Forest

Service

United States Department of Agriculture

General Technical Report 

RMRS-GTR-391

Rocky Mountain

Research Station June 2019

FIRE-BIRD: A GIS-Based Toolset 

for Applying Habitat Suitability Models 

to Inform Land Management Planning

Quresh S. Latif

Victoria A. Saab

Jessica R. Haas

Jonathan G. Dudley
Latif, Quresh S.; Saab, Victoria A.; Haas, Jessica R.; Dudley, Jonathan G. 2018. FIRE-BIRD: A 

GIS-based toolset for applying habitat suitability models to inform land management 

planning. Gen. Tech. Rep. RMRS-GTR-391. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 74 p. 

Abstract 

Habitat suitability models can inform forest management for species of conservation concern. 

Models quantify relationships between known species locations and environmental attributes, 

which are used to identify areas most likely to support species of concern. Managers can then 

limit negative human impacts in areas of high suitability or conduct habitat improvements in 

areas of marginal suitability. Model applications are computationally intensive, requiring time 

and resources not available to most managers. We developed FIRE-BIRD, an ArcGIS toolbox, 

to streamline preliminary data processing and application of habitat suitability models to forest 

management planning for disturbance-associated woodpeckers of conservation concern. Tools 

are currently developed for black-backed (Picoides arcticus) and white-headed woodpecker 

(Dryobates albolvartus) in Inland Northwest burned forests; black-backed, white-headed, and 

hairy woodpecker (D. villosus) in Northern Sierra burned forests; and white-headed woodpecker in 

Inland Northwest unburned forests. This manual provides tool operating instructions and guidelines 

to interpret resulting habitat suitability maps. The suite of species currently included makes this 

toolset best suited for postfire management and restoration treatments in dry mixed-conifer forests. 

Incorporating additional species and forest conditions in the future will broaden the scope of this 

toolset.

Keywords: decision support, disturbance-associated woodpecker, dry conifer forest, 

 forest management, habitat suitability model, cavity nest, species distribution, 

 species of conservation concern

Cover photos

Aerial view of recent wildfire in the Frank Church-River of No Return Wilderness (top left) (photo by 

Victoria A. Saab, USDA Forest Service); screenshot of dialog box for black-backed woodpecker tool 

(center left); habitat suitability index map for black-backed woodpecker at the 2015 Canyon Creek 

Fire, Oregon (bottom left); black-backed woodpecker (top right); Lewis’s woodpecker (center right); 

white-headed woodpecker (bottom right) (woodpecker photos by Tom Kogut). 

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

are in the public domain and available at no cost. Even though U.S. Forest Service publications are not 

copyrighted, they are formatted according to U.S. Department of Agriculture standards and research 

findings and formatting cannot be altered in reprints. Altering content or formatting, including the cover 

and title page, is strictly prohibited.

The 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.
Authors

Quresh S. Latif worked for 6 years as a Research Ecologist with the USDA Forest Service, 

Rocky Mountain Research Station (RMRS) in Bozeman, Montana, where he was affiliated when 

developing FIRE-BIRD and writing this manual. He received a B.S. degree in wildlife, fisheries, and 

conservation biology from University of California, Davis, and completed his Ph.D. in evolution, 

ecology, and organismal biology at the University of California, Riverside. Since then, he has 

worked as a quantitative ecologist with a focus on developing and applying population, community, 

and habitat models to inform species conservation and management. Since writing this manual, he 

has moved to a Biometrician position with Bird Conservancy of the Rockies, where he continues 

working with the Forest Service in applied ecology to inform management of forest communities 

and habitats.

Victoria A. Saab has been a Research Biologist with USDA Forest Service, Rocky Mountain 

Research Station since 1989, and an affiliate faculty member at Montana State University since 

2002. She received a B.S. degree in ecology from Oklahoma State University, an M.S. degree 

in fish and wildlife management from Montana State University, and a Ph.D. in biology from the 

University of Colorado, Boulder. Her research interests include animal distributions in relation to 

large-scale disturbances of fire and beetle outbreaks. She conducts research evaluating effects of 

land management activities on avian demographics and habitat relationships to identify appropriate 

measures for bird conservation. Since 1994 she has been leading teams of students, postdoctoral 

ecologists, and field biologists on long-term studies of fire effects on avian populations and habitats 

in dry mixed-conifer forests of the Interior West.

Jessica R. Haas is an Ecologist with USDA Forest Service, Rocky Mountain Research Station 

in Missoula, Montana. She has worked for RMRS for the past 8 years on wildfire and natural 

hazard mitigation science. Her research focuses on the development of decision support tools for 

analyzing risk of various hazards to human communities and ecosystems. Her work with RMRS 

has been used nationally to support mitigation efforts for wildfire and other natural hazards for the 

major land managers such as the Forest Service, Bureau of Land Management, and The Nature 

Conservancy, as well as various State forestry departments throughout the United States. She 

received an M.S. degree in resource conservation from the University of Montana, where she 

studied the vulnerability of national parks to climate change and exurban development.

Jonathan G. Dudley is an Ecologist with USDA Forest Service, Rocky Mountain Research Station 

at the Boise Aquatic Sciences Laboratory in Boise, Idaho. Since joining the Forest Service in 1991, 

he has contributed to numerous research projects primarily aimed at understanding relationships 

between small mammal and avian demographics, their habitats, and land management activities. 

His recent work in the Wildlife and Terrestrial Ecosystems program focuses on the influences of 

natural (wildfire, insects) and human (fire suppression, prescribed fire, postfire salvage logging, 

forest restoration) disturbances on avian communities in dry mixed-conifer forests. He received 

a B.S. degree in wildlife biology at Washington State University, Pullman, and an M.S. degree in 

Biology at Boise State University. 

Acknowledgments

This manual is the result of multiple studies and data collected by many people over 20 years. 

Funding was provided by the Joint Fire Science Program (#01-1-3-25 and #06-3-4-15), National 

Fire Plan (02.RMS.C.2), and the USDA Forest Service (Rocky Mountain Research Station; Boise, 

Fremont-Winema, Malheur, Okanogan-Wenatchee, Custer-Gallatin, Plumas, and Lassen National 

Forests; Pacific Northwest, Intermountain, Northern, and Pacific Southwest Regions). We thank 

biologists for oversight and implementation of data collection at various study sites: Chris Forristal 

and Amy Markus (Fremont-Winema National Forest), Kent Woodruff (Okanogan-Wenatchee 

National Forest), Katie Nicolato (Malheur National Forest), Larry Donohoo (Boise National Forest), 

Jodie Canfield (Custer-Gallatin National Forest and Northern Region), and Ryan Burnett and Brent 

Campos (Point Blue Conservation Science). Helpful reviews were provided by Kim Mellen-McLean 

and Josh Chapman (Pacific Northwest Region), Jodie Canfield and Courtney Frost (Northern 

Region), and Ryan Burnett and Brent Campos (Point Blue Conservation Science). We are grateful 

to the many field assistants who worked long, hot, wet, and cold days to collect the field data. 

Quresh S. Latif analyzed the data, developed habitat suitability models and ArcGIS tools, and 

wrote this manual. Victoria A. Saab conceived the idea, designed field survey protocols, supervised 

research at all stages, and substantially edited this manual. Jessica R. Haas developed ArcGIS 

tools and edited this manual. Jonathan G. Dudley designed field survey protocols, supervised data 

collection, managed data storage and compilation, and provided editorial input.

Contents

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

2. Conceptual Basis for Habitat Suitability Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.1 Black-Backed Woodpecker in Burned Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2 White-Headed Woodpecker in Unburned Forests . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.3 White-Headed Woodpecker in Burned Forests of the Inland Northwest . . . . . . . . 5

2.4 Woodpeckers in Northern Sierra Burned Forests . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3. Model Applicability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.1 Black-Backed Woodpecker Inland Northwest Model Applicability . . . . . . . . . . . . 12

3.2 White-Headed Woodpecker Unburned Forest Model Applicability . . . . . . . . . . . . 12

3.3 White-Headed Woodpecker Burned Forest Model Applicability . . . . . . . . . . . . . . 13

3.4 Applicability of Northern Sierra Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4. Instructions for Tool Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4.1 Black-Backed Woodpecker Instructions for the Inland Northwest . . . . . . . . . . . . 17

4.2 White-Headed Woodpecker Instructions for Unburned Forests . . . . . . . . . . . . . . 28

4.3 White-Headed Woodpecker Instructions for Burned Forests . . . . . . . . . . . . . . . . 32

4.4 Northern Sierra Instructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

5. Guidelines for Application and Interpretation of Habitat Suitability Models . . . . 48

5.1 Guidelines for Postfire Habitat Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

5.2 Guidelines for White-Headed Woodpecker in Unburned Forests . . . . . . . . . . . . . 55

6. Management Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

7. Troubleshooting Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Appendix A: RAVG-Based Models to Support Immediate Postfire Planning . . . . . . . 67

Appendix B: Demonstration of Model Application for Postfire Planning . . . . . . . . . . . 72

U.S. Forest Service RMRS GTR-391. 2019. 1

1. Introduction

To conserve and promote biological diversity, land managers must 

identify suitable habitat for species of conservation concern. Managers can 

then restrict potentially detrimental activities (e.g., salvage logging) to areas 

of lower habitat suitability, and target beneficial activities (e.g., restoration) 

where habitat suitability is higher (Barrows et al. 2008; Brambilla and 

Saporetti 2014; Guisan et al. 2013; Hollenbeck et al. 2011; Latif et al. 

2013, 2015). Land managers often rely on informal methods, such as verbal 

descriptions based on expert knowledge, for identifying habitat. These 

methods may have unknown reliability and limited ability to reflect complex 

habitat associations. In contrast, habitat suitability models (also called 

species distribution models; hereafter, “habitat models”) rigorously quantify 

suitable habitat by using data-driven algorithms capable of representing 

complex relationships with multiple environmental attributes (Elith and 

Leathwick 2009; Franklin 2009; Guisan and Thuiller 2005; Guisan et al. 

2013). Such information can enhance planning effectiveness and is needed 

to make management decisions robust to legal scrutiny (e.g., Defenders of 

Wildlife vs. Sally Jewell, U.S. Department of the Interior and Daniel M. 

Ashe, U.S. Fish and Wildlife Service, 4 Apr 2016, Case 9:14-cv-00246-DLC, 

Document 108). Despite numerous publications describing new models, 

few models are applied (reviewed by Guisan et al. [2013]). Application of 

habitat models is computationally intensive, requiring time and resources not 

available to most natural resource specialists at land management agencies 

(hereafter, “managers”).

Managers currently rely heavily on geographic information systems 

(GIS) technology to develop and inform their decisions and planning. In 

particular, managers commonly use ArcGIS (Esri 2015) software, which 

provides a powerful set of mapping and spatial analysis tools through a 

relatively user-friendly graphic interface. Because much of the planning 

process takes place in an ArcGIS environment, managers must at some 

point translate habitat information into map layer(s) viewable in ArcGIS. 

Habitat models are fundamentally statistical entities developed by using 

statistical software packages (e.g., R; R Core Team 2016) whose outputs do 

not readily interface with ArcGIS. Analysts often develop habitat models, 

however, with remotely sensed environmental data to facilitate translation 

of model predictions into habitat maps. Recent versions of ArcGIS software 

increasingly allow users to develop custom tools to generate and process 

spatial data layers. We leverage these new features to develop ArcGIS 

tools that help users with limited time and technical resources to translate 

statistical model predictions into habitat maps.
2 U.S. Forest Service RMRS GTR-391. 2019.

We developed a series of ArcGIS tools for applying habitat models of 

woodpeckers associated with recent disturbances to guide management of 

dry conifer forests in western North America. Large-scale disturbances, 

especially wildfire and bark beetle outbreaks, strongly influence vegetation 

structure and composition in these forests. Anthropogenic land use and 

climate change have altered disturbance timing and severity, along with 

associated structural and wildlife habitat features (Franklin and Johnson 

2012; Fulé et al. 2012; Hessburg et al. 2007, 2015; Schoennagel et al. 2004; 

Whitlock et al. 2003). In particular, many woodpecker species benefit from 

habitats generated and maintained by large-scale disturbances (Latif et al. 

2013; Russell et al. 2007; Saab et al. 2009, 2014; Wightman et al. 2010). 

State and Federal agencies are concerned with conservation of these 

woodpeckers and have legal requirements to document the effects of 

management actions on their habitats and populations. Forest managers 

must therefore consider the tradeoffs between land uses and wildlife habitat 

conservation in their planning documents. In recently disturbed forests, 

managers often try to meet conflicting objectives of conserving wildlife 

habitat while also providing economic opportunities through salvage 

logging. As new disturbances occur, model application tools targeting 

disturbed habitats could facilitate rapid identification of suitable habitat for 

woodpeckers and other disturbance-associated species. Additionally, GIS 

tools could help identify suitable habitat in disturbance-maintained dry 

forests for informing large-scale restoration and fuels reduction projects (e.g., 

those funded by the Collaborative Forest Landscape Restoration Program) 

(Mellen-McLean et al. 2013, 2015). Data used to develop models presented 

in this manual also helped the Forest Service successfully defend against 

litigation challenging commercial salvage logging after the 2002 Toolbox 

Fire in Oregon (Oregon Natural Resources Council Fund, et al., Plaintiffs, vs. 

Linda Goodman, et al., Defendants, 22 June 2004, Case No. 04-593-CO).

In this manual, we summarize the ecological basis and applicability of 

habitat models provided in ArcGIS application tools. We then provide stepby-step instructions for implementing these tools, which currently quantify 

postwildfire (hereafter, “postfire”) habitat for black-backed woodpecker 

(Picoides arcticus), white-headed woodpecker (Dryobates albolarvatus), 

and hairy woodpecker (D. villosus), along with unburned forest habitat, 

maintained by disturbance processes, for white-headed woodpecker. Finally, 

we describe how mapped predictions can be interpreted to appropriately 

inform forest management at the project scale.
U.S. Forest Service RMRS GTR-391. 2019. 3

2. Conceptual Basis for Habitat Suitability Models

2.1 Black-Backed 

Woodpecker 

in Burned Forests

Latif et al. (2013) developed habitat suitability index (HSI) models for 

nesting black-backed woodpecker with nest location data collected following 

wildfire in dry conifer forests of Oregon, Washington, and Idaho. Blackbacked woodpeckers are disturbance specialists that favor forests burned by 

wildfire across much of their range (Dixon and Saab 2000). Within burned 

forests, these birds excavate nest cavities in snags in areas of relatively high 

snag densities, which provide foraging opportunities for their preferred prey 

(i.e., bark and wood-boring beetle larvae; Scolytidae and Cerambycidae, 

respectively). Models were therefore developed by using remotely sensed 

burn severity and prefire canopy data along with a topography variable 

(cosine aspect) found in previous work to be potentially important (table 2.1).

Variable 

(abbreviation) Description

Mean, Median 

(95th percentile)

Nest Available

Cosine aspect 

(cosasp)a

Pixel cosine-transformed 

aspect derived from digital 

elevation model (unitless)

0.34, 0.51 

(-0.98, 1.00)

0.00, 0.00 

(-1.00, 1.00)

Burn severity 

(locdnbr)b

Median index of burn severity 

(change in [delta] normalized 

burn ratio prefire to postfire) 

using Landsat TM satellite 

imagery for 2-acre (3×3-cell) 

moving window (unitless)

529.3, 515.2 

(169.4, 862.3)

328.1, 297.1 

(-59.6, 806.5)

Local canopy cover 

(loccc)c

Proportion of 2-acre 

(3×3-cell) moving window 

with >40% canopy cover 

recorded before fire

0.85, 1.00 

(0.00, 1.00)

0.63, 0.89 

(0.00, 1.00)

Landscape canopy 

cover (landcc)

Proportion of 776-acre 

(0.6-mile-radius) moving 

window with >40% canopy 

cover recorded before fire

0.65, 0.62 

(0.39, 0.93)

0.59, 0.59 

(0.17, 0.95)

Table 2.1—Descriptions and descriptive statistics for environmental variables at 

nest and available sites used to develop habitat models for nesting black-backed 

woodpeckers. Descriptive statistics that equally weighted data from three surveyed 

wildfire locations (see Latif et al. 2013). Available sites represent survey units within 

which nest-searching occurred.

a

 Topography data were derived from digital elevation model data (LANDFIRE 2018).

b

 The original HSI (ensemble) model was developed with “extended assessment” burn severity 

data (MTBS 2012), wherein postfire imagery was recorded the year following wildfire. We 

provide an alternate version developed with RAVG data, however, to support immediate postfire 

planning (for details, see Appendix A).

c

 Canopy cover data were acquired from online sources as available for individual wildfire 

locations (LEMMA 2018; USFS 2018a).
4 U.S. Forest Service RMRS GTR-391. 2019.

2.1 Black-Backed Woodpecker 

in Burned Forests (continued) Using different modeling techniques and different subsets of these data, 

Latif et al. (2013) developed a series of eight models and combined their 

predictions using an ensemble approach. Resulting predictions described the 

number of models (0–8) classifying a site as suitable for nesting (hereafter, 

“ensemble predictions”). Predictions were mainly shaped by nesting affinity 

for high-severity burned sites with moderate-to-high prefire canopy cover. 

Areas classified as suitable by more models are considered more suitable, 

and areas classified as suitable by some but not all models represent areas of 

uncertainty where further surveys could improve ecological knowledge and 

help refine models (Latif et al. 2013).

Latif et al. (2015) developed and evaluated habitat suitability models 

for nesting white-headed woodpeckers in unburned dry conifer forests of 

Oregon. White-headed woodpeckers favor forests dominated by ponderosa 

pine (Pinus ponderosa) and characterized by forest canopy mosaics. In 

particular, they establish nest sites in relatively open-canopy forests but 

forage largely in more closed-canopy forests (Hollenbeck et al. 2011; 

Latif et al. 2015). To quantify these habitat associations, Latif et al. (2015) 

developed models using variables describing topography, canopy cover, and 

coverage of ponderosa pine-dominated forest compiled at a 30-m resolution 

at nest and available sites (table 2.2). They developed and evaluated two 

different types of models, but one developed by using the Maxent modeling 

technique (Phillips et al. 2006) performed especially well. Maxent model 

predictions quantified affinity for nest sites with lower local canopy cover, 

higher landscape canopy cover, and a high percentage of ponderosa-pine 

dominance.

To expedite model application particularly over large landscapes, the ArcGIS 

tool applies a simplified version of the published model. The simplified 

model excludes edge density (density of edge between high and low canopy 

cover patches), because it is computationally intensive to calculate and 

contributed negligibly to performance of the published model. Additionally, 

simplified model HSIs represent one version of the model fitted to all 

available data (published HSIs were averaged across model replicates fitted 

to resampled data subsets). In short, the simplified model generates HSIs 

virtually identical to the published model but reduces the time required for 

data processing and computation.

2.2 White-Headed 

Woodpecker in 

Unburned Forests
U.S. Forest Service RMRS GTR-391. 2019. 5

Table 2.2—Descriptions and descriptive statistics for environmental variables at 

nest and available sites used to develop habitat models for nesting white-headed 

woodpeckers in unburned forests. Descriptive statistics were calculated for samples 

balanced across subsets of nest location data (described by Latif et al. 2015). 

Available sites represent areas within which nest-searching occurred.

Variable 

(abbreviation) Description

Mean, Median 

(95th percentile)

Nest Available

Slope (slp)a pixel slope in % rise over run 3.7, 2.0 

(0.0, 16.3)

4.5, 3.0 

(0.0, 20.0)

Cosine Aspect 

(cosasp)a,b

pixel cosine-transformed 

orientation of slope (unitless)

0.05, 0.00 

(-0.99, 0.98)

0.00, 0.00 

(-0.99, 0.98)

Local canopy cover 

(loccc)c

percent canopy cover 

for 776-acre (3×3-cell) 

neighborhood

40.1, 40.6 

(8.7, 68.4)

42.9, 42.4 

(14.1, 73.8)

Landscape canopy 

cover (landcc)c

percent canopy cover for 

776-acre (0.6-mile-radius) 

neighborhood

43.2, 42.0 

(29.9, 60.1)

43.0, 42.1 

(25.0, 62.3)

Ponderosa pine 

(pipo)c

Percent ponderosa pinedominated forestd

 for 

776-acre (0.6-mile-radius) 

neighborhood

80.3, 84.0 

(49.3, 97.0)

73.3, 77.0 

(28.0, 96.9)

a

 Topography data were derived from digital elevation model data (LANDFIRE 2018).

b

 cosasp = 0 when slp ≤2%.

c

 Forest structure and composition data were derived from gradient nearest-neighbor data 

(LEMMA 2018).

d

 A pixel was classified as ponderosa pine-dominated forest if dominated or codominated by 

ponderosa pine as classified by gradient nearest-neighbor data (LEMMA 2018).

2.2 White-Headed Woodpecker in 

Unburned Forests (continued)

2.3 White-Headed 

Woodpecker 

in Burned Forests 

of the Inland 

Pacific Northwest

White-headed woodpecker nest habitat relationships in recently burned 

forests are analogous to those in unburned forests, wherein canopy mosaics 

are favored for nesting (compare Wightman et al. 2010 with Hollenbeck et 

al. 2011 and Latif et al. 2015). Wightman et al. (2010) described a postfire 

habitat model for white-headed woodpeckers. With additional data from 

two wildfire locations in Oregon (Toolbox, 2002; Canyon Creek, 2015) 

(table 2.3.1), we revised the Wightman et al. (2010) model using the Maxent 

technique to generate a new model. We briefly summarize model structure, 

development, evaluation, and rationale here. Additional details will be 

reported in a peer-reviewed manuscript (Latif et al., in preparation).

In recently burned forests, we expected white-headed woodpeckers to use 

burned areas for nest placement near unburned forest for foraging (Wightman 

et al. 2010). We modeled these relationships using Maxent (Phillips et al. 

2006) informed by remotely sensed data describing burn severity, prefire 

canopy cover, percent coverage of ponderosa pine-dominated forest, and 
6 U.S. Forest Service RMRS GTR-391. 2019.

2.3 White-Headed Woodpecker in 

Burned Forests of the Inland Pacific 

Northwest (continued)

National 

forest

Fire 

name

Ignition 

year

Years 

surveyed

Full 

extent 

(acres)

Surveyed 

extent 

(acres)

No. nest 

pixels

FremontWinema Toolbox 2002 2003‒2007 82,565 2,114a 46a

Malheur Canyon 

Creek

2015 2016‒2017 110,340 10,737; 

11,676b

47

Table 2.3.1—Summary of sampling at two wildfire locations where white-headed 

woodpecker nest surveys were conducted to inform burned forest HSI model 

development and evaluation.

a

 Non-nest sites were measured only in the 13 largest of the 22 survey units. Area surveyed = 

1,971 acres, and 33 nests were located in these 13 units.

b

 One survey unit was replaced between years. The smaller area was surveyed in 2016, and the 

larger in 2017.

topographic slope (table 2.3.2) at nest and available sites recorded at two 

wildfire locations, the 2002 Toolbox Fire (see Wightman et al. 2010) and the 

2015 Canyon Creek Fire in Oregon.

We initially developed separate models at each wildfire location and 

applied them across locations to test predictive performance. We measured 

predictive performance using the area under the receiver operating curve 

(AUC) (Fielding and Bell 1997) assessing discrimination of nest from 

non-nest sites, whereby an AUC of less than 0.5 indicates discrimination no 

better than random and an AUC of 1 indicates perfect discrimination. Models 

developed at Toolbox and Canyon Creek locations performed well when 

applied across these two locations (table 2.3.3), so we combined data from 

these locations to develop a final model. 

Unfortunately, models did not perform well at a third location, the Barry 

Point Fire (Oregon, 2011) (table 2.3.3), so we recommend restricting model 

application to conditions characteristic of the Toolbox and Canyon Creek 

locations but not Barry Point (described further in subsection 3.3 Whiteheaded Woodpecker Burned Forest Model Applicability). The final model 

provided by the ArcGIS tool describes a positive relationship with moderateto-high severity burns or open forest at the nest site scale (~2 acres) and a 

negative relationship with these features at the home range scale (776 acres) 

(table 2.3.2). Model HSIs designate suitable nesting habitat along burned 

forest edges and within mosaics of burned and unburned forest. Additionally, 

following relationships with ponderosa pine forest and topographic slope 

observed at individual wildfire locations (see also Hollenbeck et al. 2011, 

Latif et al. 2015), we suggest restricting model application to the range 

of LandPIPO and Slope values observed at nest locations (table 2.3.2) 

(described further in subsection 3.3 White-headed Woodpecker Burned Forest 

Model Applicability).
U.S. Forest Service RMRS GTR-391. 2019. 7

Table 2.3.2—Remotely sensed environmental variables used to model nesting habitat 

for white-headed woodpeckers in burned forest. Descriptive statistics (mean [SD]) are 

reported for the two study locations where the model was developed at nest sites and 

available sites representing the area surveyed.

Variable 

(abbreviation)

Toolbox 

(Oregon)

Canyon Creek 

(Oregon)

Description nest available nest available

Local-scale 

percent area 

burned or open 

(locbrnopn)a

Percentage of 2-acre 

(3×3-cell) 

neighborhood 

moderately to severely 

burned (ΔNBRb >270) 

or <10% prefire 

canopy cover

95.3 

(13)

81.6 

(32.4)

82 

(26.8)

80.4 

(28.9)

Landscapescale percent 

area burned 

or open 

(landbrnopn)a

Percentage of 

776-acre (0.6-mileradius) neighborhood 

moderately to severely 

burned (ΔNBRb >270) 

or <10% prefire 

canopy cover

61.1 

(19.7)

65.7 

(21.5)

60.7 

(14)

68.1 

(13.7)

Landscapescale 

percent area 

ponderosa 

pine forest 

(LandPIPO)c

Percentage of 

776-acre (0.6-mileradius) neighborhood 

dominated or 

codominated by 

ponderosa pined

74.9 

(7.9)

72.3 

(10.6)

59.8 

(10.1)

59.2 

(10.5)

Slopec pixel topographic slope 

as % rise over run

7.3 

(5.6)

7.8 (6.6) 21.3 

(12.9)

23.5 

(11.4)

a

 The original HSI (ensemble) model was developed with “extended assessment” burn severity 

data (MTBS 2012), wherein postfire imagery was recorded the year following wildfire. We 

provide an alternate version developed with RAVG data, however, to support immediate postfire 

planning (for details, see Appendix A).

b

 Delta normalized burn ratio

c

 LandPIPO and Slope do not directly inform modeling but are used for post hoc masking of 

the final HSI map to restrict model application to areas with LandPIPO >40% and Slope <40%, 

representing the minimum and maximum values, respectively, observed at nest locations.

d

 A pixel was classified as ponderosa pine-dominated forest if dominated or codominated by 

ponderosa pine as classified by gradient nearest-neighbor data (LEMMA 2018).

2.3 White-Headed Woodpecker in 

Burned Forests of the Inland Pacific 

Northwest (continued)
8 U.S. Forest Service RMRS GTR-391. 2019.

Table 2.3.3—Predictive performance of white-headed woodpecker burned forest 

model(s) at individual wildfire locations. AUC ≤ 0.50 indicates discrimination of nest 

from non-nest sites no better than random, whereas AUC = 1 indicates perfect 

discrimination. Boot-strapped 95% confidence intervals (parentheses) that overlapped 

0.50 were considered indicative of poor performance. Models were developed at 

Toolbox (TB) and Canyon Creek (CC) study locations and applied at these plus Barry 

Point (BP).

Developed at:

Applied at: TB CC TB and CC

TB 0.76 (0.68, 0.85) 0.72 (0.62, 0.81)a 0.72 (0.63, 0.81)

CC 0.61 (0.52, 0.7)a 0.64 (0.54, 0.73) 0.62 (0.53, 0.71)

BP 0.58 (0.39, 0.77)a 0.5 (0.31, 0.69)a 0.46 (0.27, 0.65)a

a

 AUCs measuring discrimination outside where models were originally developed are especially 

important for assessing predictive performance.

2.3 White-Headed Woodpecker in 

Burned Forests of the Inland Pacific 

Northwest (continued)

2.4 Woodpeckers 

in Northern Sierra 

Burned Forests

We developed models for several woodpecker species to inform postfire 

forest planning and habitat conservation in the northern Sierra Nevada 

of California. Given regional differences in forest structure, tree species 

composition, and woodpecker behavior (e.g., Fogg et al. 2014), we did not 

expect models developed in other regions to be applicable here. We therefore 

developed a series of models specific to this region based on nest and nonnest location data for three target species (black-backed woodpecker, hairy 

woodpecker, and white-headed woodpecker) from three wildfires that 

occurred in the Lassen National Forest over a 5-year period (table 2.4.1). 

Nests were located by searching within a priori established 200-m (700-foot) 

wide belt transects following Dudley and Saab (2003).

Timing n

Fire Ignition Sampling Nesting spp. nest non-nesta

Moonlight 2007 2009‒2012 BBWO 24 337

HAWO 46 274

WHWO 30 325

Cub 2008 2009‒2012 BBWO 19 100

HAWO 27 79

WHWO 20 112

Chips 2012 2013‒2016 BBWO 28 41

HAWO 24 40

WHWO 38 45

Table 2.4.1—Summary of sampling at three wildfires in Lassen National Forest, 

California, for woodpecker nest locations to inform HSI model development and 

evaluation. Models presented here are for three woodpecker species: black-backed 

woodpecker (BBWO), hairy woodpecker (HAWO), and white-headed woodpecker 

(WHWO). 

a

 Non-nest sites represented a random sample drawn from within survey units for nest searching.
U.S. Forest Service RMRS GTR-391. 2019. 9

2.4 Woodpeckers in Northern Sierra 

Burned Forests (continued) We briefly summarize model structure, development, evaluation, and 

rationale, leaving additional details for a peer-reviewed publication (Campos 

et al., in preparation). We initially considered metrics of topography, burn 

severity, and prefire forest structure, although only the latter two appeared 

in selected models (table 2.4.2). These variables were drawn from available 

data sources (Mayer and Laudenslayer 1988; Miller and Thode 2007; Rollins 

and Frame 2006), reflected potentially informative environmental features 

described in the literature (Latif et al. 2013, 2015; Saab et al. 2009; Tingley 

et al. 2014; Wightman et al. 2010), and differed notably between nest and 

non-nest locations (table 2.4.3). 

Table 2.4.2—Descriptions of environmental variables used to develop habitat models 

for nesting woodpeckers in burned forests of the northern Sierra Nevada Mountains. 

Local-scale variables quantified conditions at the nest site (3×3 cells; 0.81 ha), 

whereas landscape-scale variables quantified conditions approximating home range 

size inclusive of most woodpecker species (1-km radius circle; 314 ha).

Variable (abbreviation) Description

Local burn severity 

(LocCCmort)a

Median percent canopy mortality (derived from 

relativized delta normalized burn ratio) for 2-acre 

moving window (%)

Landscape burn severity 

(LandCCmort)a

Percentage of 776-acre neighborhood with >64% 

canopy mortality (derived from relativized delta 

normalized burn ratio) (%)

Local canopy cover 

(LocCC)b

Percentage of 2.5-acre moving window with >40% 

canopy cover recorded before fire (CWHR density 

class M [40–60%] and D [>60%])

Landscape canopy cover 

(LandCC)b

Percentage of 776-acre moving window with >40% 

canopy cover recorded before fire

Local large-tree dominance 

(LocTrSize)b

Percentage of 2-acre moving window dominated by 

trees of d.b.h. > 61 cm (CWHR size class 5)

a

 Burn severity data were acquired from the online repository for RAVG data (RAVG 2018).

b

 Prefire forest structure data were acquired from the California Wildlife Habitat Relationships 

(CWHR) online repository (R5 Vegetation Classification and Mapping 2018).

Table 2.4.3—Descriptive statistics (mean [SD]) for environmental variables used 

to model habitat for nesting woodpeckers in burned forests of the northern Sierra 

Nevada Mountains. Nesting species are black-backed woodpecker (BBWO), hairy 

woodpecker (HAWO), and white-headed woodpecker (WHWO). Complete variable 

names and descriptions are in table 2.4.2.

BBWO HAWO WHWO

Variable Nest Non-nest Nest Non-nest Nest Non-nest

LocCCmort 72.8 

(37)

51.4 

(43.7)

77.7 

(33.7)

49.4 

(43.7)

60.1 

(41.2)

49.8 

(43.5)

LandCCmort 33.2 

(30.3)

48.2 

(32)

41.3 

(35.7)

46.5 

(31.3)

26 

(25.9)

47.9 

(32.4)

LocCC 94.8 

(15.6)

87 

(28.7)

89.1 

(28.3)

85.9 

(29.7)

78.3 

(37.1)

85.2 

(30.5)

LandCC 79.3 

(15.8)

83 

(12.7)

84.5 

(13.5)

81.7 

(13)

79.4 

(14)

81.9 

(13.7)

LocTrSize 32.6 

(44.2)

58.5 

(43.6)

46 

(45.5)

55 

(44.9)

29.2 

(42.3)

55.3 

(44.5)
10 U.S. Forest Service RMRS GTR-391. 2019.

We used weighted logistic regression to relate binomial nest location 

data (0 = non-nest; 1 = nest) with these variables (observation weights 

and rationale follows Latif et al. 2016). For each species, we constructed 

and fitted models representing all possible combinations of environmental 

variables limited by sample size (maximum number of variables = number of 

nests/10). We then selected one model for each species according to a series 

of criteria describing predictive performance, relative fit, model-estimated 

habitat suitability at nest locations, and parsimony (table 2.4.4).

The selected models described positive relationships with nest-site 

scale burn severity for all species, negative relationships with home-range 

scale burn severity by black-backed and white-headed woodpeckers, 

positive relationships with prefire canopy cover by black-backed and hairy 

woodpeckers at various scales, and a negative relationship with largetree dominance for hairy woodpecker (table 2.4.5). Positive relationships 

with moderate- and high-severity burned nest sites are consistent with 

patterns described for other regions (Latif et al. 2013, 2016; Russell et al. 

2007). Scale-dependent relationships with burn severity for white-headed 

woodpecker are consistent with their affinity for canopy mosaics also 

described elsewhere (Hollenbeck et al. 2011; Latif et al. 2015; Wightman et 

al. 2010). 

Table 2.4.4—Performance criteria used to evaluate habitat models and select from 

candidate models for nesting woodpecker species surveyed in the northern Sierra 

Nevada Mountains.

Criterion Description How applied

RPIa Mean correlation coefficient 

(-1–1 range; n = 3 wildfires) relating 

observations with model predictions 

for each wildfire when withheld 

from model fitting via spatial crossvalidation

The model with highest 

mean RPI (n = 3 wildfires) 

that met all other criteria 

was selected.

ΔAICi 

b AIC for the i th model minus that of the 

top-ranked (lowest-AIC) model

Models where ΔAICi > 6 

were excluded.

Sensitivityc Proportion of nests classified as 

suitable at a given wildfire using a 

classification threshold that maximized 

the sum of sensitivity (proportion nests 

suitable) + specificity (proportion nonnests unsuitable) 

Models conferring 

minimum sensitivity at 

any one wildfire <0.5 were 

excluded.

Parsimonyd Statistical support for model 

coefficients based on z-statistic

Models with statistically 

unsupported coefficients 

(P > 0.05) were excluded.

a

 RSF plot index (where RSF stands for resource selection function) described by Wiens et al. 

(2008).

b

 Akaike’s Information Criterion, described by Burnham and Anderson (2002).

c

 Described by Liu et al. (2016).

d

 Described by Wiens et al. (2008). 

2.4 Woodpeckers in Northern Sierra 

Burned Forests (continued)
U.S. Forest Service RMRS GTR-391. 2019. 11

2.4 Woodpeckers in Northern Sierra 

Burned Forests (continued)

Species

Model coefficients 

(mean estimates) RPIa ΔAICi

b

Min sensitivity 

(max SSS 

threshold)

BBWO Intercept (-0.86) + LocCCmort 

(1.77) + LandCCmort (-1.65) + 

LocCC (0.8)

0.976 2.239 0.63 (0.41)

HAWO Intercept (-0.48) + LocCCmort 

(0.89) + LandCC (0.4) + 

LocTrSize (-0.47)

0.986 2.680 0.56 (0.51)

WHWO Intercept (-0.56) + LocCCmort 

(1.5) + LandCCmort (-1.83)

0.942 0.000 0.53 (0.49)

Table 2.4.5—Selected habitat models developed to inform habitat mapping for focal 

woodpecker species (black-backed woodpecker [BBWO], hairy woodpecker [HAWO], 

white-headed woodpecker [WHWO]) following wildfire in the northern Sierra Nevada 

Mountains. All environmental covariates were z-scored (centered on zero and 

divided by SD) prior to model fitting. Performance criteria used for model selection 

are reported here and described in table 2.4.4. Min sensitivity is the minimum 

proportion of nests classified suitable at any one of three wildfire locations using the 

classification threshold that maximizes sum of sensitivity and specificity (max SSS 

threshold).

a

 RSF plot index (where RSF stands for resource selection function) described by Wiens et al. 

(2008).

b

 Akaike’s Information Criterion.

Other relationships observed here, however, may be unique to Northern 

Sierra forests. For example, negative relationships with home-range scale 

burn severity by black-backed woodpecker are not observed in other regions 

(Latif et al. 2013; Russell et al. 2007) nor in more pine-dominated portions 

of the Sierra Nevada (Tingley et al. 2014). Avoidance of large-tree dominated 

sites may also reflect a particular affinity for areas with higher densities 

of smaller trees in this region (Seavy et al. 2012). These region-specific 

relationships may enhance model predictive performance within sampled 

forest types of the northern Sierra Nevada but warrant some caution in 

application elsewhere (described further in subsection 3.4 Applicability of 

Northern Sierra Models). 
12 U.S. Forest Service RMRS GTR-391. 2019.

3. Model Applicability

Models are restricted in applicability by the range of conditions within 

which they were developed. Applicability should ideally be tested with 

independent data before managers use model predictions in their planning 

and decisions (Bahn and McGill 2013; Heikkinen et al. 2012; Wenger and 

Olden 2012). Such testing is particularly critical when models are applied 

beyond the environmental range where they were originally developed. 

In light of these principles, we offer guidelines for applicability of habitat 

suitability index (HSI) models here. Additionally, we provide and describe 

application masks for particular models based on these guidelines.

Models for nesting black-backed woodpecker in burned forest were 

developed in dry conifer forests of western North America, so model 

applications should be restricted to these forests (Latif et al. 2013). We 

provide an application mask that excludes areas not characterized by dry 

conifer forests based on LANDFIRE-classified satellite imagery recorded in 

2014 (existing vegetation types included in the mask are listed in metadata 

for the mask). More specifically, models were developed and are therefore 

most applicable in recently burned and unlogged forests (≤5 years postfire) 

of Idaho, Oregon, and Washington. Models presented here are therefore not 

applicable in unburned forest (e.g., Bonnot et al. 2009; Fogg et al. 2014), and 

we expect predictive value to be lower in areas affected by salvage logging. 

Nevertheless, several lines of evidence suggest broad applicability in recently 

burned dry conifer forests: (1) Ensemble HSIs consistently characterized 

nesting distributions across the three wildfire locations representing a 

relatively broad geographic extent where models were developed, 

(2) HSIs exhibited other desirable properties for prediction (for details, see 

Latif et al. 2013), and (3) HSIs effectively discriminated nest from reference 

sites at the Canyon Creek Fire (Oregon, 2015) (V.A. Saab, unpublished data)1

(Appendix B).

The HSI model for nesting white-headed woodpecker in unburned forests 

was developed in lower elevation dry conifer forests of the East Cascade 

Mountains in eastern Oregon, and model applicability was verified in the 

Blue Mountains of western Oregon (see Maxent model in Latif et al. 2015). 

The model was developed in forests unaffected within 10 years by wildfire or 

other disturbance (e.g., insect outbreak, logging) with environmental metrics 

specific to green forests. Separate models quantify nesting habitat for whiteheaded woodpeckers in burned forests (Latif et al., in preparation; Wightman 

3.1 Black-Backed 

Woodpecker Inland 

Northwest Model 

Applicability

3.2 White-Headed 

Woodpecker Unburned 

Forest Model 

Applicability

1

 Unpublished data on file with V.A. Saab at: USDA Forest Service, Rocky Mountain 

Research Station, Bozeman, MT.
U.S. Forest Service RMRS GTR-391. 2019. 13

et al. 2010) (Appendix A). Nevertheless, canopy mosaics favored for nest 

placement are maintained largely by mixed severity fire, so applicability is 

expected to increase with increasing time since fire, assuming availability 

of contemporarily accurate environmental data. Application of this model 

should also be restricted to areas with sufficient ponderosa pine-dominated 

forest. We observed no nest locations with less than 10 percent coverage 

of ponderosa-dominated forest within a 0.6-mile-radius neighborhood 

(LEMMA 2018), so the mask accompanying this model accordingly restricts 

model application. This model should be applied with caution (i.e., predictive 

performance should be verified with independent data) in landscapes that 

deviate from conditions characterizing lower elevation conifer forests 

of Oregon. These include the North Cascade Mountains in Washington, 

where ponderosa pine is less dominant; forests of western Idaho, which are 

characterized by steeper and more rugged topography than Oregon forests; 

and southern California forests, where white-headed woodpeckers rely more 

heavily on large-seeded pine species other than ponderosa.

The HSI model for white-headed woodpecker in burned forests provided 

here was developed from data from two wildfire locations (Toolbox, 2002; 

Canyon Creek, 2015) in eastern Oregon. These wildfires burned areas 

consisting largely of lower elevation dry conifer forest strongly dominated by 

ponderosa pine (table 2.3.1). The final model exclusively quantifies whiteheaded woodpecker use of canopy mosaics (see subsection 2.4 Woodpeckers 

in Northern Sierra Burned Forests), a behavior generally characteristic of 

white-headed woodpeckers across their range (Hollenbeck et al. 2011; Latif 

et al. 2015; Wightman et al. 2010).

Despite representing a general pattern, we expect some limits to model 

applicability. Limited representation of burned locations (n = 2) across the 

species range restricts information for quantifying relationships with other 

potentially important habitat components. Specifically, ponderosa pine 

and topographic slope are identified as potentially important in previous 

work (Hollenbeck et al. 2011; Latif et al. 2015), and relationships with 

these features were retained in preliminary models developed at individual 

locations (i.e., an affinity for ponderosa pine-dominated forest at Toolbox 

and avoidance of steep slopes at Canyon Creek). Additionally, as for other 

postfire models, models are most applicable in dry conifer forest—the forest 

type in which they were developed. Accordingly, we offer an application 

mask that excludes areas with LandPIPO less than 40 percent and Slope 

greater than 40 percent, and that includes only dry conifer forest types 

identified based on LANDFIRE vegetation classifications.

We found poor predictive performance when applying this model 

at the Barry Point Fire in southern Oregon (2011) (table 2.3.3). Before 

fire, forests at this location were interspersed extensively with nonforest 

(shrubland and grassland) openings, so wildfire was probably less important 

for generating canopy mosaics. Mosaics were readily available, so whiteheaded woodpeckers may invest less effort seeking openings when selecting 

3.3 White-Headed 

Woodpecker Burned 

Forest Model 

Applicability

3.2 White-Headed Woodpecker 

Unburned Forest Model Applicability 

(continued)
14 U.S. Forest Service RMRS GTR-391. 2019.

3.3 White-Headed Woodpecker 

Burned Forest Model Applicability 

(continued)

3.4 Applicability of 

Northern Sierra Models

nest sites, compromising the predictive value of our model. Additionally, 

coverage of ponderosa pine-dominated forest (0.6-mile-radius neighborhood) 

was relatively low (mean ≈ 30 percent), which may have increased its value 

and led white-headed woodpeckers to focus more on ponderosa pine when 

selecting breeding territories and nest sites at Barry Point. Unlike at other 

locations, many nests at Barry Point were located in juniper (Juniperus 

occidentalis), further suggesting that habitat selection patterns differed at 

Barry Point.

 We therefore caution against applying our model to areas like Barry 

Point characterized before fire by extensive nonforest openings, restricted 

coverage of ponderosa pine forest, and a large juniper component. Moreover, 

we suggest users compare descriptive statistics for their project areas with 

those at model-development locations. In addition to using an appropriate 

application mask (described earlier), users should avoid application in 

landscapes whose conditions differ substantially from the characteristics 

where models were developed (see table 2.3.2).

As with other postfire models in this series, nest locations were collected 

within 5 years of wildfire, and survey units were subjected to selective-cut 

salvage logging. Logging was limited in extent and intensity, and varied 

between locations. To avoid overfitting models to conditions at individual 

locations, models excluded logging variables. That models nevertheless 

showed predictive ability between Toolbox and Canyon Creek locations 

suggests logging was not extensive enough to negate model applicability. 

Yet too much logging would be likely to compromise accuracy of remotely 

sensed data upon which models depend. We expect this model (and others in 

this series) will be most useful for informing management planning before 

implementation of salvage logging.

HSI models for woodpeckers in the northern Sierra Nevada were 

developed and are applicable in burned forests of this region. Models were 

primarily developed in Sierra Mixed Conifer, White Fir, and Red Fir forest 

types (designated by California Wildlife Habitat Relationship System 

[CWHR]) (Mayer and Laudenslayer 1988). Accordingly, we offer a mask 

that restricts application of Northern Sierra models to areas within 0.6 mile of 

relatively large forest patches (≥776 miles) of these types.

Models were developed and evaluated with data collected within 5 years 

following three wildfires that occurred in an approximately 50-mile-wide 

area (~6–12-mile spacing between fire perimeters) (table 2.4.1). Sampled 

landscapes were affected to some extent by selective harvest salvage logging. 

Nevertheless, sampling avoided extensively logged areas and models did 

not quantify relationships with logging to avoid overfitting to conditions at 

surveyed fires. Thus, we intend models for application immediately after 

wildfire and before salvage logging to inform postfire management planning. 

Models may also be applied in areas affected by limited salvage logging 

(i.e., <20 percent of the landscape treated with selective harvest) (USDA 

Forest Service 2013a,b), whereas predictive performance would be likely to 

~
U.S. Forest Service RMRS GTR-391. 2019. 15

decline in more extensively treated areas. Some environmental relationships 

quantified by these models differed from those observed in forests of other 

regions dominated more so by pine (e.g., a negative relationship with 

home-range-scale burn severity for black-backed woodpecker, in contrast 

with Tingley et al. 2014). Therefore, we expect poorer applicability in 

pine-dominated forests in drier regions of the Sierra Nevada, and in other 

mountain ranges outside California.

3.4 Applicability of Northern Sierra 

Models (continued)
16 U.S. Forest Service RMRS GTR-391. 2019.

4. Instructions for Tool Implementation

Application tools described in this manual are operated within an ArcGIS 

environment. Users must have access to ArcGIS 10, a basic understanding of 

how to operate this software, and spatial data layers for clearly defined study 

areas. Steps provided here detail how to retrieve and compile environmental 

data into GIS layers required as model inputs, and how to access and 

implement model application tools.

Currently, most tools are ideally operated within the Forest Service Citrix 

environment or another environment with access to the T drive (except tools 

for Northern Sierra woodpeckers). The optimal workflow for operating these 

tools relies on input generation tools to facilitate preliminary data processing, 

and some input generation tools require access to baseline data stored on the 

T drive. If you have access to the T drive, you can operate tools located at 

“T:\FS\RD\RMRS\Science\WTE\Research\HSI_applic_tool\TOOLBOX”. 

Alternatively, you can download and extract the entire folder structure 

needed to operate the tool at “T:\FS\RD\RMRS\Science\WTE\Research\

HSI_applic_tool\FIRE-BIRD_v0.12.zip” or from the Region 6 website 

(https://www.fs.usda.gov/main/r6/plants-animals/wildlife) under the heading 

“FIRE-BIRD: Habitat Model Application Tools for Disturbance-associated 

Woodpeckers”. Hereafter, the location “TOOLBOX” refers to either the 

folder on the T drive or the folder extracted from the FIRE-BIRD_v0.12.

zip file to a personal workstation. If you do not have access to Citrix or the 

T drive, some input generation tools will be unavailable, in which case you 

will need to follow the alternate “optional” instructions for manual input 

generation provided where relevant in these instructions.

Once you have verified access to the toolbox on Citrix or extracted the 

toolset to an alternate location, you can follow the steps provided next for 

your tool of interest. Throughout these instructions, we suggest names for the 

various files in particular steps. These tools allow you to drag and drop input 

files required for operation. You are not required to follow our suggested 

naming convention, but we nevertheless suggest doing so to make it easier to 

follow our instructions.

The primary output provided by these tools are raster layers mapping the 

relative likelihood for species occurrence, that is, habitat suitability indices 

(HSIs). In section 5 Guidelines for Application and Interpretation of Habitat 

Suitability Models, we provide additional guidelines for interpretation of 

HSIs to inform forest planning.
U.S. Forest Service RMRS GTR-391. 2019. 17

The principal output generated by the application tool for nesting blackbacked woodpecker in the Inland Northwest and northern Rocky Mountains 

describes a composite of predictions from multiple models, that is, ensemble 

predictions. Additionally, the tool allows optional generation of habitat maps 

from individual models that make up the ensemble. The study area must have 

been burned by relatively large wildfires (1,000 acres in the western United 

States) for the necessary data to be retrievable (Latif et al. 2013; Saab et al. 

2007). You can choose among various file extensions indicating format for 

output layers, of which we recommend “.tif” or “.img” formats for flexibility 

in file naming (the more restrictive default ArcGrid format leaves off any 

filename extension).

I. Retrieve and compile environmental data layers. In this step you will 

retrieve and process remotely sensed data to compile the variables 

listed in table 2.1. Pathnames for data files cannot have spaces, so 

when saving the data layers, ensure that no parent folder names 

in the file path have spaces. For example, “C:\GIS\Data\blackbacked woodpecker\dnbr.tif” is an invalid pathname. Change the name 

of the “black-backed woodpecker” folder, for example to “blackbackedwoodpecker” or “BBWO”.

A. Retrieve burn severity data:

1. Go to the Monitoring Trends in Burn Severity interactive viewer 

for querying and downloading remotely sensed wildfire data: 

https://www.mtbs.gov/viewer/index.html

4.1 Black-Backed 

Woodpecker 

Instructions for the 

Inland Northwest

Screenshot of Monitoring Trends in Burn Severity interactive 

viewer.
18 U.S. Forest Service RMRS GTR-391. 2019.

2. Follow Steps at the top of the viewer to locate your wildfire 

location. The example here shows retrieval of data for the 2015 

Canyon Creek Fire in Oregon.

a. Select “Continental U.S.” in the drop-down menu under 

Step 1.

Screenshot of Step 1 in interactive viewer.

b. When you hover your cursor over the first of three buttons 

under Step 2, the label “Select Bounding Box” will appear. 

Click on this button.

Screenshot of Step 2 in interactive viewer.

c. In the map, drag a box over the geographic region in which 

the wildfire location of interest is located. You may do this 

several times until you have zoomed in to only the area 

containing the target wildfire location.

Screenshot of Step 3 results for area and dates selected in the 

interactive viewer.

4.1 Black-Backed Woodpecker 

Instructions for the Inland Northwest 

(continued)
U.S. Forest Service RMRS GTR-391. 2019. 19

Screenshot of Step 3 in the interactive viewer.

4.1 Black-Backed Woodpecker 

Instructions for the Inland Northwest 

(continued)

d. Under Step 3, adjust the scroll bar to include the ignition 

year for the wildfire of interest. The scroll bar can include 

multiple years, in which case multiple wildfire locations may 

appear in the map viewer. There will be a chance to select 

from among multiple locations later, but to make subsequent 

steps easier, adjust the scroll bar to include only the ignition 

year for your wildfire of interest.

e. At this point, there should be only one or a few wildfire 

locations displayed in the map filter. If there are more 

than one, you can set the “fire type” filter under Step 4 to 

“Wildfire” to further reduce the size of the query. Otherwise, 

click on the arrow at the bottom right to open up the “Fire 

Bundle Downloads” window. If multiple locations are 

displayed, toggle the check boxes on the left until only 

desired locations are check-marked. Click on the “Download 

Fires” button on the top left of the “Fire Bundle Downloads” 

window. Save the zip file to your computer.

Screenshot of Step 3 results for the year and area selected, 

and Fire Bundle Downloads window.
20 U.S. Forest Service RMRS GTR-391. 2019.

4.1 Black-Backed Woodpecker 

Instructions for the Inland Northwest 

(continued)

Screenshot of directory for fire_level_tar_files.

Note on RAVG data: For assessment of recent fires where 

MTBS data are unavailable, we provide an alternate version 

of this (ensemble) model informed by RAVG data. We 

compare outputs derived from the MTBS- versus RAVGderived version of this model in Appendix A. You can obtain 

RAVG data by using the online query tool at https://www.

fs.fed.us/postfirevegcondition/index.shtml. See step I.A in 

subsection 4.4 Northern Sierra Instructions for retrieving 

RAVG data.

Screenshot of directory.

4. Within this folder, navigate to the folder named “fire_level_tar_

files” to find the “...tar.gz” file corresponding to your wildfire 

location. The file name will begin with the two-letter State 

code in which the fire was located, followed by a long series 

of numbers and finally the “.tar.gz” extension. Extract this file 

using available software as outlined in the previous step (I.A.3).

5. Review extracted files. In ArcCatalog, navigate to the folder 

containing the extracted files. In general, the fire perimeter 

polygon shapefile will end in “...burn_bndy.shp” and the burn 

severity raster will end in “...dnbr.tif” (hereafter, “burn_bndy.

shp” and “dnbr.tif”). Open the metadata.txt file and look under 

“Products List:” for explanation of the various other extracted 

files. Pay close attention to not select “...dnbr6.tif” as this will 

produce erroneous results.

3. Move the downloaded zip file to an appropriate location and 

unzip it with Winzip®, 7zip, or other file compression software. 

Right-click on the compressed file to find extraction options. The 

extracted folder will be named according to the ignition year of 

your wildfire location.
U.S. Forest Service RMRS GTR-391. 2019. 21

4.1 Black-Backed Woodpecker 

Instructions for the Inland Northwest 

(continued)

B. Compile the four input variables needed to apply the published 

HSI (ensemble) model (Latif et al. 2013) using the associated 

input generation tool described here. Note: This input generation 

tool can be operated only within the Forest Service Citrix 

environment or from an environment with access to the Forest 

Service T drive. If neither of these conditions is met, follow 

step I.C to compile inputs from alternate data sources. This 

tool requires only the burn_bndy.shp and dnbr.tif files that you 

retrieved in step I.A. The tool develops required input layers (table 

2.1) with these files and default topography and canopy cover 

data accessed automatically from the T drive. The default canopy 

cover layer provides 2012 gradient nearest neighbor (GNN) data 

from LEMMA (2018) where available (Oregon, Washington) and 

data from LANDFIRE (2018) where GNN data were not available 

(Idaho, Montana). These data may represent prefire canopy cover for 

wildfires that burned in 2012 or later unless additional disturbance 

occurred between recording the default imagery (after 2011 fire 

season but before 2012 fire season) and occurrence of the wildfire 

of interest. We have observed default imagery poorly representing 

prefire canopy cover at some locations for reasons unknown, so 

carefully inspect default canopy cover data to verify classification 

accuracy and reliability. In cases where default canopy cover data 

are deemed sufficient, proceed with steps I.B.1–6 to compile input 

layers. For wildfires that occurred before 2012 or for which 

default imagery are unlikely to accurately represent canopy 

cover immediately before wildfire (e.g., due to additional 

disturbance in the interceding period), additional steps will 

be required to compile canopy cover inputs from an alternate 

source. If alternately sourced canopy cover inputs are needed, 

follow steps I.B.1–6 but then delete resulting loccc and landcc 

layers, then follow steps I.C.4–6 (i.e., skip I.C.1–3) to generate 

replacement inputs.

1. From ArcCatalog, navigate to “TOOLBOX → Habitat Suitability 

Modeling.tbx → Input Development” and open the “Generate 

Inputs Black-backed Woodpecker Model” tool. As stated 

previously, this folder is located at “T:\FS\RD\RMRS\Science\

WTE\Research\HSI_applic_tool\TOOLBOX.” You must either 

access this location when running ArcCatalog on Citrix or 

download this entire folder onto your local computer.

2. Identify or create a folder where you want model inputs to be 

stored. Under “Workspace”, navigate to this folder. Then click 

“Add”.

3. For “Fire Perimeter”, navigate to the burn_bndy.shp file 

downloaded from MTBS (step I.A). You can either navigate 

from outside the tool interface and drag and drop this file into the 

“Fire Perimeter” box, or navigate from within the tool and click 

“Add”.
22 U.S. Forest Service RMRS GTR-391. 2019.

4.1 Black-Backed Woodpecker 

Instructions for the Inland Northwest 

(continued)

4. For “dNBR”, use the dnbr.tif file downloaded from MTBS (step 

I.A). Either navigate from outside the tool interface and drag and 

drop dnbr.tif, or navigate from the “dNBR” box and click “Add.”

5. Click OK (all outputs will be stored in the INPUTS folder in 

your workspace).

6. Close this dialog when completed successfully.

C. Optional—Steps for compiling input layers (locdnbr, loccc, landcc, 

and cosasp) from alternate data sources are provided here. Follow 

these steps if you don’t have access to the Forest Service T drive, 

or if default canopy cover layers are insufficient. These steps 

assume that you have obtained four layers for your project area: 

1) dnbr.tif and burn_bndy.shp files retrieved in step I.A, 2) a 30-mpixel elevation raster layer (e.g., a digital elevation model layer from 

LANDFIRE), and 3) a 30-m-pixel raster layer that either provides 

continuous canopy cover (percent) or classifies high (>40 percent) 

versus low (<40 percent). Raster layers (dnbr.tif, elevation, and 

canopy cover) should cover all areas inside the burn_bndy.shp file 

and extend at least 0.6 mile beyond (see step I.A.3). If this criterion 

is not met, layers should cover all areas within and at least 0.6 mile 

outside study units relevant to management planning or decisions; 

use a shapefile describing study unit boundaries in place of burn_

bndy.shp in step I.C.2.d (following). If you have access to the T drive 

but require alternately sourced canopy cover data, follow steps 

I.B.1–5, but then delete resulting loccc and landcc layers. Then 

follow steps I.C.4–6 (i.e., skip step I.C.1–3) to generate replacement 

inputs. Note: We suggest following file naming directions as stated 

below to keep track of different files referenced in these instructions.

1. Identify or create a folder where you want model inputs to be 

stored. Place intermediate and final layers generated from steps 

I.C.2–4 below in this folder. The remainder of these steps will 

refer to this folder as your “workspace.”

2. Apply the “Focal Statistics” tool to dnbr.tif (downloaded in step 

I.A.3) to generate the locdnbr input layer.

a. Open the tool from ArcCatalog (TOOLBOX → Habitat 

Suitability Modeling.tbx → Input Development → Focal 

Statistics).

b. Designate dnbr.tif as the “Input Raster”.

c. Apply the default neighborhood of 3×3 cells with “Statistics 

type” set to “Median”. Name the resulting layer “lcdnbrpre”.

d. Clip lcdnbrpre to the fire perimeter to produce final locdnbr 

input layer. Open TOOLBOX → Habitat Suitability 

Modeling.tbx → Input Development → Extract by Mask. 

Designate lcdnbrpre as the Input layer and burn_bndy.shp as 

the mask. Designate your workspace as the output location 

and name the output file “locdnbr”.
U.S. Forest Service RMRS GTR-391. 2019. 23

4.1 Black-Backed Woodpecker 

Instructions for the Inland Northwest 

(continued)

3. Identify or create a folder where you want model inputs to be 

stored. Place intermediate and final layers generated from steps 

I.C.2–4 in this folder. 

4. Apply the “Focal Statistics” tool to dnbr.tif (downloaded in step 

I.A.3) to generate the locdnbr input layer.

5. Identify or create a folder where you want model inputs to be 

stored. Place intermediate and final layers generated from steps 

I.C.2–4 (following) in this folder. The remainder of these steps 

will refer to this folder as your “workspace.”

6. Apply the “Focal Statistics” tool to dnbr.tif (downloaded in step 

I.A.3) to generate the locdnbr input layer.

a. Open the tool from ArcCatalog (TOOLBOX → Habitat 

Suitability Modeling.tbx → Input Development → Focal 

Statistics).

b. Designate dnbr.tif as the “Input Raster”.

c. Apply the default neighborhood of 3×3 cells with “Statistics 

type” set to “Median”. Name the resulting layer “lcdnbrpre”.

d. Clip lcdnbrpre to the fire perimeter to produce final locdnbr 

input layer. Open TOOLBOX → Habitat Suitability 

Modeling.tbx → Input Development → Extract by Mask. 

Designate lcdnbrpre as the Input layer and burn_bndy.shp as 

the mask. Designate your workspace as the output location 

and name the output file “locdnbr”.

7. Generate cosine aspect input layer (cosasp) from raw elevation.

a. Before following these instructions, locate a raster layer that 

describes elevation at 30-m resolution (e.g., digital elevation 

model layer from LANDFIRE).

b. Apply the “Aspect” tool to this layer (TOOLBOX → Habitat 

Suitability Modeling.tbx → Input Development → Aspect).

c. The resulting raster layer will be in degrees. You can 

calculate cosine aspect using the raster calculator tool 

included with ArcGIS software, but doing so requires 

conversion of aspect from degrees to radians. To make this 

step simpler, we recommend using the “Cosine (degrees)” 

tool that we have included under TOOLBOX → Habitat 

Suitability Modeling.tbx → Input Development. Doubleclick on the “Cosine (degrees)” tool, drag and drop the 

aspect raster generated in the previous step (I.C.7.b) as the 

input for this tool, and name the resulting tool “casppre”.

8. Begin with a raw canopy cover layer that either describes a 

continuous percentage or classifies high (>40 percent) versus low 

(<40 percent) canopy cover at a 30-m resolution. The available 

sources for retrieving such data will depend on wildfire timing 

and geographic region of your study location (e.g., LEMMA 

2018; LANDFIRE 2018; USFS 2018a). Once you obtain this 
24 U.S. Forest Service RMRS GTR-391. 2019.

4.1 Black-Backed Woodpecker 

Instructions for the Inland Northwest 

(continued)

layer, carry out the following steps to compile loccc and landcc 

layers. If the raw canopy cover layer is categorical and classifies 

high (>40 percent) with raster value = 1 versus low (<40 percent) 

with raster value = 0, name this file “ccov_ovr40”, and skip to 

step I.C.9. If the raw layer does not match this structure exactly 

but contains sufficient information to discriminate high (>40 

percent) from low (<40 percent) canopy cover, name it “ccov” 

and follow steps I.C.8.a–c, which direct use of the “Reclassify” 

tool to classify high (>40 percent) versus low (<40 percent) 

canopy cover. If the raw canopy cover layer is not continuous 

and does not include the 40-percent cutoff for defining 

categories, you will need to seek an alternate data source 

(e.g., a forest- or region-specific data repository).

a. In ArcToolbox, navigate to TOOLBOX → Habitat 

Suitability Modeling.tbx → Input Development → 

Reclassify.

b. Designate “ccov” as the “Input raster”. Adjust the 

“Reclassification” table to match the image below. If the raw 

layer is continuous, your table will look like the one depicted 

below. Otherwise, manipulate the table however necessary to 

generate two classes: value = 0 for <40 percent and value = 1 

for >40 percent.

Screenshot of the Reclassify function in ArcToolbox.

c. Select an appropriate location and name this file “ccov_

ovr40”.

9. Apply the “Focal Statistics” tool to the “ccov_ovr40” layer to 

generate local- and landscape-scale canopy cover layers.

a. Open the tool from ArcCatalog (Spatial Analyst Tools → 

Neighborhood → Focal Statistics).

b. Designate ccov_ovr40 as the “Input Raster”.

c. Apply the default neighborhood of 3×3 cells with “Statistics 

type” set to “Mean”. Name the resulting layer “lccpre”.
U.S. Forest Service RMRS GTR-391. 2019. 25

d. Apply a 1-km-radius neighborhood (select the “Circle” 

option under “Neighborhood”, select the “Map” option for 

“Units”, and use 1000 for the “Radius”), set “Statistics type” 

to “Mean”, and name the resulting layer “ldcpre”.

e. Verify that lccpre and ldcpre are proportions (range: 0–1) and 

not percentages (range: 0–100). Right-click on each layer, 

click “Properties” at the bottom of the drop-down menu, and 

scroll to the bottom. Inspect minimum and maximum values 

to verify each layer is a proportion.

10. Clip lccpre, ldcpre, and casppre using locdnbr (generated in step 

I.A.3) as a mask and snapping layer to generate the final loccc, 

landcc, and cosasp layers.

a. Open the “Extract by Mask” tool (TOOLBOX → Habitat 

Suitability Modeling.tbx → Input Development → Extract 

by Mask).

b. Designate the “loccpre” layer as the Input Raster and locdnbr 

as the mask data. Open the “Environments” window.

c. Under “Output coordinates”, import the coordinate system 

from locdnbr.

d. Under “Processing Extent”, set locdnbr as the “Snap Raster”.

e. Click OK.

f. For “Output raster”, navigate to the location containing 

locdnbr and cosasp layers (i.e., INPUTS folder generated in 

step I.C.3) and name the output layer “loccc”.

g. Repeat steps I.C.10.a–f for ldcpre and casppre to generate 

landcc and cosasp input layers, respectively.

II. Install scipy module (Note: If running the tool on Citrix, you can skip 

to step III): Before running the GIS model application tool, install 

the “scipy” PythonTM module upon which the tool relies. Open the 

“TOOLBOX” folder from Windows® Explorer. We have provided two 

executable files in this folder for installing the “scipy” module. The 

correct file to run will depend on which version of Python is installed 

on your computer. If you have ArcGIS 10.1 or newer, double-click on 

the scipy-0.14.0-win32-superpack-python2.7.exe. If 

you have ArcGIS 10.0, double-click on the scipy-0.14.0-win32-

superpack-python2.6.exe.

4.1 Black-Backed Woodpecker 

Instructions for the Inland Northwest 

(continued)
26 U.S. Forest Service RMRS GTR-391. 2019.

4.1 Black-Backed Woodpecker 

Instructions for the Inland Northwest 

(continued)

Screenshot of directory showing location of scipy Python modules.

Follow the install prompts. Ensure that the directory where you install 

the scipy package corresponds to the ArcGIS install locations:

A. For ArcGIS10.0 – C:\Python26\ArcGIS10.0\Lib\site-packages

B. For ArcGIS10.1 – C:\Python26\ArcGIS10.1\Lib\site-packages

III. Run model application tool:

A. From ArcCatalog, navigate to TOOLBOX → Habitat Suitability 

Modeling.tbx → HSI models, and open the “Black Backed 

Woodpecker” tool.

Screenshot of filepath showing location of tool for the black- backed 

woodpecker HSI model.

B. For “Cosine Aspect”, “dNBR”, “Local canopy cover”, and 

“Landscape canopy cover” inputs, use cosasp, locdnbr, loccc, and 

landcc input rasters, respectively, located in the INPUTS folder in 

your workspace (see step I.B or I.C). Either navigate to the INPUTS 

folder from outside the “Black-backed Woodpecker” tool dialog 

box and drag and drop each input layer, or navigate from within the 

dialog box and click “Add” to designate each layer.
U.S. Forest Service RMRS GTR-391. 2019. 27

Screenshot of dialog box for black-backed woodpecker tool.

C. Designate an appropriate location and filename for the Output 

Ensemble HSI raster. Click the button with the folder icon to the 

right, locate your workspace, and create a meaningful name, such 

as “BBWO_ensemble_HSI.tif”. Again, you can specify alternate 

file formats for rasters, but we recommend “.img” or “.tif” because 

these allow longer filenames than the default ArcGrid format (which 

leaves off any filename extension).

D. Select masking option. You must either check the “no mask” box 

or provide a raster layer to serve as a mask for the final HSI output 

layer. If neither is done, the tool will produce an error. Any raster 

layer can function as a mask, whereby pixels in the mask layer with 

NODATA will indicate which values to be dropped (i.e., converted 

to NODATA) in the final HSI output. We provide a potential mask 

at TOOLBOX → masks → BBWO → dcfmask. This mask includes 

all areas characterized as dry conifer forest, which we identified 

as 20 LANDFIRE vegetation types (listed in metadata) based on 

2014 imagery. Alternatively, you may provide a comparable mask 

that covers your project area or check the “no mask” box, bearing 

in mind limitations to model applicability described earlier (see 

subsection 3.1 Black-Backed Woodpecker Inland Northwest Model 

Applicability).

E. If you desire individual HSI model outputs (i.e., the components of 

the ensemble output), check the “Output component models?” box 

and designate a folder where you want these outputs to be stored. A 

folder named “OUTPUTS” will be automatically generated within 

the designated folder, and individual HSI model layers will be stored 

in the “OUTPUTS” folder. Click on OK to run the model. A series 

of progress bars will flash. If necessary, close the “Results” window 

once “succeeded” is displayed. To view the ensemble model output, 

navigate to the output file (“BBWO_ensemble_HSI.tif”), and select 

the preview pane tab. Select “Geography” in the drop-down menu at 

the bottom of the preview pane to preview the HSI map, or “Table” 

to view pixel counts for each suitability level (0–8; i.e., the number 

of models predicting each pixel as suitable).

4.1 Black-Backed Woodpecker 

Instructions for the Inland Northwest 

(continued)
28 U.S. Forest Service RMRS GTR-391. 2019.

4.2 White-Headed 

Woodpecker 

Instructions for 

Unburned Forests

The output generated by the application tool for nesting white-headed 

woodpecker in unburned forests is a 30-m-resolution raster layer with HSI 

values ranging from 0 to 1 (i.e., least to most suitable for nesting). We have 

posted habitat maps generated by this model for forest conditions recorded 

in Oregon (where the model was developed and evaluated) during 2002 and 

2012 (T:\FS\RD\RMRS\Science\WTE\Research\RMRS-WHWO\Oregon_

hsi_maps\WHWO_OR_HSI_maps.gdb). We intend the application tool for 

landscapes not reflected by default maps. Landscapes reflected by default 

maps include:

1. Landscapes requiring adjustment for disturbance between recording of 

default imagery and the time period of interest.

2. Landscapes just outside Oregon State boundaries but still within 

conditions similar to where the model was developed (e.g., southern 

Washington, western Idaho, and northern California). If you are applying 

the model outside of Oregon, be aware of the increasing need to evaluate 

the model with independent data from the targeted project area as one 

moves farther from Oregon.

3. Projected landscapes representing future or historical conditions under 

alternate climate or management scenarios.

In these situations, you will need to obtain relevant spatial data describing 

continuous percent canopy cover and the distribution of forests dominated by 

ponderosa pine. 

There are two disclaimers for you to acknowledge before following 

instructions for this tool. First, additional data processing steps will be 

required if you are not operating ArcGIS within the Forest Service Citrix 

environment and if you do not have access to the Forest Service T drive (also 

true for Black-backed Woodpecker tool). We provide additional “optional” 

data processing instructions as we did for black-backed woodpecker in 

burned forest. Second, Python imposes memory limits on the implementation 

of ArcGIS tools. Unlike burned forest projects, unburned forest project areas 

could conceivably be large enough for memory to be limiting. 

In such cases, you will need to break up your project area into subunits, 

implement the tool following our instructions for each subunit, and then 

stitch resulting subunit output layers together using one of two raster mosaic 

tools (ArcToolbox → Data Management Tools → Raster → Raster Dataset 

→ Mosaic, or Mosaic to New Raster in same location). We have successfully 

implemented the tool for study areas whose input layers consist of about 

16 million pixels (1.4 million ha, or 3.5 million acres) but failed with study 

areas consisting of ~30 million pixels (2.7 million ha, or 6.7 million acres). 

Thus, for large project areas, we suggest aiming for subunits no larger than 

16 million pixels in extent. For irregularly shaped project areas, be aware 

that memory is limited by the entire extent of input layers, not just the area 

for which environmental values are compiled (i.e., including NoData pixels). 

Thus, you should check the total number of pixels contained in input raster 

layers (right-click on layer and see Properties) before implementing this tool 

or if an error is reported when you attempt implementation.
U.S. Forest Service RMRS GTR-391. 2019. 29

4.2 White-Headed Woodpecker 

Instructions for Unburned Forests 

(continued)

I. Retrieve and compile data layers. In this step you will process remotely 

sensed data to compile the variables listed in table 2.2. Pathnames for 

data files cannot have spaces, so when saving the data layers, ensure that 

no parent folders in the file pathnames have spaces. For example, “C:\

GIS\Data\white-headed woodpecker\cancov.tif” is an invalid pathname. 

Change the name of the “white-headed woodpecker” folder, for example 

to “white-headedwoodpecker” or “WHWO”.

A. Project_area shapefile—Obtain or create a polygon shapefile that 

delineates the entire area within which habitat suitability needs to be 

mapped. Label this shapefile “Project_area.shp”.

B. Canopy cover layer—Obtain or generate a raster layer that quantifies 

continuous canopy cover (percent) at a 30-m-pixel resolution for the 

landscape of interest. This layer needs to cover all areas inside the 

Project_area.shp boundaries and extend at least 0.6 mile outside this 

area. Name this layer “cancov”.

C. Ponderosa pine layer—Obtain or generate a 30-m-resolution raster 

layer that indicates whether or not each pixel is classified as forest 

dominated or codominated by ponderosa pine (PIPO). This layer 

should be valued so that 0 = non-PIPO and 1 = PIPO dominant or 

codominant forest. Additionally, this layer needs to cover all areas 

inside the Project_area.shp polygon and within 0.6 mile outside the 

Project_area.shp polygon boundaries. Name this layer “pipo_class”.

D. If operating in the Citrix environment or a local environment with 

access to the Forest Service T drive, run input generation tool. If the 

T drive is inaccessible, skip to step E for guidelines on generating 

inputs without the input generation tool.

1. From ArcCatalog, navigate to the TOOLBOX → Habitat 

Suitability Modeling.tbx → Input Development, and open the 

“Generate Inputs White-headed Woodpecker Unburned Model” 

tool.

2. Under “Workspace”, navigate to a folder where you want model 

inputs to be stored.

3. Under “Project Area Boundary”, navigate to the Project_Area.

shp file.

4. For “Percent canopy cover”, use the cancov layer (step I.B), and 

for “Ponderosa pine”, use the pipo_class layer (step I.C).

5. Click OK (all outputs will be stored in the INPUTS folder in 

your workspace).

E. Optional—Guidelines for generating inputs for White-headed 

Woodpecker model application tool for unburned forest without the 

input generation tool (i.e., when you do not have access to the Forest 

Service T drive):

1. Apply the “Focal Statistics” tool to the “cancov” layer to 

generate local- and landscape-scale canopy cover layers.
30 U.S. Forest Service RMRS GTR-391. 2019.

a. Open the tool from ArcCatalog (TOOLBOX → Habitat 

Suitability Modeling.tbx → Input Development → Focal 

Statistics).

b. Designate cancov (see step I.B) as the “Input Raster”.

c. Apply the default neighborhood of 3×3 cells with “Statistics 

type” set to “Mean”. Name the resulting layer “lccpre”.

d. Repeat steps I.E.1a–1c, and apply a 1-km-radius 

neighborhood (select the “Circle” option under 

“Neighborhood”, select the “Map” option for “Units”, and 

use 1000 for the “Radius”), set “Statistics type” to “Mean”, 

and name the resulting layer “ldcpre”.

2. Apply the “Focal Statistics” tool to the “pipo_class” layer to 

generate a layer describing the percent coverage of ponderosa 

pine-dominated forest.

a. Open the tool from ArcCatalog (TOOLBOX → Habitat 

Suitability Modeling.tbx → Input Development → Focal 

Statistics).

b. Designate “pipo_class” (see step I.B) as the “Input Raster”.

c. Apply a 1-km-radius neighborhood (select the “Circle” 

option under “Neighborhood”, select the “Map” option for 

“Units”, and use 1000 for the “Radius”), set “Statistics type” 

to “Mean”, and name the resulting layer “pipopre”.

3. Clip lccpre, ldcpre, and pipopre using the “Project_Area.shp” 

polygon layer (which you created in step I.A).

a. Open the “Extract by Mask” tool (TOOLBOX → Habitat 

Suitability Modeling.tbx → Input Development → Extract 

by Mask). Designate the “lccpre” as the “Input Raster” and 

the “Project_Area.shp” file as the mask.

b. Open the Environments Window within this tool. Under 

“Output coordinates”, import the coordinate system from 

“Project_Area.shp”. Under “Processing Extent”, set 

“cancov” as the “Snap Raster”. Note: It does not matter 

which coordinate system or snap raster is used, as long as 

you use the same one for all input layers.

c. Designate an appropriate location for all input layers (e.g., 

an INPUTS folder in your workspace), name the output layer 

“loccc”, and click “OK”.

d. Repeat steps I.E.3a–3c for “ldcpre” and “pipopre” and name 

respective output files “landcc” and “pipo”.

4.2 White-Headed Woodpecker 

Instructions for Unburned Forests 

(continued)
U.S. Forest Service RMRS GTR-391. 2019. 31

4.2 White-Headed Woodpecker 

Instructions for Unburned Forests 

(continued)

II. Run model application tool:

A. From ArcCatalog, navigate to TOOLBOX → Habitat Suitability 

Modeling.tbx → HSI Models, and open the “White-Headed 

Woodpecker Unburned” tool.

Screenshot of the filepath for the tools for HSI models.

B. For “Local canopy cover”, “Landscape canopy cover”, “Cosine 

aspect”, “Ponderosa pine”, and “Slope”, use loccc, landcc, cosasp, 

pipo, and slp input rasters, respectively, located in the INPUTS 

folder in your workspace (see step I.D or I.E). Either navigate to the 

INPUTS folder from outside the tool dialog box and drag and drop 

each input layer, or navigate from within the dialog box and click 

“Add” to designate each layer.

Screenshot of the dialog box for the tool for the white-headed 

woodpecker in unburned forest HSI model.

C. For “Output HSI raster”, designate an appropriate location and 

filename for the output raster. Click the button with the folder icon to 

the right, locate your workspace, and create a meaningful name, such 

as “WHWO_unburned_HSI.tif”. Again, you can specify alternate 

file formats for rasters, but we recommend “.img” or “.tif” because 

these allow longer filenames than the default ArcGrid format (which 

leaves off a filename extension).
32 U.S. Forest Service RMRS GTR-391. 2019.

D. Select masking option. You must either check the “no mask” box 

or provide a raster layer to serve as a mask for the final HSI output 

layer. If neither is done, the tool will produce an error. Any raster 

layer can function as a mask, whereby pixels in the mask layer with 

NODATA will indicate which values to be dropped (i.e., converted 

to NODATA) in the final HSI output. We provide a potential mask 

at TOOLBOX → masks → WHWO_unb → mskpin10. This mask 

includes all areas with 10 percent or more ponderosa pine-dominated 

forest within a 0.6-mile-radius neighborhood and covers areas 

where GNN data are available (Washington, Oregon, and northern 

California). Alternatively, you may provide a comparable mask 

that covers your project area or check the “no mask” box, bearing 

in mind limitations to model applicability described earlier (see 

subsection 3.2 White-Headed Woodpecker Unburned Forest Model 

Applicability).

E. Click on OK to run the model. A series of progress bars will flash. 

If necessary, close the “Results” window once “succeeded” is 

displayed. To view the ensemble model output, navigate to the output 

file (e.g., “WHWO_unburned_HSI.tif”) and select the preview pane 

tab.

4.2 White-Headed Woodpecker 

Instructions for Unburned Forests 

(continued)

The principal output generated by the application tool for white-headed 

woodpecker in burned forests is a 30-m-resolution raster layer with HSI 

values ranging from 0 to 1 (i.e., least to most suitable for nesting).The study 

area must have been burned by relatively large wildfires (1,000 acres in 

the western United States) for the necessary input data to be retrievable. As 

with all ArcGIS tools, the user can choose among various file extensions 

indicating format for output layers, of which we recommend “.tif” or “.img” 

formats for flexibility in file naming (the more restrictive default ArcGrid 

file naming leaves off a filename extension). Additional data processing steps 

will be required if users do not have access to the Forest Service T drive. We 

provide “optional” data processing instructions for these users in step I.C.

I. Retrieve and compile data layers. In this step you will process remotely 

sensed data to compile the variables listed in table 2.3.2. Pathnames for 

data files cannot have spaces, so when saving the data layers, ensure that 

no parent folders in the file pathnames have spaces. For example, “C:\

GIS\Data\white-headed woodpecker\cancov.tif” is an invalid pathname. 

Change the name of the “white-headed woodpecker” folder, for example 

to “white-headedwoodpecker” or “WHWO”.

A. Follow instructions in step I.A in subsection 4.1 Black-Backed 

Woodpecker Instructions for the Inland Northwest to retrieve MTBS 

burn severity data for the wildfire relevant to your project. As noted 

in subsection 4.1, the fire perimeter polygon shapefile will end in 

“...burn_bndy.shp” and the burn severity raster will end in “...dnbr.

tif” (hereafter, burn_bndy.shp and dnbr.tif, respectively). Note on 

RAVG data: For assessment of recent fires where MTBS data 

are unavailable, we provide an alternate version of this model 

4.3 White-Headed 

Woodpecker 

Instructions for 

Burned Forests
U.S. Forest Service RMRS GTR-391. 2019. 33

4.3 White-Headed Woodpecker 

Instructions for Burned Forests 

(continued)

informed by RAVG data. We compare outputs derived from 

MTBS- versus RAVG-derived models in Appendix A. You may 

obtain RAVG data using the online query tool available at 

https://www.fs.fed.us/postfirevegcondition/index.shtml. See step 

I.A in subsection 4.4 Northern Sierra Instructions for retrieving 

RAVG data.

B. Compile the two input variables needed for model application (table 

2.3.2) using the associated input generation tool described here. 

Note: This input generation tool can be operated only within the 

Forest Service Citrix environment or from an environment with 

access to the Forest Service T drive. If neither of these conditions 

is met, follow step I.C for input compilation. This tool requires 

only dnbr.tif and burn_bndy.shp files from the user (retrieved in 

step I.A), which it combines with default prefire canopy cover data 

to compile inputs. The default canopy cover data represent 2012 

gradient nearest neighbor (GNN) data from LEMMA (2018). These 

data may represent prefire canopy cover for wildfires that burned 

in 2012 or later unless additional disturbance occurred between 

recording default imagery (after 2011 but before 2012 fire seasons) 

and occurrence of the wildfire of interest. We have observed default 

imagery poorly representing prefire canopy cover at some locations 

for reasons unknown, so you should carefully inspect default canopy 

cover data to verify they make sense and are reasonably accurate. In 

cases where default canopy cover data are deemed sufficient, proceed 

with steps I.B.1–6 to compile input layers. For wildfires that 

occurred before 2012 or for which default imagery are unlikely 

to accurately represent canopy coverage immediately before 

wildfire (e.g., due to additional disturbance in the interceding 

period), follow steps in I.C to use data from an alternate source.

1. From ArcCatalog, navigate to the TOOLBOX → Habitat 

Suitability Modeling.tbx → Input Development, and open the 

“Generate Inputs White-headed Woodpecker Burned Model” 

tool.

2. Identify or create a folder where you want model inputs to be 

stored. Under “Workspace”, navigate to this folder. Then click 

“Add”.

3. For “dNBR”, use dnbr.tif downloaded from MTBS (see step 

I.A). You can either navigate from outside the tool interface and 

drag and drop dnbr.tif, or navigate from the “dNBR” box and 

click “Add”.

4. For “Fire Perimeter”, navigate to burn_bndy.shp downloaded 

from MTBS (step I.A). You can either navigate from outside 

the tool interface and drag and drop the burn_bndy.shp into the 

“Fire Perimeter” box, or navigate from within the tool and click 

“Add”.

5. Click OK (all outputs will be stored in the INPUTS folder in 

your workspace).

6. Close this dialog when completed successfully.
34 U.S. Forest Service RMRS GTR-391. 2019.

C. Optional—Steps for compiling input layers (locbrnopn and 

landbrnopn) from alternate data sources are described here. These 

instructions are for users who lack access to the Forest Service 

T drive, or if default canopy cover layers do not cover project 

areas. These steps assume you have obtained three layers for your 

project area: (1) a dnbr.tif file describing burn severity (see step I.A), 

(2) a burn_bndy.shp file delineating the fire perimeter (see step I.A), 

and (3) a 30-m-pixel raster layer that provides continuous canopy 

cover (percent). Raster layers (1, 3) should cover all areas inside and 

at least 0.6 mile outside burn_bndy.shp. If coverage does not meet 

this criterion, layers should cover all areas within and at least 1 km 

outside study units relevant to management planning or decisions, 

in which case you should use a shapefile describing study unit 

boundaries in place of burn_bndy.shp in step I.C.6 (following). Note: 

We suggest following file naming directions as stated below to more 

easily keep track of files referenced in these instructions.

1. Identify or create a folder named “INPUTS” in your workspace. 

Place intermediate and final layers generated from steps I.C.2–6 

in this folder. The remainder of these steps will refer to this 

folder as the INPUTS folder.

2. Classify dnbr.tif (see step I.A) to generate an intermediate “hisev.

tif” layer.

a. Open the Reclassify tool (TOOLBOX → Habitat Suitability 

Modeling.tbx → Input Development → Reclassify).

b. Designate dnbr.tif as the “Input Raster”. Adjust the 

“Reclassification” table to match the image below to 

generate two classes: value = 0 for dNBR < 270 and value = 

1 for dNBR > 270.

Dialog box for the Reclassify tool for burn severity.

c. Browse to your INPUTS folder and name the output file 

“hisev.tif”.

3. Classify your continuous canopy cover layer (see step I.B) 

(hereafter, “cancov.tif”) to generate an intermediate “ccopen.tif” 

layer.

4.3 White-Headed Woodpecker 

Instructions for Burned Forests 

(continued)
U.S. Forest Service RMRS GTR-391. 2019. 35

a. Open the Reclassify tool (TOOLBOX → Habitat Suitability 

Modeling.tbx → Input Development → Reclassify).

b. Designate “cancov.tif” as the “Input Raster”. Adjust the 

“Reclassification” table to match the image below to 

generate two classes: value = 1 for cancov < 10 and value = 

0 for cancov > 10.

4.3 White-Headed Woodpecker 

Instructions for Burned Forests 

(continued)

Dialog box for the Reclassify tool for the continuous canopy 

cover layer.

c. Browse to your INPUTS folder and name the output file 

“ccopen.tif”. Click OK.

4. Calculate burn or open layer.

a. Open the “Raster Calculator” tool (TOOLBOX → Habitat 

Suitability Modeling.tbx → Input Development → Raster 

Calculator).

b. In the expression box, write a statement that adds “ccopen” 

(step I.C.3) and “hisev” (step I.C.2), and then assigns “1” 

to pixels when ccopen + hisev > 0 and “0” when ccopen + 

hisev = 0 using the “Con” function. The final expression will 

be of the form “Con((“workspace/hisev.tif” + “workspace/

ccopen.tif”) > 0, 1, 0)” (see example screenshot below).

Screenshot of a conditional statement in the Raster 

calculation tool. 

c. Name the output raster “BrnOpn.tif” and set its location to 

your INPUTS folder. Click OK.
36 U.S. Forest Service RMRS GTR-391. 2019.

4.3 White-Headed Woodpecker 

Instructions for Burned Forests 

(continued)

5. Apply the “Focal Statistics” to “BrnOpn.tif” (step I.C.4) and 

further process with “Raster Calculator” to generate layers 

describing percent area burned or open.

a. Open the Focal Statistics tool (TOOLBOX → Habitat 

Suitability Modeling.tbx → Input Development → Focal 

Statistics).

b. Designate “BrnOpn.tif” as the “Input Raster”.

c. Apply the default neighborhood of 3×3 cells with “Statistics 

type” set to “Mean”. Name the resulting layer “locbrnopn_

prp.tif”.

d. Run the Focal Statistics tool again (Repeat steps I.C.5.a–b), 

this time setting the neighborhood to “circular”, size to 

radius = 1000, and units = “Map”. Set Statistics type = 

“Mean” and select “Ignore NoData in calculations” 

(see screenshot below). Name the resulting layer 

“landbrnopn_prp.tif”.

Dialog box for neighborhood and statistics type.

e. For each layer generated in the last step (“locbrnopn_prp.

tif” and “landbrnopn_prp.tif”), apply the Raster Calculator 

tool again (TOOLBOX → Habitat Suitability Modeling.

tbx → Input Development → Raster Calculator) to rescale 

to percentages; that is, multiply “locbrnopn_prp.tif” and 

“landbrnopn_prp.tif” by 100. Designate the INPUTS folder 

as the output location and name the output files “locbrnopn_

premask.tif” and “landbrnopn_premask.tif”, respectively.

6. Clip percent area (burned or open) layers to the fire 

perimeter to produce final input layers. Open TOOLBOX → 

Habitat Suitability Modeling.tbx → Input Development → 

Extract by Mask. Successively designate layers generated 

in the previous step (I.C.5) (“locbrnopn_premask.tif” and 

“landbrnopn_premask.tif”) as Input layers and burn_bndy.

shp as the mask. Designate the INPUTS folder as the output 
U.S. Forest Service RMRS GTR-391. 2019. 37

location and name the output files “locbrnopn.tif” and 

“landbrnopn.tif”. For “landbrnopn.tif”, set the snapping layer 

(Under “Environments... → Processing Extent → Snap 

Raster” at the bottom of the “Extract by Mask” window) to 

“locbrnopn.tif” to ensure both layers are snapped together.

7. To remain organized and ensure input layers can be easily 

found in the future, delete all intermediate layers (hisev.tif, 

ccopen.tif, BrnOpn.tif, locbrnopn_prp.tif, landbrnopn_prp.tif, 

locbrnopn_premask.tif, and landbrnopn_premask.tif) or move 

them to another appropriate location (e.g., a subfolder named 

“intermediates” in your INPUTS folder in your workspace).

II. Run model application tool:

A. From ArcCatalog, navigate to “TOOLBOX → Habitat Suitability 

Modeling.tbx → HSI Models” and open the “White-headed 

Woodpecker Burned” tool.

Screenshot of the filepath for the tools for HSI models.

B. For “Local-scale percent area burned or open” and “Landscape-scale 

percent area burned or open”, use “locbrnopn.tif” and “landbrnopn.

tif” input rasters, respectively (see steps I.A–C). You can either 

navigate to the INPUTS folder from outside the tool dialog box and 

drag and drop each input layer, or navigate from within the dialog 

box and click “Add” to designate each layer.

C. For “Output HSI raster”, designate an appropriate location and 

filename for the output raster. Click the button with the folder icon 

to the right, locate your workspace, and create a meaningful name, 

such as “WHWO_burned_HSI.tif”. Again, you can specify alternate 

file formats for rasters, but we recommend “.img” or “.tif” because 

these allow longer filenames than the default ArcGrid format (which 

leaves off any filename extension).

D. Select masking option. You must either check the “no mask” box 

or provide a raster layer to serve as a mask for the final HSI output 

layer. If neither is done, the tool will produce an error. Any raster 

layer can function as a mask, whereby pixels in the mask layer with 

NODATA will indicate which values to be dropped (i.e., converted 

to NODATA) in the final HSI output. We provide a potential 

mask at TOOLBOX → masks → WHWO_brn → maskfps. This 

4.3 White-Headed Woodpecker 

Instructions for Burned Forests 

(continued)
38 U.S. Forest Service RMRS GTR-391. 2019.

mask includes all areas characterized as dry conifer forest with 10 

percent or more ponderosa-dominated forest within a 1-km-radius 

neighborhood and a slope of 40 percent or less. The mask covers 

only areas where GNN data are available (Washington, Oregon, and 

northern California). Alternatively, you may provide a comparable 

mask that covers your project area or check the “no mask” box, 

bearing in mind limitations to model applicability described 

earlier (see 3.2 White-headed Woodpecker Burned Forest Model 

Applicability). With a mask selected, the input window for this tool 

will resemble the screenshot below.

Dialog box for the masking option for the white-head woodpecker 

HSI model.

E. Click on OK to run the model. A series of progress bars will flash. 

If necessary, close the “Results” window once “succeeded” is 

displayed. To view the ensemble model output, navigate to the output 

file (e.g., “WHWO_burned_HSI.tif”) and select the preview pane 

tab.

4.3 White-Headed Woodpecker 

Instructions for Burned Forests 

(continued)

We provide three model application tools (one per woodpecker species) 

for northern Sierra Nevada forests. Outputs generated by these tools are 

30-m-resolution raster layer with HSI values ranging from 0 to 1 (i.e., least 

to most suitable for nesting). As for other tools, we recommend using “.tif” 

or “.img” extensions for output layers for flexibility in filename length (the 

ArcGrid format leaves off any filename extension). The study area must have 

been burned by sufficiently large wildfires (>1,000 acres) for the necessary 

data to be retrievable.

I. Retrieve and compile environmental data layers. In this step you will 

retrieve remotely sensed data from Forest Service websites and input 

them into the Input Generation Tool to compile the variables listed in 

table 2.4.2. None of the files you download will need to be altered from 

4.4 Northern Sierra 

Instructions
U.S. Forest Service RMRS GTR-391. 2019. 39

4.4 Northern Sierra Instructions 

(continued)

their downloaded form. Pathnames for data files used in these tools 

cannot have spaces, so when saving the environmental data layers to 

your workstation, ensure that no parent folders in the file pathnames have 

spaces. For example, “C:\GIS\Data\Northern Sierra\rdnbr_cc.tif” is an 

invalid pathname. Change the name of the “Northern Sierra” folder, for 

example to “NorthernSierra” or “Northern_Sierra”.

A. Retrieve burn severity data:

1. Go to the Forest Service Post-Fire Vegetation Conditions website 

https://www.fs.fed.us/postfirevegcondition/index.shtml and enter 

the necessary information for your fire in the query builder. In 

this example, we are looking for the 2013 Rim Fire in California. 

If a window like the one below is not visible in your web 

browser at the web page in the link above, ensure that Adobe 

Flash Player is enabled in your web browser, then refresh the 

web page.

Screenshot of Step 2, Query Builder, window in RAVG Data 

Access and Summaries.
40 U.S. Forest Service RMRS GTR-391. 2019.

2. Click on the compressed file icon under the Download Data 

column to download the GIS data packet for your fire, then unzip 

the contents into your GIS workspace.

Screenshot of Step 3, Results and Analysis, window in RAVG 

Data Access and Summaries.

3. See the metadata document in the extracted file folder for 

explanation of the files associated with your download. In 

general, the fire perimeter polygon shapefile will end in “...

burn_bndy...” and the burn severity raster will end in “_cc_alb.

tif” or “_cc.tif” (hereafter, “fire perimeter” and “burn severity” 

layers, respectively).

4.4 Northern Sierra Instructions 

(continued)
U.S. Forest Service RMRS GTR-391. 2019. 41

4.4 Northern Sierra Instructions 

(continued)

Screenshot of directory for RAVG data files.

B. Retrieve prefire forest structure data:

1. Navigate to the Region 5 Vegetation Classification and Mapping 

web page https://www.fs.usda.gov/detail/r5/landmanagement/

resourcemanagement/?cid=stelprdb5347192. Identify the Zone 

containing your fire of interest using the CALVEG Mapping 

Zones map on the web page. If your fire of interest is near the 

border of a Zone, you may also need vegetation data from an 

adjacent Zone to get complete coverage of the fire perimeter 

and a 1-km buffer area outside the perimeter. Once you have 

identified your Zone(s), click on “Download Existing Vegetation 

Zones, Keys and Descriptions”.

Screenshot of the CALVEG Mapping Zones Map at the Region 5 

Vegetation Classification and Mapping website.
42 U.S. Forest Service RMRS GTR-391. 2019.

4.4 Northern Sierra Instructions 

(continued)

2. A table will appear below. Click on the Zone containing your 

fire of interest under the Spatial Data column of the table. If 

your fire of interest is near the border of a Zone, you may also 

need to download data for adjacent Zone(s) to get complete 

coverage of the fire perimeter and a 1-km buffer area outside the 

perimeter. Unzip the file and place the geodatabase into your GIS 

workspace.

Screenshot of the table of spatial datasets available by CALVEG 

Mapping Zone.

3. On the FSGeodata Clearinghouse web page, there is a “Search 

by keyword” option above the table of feature classes. Type 

“Existing Vegetation” into the “Search by keyword” text box and 

click the “go” button.

Screenshot of the “Search by keyword” option at the FSGeodata 

Clearinghouse “Dowload National Datasets” web page.
U.S. Forest Service RMRS GTR-391. 2019. 43

4.4 Northern Sierra Instructions 

(continued)

4. Scroll down to your Zone of interest. In our example, we are 

looking for the “South Sierra” Zone in Region 5. Once you have 

found your Zone, click the “ESRI geodatabase” hyperlink below 

the Zone name to download the Zone data. Unzip the file and 

place the geodatabase into your GIS workspace.

Screenshot of descriptions of and links for Existing Vegetation 

geodatabases for two Region 5 Zones.

C. Now you will compile the input variables needed to apply any of 

the habitat models for Northern Sierra woodpeckers (tables 2.4.2, 

2.4.5) using the associated “Input Generation Northern Sierra” tool. 

The following subtasks are best performed entirely from ArcCatalog. 

Unlike for other toolsets, the input generation tool for Northern 

Sierra woodpeckers does not require access to data layers on the 

T drive and therefore can be operated in any environment after 

retrieval of raw data as described in steps I.A and I.B.

1. From ArcCatalog, navigate to “TOOLBOX → Habitat Suitability 

Modeling.tbx → Input Development” and open the “Input 

Generation Northern Sierra” tool by double-clicking it. This 

folder is located at “T:\FS\RD\RMRS\Science\WTE\Research\

HSI_applic_tool\TOOLBOX” on Citrix or the “TOOLBOX” 

folder extracted to your local workstation.

Screenshot of the dialog box for the “Input Generation Northern 

Sierra” tool.
44 U.S. Forest Service RMRS GTR-391. 2019.

4.4 Northern Sierra Instructions 

(continued)

2. Identify or create a folder where you want the layers generated 

from this tool to be stored. Under “workspace”, navigate to this 

folder. Then click “Add”. Alternatively, you can drag and drop 

the desired folder to this window from ArcCatalog.

3. For “fire perimeter”, navigate to the fire perimeter shapefile 

downloaded along with RAVG data. You can either navigate 

from outside the tool interface and drag and drop the perimeter 

shapefile into the “Fire Perimeter” box from ArcCatalog, or 

navigate from within the tool and click “Add”.

4. If you desire the output file to be in a different coordinate system 

from RAVG data (i.e., the fire perimeter shapefile), provide 

a spatial layer (shapefile or raster) that represents the desired 

coordinate system (drag and drop, or navigate from within the 

tool). Otherwise, leave blank.

5. For “burn severity”, use the % canopy mortality layer 

downloaded with RAVG data (step I.A; drag and drop, or 

navigate from within the tool). The filename for this layer may 

vary but should include “cc” (e.g., “_cc.img”, “_cc.tif”, or 

“_cc_alb.tif”) and not “cc5”. You may need to review metadata 

for downloaded layers (click on the “Description” tab when 

previewing in ArcCatalog) to identify the right layer. The values 

in the % canopy mortality layer are derived from the relativized 

delta-normalized burn ratio (RdNBR) and should range from 

0 to 100. If no such layer exists, you can contact the Region 5 

Remote Sensing Laboratory for an appropriate file.

6. For “CWHR polygons”, provide the Existing Vegetation polygon 

file(s) retrieved in step I.B (drag and drop, or navigate from 

within the tool). The attribute table for each polygon file should 

contain “CWHR_DENSITY” and “CWHR_SIZE” fields, from 

which input layers are derived. You can verify existence of these 

fields by previewing the attribute table for each polygon file in 

ArcCatalog (go to “Preview” tab at the top, select “Table” in 

the drop-down menu at the bottom, and scroll to the right; see 

screenshot below).
U.S. Forest Service RMRS GTR-391. 2019. 45

4.4 Northern Sierra Instructions 

(continued)

Screenshot of preview of details for Existing Vegetation polygon 

files.

7. Click OK. All outputs will be stored under “predictors” in your 

workspace. Open this folder and verify presence of the following 

layers: “locccmort.tif”, “landccmort.tif”, “loccc.tif”, “landcc.tif”, 

“loctrsize.tif”.

8. Optional—The “_scratch” directory generated in your workspace 

when you implemented this tool can be deleted upon successful 

completion. 

II. Run any or all model application tools as desired. Now you will use the 

layers in the “predictors” file as inputs for the species-specific habitat 

suitability models for Northern Sierra woodpeckers. The following tasks 

are best performed entirely from ArcCatalog.

A. From ArcCatalog, navigate to “TOOLBOX → Habitat Suitability 

Modeling.tbx”, open the “HSI models” toolbox, and then open the 

desired tool named “Northern Sierra...”, for example, “Northern 

Sierra BBWO”, by double-clicking it.
46 U.S. Forest Service RMRS GTR-391. 2019.

4.4 Northern Sierra Instructions 

(continued)

Screenshot of the filepath for the tools for HSI models.

B. For the variables listed in table 2.4.2 (abbreviations: LocCCmort, 

LandCCmort, LocCC, LandCC, and LocTrSize), use “locccmort.tif”, 

“landccmort.tif”, “loccc.tif”, “landcc.tif”, “loctrsize.tif”, respectively 

(generated in step I.C). You can either navigate to the “predictors” 

folder in ArcCatalog and drag and drop each input layer, or navigate 

from within the dialog box and click “Add” to designate each layer. 

A subset of two or three of these input layers will be required for any 

one tool.

Screenshot of dialog box for black-backed woodpecker in burned 

forest in the northern Sierra Nevada.

C. For the “Output” parameter, designate an appropriate location and 

filename for the output HSI raster. Click the button with the folder 

icon to the right, locate your workspace or any other desired location, 

and create a meaningful name, such as “HSI_BBWO.tif”. Again, we 

recommend “.img” or “.tif” file extensions to allow longer filenames 

(the ArcGrid format leaves off any filename extension).

D. Select masking options. You must either check the “no mask” box 

or provide a raster layer to serve as a mask for the final HSI output 

layer. If neither is done, the tool will produce an error. Any raster 

layer can function as a mask, whereby pixels in the mask layer with 

NODATA will indicate which values to be dropped (i.e., converted 

to NODATA) in the final HSI output. We provide a potential mask 
U.S. Forest Service RMRS GTR-391. 2019. 47

4.4 Northern Sierra Instructions 

(continued) (TOOLBOX → masks → NSierra → mask) whose rationale was 

described earlier in 3.4 Applicability of Northern Sierra Models . If 

this mask includes the entire study area (i.e., study area contains no 

NODATA pixels), you may check the “no mask” box and proceed 

with tool implementation. On the other hand, if large portions of the 

study area are excluded, the safest course would be to specify the 

suggested mask or an equivalent layer relevant to your project area.

E. Click on OK to run the model. A series of progress bars will flash. 

If necessary, close the “Results” window once “succeeded” is 

displayed. To view the ensemble model output, navigate to the output 

file (e.g., “HSI_BBWO.tif”), and select the preview pane tab.
48 U.S. Forest Service RMRS GTR-391. 2019.

5. Guidelines for Application and Interpretation 

of Habitat Suitability Indices

We provide both general and model-specific guidelines for displaying 

and applying habitat suitability index (HSI) maps to support forest planning. 

In general, we anticipate managers’ need to display model predictions as 

suitability categories (e.g., low, moderate, and high suitability habitat). We 

therefore provide guidelines for defining and interpreting such categories.

In general, HSI maps depicting suitability categories can be displayed in 

ArcMap by following these steps:

1. In ArcMap, open either a new map document or an existing one 

containing relevant layers for project planning.

2. Open the “Catalog” window (look for button). 

3. From the “Catalog” window, navigate to the output HSI map layer 

generated from the model application tool (e.g., “BBWO_..._HSI_

ensemble_output.tif”, “HSI_HAWO.tif”), and drag and drop this layer 

into the ArcMap table of contents window (left-hand side of screen).

4. In ArcMap Table of Contents, right-click on the HSI output layer and go 

to Properties → Symbology. Select the “Classified” option in the menu 

on the left.

Screenshot of dialog box for Layer Properties.

From this window, select the number of categories desired, the HSI 

thresholds that define desired categories (click on the “Classify” button 

on the right), and the color scheme for mapping. We provide guidance for 

defining categories for particular models later in this section.
U.S. Forest Service RMRS GTR-391. 2019. 49

We anticipate three basic questions for forest managers with woodpecker 

habitat conservation objectives:

1. Where is suitable nesting habitat within the project area?

2. Where and how could management activities positively or negatively 

impact habitat?

3. How much suitable habitat is needed for population persistence?

To help address these questions, we relate HSIs with apparent or hatched 

nest densities observed at locations where models were developed. We 

use the relationship between HSIs and nest densities to inform suitability 

categories and their interpretation. Hatched nests are highly detectable with 

our survey methods (Russell et al. 2009), so we related hatched nest densities 

with HSIs where possible. At locations where hatching status had not been 

consistently recorded, we related apparent nest densities with HSIs. Apparent 

nest densities are subject to negative detection bias, but we expect this bias 

to be sufficiently independent of habitat for apparent densities to accurately 

reflect HSI-related variation and differences among suitability classes. 

We generated 95-percent confidence intervals for apparent or hatched 

nest densities within suitability categories using nonparametric bootstrapping 

(Efron and Tibshirani 1986) with transects (Northern Sierra and whiteheaded woodpecker unburned forest models) or 600-m cells (black-backed 

and white-headed woodpecker burned-forest models) as sampling units. We 

caution that density estimates may not fully account for regional variation 

in population density or time since disturbance. Nevertheless, they indicate 

differences in nest densities among low, moderate, and high suitability 

habitat at sampled locations within 5 years following wildfire. In addition, 

we work through a hypothetical example of model application to inform 

postfire management strategies at the Canyon Creek Fire in the Malheur 

National Forest (Appendix B).

HSI models provided here describe the relative suitability of sites for 

nesting, and therefore cannot be used in isolation to inform how much habitat 

to conserve (question 3). To address this question, managers must consider 

population ecology and management objectives along with the distribution 

of modeled habitat. Relating HSIs with nest densities can help inform 

applications with particular population targets. Ideally, we would relate 

additional population parameters, such as abundance, fitness, and population 

viability, with habitat to develop meaningful management objectives. 

Such data are often unavailable and prohibitively costly to collect for most 

woodpecker populations.

We therefore suggest a comparative approach for forest planning. We 

can compare the amount of highly or moderately-to-highly suitable habitat 

retained among alternative management scenarios, relative to what was 

present historically across landscapes of interest. The aim of management 

objectives founded on a comparative approach is to maintain levels of 

habitat associated with persistent populations in other areas or time periods. 

Continued testing and refinement of models with newly acquired independent 

data will improve model predictive performance, which will concomitantly 

improve the information provided by this approach.
50 U.S. Forest Service RMRS GTR-391. 2019.

We expect application of postfire habitat models primarily to inform 

management plans needing to accommodate both resource extraction (i.e., 

salvage logging) and woodpecker habitat conservation. To support such 

application, we identify suitability categories based on HSI relationships 

with hatched-nest densities (where available) or apparent nest densities 

(where hatching status was uncertain) in burned forests where models 

were developed (see also Latif et al. 2013, in preparation; Campos et al., 

in preparation). In all cases, observed nest densities increased from low to 

moderate and from moderate to high suitability classes (tables 5.1.1–3, 

figs. 5.1.1–3). Our observations suggest some variation among study 

locations (table 5.1.1). Nevertheless, nest densities consistently increased 

with increasing predicted suitability. Reserves set aside for habitat 

conservation should therefore proportionately favor areas classified as high 

over moderate and moderate over low suitability.

5.1 Guidelines for 

Postfire Habitat Models

Table 5.1.1—Observed nest densities (per 1,000 ac) at locations habitat suitability 

models were developed for nesting black-backed woodpeckers. Low, moderate, and 

high suitability classes correspond with ensemble model predictions of 0–2, 3–5, and 

6–8, respectively. Percent nests = the expected value given even sampling across 

categories. Area surveyed represents the extent surveyed each year multiplied by 

study duration at each location. 95% confidence limits (error bars) were bootstrapped 

using 600 m cells as sampling units (n = 67, 83, and 176 for Star Gulch, Tripod, and 

Toolbox locations, respectively). Note: Values in this table are for HSIs based 

on MTBS (extended assessment) burn severity. For equivalent RAVG-based 

(immediate assessment) HSIs, refer to values in table A1.

Fire location Habitat suitability (HSI) class

Quantity Low Moderate High

Star Gulch (1994 Idaho)a

Density 0.22 (0, 0.56) 2.07 (0.32, 4.51) 3.92 (2.55, 5.35)

Percent nests 4 (0, 10) 33 (7, 54) 63 (45, 89)

Area surveyed (acres) 22,214.2 7,153.6 17,011.9

Tripod (2006 Washington)a

Density 0 (0, 0) 1.96 (0, 4.72) 9.86 (4.3, 16.1)

Percent nests 0 (0, 0) 17 (0, 43) 83 (57, 100)

Area surveyed (acres) 12,570.2 2,519.9 3,756.6

Toolbox (2002 Oregon)a

Density 1.28 (0.41, 2.26) 5.92 (3.41, 8.84) 11.03 (8.38,1 3.93)

Percent nests 7 (2, 12) 32 (21, 44) 61 (49, 73)

Area surveyed (acres) 11,586.0 7,930.4 15,893.5

All three firesb

Density 0.43 (0.16, 0.75) 3.57 (2.18, 5.17) 7.71 (6.25, 9.33)

a

 To inform potential densities within suitability classes, we suggest referencing densities 

associated with locations listed here that are most similar in environmental conditions to the 

project area of interest. Percent ponderosa pine and median percent slope are 64 percent and 

47 percent at Star Gulch, 55 percent and 37 percent at Tripod, and 82 percent and 14 percent at 

Toolbox (for these and additional relevant data, see Hollenbeck et al. 2013).

b

 Values represented by red circles and error bars in figure 5.1.1.
U.S. Forest Service RMRS GTR-391. 2019. 51

5.1 Guidelines for Postfire 

Habitat Models (continued)

Table 5.1.2—Density of hatched nests (per 1000 ac) in suitability classes defined 

by HSI thresholds (0.34, 0.6) for white-headed woodpeckers in burned forest. 

Models were developed with data from Toolbox and Canyon Creek wildfire 

locations (Oregon). 95% CLs (in parentheses) were generated with non-parametric 

bootstrapping. Values for “percent nests” are the expected percent of hatched 

nests assuming equal area sampling across suitability classes. Area surveyed was 

calculated as the proportion of sites representing the surveyed area in each suitability 

class multiplied by the total area surveyed at each location. Note: Values in this 

table are for a HSI model based on MTBS (extended assessment) burn severity. 

For the equivalent RAVG-based (immediate assessment) model, refer to values 

in table A2.

Location Habitat suitability (HSI) class

Quantity Low Moderate High

Toolbox (2002)

Density 0.28 (0.06, 0.54) 1.52 (0.9, 2.2) 3.97 (1.93, 6.22)

Percent nests 5 (1, 10) 26 (16, 43) 69 (50, 80)

Area surveyed (acres) 17,592.3 15,765.1 3,780.7

Canyon Creek (2015)

Density 0.66 (0.23, 1.2) 3.11 (1.99, 4.3) 6.12 (2.71, 10.11)

Percent nests 7 (2, 14) 31 (19, 50) 62 (41, 76)

Area surveyed (acres) 12,114.4 8,673.8 1,633.4

Both firesa

Density 0.44 (0.21, 0.71) 2.09 (1.49, 2.7) 4.68 (2.91, 6.68)

a

 Values represented by red circles and error bars in figure 5.1.2.

We recommend managers consider several other factors along with 

modeled HSIs when planning management of recently burned forests. 

Studies in Idaho found negative effects of salvage logging on nest density 

and nest survival for several woodpecker species (Saab et al. 2007). Barkdrilling species, including black-backed and hairy woodpecker, favor 

and may require nest sites surrounded by relatively high snag densities 

for foraging (Dudley et al. 2012). Other species, such as white-headed 

woodpecker, may forage more in nearby green forest (Wightman et al. 2010). 

Forest conditions within 0.6 mile of the nest site therefore inform modeled 

HSIs, and so to conservatively maintain the HSI of a given patch, logging 

should be restricted within 0.6 mile of that patch. Conserving areas within 

0.6 mile of potential nesting habitat will ensure that associated foraging 

habitat is maintained even if surrounding areas are lower in suitability for 

nesting. 

Removal of roadside hazard trees can severely impact burned forest 

landscapes, especially in areas of high road density. If road closings are 

not feasible, habitat reserves should be located in areas with minimal road 

densities, and model applications should account for areas affected by 

roadside tree removal (e.g., by discounting prefire canopy cover input values 

within affected areas). Although topographic slope does little to inform 
52 U.S. Forest Service RMRS GTR-391. 2019.

Table 5.1.3—Number of nests and observed nest densities where habitat suitability 

models were developed for nesting woodpeckers in the Northern Sierra Nevada 

Mountains by suitability class. Habitat suitability index (HSI) bins define suitability 

classes. All surveys were conducted within 5 years following wildfire. Expected 

percent nest values assume observed densities and equal area surveyed in each 

suitability categories. Density and percent nest values in parentheses are 95 percent 

CLs generated by transect-level boot-strapping (n = 45 transects replicated 5000 

times).

Values by habitat suitability (HSI) class

Species Summary quantity Low Moderate High

Black-backed 

woodpecker

HSI bins 0‒0.41 0.41‒0.65 0.65‒1

No. nests 8 17 29

Area surveyed (acres) 5,714.3 1,976.7 1,193.4

Density (nests per 

1,000 acres)

1.4 

(0.5, 2.4)

8.6 

(4.3, 13.8)

24.3 

(9.4, 40.8)

Expected % nests 4 

(2, 9)

25 

(14, 45)

71 

(49, 83)

Hairy 

woodpecker

HSI bins 0‒0.51 0.51‒0.7 0.7‒1

No. nests 18 45 27

Area surveyed (acres) 5,294.1 2,678.6 857.1

Density (nests per 

1,000 acres)

3.4 

(1.6, 5.3)

16.8 

(11.8, 23.1)

31.5 

(17.3, 52)

Expected % nests 6 

(3, 10)

33 

(23, 47)

61 

(45, 72)

White-headed 

woodpecker

HSI bins 0‒0.49 0.49‒0.75 0.75‒1

No. nests 16 26 30

Area surveyed (acres) 5,925.9 2,148.8 859.6

Density (nests per 

1,000 acres)

2.7 

(1.4, 4.2)

12.1 

(7.2, 18.6)

34.9 

(17.2, 53.2)

Expected % nests 5 

(3, 10)

24 

(16, 39)

70 

(53, 80)

HSI models here, woodpeckers tend to favor relatively gentle slopes at 

some locations (Hollenbeck et al. 2011; Latif et al. 2016). Consequently, 

we recommend habitat reserves that include gentle slopes as available 

within project areas. Finally, HSI models strictly inform nesting densities, 

so managers may need to consider additional information on other fitness 

components such as nest survival as is available. In particular, nest survival 

for black-backed woodpecker in the Inland Northwest and Rocky Mountains 

can decrease with increasing proximity to unburned edge, possibly reflecting 

nest predator refugia provided by unburned forest (Saab et al. 2011).

 Disturbance-associated woodpeckers are mobile and populations 

persist largely through colonization of newly burned forests mediated by 

dispersal (Dixon and Saab 2000; Siegel et al. 2015). Given the importance 

of dispersal across locations for maintaining populations, habitat targets 

5.1 Guidelines for Postfire 

Habitat Models (continued)
U.S. Forest Service RMRS GTR-391. 2019. 53

Figure 5.1.1—Observed nest densities related with ensemble HSI (i.e., the number 

of models classifying a given site as suitable) for black-backed woodpeckers in the 

Inland Northwest. Small black dots are values for individual HSI levels. Large red 

dots represent observed densities averaged (mean) across locations (Star Gulch 

[Idaho], Toolbox [Oregon], and Tripod [Washington]) within suitability categories 

(low, moderate, and high) and are plotted at mean HSI values. Error bars represent 

95-percent confidence intervals, which were bootstrapped by using 600-m cells as 

sampling units (n = 326 across all three locations). Note: Values in this figure are 

for HSIs based on MTBS (extended assessment) burn severity. For equivalent 

RAVG-based (immediate assessment) HSIs, refer to figure A1.

Figure 5.1.2—Densities of hatched nests for white-headed woodpeckers along 

an HSI gradient in burned forest. Low, moderate, and high suitability classes are 

differentiated by two HSI thresholds, one that maximizes the sum of sensitivity and 

specificity (maxSSS) and the other placed at a natural break in densities for equalarea moving-window bins in this figure (small dots) and the distribution of nest site 

HSIs (rug bars). Large circles and error bars are density estimates and bootstrapped 

95-percent confidence intervals for habitat suitability classes, respectively. 

Note: Values in this figure are for an HSI model based on MTBS (extended 

assessment) burn severity. For the equivalent RAVG-based (immediate 

assessment) model, refer to figure A2.

5.1 Guidelines for Postfire 

Habitat Models (continued)
54 U.S. Forest Service RMRS GTR-391. 2019.

5.1 Guidelines for Postfire 

Habitat Models (continued)

Figure 5.1.3—Observed nest densities (not corrected for detectability) related with 

HSI values for woodpeckers in the northern Sierra Nevada. Species are black-backed 

woodpecker (BBWO), hairy woodpecker (HAWO), and white-headed woodpecker 

(WHWO). Small open dots are values for equal-area moving-window bins, and large 

closed dots are values for suggested suitability categories. Error bars represent 

95-percent confidence intervals generated by transect-level bootstrapping (n = 45 

transects replicated 5,000 times). Rug plot shows HSI values for nest locations. The 

low-to-moderate HSI threshold maximizes the sum of sensitivity (proportion of nests 

classified as moderate) and specificity (proportion of landscape classified as low) 

(maxSSS). 

at any one location should be placed in a landscape context. Thus, we 

recommend analyzing habitat dynamics and comparing the implications 

of alternate management scenarios for habitat suitability across landscapes 

large enough to include multiple neighboring locations (e.g., entire national 

forests) to inform conservation objectives for any one wildfire location. In 

conjunction with tools presented here, development of tools that streamline 

analysis of landscape-scale habitat dynamics was partially funded and is 

ongoing (National Fire Plan Funded Proposal FY16-FY18; Saab, Latif, and 

Haas FRF23516). Summaries of observed nesting densities (tables 5.1.1–3) 

provide further context for comparing alternate management scenarios with 

historically derived benchmarks; bear in mind these caveats:

1. We expect habitat models to be most applicable in forests proposed for 

tree harvest. Woodpeckers are likely to make some use of selectively 

logged forests with potentially similar reproductive success (Forristal 
U.S. Forest Service RMRS GTR-391. 2019. 55

5.1 Guidelines for Postfire 

Habitat Models (continued) 2009; Saab et al. 2007, 2011). Further study may allow precise 

estimation of population densities with particular treatments and prefire 

conditions, which could inform refinement of salvage logging plans to 

reflect nuanced population responses.

2. Ensemble predictions for black-backed woodpecker reflect not only 

relative habitat suitability but also modeling uncertainty. Specifically, the 

moderate suitability classification indicates areas where models disagree 

and suitability is therefore less certain (Latif et al. 2013). Additionally, 

we are least confident in nest density estimates for moderate suitability 

areas because locations where models were developed contained 

relatively limited moderate suitability habitat. In contrast, model 

disagreement and the extent of moderate suitability habitat tend to be 

higher at locations with environmental conditions that deviate from 

where models were developed (Latif et al. 2013). Thus, we have less 

confidence in the predictive value of ensemble predictions in areas with 

proportionately extensive moderate suitability habitat (HSI = 3–5). For 

such project areas, monitoring the effects of management treatments 

and evaluation (and possible refinement) of model predictions with 

independent data (ideally nest locations) would be needed.

3. HSIs most explicitly describe habitat suitability for nesting but also 

implicitly contain information on surrounding foraging habitat within 

0.6-mile-radius neighborhoods. Other studies explicitly quantify foraging 

habitat, home range size, and relationships between the two (Dudley and 

Saab 2007; Dudley et al. 2012; Tingley et al. 2014). Remotely sensed 

data available for most wildfire locations (i.e., burn severity, prefire 

canopy cover) typically lack the resolution for applying published 

models that quantify foraging habitat suitability. For locations with 

fine-scale data quantifying snag densities and distributions (e.g., field 

measurements or LiDAR), home range size and foraging habitat data 

could improve and clarify predictions of population density (e.g., Tingley 

et al. 2016).

We expect HSIs for nesting white-headed woodpecker in unburned 

forests to primarily inform large-scale forest restoration, such as restoration 

projects funded by the Collaborative Forest Landscape Restoration Program 

(CFLRP) (https://www.fs.fed.us/restoration/CFLRP/). Managers can limit or 

defer restoration treatments (usually thinning or prescribed burning for fuels 

reduction) in areas characterized as high suitability and implement treatments 

in areas where habitat suitability is limited by a lack of canopy openings 

(Hollenbeck et al. 2011). Managers could also apply the HSI model to 

hypothetical landscapes representing projected conditions under alternative 

management or climate scenarios. HSI maps representing conditions in 

Oregon in 2002 and 2012 are posted on the T drive (T:\FS\RD\RMRS\

Science\WTE\Research\RMRS-WHWO\Oregon_hsi_maps\WHWO_OR_

HSI_maps.gdb). The tool for white-headed woodpecker in unburned forest 

supplements these data by facilitating model application to projected future 

scenarios or landscapes affected by disturbance relatively recently.

5.2 Guidelines 

for White-headed 

Woodpecker in 

Unburned Forests
56 U.S. Forest Service RMRS GTR-391. 2019.

We anticipate forest managers using HSI maps to identify areas where 

restoration could improve habitat conditions for white-headed woodpecker 

or to evaluate the relative benefits of alterative management strategies. To 

support such applications, we have identified suitability categories based on 

HSI relationships with observed nest densities in unburned forests of Oregon 

(table 5.2, fig. 5.2). Depending on specific management goals, we suggest 

HSI thresholds of 0.4 or 0.49 to distinguish between low, moderate, and high 

suitability categories. These categories are analogous to those evaluated by 

Latif et al. (2015), but optimized for the simplified model applied here, such 

that categories meaningfully differentiate observed densities across multiple 

national forests representing different geographic regions (table 5.2). 

Table 5.2—Number of nests, area surveyed, and observed nest densities (not 

corrected for imperfect detection) in habitat suitability categories for white-headed 

woodpecker in unburned forests of Oregon. Suitability categories are low (HSI < 0.4), 

moderate (0.4 ≤ HSI < 0.49), and high (HSI ≥ 0.49). The model was originally 

developed with data from the Deschutes and Fremont-Winema National Forests, and 

data from the Malheur National Forest were collected following model development.

Values by habitat suitability (HSI) class

National forest Summary quantity Low Moderate High

Deschutes No. nests 3 4 10

Area surveyed (acres) 350.4 207.2 291.6

Density (nests per 

1,000 acres)

8.6 19.3 34.3

Expected % nests 14 31 55

Fremont-Winema No. nests 3 0 4

Area surveyed (acres) 690.3 267.5 172

Density (nests per 

1,000 acres)

4.3 0 23.3

Expected % nests 16 0 84

Malheur No. nests 3 12 24

Area surveyed (acres) 1,130.2 622.5 600.9

Density (nests per 

1,000 acres)

2.7 19.3 39.9

Expected % nests 4 31 65

All three forests No. nests 9 16 38

Area surveyed (acres) 2,175.1 1,162.1 1,133.4

Density (nests per 

1,000 acres)

4.1 

(1.8, 6.7)

13.8 

(5.5, 23.6)

33.5 

(21.7, 47.8)

Expected % nests 8 (3, 15) 27 (12, 41) 65 (51, 79)

5.2 Guidelines for White-headed 

Woodpecker in Unburned Forests 

(continued)
U.S. Forest Service RMRS GTR-391. 2019. 57

5.2 Guidelines for White-headed 

Woodpecker in Unburned Forests 

(continued)

Figure 5.2—Observed nest densities related with HSI values for white-headed 

woodpecker in unburned forests of Oregon. Small open dots are values for equalarea moving-window bins, and large closed dots are values for suggested suitability 

categories. Density values are for areas within 0.2 mile of transects where survey 

effort was highest and evenly distributed. Error bars represent 95-percent confidence 

intervals generated by transect-level bootstrapping (n = 57 transects replicated 5,000 

times). Rug plot shows HSI values for nest locations (n = 63). The moderate-to-high 

HSI threshold (0.49) maximizes the sum of sensitivity (proportion of nests classified 

as high) and specificity (proportion of landscape classified as low or moderate) 

(maxSSS).

Managers can define alternative suitability categories to accommodate 

particular objectives by considering how observed nest densities relate with 

HSIs (fig. 5.2). Managers can then plan or evaluate forest restoration by 

considering how treatments are likely to affect or have affected the amount 

and distribution of low, moderate, or high suitability habitat within project 

areas. Additionally, managers can further gauge the potential implications of 

treatments for populations by considering observed nest densities at sampled 

locations (table 5.2, fig. 5.2).

The HSI model presented here should be applied outside Oregon with 

caution, particularly in landscapes with conditions that differ substantially 

from those where models were developed (see Latif et al. 2015). For 

example, we facilitated application of this model to inform sampling design 

for monitoring white-headed woodpecker at the Nez Perce-Clearwater 

National Forest in Idaho. The model assigned extremely low HSI values to 

most areas because the Idaho study area was characterized by much greater 
58 U.S. Forest Service RMRS GTR-391. 2019.

topographic relief than Oregon study areas. An alternate model fitted to 

available data that excluded topographic predictors (i.e., Slope and Cosine 

Aspect; table 2.2) appeared more useful and may be added to the GIS toolbox 

described here following evaluation with independent data in Idaho.

When using HSIs to inform conservation or restoration, managers need 

to carefully consider environmental inputs used to calculate HSIs. HSIs 

are calculated in part by using variables representing forest structure over a 

0.6-mile-radius area (table 2.2), so management decisions need to include 

areas within 0.6 mile of target locations. Additionally, evaluating whether 

suitability at potential project areas is mainly limited by the arrangement 

of open- and closed-canopy forests or by the absence of ponderosa pine 

within 0.6 mile of these areas is necessary to inform restoration treatments. 

We expect restoration treatments to have greater potential to primarily 

improve habitat suitability in the near term by encouraging canopy mosaics, 

but treatments could also benefit habitat suitability in the long term if they 

encourage ponderosa pine dominance. To best inform their decisions, forest 

managers should ideally compare HSI maps generated for landscapes 

explicitly representing projected results of alternative management options.

We did not attempt to relate HSIs with observed nest densities. 

Anticipated uses of this HSI model will focus on relative differences in 

amount of suitable habitat under alternative scenarios rather than determining 

an absolute amount of habitat to conserve. Effectiveness monitoring studies 

of Collaborative Forest Landscape Restoration treatments are generating 

data for evaluating treatment effects on white-headed woodpecker nesting 

densities and habitat.

5.2 Guidelines for White-headed 

Woodpecker in Unburned Forests 

(continued)
U.S. Forest Service RMRS GTR-391. 2019. 59

6. Management Implications

Despite widespread development of models intended to inform habitat 

management, accessibility limits their application (Guisan et al. 2013). 

The tools described here are designed to improve accessibility of habitat 

suitability models for disturbance-associated woodpeckers to assist forest 

management decisionmaking in western North America. Our approach 

consists of integrating model application tools into software commonly used 

for management planning (ArcGIS) and providing guidelines for interpreting 

HSI maps in relation to underlying species ecology.

Thus far, we have provided model application tools for woodpecker 

species of conservation concern in mixed conifer forests to assess various 

management activities. Several woodpecker species inhabit forests recently 

burned by wildfire, for which habitat models are powerful tools for planning 

postfire salvage logging activities. Additionally, in the Inland Northwest, the 

white-headed woodpecker specializes on ponderosa pine-dominated forests 

characterized by canopy mosaics, wherein habitat models for unburned 

forests could inform large-scale forest restoration treatments.

We will continue to develop habitat models for other woodpecker 

species that will broaden the application and information for science-based 

management decisions. For example, habitat models for Lewis’s woodpecker 

(Melanerpes lewis) in burned forests would further inform salvage planning. 

Black-backed, white-headed, hairy, and Lewis’s woodpecker use a range of 

habitat conditions for nesting within burned forests, such that managing for 

multiple woodpecker species is likely to result in conditions beneficial for a 

variety of other species (Saab et al. 2009, 2011). After further development 

and refinement of habitat suitability models for woodpeckers and other 

species adapted to disturbance-maintained forests, additional application 

tools will be incorporated into this series.

Although this series of application tools is currently geared to habitat 

conservation for woodpeckers nesting in dry conifer forests, we expect the 

approach to be broadly applicable. Additional model application tools could 

facilitate broader use of habitat models for a variety of species to inform 

management decisions and planning.
60 U.S. Forest Service RMRS GTR-391. 2019.

7. Troubleshooting Guide

Problem 1.—When I am attempting to retrieve RAVG burn severity data for 

my project area, the data retrieval window is not visible on the website as 

shown in the instructions (section 4.4).

Solution 1.—You may not have Adobe Flash Player installed or enabled. 

Search for “install or enable Adobe Flash Player” on the Internet to 

find instructions to install, enable, and check the status of Adobe Flash 

Player. If Adobe Flash Player is enabled but the online data retrieval tool 

remains unavailable, you can contact the RAVG program directly via 

email (rapid_assessment_post_fire@fs.fed.us) or through the program’s 

website (https://www.fs.fed.us/postfirevegcondition/contact/index.php).

Problem 2.—I received an error when attempting to run one of the 

input generation tools for black-backed or white-headed woodpecker 

in burned forests of the Inland Northwest (e.g., Failed to execute 

(BBWOINPUTS).).

Solution 2.1.—If the error notification indicates that default layers 

on the T drive were not found, try closing and reopening the tool, and 

rerunning.

Solution 2.2.—The wildfire perimeter may fall outside the spatial extent 

where default data are available. You may need to truncate the fire 

perimeter shapefile to include only areas where default data are available, 

or develop model inputs manually (see step I.C in section 4.1 or 4.3). 

You can view default canopy cover and topography data at: T:\FS\RD\

RMRS\Science\WTE\Research\HSI_applic_tool\PA_RASTERS

Solution 2.3.—The extents of burn_bndy.shp and dnbr.tif files may not 

match. Make sure the two files are for the same fire, and check to make 

sure the coordinate systems are identical for both files.

Problem 3.—When attempting to operate the input generation tool for the 

Northern Sierras, I receive errors that resemble the following:

Error: “000732: <value>: Dataset <value> does not 

exist or is not supported.”

ExecuteError: Failed to execute. Parameters are not 

valid.

ERROR 000732: Input Features: Dataset ‘...’ does not 

exist or is not supported

Failed to execute (PolygonToRaster).
U.S. Forest Service RMRS GTR-391. 2019. 61

Solution 3.—Check the folder names and pathways for input files. 

Filenames and pathways should contain no spaces. In particular, the most 

likely reason for this error is an invalid pathname for Existing Vegetation 

polygons downloaded from the Region 5 website. If the file geodatabase 

containing the polygons is embedded within a file of the same name, you 

will receive this error. For example, these paths result in an error:

C:\GIS\vegetation\eveg\ExistingVegR5_SouthSierra1995_

2016_v1.gdb\ExistingVegR5_SouthSierra1995_2016_v1.gdb\

ExistingVegR5_SouthSierra1995_2016_v1

C:\GIS\vegetation\Existing Veg\ExistingVegR5_SouthSierra1995_

2016_v1.gdb \ExistingVegR5_SouthSierra1995_2016_v1

But this path is acceptable:

C:\GIS\vegetation\eveg\ExistingVegR5_SouthSierra1995_2016_

v1.gdb\ExistingVegR5_SouthSierra1995_2016_v1
62 U.S. Forest Service RMRS GTR-391. 2019.

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U.S. Forest Service RMRS GTR-391. 2019. 67

Appendix A: RAVG-Based Models to Support 

Immediate Postfire Planning

Woodpecker habitat models for burned forest in the Inland Northwest 

were originally developed with burn severity data available under the 

Monitoring Trends in Burn Severity (MTBS) program. Since 1984, the 

MTBS program has provided standardized and ecologically relevant data 

for monitoring U.S. wildfire patterns in slow-growth systems, such as 

forests, by contrasting prefire satellite imagery with imagery recorded 1 year 

postfire during the growing season (i.e., extended assessment). These data 

are particularly relevant for quantifying woodpecker habitat in burned forests 

because the timing of postfire imagery is suited to reflect the substantial 

tree mortality (and snag generation) during the first year after wildfire. 

Postfire forest planning, however, is usually started well before MTBS data 

become available. To support more proximate analysis needs, burn severity 

data based on an immediate assessment (within 45 days following wildfire 

containment) are also available for wildfires on National Forest System 

lands through the Rapid Assessment of Vegetation Condition after Wildfire 

(RAVG) process.

In recognizing the need for rapid postfire assessment, we provide 

alternate RAVG-derived model versions (see tables 2.1, 2.3.2). RAVG-based 

habitat suitability index (HSI) models are structured the same as MTBSbased models, but coefficient values quantify nesting relationships with 

RAVG rather than MTBS burn severity. Tools for obtaining HSI maps from 

RAVG-based models are operated as follows:

1. Obtain RAVG-based burn severity data following step I.A in section 4.4 

Apply input development instructions (step I.A in sections 4.1 and 4.3).

2. Apply input development tools using RAVG (instead of MTBS) dNBR 

(steps I.B–C in sections 4.1 and 4.3).

3. Apply RAVG versions of model application tools with resulting inputs. 

Navigate to TOOLBOX Habitat Suitability Modeling.tbx  HSI 

models, and open the “Black-backed Woodpecker (RAVG)” or “Whiteheaded Woodpecker Burned (RAVG)” tool. Then follow instructions in 

step III, section 4.1 or step II, section 4.3, respectively, to operate these 

tools.

To support application of RAVG-derived HSI maps where needed, 

we related RAVG HSIs with nest densities and compared MTBS versus 

RAVG HSIs. For both species, nest densities were sufficiently related 

with RAVG-based HSIs to inform management (tables A.1, A.2; figs. A.1, 

A.2). Considering these relationships, we modified HSI thresholds for 

RAVG-based black-backed woodpecker HSI models for classifying low, 

moderate, and high suitability habitat (table A1, fig. A1). For white-headed 
68 U.S. Forest Service RMRS GTR-391. 2019.

woodpeckers, we recommend the same thresholds for RAVG HSI models 

as those identified for MTBS models (table A.2, fig. A.2). We compared 

MTBS to RAVG HSIs at three relatively large 2015 wildfires in Oregon 

and Washington (fig. A.3) that burned extensively across forest types and 

conditions where models were applicable (i.e., areas included in application 

masks). RAVG HSIs and suitability classifications were strongly correlated 

with MTBS versions for both species at all three locations (table A.3). 

Consequently, RAVG and MTBS HSI models identified similar areas as 

suitable (e.g., fig. A.4).

Consistent with our expectations given the ecological relevance of 

MTBS burn severity, nest densities related more closely with MTBS than 

RAVG HSIs (compare tables A.1, A.2 and figures A.1, A.2 with tables 5.1.1, 

5.1.2 and figures 5.1.1, 5.1.2, respectively). We therefore recommend using 

MTBS-based models when possible for black-backed and white-headed 

woodpecker in the Inland Northwest, but offer RAVG-based alternatives 

to support planning needs following wildfire before MTBS data become 

available.

Table A.1—Observed nest densities (per 1,000 ac) at model development locations 

for nesting black-backed woodpeckers. Low, moderate, and high suitability classes 

correspond with RAVG-based ensemble model predictions of 0–2, 3–6, and 7–8, 

respectively. Percent nests = the expected value given even sampling across 

categories. Area surveyed represents the extent surveyed each year multiplied by 

study duration at each location. 95 percent confidence limits were bootstrapped 

using 600 m cells as sampling units (n = 67, 83, and 176 for Star Gulch, Tripod, and 

Toolbox locations, respectively).

Fire location Habitat suitability (HSI) class

Quantity Low Moderate High

Star Gulch (1994 Idaho)

Density 0.22 (0, 0.56) 2.07 (0.32, 4.51) 3.92 (2.55, 5.35)

Percent nests 4 (0, 10) 33 (7, 54) 63 (45, 89)

Area surveyed (acres) 15,803 14,114 16,463

Tripod (2006 Washington)

Density 0 (0, 0) 1.96 (0, 4.72) 9.86 (4.3, 16.1)

Percent nests 0 (0, 0) 17 (0, 43) 83 (57, 100)

Area surveyed (ha) 4,637 1,354 1,643

Toolbox (2002 Oregon)

Density 1.28 (0.41, 2.26) 5.92 (3.41, 8.84) 11.03 (8.38, 13.93)

Percent nests 7 (2, 12) 32 (21, 44) 61 (49, 73)

Area surveyed (acres) 10,569 9,122 15,719

All three firesa

Density 0.31 (0.06, 0.66) 3.17 (2.06, 4.43) 7.63 (6.09, 9.24)

a

 Values represented by red circles and error bars in figure A.1.
U.S. Forest Service RMRS GTR-391. 2019. 69

Location Habitat suitability (HSI) class

Quantity Low Moderate High

Toolbox

Density 0.31 (0.1, 0.57) 1.24 (0.66, 1.85) 4.43 (2.57, 6.58)

Percent nests 5 (2, 10) 21 (11, 33) 74 (60, 84)

Area surveyed (acres) 19,319 12,857 4,962

Canyon Creek

Density 0.8 (0.36, 1.34) 3.05 (1.84, 4.3) 8.41 (4.4, 13.27)

Percent nests 7 (3, 12) 25 (14, 40) 69 (51, 81)

Area surveyed (acres) 13,774 7,221 1,427

Both firesa

Density 0.51 (0.28, 0.77) 1.91 (1.32, 2.55) 5.33 (3.57, 7.25)

Table A.2—Density of hatched nests (per 1,000 ac) in suitability classes defined 

by alternative RAVG-based HSI model thresholds (0.34, 0.6) for white-headed 

woodpeckers in burned forest. Models were developed at Toolbox and Canyon Creek 

wildfire locations (Oregon). 95 percent confidence intervals (in parentheses) were 

generated with non-parametric bootstrapping. Values for “percent nests” are the 

expected percent of hatched nests assuming equal area sampling across suitability 

classes. Area surveyed was calculated as the proportion of sites representing the 

surveyed area in each suitability class multiplied by the total area surveyed at each 

location.

a

 Values represented by red circles and error bars in figure A.2.

Figure A.1—Observed nest densities related with RAVG-based ensemble HSI 

(i.e., the number of models classifying a given site as suitable) for black-backed 

woodpeckers in the Inland Northwest. Small black dots are values for individual HSI 

levels. Large red dots represent observed densities averaged (mean) across locations 

(Star Gulch [Idaho], Toolbox [Oregon], and Tripod [Washington]) within suitability 

categories (low, moderate, and high) and are plotted at mean HSI values. Error bars 

represent 95-percent confidence intervals, which were bootstrapped by using 600-m 

cells as sampling units (n = 326 across all three locations).
70 U.S. Forest Service RMRS GTR-391. 2019.

Figure A.2—Densities of hatched nests for white-headed woodpeckers along an 

alternate RAVG-based HSI gradient in burned forest. Low, moderate, and high 

suitability classes are differentiated by two HSI thresholds, one that maximizes the 

sum of sensitivity and specificity (maxSSS) and the other placed at a natural break 

in densities for equal-area moving-window bins in this figure (small dots) and the 

distribution of nest site HSIs (rug bars). Large circles and error bars are density 

estimates and bootstrapped 95-percent confidence intervals for habitat suitability 

classes, respectively.

Figure A.3—Wildfires in 2015 in Oregon (Cornet-Windy Ridge, Canyon Creek) and 

Washington (North Star) used to compare MTBS- to RAVG-based HSIs for blackbacked and white-headed woodpeckers.
U.S. Forest Service RMRS GTR-391. 2019. 71

Table A.3—Correspondence between original (MTBS-based) and alternative (RAVGbased) HSI models at three 2015 wildfires in Washington (North Star) and Oregon 

(Canyon Creek, Cornet-Windy Ridge). Correlation coefficients (Pearson’s r) relate 

continuous HSI values for 30-m pixels. The percent of pixels classified the same (low, 

moderate, or high suitability) using recommended MTBS- versus RAVG-based HSI 

thresholds are also reported.

Pearson’s r % Consistently classified

Location BBWO WHWO BBWO WHWO

North Star 0.689 0.576 62 70

Canyon Creek 0.852 0.814 74 81

Cornet-Windy Ridge 0.762 0.716 69 74

All 0.741 0.700 66 75

Figure A.4—Burn severity (top row) and HSI maps (bottom two rows) for blackbacked (BBWO) and white-headed woodpecker (WHWO) at the Canyon Creek Fire 

(Oregon, 2015). Versions derived from MTBS versus RAVG are compared. HSI maps 

are masked to exclude areas outside the range of conditions where models were 

developed.
72 U.S. Forest Service RMRS GTR-391. 2019.

Appendix B: Demonstration of Model Application 

for Postfire Planning

We demonstrate the use of woodpecker habitat suitability index (HSI) 

models to inform postfire management using the Canyon Creek Fire 

(Oregon, 2015) as a case study. We consider a hypothetical scenario wherein 

managers have identified a series of six potential management units with 

opportunity for salvage logging (fig. B.1). The units considered were actual 

management units identified during the postfire planning process at Canyon 

Creek, and treatment units are the areas within management units where 

salvage logging was possible and implemented after slope, accessibility, and 

economic viability were considered. Thus, these units are realistic, but we 

constructed a simplified decision scenario purely to demonstrate how HSI 

models could inform postfire planning.

We assume a minimum sale area of 700 acres for economic desirability 

for salvage logging given contemporaneous market conditions. For this 

example, we considered a postfire management plan that allowed a maximum 

of 50 breeding pairs to be impacted by logging (i.e., occur within logged 

areas) without compromising conservation targets. In theory, these targets 

would be developed from a regional assessment of current population status. 

For simplicity’s sake, we focus this scenario on one species, the black-backed 

Figure B.1—Potential units considered in a hypothetical postfire management 

scenario for the Canyon Creek Fire (Oregon, 2015). Scenario options entail salvage 

logging on different combinations of management units. Treatment units are the areas 

within management units with logging opportunity.
U.S. Forest Service RMRS GTR-391. 2019. 73

woodpecker, with clearly documented negative relationships with logging 

(Saab et al. 2007). We consider potential effects on nesting and foraging 

within 0.6 mile of treated areas. We used HSI models based on Monitoring 

Trends in Burn Severity (MTBS) data to inform planning in this scenario, 

but the principles demonstrated are also relevant to model application based 

on Rapid Assessment of Vegetation Condition after Wildfire (RAVG) data. 

The HSI map relevant to this scenario is shown in figure A4 (middle row, left 

column), and nest density relationships with modeled suitability are in 

table 5.1.1 and figure 5.1.1.

Scenario options where four of six management units were included 

in the logging sale unit met our hypothetical economic benchmark of 700 

acres treated. Of these, the upper 95-percent confidence limit for potentially 

affected nest numbers fell below our maximum allowable number of 50 

nests in treated areas for seven options (table B.1). Assuming timber volume 

(and consequent economic desirability) for a given sale is related to the area 

treated, scenarios G and F appear most desirable. Of these, the number of 

nesting pairs whose foraging or nesting habitat could be affected (i.e., the 

0.6-mile-radius footprint for treatment areas), was smaller for option G. 

Thus, option G was most desirable to meet the multiple objectives and 

criteria defined in our hypothetical scenario.

Our hypothetical planning scenario ignores several likely realities 

for managers engaged in postfire forest planning. We focused only on 

one woodpecker species, whereas multiple species could be of concern, 

including those not necessarily represented in our toolset (e.g., Lewis’s 

woodpecker). We considered economic viability of salvage logging units 

Area (acres) BBWO habitat (acres) Number of potential BBWO nests

Option Units treated treated affected treated affected treated affected

A 3, 4, 5, 6 721 11,020 649 4,685 32 (23, 42) 256 (168, 355)

B 2, 3, 4, 5 722 11,132 598 5,149 31 (22, 40) 281 (189, 385)

C 2, 3, 5, 6 734 10,129 582 4,437 28 (20, 37) 239 (158, 331)

D 1, 3, 5, 6 751 9,550 647 4,509 32 (23, 42) 243 (163, 333)

E 1, 2, 3, 5 752 9,576 596 4,736 30 (22, 40) 256 (173, 348)

F 2, 3, 4, 6 809 10,238 657 5,075 32 (23, 43) 269 (182, 367)

G 1, 2, 3, 6 839 8,188 655 4,396 32 (23, 43) 230 (157, 312)

Table B.1—Alternative options consisting of different combinations of management units selected for postfire salvage logging 

at the Canyon Creek Fire (Oregon, 2015). Unit numbers correspond with those in figure B1. Black-backed woodpecker 

(BBWO) habitat is the sum of moderate and high suitability areas, and potential nests (and 95 percent boot-strapped 

confidence limits) were estimated assuming similar densities to those observed at HSI model development locations (see 

fig. 5.1.1). When calculating potential nest numbers, densities were multiplied by 5 to reflect assumed viability of BBWO 

habitat for 5 years following wildfire and development of HSI models within this timeframe. Values for “treated” represent the 

total area actually logged. Values for “affected” represent the area within 1-km of logging, within which nesting pairs could 

experience reduced foraging opportunities due to logging.
74 U.S. Forest Service RMRS GTR-391. 2019.

in terms of the potential area treated, whereas managers probably would 

instead quantify timber volume, which may not correlate perfectly with area 

treatable. We ignored roadside hazard tree removal, which was extensive 

at the Canyon Creek Fire. With the high prioritization of human safety, 

roadside treatments may be non-negotiable, in which case assessments of 

impacts to woodpeckers should account for hazard tree removal. With all 

these considerations, postfire forest planning will be substantially more 

complicated than depicted in our example. Nevertheless, this example 

demonstrates how to apply HSI maps and related nest densities during the 

planning process.

Saab, V.A.; Russell, R.E.; Dudley, J.G. 2007. Nest densities of cavity-nesting 

birds in relation to postfire salvage logging and time since wildfire. Condor. 

109: 97–108.

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