lindl.) J. Bucholz) and foxtail pine (Pinus balfouriana Balf.), are restricted primarily or completely to the southern Sierra Nevada. 

Such clinal changes can have major effects on the fire regime, even within a single 

vegetation or PFR type. As noted in Van de Water and Safford (2011), different FRI 

measures (mean, median, minimum, maximum) may be of more use in different 

parts of the PFR range. Where local data on pre-Euro-American settlement fire regimes are available, they should be consulted (see Van de Water and Safford [2011] 

for a comprehensive list of pre-2011 references). Spatial relationships between PFRs 

can also have a major impact on local fire regimes. For example, where vegetation 

types of very different flammabilities are juxtaposed, fire frequencies will be locally affected. Vegetation patch sizes are also important and can be an important 

determinant of the local fire regime (Agee 1998, Bond and van Wilgen 1996). Our 

FRID mapping products are available for all Forest Service units in California, so 

assessments at finer scales are possible.
43

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Another scalar issue is related to the numerical resolution of the FRID data and 

the resolution at which they are applied. Condition classes based on some aspect of 

fire regime or its effects on ecosystem status (e.g., vegetation structure, such as in 

the FRCC program [Hann and Strohm 2003]) have become widely used measures 

of ecosystem status and management progress in reducing fuels or restoring forest 

structure. The green-yellow-red (good-poor-bad) color scheme of many condition 

class maps or graphics (e.g., fig. 9) is a useful and eye-catching method for highlighting areas in need of management intervention, but it can hide important variation in the analyzed ecosystems and landscapes, and it can suggest one-size-fits-all 

remedies for situations that require nuanced consideration. Our addition of the 

negative cc to the classic green-yellow-red (CC1-2-3) scheme for the mean PFRID 

metric (Safford et al. 2011) was an attempt to allow managers to recognize the very 

different ecological situations and management challenges created by departures 

caused by overly frequent fire. Broad categories like condition classes make wide 

generalizations possible, which may be useful for political, budgetary, and educational purposes. However, we recommend that the PFRID metrics be considered 

principally in their raw, unclassified form (e.g., 53 percent departure rather than 

“CC 2”) and with a keen eye to local conditions and information that can provide 

a more reasoned and realistic assessment at scales that matter to on-the-ground 

management.

A third scalar issue pertains to the temporal scale of comparison between the 

reference FRI information and current FRIs. Our PFRID metrics are set up to compare two relatively long-term data sets: the current FRIs, which in this study are 

obtained from the 103 (inclusive) years between 1908 and 2010, and the pre-EuroAmerican settlement FRIs, which were primarily derived from the two to five 

centuries before 1850. Fire management in California has passed through a number 

of different philosophical and tactical phases (Stephens and Sugihara 2006), and 

the effects of these different management periods on fire occurrence are masked 

or diluted by a metric based on averages from 103 years of data. The best example 

of this effect is in the comparisons we made between Yosemite, Sequoia and Kings 

Canyon NPs and the adjacent Sierra Nevada national forests. Extensive management of natural ignited fires for resource benefits began in the national parks in the 

1970s, and a PFRID comparison using only the current FRIs since that time would 

probably show much greater differences between the national parks and the national 

forests, which continue to suppress most wildfires. 
44

research paper psw-rp-266

Data quality considerations—

The PFR data provided in Van de Water and Safford (2011) are not infallible, and 

we know of a number of situations where future data collection and finer differentiation of fire regimes are recommendable. For example, Jeffrey pine was placed 

into the yellow pine PFR by Van de Water and Safford (2011), but it is very stress 

tolerant and often found in situations where low site productivity notably reduces 

growth and fuel accumulation. Such sites simply cannot support the very high fire 

frequencies associated with modal Jeffrey pine sites. Examples include ultramafic 

“serpentine” soils in NW California and the Sierra Nevada, where Jeffrey pine is 

often the dominant tree species (Safford and Mallek 2010). The high positive FRID 

found for the Upper and Lower Scott Mountains ecological subsections in the eastern Klamath Mountains, which are largely underlain by ultramafic substrates, is 

thus almost certainly an overestimate of departure. In the Sierra Nevada on nonserpentine sites, Jeffrey pine grows extensively on the east side of the range at moderate elevations and on the west side in upper montane forests, where it generally 

occupies rocky, exposed sites with California juniper and other stress tolerators. 

The reference FRI in these low-productivity west-side sites, nested in forest with 

mean presettlement FRIs > 40 years, is likewise surely longer than the standard yellow pine PFR (Van de Water and Safford 2011). The many shrub and closed-cone 

conifer types grouped together under the huge PFR category “chaparral and serotinous conifers” by Van de Water and Safford (2011) also include variability in FRIs 

across climatic and edaphic clines that may be better incorporated into a number of 

groups. 

The fire perimeter data upon which the FRID polygons are based are also far 

from perfect. Because the database focus is on the fire perimeter, many unburned 

inclusions within fires are missed. Older fire perimeters (pre-World War II) are 

notoriously inexact, and some fires are represented simply by circular polygons of 

the appropriate fire size centered on the approximate fire location. Some portions of 

California are missing records of most fires before 1950. Most prescribed fires are 

missing from the database, although we are currently working with the Forest Service and NPS to remedy this deficiency. With all of these problems, however, the 

California Fire Perimeters database (FRAP 2011) is the most extensive and complete data source for fire location, size, and shape in the world to our knowledge. 

It is considered approximately complete for fires over 4 ha in size back to 1950, 

and mostly comprehensive for Forest Service fires to about 1908, when the agency 

began to require the collection of data on fire location and size (see McKelvey and 

Busse 1996, Miller et al. 2009).
45

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Optimally, we would base an assessment of current FRID from pre-EuroAmerican settlement conditions on a vegetation map of average presettlement 

conditions. Obviously such a map does not exist. We considered using potential 

vegetation maps such as the Küchler map of California from 1976 (Barbour and 

Major 1988) or the Biophysical Settings (BpS) map from the LANDFIRE project 

(Rollins 2009), but the former was drawn qualitatively on a very broad-scale map 

of the state before the advent of geographic information systems, and the latter 

has too many inaccuracies at the subregional scale. The Forest Service inventoried 

California’s nondesert wildlands in the 1920s and 1930s, but this was 70 to 80 years 

after settlement (although only a few decades after the beginnings of federal fire 

suppression), and the project was halted after about 60 percent of the area had been 

mapped (Wieslander 1935). Our decision to go with the most accurate statewide 

map of existing vegetation means that any substantial changes in vegetation over 

the time since 1850 could have an impact on the accuracy of our metrics. One of 

the best documented widespread changes has been the decrease in pine dominance 

and the increase in fire-intolerant species in lower and middle elevation forests in 

northern California (and higher elevation southern California) owing to 19th- and 

20th-century timber harvest and 20th century fire suppression (Barbour et al. 2007, 

Minnich et al. 1995, Sugihara et al. 2006, Thorne et al. 2008). Many forest stands 

mapped today as mixed conifer would probably have been mapped as yellow pine 

in the mid-19th century. In these cases, our FRID measures of current departure 

from presettlement FRIs understate the actual magnitude of change, as the FRID 

measures are being calculated based on the mixed-conifer reference FRIs, which 

are up to 45 percent longer (in the case of moist mixed conifer) than the yellow pine 

reference FRIs (Van de Water and Safford 2011). In other cases, the reverse may 

be true. For example, comparison of the 1930s Forest Service maps of the Sierra 

Nevada (Wieslander 1935) with the current EVEG maps suggests that some areas 

originally mapped as subalpine forest are now dominated by red fir, which would 

reduce the presettlement baseline mean FRI from 133 years to 40 years. In this 

case, our FRID measures are somewhat more difficult to interpret, as the vegetation 

change is more likely due to climate warming than human management (Dolanc et 

al. 2012), and management attempts to reverse the trend may be counterproductive. 

Consideration of changing climate—

Ecosystem transformations caused by directional climate change form the basis for 

a recent wave of concern regarding the usefulness and applicability of historical 

data to contemporary and, more importantly, future management problems (Millar 

et al. 2007, Stephenson et al. 2010, Wiens et al. 2012). The traditional assumption 

that ecosystem patterns and processes vary about some long-term mean (i.e., that 
46

research paper psw-rp-266

they exhibit stationarity) was always untenable, but it has become even more so 

in the “Anthropocene” Epoch (Steffen et al. 2007). Changes in the environmental 

baseline resulting from climate change, or human land use, or invasive species, 

etc., make the uncritical use of historical data as a management target less and less 

defensible, but this does not reduce the value of historical data; indeed, the less we 

know about the future, the more we will have to rely on insight gained from our 

experiences with the past (Wiens et al. 2012). In the case of fire-suppressed forest 

types that historically burned at high frequency, human management has pushed 

the range of variation for fire occurrence far below the HRV. Modeled or inferred 

considerations of fire frequency over the next 50 to 100 years nearly unanimously 

project increasing potential for wildfire, perhaps even above levels that reigned 

when Euro-Americans settled California (see below). In this case, restoration of 

HRV conditions is a logical first step and might be treated as a waypoint toward the 

ultimate goal of increasing resilience to a much warmer future (Safford et al. 2012). 

Management application of FRID data—

Fire return interval departure data can provide a template for assessing ecosystem 

conditions and evaluating landscapes for restoration need. Yosemite, Sequoia, and 

Kings Canyon NPs have used the NPS-FRID index for such purposes for over two 

decades (Caprio et al. 1997, Keifer et al. 2000, van Wagtendonk et al. 2002). At the 

simplest level, relationships between FRID and ecosystem type (as represented by 

our PFRs) can help direct broad landscape-level strategies. In northern California, 

high-elevation forests (red fir, western white pine, subalpine) are only moderately 

departed from historical conditions and—ecologically speaking—tend not to be 

high-payoff landscapes for fuels reduction, wildland fire use for ecological benefit, 

or other types of restoration management (Agee 2005). However, these are often the 

safest and easiest places to carry out such management (and climate warming is increasing concern for lower elevation red fir forests, which are transitional from the 

mixed-conifer belt). Areas of extreme departure, especially when evaluated against 

min or max PFRID, are probably at or beyond the HRV for fire frequency. These 

landscapes, mostly shrublands in southern California and low to middle elevation 

forests in northern California, may seem like logical places to focus ecological 

restoration efforts, but in some cases, they may be too difficult, too remote, too expensive, or too controversial to actively manage. The projected future environment 

of the restoration landscape will need to be considered in restoration planning, and 

management targets may need to be adjusted. The FRIs documented in the database 

do not need to represent the long-term target condition for the restored landscape, 

but they provide an idea of the range of fire frequencies that might best promote 

sustainability of the reference ecosystem type. 
47

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Future projections suggest even more ecological potential for wildfire in most 

Western U.S. forests than was the case during our pre-Euro-American settlement 

reference period (Lenihan et al. 2003, National Research Council 2011, Westerling 

et al. 2011), when fire was much more common than today. Climate-driven projections also suggest higher levels of drought and stress-related susceptibility to insect 

attack and disease (Evangelista et al. 2011, Sturrock et al. 2011). These projections 

suggest that a serious management effort will be required to increase the resilience 

of fire- and drought-prone landscapes to future environmental stressors. Our mean 

PFRID metric can be used in conjunction with the NPS-FRID index to identify 

recently burned locations that have experienced fire frequencies in the 20th century 

that are within or near HRV. Lydersen and North (2012) recently used a similar protocol to identify frequently burned mixed-conifer stands in the Sierra Nevada and 

conducted ground sampling to develop a picture of forest structure in fire-resilient 

stands. These are places to begin use of prescribed or managed wildland fire to 

ensure long-term maintenance of fire-resilient composition and structure. Such 

places could also be used as core areas around which to expand restoration efforts 

into the surrounding landscape. 

As restoration efforts proceed, FRID data can be used to track progress and 

measure management success. Miller and Davis (2009) carried out fire modeling in 

two watersheds of Sequoia-Kings Canyon and Yosemite NPs based on suppressed 

lightning ignitions that had occurred during a previous 20-year period. At the end 

of their study period, they assessed the outcome of their exercise by generating 

a hypothetical map of the NPS-FRID index and comparing it to the actual FRID 

map that existed without the modeled fires. Tracking of fire restoration efforts 

at the broader landscape scale could be accomplished with a similar comparative 

protocol. For short-term monitoring, the NPS-FRID index may be the most useful 

performance measure, as it considers only the time since the most recent fire. In the 

end, repeated fires at appropriate levels of severity will be required to sufficiently 

restore the fire regime, vegetation structure and composition, wildlife habitat, and 

other ecosystem patterns and processes in frequent-fire forest types. In southern 

California shrubland ecosystems, on the other hand, the focus should be on reducing fire frequencies. Measures like mean, min, and max PFRID, which are better 

at evaluating the frequency of fire in an ecosystem over time, will be more helpful 

in targeting and tracking a long-term strategy to promote resilience. The final goal 

should not be a slavish adherence to the mean pre-Euro-American settlement fire 

frequencies listed in Van de Water and Safford (2011) and elsewhere. However, 

these values and their ranges can be used profitably as short- or medium-term targets for restoration efforts, in the understanding that the long-term goal is not some 
48

research paper psw-rp-266

static picture postcard of a presettlement landscape, but a dynamic ecosystem that is 

more resilient to disturbance, a warming climate, and all of the other stressors that 

will come with global change. 

When using the PFRID measures, managers may have the tendency to focus 

their restoration efforts on areas mapped as CCs 3 or -3, because current management policies are focused on highly departed lands. In most cases, this is probably 

a reasonable course of action, although—as noted above—for most situations we 

recommend use of the raw FRID data rather than the condition class categories. The 

extent to which the CC 2 and 3 or -2 and -3 boundaries might represent a reliable 

ecological warning bell can be gauged by the information provided in table 1. Table 

1 compares the mean minimum and mean maximum pre-Euro-American settlement 

FRIs from Van de Water and Safford (2011) with the FRIs associated with the CC 2 

and 3 (+67 percent departure) and CC -2 and -3 (-67 percent departure) boundaries. 

The 33 percent and 67 percent cutoffs used to identify CCs 1, 2, and 3 were based 

on simple division of the 100 percent maximum departure into thirds (Hann 2004, 

Hann and Strom 2003), thus there is no underlying statistical distribution of fire 

frequencies assumed. This leads to different relationships between the range of 

presettlement FRIs associated with each PFR and the FRIs represented by the CC 2 

and 3 and CC -2 and -3 boundaries (table 1, fig. 15). 

Looking at table 1, some generalizations can be made about these differences. 

For example, most tree-dominated PFRs show longer mean maximum FRIs than 

the FRIs associated with the CC 3 boundary (e.g., fig. 15), while shrub-dominated 

PFRs in table 1 universally show shorter mean maximum presettlement FRIs than 

the FRIs represented by the CC 3 boundary (table 1). These differences are probably due both to differences in sample size (there are many fewer studies of fire history in shrub vs. forest ecosystems) (Van de Water and Safford 2011), and inherent 

differences in the distributions of fire frequencies in the two ecosystem types (e.g., 

Grissino-Mayer 1999, Moritz 2003). For tree-dominated PFRs with longer mean 

maximum FRIs, managers might want to treat the mean maximum FRI as a higher 

class of departure (“extreme?”). For shrub PFRs with shorter mean maximum FRIs, 

CC 3 is a more conservative measure of departure than the mean maximum FRI. 

On the negative side of the PFRID scale, for most shrub PFRs the mean minimum 

presettlement FRI is longer than the FRI represented by the CC -3 boundary (table 

1). In practical terms, this means that for PFRs like chaparral/serotinous conifers 

or semidesert chaparral, both with extensive distributions in southern California, 

use of the CC -3 category as an ecological alarm bell for overly frequent fire may 

actually underestimate the magnitude of the ecological departure. 
49

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Figure. 15—Elevational gradient (left to right: low elevation to high) of six forest presettlement fire 

regimes (PFRs) on the west slope of the Sierra Nevada, comparing the condition class (CC) boundaries for each PFR with the approximate range of pre-Euro-American settlement fire return intervals 

(FRIs) (the area between mean maximum [“max FRI”] and mean minimum [“min FRI”]). Condition 

class color scheme corresponds to figure 9. YP = yellow pine, DMC = dry mixed conifer, MMC = 

moist mixed conifer, RF = red fir, WWP = western white pine, and SA = subalpine forest.

Fire return interval departure users should pay careful attention to the various 

limitations and caveats inherent to the tool. As discussed above, these include scalar 

issues, issues with data quality and extent, issues with the reference baseline, and 

issues with interpretation. We finish by reminding the reader that FRID analysis 

does not include information on aspects of the fire regime other than fire frequency 

calibrated by vegetation type. Fire occurrence and behavior are driven by such factors as topography, weather, and fuel conditions that were not directly considered in 

our analysis. FRID is a useful broad-scale planning tool, but proper interpretation 

at scales meaningful to resource managers will require concurrent consideration of 

other sources of information as well, such as site history, fuel loading and vegetation structure, topography, weather, and other components of the fire regime, 

including fire size, severity, and spatial pattern. 

Acknowledgments

Carol Clark (Forest Service Remote Sensing Lab) did most of the geographic 

information system work: we are heavily indebted to her tireless help. We thank 

Dave Schmidt of The Nature Conservancy for aiding in the development of the 

first-generation FRID maps for southern California and the supporting metadata, 

which formed a basis for this paper. Jan Beyers, Tony Caprio, Mary Beth Keifer, 
50

research paper psw-rp-266

Susie Kocher, Jonathan Long, Jay Miller, Sylvia Mori, Sarah Sawyer, Carl Skinner, Jan van Wagtendonk, and Davis Weise kindly provided review, discussion, or 

comments on early results and drafts. Funding for this project was provided by the 

USDA Forest Service, Pacific Southwest Region Ecology Program.

English Equivalents

When you know: Multiply by: To find:

Millimeters (mm) 0.0394 Inches

Meters(m) 0.394 Feet

Kilometers (km) 0.621 Miles

Hectares (ha) 2.47 Acres

Square kilometers (km2

) 0.386 Square miles

Degrees Celsius (°C) 1.8 °C + 32 Degrees Fahrenheit

Literature Cited

Agee, J.K. 1993. Fire ecology of the Pacific Northwest. Washington, DC: Island 

Press. 505 p.

Agee, J.K. 1998. The landscape ecology of western forest fire regimes. Northwest 

Science. 72: 24–34.

Agee, J.K. 2005. The role of silviculture in restoring fire-adapted ecosystems. In: 

Powers, R.F., ed. Restoring fire-adapted ecosystems: proceedings of the 2005 

national silviculture workshop. Gen. Tech. Rep. PSW-GTR-203. Albany, CA: 

U.S. Department of Agriculture, Forest Service, Pacific Southwest Research 

Station: ix–xviii.

Anderson, M.K. 2005. Tending the wild: Native American knowledge and the 

management of California’s natural resources. Berkeley, CA: University of 

California Press. 555 p.

Armstrong, C.F.; Stidd, C.K. 1967. A moisture-balance profile on the Sierra 

Nevada. Journal of Hydrology. 5: 258–268.

Barbour, M.G.; Berg, N.H.; Kittel, T.G.F.; Kunz, M.E. 2002. Snowpack and 

the distribution of a major vegetation ecotone in the Sierra Nevada of California. 

Journal of Biogeography. 18: 141–149.

Barbour, M.G.; Keeler-Wolf, T.; Schoenherr, A.A. 2007. Terrestrial vegetation of 

California. 3rd ed. Berkeley, CA: University of California Press. 730 p.
51

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Barbour, M.G.; Major, J. 1988. Terrestrial vegetation of California. 2nd ed. 

Special Publication No. 9. Sacramento, CA: California Native Plant Society.

1002 p.

Billings, W.D. 1994. Ecological effects of cheatgrass and resultant fire on 

ecosystems in the western Great Basin. In: Monsen, S.B.; Kitchen, S.G., eds. 

Proceedings of ecology and management of annual rangelands. Gen. Tech. Rep. 

INT-GTR-313. Ogden, UT: U.S. Department of Agriculture, Forest Service, 

Intermountain Research Station: 22–30.

Bond, W.J.; van Wilgen, B.W. 1996. Fire and plants. London: Chapman and Hall. 

263 p.

Bontrager, D.R.; Erickson, R.A.; Hamilton, R.A. 1995. Impacts of the October 

1993 Laguna Canyon Fire on California gnatcatchers and cactus wrens. In: 

Keeley, J.E.; Scott, T., eds. Brushfires in California wildlands: ecology and 

resource management. Fairfield, WA: International Association of Wildland

Fire: 69–76.

Brooks, M.L.; Minnich, R.A. 2006. Southeastern deserts bioregion. In: Sugihara, 

N.G.; van Wagtendonk, J.W.; Shaffer, K.E.; Fites-Kaufman, J.; Thode, A.E., eds. 

Fire in California’s ecosystems. Berkeley, CA: University of California Press: 

391–414.

Cannon, S.H.; Gartner, J.E. 2005. Wildfire-related debris flow from a hazards 

perspective. In: Jakob, M.; Hungr, O., eds. Debris flow hazards and related 

phenomena. Berlin: Springer Verlag: 363–385.

Caprio, A.C.; Conover, C.; Keifer, M.B.; Lineback, P. 1997. Fire management 

and GIS: a framework for identifying and prioritizing fire planning needs. 

In: Sugihara, N.G.; Morales, M.E.; Morales, T.J., eds. Proceedings of the 

conference on fire in California ecosystems: integrating ecology, prevention, and 

management. Davis, CA: Association for Fire Ecology. Unconventional paging.

Caprio, A.C.; Graber, D.M. 2000. Returning fire to the mountains: Can we 

successfully restore the ecological role of pre-Euro-American fire regimes to 

the Sierra Nevada? In: Cole, D.N.; McCool, S.F.; Borrie, W.T.; O’Loughlin, J., 

eds. Wilderness science in a time of change—volume 5: Wilderness ecosystems, 

threats, and management. Conference proceedings RMRS-P-15-VOL-5. Ogden, 

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

Station: 233–241.
52

research paper psw-rp-266

Connell, J.H. 1978. Diversity in tropical rain forests and coral reefs. Science.

199: 1302–1310.

Cope, A.B. 1993. Abies magnifica. In: Fire effects information system. U.S. 

Department of Agriculture, Forest Service. http://www.fs.fed.us/database/feis/. 

(22 July 2012).

D’Antonio, C.M.; Berlow, E.L.; Haubensak, K.L. 2004. Invasive exotic plant 

species in Sierra Nevada ecosystems. In: Murphy, D.D.; Stive, P.A., eds. 

Proceedings of the Sierra Nevada science symposium: science for management 

and conservation. Gen. Tech. Rep. PSW-GTR-193. Albany, CA: U.S. Department 

of Agriculture, Forest Service, Pacific Southwest Research Station: 175–184.

Dolanc, C.R.; Thorne, J.H.; Safford, H.D. 2012. Widespread shifts in the 

demographic structure of subalpine forests in the Sierra Nevada, California, 

1934–2007. Global Ecology and Biogeography. 22: 264–276. 

Egan, D.; Howell, E.A., eds. 2001. The historical ecology handbook: a restorationist’s guide to reference ecosystems. Washington, DC: Island Press. 488 p.

Environmental Systems Research Institute [ESRI]. 2008. ArcGIS. Version 9.3. 

Redlands, CA: Environmental Systems Research Institute.

Evangelista, P.H.; Kumar, S.; Stohlgren, T.J.; Young, N.E. 2011. Assessing 

forest vulnerability and the potential distribution of pine beetles under current 

and future climate scenarios in the interior West of the US. Forest Ecology and 

Management. 262: 307–316.

Federal Aviation Administration [FAA]. 1975. Aviation weather. FAA Advisory 

Circular AC 00-6A. Washington, DC: Federal Aviation Administration and 

National Weather Service.

Fire and Resource Assessment Program [FRAP]. 2011. Fire perimeters 

[Database]. Sacramento, CA: California Department of Forestry and Fire 

Protection. http://www.frap.cdf.ca.gov/data/frapgisdata/select.asp. (12 May 2011).

Fonda, R.W.; Belanger, L.A.; Burley, L.L. 1998. Burning characteristics of 

western conifer needles. Northwest Science. 72: 1–9.

Franklin, J.; Woodcock, C.E.; Warbingon, R. 2000. Digital vegetation maps of 

forest lands in California integrating satellite imagery, GIS modeling, and field 

data in support of resource management. Photogrammetric Engineering and 

Remote Sensing. 66: 1209–1217.
53

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Fulé, P.Z. 2008. Does it make sense to restore wildland fire in changing climate? 

Restoration Ecology. 16: 526–531.

Gouvenain, R.C. de; Ansary, A.G. 2006. Association between fire return interval 

and population dynamics in four California populations of Tecate cypress 

(Cupressus forbesii). Southwestern Naturalist. 51: 447–454.

Greenlee, J.M.; Langenheim, J.H. 1990. Historic fire regimes and their relation to 

vegetation patterns in the Monterey Bay area of California. American Midland 

Naturalist. 124: 239–253.

Grissino-Mayer, H.D. 1999. Modeling fire interval data from the American 

Southwest with the Weibull distribution. International Journal of Wildland Fire. 

9: 37–50.

Halsey, R. 2004. Fire, chaparral and survival in southern California. San Diego, 

CA: Sunbelt Publications. 232 p.

Hammer, R.B.; Radeloff, V.C.; Fried, J.S.; Stewart, S.I. 2007. Wildlandurban interface housing growth during the 1990s in California, Oregon, and 

Washington. International Journal of Wildland Fire. 16: 255–265.

Hann, W.J. 2004. Mapping fire regime condition class: a method for watershed

and project scale analysis. In: Engstrom, R.T.; Galley, K.E.M.; de Groot, W.J. 

eds. Proceedings of the 22nd Tall Timbers fire ecology conference: fire in 

temperate, boreal, and montane ecosystems. Tallahassee, FL: Tall Timbers 

Research Station: 22–24. http://webharvest.gov/peth04/20041015014144/http://

frcc.gov/assoc/HannTallTim2003FrccPaper.pdf. (November 20, 2012).

Hann, W.J.; Strohm, D.J. 2003. Fire regime condition class and associated data 

for fire and fuels planning: methods and applications. In: Omi, P.N.; Joyce, L.A., 

eds. Fire, fuel treatments, and ecological restoration. Conference proceedings 

RMRS-P-29. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station: 397–434.

Hardy, C.C.; Menakis, J.P.; Long, D.G.; Brown, J.K.; Bunnell, D.L. 1998.

Mapping historic fire regimes for the western United States: integrating remote 

sensing and biophysical data. In: Proceedings of the seventh biennial forest 

service remote sensing applications conference. Bethesda, MD: American 

Society for Photogrammetry and Remote Sensing: 288–300.
54

research paper psw-rp-266

Heinselman, M.L. 1978. Fire in wilderness ecosystems. In: Hendee, J.C.; Stankey, 

G.H.; Lucas, R.C., eds. Wilderness management. Misc. Publ. 1365. Washington, 

DC: U.S. Department of Agriculture, Forest Service: 249–278.

Huston, M.A. 1994. Biological diversity: the coexistence of species on changing 

landscapes. Cambridge, United Kingdom: Cambridge University Press. 708 p.

Huston, M.A. 2003. Understanding the effects of fire and other mortality-causing 

disturbances on species diversity. In: Burrows, N.C.; Abbott, I., eds. Fire 

in south-western Australian forests: impacts and management. Leiden, The 

Netherlands: Backhuys Publishers: 51–84.

Johnson, E.A.; Gutsell, S.L. 1994. Fire frequency models, methods and 

interpretation. Advances in Ecological Research. 25: 239–287.

Keeley, J.E. 1982. Distribution of lightning and man-caused wildfires in 

California. In: Conrad, C.E.; Oechel, W.C., eds. Symposium on dynamics and 

management of Mediterranean-type ecosystems. Gen. Tech. Rep. PSW-GTR-58. 

Berkeley, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest 

Forest and Range Experiment Station: 431–437.

Keeley, J.E.; Bond, W.J.; Bradstock, R.A.; Pausas, J.G.; Rundel, P.W. 2011.

Fire in Mediterranean ecosystems: ecology, evolution and management. New 

York: Cambridge University Press. 515 p.

Keeley, J.E.; Fotheringham, C.J. 2001. Historic fire regime in southern California 

shrublands. Conservation Biology. 15: 1536–548. 

Keeley, J.E.; Safford, H.; Fotheringham, C.J.; Franklin, J.; Moritz, M. 2009.

The 2007 southern California wildfires: lessons in complexity. Journal of 

Forestry. 107: 287–296.

Keeley, J.E.; Zedler, P.H. 2009. Large, high-intensity fire events in southern 

California shrublands: debunking the fine-grain age patch model. Ecological 

Applications. 19: 69–94.

Keifer, M.; Caprio, A.; Lineback, P.; Folger, K. 2000. Incorporating a GIS model 

of ecological need into fire management planning. In: Neuenschwander, L.F.; 

Ryan, K.C.; Gollberg, G.E., eds. Joint Fire Science conference and workshop 

proceedings: crossing the millennium—integrating spatial technologies and 

ecological principles for a new age in fire management. Boise, ID; Moscow, 

ID; and Fairfield, WA: University of Idaho and the International Association of 

Wildland Fire. 1: 122–130.
55

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Kilgore, B.M. 1981. Fire in ecosystem distribution and structure: western forests 

and scrublands. In: Mooney, H.A.; Bonnicksen, T.M.; Christensen, N.L.; 

Lotan, J.E.; Reiners, R.A., eds. Proceedings of the conference: fire regimes and 

ecosystem properties. Gen. Tech. Rep. WO-GTR-26. Washington, DC: U.S. 

Department of Agriculture, Forest Service: 58–89.

Landres, P.B.; Morgan, P.; Swanson, F.J. 1999. Overview of the use of natural 

variability concepts in managing ecological systems. Ecological Applications.

9: 1179–1188.

Lenihan, J.M.; Drapek, R.; Bachelet, D.; Neilson, R.P. 2003. Climate change 

effects on vegetation distribution, carbon, and fire in California. Ecological 

Applications. 13: 1667–1681.

Link, S.O.; Keeler, C.W.; Hill, R.W.; Hagen, E. 2006. Bromus tectorum cover 

mapping and fire risk. International Journal of Wildland Fire. 15: 113–119.

Lydersen, J.; North, M. 2012. Topographic variation in structure of mixed-conifer 

forests under an active-fire regime. Ecosystems. 15: 1134-1146. DOI: 10.1007/

s10021-012-9573-8.

Major, J. 1988. California climate in relation to vegetation. In: Barbour, M.G.; 

Major, J., eds. Terrestrial vegetation of California. 2nd ed. Sacramento, CA: 

California Native Plant Society: 11–74.

McCune, B.; Mefford, M.J. 2006. PC-ORD multivariate analysis of ecological 

data. Version 5. Gleneden Beach, OR: MJM Software Design.

McGinnis, T.W.; Keeley, J.E.; Stephens, S.L.; Roller, G.B. 2010. Fuel buildup 

and potential fire behavior after stand-replacing fires, logging fire-killed trees 

and herbicide shrub removal in Sierra Nevada forests. Forest Ecology and 

Management. 260: 22–35.

McKelvey, K.S.; Busse, K.K. 1996. Twentieth century fire patterns on Forest 

Service lands. In: Sierra Nevada Ecosystems Project: final report to Congress. 

Davis, CA: University of California: 1119–1138.

Merriam, K.E.; Keeley, J.E.; Beyers, J.L. 2006. Fuel breaks affect nonnative 

species abundance in Californian plant communities. Ecological Applications. 

16: 515–527.

Miles, S.R.; Goudey, C.B. 1997. Ecological subregions of California: section and 

subsection descriptions. Tech. Pap. R5-EM-TP-005. San Francisco, CA: U.S. 

Department of Agriculture, Forest Service.
56

research paper psw-rp-266

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.

Miller, C.; Davis, B. 2009. Quantifying the consequences of fire suppression in 

two California National Parks. George Wright Forum. 26(1): 76–88.

Miller, J.D.; Safford, H.D. 2012. Trends in wildfire severity: 1984 to 2010 in the 

Sierra Nevada, Modoc Plateau, and southern Cascades, California, USA. Fire 

Ecology. 8(3): 41–57.

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.D.; Skinner, C.N.; Safford, H.D.; Knapp, E.E.; Ramirez, C.M. 

2012. Trends and causes of severity, size, and number of fires in northwestern 

California, USA. Ecological Applications. 22: 184–203.

Minnich, R.A. 1986. Snow levels and amounts in the mountains of southern 

California. Journal of Hydrology. 89: 37–58.

Minnich, R.A. 2007. Climate, paleoclimate, and paleovegetation. In: Barbour, 

M.G.; Keeler-Wolf, T.; Schoenherr, A.A., eds. Terrestrial vegetation of 

California. 3rd ed. Berkeley, CA: University of California Press: 43–70.

Minnich, R.A.; Barbour, M.G.; Burke, J.H.; Fernau, R.F. 1995. Sixty years 

of change in Californian conifer forests of the San Bernardino Mountains. 

Conservation Biology. 9: 902–914.

Moritz, M.A. 1997. Analyzing extreme disturbance events: fire in Los Padres 

National Forest. Ecological Applications. 7: 1252–1262.

Moritz, M.A. 2003. Spatiotemporal analysis of controls on shrubland fire regimes: 

age dependency and fire hazard. Ecology. 84: 351–361.

National Research Council. 2011. Climate stabilization targets: emissions, 

concentrations, and impacts over decades to millennia. Washington, DC:

The National Academies Press. 289 p.

North, M.; Stine, P.; O’Hara, K.; Zielinski, W.; Stephens, S. 2009. An 

ecosystem management strategy for Sierran mixed-conifer forests. Gen. Tech. 

Rep. PSW-GTR-220. Albany, CA: U.S. Department of Agriculture, Forest 

Service, Pacific Southwest Research Station. 49 p.
57

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

Orville, R.E. 2008. Development of the National Lightning Detection Network. 

Bulletin of the American Meteorological Society. 89: 180–190.

Overpeck, J.T.; Rind, D.; Goldberg, R. 1990. Climate-induced changes in forest 

disturbance and vegetation. Nature. 343: 51–53.

Pickett, S.T.A.; White, P.S., eds. 1985. The ecology of natural disturbance and 

patch dynamics. Orlando, FL: Academic Press. 472 p.

Potter, D.A. 1998. Forested communities of the upper montane in the central and 

southern Sierra Nevada. Gen. Tech. Rep. PSW-GTR-169. Albany, CA: U.S. 

Department of Agriculture, Forest Service, Pacific Southwest Research Station. 

319 p.

PRISM Group. 2004. Climate normals, 1971–2000 [Database]. Corvallis, OR: 

Oregon State University. http://www.prismclimate.org. (4 May 2011).

Rollins, M.G. 2009. LANDFIRE: a nationally consistent vegetation, wildland fire, 

and fuel assessment. International Journal of Wildland Fire. 18: 235–249.

Safford, H.D. 2007. Man and fire in southern California: doing the math. 

Fremontia. 35(4): 25–29.

Safford, H.D.; Hayward, G.D.; Heller, N.E.; Wiens, J.A. 2012. Historical 

ecology, climate change, and resource management: Can the past still inform 

the future? In: Wiens, J.A.; Hayward, G.D.; Safford, H.D.; Giffen, C.M., 

eds. Historical environmental variation in conservation and natural resource 

management. New York: John Wiley and Sons: 46–62.

Safford, H.D.; Mallek, C.R. 2010. Disturbance and diversity in low productivity 

ecosystems. In: Harrison, S.P.; Rajakaruna, N., eds. Serpentine: the evolution 

and ecology of a model system. Berkeley, CA: University of California Press: 

249–274.

Safford, H.D.; Van de Water, K.; Schmidt, D. 2011. California Fire Return 

Interval Departure (FRID) map, 2010 version. U.S. Department of Agriculture, 

Forest Service, Pacific Southwest Region, and The Nature ConservancyCalifornia. http://www.fs.usda.gov/main/r5/landmanagement/gis/.

(8 August 2011).

Skinner, C.N.; Chang, C. 1996. Fire regimes, past and present. In: Sierra Nevada 

Ecosystem Project final report to Congress. Wildland Resources Center Report 

37. Davis, CA: University of California: 1041–1069.
58

research paper psw-rp-266

Skinner, C.N.; Taylor, A.H. 2006. South Cascades bioregion. In: Sugihara, N.G.; 

van Wagtendonk, J.W.; Shaffer, K.E.; Fites-Kaufman, J.; Thode, A.E., eds. Fire in 

California’s ecosystems. Berkeley, CA: University of California Press: 195–224.

Starr, K. 2005. California. A history. New York, NY: Random House. 400 p.

Steffen, W.; Crutzen, P.J.; McNeill, J.R. 2007. The Anthropocene: Are humans 

now overwhelming the great forces of nature? AMBIO. 36: 614–621.

Stephens, S.L.; Sugihara, N.G. 2006. Fire management and policy since European 

settlement. In: Sugihara, N.G.; van Wagtendonk, J.W.; Shaffer, K.E.; FitesKaufman, J.; Thode, A.E., eds. Fire in California’s ecosystems. Berkeley, CA: 

University of California Press: 431–443.

Stephenson, N.L.; Millar, C.I.; Cole, D.N. 2010. Shifting environmental 

foundations: the unprecedented and unpredictable future. In: Cole, D.N.; Yung, 

L., eds. Beyond naturalness: rethinking park and wilderness stewardship in an 

era of rapid change. Washington, DC: Island Press: 50–66.

Sturrock, R.N.; Frankel, S.J.; Brown, A.V.; Hennon, P.E.; Kliejunas, J.T. 2011.

Climate change and forest diseases. Paper 143. U.S. Department of Agriculture, 

Forest Service, and University of Nebraska-Lincoln Faculty Publications.

http://digitalcommons.unl.edu/usdafsfacpub/143. (6 June 2012).

Sugihara, N.G.; van Wagtendonk, J.W.; Shaffer, K.E.; Fites-Kaufman, 

J.; Thode, A.E., eds. 2006. Fire in California’s ecosystems. Berkeley, CA: 

University of California Press. 596 p.

Swetnam, T.W. 1993. Fire history and climate change in giant Sequoia groves. 

Science. 262: 885–889.

Syphard, A.D.; Radeloff, V.C.; Keeley, J.E.; Hawbaker, T.J.; Clayton, M.K.; 

Stewart, S.I.; Hammer, R.B. 2007. Human influence on California fire regimes. 

Ecological Applications. 17: 1388–1402.

Thorne, J.H.; Morgan, B.J.; Kennedy, J.A. 2008. Vegetation change over sixty 

years in the central Sierra Nevada, California, USA. Madroño. 55: 223–237.

Turner, M.G.; Gardner, R.H.; Dale, V.H.; O’Neill, R.V. 1989. Predicting the 

spread of disturbance across heterogeneous landscapes. Oikos. 55: 121–129.

Van de Water, K.M.; Safford, H.D. 2011. A summary of fire frequency estimates 

for California vegetation before Euro-American settlement. Fire Ecology. 7(3): 

26–58.
59

Using Fire Return Interval Departure (FRID) Analysis to Map Spatial and Temporal Changes in Fire Frequency

van Wagtendonk, J.W. 2007. The history and evolution of wildland fire use. Fire 

Ecology. 3(2): 3–17.

van Wagtendonk, J.W.; Cayan, D.R. 2008. Temporal and spatial distribution of 

lightning strikes in California in relation to large-scale weather patterns. Fire 

Ecology. 4(1): 34–56.

van Wagtendonk, J.W.; van Wagtendonk, K.A.; Meyer, J.B.; Paintner, K.J. 

2002. The use of geographic information for fire management planning in 

Yosemite National Park. Applied Geography. 19: 19–39.

Von Hippel, P.T. 2005. Mean, median, and skew: correcting a textbook rule. 

Journal of Statistics Education. 13(2). http://www.amstat.org/publications/jse/

v13n2/vonhippel.html. (November 20, 2012). 

Wells, W.G., II. 1987. The effects of fire on the generation of debris flows in 

southern California. Reviews in Engineering Geology. VII: 105–114.

Westerling, A.L.; Bryant, B.P.; Preisler, H.K.; Holmes, T.P.; Hidalgo, H.G.; 

Das, T.; Shrestha, S.R. 2011. Climate change and growth scenarios for 

California wildfire. Climatic Change. 109(Suppl. 1): S445–463.

Westerling, A.L.; Hidalgo, H.G.; Cayan, D.R.; Swetnam, T.W. 2006. Warming 

and earlier spring increase western U.S. forest wildfire activity. Science. 313: 

940–943.

Whisenant, S.G. 1990. Changing fire frequencies on Idaho’s Snake River Plains: 

ecological and management implications. In: McArthur, E.D.; Romney, E.M.; 

Smith, S.D.; Tueller, P.T., comps. Proceedings, symposium on cheatgrass 

invasion, shrub die-off, and other aspects of shrub biology and management. 

Gen. Tech. Rep. INT-GTR-276. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Research Station: 4–10.

Wiens, J.A.; Hayward, G.D.; Safford, H.D.; Giffen, C.M., eds. 2012. Historical 

environmental variation in conservation and natural resource management. New 

York: John Wiley and Sons. 

Wieslander, A.E. 1935. A vegetation type map for California. Madroño.

3: 140–144. 

Zedler, P.H.; Gautier, C.R.; McMaster, G.S. 1983. Vegetation change in response 

to extreme events: the effect of a short interval between fires in California 

chaparral and coastal scrub. Ecology. 64: 809–818.
This publication is available online at www.fs.fed.us/psw/. You may also order 

additional copies of it by sending your mailing information in label form through 

one of the following means. Please specify the publication title and series 

number.

Fort Collins Service Center

Web site http://www.fs.fed.us/psw/

Telephone (970) 498-1392

FAX (970) 498-1122

E-mail rschneider@fs.fed.us

Mailing address Publications Distribution 

Rocky Mountain Research Station 

240 West Prospect Road 

Fort Collins, CO 80526-2098

Pacific Southwest Research Station

800 Buchanan Street

Albany, CA 94710