Open-File Report 2014-1050

U.S. Department of the Interior

U.S. Geological Survey

 

 

 

Projecting Climate Effects on Birds and Reptiles of the 

Southwestern United States
Cover photo description clockwise from upper left: Virginia’s Warbler (Vermivora virginiae) 

photo credit to P. Dotson/Utah Division of Wildlife Resources; Desert Tortoise (Sonoran 

Population) (Gopherus agassizii) photo credit to Cecil Schwalbe/U.S. Geological Survey and 

University of Arizona; Pinyon Jay (Gymnorhinus cyanocephalus) photo credit to Dave 

Menke/U.S. Fish and Wildlife Service; and Arizona Black Rattlesnake (Crotalus cerberus) photo 

credit to Erika Nowak/U.S. Geological Survey and Northern Arizona University. 
Projecting Climate Effects on Birds and Reptiles of the 

Southwestern United States

By Charles van Riper III, James R. Hatten, J. Tom Giermakowski, David Mattson, Jennifer A. Holmes, 

Matthew J. Johnson, Erika M. Nowak, Kirsten Ironside, Michael Peters, Paul Heinrich, K. L. Cole, C. Truettner, 

and Cecil R. Schwalbe

Open-File Report 2014–1050

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2014

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Suggested citation:

van Riper, C., III., Hatten, J.R., Giermakowski, J.T., Mattson, D., Holmes, J.A., Johnson, M.J., Nowak, E.M.,

Ironside, K., Peters, M., Heinrich, P., Cole, K.L., Truettner, C., and Schwalbe, C.R., 2014, Projecting 

climate effects on birds and reptiles of the Southwestern United States: U.S. Geological Survey Open-File 

Report 2014‒1050, 100 p., http://dx.doi.org/10.3133/ofr20141050.

ISSN 2331-1258 (online)

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain

copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be 

secured from the copyright owner.
iii

Contents

Executive Summary.................................................................................................................................................... 1

Introduction................................................................................................................................................................. 2

Methods...................................................................................................................................................................... 4

Overall Project Design ............................................................................................................................................ 4

Selection of Bird and Reptile Species for Modeling................................................................................................. 5

Registration of Location Data and Model Sample Sizes ......................................................................................... 7

Conceptual Models ................................................................................................................................................. 7

Modeling Species Distributions ............................................................................................................................... 9

Projecting Distributions ......................................................................................................................................... 10

Data Delivery ........................................................................................................................................................ 10

Website Application............................................................................................................................................... 10

Results...................................................................................................................................................................... 11

Bird Species-Specific Projections.......................................................................................................................... 11

Reptile Species-Specific Projection....................................................................................................................... 12

Summary of Results ................................................................................................................................................. 14

Overall General Patterns of Climate Change Influences on Birds and Reptiles.................................................... 16

Insert 1: Detailed Project Justification and Methods ................................................................................................. 18

Insert 2: The Southwestern NCCWSC Advisory Team Development....................................................................... 27

Insert 3: Bird and Reptile Distribution Data............................................................................................................... 34

Insert 4: Explanatory Variables................................................................................................................................. 37

Insert 5: Modeling Plant Species Distributions.......................................................................................................... 41

Insert 6: Modeling Bird Distributions ......................................................................................................................... 45

Insert 7: Modeling Reptile Distributions .................................................................................................................... 47

Insert 8: Data Storage and Delivery.......................................................................................................................... 49

Insert 9: Website Application .................................................................................................................................... 53

Insert 10: Bird Species Count ................................................................................................................................... 56

Insert 11: Reptile Species Accounts ......................................................................................................................... 74

References Cited...................................................................................................................................................... 91
iv

Figures - Main text

Figure 1. Conceptual model of the overall project design, including project elements and the linkage and 

sequencing logic that was employed. .................................................................................................................5

Figure 2. Conceptual model of factors that might possibly affect sage thrasher range and population 

dynamics with changing environments, including explanatory variables used to model current range...............8

Figure 3. Graph showing percent change in projected breeding distribution of birds by 2099 in relation

to total percent relative influence of static variables (TRI, insolation, soil sandiness) for a species-specific 

model................................................................................................................................................................15

Figure 4. Graph showing relation between percent projected distribution gain or loss and the median 

temperature of a species current distribution....................................................................................................17

Figures - Inserts

Figure 3.1. Graph showing summary of occurrence data compiled for reptiles, expressed as

unique grid cells that match the environmental data used for modeling distribution. ........................................36

Figure 5.1. Conceptual model of the process used to create future plant species projections,

including effects of migration rates, for plants used in modeling animal species ranges. ................................. 41

Figure 5.2. Model-generated example showing projected changes in a plant species distribution 

incorporating the effects of migration for the three periods 2010–39, 2040–69, and 2070–99 .........................44

Figure 8.1. Screen capture showing the home page of the Southwest Plant Atlas (Southwest Plant Atlas, 

http://www4.nau.edu/swplantatlas/endemic.html) .............................................................................................50

Figure 8.2. Screen capture of Southwest Plant Atlas species website page for pinyon pine (Pinus edulis) 

showing the layout of the model output and explanatory variable selection......................................................51

Figure 8.3. Screen capture showing species website pages of the Southwest Animal Atlas for 

pinyon jay (Gymnorhinus cyanocephalus) and Arizona black rattlesnake (Crotalus Cerberus) including the 

species description and conceptual model diagrams ........................................................................................52

Figure 9.1. Consecutive screen captures of project website showing distribution data for

Pinus ponderosa in the western United States (darker blue-green indicates greater likelihood) ......................54

Figure 10.1. Amphispiza bilineata................................................................................................................... 56

Figure 10.2. Conceptual model of factors affecting black-throated sparrow range and population dynamics, 

including explanatory variables used to model current range ........................................................................... 56

Figure 10.3. Predicted black-throated sparrow range in 2099, showing levels of agreement among 

predictions based on 5 different statistically downscaled GCMs plus a 6th representing an ensemble of 

22 GCMs .......................................................................................................................................................... 57

Figure 10.4. Areas where ≥5 GCM projections predict there will be black-throated sparrow range in 2099 

compared to range in 2000, showing areas of range loss, range gain, and range persistence between 2000 

and 2099........................................................................................................................................................... 57

Figure 10.5. Relative influence of variables retained in the best model of current range, as measured by 

the chi-squared statistic .................................................................................................................................... 58

Figure 10.6. Measures of range change among four decadal intervals for the period 2000 through 2099...... 58

Figure 10.7. Vireo vicinior ............................................................................................................................... 59

Figure 10.8. Conceptual model of factors affecting gray vireo range and population dynamics, including 

explanatory variables used to model current range ......................................................................................... 59

Figure 10.9. Predicted gray vireo range in 2099, showing levels of agreement among predictions based on 

5 different statistically downscaled GCMs plus a 6th representing an ensemble of 22 GCMs.......................... 60
v 

Figure 10.10. Areas where ≥5 GCM projections predict there will be gray vireo range in 2099 compared to 

range in 2000, showing areas of range loss, range gain, and range persistence between 2000 and 2099...... 60

Figure 10.11. Relative influence of variables retained in the best model of current range, as measured by 

the chi-squared statistic .................................................................................................................................... 61

Figure 10.12. Measures of range change among four decadal intervals for the period 2000 through 2099.... 61

Figure 10.13. Vermivora virginiae ................................................................................................................... 62

Figure 10.14. Conceptual model of factors affecting Virginia’s warbler range and population dynamics, 

including explanatory variables used to model current range .......................................................................... 62

Figure 10.15. Predicted Virginia’s warbler range in 2099, showing levels of agreement among predictions 

based on 5 different statistically downscaled GCMs plus a 6th representing an ensemble of 22 GCMs .......... 63

Figure 10.16. Areas where ≥5 GCM projections predict there will be Virginia’s warbler range in 2099 

compared to range in 2000, showing areas of range loss, range gain, and range persistence between 2000 

and 2099........................................................................................................................................................... 63

Figure 10.17. Relative influence of variables retained in the best model of current range, as measured by

the chi-squared statistic ................................................................................................................................... 64

Figure 10.18. Measures of range change among four decadal intervals for the period 2010 through 2099.... 64

Figure 10.19. Oreoscoptes montanus............................................................................................................. 65

Figure 10.20. Conceptual model of factors affecting Virginia’s warbler range and population dynamics, 

including explanatory variables used to model current range .......................................................................... 65

Figure 10.21. Predicted sage thrasher range in 2099, showing levels of agreement among predictions based 

on 5 different statistically downscaled GCMs plus a 6th representing an ensemble of 22 GCMs..................... 66

Figure 10.22. Areas where ≥5 GCM projections predict there will be sage thrasher range in 2099 compared 

to range in 2000, showing areas of range loss, range gain, and range persistence between 2000 and 2099.. 66

Figure 10.23. Relative influence of variables retained in the best model of current range, as measured by the 

chi-squared statistic.......................................................................................................................................... 67

Figure 10.24. Measures of range change among four decadal intervals for the period 2000 through 2099.... 67

Figure 10.25. Gymnorhinus cynanocephalus.................................................................................................. 68

Figure 10.26. Conceptual model of factors affecting pinyon jay range and population dynamics, including 

explanatory variables used to model current pinyon jay range ......................................................................... 68

Figure 10.27. Predicted pinyon jay range in 2099, showing levels of agreement among predictions based on 

5 different statistically downscaled GCMs plus a 6th representing an ensemble of 22 GCMs.......................... 69 

Figure 10.28. Areas where ≥5 GCM projections predict there will be pinyon jay range in 2099 compared to 

range in 2000, showing areas of range loss, range gain, and range persistence between 2000 and 2099...... 69

Figure 10.29. Areas where ≥5 GCM projections predict there will be pinyon jay range in 2099, showing 

differences in modeled probabilities of range between 2000 and 2099 ............................................................ 69

Figure 10.30. Relative influence of variables retained in the best model of current range, as measured by the 

chi-squared statistic.......................................................................................................................................... 70

Figure 10.31. Measures of range change among four decadal intervals for the period 2000 through 2099.... 70

Figure 10.32. Sitta pygmaea........................................................................................................................... 71

Figure 10.33. Conceptual model of factors affecting pygmy nuthatch range and population dynamics, 

including explanatory variables used to model current range .......................................................................... 71

Figure 10.34. Predicted pygmy nuthatch range in 2099, showing levels of agreement among predictions 

based on 5 different statistically downscaled GCMs plus a 6th representing an ensemble of 22 GCMs .......... 72

Figure 10.35. Areas where ≥5 GCM projections predict there will be pygmy nuthatch range in 2099 

compared to range in 2000, showing areas of range loss, range gain, and range persistence between 2000 

and 2099........................................................................................................................................................... 72

Figure 10.36. Relative influence of variables retained in the best model of current range, as measured by 

the chi-squared statistic .................................................................................................................................... 73 
vi

Figure 10.37. Measures of range change among four decadal intervals for the period 2000 through 2099... 73

Figure 10.38. Sphyrapicus thyroideus............................................................................................................. 74

Figure 10.39. Conceptual model of factors affecting Williamson’s sapsucker range and population 

dynamics, including explanatory variables used to model current range .......................................................... 74

Figure 10.40. Predicted Williamson’s sapsucker range in 2099, showing levels of agreement among 

predictions based on 5 different statistically downscaled GCMs plus a 6th representing an ensemble of 

22 GCMs .......................................................................................................................................................... 75

Figure 10.41. Areas where ≥5 GCM projections predict there will be Williamson’s sapsucker range in 2099 

compared to range in 2000, showing areas of range loss, range gain, and range persistence between 2000 

and 2099...........................................................................................................................................................75 

Figure 10.42. Relative influence of variables retained in the best model of current range, as measured by 

the chi-squared statistic .................................................................................................................................... 76

Figure 10.43. Measures of range change among four decadal intervals for the period 2000 through 2099..... 76

Figure 11.1. Aspidoscelis velox, Arizona......................................................................................................... 77

Figure 11.2. Conceptual model of factors affecting suitable habitat for plateau striped whiptail and 

population dynamics, including explanatory variables used to model current suitable habitat for plateau 

striped whiptail.................................................................................................................................................. 77

Figure 11.3a. Illustration of Western United States showing projected range in 2099, showing levels of 

agreement among predictions based on 5 different statistically downscaled global circulation models (GCM) 

plus a 6th representing an ensemble of 22 GCMs............................................................................................ 78

Figure 11.3b. Illustration of Western United States showing modeled areas (including vegetation variables) 

where projections of greater than or equal to five global circulation models project there will be suitable habitat 

in 2099, showing areas of habitat loss, habitat gain, and habitat persistence between 2010 and 2099 ........... 78

Figure 11.3c. Illustration of Western United States showing modeled areas (excluding vegetation 

variables) where projections of greater than or equal to five global circulation models project there will be 

suitable habitat in 2099, showing areas of habitat loss, habitat gain, and habitat persistence between 2010 

and 2099........................................................................................................................................................... 78

Figure 11.4. Graph showing percent contribution of variables retained in the best model of current range.... 79

Figure 11.5. Measures of projected change in range at end of three different decades as a percent of 

current modeled range (year 2010) .................................................................................................................. 79

Figure 11.6. Crotalus Cerberus....................................................................................................................... 80

Figure 11.7. Conceptual model of factors affecting suitable habitat for Arizona black rattlesnake and 

population dynamics, including explanatory variables used to model current suitable habitat for Arizona 

black rattlesnake............................................................................................................................................... 80

Figure 11.8a. Illustration of Western United States showing predicted range in 2099, showing levels of 

agreement among predictions based on 5 different statistically downscaled global circulation models 

(GCM) plus a 6th representing an ensemble of 22 GCMs ................................................................................ 81

Figure 11.8b. Illustration of Western United States showing modeled areas (including vegetation 

variables) where projections of greater than or equal to five global circulation models predict there will be 

suitable habitat in 2099, showing areas of habitat loss, habitat gain, and habitat persistence between 2010 

and 2099........................................................................................................................................................... 81

Figure 11.8c. Illustration of Western United States showing modeled areas (excluding vegetation 

variables) where projections of greater than or equal to five global circulation models predict there will be 

suitable habitat in 2099, showing areas of habitat loss, habitat gain, and habitat persistence between 2010 

and 2099........................................................................................................................................................... 81

Figure 11.9. Graph showing percent contribution of variables retained in the best model of current range.... 82

Figure 11.10. Measures of projected change in range at end of three different decades as a percent of 

current modeled range (year 2010) .................................................................................................................. 82
vii

Figure 11.11. Gopherus agassizii ................................................................................................................... 83

Figure 11.12. Conceptual model of factors affecting suitable habitat for Sonoran desert (Morafka’s) tortoise, 

and population dynamics, including explanatory variables used to model current suitable habitat for Sonoran 

desert tortoise................................................................................................................................................... 83

Figure 11.13a. Illustration of Western United States showing predicted range in 2099, showing levels of 

agreement among predictions based on 5 different statistically downscaled global circulation models (GCM) 

plus a 6th representing an ensemble of 22 GCMs............................................................................................ 84

Figure 11.13b. Illustration of Western United States showing modeled areas (including vegetation 

variables) where projections of greater than or equal to five global circulation models predict there will be 

suitable habitat in 2099, showing areas of habitat loss, habitat gain, and habitat persistence between 2010 

and 2099........................................................................................................................................................... 84

Figure 11.13c . Illustration of Western United States showing modeled areas (excluding vegetation 

variables) where projections of greater than or equal to five global circulation models predict there will be 

suitable habitat in 2099, showing areas of habitat loss, habitat gain, and habitat persistence between 2010 

and 2099........................................................................................................................................................... 84

Figure 11.14. Graph showing percent contribution of variables retained in the best model of current range .. 85

Figure 11.15. Measures of projected change in range at end of three different decades as a percent of 

current modeled range (year 2010) ................................................................................................................. 85

Figure 11.16. Holbrookia maculate ................................................................................................................. 86

Figure 11.17. Conceptual model of factors affecting suitable habitat for common lesser earless lizard and

population dynamics, including explanatory variables used to model current suitable habitat for lesser 

earless lizard..................................................................................................................................................... 86

Figure 11.18a. Illustration of Western United States showing predicted range in 2099, showing levels of 

agreement among predictions based on 5 different statistically downscaled global circulation models (GCM) 

plus a 6th representing an ensemble of 22 GCMs............................................................................................ 87

Figure 11.18b. Illustration of Western United States showing modeled areas (including vegetation 

variables) where projections of greater than or equal to five global circulation models predict there will be 

suitable habitat in 2099, showing areas of habitat loss, habitat gain, and habitat persistence between 2010 

and 2099........................................................................................................................................................... 87

Figure 11.18c. Illustration of Western United States showing modeled areas (excluding vegetation 

variables) where projections of greater than or equal to five global circulation models predict there will be 

suitable habitat in 2099, showing areas of habitat loss, habitat gain, and habitat persistence between 2010 

and 2099........................................................................................................................................................... 87

Figure 11.19. Graph showing percent contribution of variables retained in the best model of current range .. 88

Figure 11.20. Measures of projected change in range at end of three different decades as a percent of 

current modeled range (year 2010) .................................................................................................................. 88

Figure 11.21. Sauromalus ater........................................................................................................................ 89

Figure 11.22. Conceptual model of factors affecting suitable habitat for common chuckwalla and population 

dynamics, including explanatory variables used to model current suitable habitat for common chuckwalla.... 89

Figure 11.23a. Illustration of Western United States showing predicted range in 2099, showing levels of 

agreement among predictions based on 5 different statistically downscaled global circulation models (GCM) 

plus a 6th representing an ensemble of 22 GCMs............................................................................................ 90

Figure 11.23b. Illustration of Western United States showing modeled areas (including vegetation 

variables) where projections of greater than or equal to five global circulation models predict there will be 

suitable habitat in 2099, showing areas of habitat loss, habitat gain, and habitat persistence between 2010 

and 2099........................................................................................................................................................... 90
viii

Figure 11.23c. Illustration of Western United States showing modeled areas (excluding vegetation 

variables) where projections of greater than or equal to five global circulation models predict there will be 

suitable habitat in 2099, showing areas of habitat loss, habitat gain, and habitat persistence between 2010 

and 2099........................................................................................................................................................... 90

Figure 11.24. Graph showing percent contribution of variables retained in the best model of current range .. 91

Figure 11.25. Measures of projected change in range at end of three different decades as a percent of 

current modeled range (year 2010) .................................................................................................................. 91

Tables - Main text

Table 1. Ranking criteria used to prioritize southwestern bird and reptile species for analysis, modeling, 

and projecting .....................................................................................................................................................6

Table 2. Bird and reptile species selected for analysis, modeling, and projecting .............................................7

Table 3. Projected change in area of modeled distribution by 2099 as percent of baseline (1980‒2009) 

suitable areas for birds, at an 83 percent GCM-distribution model agreement threshold, for models with 

and without the effects of plant distributions .....................................................................................................14

Table 4. Projected change in area of modeled distribution by 2099, as percent of baseline (1940-2009) 

suitable areas for reptiles, at an 83 percent GCM-distribution model agreement threshold, for models

with and without the effects of plant distributions..............................................................................................16

Tables - Inserts

Table 2.1. The selection process and final membership of the southwestern National Climate 

Change and Wildlife Science Center Advisory Team. ......................................................................................28

Table 2.2. Guidelines of the southwestern National Climate Change and Wildlife Science Center 

Advisory Team..................................................................................................................................................29

Table 2.3. April 1, 2010, AT meeting at the BLM National Training Center, Phoenix, Arizona. .......................31

Table 3.1. Breeding season of each bird species selected for National Climate Change and Wildlife 

Science Center projecting of climate impacts on southwest wildlife. ................................................................34

Table 5.1. Plants species for which current and future potential suitable climates were modeled...................43
ix

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

meter (m) 1.094 yard (yd) 

kilometer (km) 0.6214 mile (mi)

kilometer (km) 0.5400 mile, nautical (nmi) 

Area

square kilometer (km2) 247.1 acre

square kilometer (km2) 0.3861 square mile (mi2)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

°C=(°F-32)/1.8
x 

Author Information:

van Riper, Charles, III

ST Research Ecologist

U.S. Geological Survey

Southwest Biological Science Center 

Sonoran Desert Research Station

School of Natural Resources and the Environment

University of Arizona

1110 E. South Campus Dr., Room 123

Tucson, AZ 85721-0033

Hatten, J.R.

Research Biogeographer

U.S. Geological Survey

Western Fisheries Research Center

Columbia River Research Laboratory

5501-A Cook-Underwood Rd.

Cook, WA 98605-9717

Giermakowski, J.T.

Sr. Collection Manager

Museum of Southwestern Biology

University of New Mexico

MSC03 2020, Dept. Biology

Albuquerque, NM 87131-0001

 

Mattson, D.

Research Wildlife Biologist 

U.S. Geological Survey

Southwest Biological Science Center

Colorado Plateau Research Station

Northern Arizona University

P.O. Box 5614,

Flagstaff, AZ 86011

 

Holmes, J.A.

Senior Wildlife Biologist

U.S. Geological Survey

Southwest Biological Science Center

Colorado Plateau Research Station

Northern Arizona University

PO Box 5614

Flagstaff, AZ 86001

Johnson, M.J.

CPRS Director/Ecologist

U.S. Geological Survey

Southwest Biological Science Center

Colorado Plateau Research Station

Northern Arizona University

P.O. Box 5614

Flagstaff, AZ 86011

Nowak, E.M.

Herpetologist

U.S. Geological Survey

Southwest Biological Science Center

Colorado Plateau Research Station

Northern Arizona University

P.O. Box 5614,

Flagstaff, AZ 86011

Ironside, K.

Merriam-Powell Center for Environmental Research

School of Earth Sciences and Environmental 

Sustainability

Northern Arizona University

800 S. Beaver St., Room #330

Flagstaff, AZ 86011

Peters, M.

Research Assistant Affiliate

Merriam-Powell Center for Environmental Research

School of Earth Sciences and Environmental 

Sustainability

Northern Arizona University

800 S. Beaver St.,

Flagstaff, AZ 86011

Heinrich, P. 

Network Administrator

Geospatial Research and Information Laboratory

Northern Arizona University

Flagstaff, AZ 86011

Cole, K.L.

U.S. Geological Survey

Southwest Biological Science Center

Colorado Plateau Research Station

Northern Arizona University

P.O. Box 5614,

Flagstaff, AZ 86011

Truettner, C.M.

Research Ecologist

Merriam-Powell Center for Environmental Research

School of Earth Sciences and Environmental 

Sustainability

Northern Arizona University 

800 S. Beaver St., Room #330

Flagstaff, AZ 86011

Schwalbe, C.R.

Emeritus, U.S. Geological Survey

Southwest Biological Science Center 

Sonoran Desert Research Station

School of Natural Resources and the Environment

University of Arizona

1110 E. South Campus Dr., Room 123

Tucson, AZ 85721-0033
1 

Projecting Climate Effects on Birds and Reptiles of the 

Southwestern United States

By Charles van Riper III,James R. Hatten, J. Tom Giermakowski, David Mattson, Jennifer A. Holmes, 

Matthew J. Johnson, Erika M. Nowak, Kirsten Ironside, Michael Peters, Paul Heinrich, K.L. Cole, C. Truettner, 

and Cecil R. Schwalbe

Executive Summary

• We modeled the current and future breeding ranges of seven bird and five reptile species in the 

Southwestern United States with sets of landscape, biotic (plant), and climatic global circulation 

model (GCM) variables. 

• For modeling purposes, we used PRISM data to characterize the climate of the Western United 

States between 1980 and 2009 (baseline for birds) and between 1940 and 2009 (baseline for 

reptiles). In contrast, we used a pre-selected set of GCMs that are known to be good predictors 

of southwestern climate (five individual and one ensemble GCM), for the A1B emission 

scenario, to characterize future climatic conditions in three time periods (2010–39; 2040–69; 

and, 2070–99). 

• Our modeling approach relied on conceptual models for each target species to inform selection 

of candidate explanatory variables and to interpret the ecological meaning of developed 

probabilistic distribution models. We employed logistic regression and maximum entropy 

modeling techniques to create a set of probabilistic models for each target species.

• We considered climatic, landscape, and plant variables when developing and testing our 

probabilistic models. Climatic variables included the maximum and minimum mean monthly 

and seasonal temperature and precipitation for three time periods. Landscape features included 

terrain ruggedness and insolation. We also considered plant species distributions as candidate 

explanatory variables where prior ecological knowledge implicated a strong association 

between a plant and animal species.

• Projected changes in range varied widely among species, from major losses to major gains.

• Breeding bird ranges exhibited greater expansions and contractions than did reptile species. 

• We project range losses for Williamson’s sapsucker and pygmy nuthatch of a magnitude that 

could move these two species close to extinction within the next century. Although both species 

currently have a relatively limited distribution, they can be locally common, and neither are 

presently considered candidates for prospective endangerment. 

• We project range losses of over 40 percent, from its current extent of occurrence, for the plateau 

striped whiptail, Arizona black rattlesnake, and common lesser earless lizard. Currently, these 

reptile species are thought to be common or at least locally abundant throughout their ranges. 

• The total contribution of plants in each distribution model was very small, but models that 

contained at least one plant always outperformed models with only physical variables (climatic 

or landscape). The magnitude of change in projected range increased further into the future, 

especially when a plant was in the model.
2 

• Among bird species, those that had the strongest association with a landscape feature during the 

breeding season, such as terrain ruggedness and insolation, exhibited the smallest contractions 

in projected breeding range in the future. In contrast, bird species that had weak associations 

with landscape features, but strong climatic associations, suffered the greatest breeding range 

contractions. Thus, landscape effects appeared to buffer some of the negative effects of climate 

change for some species. 

• Among bird species, magnitude of change in projected breeding range was positively related to 

the annual average temperature of their baseline distribution, thus species with the warmest 

breeding ranges exhibited the greatest changes in future breeding ranges. This pattern was not 

evident for reptiles, but might exist if additional species were included in the model.

• Our results provide managers with a series of projected range maps that will enable scientists, 

concerned citizens, and wildlife managers to identify what the potential effects of climate 

change will be on bird and reptile distributions in the Western United States. We hope that our 

results can be used in proactive ways to mitigate some of the potential effects of climate change 

on selected species. 

Introduction

Global circulation models (GCM) of future climates predict extreme change for the 

Southwestern United States (Solomon and others, 2007; Diffenbaugh and others, 2008). 

Temperatures are projected to increase 3.5–4°C within the next 60–90 years while precipitation is 

projected to decline by 5–20 percent (Seager and others, 2007). The coalescing effect of these 

changes in temperature and precipitation in this already arid region will profoundly affect the 

distributions and viability of plant and animal populations. Region-wide ecosystem processes and 

system dynamics will be especially sensitive to relatively small shifts in climate in the arid and 

semi-arid environments (Millennium Ecosystem Assessment, 2005). 

Managers of public lands and associated wildlife resources in the Southwestern United 

States need information about climate change consequences so they can better adapt management 

strategies to help conserve and sustain habitats of wildlife species. Prediction of coarse-scale 

changes, such as shifts in the distribution of dominant plant species, are needed to identify those 

habitats most vulnerable to climate change and to aid in prioritizing conservation efforts (U.S. Fish 

and Wildlife Service, 2010; Rowland and others, 2011). Dominant vegetation structure is a major 

determinant of the distribution of many animal species. Probabilistic predictions of future wildlife 

species distributions, directly linked to climate change, are needed to provide managers with 

empirical assessments of the future prospects for all species and especially those of special interest. 

We report here on a project that utilized downscaled climate projections for the A1B 

emission scenario (a balanced emphasis on all energy sources in the next century) to project 

potential changes in birds and reptile ranges throughout the Southwestern United States. We used 

species distribution models (also referred to as ecological niche, bioclimatic, or habitat distribution 

models) to estimate losses and gains in projected ranges of select upland species in response to 

projected climate change. Following the admonitions and advice of previous researchers (for 

example, Luoto and Heikkinen, 2008; Thuiller and others, 2008; Virkkala and others, 2010; 

McMahon and others, 2011), we included the cascading effects of static landscape features and 

changing plant species distributions to assess the potential dynamics of habitat change, including 

thresholds and tipping points. 
3 

Our study area focused on the Sonoran Desert and Colorado Plateau ecosystems within 

Arizona, western New Mexico, Utah, southwestern Colorado, and southeastern California, but also 

included the rest of the Western United States. Focal wildlife species included resident and 

migratory birds and reptiles, which compose the majority of vertebrate biodiversity in this region 

(Boykin and others, 2007). Focal species were selected based on several sources of information, 

including input from a Resource Managers Advisory Group as well as wildlife and conservation 

partners. To assure relevance and responsiveness to real world management needs, we established 

an advisory team to provide guidance and feedback on all aspects of the project including planning, 

species prioritization, data acquisition and management, and product delivery tools. 

This interdisciplinary project was undertaken by a diverse team of U.S. Geological Survey 

(USGS) and university-based scientists. Our research built on previous efforts of USGS researchers 

and collaborators supported by the U.S. Department of Energy National Institute for Climate 

Change Research (NICCR) and the USGS/National Park Service Natural Resource Protection 

Program (NRPP). These earlier efforts included development of downscaled databases of the most 

recent GCM results from the Fourth Assessment Report (AR4) dataset (Solomon and others, 2007), 

and evaluation of all 22 GCMs for their ability to hindcast 20th century precipitation seasonality in 

the arid southwest. The best of these were statistically downscaled to a 4-km grid for the entire 

Western United States (Garfin and others, 2010). This foundational work was essential to the 

research reported herein because, although all GCMs project increases in temperatures, annually 

and in all seasons, they differ greatly in their estimations of future precipitation seasonality. Thus, 

we concentrated on a range of GCMs that were successful at depicting historical shifts among 

seasonal precipitation peaks that dictate the current biogeographic limits for many plant and animal 

species of the Southwestern United States. Based on these results we selected five of the best-fitting 

models for projecting plant and animal species distributions. These five models encompassed the 

spectrum of plausible future climates or produced results that closely matched recent 20th century 

climates. Additionally, we averaged the results of the 22 models to create an ensemble future 

scenario for projections. Recognizing that Mauer and others (2007) and Mearns and others (2009) 

provide easy-to-access statistically downscaled data sets, we chose to follow Garfin and others 

(2010); their paper provides details on how we downscaled and evaluated GCMs for this project. 

We benefitted not only from our expertise in applying downscaled models of future climate 

change to project ongoing and future changes in plant and habitat distributions (Cole and others, 

2008), but also from previous work with inventory and monitoring (I&M) efforts in National Parks 

of the Southern Colorado Plateau and Sonoran Desert Networks; strong relationships with resource 

and wildlife management agencies and interest groups; and multi-disciplinary expertise in reptiles 

and birds. Our experience confirmed that successful modeling efforts are dependent upon expert 

input regarding the ecology of each focal species. 

Our multidisciplinary project focused on meeting four objectives: 

1. Identify and prioritize plant and animal species of interest to land managers in relation to 

prospective climate change; 

2. Develop spatially explicit models to project and explain changes in distributions of selected 

bird and reptile species;

3. Incorporate the effects of current and projected future ecological dependencies of animal 

distributions on plant species distributions; and 

4. Develop web-based tools for land managers that include: access to and application of 

downscaled climate models; identification of potential changes in ranges of target species; 

and prioritized species and geographic locations most vulnerable to global climate change.
4 

In an effort to keep this initial National Climate Change and Wildlife Science Center 

(NCCWSC) project summary document to a manageable size, we have opted to provide detailed 

conceptual and methodological information into 11 Inserts attached to the main document. In these 

documents, Insert 1 details project conceptual design and justifications associated with our 

southwestern NCCWSC project. It explains why the focus of our effort was on documenting 

changes in species distribution patterns, why we chose to do a few species in detail rather than 

examining many species at a coarse level, and how and why we chose the focal bird and reptile 

species. Insert 1 also details how we examined distributional data, the probabilities and thresholds 

of each species occurrence, what limiters and drivers were responsible for distributional patterns, 

and what information we decided to include and what to exclude from our modeling process. Insert 

1 ends with a discussion on hypotheses associated with our conceptual models and a large section 

outlining uncertainties recognized within the modeling process. In Insert 2, we outline specifics 

about the advisory team of managers and interested stakeholders that assisted us with this global 

climate change project. Inserts 3 and 4 detail explanatory variables and how we utilized bird and 

reptile distribution data, whereas Insert 5 does the same for plants. Insert 6 provides details on how 

we modeled and tested bird distributional changes associated with changing climate, and Insert 7 

does the same for reptiles. Insert 8 explains in detail how we managed and stored data from this 

project, and Insert 9 details our website development and the tools that were developed for land 

manager use. The final two inserts (10 and 11) provide specifics on the individual bird and reptile 

species accounts, giving a general background, the conceptual models that we utilized, the maps of 

expected distributional changes, and a summary of the importance of each variable to the final 

model. 

Methods

Overall Project Design

Our project consisted of nine distinct elements or activities (rounded boxes in figure 1). We 

first selected bird and reptile species to model based on a number of criteria that attended to 

representativeness and our ability to model a species distribution. Second, we thoroughly reviewed 

the literature to synopsize our current understanding of each species ecology, as the basis for 

developing conceptual models of ecological relations. These models helped us to identify candidate 

explanatory variables. Third, we then conducted data development by assembling a Geographic 

Information System (GIS) that contained layers of data corresponding to our candidate explanatory 

and response variables for the entire extent of the Western United States. Parallel to our data-centric 

activities, we also developed, fourth, our ecological scenarios and, fifth, our climate scenarios. The 

ecological scenarios consisted of models with and without inclusions of plant species distributions 

as explanatory variables, considering the effects of static features as a distinct model component. 

Our climate scenarios consisted of carefully selected global circulation models (GCMs) that we 

used to project climate at three set future time intervals. Sixth, at the heart of our project, we 

developed statistical models to explain current species distributions; and, seventh, evaluated the 

performance of each model. Eighth, we projected future distributions of each of our selected 

species. Finally, we evaluated changes in projected animal species ranges as well as overall 

uncertainty in our projections. We provide detailed explanations of each of these elements in 

Insert 1. 
5 

Figure 1. Conceptual model of our overall project design, including project elements and the linkage and 

sequencing logic that was employed. 

Selection of Bird and Reptile Species for Modeling

We developed a list of candidate species for modeling based on input from our NCCWSC 

Advisory Team (for details on the Advisory Team and species selection, see Insert 2). The species 

list was based on recommendations of the managers, who were selected at a formational meeting 

during October 2009. We also incorporated results of a survey that was sent to southwestern land 

managers and conservation community members. We then reviewed these lists and removed some 

species and added others according to the following: 

• The bulk of a species range is within the interior western U.S. 

• The species are present within a substantial portion of our study area (for example, this excludes 

rare species with limited ranges). 

• There is sufficient information available about the ecology of candidate species.

• The species has not been extirpated over a substantial portion of its range, or we have good 

historic range maps on the historic distribution. 

• There is adequate present distribution information; the extent of distribution is represented well 

by available samples.

• There is access to sufficient candidate explanatory variables in spatial GIS form to warrant 

modeling. 

• Different functional groups (for example, long- and short-lived species, predators versus 

herbivores) are represented for both birds and reptiles. 

• Species that are currently common, but with a likelihood of being sensitive to climate change 

are represented.
6 

Table 1. Ranking criteria used to prioritize southwestern bird and reptile species for analysis, modeling, and 

projecting. 

Ranking criteria Score= 3 Score= 2 Score= 1

Data Availability1 easily available difficult to obtain very difficult/nonexistent

Ancillary Data Availability2 easily available difficult to obtain very difficult / complex

Sample Size 

(# presence records)3

high moderate low

Data Type presence/absence presence only unknown

Species Detectability high moderate low

Species Current Range entirely within 

western US

western US and 

Mexico and/or Canada

west, mid-west and east 

US, or mostly Mexico

Data Distribution4 across range moderate limited/unknown 

Association with plant species high/strong moderate low

Association with plant community high/strong moderate low

Species of Conservation Concern5 high moderate low/commonly found

Perceived vulnerability to Climate 

Warming6

high medium low

1

Scoring for Data Availability of Birds: 3=sufficient data available from–Avian Knowledge Network (AKN)/EBird; 

2=significant proportion of records in sources other than AKN/EBird (for example, endangered species), 1= insufficient 

data in AKN/EBird and no known additional data sources. For reptiles, we relied on our knowledge of databases. 

2 Ancillary Data includes all data other than reptile or bird presence/absence data; that is, data for other variables (plant 

species distribution data, soil texture data); to be included in the modeling, as identified in the conceptual models. 

3

Scoring for Sample Size (number of presence records): 1=less than 1,000 presence records, 2=1,000–5,000 presence 

records, 3=greater than 5,000 records from EBird. For reptiles, we estimated the expected relative sample sizes.

4

Data Distribution refers to the availability of presence data in relation to the species range and whether there are data 

from across the entire range, or from only a limited portion of the range.

5

The following scoring applied to bird Species of Conservation Concern: If a species was listed as of continental 

concern, a regional stewardship species, of regional concern, or as a continental stewardship species for any western 

Bird Conservation Region by Partners in Flight (PIF) score = 1. If the species is listed on the 2007 Audubon WatchList, 

the species WatchList score = 1. If a species is on the U.S. Fish and Wildlife Service (USFWS) Birds of Conservation 

Concern List (2008), its USFWS score = 1. If a species is a Candidate for listing, listed as Threatened, or listed as 

Endangered under the Federal Endangered Species Act (ESA) list, its ESA score = 1. Then we added 

PIF+WatchList+USFW+ESA and values of 3 or 4 were scored as Species of Conservation Concern = 3, 2 = 2, and 1=1.

A similar process was used for reptiles, based on a compilation developed by Southwest Partners in Amphibian and 

Reptile Conservation. While the list compilation is not comprehensive, it serves as an objective, relative measure 

indicating species of concern for regional federal and state agencies. We counted the number of times a species was 

listed as a conservation priority by U.S. Forest Service and Bureau of Land Management regions, the Navajo Nation, 

and the USFWS (for example, ESA). A high score of 3 indicated 14–7 occurrences of the species on federal lists; a 

moderate score of 2 indicated 6-1 list occurrences, and a score of 1indicated the species was not on any list.

6

Perceived Vulnerability to Climate Warming for birds was taken from the State of the Birds Report 2010 which lists 

Climate Change Vulnerability Scores as high, medium, or low. No similar document exists for reptiles, so Nowak, 

Schwalbe, and Giermakowski used their expert opinion. 
7 

 We then ranked the species by, first, assigning a score of 1 to 3 to each criterion for each 

species (table 1). Then, individual criteria scores were summed to give an overall score for each 

species. Then the species overall scores were used to produce ranked lists for birds and reptiles, 

with the species with the highest score having the highest rank. We used the ranked lists to select 

six upland reptile and seven bird species (table 2) that we felt represented a broad range of natural 

and life histories.

Table 2. Bird and reptile species selected for analysis, modeling, and projecting.

Bird species Reptile species

Black-throated sparrow Striped plateau whiptail

Gray vireo Arizona black rattlesnake

Pinyon jay Sonoran desert tortoise

Pygmy nuthatch Lesser earless lizard

Sage thrasher Common chuckwalla

Virginia’s warbler

Williamson’s sapsucker

Because of constraints and the complexity of robustly modeling the potential changes in 

hydrology, no riparian species (riparian birds, amphibians, and gartersnakes) were included in this 

project. For additional details about bird and reptile distributions and datasets, refer to Insert 3.

Registration of Location Data and Sample Sizes

We modified the number of bird and reptile location records to one per grid cell 

(approximately 16 km2) to minimize sampling bias, which is especially important for correlative 

modeling (Phillips and others, 2009). We registered all georeferenced point range data for birds and 

reptiles by cells that we used for our spatial modeling. Once registered, each grid cell containing 

presence locations constituted one record of occurrence for an individual species. This registration 

of locational data to a uniform spatial grid allowed us to directly and systematically relate locations 

to explanatory environmental variables registered within the same grid, and guarded against finescale spatial and temporal autocorrelation, while accommodating detailed spatial uncertainty in 

recorded locations. This process substantially reduced the number of presence observations used for 

spatial modeling. In the case of reptiles, the original 23,954 georeferenced records were reduced to 

5,087 grid cells representing the presence of five different taxa (see Insert 3, fig. A-1). The number 

of cells where presence was documented varied from about 200 to about 3,000 for individual 

species, which is sufficient for reliably modeling species distributions using logistic regression and 

Maxent (Guisan and Zimmermann, 2000; Kadmon and others, 2003; Wicz and others, 2008). 

Conceptual Models

We developed conceptual models for each species; figure 2 is an example for the sage 

thrasher.
8 

Figure 2. Conceptual model of factors that affect sage thrasher range and population dynamics with 

changing environments, including explanatory variables used to model current range (in green and circled 

red). The relative strength of different effects is denoted by the width of connecting arrows. 

The sage thrasher conceptual model (fig. 2) is an example of an initial conceptual model 

that comprised our a priori hypotheses regarding determinants of range, and a concluding 

conceptual model that shows the relative importance of each of those effects. To develop our initial 

conceptual models, we first conducted a thorough literature review and/or used our own data to 

identify suites of factors likely to determine survival and reproduction of a given species. Our 

conceptual models illustrate causally how a species population demography (that is, survival and 

reproduction) is linked through proximal physiological and metabolic drivers to spatial and 

temporal variables that are available to use in statistical modeling. As part of this exercise, we 

identified the nature of prospective relations (fig. 2, positive or negative symbols), as well as the 

likelihood of any interactions. We differentiated four categories of factors within each conceptual 

model: (1) proximal physiological and metabolic drivers affecting a species survival and 

reproduction, (2) conceptually intermediate ecological phenomena affecting (1), (3) distal 

environmental measures which were candidate explanatory variables in statistical models, and (4) 

key ecological phenomena for which we had no measured surrogate that could be modeled. Taken 

together, these factors provided a well-informed logic for selecting candidate explanatory variables, 

a sound basis for interpreting any effects that emerged from our statistical modeling, and a means 

of identifying key factors that were not accounted for in any quantitative analysis. 

Our concluding conceptual model incorporated the results of our statistical analyses (fig. 2, 

boxes circled in red and with larger arrows). Effects that were either not supported or weakly 

supported by our quantitative results were dropped from the conceptual models (fig. 2, green boxes 

not circled in red). Effects that were moderately or strongly supported were given amplified 

importance through the graphic form of enlarging the arrows denoting effects (fig. 2). 
9 

For a detailed summary of the explanatory variables in our animal modeling, see Insert 4. 

For more details on how we went about modeling and incorporating the distributions of plant 

species, see Insert 5.

Modeling Species Distributions

We used an eight-step process to model current and future ranges of selected species: (1) we 

developed a conceptual model of each species life history; (2) we assembled a spatial database that 

characterized baseline (1980‒2009 for birds; 1940–2009 for reptiles) and future (2010‒99) climates 

throughout the western U.S., plus topographic and vegetation features; (3) we used logistic 

regression for birds and the Maxent algorithm for reptiles to identify significant associations 

between species locations and biophysical variables, and to construct cell-based probabilistic 

models of species occurrence; (4) we evaluated each model in terms of error rates and projective 

efficiency, given the supporting data; (5) we created distribution maps for each focal species in the 

Western United States; (6) for birds, we challenged each map with an independent breedinglocation dataset; (7) we modeled distribution for each species using future projections of climate 

and plant distributions for three time intervals (2010–39, 2040–69, 2070–99) with six GCMs (that 

is, 18 simulations); and, (8) we created maps of projected future distributions for each focal species, 

and quantified change between baseline and future projected range. For each GCM simulation, we 

created species distribution models with and without plant distributions, which allowed us to clearly 

distinguish the modifying effects of plant distributions in our models. 

We modeled current and future distributions of reptiles using the Maxent algorithm 

(Maxent, version 3.3.3k, available at http://www.cs.princeton.edu/~schapire/maxent/; Phillips and 

others, 2006; Phillips and Dudík, 2008). Maxent uses a maximum entropy probability distribution 

to contrast occurrence data with background environmental data and estimates a probability 

distribution that has the maximum entropy (that is, most dispersed, or uniform) given certain 

constraints. The constraints are that the expected values of each feature (for example, a climate 

variable) must equal the empirical average (the average value at known occurrence points; Phillips 

and others, 2006). Maxent is one of the best algorithms to calculate the likely suitability of 

landscape for a species when presence or absence data are not available (Elith and others, 2006; 

Elith and Leathwick, 2009; Elith and others, 2011). Unlike birds, absence from specific areas for 

many species of reptiles is difficult to confirm because of their cryptic habits and low detectability. 

In addition, for many reptile species, available data only document their presence. Models built 

with presence-only data do not incorporate information on the frequency of occurrence of a species 

in a region, and therefore cannot accurately predict probability of presence (MacKenzie and others, 

2002; Guisan and Thuiller, 2005). However, such models can be used to estimate a relative index of 

the suitability of landscape for a species (Elith and others, 2006). For additional details about bird 

model development, fit, and verification, see Insert 6; for more details on reptiles, see Insert 7. 
10

Projecting Distributions

Once we finalized models depicting current distribution, we projected two distribution 

models (one with and one without vegetation) obtained from baseline climatic conditions according 

to 18 different future climatic scenarios (three different time periods and six different GCMs). In 

the case of Maxent, we applied the option to “fade by clamping” because we applied the models to 

future climates with unknown variable ranges. This option reduced projections at each point by the 

difference between clamped and non-clamped output (Phillips and Dudík, 2008). We provided 

results as the logistic model output. This allowed us to represent landscape suitability for each cell 

as a scaled index (Phillips and Dudik, 2008) and, from that, calculate changes in projected ranges 

based on predefined output thresholds above which projections were considered to be suitable for 

the given species (Pearson and others, 2007). Although there are many ways to calculate these 

thresholds (Liu and others, 2005), the choice depends on the purpose of the model (Lobo and 

others, 2008). We calculated thresholds by equalizing sensitivity and specificity for training 

samples, assuming that such an approach equalized the risks of over- and under-specifying 

(Freeman and Moisen, 2008). We then calculated the percentage of range “gained” or “lost” based 

on where five out of six GCM-habitat models concurred on the delineation of potential range (83 

percent agreement threshold). We calculated range change considering scenarios with and without 

the effects of unlimited dispersal, either by the focal animal species alone or by plant species in 

models as independent variables. Calculated range losses would have been less if we had adopted 

less stringent thresholds (for example, agreement among 3 or 50 percent of projections) for 

delineating future range. 

Data Delivery

Data delivery for this project can be divided into two overlapping frameworks: (1) data 

delivery and archiving for project scientists and (2) data delivery and archiving of final products for 

all stakeholders. For final archiving and data delivery to the public, we used Federal Geographic 

Data Committee (FGDC) metadata and national metadata clearinghouses for documentation and 

discovery of data and websites, and a web mapping application for final delivery. For details on 

data storage and delivery, see Insert 8. 

Website Application

This aspect of the project was an effort to provide managers and other stakeholders with a 

useful tool that will present a simple way for understanding how the ranges of a select group of 

animal and plant species will be affected by global climate change. To accomplish this, a website 

was developed that will serve as the primary interface between groups of users and the spatial data 

assembled by project scientists. The primary goal of our website design is to display projected timeseries data according to various models. For additional details about the website design and 

development, refer to Insert 9. 
11

Results

For purposes of this report we have chosen to present our modeling results for each species 

in the form of a standardized 3-page species account (Inserts 10 and 11). Here, we summarize our 

species-specific results and provide a brief synoptic summary of our projections for each species 

group—birds and reptiles. Each species account contains three basic informational themes. The first 

page contains information about the species natural history with a conceptual model that 

encapsulates our prior understanding of key ecological and environmental relations, modified to 

incorporate the results of our statistical models. The second page presents habitat maps showing 

projected distributions for the 2070‒2099 time period and areas of range loss, gain, and range 

persistence between 2000 and the 2070-2099 time period. The third and final page presents 

information in non-spatial form about model performance, model content, and summary statistics 

for distributional gains and losses in habitat by future time interval. More complete results for each 

species can be accessed at our project web portal (ftp://grail1.bio.nau.edu/) and interested persons 

can obtain the password from Paul Henrich (Paul.Henrich@nau.edu). The complete results include 

projected geospatial changes in habitat distribution for each species permuted by time interval and 

GCM, along with options for overlaying reference information such as state boundaries, land 

ownership, and major highways. 

Bird Species-Specific Projections

Climate changes for the Western and Southwestern United States will affect bird species in 

unique ways, based largely on the natural history and habitat needs of each species. Specifics of 

how these changes will differentially affect each of the bird species we included in the modeling are 

detailed in Insert 10. 

Black-throated sparrow—Explanatory variables in the best-fit model for the black-throated 

sparrow included, in decreasing rank order, terrain ruggedness, mean spring precipitation, the range 

of creosote bush (Larrea tridentata), mean winter temperatures, and minimum June temperature. 

This sparrow’s breeding range is projected to increase by 34–47 percent between 2010 and 2099 

when an 83 percent agreement threshold was applied (see Insert 10, p. 59).

Gray vireo—Explanatory variables in the best-fit model for the gray vireo (which inhabits 

some of the hottest, most arid regions of the Southwestern United States) are, in decreasing rank 

order, maximum breeding season temperature, terrain ruggedness, the range of Utah juniper 

(Juniperus osteosperma), insolation (incident solar radiation), and mean breeding season 

precipitation. The gray vireo’s breeding range is projected to increase from 58 –71 percent between 

2010 and 2099 when an 83 percent agreement threshold was applied (see Insert 10, p. 62). 

Virginia’s warbler—The Virginia’s warbler presently has a relatively restricted breeding 

range, inhabiting scrubby habitats below the pine belt in the northern part, and scrubby habitats 

within surrounding conifers in the southern part of its range (Olson and Martin, 1999). Explanatory 

variables in the best-fit model for the Virginia’s warbler included, in decreasing rank order, terrain 

ruggedness, the range of Gambel oak (Quercus gambelii), mean winter precipitation, mean summer 

temperature, mean summer precipitation, and insolation. The Virginia’s warbler’s breeding range is 

projected to decrease slightly, by 1.5–7 percent, between 2010 and 2099 when an 83 percent 

agreement threshold was applied (see Insert 10, p. 65). 
12

Sage thrasher—Considered a sage obligate, this bird species is generally dependent on 

large patches and expanses of sagebrush steppe for successful breeding. The distribution of sage 

thrasher had one of the strongest associations with the distribution of a plant species. Explanatory 

variables in the best-fit model for the sage thrasher were, in rank decreasing order, mean winter 

precipitation, maximum June temperature, mean July precipitation, the range of big sagebrush 

(Artemisia tridentata), percent sand in the soil, and terrain ruggedness. We were unable to project 

the distribution of big sagebrush as we lacked an adequate distribution model for this plant species, 

and therefore we projected the sage thrasher distributions using only the model without plant 

distributions. The thrasher breeding range is projected to decrease by 78 percent between 2010 and 

2099 when an 83 percent agreement threshold was applied (see Insert 10, p. 68). 

Pinyon jay—This jay species coevolved as a non-migratory mutualist with pinyon pines. 

Not surprisingly, the distribution of the pinyon jay has strong associations with the distributions of 

pinyon pines which provide the bird's main food resources (Marzulff and Balda, 1992). Explanatory 

variables in the best-fit model for the pinyon jay are, in decreasing rank order, mean winter 

precipitation, maximum summer temperatures, the range of pinyon pine (Pinus edulis), single-leaf 

pinyon (Pinus monophylla), and Jeffrey pine (Pinus jeffreyi), terrain ruggedness, and insolation. 

The pinyon jay’s breeding range is projected to decrease by 25–31 percent between 2010 and 2099 

when an 83 percent agreement threshold was applied (see Insert 10, p. 71). 

Pygmy nuthatch—Another pine-associated bird species in the southwest is the pygmy 

nuthatch. Explanatory variables in the best-fit model for the pygmy nuthatch included, in 

decreasing rank order, mean fall precipitation, mean winter temperatures, mean winter 

precipitation, the range of ponderosa pine (Pinus ponderosa), minimum summer temperature, and 

terrain ruggedness. The pygmy nuthatch’s breeding range is projected to decrease by 75–81 percent 

between 2010 and 2099 when an 83 percent agreement threshold was applied (see Insert 10, p. 74).

Williamson’s sapsucker—This species inhabits open coniferous and mixed coniferousdeciduous forests of western United States. Explanatory variables in the best-fit model for the 

sapsucker are, in decreasing rank order, the range of Jeffrey pine (Pinus jeffreyi), mean breeding 

season temperatures, the range of Douglas fir (Pseudotsuga menziesii), the range of ponderosa pine, 

winter precipitation, mean breeding season precipitation, and terrain ruggedness. The Williamson’s 

sapsucker breeding range is projected to decrease by 73–78 percent between 2010 and 2099 when 

an 83 percent agreement threshold was applied (see Insert 10, p. 77). 

Reptile Species-Specific Projection

Our modeling results suggest that future climate change will negatively affect the 

distributions of reptiles in the Western and Southwestern United States. The following is a synopsis 

of projections for each of the five reptiles that we included in the modeling during this project.

Plateau striped whiptail—This medium-sized (to 90 mm) lizard inhabits a variety of 

grassland, and shrub habitats of the Colorado Plateau and central New Mexico. The whiptail is 

found ranging to elevations as high as 2440 m. Because of its strong association with grasslands, 

the distribution of black grama (Bouteloua eriopoda) has the strongest effect of all explanatory 

variables in the best-fit model of distribution for this lizard. Other variables include, in decreasing 

rank order of effect, winter average temperatures, insolation, total summer (July‒September) 

precipitation, and terrain ruggedness. The range was projected to decrease by 42 percent, assuming 

no dispersal, or by 17 percent with unlimited dispersal when an 83 percent agreement threshold was 

applied (see Insert 11, p. 80). 
13

Arizona black rattlesnake—This montane rattlesnake is found exclusively in Arizona and 

western New Mexico. It typically inhabits pine-oak woodlands or chaparral habitats. At higher 

elevations, individuals rely on volcanic outcrops and talus slopes for cover and hibernation whereas 

at lower elevations they are restricted to cool, moist drainages. Explanatory variables in the best-fit 

model for the Arizona black rattlesnake, in decreasing rank order of effect, were minimum winter 

(October–April) temperature, terrain ruggedness, total summer precipitation, rock type, insolation, 

geology and distribution of Gambel oak (which was strongly correlated with distributions of other 

oaks). Regardless of dispersal assumptions, range of this snake was projected to decrease between 

32 and 46 percent when an 83 percent agreement threshold was applied (see Insert 11, p. 83).

Desert tortoise —The Sonoran population of the desert tortoise (Morafka’s Desert 

Tortoise), recently recognized as a separate species, is found throughout the Arizona Upland 

subdivision of the Sonoran desert, specifically in Sonoran uplands with rocky outcrops and alluvial 

fans. Explanatory variables for this tortoise that are most important in the best-fit models, in 

decreasing rank order, were the extent of brittlebush (Encelia farinosa), average winter 

temperatures, total summer precipitation, and winter minimum temperatures. Although brittlebush 

is not directly associated with tortoises, it is likely that its importance in the model is because of 

how the extent of the plant’s distribution relates to the extents of semidesert and desert grasslands 

as well as desert scrub habitats. Desert tortoise is the only species of reptile for which projections 

do not include a decrease in suitable habitat by 2099 but only when unlimited dispersal is assumed. 

However, assuming no dispersal, models indicate a slight decrease (-1 percent) in the extent of 

suitable areas (see Insert 11, p. 86).

Common lesser earless lizard —This species is a locally common and widespread lizard 

found across short-grass prairie and open habitats between the Southern Colorado Plateau and 

western edge of the Great Plains. Explanatory variables in the best-fit models for the common 

earless lizard were, in decreasing rank order, range of black grama, total monsoonal 

(July‒September) precipitation, winter average temperatures, summer average temperatures, winter 

precipitation, early summer (May‒June) precipitation, and soil depth. Depending on dispersal 

assumptions, the range of this lizard is projected to decrease by 22–49 percent from 2010 to 2099 

when an 83 percent agreement threshold was applied (see Insert 11, p. 89). 

Common chuckwalla—This a large lizard found across the Mojave and Sonoran deserts of 

the Southwestern United States. Best-fit models of its range included three main explanatory 

variables which were, in rank order of importance, the range of creosote bush, the range of 

brittlebush, and May-June precipitation. Regardless of dispersal assumptions, projected ranges are 

likely to decrease by between 13 and 23 percent from 2010 to 2099 when an 83 percent agreement 

threshold was applied (see Insert 11, p. 92).
14

Summary of Results

Projected changes for birds—Projected breeding distributions for each bird species varied 

widely within and among the three time periods (and six GCMs) that we examined (Insert 10). We 

determined that the magnitude of change was largely dependent upon the GCM-distribution model 

agreement threshold. At an 83 percent agreement threshold, black-throated sparrow and gray vireo 

are projected to experience major gains in breeding habitat (table 3). By contrast, pygmy nuthatch, 

sage thrasher, and Williamson’s sapsucker are projected to experience large losses in breeding 

habitat. Thus, these last three species might be expected in the future to experience large population 

declines (table 3). 

Table 3. Projected change in area of modeled distribution by 2099 as percent of baseline (1980–2009)

suitable areas for birds, at an 83 percent GCM-distribution model agreement threshold, for models with and 

without the effects of plant distributions. 

Species Models with plant distributions Models without plant distributions

Black-throated sparrow +34% +47%

Gray vireo +58% +71%

Pinyon jay -31% -25%

Pygmy nuthatch -81% -75%

Sage thrasher N/A -78%

Virginia’s warbler -7% -2%

Williamson’s sapsucker -78% -73%

In all of our bird models, inclusion of plant distribution effects led to more negative 

projections—either less gains or greater losses in breeding range. All bird models also contained a 

quadratic term that introduced an effect of coarse-grain terrain ruggedness (TRI). An additional 

three models (gray vireo, Virginia’s warbler, pinyon jay) included an effect of insolation. The 

combined effects of static features (TRI, insolation, and, in one model, soil sandiness; calculated as 

the sum of χ

2

s for all static feature effects) in each bird model was positively related to the 

magnitude of change in breeding habitat by 2099 (fig. 3). Overall, we determined that the greater 

the aggregate effect of static features, the greater the increase in projected bird breeding range, or 

the less the projected loss in numbers of individuals. 
15

Figure 3. Graph showing percent change in projected breeding distribution of birds by 2099 in relation to 

total percent relative influence of static variables (TRI, insolation, soil sandiness) for a species-specific model. 

Green diamonds denote species projected to experience major distributional gains in habitat (black-throated 

sparrow and gray vireo); red diamonds denote major distributional losses in habitat (pygmy nuthatch, sage 

thrasher, Williamson’s sapsucker); and pink diamonds denote little projected change (pinyon jay and Virginia’s 

warbler).

Projected changes for reptiles—We quantified the effect of dispersal on projected 

distributions of reptiles at two extremes: (1) we assumed no dispersal into newly suitable habitats, 

and (2) we calculated the maximum nearest neighbor between all known records of a species and 

used that as a buffer distance representing the potential of unlimited dispersal. Differences in 

modeled suitable areas with and without dispersal are given in table 4. 
16

Table 4. Projected change in area of modeled distribution by 2099, as percent of baseline (1940–2009) 

suitable areas for reptiles, at an 83 percent GCM-distribution model agreement threshold, for models with and 

without the effects of plant distributions.

Models with plant distributions Models without plant distributions

Species Unlimited dispersal No dispersal Unlimited dispersal No dispersal

Striped Plateau 

Whiptail

-17% -42% -22% -42% 

Arizona Black 

Rattlesnake

-32% -36% -44% -46%

Sonoran Desert 

Tortoise

+21% -1% +25% 0%

Lesser Earless Lizard -22% -45% -34% -49%

Common Chuckwalla -14% -14% -22% -23%

Most reptiles were projected to experience one-third range distributional losses by 2099, 

regardless of dispersal potential or inclusion of plant species distributions (Insert 11). The sole 

exception to this pattern was the Sonoran desert tortoise, for which we projected increase in suitable 

area but only when assuming unlimited dispersal. Unlike the bird models, only one reptile model 

included an effect of terrain ruggedness; and only three of the five models included any effect of a 

static variable. Measures of insolation, soil composition, and geologic features each were important 

in two models. Inclusion of plant species distributions had an inconsistent and generally lesser 

effect on future projections compared to the more uniform effects of these variables in bird 

distribution models (see more specifics in Insert 11). 

Overall General Patterns of Climate Change Influences on Birds and Reptiles

Considering all of the species that we modeled, there appeared to be a positive relation 

between projected distributional gains or losses and the average annual temperature of the species 

current distribution (fig. 4). 
17

Figure 4. Graph showing relation between percent projected distribution gain or loss and the median 

temperature of a species current distribution. Green diamonds denote bird species and orange diamonds 

denote reptile species.

The warmer the current range, the greater the projected distributional gain or the less the 

projected loss. This pattern was more evident for birds than reptiles. Coarse-grain terrain 

ruggedness index (TRI) also was a variable that frequently contributed significantly to bird models 

but had less of an influence on the reptile species. Insolation, on the other hand, was an important 

variable in all animal models (see Inserts 10 and 11). In summary, we found that changing climates 

will not uniformly affect all species and that some bird species will be affected differently than 

some reptile species. Overall, some species will gain in suitable areas and thus possibly experience 

increases in population numbers, while other species will experience distribution contractions and 

likely shrinking populations. Land managers should be aware of these potential changes so that 

they can adjust their management practices accordingly.
18

Insert 1: Detailed Project Justification and Methods

Distributions: Our Focus of Attention

The goal of our project was to project the future breeding ranges of birds and reptiles, based 

upon spatially explicit environmental models obtained under baseline climatic conditions (1980–

2009 for birds and 1940–2009 for reptiles), and for three future time period intervals (2010–39; 

2040–69; 2070–99). The scale of our modeling was both coarse and broad because species 

distributions are hundreds of thousands to millions of square kilometers, and projected changes in 

distributions could be larger than our focal study area. Consequently, although our focal area was 

the Southwest, our larger study area had to encompass regions where species might expand, which 

included the entire western United States.

The resolution, or grain, of our analysis was correspondingly coarse. Our minimum 

mapping unit was 16 km2 (4 × 4 km squares, or grid cells), which is of a size comparable to very 

large project areas in most land management agencies, and the minimum useful grain of most 

climate projections. Because our minimum resolution transcended the scale of most management 

actions, this project was not designed to inform matters such as short-term rangeland and forest 

management practices on individual management units. Rather, we offered information that will 

help managers to better anticipate the nature and magnitude of climate-driven changes in wildlife 

habitats and potential distributions, with implications for planning at regional and continental 

scales.

We offer explanations for projected changes that are rooted in a thorough understanding of 

the natural history of each species, with the potential for extrapolation to ecologically similar 

species. Our attention to selecting representative species and to grounding our work in ecological 

knowledge was the basis for a relatively broad scope of inference, despite focusing on 

comparatively few species. Inference was based on an understanding of context, contingency, and 

connection, which is what we offer in our results. 

A Few Well, Versus Many Poorly

We assumed that our results would be of greater value, and provide a broader scope of 

inference, if we modeled a few key representative species well, rather than many species poorly.

Projecting is a chancy business (Berteaux and others, 2006; Jackson and others, 2009). Projections 

that ignore or make little use of prior ecological knowledge in both the design and interpretation of 

models are likely to produce nonsensical results at best, or misleading results at worst (Austin, 

2002; Barry and Elith, 2006; Berteaux and others, 2006; Elith and Leathwick, 2009). Several prior 

research efforts have run tens to hundreds of species through automated modeling procedures, 

attentive only to climate envelopes, and automatically processing scores of climate variables, in an 

attempt to produce comprehensive results for large groups of species (for example, Huntley and 

others, 2004; Elith and others, 2006; Lawler and others, 2009; Kissling and others, 2010). It is 

important to understand that animal species respond to more than just climate events, thus we 

choose to model fewer species and with non-climatic variables.
19

Our Focal Species, and Why

We modeled the ranges of bird and reptile species for which we had sufficient data and 

ecological knowledge—enough data with enough coverage to support our quantitative work, and 

enough information on ecological relations to support the development of conceptual models (see 

Thuiller and others, 2004; Elith and Leathwick, 2009; Babet-Massin and others, 2010). We focused 

on birds and reptiles because they covered the majority of terrestrial vertebrate diversity in the 

Southwest, including species with a wide range of life histories (Boykin and others, 2007). In the 

broadest sense, these species represent, on the one hand, highly mobile endotherms that would 

presumably be able to respond to changes in range conditions (birds), versus, on the other hand, 

ectotherms with limited dispersal potential that would presumably be quite sensitive to climate 

(reptiles). This diversity matched our goal of learning how species with different dispersal 

capacities and life histories might respond to changing climate patterns. We also required the 

ranges to either be mainly contained within, or encompass a significant portion of the interior 

Southwestern United States, and for occurrence data to coincide roughly with the species known 

historic distribution (Kadmon and others, 2003; Thuiller and others, 2004; Sinclair and others, 

2010; Veloz and others, 2012). This last condition pertained to our focus on climate-related effects 

on distributions, rather than distribution as an artifact of direct human effects—which is the typical 

reason why most current distributions differ dramatically from those of the past. 

Given these criteria, certain categories of species were excluded from consideration, 

including some that were originally of interest to managers. The distributions of intrinsically rare 

species were not modeled for two reasons. First, a limited distribution does not support the reliable 

detection of climate-related or other broad-scale effects, because of intrinsically limited sample 

sizes (at a grain of 16 km2

) and intrinsically limited span of variation in potential environmental 

drivers (Elith and Leathwick, 2009). Second, many intrinsically rare species are so because of 

historical and evolutionary artifacts (for example, migration followed by isolation), or because of 

association with highly anomalous environments. As a consequence, we did not model endangered, 

threatened, or otherwise at-risk species with restricted distributions. Historically wide-ranging but 

more recently widely-extirpated species, such as the yellow-billed cuckoo (Coccyzus americanus), 

also were not considered good candidates, especially if we lacked reliable data for historic 

distributions. Virtually all recent extirpations have been the result of direct human causes, including 

over-harvest and the destruction of critical habitats such as native riparian corridors (Collar and 

others, 1997; Czech and Krausman, 1997). Given our interest in climate change, we needed 

distribution data that was more likely to reflect climate limiters rather than recent historical human 

activities (Sinclair and others, 2010). 

Data on Distributions

The scale of our modeling and projections matched the scale of regional or continental 

occurrence data for most bird and reptile species. Broad-scale occurrence data can typically be 

obtained at the resolution of watersheds or areas as large as 64 km2

. Occurrence also is reckoned at 

a coarse temporal scale, typically decades. For purposes of our analysis, a species was considered 

present if there was a single record within an approximately 16 km2 cell within the last several 

decades. Our temporal window for determining presence differed between birds and reptiles 

because of differences in the distribution of observations in time between these two groups. The 

vast majority of bird observations in AKN/EBird have been recorded since the 1970s, whereas 

observations of reptiles are much more evenly distributed going back to the 1940s. Our objective 

was to match the temporal reckonings of past climate with past species distributions for both birds 

and reptiles, which led us to truncate bird observations at 1990 and reptile observations at 1940. 
20

Our estimates of past climate correspondingly varied between birds and reptiles, with climate 

averages for our bird models encompassing 1980–2009 and climate averages for our reptile models 

encompassing 1940–2009. 

Ascertaining species presence (or absence) at the grain of 16 km2 over several decades is at 

about the coarsest demographic resolution possible where we could still retain meaningful 

information with which to work. Working at this scale we lost information about most demographic 

processes, including birth, death, immigration, and emigration rates, as well as ultimate metrics 

such as density. Nonetheless, we expected coarse-grained presence to be significantly correlated 

with all of these processes at broader scales. Therefore, we expected to detect a species where 

densities where higher, and where birth and immigration rates were correspondingly higher and 

death rates lower. Even so, our model results are not likely to be sensitive to finer-scale 

demographic phenomena such as density, and do not support or inform management at that finer 

scale. 

The Nature of Distributions: Probabilities and Thresholds

Distributions are intrinsically difficult to quantify. Some might be deceived into thinking 

otherwise by the typical black and white, binary ranges that are shown in most maps. But whether a 

species is “present” or not necessarily varies with the spatial and temporal grain of record-keeping, 

and with the intensity of survey efforts. In other words, a species is more likely to be “present” if 

this determination is made for larger grid cells, based on data collected over longer spans of time, 

and involving more intensive survey efforts. As survey effort and the spatial and temporal span 

increase, so do the sample unit-specific probabilities that a species will be documented as present. 

But much of this fuzzy information gets lost in either polygonal range maps or presence-absence or 

presence-only data. Even so, the underlying probabilistic sampling processes are real and 

important, and a necessary part of interpreting present and future projected ranges (Austin, 2002; 

Legendre and others, 2002; Barry and Elith, 2006; Elith and Leathwick, 2009). 

The probabilistic or fuzzy nature of distributions was captured in the output of modeling 

methods that we used for this project: logistic regression and Maxent. Both methods produced 

probabilities, which in the case of logistic regression was obtained by back-transforming the 

modeled log odds. These spatially-explicit probabilities more fully capture the information 

contained in models than do binary surfaces that are more commonly recognized as range maps. 

Yet some analyses and some visual applications required that we translate a predicted surface of 

occurrence probabilities into a binary (present versus absent) map for a given species. 

The production of binary maps required setting a threshold in terms of modeled 

probabilities. Wherever a value was above the specified threshold, the associated cell was 

considered to be range for this species. Wherever values were below this threshold, the cell was not 

deemed to be part of the animal’s range. But what constitutes “true” presence reckoned in terms of 

modeled probabilities? There probably is no knowable answer, given that we could not estimate all 

of the underlying sampling processes that went into producing the presence-only or presenceabsence data used for our modeling (Guisan and Zimmermann, 2000; Barry and Elith, 2006; Elith 

and Leathwick, 2009). Nor is there uniform agreement in concept regarding what does or does not 

constitute “presence.” As a result, we set thresholds as values that equalize training sensitivity and 

specificity on a premise that such an approach equalized the risks of over- and under-specifying 

(Freeman and Moisen, 2008). 
21

Limiters and Drivers of Distributions

Our focus on distributions and the large extent and coarse resolution of our analysis had 

implications for the nature and scale of ecological drivers. Most managers operate at the spatial 

extent of hectares or small watersheds, and focus on fine- or mesoscale manipulations of the 

structure and composition of vegetation or animal populations. Interest is typically in densities and 

vital rates of species. But the scale of our analysis and the scale of ecological processes that 

determine species distributions did not match this traditional focus.

The determinants and drivers of species ranges typically operate largely (but not wholly) at 

the limits of species tolerances (Guisan and Zimmermann, 2000; Elith and Leathwick, 2009). For 

vertebrate animals, these factors include broad-scale coarse-grain phenomena such as climate, 

insolation (incident solar radiation), regional soil characteristics, regional geomorphology, regional 

land use patterns, and, in cases where there is direct dependence, the distribution of plant species 

(for example, Schweiger and others, 2008; Austin and Van Neil, 2011; Stanton and others, 2012). 

As a bottom line, our results were not intended to—nor should they—inform finer-scale 

management of resources, both as a consequence of the grain of our analysis and the scale and 

delimiting nature of underlying ecological processes. 

Explanatory Variables

Logistical criteria—Our goals imposed some stringent demands upon the variables that we 

used in our explanatory predictive models. Any candidate variable had to be represented by digital 

spatial data that encompassed the entire extent of not only our focal area, but also the area into 

which species might expand. In many of the bird cases this comprised the entire western United 

States. The variable also had to have uniform meaning relative to its referent classes and other 

indices throughout this extent (Longley and others, 2005). More important, we had to be able to 

project future spatial configurations of our explanatory variables (Berteaux and others, 2006; Elith 

and Leathwick, 2009; Jackson and others, 2009). 

These considerations debarred using many databases related to vegetation structure and 

composition, such as products of GAP analysis or remote-sensing based estimates of vegetation 

cover and productivity. We did use the distributions of select plant species as explanatory variables, 

but only because our modeling of animal species distributions were attached to the results of 

another major undertaking of our larger southwestern NCCWSC project that was devoted to 

modeling and projecting only plant species distributions (Cole and others, 2008; Garfin and others, 

2010). Features that we assumed would be static, at least for 99 years, were not constrained to the 

need of being projected. These variables included measures of geology, geomorphology, soils, and 

solar insolation. 

What we did NOT include—We included most factors—as explanatory variables—that we 

thought were important to determining distributions of the species that we modeled. Most 

prominently, these included climate, soils, geomorphology, geology, hydrology, land use, and plant 

species distributions, all at a coarse scale. However, there were other factors that we did not 

include, which were nonetheless likely to influence current and future animal distributions. These 

factors included disease, vectors, predation, inter-specific competition, and major transformations 

of vegetation structure and composition owing to changes in land use, fire regimes, and the 

invasion of non-native species (Austin, 2002; Ibáñez and others, 2006). 
22

The future effects of diseases and insects are imponderables. Most major disease or vector 

effects on species distributions have been driven by the outbreak of particularly virulent non-native 

diseases and vectors (van Riper and others, 1986) or mutant species or strains (De Leo and others, 

2002). Although important, such phenomena are so complex as to be unpredictable. We had no way 

to introduce or project such effects. Nonetheless, the recent histories of white-nose syndrome and 

bats, of West Nile virus and birds, and of chytridiomycosis and amphibians are instructive (Friend 

and others, 2001; Daszak and others, 2003; Foley and others, 2010). 

Invasions by non-native species and large-scale changes in fire regimes are better suited to 

projecting, but well beyond the scope of our project. If researchers at some future time were to 

reliably predict spatially-explicit patterns of fire regimes or invasions by non-native species, while 

explicitly accounting for climate change, such results would present a great opportunity to integrate 

the effects of fire and invasive species into projections of animal distributions. 

Finally, predation and competition were conceivably within the purview of this project, but 

not accounted for because of time constraints and other priorities. We know that predation and 

competition can be powerful forces, even to the extent of configuring species distributions (Austin, 

2002; Guisan and Thuiller, 2005; McMahon and others, 2011). Human predation on a number of 

vertebrates has led to extirpations over large areas (Collar and others, 1997; Czech and Krausman, 

1997). Spotted owls (Strix occidentalis) are probably declining in distribution in the Northwestern 

U.S. because of competition with (and other effects of) the barred owl (Strix varia; Kelley and 

others, 2003; Gutiérrez and others, 2007). Native leopard frogs (Lithobates sp.) are being similarly 

affected by introduced bullfrogs (Lithobates catesbeiana) and crayfish in the Southwestern United 

States (Hayes and Jennings, 1986; Kats and Ferrer, 2003). These kinds of effects could be modeled 

by introducing other species distributions as explanatory variables in instances where the predator 

or competitor was known. We see this as a logical next step for the work started under the auspices 

of this project. 

Plant species—We included plant distributions as explanatory variables in instances where 

we had reason to believe an animal was in some way directly dependent on food or other conditions 

provided by the plant. The widespread dependence of pinyon jays (Gymnorhinus cyanocephalus) 

on the seeds of pinyon pines (Pinus sp.) is a classic case (Marzluff and Balda, 1992). Given that in 

our larger project we also modeled current and future distributions of plants (Cole and others, 2008; 

Garfin and others, 2010), we were in a position to project the future distributions of plants in 

instances where the plant species efficiently predicted an animal’s distribution—in a way that we 

could readily explain. Plants potentially had a substantial effect on future projections of animal 

distributions because of the intrinsically limited mobility of many plant species (Araújo and others, 

2006; Schweiger and others, 2008). Even though climate envelopes may expand rapidly, 

colonization by plants may occur at a much slower pace, and where an animal is dependent on the 

plant, expansion of the animal’s distribution would be correspondingly curbed. We dealt with this 

phenomenon by introducing a “mask” around current plant distributions that represented the 

distance we expected the plant to migrate within a projected time increment, which in turn affected 

expansion of an animal’s distribution, mediated through the overall structure of the projective 

model. 
23

The use of plant distributions as explanatory variables in models of animal distributions is 

conceptually demanding. There are many instances where a plant species may act strictly as an 

environmental surrogate, or proxy, without any direct link to the life history of the focal animal. In 

these cases, the plant species distribution will receive considerable statistical support—as a 

statistically efficient variable. One plant-related variable could capture the current concatenation of 

several climate and other variables. This circumstance may work well if the sole aim is to 

efficiently represent current patterns statistically. But one could be misled and left searching for a 

post hoc explanation if the aim is to explain and project ranges. Certain configurations of climate 

associated with a plant species distribution today may uncouple and reconfigure in novel ways in 

the future, with implications for a given animal’s distribution. Using a plant surrogate would not 

allow us to track these kinds of novel configurations. And if climate rather than a plant were truly 

determining where an animal could live, our introduction of a migratory mask for the plant, with an 

effect on the animal, would be spurious. We relied heavily on our review of the literature, and 

related conceptual models to navigate through these difficulties. We also produced models with and 

without plant species variables for each animal that we considered, so as to consistently provide at 

least one statistical explanation that did not depend on plant predictors. 

Hydrology and riparian vegetation—Riparian bird and reptile species are of considerable 

interest to managers in the Southwestern United States—including the southwestern willow 

flycatcher (Empidonax traillii extimus), yellow-billed cuckoo, leopard frogs, and narrow-headed 

garter snakes (Thamnophis rufipunctatus). Water is limited and quite influential in this semi-arid to 

arid region, and riparian areas, rivers, and streams have been dramatically altered to serve human 

purposes other than the conservation of biodiversity (Reisner, 1986; Barnett and others, 2008). But 

hydrology is another difficult-to-work-with explanatory variable, especially if one is interested in 

projecting its future configurations. Hydrology is a function of climate inputs, watershed 

characteristics, and local stream reach features. Riparian vegetation, which is often more directly 

relevant to terrestrial vertebrates, is denoted by structure and composition, which is a function not 

only of local hydrology, but also streamside geomorphology and history (Shafroth and others, 

2002; Webb and Leake, 2006). 

Rapid advances are occurring in our collective ability to model current and future 

hydrology, primarily because water and riparian areas are such important resources. The NHDPlus 

database (NHDPlus Version 2, http://www.horizon-systems.com/nhdplus/) provides a 

comprehensive spatial representation of current hydrology for our study area at the resolution of 

individual stream reaches, based on models of historic hydrologic data. This is an important asset in 

modeling the distributions of riparian animal species; however, predicting hydrology using GCM 

projections is ongoing and far from comprehensive for our larger study area (for example, 

Christensen and Lettenmaier, 2007). We were in a position to model the current distributions of 

riparian animal species, but chose not to predict where they would be in the future. 

Conceptual Models

Hypotheses—We relied upon conceptual models to synthesize, represent, and relate 

relevant life history information about our focal animal species, as a basis for designing and 

interpreting our statistical species distribution models. We selected focal species partly on the basis 

of whether existing information was sufficient to identify factors that conceivably determined its 

distribution. We were particularly wary of dredging data, which is typified by exploring dozens of 

effects without any prior basis for plausibility or functional interpretation. 
24

Statistical fit alone is a not a good criterion for making future projections, primarily because 

some variables may correlate well with current range, but mask some important effects that are 

functionally uncorrelated, and prone to “go in different directions” under future climates (Guisan 

and Zimmermann, 2000). This kind of risk would be amplified if some statistically-buried effects 

were “pinned” to the landscape, as would be the case with geomorphology or solar radiation, while 

others were highly mobile, as in the case of climate. Moreover, lack of attention to prior knowledge 

increases the odds that some factors critical for explanation and prediction may be overlooked 

entirely. 

Conceptual models were not only a key means of disciplining our thinking, but also an 

efficient way to articulate hypotheses about how the world worked in ways relevant to explaining 

why species occurred where they did. We carefully reviewed the literature to identify a suite of 

factors likely to determine where a species lived, died, or reproduced, along with any functional 

explanation. We then interrelated these factors to form a logical picture of the determinants and 

contingencies of the species distribution. We also identified the nature of prospective relations 

(positive or negative), and the likelihood of any interactions that required an interaction term in a 

statistical model. These conceptual maps were then carefully followed in constructing candidate 

statistical models for explaining and projecting species distributions. 

In addition to bounding the range of the plausible—and the interpretable—we also viewed 

our conceptual models as hypotheses to be tested statistically by our quantitative models, realizing 

that all of our tests were “weak” rather than “strong,” at least in conventional scientific terms 

(Barry and Elith, 2006; Berteaux and others, 2006; Elith and Leathwick, 2009; Jackson and others, 

2009). Variables that had major effects and were retained in our best statistical models supported 

the effect hypothesized in our conceptual models. The effects embodied by variables that were not 

retained in any model were not necessarily rejected, but rather provisionally relegated to the 

category of minor, at least at the scale of delineating and determining distributions. 

Logic and explanation—None of the variables that we used in our statistical models 

directly measured ecological processes or states that, in turn, directly affected vital demographic 

processes. In a proximal sense, species live or die on the basis of physiological function, including 

thermoregulation, metabolism, and essential chemical and other reactions. These functions are 

sustained in turn by ingestion of essential nutrients and the metabolism of those nutrients to form 

adipose or other reserves. Physiological homeostasis also is sustained by avoiding physical damage, 

whether by predators or any other injurious agent. But we had no means of measuring any of these 

individual-specific phenomena in a spatially-explicit manner at the scales we were using, and in a 

way that could be projected into the future. 

Put another way, all of the variables that we used in our statistical models were surrogates 

for effects operating conceptually nearer to the vital processes that, in reality, determined whether a 

species occupied or otherwise survived in a given 16-km2

 piece of ground. We carefully identified 

proximal vital processes, and back-tracked to measures that we could adequately explain within our 

models (fig. 1 on p. 5; fig. 2 on p. 8). Importantly, these conceptually more distant measures were 

thus related in an interpretable way to a proximal vital process. Our conceptual models allowed us 

to explicitly track how each variable that entered into a statistical model related to ultimate vital 

rates through intervening distinguishable ecological processes. This provided a basis for identifying 

plausible candidate explanatory variables and interpreting statistical models at all stages of 

development. 
25

We produced two conceptual models for each species—an initial model that informed 

selection of candidate variables for statistical models and a concluding conceptual model that 

integrated the results of our statistical analyses. This concluding conceptual model resulted from 

parsing, pruning, and otherwise refining our original conceptual model. Effects that were not 

supported by our quantitative results were removed from the conceptual models. Effects that were 

strongly supported were given amplified importance through the graphic form of enlarging the 

arrows denoting effects (see “Winter ppt” in figs. 1–2). These revisions of our original models 

represented an essential aspect of scientific practice that entails the modification of original 

hypotheses—or informed speculations about how the world might work—in light of new evidence 

(Hilborn and Mangel, 1997; Burnham and Anderson, 2002). Our models constituted new evidence, 

and our final conceptual models represented our revised understanding of how a given species 

distribution was determined based on the results of this project. 

Uncertainty

Uncertainty entered into our analyses in many ways, and at virtually all steps of the process. 

Without being exhaustive, there was uncertainty about the match between our species observation 

data and the “true” current distribution (and, throughout, we will tacitly insert quotations around 

“true” given the conceptual and philosophical ambiguity of this concept). Likewise, there was 

uncertainty in the match between our explanatory measures and the true underlying environment. 

There was uncertainty in the behavioral, genetic, and (related) taxonomic homogeneity of each 

species, with implications for the homogeneity of its response to the environment, including climate 

change (Parmesan, 2006). There was uncertainty in the effects of using different modeling methods, 

and choices of approach within modeling methods (Araújo and others, 2005; Elith and others, 2006; 

Pearson and others, 2006)—for one, the difference between results from Maxent and logistic 

regression and how spatial autocorrelation was or was not dealt with in each model. There also was 

uncertainty regarding the sufficiency of our candidate explanatory variables, and the resulting 

specification of models based simply on which variables were or were not considered (Burnham 

and Anderson, 2002). There was uncertainty in how the modeled explanatory variables related in 

concept to underlying ecologic and demographic phenomena. There was also uncertainty in the 

projected future climate based on a wide range of variation and uncertainty in each statisticallydownscaled global circulation model (GCM). 

This litany of uncertainty could be construed as cause for undue caution. However, all 

exercises in explanation and projection are affected by a similar suite. We conscientiously dealt 

with these uncertainties as we were able. Our first task was to simply recognize each uncertainty, its 

nature, and its source. We were then in a position to determine how best to deal with uncertainty in 

the GCM models. Some uncertainties were beset by imponderables. For example, we were never in 

a position to determine whether we had identified the “true” set of candidate variables for 

explaining a species distribution (Burnham and Anderson, 2002). This was in the category of not 

knowing what we did not know. On the other hand, there were numerous opportunities for us to 

either reduce or clarify uncertainty, either in concept or by explicit quantitative means.
26

Perhaps the most straight-forward means we used to clarify uncertainty was in the match of 

our data to our models. There are numerous metrics for assessing model adequacy, including 

“confusion” matrices (that is, calculated percentages of false negatives, false positives, true 

negatives, and true positives generated by a given model), and assessments of predictive signal, 

including area under the Receiver Operating Characteristic (ROC) curve (Fielding and Bell, 1997; 

Guisan and Zimmermann, 2000). We used both of these measures. We also used a subset of the 

data withheld from developing any given model to test it—the difference between “training” data 

and “test” data (Muñoz and Felicisimo, 2004). None of the methods constituted a strong test of our 

models, but they nonetheless were a relatively unambiguous means of assessing how well our 

different models described and projected existing animal distribution data. 

We relied primarily on sensitivity analysis to quantify the consequences of different choices 

regarding method or approach. Sensitivity analysis simply asks the question: What would have 

been the results doing something one way versus another (Saltelli and others, 2008)? In each case 

we had decided on a particular path to take, with consequences for all that followed. But sensitivity 

analysis allowed us to document the consequences at any given juncture. A sampling of choices 

includes whether to use logistic regression or Maxent, or what threshold to apply to projected 

distribution probabilities to best develop a binary range map. Sensitivity analysis does not support 

definitive conclusions regarding the “best” course of action, but it did allow us to track the 

consequences of choices that we did make. 

We dealt with uncertainty in climate projections, first, by selecting GCMs that performed 

well in our region and, second, by identifying spatial areas of agreement and disagreement between 

specific GCM projections. We relied on a previous analysis of GCM performance (Garfin and 

others, 2010) to identify 14 out of 22 GCMs that showed the best agreement in terms of 

precipitation seasonality of the western United States, with particular attention to the summer-fall 

monsoon. We then selected 5 of those top 14 models that were either statistically the best 

performers, or captured the range of variation in projected climates, in terms of wet-dry and coolerhotter extremes. We projected future species ranges by running our models of current range on each 

of the six climate projections (one each for the 5 models, plus a sixth representing an aggregate of 

all 22 GCMs—our “ensemble” projection). Once that was done, we then looked for areas of 

agreement among the six projections, calculated spatially in terms of the number of GCMs that 

were in agreement. The best case occurred where all six models agreed about whether a species 

would be present or not. Although we will never know the true future until it happens, we think this 

reckoning of uncertainty among GCMs provides a robust basis for reaching conclusions about 

future animal distribution patterns.

Finally, we relied heavily on conceptual models and our understanding of prior ecological 

research to reduce uncertainty in both model specification and interpretation. This approach using 

conceptual models allowed us to identify candidate variables that were highly plausible and linked 

in logical and known ways to vital underlying ecological and demographic processes. The 

conceptual models also allowed us to identify factors that we hypothesized were important, but for 

which we could not identify any operational surrogates for entry into statistical models. Some of 

these factors included specific predators or diseases. Even though we were not able to quantify the 

effects of these factors, we at least knew what was not included, which, when used for 

interpretation, reduced the overall uncertainty of our models and their projections. 
27

Insert 2: The Southwestern NCCWSC Advisory Team Development

Pre-Advisory Team Efforts

Prior to constituting the NCCWSC project advisory team (AT), input on project design was 

solicited from a wide range of southwestern managers and stakeholders who elected to participate 

in a workshop held on October 5, 2009. A brief description of the stakeholder engagement process 

follows, featuring workshops and meetings specifically designed to elicit input. 

October 5, 2009, Inaugural stakeholder workshop, High Country Conference Center, 

Flagstaff, Arizona—This workshop was convened as part of the 10th Biennial Conference of 

Research on the Colorado Plateau, and designed to capitalize on the presence of a large number of 

people potentially interested in this project and its products. Participation was solicited, and an 

invitation extended to all who were interested. Workshop topics included: (1) a NCCWSC project 

overview; (2) developing criteria for selecting species to model; (3) candidate species; (4) 

considerations and constraints affecting species distribution modeling; and (5) sources of species 

distribution data. 

October 2009-March 2010—Following the October 2009 workshop, input was solicited by 

e-mail from workshop participants regarding: (1) Advisory Team (AT) nominations; (2) criteria for 

species selection; (3) prioritization of prospective focal species identified during the 2009 

workshop; and (4) additional suggested species. Five AT respondents prioritized the distributed 

species list, and four AT respondents offered comments regarding additional candidates. We 

initially considered modeling mammal species distributions, but decided during these early 

deliberations not to model this taxonomic group. We considered data availability and reliability to 

be problematic, and some of the recommended species (for example, pronghorn [Antilocapra 

americana] and Arizona gray squirrel [Sciurus arizonensis]) exhibited distributions that were either 

too restricted to be reliably modeled, or were artifacts of historic anthropogenic influences. We 

excluded most imperiled or critically imperiled wildlife species for similar reasons, despite the 

continued advocacy of some stakeholders that such species be included. 

Advisory Team Efforts

The southwestern NCCWSC project AT was formally constituted on 1 April 2010, after a 6 

month nomination and solicitation process. An additional three members were added on September 

7, 2010. The full AT consisted of nine members representing federal and state bureaus as well as 

non-governmental organizations. The U.S. Fish and Wildlife Service (FWS), National Park Service 

(NPS), U.S. Forest Service (FS), Bureau of Land Management (BLM), Arizona Game and Fish 

Department, The Nature Conservancy, Sky Island Alliance, and Center for Biological Diversity 

were organizations represented by the AT (table 2.1). 
28

Table 2.1. The selection process and final membership of the southwestern National Climate Change and 

Wildlife Science Center Advisory Team.

• Over 20 nominations received from a wide range of interests with the goal of establishing a 

team composed of: 

o Three representatives from federal resource management agencies or tribal governments

o One to two representatives from state wildlife agencies

o Two representatives from academic or science institutions 

o Two representatives from non-governmental organizations (NGOs) 

• Upon review of nominations, interviews and reference checks, the research team selected the 

Advisory Team member as follows: 

o Federal:

 Lisa Thomas, National Park Service, Southern Colorado Plateau I&M 

Ecologist and Network Manager 

 Karl Ford PhD., BLM National Operations Center, Project Leader for Rapid 

Ecoregional Assessments of the Colorado Plateau and Sonoran Desert; 

climate change science information

 Bill Noble, U.S. Forest Service, Wildlife Biologist, Four Forests Restoration 

Initiative

 Brenda Smith, U.S. Fish and Wildlife Service, Northern Arizona Field Office 

Director

o State: 

 Brian Wakeling, Arizona Game and Fish, Assistant Director for Game Species 

Management

o Academic (also State): 

 Laura Huenneke PhD., Vice President for Research, Northern Arizona 

University

o NGOs: 

 Marcos Robles PhD., Ecologist for Climate Change, The Nature Conservancy 

 Taylor McKinnon PhD., Conservation Biologist, Center for Biological 

Diversity

 Louise Misztal, GIS specialist and Ecologist, Sky Islands Alliance

Operations of the AT were governed by a set of guidelines outlining roles and 

responsibilities that were adopted at the first AT meeting (table 2.2). 
29

Table 2.2. U.S. Geological Survey Southwest Biological Science Center NCCWSC Project - Projecting 

Climate Impacts on Southwestern Wildlife - Advisory Team Guidelines

U.S. Geological Survey Southwest Biological Science Center 

NCCWSC Project - Projecting Climate Impacts on Southwestern Wildlife

Advisory Team Guidelines

PURPOSE: The purpose of this document is to define the roles, responsibilities, operating 

principles, and operations for the Advisory Team associated with the U.S. Geological Survey 

National Climate Change and Wildlife Science Center (NCCWSC) project entitled “Projecting 

climate impacts on wildlife of the arid Southwest at regional and local scales using downscaled 

climate models,” Lead P.I. Dr. Charles van Riper (now referenced as “Projecting Climate

Impacts on Southwestern Wildlife”). These guidelines are intended to provide clarity in order to 

facilitate effective and efficient activities and communications among the Advisory Team (AT) 

and the Research Team (RT). 

I. OBJECTIVES OF TEAM:

• To foster communication and collaboration between stakeholders and the U.S. Geological 

Survey (USGS) staff and research collaborators involved with this project. 

• Through two-way communication, support the NCCWS Center and project goal of 

providing scientific and technical information to help natural resource managers cope 

with a changing climate.

• To assure that the project is focused on key manager and stakeholder priorities. 

• To deliver results and information that is understandable and useful for managers and 

stakeholders.

• To promote opportunities for related collaborations in management and research through 

a variety of means such as information exchange, data sharing, leveraging activities and 

complementary research and monitoring. 

• To build common goals and organizational “buy-in” for this project as a cornerstone for 

climate change research and adaptive resource management in the southwest.

II. MEMBER ROLES AND RESPONSIBILITIES

• Promote ongoing dialog and two-way communication by 1) representing and 

communicating their organization’s perspectives and 2) keeping their organization 

informed about and engaged in the project. 

• Inform the project team about manager and stakeholder needs and priorities. 

• Advise the project team about focal species selection and prioritization. 

• Identify data sources and facilitate data sharing and access in support of the project.

• Advise the project team about how to develop and deliver project results to maximize 

their utility for managers and stakeholders (e.g. technology transfer and decision support 

tools).

• Provide overall feedback to the RT and USGS on the project and other climate change 

research needs and priorities.

• Identify and promote opportunities for collaborations relevant to this project and climate 

change research and adaptive resource management in the southwest. 
30

III. OPERATING PRINCIPLES

A. USGS Leadership and Liaison: The primary USGS liaison for the AT is Kate Kitchell, 

Module II leader and SBSC Acting Center Director. The liaison is responsible for 

coordinating, facilitating, and assuring support for the AT; assuring follow-through on 

AT input; and responsiveness from the RT to the AT. Individual interactions between AT 

and RT members are welcome at any time throughout the project process. 

B. Advisory Team Leadership: The Advisory Team may choose to select a team leader 

who may serve as the primary coordinator and spokesperson for the group. This leader 

could also take on facilitation roles carried out by the USGS Liaison and subsequently 

incorporated into these guidelines. 

C. Decision-making: The AT will make decisions or recommendations to the USGS by 

consensus. They may choose to establish further decision-making procedures through a 

consensus process. 

D. Record keeping and reporting: USGS will be responsible for keeping and distributing 

notes from all meetings and conference calls. Within one week of meetings/calls, notes 

will be circulated in draft to Advisory and RT members for review and revision prior to 

final distribution. Notes will be posted on the USGS SBSC website and USGS NCCWSC 

website.

E. Confidentiality of unpublished/preliminary data and results: Unpublished and 

preliminary information about the project will be shared with AT members in order to 

facilitate project goals. However, in accordance with USGS policy, such information is 

not available for public release until it has undergone full quality control, peer review, 

and publication. Therefore, AT members are expected to maintain confidentiality of this 

information. Release or distribution of selected information may be allowed on a case-bycase basis upon request to and approval by the USGS Liaison and Project Lead PI (van 

Riper).

F. Frequency of meetings and conference calls: The AT will meet no more than 3 times 

per calendar year. Each meeting will be no more than 2 days total (including travel) 

unless requested by a consensus of AT members. Conference calls may be scheduled 

between meetings in order to accomplish AT objectives. Meetings will be scheduled with 

a minimum of 6 weeks’ notice in order to maximize participation. 

G. Outreach and communication: Outreach products will be developed and distributed 

initially by USGS and further distributed with the assistance of AT members.

Recommendations from the AT for outreach and communication actions and products are 

welcome and encouraged.

IV. COMPOSITION MEMBERSHIP

The AT membership is composed of a minimum of 8 members representing federal, state, 

academic/research, and non-governmental organizations who bring technical expertise, diverse 

perspectives, geographic representation of the study region, and demonstrated abilities to work in 

a collaborative context. Up to 3 additional members may be added to facilitate project goals.

Recommendations for additional members and rationale may be provided to the USGS Liaison at 

any time during the course of the project. These will be evaluated and vetted with the AT and RT 

to meet project goals.
31

V. FUNDING FOR ADVISORY TEAM ACTIVITIES

All costs and expenses associated with the AT will be covered by the USGS, subject to the 

approval of the USGS liaison. USGS will reimburse AT members for said expenses (contact 

Serena Mankiller smankiller@usgs.gov). 

VI. TERM

This Advisory Team will remain in effect through the duration of this project and the public 

release of project primary results and products. Expected sunset date is June 2012. 

VII. REVIEW AND REVISION OF GUIDELINES

These guidelines may be reviewed in response to substantive issues or needs raised by AT 

members or USGS. Revisions would be developed and vetted with the AT and RT, and 

implemented upon consensus among AT members. 

The AT met on five different occasions to review the southwestern NCCWSC project and to 

offer feedback. 

April 1, 2010, AT meeting, BLM National Training Center, Phoenix, Arizona—This 

meeting inaugurated the AT and established its guidelines. Agenda topics included: (1) introduction 

of the AT members; (2) consensus on and clarification of AT guidelines; (3) project overview and 

design; and (4) solicitation of input from the AT on project focus and objectives; selection of focal 

species; data sources and data access; products and information transfer; and communications and 

potential collaborations (table 2.3).

Table 2.3. April 1, 2010, AT meeting at the BLM National Training Center, Phoenix, Arizona.

USGS – Advisory Team Meeting

NCCWSC Project - Projecting Climate Impacts on Southwestern Wildlife

Location - BLM National Training Center in Phoenix 

April 1, 2010; Time: 9:30-4:00 – (working lunch) 

Facilitator – Kate Kitchell and Charles van Riper III; Note taker – Serena Mankiller

Meeting Objectives:

1. Become acquainted with the Advisory Team Members and their respective activities and 

interests related to this project.

2. Clarify roles, responsibilities, and operating principles for Advisory Team - (hand out) 

3. Learn about purpose, objectives, methods, data, progress, and outcomes of the project 

4. Initiate process to seek input from the Advisory Team on the following (including, but not 

limited to):

• Project focus and objectives 

• Focal species

• Available data sources and access to the data

• Project outputs and science transfer

• Opportunities for collaborations and communications 
32

Meeting Agenda:

9:30 – 9:45 Welcome, introductions, agenda review – Kate

9:45 – 10:00 Update on USGS activities related to NCCWSC (LCCs, CSCs) - Kate & Charles

10:00 – 11:15 Advisory Team member introductions – summary reports of relevant activities 

(10 Minutes each max.)

11:15 – 11:45 Discussion of Advisory Team guidelines (see advance material) 

11:45 – 12:15 Lunch Break (continue with working lunch) 

12:15 – 1:45 Comprehensive project report 

 (Purpose, objectives, methods, data, progress, outcomes) 

• Overview & Introduction (10 min.) Charles van Riper III

• Module I: climate-plant modeling (30 min.) Ken Cole et al. 

• Module III: habitat & wildlife modeling (30 min.) Dave Mattson et al.

• Module IV: data and web-based products (20 min.) Paul Heinrich & Michael Peters

 

1:45 – 2:00 Break

 

2:00 – 3:30 Discussion and input from the advisory team about: 

• Overall project direction, objectives, and relevance (20 min.) 

o What are the priority issues, concerns and questions for stakeholders related to 

climate change and wildlife?

o How can this project be developed and implemented to respond to these needs? 

• Methods (Modules I & III) (10 min.) 

• Focal species – criteria, additions, deletions, adjustments? (15 min.) 

• Data availability and sharing (15 min.) 

o What information and data do you know of that would benefit this project? 

o How can it be accessed and shared?

o What limitations or constraints need to be considered? 

• Products & outputs: making research results useful and relevant for managers and partners 

(20 min.) 

o What do managers need to assist them? 

o What formats (e.g. web, fact sheets, reports, decision trees) are most useful? 

o Do the types of outputs we envision seem useful? If not, how should we adjust? 

• Recommendations on outreach and communications materials (e.g. fact sheet) (5 min.) 

• Other (5 min.) 

3:30 − 4:00 Action items, next steps & feedback on meeting - Kate

• Summarize

• Reimbursement reminder

• Next meeting/call

• Notes
33

September 7–8, 2010, 2nd AT meeting, USFS Rocky Mountain Research Station, 

Flagstaff, Arizona—The agenda of this meeting included: (1) introduction of the final AT 

members; (2) project overview provided by scientists; (3) update on project progress, including the 

results of prototype models for pinyon jay, Virginia’s warbler, and Arizona black rattlesnake; and 

(4) solicitation of AT input on methods for representing and displaying uncertainty; modeling 

methods for plant and animal distributions; a finalized list of focal species; and the form of products 

and the methods for presenting them. Of note, the AT clarified at this meeting that we would prefer 

if the southwestern NCCWSC model fewer species well rather than do many species poorly. We 

also discussed the possibility of modeling vegetation structure, which we decided was too 

problematic, especially when tasked with projecting changes in vegetation structure under future 

climates.

June 20–21, 2011, 3rd AT meeting, USGS Flagstaff Science Center, Flagstaff, Arizona—

The agenda of the third AT meeting included: (1) a review of the project design and related 

considerations; (2) an update on progress; (3) final agreement on species to be modeled (see 

below); (4) data and product delivery; and (5) design and performance of the project website. This 

meeting offered the AT its first opportunity to interact with the project website and offer explicit 

feedback on the basis of hands-on use. All participants concluded at this meeting that there was a 

need to add a bird species associated with higher-elevation mixed conifer forests, which led to the 

addition and inclusion of Williamson’s sapsucker.

March6-7, 2011, Final AT meeting, Northern Arizona University, Applied Research & 

Development Building, Flagstaff, Arizona—At this final meeting we presented near-final results 

of the project to the AT. The AT provided feedback on models, overall results, form of project 

outputs, potential uses by managers, website features, and website navigation. 
34

Insert 3: Bird and Reptile Distribution Data

Bird Distribution Data

We acquired and processed three types of bird location data: (1) presence only; (2) presence 

and absence; and, (3) polygonal range maps. Species presence and absence were acquired from the 

Avian Knowledge Network (AKN) website (Avian Knowledge Network, 

http://www.avianknowledge.net). The AKN is an organization that assembles locational 

observations of birds from a wide range of sources. The assembled data come from bird 

monitoring, bird banding, and broad-scale citizen based bird surveillance programs. 

We acquired presence data by species for the period 1990–July 2009, for the entire United 

States. We first imported these data into Microsoft Access® databases from downloaded ASCII text 

files. Once imported, these data were manipulated in Access® to remove unwanted attributes and 

records identified on the basis of project specifics. We then converted the presence databases into 

Environmental Systems Research Institute (ESRI) ArcGIS® layers. We then analyzed the GIS 

layers to remove records such as those that were outside project study area, from unwanted 

collections, and outside the species breeding dates. Birds of North America accounts (Dobbs and 

others, 1997; Barlow and others, 1999; Olson and Martin, 1999; Reynolds and others, 1999; 

Kingery and Ghalambor, 2001; Balda, 2002; Johnson and others, 2002) were used to estimate 

breeding season for each bird species (table 3.1).

Table 3.1 Breeding season of each bird species selected for National Climate Change and Wildlife Science 

Center projecting of climate impacts on southwest wildlife.

Bird species Breeding season

Black-throated sparrow March 15 – August 31

Gray vireo April 15 – August 15

Pinyon jay February 1 – July 15

Pygmy nuthatch March 15 – August 15

Sage thrasher April 1 – August 15

Virginia’s warbler May 1 – July 31

Williamson’s sapsucker April 15 – July 31

We acquired and processed absence observation data similarly to the presence observations, 

although the presence-absence data had to be acquired as sample events. The sample event data 

included records for all species in the United States from 1990 to July 2009. Sample event data 

were all analyzed within ArcGIS® to produce absence observation events for each species.

Nature Serve collects, manages, and disseminates detailed local information on plants, 

animals, and ecosystems. We acquired species range maps from the Nature Serve website (Nature 

Serve, http://www.natureserve.org/explorer/). Range maps were gathered by taxonomic family and 

extracted as ESRI shapefiles for each desired species. We analyzed the shapefiles within ArcGIS®

to remove all but each species breeding range. We further used ArcGIS® to generate 1 degree

buffers around breeding range polygon(s), and then used these buffered ranges to clip out all 

observations of the corresponding species that fell outside our study area. All three data types 

(presence and absence observation events, and range maps) were delivered for analysis in shapefile 

format with a standard spatial reference of Geographic Coordinate System, World Geodetic System

(1972). 
35

Reptile Distribution Data

Reptile distribution data are generally less readily available than are data about birds. 

Nevertheless, we compiled 31,788 occurrence records of five selected taxa of reptiles from a 

variety of sources, including natural history museums, inventory and monitoring programs, and 

other research databases. We obtained the majority of records from the HerpNET data portal 

(HerpNET, http://www.herpnet.org) and Global Biodiversity Information Facility (GBIF; Accessed 

through GBIF Data Portal, http://data.gbif.org), as well as National Park Service Inventory and 

Monitoring Network databases (Northern Colorado Plateau, Southern Colorado Plateau, Sonoran, 

Chihuahuan, and Mojave; Persons and Nowak, 2006a, 2006b; Persons and others, 2006; Persons 

and Nowak, 2007). Other sources of data included Natural Heritage programs, studies conducted 

under scientific collecting permits obtained from state wildlife agencies, and individual research 

programs or books in preparation, for which we directly contacted authors and investigators.

Records of occurrence for our selected taxa in the Western United States included data 

collected between 1852 and 2010, although the majority of records (58 percent) were obtained after 

1980. We were not able to confirm the taxonomic determination of each specimen or observation 

but we did scrutinize individual data points that we deemed suspect because of their geographic 

position and excluded such uncertain data from analyses. Because much of the location information 

associated with obtained data was only descriptive (that is, not geo-referenced), we assigned 

geographic coordinates either automatically through specialized programs (GEOLocate ver. 3.21; 

Rios and Bart, 2010) or manually by finding the location using Google EarthTM (Google Inc., 

Mountain View, California) and other resources within a geographical information system 

(ArcGIS® ver. 10, ESRI Inc., Redlands, California). In cases where coordinates were generated by 

software (unsupervised), we reviewed the correspondence of individual sets of coordinates to the 

locality description.

We reduced the number of records to one per grid cell (approximately 16 km2) to minimize 

sampling bias, which is especially important for correlative modeling (Phillips and others, 2009). 

We registered all georeferenced point range data for reptiles by cells that were used for our spatial 

modeling. Once registered, each grid cell containing one or more presence locations constituted our 

record of presences for individual species. This process substantially reduced the number of 

presence observations used for spatial modeling. In the case of reptiles, the original 23,954 

georeferenced records were reduced to 5,087 grid cells, or points, representing the presence of five 

different taxa (fig. 3.1). 
36

Figure 3.1 Graph showing summary of occurrence data compiled for reptiles, expressed as unique grid 

cells that match the environmental data used for modeling distribution.

The number of cells where presence was documented varied from about 200 to about 3,000 

for individual species, which is considered sufficient for reliably modeling species distributions 

using logistic regression and Maxent (Guisan and Zimmermann, 2000; Kadmon and others, 2003; 

Wicz and others, 2008). 
37

Insert 4: Explanatory Variables

We identified candidate explanatory variables on the basis of several key criteria. These 

variables needed to be represented by digital spatial data that encompassed the entire extent of not 

only the Southwestern United States focal area, but also the area into which species might expand, 

in most cases comprising the entire Western United Sates. The variables also had to have uniform 

meaning relative to its referent classes throughout this extent. More important, we had to be able to 

project future spatial configurations of our explanatory variables. 

These considerations debarred using many databases related to vegetation structure and 

composition, such as GAP or remote-sensing based estimates of vegetation cover and density. We 

did use the distributions of select plant species as explanatory variables, but only because our 

modeling of animal species ranges were incorporated into the results of another major undertaking 

of our larger project that was devoted to modeling and projecting plant species ranges (Cole and 

others, 2008; Garfin and others, 2010). Features that we assumed would be static, at least for 99 

years, were not constrained to the need of being projected. These variables included measures of 

geology, geomorphology, soils, and solar insolation. 

Climate variables (temperature and precipitation)—Climate was the key factor and 

foundation of our models, and we obtained climate data from the PRISM (Parameter-elevation 

Regressions on Independent Slopes Model) climate dataset (PRISM Climate Group, Oregon State 

University, http://prism.oregonstate.edu, accessed September 26, 2013). PRISM’s average 

maximum and minimum monthly temperatures were averaged together to calculate monthly means 

that corresponded with the mean monthly temperatures provided in the Intergovernmental Panel on 

Climate Change (IPCC) AR4 GCM simulation results. We obtained IPCC AR4 GCM results from 

the World Climate Research Programme's (WCRP) Coupled Model Intercomparison Project phase 

3 (CMIP3) multi-model dataset (Meehl and others, 2007; Solomon and others, 2007). Details on the 

models and their configurations are available at http://www-pcmdi.llnl.gov (Lawrence Livermore 

National Laboratory, Program for Climate Model Diagnosis and Intercomparison, accessed 

September 11, 2013, at http://www-pcmdi.llnl.gov). 

We analyzed the 22 IPCC models (48 simulations) incorporating the effects of estimated 

greenhouse gas and aerosol changes from the late 19th century through 1999, and then incorporated 

the IPCC Special Report on Emissions Scenarios (SRES) A1B scenario to 2100 (Intergovernmental 

Panel on Climate Change, 2000). Results for each GCM simulation were statistically downscaled to 

a 2.5-minute grid (approximately 4-km grid), as described by Garfin and others (2010). Each 

downscaled model was evaluated based on its ability to simulate precipitation seasonality in the 

western United States and on its accurate representation of a range of projected future climates for 

the southwest region (Garfin and others, 2010). Our focus was to examine the proximate climate 

response only in our region, and we did not examine GCM abilities to reproduce models such as 

tropical ocean circulation, Asian monsoon, or Arctic winds. We selected five different GCMs from 

the top 14 best performing models as a basis for climate projections, including the two that were top 

ranked (ukmo_hadgem1 and mpi_echam5) and three others that represented a spectrum of projected 

climates in terms of warmth and aridity (cnrm_cm3, csiro_mk3, and ncar_ccsm3). 
38

The following climate variables were specified as a candidate in at least one of our 

statistical models: 

• Breeding season average temperature—The average of each bird species breeding season 

monthly average temperature, 1980‒2009. 

• Breeding season average maximum average temperatures & average minimum average 

temperatures—The average of the maximum average breeding season monthly temperature, 

1980–2009, and the average of the minimum average breeding season monthly temperature, 

1980–2009. 

• Breeding season total precipitation (PPT)—The mean annual total precipitation for breeding 

season months, 1980–2009. 

• Spring, summer, fall, and winter average maximum average temperatures & average minimum 

average temperatures—The average of the maximum average monthly spring (March through 

May), summer (June through August), fall (September through November), and winter 

(December through February) temperatures, 1980–2009, and the average of the minimum 

average monthly spring, summer, fall, and winter temperatures, 1940 or 1980–2009. 

• Spring, summer, fall, and winter total PPT—The mean of the yearly total spring (March 

through May), summer (June through August), fall (September through November), and winter 

(December through February) precipitation, 1940 or 1980–2009. 

• Southwest winter total PPT—The mean of the yearly total November through March 

precipitation, 1940 or 1980–2009. 

• Southwest spring total PPT—The mean of the yearly total May through June precipitation, 

1940 or 1980–2009. 

• Southwest monsoon total PPT—The mean of the yearly total July through September 

precipitation, 1940 or 1980–2009. 

• Monthly PPT—The average of monthly precipitation, 1940 or 1980–2009. 

Insolation—Incoming solar radiation (insolation) is the primary energy that drives many of 

the Earth's physical and biological processes. On a global scale, variation in insolation results from 

the Earth’s tilt and rotation on its axis and its revolution around the Sun. On a landscape scale, 

slope, aspect, elevation, and shadows create highly variable spatial and temporal insolation patterns 

(Dubayah and Rich, 1995). Insolation increases with steeper slope, more southerly aspect, and 

higher elevation (Barry and Chorley, 1976; Brady and Weil, 2002). This variability contributes to 

microclimate differences, including factors such as air and soil temperature regimes, 

evapotranspiration, snow melt patterns, soil moisture, and light available for photosynthesis. In the 

northern hemisphere, during the winter, insolation decreases with increasing latitude. During the 

summer this relationship reverses and insolation increases with latitude (Barry and Chorley, 1976). 

Topographic effects on insolation are most pronounced in the northern hemisphere during winter 

solstice, when the sun is at its nadir (Stiling, 1996). 

We used ArcGIS® 9.3 Spatial Analyst Area solar radiation tool to develop two layers that 

quantified the amounts of solar radiation received on January 1 and May 1. Because the tool only 

allows one latitudinal input, a 90-meter digital elevation model (DEM) was clipped in slightly 

overlapping quarter decimal degree latitudinal bands. We ran the solar analyst tool for each band, 

and then mosaiced each together using the “blend” function. 
39

Geology and landform—Abiotic habitat variables are important habitat components for 

birds and reptiles, particularly during the overwinter (reptile denning) period, during the hot 

summer months, and during nesting and development of juveniles (Van Devender, 2002; Schofer, 

2007; Ernst and Lovich, 2009; Nowak and Arundel, unpub. data, 2009). Volcanic formations and 

talus fields, boulders, caliche soils, and other outcrops provide important overwinter denning and 

summer sheltering habitat for reptiles, especially in high elevation habitats. We used the following 

variables to denote these potential effects: 

• Terrain ruggedness index (TRI)—A statistical measure of how "rough" the landscape is (Riley 

and Malecki, 2001). An area with many mountains or canyons has high terrain ruggedness, 

whereas a flat plain has low terrain ruggedness. TRI was calculated at two spatial scales, 90-m 

and 4-km grid cells.

• Lithology—A dataset on surficial lithology classes for the conterminous United States was 

obtained from the U.S. Geological Survey Ecosystem Mapping Project (U.S. Geological 

Survey, GEOSS, http://rmgsc.cr.usgs.gov/ecosystems/usa.shtml#SL). The layer classes were 

derived from the U.S. Geological Survey map "Surficial Materials in the conterminous United 

States", which was based on texture, internal structure, thickness, and environment of 

deposition or formation of materials. This original map was produced from a compilation of 

regional surficial and bedrock geology source maps using broadly defined common map units 

for the purpose of providing an overview of the existing data and knowledge. The dataset 

includes 18 lithology classes that typically control or influence the distribution of vegetation 

types.

• Rocktype—From: Schruben and others (Geology of the Conterminous United States at 

1:2,500,000 Scale A Digital Representation of the 1974 P.B. King and H. M. Beikman Map). 

The Geologic Map of the United States is primarily a bedrock map, which depicts geologic 

materials present beneath the soil or relatively thin mantles of surficial deposits, not necessarily 

the surficial materials themselves. For example, the map does not depict the glacial deposits in 

the northern States, the widespread eolian deposits in the high plains, and the high-level gravels 

that mantle older Tertiary and pre-Tertiary units in much of the Atlantic and Gulf Coastal 

Plains.

• Landform—From: Manis and others (2001). Preclassification: An Ecologically Predictive 

Landform Model. Gap Analysis Program Bulletin No. 10, December 2001. USGS GAP 

Analysis Program. Moscow, Idaho. (U.S. Geological Survey, 1999, 

http://earth.gis.usu.edu/swgap/data/metadata/landform_albers.htm). We created this layer from 

30-m DEMs that were resampled into a 90-m DEM and then partitioned into 115 slightly 

overlapping latitudinal strips. We ran the aml script developed by Manis and others (2001) for 

each strip, after which we merged and analyzed the output for artifacts. Once artifacts were 

removed, we extracted and added a water body class from resampled 30-m USGS Landfiredata.

Soils type and structure—Soil composition and texture play an important part in the nesting 

ecology of reptiles, particularly gravelly soils and sandy or friable soils. Soil moisture is 

particularly important in determining hatching success of egg-laying reptiles, as well as overwinter 

survival of juveniles. We used the following data to denote the effects of soils: 

• Depth to lithic layer—From the Natural Resources Conservation Service (NRCS) U.S. general 

soils map (Soil Survey Staff, Natural Resources Conservation Service, United States 

Department of Agriculture, U.S. General Soil Map [STATSGO2]). 
40

• Drain index—We joined a drainage index table developed by the USDA Forest Service and 

Michigan State University (USDA, http://foresthealth.fs.usda.gov/portal [accessed 9/11/2013]) 

with the map unit key field of the U.S. General Soil Map (STATSGO2 

http://websoilsurvey.nrcs.usda.gov [accessed 9/30/2013]).) to form a measure of the long-term 

wetness of a soil. The Drainage Index (DI) indicates the amount of water that a soil contains 

and is not meant to mimic the concept of "plant available water," which is mostly dependent 

upon soil texture. The DI only loosely (secondarily) takes soil texture into consideration. The 

main factors affecting DI are the depth to the water table, soil moisture regime and volume, and 

(lastly) texture. The DI is calculated from the soil taxonomic subgroup classification in the U.S. 

system of soil taxonomy, along with its textural family and slope class. 

• Percent of sand, silt, and clay—We generated three soil texture layers, in the form of 

percentages of soil particle size classes (sand, silt, or clay) for the study area using the NRCS 

U.S. General Soils Map (U.S.D.A. http://websoilsurvey.nrcs.usda.gov [accessed 09/30/2013]).

We used default settings of the NRCS Soil Data Viewer 5.2 tool in ArcGIS 9.2 to attribute the 

soil data map for percent sand, silt and clay. We used the majority function in ArcGIS® 9.3 to 

convert the resulting polygon map to a 4-km grid. 

Distribution of plant species—We utilized two different methods to develop presenceabsence maps of current distributions for plant species that were potentially important to birds and 

reptiles. Our use of methods depended on the spatial distribution of occurrence records. For plant 

species with sparse or patchily distributed occurrence data we used heads-up digitizing that used 

information on elevational distribution, occurrence by plant community types, and expert opinion. 

For species that had been systematically and intensely sampled, as in the Forest Inventory and 

Analysis (FIA) databases, we used ArcGIS® 9.3 Geostatistical Analyst to interpolate presenceabsence range maps. We registered ranges to a 4-km resolution grid using radial splines that 

encompassed an 8‒12 cell neighborhood and a threshold of 0.10. 

In addition to FIA, data sources of plant presence and absences included the LANDFIRE 

Reference Database (2007), the Central Mojave field data (Thomas and others, 2002), the 

southwest ReGAP project (http://earth.gis.usu.edu/swgap/trainingsites.html [accessed 9/12/2013]),

the participants of the Consortium of California Herbaria (http://ucjeps.berkeley.edu/consortium/, 

[accessed 9/12/2013]), the Global Biodiversity Information Facility (http://www.gbif.org, [accessed 

9/12/2013]/), and the Southwest Environmental Information Network (SEINet 

http://swbiodiversity.org/seinet/projects/index.php, [accessed 9/12/2013]).

We used methods described in Cole and others (2011) to project the future distributions of 

plant species that were found to have explanatory power in the bird and reptile distribution models. 

Future distributions were based on models of current range, projected changes in climate, and the 

estimated migration rate of the affected plant species, for more information see Insert 5. 

Densities of humans (Human Influence Index)—We obtained an index of human 

influence from the Wildlife Conservation Society (WCS) and the Center for International Earth 

Science Information Network (CIESIN) at Columbia University (Sanderson and others, 2002). The 

Human Influence Index was produced by overlaying data layers that represent the various factors 

presumed to exert an influence on ecosystems, such as human population distribution, urban areas, 

roads, navigable rivers, and agricultural land uses. The human “footprint” has been identified as an 

important covariate in the presence and persistence of several species of reptiles, with reductions in 

the extent and quality of habitat by human land use or other activities having caused significant 

declines in populations of desert tortoises (Berry and others, 2002; Tracy and others, 2004) and 

Arizona black rattlesnakes.
41

Insert 5: Modeling Plant Species Distributions

We modeled plant species distributions after Cole and others (2008), based on the concept 

of climate limits (fig. 5.1). 

Figure 5.1. Conceptual model of the process used to create future plant species projections, including 

effects of migration rates, for plants used in modeling animal species ranges.
42

Climatic limiters provide important insights into the ability of species to adapt to future 

climates. We used a method called ClimLim to infer climatic limits for a given species based on its 

documented current distribution. This method is similar to the approach described by Arundel 

(2005). We gathered presence and absence data for individual plants from various published 

sources, such as the Forest Inventory Analysis, and created ranges for each species either by 

interpolation or hand digitizing. The ClimLim program associates the 36 climate variables available 

from PRISM with species presence and absence points, calculates which variables are associated 

with the edges of the species current distributions, and ranks their importance. We used the climate 

variables identified as limiters by ClimLim as candidate explanatory variables in logistic regression 

models, and then applied the resulting logistic regression equations to the various GCM grids to 

project potential suitable climates, which corresponded with probabilities of species occurrences. 

We also applied the logistic regression models to the original PRISM climate data to project current 

potential distribution. In total, we estimated the current and future potential suitable climate for 50 

dominant plant species (table 5.1) for the periods 2010–39, 2040–69, and 2070–99. 

Plant migration rates were integrated into future projections for 17 of the 50 plant species 

included in the model, utilizing a migration rate of 50 m/year for long-lived "climax" species, and a 

rate of 500 m/year for early successional species (table 5.1). 
43

Table 5.1. Plants species for which current and future potential suitable climates were modeled. Migration 

estimates were made for a subset of the indicated species.

Species Common name Migration preformed

Juniperus deppeana alligator juniper X

Juniperus monosperma One-seed juniper X

Juniperus osteosperma Utah juniper X

Juniperus scopulorum Rocky Mountain juniper X

Abies concolor white fir X

Abies lasiocarpa subalpine for X

Abies lasiocarpa var. arizonica corkbark fir X

Picea engelmannii Engelmann spruce X

Picea pungens blue spruce X

Pinus aristata bristlecone pine X

Pinus edulis pinyon pine X

Pinus flexilis limber pine X

Pinus jeffreyi Jeffrey pine

Pinus monophylla var. fallax singleleaf pinyon X

Pinus monophylla var. monophylla singleleaf pinyon

Pinus ponderosa ponderosa pine X

Pinus strobiformis southwestern white pine X

Pseudotsuga menziesii Douglas spruce X

Rhus ovate sugar sumac

Rhus trilobata skunkbush

Baccharis pteronioides yerba de pasmo

Encelia farinosa brittlebush

Gutierrezia sarothrae broom snakeweed

Tetradymia canescens gray horsebrush

Carnegiea gigantea saguaro

Salsola tragus tumbleweed

Arctostaphylos pungens pointleaf manzanita

Acacia greggii Catclaw acacia

Robinia neomexicana New Mexico locust

Quercus arizonica Arizona white oak

Quercus emoryi Emory oak

Quercus gambelii Gambel oak X

Quercus turbinella scrub oak

Garrya wrightii wright silktassel

Ceanothus greggii desert ceanothus

Cercocarpus ledifolius curl-leaf mountain mahogany

Cercocarpus montanus alderleaf mountain mahogany

Coleogyne ramosissima blackbrush

Populus tremuloides quaking aspen

Nolina microcarpa beargrass

Achnatherum hymenoides Indian ricegrass

Bouteloua eriopoda black grama

Bouteloua gracilis blue grama

Bromus rubens foxtail brome

Bromus tectorum cheatgrass

Centaurea diffusa diffuse knapweed

Elymus elymoides bottlebrush squirreltail

Muhlenbergia montana mountain muhly

Pleuraphis jamesii galleta

Poa fendleriana muttongrass
44

These estimates were used to create “buffers” around current ranges of a size determined by 

the migration rate and the duration of each time interval. We then intersected the buffer and the 

current range with the potential suitable climate for each time interval. The output represented the 

projected distribution for the next time interval. This process provided a coarse estimate of where 

the given species could no longer live (unsuitable); where the climate conditions were suitable but 

natural migration rates were not sufficient to keep pace with change (assisted migration); and where 

unsuitable habitat at a previous time interval became suitable again (recolonize). These estimates 

did not incorporate anomalous biotic as well as some abiotic factors that determine species 

migration. Potential adaptive strategies also were not accounted for by these models. Figure 5.2 

shows a sample of the pinyon pine maps generated as the results of this project. 

Figure 5.2. Model-generated example showing projected changes in a plant species distribution 

incorporating the effects of migration for the three periods 2010–39, 2040–69, and 2070–99. The featured 

species is pinyon pine (Pinus edulis), which we used as part of the model to project future distributions of 

pinyon jay. Projections are based on the ensemble future warming scenario, which is the average of the 

results of the 22 global climate models developed by the Intergovernmental Panel on Climate Change Fourth 

Assessment Report (Solomon and others, 2007).
45

Insert 6: Modeling Bird Distributions

Statistical design—We developed an environmental dataset for spatial modeling bird 

distributions by using a case–control sampling design (Keating and Cherry, 2004). Specifically, we 

compared a set of randomly selected presence locations with a set of absence locations, using 80 

percent of presence-absence locations for model construction and 20 percent for model testing. 

Absence data were selected randomly from areas that had been searched between 1990 and 2009 

and found to not contain a given bird species. Whenever a cell contained both presence and absence 

data, given that one year may contain the focal species and the next year not, we prioritized 

presence over absence. Thus, any cell between 1990 and 2009 that contained a presence record of a 

focal species was assigned a presence rating. Presence-absence observations were assigned to the 

center of the cell (centroid), thus all detections were at least 4 km apart from one another. For 

robust estimation of each parameter, we ensured that there were at least 10 presence observations 

per parameter in every candidate model (Peduzzi and others, 1996). 

We initially conducted an intensive analysis of two bird species (pinyon jay and Virginia’s 

warbler) to discern how spatial auto-correlation (Legendre, 1993) affects model parameters, and to 

ascertain how Maxent and logistic regression differ in predictive capabilities. Specifically, we 

hoped to find how birds and habitat correlate at multiple scales of analyses, and to build a 

crosswalk between model results obtained from Maxent and logistic regression. Although we could 

not conduct intensive modeling for every bird species, we hoped to gain greater understanding of 

our overall results through our seven intensively modeled subsets of bird species. 

Spatially explicit modeling—We used binary logistic regression, considering both 

univariate and multivariate models, to identify environmental associations and to develop equations 

useful for spatially explicit (GIS-based) distribution models. Logistic regression is ideal for 

evaluating relationships between predictor variables and a species range because presence-absence 

data are binary (Keating and Cherry, 2004). We used Arc/Info® GRID (Environmental Systems 

Research Institute, 1992) to calculate and map the probability that a species would be present 

within 16-km2 (4 km × 4 km) cells. We calculated the relative probability (P) with the following 

equation: 

P = e

g(x)

 / 1 + e

g(x)

 (1)

where : 

g(x) is the linear combination of parameter estimates obtained from the logistic 

regression (Hosmer and Lemeshow, 1989). In equation 1, the relative suitability 

of an area is linked to the probability of breeding season occupancy based on the 

logistic regression under consideration. Each model assigned cells a probability 

between 0 and 99 percent. 

 e an exponential function, approximately equal to 2.718281828. 
46

Multivariate model selection and fit—We only considered variables derived from our 

conceptual models for model development and verification, as opposed to using large datasets tied 

to automated forward or backward stepping routines and Akaike’s Information Criterion (AIC) 

selection. We screened variables for collinearity by examining pairwise Pearson’s correlation 

coefficients among all pairs of variables, then evaluated the significance of each covariate in our 

models with the G-statistic, which is analogous to the χ2 statistic, produced from backwards 

stepping and a log-likelihood ratio test (Hosmer and Lemeshow, 1989). We checked for linearity 

between the logit and continuous variables through the incorporation of quadratic and cubic higherorder terms). Given the importance of habitat suitability in our analyses, we relied upon area-underthe-curve (AUC; Egan, 1975) and a classification table (Story and Congalton, 1986) to determine 

the performance of each candidate model. We did not rely solely on AIC (Burnham and Anderson, 

2002) in our selection process because it consistently supported very large and cumbersome models 

that were difficult to interpret and performed little or no better than far simpler models. For brevity 

sake, we only present models that achieved the highest classification accuracies and were 

ecologically interpretable.

Evaluation of included variables—We evaluated the relative contribution of variables 

included in each logistic regression model by the Wald χ

2

 statistic, which is proportional to the 

contribution of a variable to explaining variation in the response, and is conditional on the model. 

Model verification—Spatial accuracies of our logistic-regression models were assessed 

with an independent dataset (that is, withheld bird species presence locations not used in model 

development). Model accuracies depended upon a movable probability cutpoint used to delineate 

(extract) suitable versus unsuitable breeding locations (cells) from the probability grid. For our 

analyses, we selected probability cutpoints (thresholds) that roughly balanced commission and 

omission errors. Model accuracies were calculated as model sensitivity (1−omission error), 

specificity (1−commission), and overall accuracy (sensitivity + specificity/2). Presence locations 

that fell outside of a projected distribution were counted as an omission error (Story and Congalton, 

1986), whereas cells that were predicted to be occupied but found to be empty were counted as a 

commission error.
47

Insert 7: Modeling Reptile Distributions 

Statistical design and spatially explicit modeling—All reptile models were fitted in Maxent 

by taking advantage of built-in functions for random seeds, background selection, cross-validation, 

and model averaging (Phillips and Dudík, 2008). Although choice of background data can have 

important effects on projections (VanDerWal and others, 2009), we used a random background set 

of data from the entire set of possible locations throughout western United States to serve as 

pseudo-absences. A large number of locations (10,000) from a broad range of conditions was used 

to ensure good representation of all possible environments, which is important when models are to 

be projected into very different conditions, as would be the case with future climates (Elith and 

others, 2010). We debiased background samples by weighing samples according to variations in 

cell area because all covariate data for this study were unprojected. We then fitted models using 

five-fold cross-validation to estimate errors around fitted functions and to determine predictive 

performance using withheld data (Elith and others, 2010). In practice this meant that the algorithm 

randomly selected and withheld 20 percent of the cells containing presences for use in model 

testing for each “fold.” The remaining 80 percent of reptile species presences were used for model 

training.

Multivariable model selection—All variables that we identified as important in the 

conceptual models for each reptile taxon were initially included in a model, although we did not 

include any variables that would account for spatial patterns in the distributional data. For the most 

part we used default settings in Maxent for model development, but restricted variable specification 

to hinged, quadratic, and categorical features (in machine learning language, features are 

transformations of variables into functions). These features produce model projections similar to 

those based on generalized linear or additive models (Phillips and Dudík, 2008; Elith and others, 

2010), which made the models produced by Maxent comparable to those developed for birds using 

logistic regression. We did not select for automatic examination of interacting variables because of 

the complexity of interpreting ad hoc interactions, nor did we select linear or threshold features 

because these tend to be redundant with hinge features (Elith and others, 2010). Default 

regularization parameters were selected for all models because we did not have data on the relative 

or absolute detectability of the different reptile species.

We examined correlations among all continuous variables by calculating the Pearson 

pairwise correlation coefficients using “ENM Tools” (Ecological Niche Modeling Tools software 

by Warren and others, 2010). However, we did not exclude correlated variables because the 

strength of correlations varied spatially and because analyses also have shown that Maxent is more 

stable to the effects of correlated variables compared to stepwise regression (Hastie and others, 

2009). To choose whether models should include the effects of plant species distributions, we first 

calculated raw outputs from Maxent models to select the most parsimonious model based on AIC, 

AICc, and BIC (Burnham and Anderson, 2002; Warren and Seifert, 2011). Then, using the most 

parsimonious model, we calculated both models that included and excluded the effect of 

distribution of different plant species. 
48

Model and variable evaluation—We evaluated in several ways variables that were included 

in the final models, using the built-in jackknife test that quantifies the contribution of each variable 

to the model (expressed as percent and based on increase in gain; Phillips and others, 2006) as well 

as permutation importance (Phillips and Dudik, 2008). We then ran models excluding variables 

with less than a 5 percent contribution and examined differences in area under the curve (AUC) for 

training and test data. We used AUC of the receiver operating characteristic (ROC) as a thresholdindependent measure of model performance (Elith and others, 2006). We plotted ROC for all 

possible thresholds, with sensitivity (true positive rate) on the y-axis and 1-specificity (false 

positive rate) on the x-axis (Fawcett, 2003). We characterized the AUC performance of each model 

at all possible thresholds, summarized by a number ranging from 0 to 1, where 1 indicates perfect 

model performance, 0.5 indicates the equivalent of a random process (presence and background not 

different), and less than 0.5 indicates performance worse than random (Phillips and others, 2006). 

Projecting changes in ranges—Once adopted, we projected the final model of current 

conditions according to 15 different future scenarios (three different time periods and five different 

GCMs). In the case of Maxent, we applied the option to “fade by clamping” because we applied the 

models to future climates with unknown variable ranges. This option reduced predictions at each 

point by the difference between clamped and non-clamped output (Phillips and Dudík, 2008). Our 

model results are provided as the logistic model output. This allowed us to represent potential 

habitat in each cell as a scaled index (Phillips and Dudik, 2008) and, from that, calculate changes in 

projected habitats based on predefined output thresholds above which projections were considered 

to be suitable for the given species (Pearson and others, 2007). Although there are many ways to 

calculate these thresholds (Liu and others, 2005), the choice depends on the purpose of the model 

(Lobo and others, 2008). We calculated thresholds by equalizing sensitivity and specificity for 

training samples, assuming that such an approach equalized the risks of over- and under-specifying 

(Freeman and Moisen, 2008). We then calculated the percentage of habitats “gained” or “lost” by 

using model ensembles of five out of six models being concordant (>83 percent concordance) and 

compared statistics generated on area assuming either no dispersal or unlimited dispersal. 
49

Insert 8: Data Storage and Delivery 

It was decided that the most reasonable repository for all of the data from this NCCWSC 

project would be at the Geospatial Research and Information Laboratory (GRAIL) site at Northern 

Arizona University (NAU). There were already a number of large data sets being managed by 

GRAIL, and the NCCWSC data would complement existing Colorado Plateau information. 

Moreover, we felt that access to data would be easier at a public state institution rather than through 

a federal agency. To this end, a computer server was purchased and dedicated solely at GRAIL for 

Southwest NCCWSC data storage. 

Data delivery for this project was divided into two overlapping frameworks: (1) data 

delivery and archiving for NCCWSC project scientists; and, (2) data delivery and archiving of final 

products to all stakeholders, including DOI agency personnel, state and tribal agencies, and the 

public. In order to provide rapid and secure access to preliminary data and working datasets to 

project scientists, we used several tools, including ftp, ssh, and ESRI geodatabases. For final 

archiving and data delivery to stakeholders, we will use FGDC metadata and national metadata 

clearinghouses for documentation and discovery of data and websites, and a web mapping 

application for final delivery.

Our project ftp site is: ftp://grail1.bio.nau.edu/. This ftp site is being used to house both 

input data and preliminary model output. As of June 2012 there were 95 gigabytes (GB) of 

NCCWSC data stored on the ftp server. This ftp server now requires a login because of new 

security vulnerabilities within the ftp protocol. Final data products will be made available through 

public access websites.

Our metadata clearinghouse is: http://mprlsrvr1.bio.nau.edu/metadataexplorer/

We have now uploaded metadata for more than 50 datasets to the Geodata.gov 

(http://catalog.data.gov/dataset). We will continue to upload additional metadata as datasets are 

documented.

Much of the working data for the southwest NCCWSC project is stored in an ArcSDE®

geodatabase and processed using ArcGIS® and customized scripts. The geodatabase for this project, 

housed within GRAIL at NAU, is approximately 30 GB and comprises 30 raster catalogs, each of 

which includes a few dozen raster datasets. 

Several feature classes and custom tool boxes also are included in this NCCWSC 

geodatabase. In addition, there are multiple free-standing SQL databases and stored procedures 

which were used by the project programmer and GIS Analysts to format, select, and analyze 

various climate datasets (figs. 8.1, 8.2, 8.3). 
50

Figure 8.1. Screen capture showing home page of the Southwest Plant Atlas (Southwest Plant Atlas, 

http://www4.nau.edu/swplantatlas/endemic.html). This website contains the model output and explanatory 

material for the plant species distribution modeling effort.
51

Figure 8.2. Screen capture of Southwest Plant Atlas (Southwest Plant Atlas, 

http://www4.nau.edu/swplantatlas/endemic.html) species website page for pinyon pine (Pinus edulis) showing 

the layout of the model output and explanatory variable selection.
52

Figure 8.3. Screen capture showing species website pages of the Southwest Animal Atlas (Southwest Plant 

Atlas, http://www4.nau.edu/swplantatlas/endemic.html) for pinyon jay (Gymnorhinus cyanocephalus) and 

Arizona black rattlesnake (Crotalus Cerberus) including the species description and conceptual model 

diagrams. The first pane (pinyon jay) shows the a priori version of conceptual model and the resulting final 

model after incorporating results from the statistical models. Both the original conceptual model and resulting 

final model are shown for every bird and reptile species that was modeled in the southwestern NCCWSC 

project. Additionally, by clicking on boxes in the conceptual models, one is directed to a link that explains each 

variable or process.

All southwest NCCWSC datasets are made available to the mapping application (and 

directly to any ArcGIS® user) through REST (Representative State Transfer) services hosted on 

cpbc.bio.nau.edu. A catalog of these REST services can be seen at: 

http://cpbc.bio.nau.edu/CPBC_ArcGIS/rest/services. End users may directly connect to any of 

these services using ArcMap or other REST enabled mapping tools.

Finally, we have populated http://www4.nau.edu/swplantatlas/maps.html with data and 

background information on modeling techniques and rational. These websites were designed using 

a standard html and css framework with virtual directories pointing to large file servers to hold the 

large number of map images for each species. As of June 2012, all plant and animal species 

included in our models have been added to the websites. 
53

Insert 9: Website Application 

This southwest NCCWSC project is an ambitious effort to provide managers and other 

stakeholders with an understanding of how the ranges of a select group of animal and plant species 

in Southwestern United States will be affected by global climate change. To accomplish this goal, a 

website has been developed that will serve as the primary interface between the stakeholder group 

of users and the spatial data assembled by project scientists.

We considered various approaches to presenting our southwest NCCWSC data in a 

manageable and maximally informative way. Typically, most web-based approaches collapse a 

time-series of spatial data into a single spatial extent and place the burden of contextualizing the 

data on the end-user.

Another approach, exemplified by the Point Reyes Bird Observatory California Avian Data 

Center website (California Avian Data Center, 

http://data.prbo.org/cadc2/index.php?page=climate-change-distribution), is to present a current 

species range juxtaposed by a single, projected future range. This was adopted by the southwestern 

NCCWSC as a model for our first website design, but has a number of usability issues, the most 

critical being the inability to compare selected time-periods and models at a glance. Nonetheless, 

this design imposes fewer demands on the end-user: no downloading or sequential loading of data 

required. It is a reasonable, if incomplete, solution for displaying a time-series at varying spatial 

scales.

Our final design is unlike any currently extant websites. We relied heavily on the work of 

Edward Tufte (Tufte, 2013), who advocates presenting “slices of information” in “parallels of time 

and space,” enabling users to see trends and make comparisons. Our website not only incorporates 

these ideas, but also extends them. Static displays are now dynamic—users may select from a menu 

of time-periods and models to produce custom displays of data. Additionally, a “tethered” zoom 

feature allows a selected area of interest within a single window to be presented in all windows; this 

allows an end-user to immediately see variation across time and among models at different spatial 

scales (see fig. 9.1).
54

Figure 9.1. Consecutive screen captures of the project website showing distribution data for Pinus 

ponderosa in the western United States (darker blue-green indicates greater likelihood). The columns 

represent time, and the rows represent the individual models used for projection (in this case, Centre National 

de Recherches Meteorologiques (CNRM) (http://wwwpcmdi.llnl.gov/ipcc/model_documentation/CNRM-CM3.htm [accessed 9/12/2013] and Commonwealth 

Scientific and Industrial Research Organization (CSIRO) (http://www.csiro.au/[accessed 9/12/2013]). The 

bright-blue bounding box in the upper image reflects the user chosen window for zooming and the bottom 

shows the change—Montana, Idaho, and Wyoming are now the only visible state names. Notice that not only 

do all cells maintain the same spatial extents at any given scale, they also allow end-users to identify trends 

and make immediate comparisons/contrasts. 
55

This southwestern NCCWSC website design is a major advance in building displays that 

allow users to fully explore multi-dimensional datasets. However, the website is still in a 

development stage and a number of features have been suggested by the Advisory Team to 

complete its functionality. This includes a suite of analytic tools that allow users to quantify various 

properties of the displayed imagery; for example, calculating the area where a retraction in range 

has occurred. Additionally, under consideration is a blink comparator—this would be the software 

analog of the mechanical device used by astronomers to track foreground changes against a static 

background of stars. In this case, the static image is one of the distribution images from a particular 

time period and model, over which another image is quickly inserted and removed. 

Web Site Development Methods

The primary goal of our website design is to display projected time-series data according to 

various model outputs. This immediately suggests a dynamic matrix or grid as the key design 

element. Unfortunately, there are no easily accessible extant technologies for addressing this 

approach. The mapping application uses data housed in an ESRI geodatabase and served as ESRI 

mapping services through ArcGIS® Server 10 at 

http://cpbc.bio.nau.edu/CPBC_ArcGIS/rest/services (ArcGIS® Server 10 at 

http://cpbc.bio.nau.edu/CPBC_ArcGIS/rest/services. [accessed 9/12/2013]). Both raster (bitmap) 

and vector graphics are served in this manner. 

Outside of the key design consideration noted above, a number of usability principles have 

been incorporated into our web interface. A small sampling includes having no menu item more 

than two levels deep, frequently used tools having a larger design footprint than is found in a 

typical mapping application, storage of user selections so that a return visit brings the user back to 

the previous state, and last (but not least), a minimalist interface for advanced users. Future work 

will center on testing website usability with the AT members.

For those technically inclined, a brief summary of the tools used to build this website and a 

rationale follows. The core technology behind the website is Actionscript®. Language features like 

composite user interface components, advanced datagrids, and item renderers have allowed us to 

construct a website that is simple to use and yet offers a powerful way of accessing large 

collections of multi-dimensional data—without the output being unsightly. Additionally, 

Actionscript® has the ability to use C code to do processor-intensive tasks. In our case, we are using 

a C-based shapefile parsing library to display vector data inside of our web application. This 

functionality, currently being integrated, should provide a measurable boost to performance. 

We have scoured various forums for any hint that ESRI considers multiple maps in a single 

web-based application as a design goal but could find no extant working examples. Flex-based 

Bing® maps rely on ESRI’s technology and were excluded from consideration. Google MapsTM 

does provide software tools that allow user-generated bitmap data to be overlaid on its imagery 

layers. This allows end-users to see orienting features like state boundaries or highways as they 

navigate the map. Other sources, including Openscales and Openlayers for instance, have similar 

features but lack the technical aspects required by the southwestern NCCWSC stakeholder end 

users.
56

Insert 10: Bird Species Count

BLACK-THROATED SPARROW

Amphispiza bilineata

The black-throated sparrow inhabits arid upland habitats of the 

southwestern United States and Mexico, and breeds as far north as 

Washington. Throughout its range it prefers semi-open habitat with evenly 

spaced shrubs and trees 1-3 m high. Habitat and vegetation density may

influence habitat selection more than specific shrub species, although it is

closely associated with creosote bush (Larrea tridentata) throughout the

southern part of its range. During the breeding season it is a groundforaging omnivore, eating a variety of insects and seeds. Their food 

resourcesin arid environments depend on climatic conditions. Wet years

yield more arthropods and seeds than do drier years. Initiation of nest

building by the sparrow is variable depending on rainfall, elevation, and 

food availability. In desert areas, nesting is initiated by midsummer rains, 

but nesting may also start in early spring in years of adequate winter

rainfall. In southern New Mexico and central Arizona, their nests tend to be 

oriented in northerly and easterly directions, which is thought to provide 

early-morning sun, maximum shade, and shelter from afternoon sun and 

southwesterly winds.

Figure 10.2. Conceptual model of factors affecting black-throated sparrow range and 

population dynamics, including explanatory variables used to model current range (in green 

and circled red). The relative strength of different effects is denoted by the width of connecting 

arrows.

Figure 10.1. Amphispiza bilineata, 

(photo by Jim Rorabaugh/U.S. Fish and 

Wildlife Service).
57

RANGE PROJECTIONS - 2099

Figure 10.3. Predicted black-throated 

sparrow range in 2099, showing levels

of agreement among predictions based 

on 5 different statistically downscaled 

GCMs plus a 6th representing an 

ensemble of 22 GCMs. White denotes 

areas where all projections agree there 

will not be sparrow range, whereas blue 

denotes areas where all agree there will 

be sparrow range.

Number of projections agreeing 

that there will be black-throated 

sparrow range in 2099.

Figure 10.4 . Areas where ≥5 GCM 

projections predict there will be blackthroated sparrow range in 2099 compared 

to range in 2000, showing areas of range 

loss (red), range gain (green), and range 

persistence (blue) between 2000 and 2099.
58

Model accuracy 77%

AUC with plant species ranges 0.838

AUC without plant species ranges 0.836

1. Terrain ruggedness(TRI) quadratic

2. Mean spring precipitation (PPTAVR_spr) monotonic -

3. Creosote (Larrea tridentata) range (LATR) monotonic +

4. Mean winter (December-February) temperatures(TMPAVG_win) monotonic -

5. Minimum temperature – June (TMPMIN_06) monotonic +

MODEL RESULTS AUC: Area under the curve

 

Rank order of variable influence in the model Relationship

Figure 10.5. Relative influence of variables retained in the 

best model of current range, as measured by the chisquared statistic. Green denotes a positive effect; red 

denotes a negative effect. Variables with a “2” denote a 

quadratic term.

Figure 10.6. Measures of range change among four decadal intervals for the period 2000 through 2099. The figure on 

the left shows extent of modeled current or projected future range reckoned in terms of area. The figure on the right 

shows the change in range extent as a percent of current range. Green bars are results produced by the model that 

includes the effect of plant species ranges; red bars are results produced by the model that does not include plant range 

effects. Values were calculated based on areas of agreement of at least 5 of the 6 climate projections.
59

GRAY VIREO

Vireo vicinior

The gray vireo inhabits some of the hottest, most arid regions of the 

southwestern United States. It is a short-distance migrant and withdraws 

completely from most of its breeding range in winter. It prefers mixed juniper,

pinyon, and oak scrub associations, chaparral in hot, arid 

mountains, and high plains scrubland. It eats a variety of arthropods, including 

large grasshoppers, cicadas, and caterpillars, which it gleans from the leaves,

twigs, branches and trunks of small trees or twigs and branches of shrubby 

vegetation foliage. Gray vireo nests are often constructed on west- or northfacing tree forks. The nesting pair alternate brooding, which is continuous from 

hatching of first nestling and diminishes after the 6th day. Eggs are vulnerable to

exposure to sun, and both adults have been observed shading nestlings. This

species is on the 2007 (most recent) WatchList for United States Birds and the 

USFWS Federal list of birds of conservation concern due to its rarity. 

Figure 10.8. Conceptual model of factors affecting gray vireo range and population 

dynamics, including explanatory variables used to model current range (in green and 

circled red). The relative strength of different effects is denoted by the width of connecting 

arrows.

Figure 10.7. Vireo vicinior, photo 

by Brian E. Small/Utah Division 

of Wildlife Resources
60

RANGE PROJECTIONS - 2099

Number of projections agreeing 

that there will be gray vireo 

range in 2099.

Figure 10.9. Predicted gray vireo range 

in 2099, showing levels of agreement 

among predictions based on 5 different 

statistically downscaled GCMs plus a 

6th representing an ensemble of 22 

GCMs. White denotes areas where all 

projections agree there will not be vireo 

range, whereas blue denotes areas

where all agree there will be vireo range.

Figure 10.10. Areas where ≥5 GCM 

projections predict there will be gray vireo 

range in 2099 compared to range in 2000, 

showing areas of range loss (red), range 

gain (green), and range persistence (blue) 

between 2000 and 2099.
61

Model accuracy 70%

AUC with plant species ranges 0.75

AUC without plant species ranges 0.73

MODEL RESULTS

Rank order of variable influence in the model Relationship

1. Maximum breeding season (April-August) temperature (TMPmax_aa) quadratic

2. Terrain ruggedness(TRI) quadratic

3. Juniperous osteosperma range (JUOS_rbf) monotonic +

4. Solar insolation (Solar_May) monotonic +

5. Mean breeding season (April-August) precipitation monotonic +

Figure 10.11. Relative influence of variables 

retained in the best model of current range, 

as measured by the chi-squared statistic. 

Green denotes a positive effect; red denotes 

a negative effect. Variables with a "2" 

denote a quadratic term.

Figure 10.12. Measures of range change among four decadal intervals for the period 2000 through 2099. The figure on the 

left shows extent of modeled current or projected future range reckoned in terms of area. The figure on the right shows the 

change in range extent as a percent of current range. Green bars are results produced by the model that includes the effect 

of plant species ranges; red bars are results produced by the model that does not include plant range effects. Values were 

calculated based on areas of agreement of at least 5 of the 6 climate projections.
62

VIRGINIA'S WARBLER

Vermivora virginiae

The Virginia’s warbler has a relatively restricted breeding range, inhabiting scrubby 

habitats below the pine belt in northern part of its range, and scrubby habitats within

surrounding conifers in southern part of its range. For breeding habitat, it shows a 

strong association for steep draws, drainages, or slopes with oak or other shrubby 

vegetation. In Arizona, this species tends to select locust (Robinia neomexicana)- or oak

(Quercus sp.)- dominated sites, which indicate a preference for drier conditions and 

higher daytime temperatures. In addition, pine-oak-locust habitat with more mesic

maple (Acer spp.) had territories established before the same habitat without the maple

component, suggesting a preference for moister microhabitats when available. The

Virginia’s warbler is insectivorous and leafy vegetation is especially important for 

foraging. In southern Arizona it spends a disproportionate amount of time foraging in

oak, even though coniferous foraging habitat is four times more abundant. They build

their nests on the ground, and nesting activity begins soon after pairing but may be 

delayed if snow cover persists, and in periods of poor weather. Nests may also be 

abandoned in periods of inclement weather. This species is on the 2007 (most recent)

WatchList for United States Birds due to its rarity. 

Figure 10.14. Conceptual model of factors affecting Virginia’s warbler range and 

population dynamics, including explanatory variables used to model current range (in 

green and circled red). The relative strength of different effects is denoted by the width of

connecting arrows.

Figure 10.13.

Vermivora virginiae, 

Photo by P. 

Dotson/Utah Division 

of Wildlife Resources
63

RANGE PROJECTIONS - 2099

Number of projections agreeing 

that there will be Virginia’s 

warbler range in 2099.

Figure 10.15. Predicted Virginia’s warbler 

range in 2099, showing levels of 

agreement among predictions based on 5 

different statistically downscaled GCMs 

plus a 6th representing an ensemble of 22 

GCMs. White denotes areas where all

projections agree there will not be

warbler range, whereas blue denotes

areas where all agree there will be

warbler range.

Figure 10.16. Areas where ≥5 GCM 

projections predict there will be 

Virginia’s warbler range in

2099 compared to range in 2000, showing 

areas of range loss (red), range gain 

(green), and range persistence (blue) 

between 2000 and 2099.
64

MODEL RESULTS 

Area Under the Curve (AUC) Model accuracy 73%

AUC with plant species ranges 0.78

AUC without plant species ranges 0.76

Rank order of variable influence in the model Relationship

1. Terrain ruggedness(TRI) quadratic

2. Oak (Quercus gambelii) range (qugabf) monotonic +

3. Mean winter (Dec. – Feb.) precipitation (pptavg_wk) monotonic -

4. Mean summer (June – Aug.) temperature (tmpavg_sk) quadratic

5. Mean summer (June – Aug.) precipitation (pptavg_sk) quadratic

6. Solar insolation (solarmayk) quadratic

Figure 10.17. Relative influence of 

variables retained in the best model of 

current range, as measured by the chisquared statistic. Green denotes a 

positive effect; red denotes a negative 

effect. Variables with a "2" denote a 

quadratic term.

Figure 10.18. Measures of range change among four decadal intervals for the period 2010 through 2099. The 

figure on the top shows extent of modeled current or projected future range reckoned in terms of area. The 

figure on the bottom shows the change in range extent as a percent of current range. Green bars are results 

produced by the model that includes the effect of plant species ranges; red bars are results produced by the 

model that does not include plant range effects. Values were calculated based on areas of agreement of at 

least 5 of the 6 climate projections.
65

SAGE THRASHER

Oreoscoptes montanus

The sage thrasher is considered a sage obligate, and is generally dependent on large 

patches and expanses of sagebrush steppe for successful breeding. It primarily inhabits

areas dominated by big sagebrush (Artemisia tridentate), but has been noted in black

greasewood (Sarcobatus vermiculatus) habitat in areas in Utah and Nevada, and 

bitterbrush (Pursia tridentate) habitat in Washington. Within its range it is more abundant 

in areas with high sagebrush cover, tall shrubs, woody cover, and bare ground and less

abundant in areas with higher percent cover of grasses. In Washington, occurrence was

greater on loamy and shallow soils than on sandy soils. Besides the impacts of habitat loss,

little is known about factorsregulating sage thrasher numbers throughout their range. 

Temperature and insolation appear to affect nest site selection. Nests are oriented with

more easterly exposuresthan westerly ones, and nest placement patterns are generally

consistent with the hypothesis that nests are placed so as to guard against heat loss at

night, to avoid direct exposure to the sun during the heat of the day, and to gain heat

early in the morning. Temperature and precipitation likely have a greater effect on the

distribution of the sage thrasher’s habitat. Sagebrush grows in areas with cold winters and 

summer drought. Recruitment of subspecies of big sagebrush is dependent upon 

precipitation amounts during specific time periods, depending on the subspecies.

Figure 10.20. Conceptual model of factors affecting Virginia’s warbler range and 

population dynamics, including explanatory variables used to model current range (in 

green and circled red). The relative strength of different effects is denoted by the width of 

connecting arrows.

Figure 10.19. 

Oreoscoptes montanus, 

photo by Dave 

Menke/U.S. Fish and 

Wildlife Service.
66

RANGE PROJECTIONS - 2099

Number of projections agreeing 

that there will be sage thrasher 

range in 2099.

Figure 10.21. Predicted sage thrasher 

range in 2099, showing levels of 

agreement among predictions based on 

5 different statistically downscaled 

GCMs plus a 6th representing an 

ensemble of 22 GCMs. White denotes 

areas where all projections agree there 

will not be thrasher range, whereas blue 

denotes areas where all agree there will 

be thrasher range.

Figure 10.22. Areas where ≥5 GCM 

projections predict there will be sage 

thrasher range in 2099 compared to range 

in 2000, showing areas of range loss (red), 

range gain (green), and range persistence 

(blue) between 2000 and 2099. Note: the 

predicted range of sagebrush (Artemisia 

tridentata) was not modeled.
67

MODEL RESULTS Model accuracy 70% Area Under the Curve (AUC) AUC with plant species ranges 0.80

AUC without plant species ranges 0.79

Rank order of variable influence in the model Relationship

1. Mean winter (Dec. – Feb.) precipitation (PPTAVG_W) monotonic - 

2. Maximum June temperature (TMPMAX_06) monotonic -

3. Mean July precipitation (PPTAVG_07) monotonic -

4. Sagebrush (Artemisia tridentata) range (ARTR) monotonic +

5. Percent sand in soil (PRESAND) monotonic -

6. Terrain ruggedness(TRI) quadratic

Figure 10.23. Relative influence of variables retained in the best model of current range, as measured by the 

chi-squared statistic. Green denotes a positive effect; red denotes a negative effect. Variables with a "2" 

denote a quadratic term.

Figure 10.24. Measures of range change among four decadal intervals for the period 2000 through 2099. The figure on 

the left shows extent of modeled current or projected future range (without plant ranges) reckoned in terms of area. 

The figure on the right shows the change in range extent as a percent of current range. Green bars are results produced 

by the model that includes the effect of plant species ranges; red bars are results produced by the model that does not 

include plant range effects. Plant range effects were not predicted for the sage thrasher. Values were calculated based 

on areas of agreement of at least 5 of the 6 climate projections.
68

PINYON JAY

Gymnorhinus cynanocephalus

Pinyon jays coevolved as non-migratory mutualists with pinyon pines. Jays

disperse the large wingless seed of pinyon pines over long distances, which

can result in relatively rapid colonization. At the same time, the jays obtain

energy and nutrientsfrom the seeds. Although closely associated with

pinyon pines in most areas, in parts of its range pinyon jays inhabit

ponderosa pine (Pinus ponderosa) and Jeffrey pine (P. jeffreyi) forests. During 

some years, especially when pinyon pine cones are rare or absent, jays

harvest and cache large numbers of ponderosa or Jeffrey pine seeds. Pinyon 

jays prefer to cache seeds at the base of large trees, boulders, or at the base

of cliffs, where less snow accumulates and where the snow that does 

accumulate melts more rapidly. Pinyon jays may assess insolation when nest

building, as in northern Arizona where >50% of nests are located on the

south-facing sides of trees. Females in south-facing nests receive up to 40% 

more solar energy compared to females in

north-facing nests, which matters, because pinyon jays are among the earliest

nesting passerines in the United States.

Figure 10.26. Conceptual model of factors affecting pinyon jay range and population 

dynamics, including explanatory variables used to model current pinyon jay range (in green 

and circled red). The relative strength of different effects is denoted by the width of 

connecting arrows.

Figure 10.25. Gymnorhinus 

cynanocephalus, photo by 

Dave Menke/U.S. Fish and 

Wildlife Service
69

RANGE PROJECTIONS - 2099

Figure 10.27. Predicted pinyon jay range in 2099, 

showing levels of agreement among predictions 

based on 5 different statistically downscaled GCMs 

plus a 6th representing an ensemble of 22 GCMs. 

White denotes areas where all projections agree 

there will not be pinyon jay range, whereas blue 

denotes areas where all agree there will be pinyon jay 

range.

Number of projections 

agreeing that there will be 

pinyon jay range in 2099.

Figure 10.28. Areas where ≥5 GCM projections 

predict there will be pinyon jay range in 2099 

compared to range in 2000, showing areas of 

range loss (red), range gain (green), and range 

persistence (blue) between 2000 and 2099.

Figure 10.29. Areas where ≥5 GCM projections 

predict there will be pinyon jay range in 2099, 

showing differences in modeled probabilities of 

range between 2000 and 2099. Darker blue 

denotes increased probability of range whereas 

darker red denotes decreased probability of 

range.

Difference in modeled 

probabilities between

2000 and 2099.
70

Model accuracy 73%

AUC with plant species ranges 0.80

AUC without plant species ranges 0.79

MODEL RESULTS Area Under the Curve (AUC)

Rank order of variable influence in the model Relationship

1. Mean winter (December-February) precipitation monotonic -

2. Mean maximum summer (May-September) temperatures quadratic

3. Pinus edulis range (pied) monotonic +

4. Pinus monophylla range (pimo) monotonic +

5. Pinus jeffreyi range (pije) monotonic +

6. Terrain ruggedness(TRI) quadratic

7. May solar insolation monotonic +

Figure 10.30. Relative 

influence of variables retained 

in the best model of current 

range, as measured by the chisquared statistic.

Green denotes a positive 

effect; red denotes a negative 

effect. Variables with a “2” 

denote a quadratic term.

Figure 10.31. Measures of range change among four decadal intervals for the period 2000 through 2099. 

The figure on the left shows extent of modeled current or projected future range reckoned in terms of area. 

The figure on the right shows the change in range extent as a percent of current range. Green bars are 

results produced by the model that includes the effect of plant species ranges; red bars are results produced 

by the model that does not include plant range effects. Values were calculated based on areas of agreement 

of at least 5 of the 6 climate projections.
71

PYGMY NUTHATCH

Sitta pygmaea

The pygmy nuthatch has a strong and almost exclusive preference for longneedled pines including ponderosa, Jeffrey, and similar species. It depends on 

cavities for nesting and roosting and has clear seasonal cavity

preferences, related to thermal insulation and ventilation. Seventy percent of nest

cavity openings face southward, othersface south and east, away from prevailing 

winds. Selection of the roost cavity is affected by weather and by characteristics of 

the cavity (such as type of wood, thickness of bole, size of entrance, and depth of 

cavity) that afford protection from outside ambient temperatures. Temperature and 

presence of snow influence both time of roosting and size of roost flock. Flocks roost

earlier as temperatures drop and flock size increases when snow covers ground. 

While roosting they use protected sites, huddling, and hypothermia for energy-saving 

mechanisms. The pygmy nuthatch feeds almost exclusively in pines and eats weevils,

leaf and bark beetles, and pine seeds. Energy assimilated increases with rising 

ambient temp and barometric pressure, and decreases with higher wind speeds and 

precipitation. Temperature and wind speed have greatest effect 

- individuals are less active on colder and windier days, when they are more apt to

roost in the shelter of a branch or in a cavity. 

Figure 10.33. Conceptual model of factors affecting pygmy nuthatch range and 

population dynamics, including explanatory variables used to model current range (in 

green and circled red). The relative strength of different effects is denoted by the width of 

connecting arrows.

Figure 10.32. Sitta pygmaea, 

photo by nps.gov
72

RANGE PROJECTIONS - 2099

Figure 10.34. Predicted pygmy nuthatch 

range in 2099, showing levels of 

agreement among predictions based on 5 

different statistically downscaled GCMs 

plus a 6th representing an ensemble of 22 

GCMs. White denotes areas where all

projections agree there will not be 

nuthatch range, whereas blue denotes 

areas where all agree there will be 

nuthatch range.

Number of projections agreeing 

that there will be pygmy 

nuthatch range in 2099.

Figure 10.35. Areas where ≥5 GCM 

projections predict there will be pygmy 

nuthatch range in 2099 compared to range 

in 2000, showing areas of range loss (red), 

range gain (green), and range persistence 

(blue) between 2000 and2099.
73

MODEL RESULTS Model accuracy 78% Area Under the Curve (AUC) AUC with plant species ranges 0.86

AUC without plant species ranges 0.85

Rank order of variable influence in the model Relationship

1. Mean fall (Sept. – Nov.) precipitation (pptavg_f) quadratic

2. Mean winter (Dec. – Feb.) temperatures (tmpavg_w) quadratic

3. Mean winter (Dec. – Feb.) precipitation (pptavg_w) quadratic

4. Pinus ponderosa range (pipo) monotonic +

5. Minimum summer (June – Aug.) temperature (tmpmin_s) quadratic

6. Terrain ruggedness(TRI) quadratic

Figure 10.36. Relative influence of 

variables retained in the best model of 

current range, as measured by the chisquared statistic. Green denotes a 

positive effect; red denotes a negative 

effect. Variables with a “2” denote a 

quadratic term.

Figure 10.37 . Measures of range change among four decadal intervals for the period 2000 through 2099. The figure 

on the left shows extent of modeled current or projected future range reckoned in terms of area. The figure on the 

right shows the change in range extent as a percent of current range. Green bars are results produced by the model 

that includes the effect of plant species ranges; red bars are results produced by the model that does not include plant 

range effects. Values were calculated based on areas of agreement of at least 5 of the 6 climate projections.
74

Williamson’s sapsucker

Sphyrapicus thyroideus

The Williamson’ssapsucker inhabits open coniferous and mixed coniferousdeciduous forests of western North America, and has been considered a sensitive 

indicator species because of its specific habitat requirements. Like other

sapsuckers, it drills conspicuous rings of holes (“sap wells”) into tree trunks,

specializing on eating coniferous sap and phloem. Breeders switch to a diet of 

ants during the nestling period. In the Southwest it occupies mixed conifer

forests, but may be more common in ponderosa pine (Pinus ponderosa) 

dominated forests in other areas. Acrossits range it breeds at middle to high 

elevationsin montane spruce-fir (Picea-Abies), Douglasfir (Pseudotsuga 

menziesii), lodgepole pine (Pinus contorta) and ponderosa pine forests, as well as

mixed deciduous-coniferous forest with quaking aspen (Populus tremuloides). It 

has a preference for snags as nest sites and in most places aspen snags are 

preferred over conifer snags. This species is on the 2007 (most recent) WatchList 

for United States Birds and the USFWS Federal list of birds of conservation 

concern due to its rarity. 

Figure 10.39 . Conceptual model of factors affecting Williamson’s sapsucker range and 

population dynamics, including explanatory variables used to model current range (in green 

and circled red). The relative strength of different effects is denoted by the width of 

connecting arrows.

Figure 10.38. Sphyrapicus 

thyroideus, photo by Sally 

King/U.S. Fish and Wildlife 

Service
75

RANGE PROJECTIONS - 2099

Number of projections agreeing 

that there will be Williamson’s 

sapsucker range in 2099.

Figure 10.40. Predicted Williamson’s 

sapsucker range in 2099, showing levels 

of agreement among predictions based 

on 5 different statistically downscaled 

GCMs plus a 6th representing an 

ensemble of 22 GCMs. White denotes 

areas where all projections agree there 

will not be sapsucker range, whereas 

blue denotes areas where all agree 

there will be sapsucker range.

Figure 10.41. Areas where ≥5 GCM 

projections predict there will be 

Williamson’s sapsucker range in 2099 

compared to range in 2000, showing areas 

of range loss (red), range gain (green), and 

range persistence (blue) between 2000 

and 2099.
76

MODEL RESULTS Model accuracy 81%

 Area Under the Curve (AUC) AUC with plant species ranges 0.87

 AUC without plant species ranges 0.85

Rank order of variable influence in the model Relationship

1. Pinus jeffreyi range (pije) monotonic + 

2. Mean breeding season (April-July) temperatures quadratic

3. Pseudotsuga menzeisii range (psmeg) monotonic +

4. Pinus ponderosa range (pipo) monotonic +

5. Mean breeding season (April-July) precipitation quadratic

6. Terrain ruggedness(TRI) quadratic

Figure 10.42. Relative influence of variables retained in the best model of current range, as measured by the chisquared statistic. Green denotes a positive effect; red denotes a negative effect. Variables with a “2” denote a 

quadratic term.

Figure 10.43 . Measures of range change among four decadal intervals for the period 2000 through 2099. The figure on the 

left shows extent of modeled current or projected future range reckoned in terms of area. The figure on the right shows the 

change in range extent as a percent of current range. Green bars are results produced by the model that includes the effect 

of plant species ranges; red bars are results produced by the model that does not include plant range effects. Values were 

calculated based on areas of agreement of at least 5 of the 6 climate projections.
77

Insert 11: Reptile Species Accounts

PLATEAU STRIPED WHIPTAIL

Aspidoscelis velox

The plateau striped whiptail lizard species complex inhabits a variety of 

grassland and scrub and shrub habitats on the Colorado Plateau and in central

New Mexico, at elevations ranging from 1,400 to 2,440 m. This is a mediumsized (70–90 mm) all-female species. Unisexual whiptails reproduce by 

parthenogenesis, in which unfertilized eggs hatch into clones of the mother.

Adults live for at least 4–5 years. Adults are typically active between May and 

mid-September while juveniles are active later into the fall. Female Plateau

Striped Whiptails lay three to five eggs in June, and hatchlings emerge

between July and September. Whiptail lizards are actively-foraging diurnal

insectivores known for running after insect and hatchling lizard prey, and 

typically maintain body temperatures around 38 degrees Celsius when active.

Where present, individuals of Plateau Striped Whiptails are usually abundant. 

Given their size and unisexual reproductive mode, they may create significant 

competition for prey where they co-occur with bisexual whiptail and other

lizard species.

Figure 11.2. Conceptual model of factors affecting suitable habitat for plateau striped 

whiptail and population dynamics, including explanatory variables used to model current 

suitable habitat for plateau striped whiptail (in green and circled red). The relative 

strength of different effects is denoted by the width of connecting arrows.

Figure 11.1. Aspidoscelis velox, 

Arizona, photo by Trevor Persons, 

Northern Arizona University
78

Range Projections–2099

Number of projections agreeing where there will be

suitable habitat in 2099.

Figure 11.3a. Illustration of Western United States 

showing projected range in 2099, showing levels of 

agreement among predictions based on 5 different 

statistically downscaled global circulation models 

(GCM) plus a 6th representing an ensemble of 22 

GCMs. White denotes areas where all projections 

agree there will not be suitable habitat, whereas blue 

denotes areas where all models agree that suitable 

habitat will exist.

Figure 11.3b. Illustration of Western United States 

showing modeled areas (including vegetation 

variables) where projections of greater than or 

equal to five global circulation models project 

there will be suitable habitat in 2099, showing 

areas of habitat loss (red), habitat gain (green), 

and habitat persistence (blue) between 2010 and 

2099.

Figure 11.3c. Illustration of Western United 

States showing modeled areas (excluding 

vegetation variables) where projections of 

greater than or equal to five global circulation 

models project there will be suitable habitat in 

2099, showing areas of habitat loss (red), habitat 

gain (green), and habitat persistence (blue) 

between 2010 and 2099.
79

Model Results

Area Under the Curve (AUC)

AUC with plant species ranges 0.924

AUC without plant species ranges 0.926

Summary of variable importance

Figure 11.4. Graph 

showing percent 

contribution of 

variables retained in 

the best model of 

current range. 

Variable contributions 

should be interpreted 

with caution when the 

predictor variables are 

correlated. 

Projected change in area of suitable habitat

Unlimited dispersal 

No dispersal

Figure 11.5. Measures of projected change in range at 

end of three different decades as a percent of current 

modeled range (year 2010). Green bars are results 

produced by the model that includes the effect of plant 

species ranges; red bars are results produced by the 

model that does not include plant range effects. Values 

were calculated based on areas of agreement of at 

least five of the six climate projections.
80

ARIZONA BLACK RATTLESNAKE

Crotalus cerberus

The Arizona black rattlesnake is a high-elevation medium-sized (<1,000 

mm) rattlesnake found primarily in Arizona and extreme western New 

Mexico. Adults are dark brown or black in background color, with white- or 

yellow-edged barker hexagonal blotches. Juveniles are born with a grey

background and dark blotches. Arizona black rattlesnakes are typically

found in pine-oak woodlands or chaparral habitats. At high elevations, they

appear to rely on south, west, or east-facing blocky volcanic outcrops 

surrounded by talus, to survive freezing winter temperatures. In lower 

deserts, the species appears restricted to cool, moist drainages, and may

favor hibernation locations with northern or western aspects. Adults eat

small mammals, birds, and lizards, while juveniles eat lizards. The species is

long-lived and very social, with all ages hibernating in large groups, and 

pregnant females gestating and giving birth in smaller groups. This sociality

is possibly due to limited suitable hibernation and rookery sites, or 

relatedness.

Figure 11.7. Conceptual model of factors affecting suitable habitat for Arizona black 

rattlesnake and population dynamics, including explanatory variables used to model 

current suitable habitat for Arizona black rattlesnake (in green and circled red). The 

relative strength of different effects is denoted by the width of connecting arrows.

Figure 11.6. Crotalus Cerberus, 

photo by Erika Nowak, U.S.G.S., 

and Northern Arizona 

University
81

PROJECTIONS - 2099

Number of projections agreeing where there will be

suitable habitat in 2099.

Figure 11.8a. Illustration of Western United States 

showing predicted range in 2099, showing levels of 

agreement among predictions based on 5 different 

statistically downscaled global circulation models 

(GCM) plus a 6th representing an ensemble of 22 

GCMs. White denotes areas where all projections 

agree there will not be suitable habitat, whereas blue 

denotes areas where all models agree that suitable 

habitat will exist.

Figure 11.8b. Illustration of Western United 

States showing modeled areas (including 

vegetation variables) where projections of 

greater than or equal to five global circulation 

models predict there will be suitable habitat in 

2099, showing areas of habitat loss (red), habitat 

gain (green), and habitat persistence (blue)

between 2010 and 2099.

Figure 11.8c. Illustration of Western United 

States showing modeled areas (excluding 

vegetation variables) where projections of 

greater than or equal to five global circulation 

models predict there will be suitable habitat in 

2099, showing areas of habitat loss (red), habitat 

gain (green), and habitat persistence (blue) 

between 2010 and 2099.
82

MODEL RESULTS 

Area Under the Curve (AUC) AUC with plant species ranges 0.98

AUC without plant species ranges 0.98

Summary of variable importance

Figure 11.9. Graph 

showing percent 

contribution of 

variables retained in 

the best model of 

current range. Variable 

contributions should be 

interpreted with 

caution when the 

predictor variables are 

correlated.

Projected change in area of suitable habitat

Unlimited dispersal No dispersal

Figure 11.10. Measures of projected change in range at end of three different decades as a percent of current modeled 

range (year 2010). Green bars are results produced by the model that include the effect of plant species ranges; red bars 

are results produced by the model that does not include plant range effects. Values were calculated based on areas of 

agreement of at least five of the six climate projections.
83

DESERT TORTOISE

Gopherus agassizii

The desert tortoise is found acrossthe Mojave and Sonoran deserts of the US

and Mexico. Recent work suggests that the Sonoran Desert population is

genetically distinct; this species (G. morafkai) occursin upland Sonoran Desert 

scrub and grassland associations with rocky outcrops and alluvial fans, at

elevations up to 1,420 m. Individuals use burrows that they excavate under

outcrops, boulders, or caliche caves for retreat and hibernation sites. Tortoises

feed on perennial and winter annual plants; favored forage plants include 

grasses, mallows, and desert vine. Individuals are particularly active during 

mid-March to May and late July to late September following rainfall-induced

increases in plant productivity. To survive during dry periods, tortoises cease

feeding, and rely on water stored in their bladders and very low metabolic 

rates. Female Sonoran tortoiseslay 1–12 eggs in a single clutch in late June 

and July. Hatchlings emerge in early fall following monsoon rains, and are 

particularly susceptible to desiccation and starvation during dry years. This

very long-lived species is facing many conservation threats, including habitat

loss, overgrazing, invasive plants, disease, and subsidized predators (such as,

dogs and ravens [Corvus corax]).

Figure 11.12. Conceptual model of factors affecting suitable habitat for Sonoran desert 

(Morafka’s) tortoise, and population dynamics, including explanatory variables used to model 

current suitable habitat for Sonoran desert tortoise (in green and circled red). The relative 

strength of different effects is denoted by the width of connecting arrows.

Figure 11.11. Gopherus agassizii, 

photo by Cecil Schwalbe/University of 

Arizona SNRE and U.S.G.S. Sonoran 

Desert Research Station
84

PROJECTIONS - 2099

Number of projections agreeing where there will be

suitable habitat in 2099.

Figure 11.13a. Illustration of Western United States 

showing predicted range in 2099, showing levels of 

agreement among predictions based on 5 different 

statistically downscaled global circulation models 

(GCM) plus a 6th representing an ensemble of 22 

GCMs. White denotes areas where all projections 

agree there will not be suitable habitat, whereas blue 

denotes areas where all models agree that suitable 

habitat will exist.

Figure 11.13b. Illustration of Western United 

States showing modeled areas (including 

vegetation variables) where projections of 

greater than or equal to five global circulation 

models predict there will be suitable habitat in 

2099, showing areas of habitat loss (red), habitat 

gain (green), and habitat persistence (blue) 

between 2010 and 2099.

 

Figure 11.13c . Illustration of Western United States 

showing modeled areas (excluding vegetation 

variables) where projections of greater than or equal 

to five global circulation models predict there will be 

suitable habitat in 2099, showing areas of habitat 

loss (red), habitat gain (green), and habitat 

persistence (blue) between 2010 and 2099.
85

MODEL RESULTS 

Area Under the Curve (AUC)

AUC with plant species ranges 0.97

AUC without plant species ranges 0.96

Summary of variable importance

Figure 11.14. Graph 

showing percent 

contribution of variables 

retained in the best model 

of current range. Variable 

contributions should be 

interpreted with caution 

when the predictor 

variables are correlated.

Projected change in area of suitable habitat

 

Unlimited dispersal 

 No dispersal 

Figure 11.15. Measures of projected change in range at end of 

three different decades as a percent of current modeled range (year 

2010). Green bars are results produced by the model that include 

the effect of plant species ranges; red bars are results produced by 

the model that does not include plant range effects. Values were 

calculated based on areas of agreement of at least five of the six 

climate projections.

 
86

LESSER EARLESS LIZARD

Holbrookia maculata

The lesser earless species complex is found across short-grass prairie and 

open habitats of the southern Colorado Plateau, New Mexico, Texas,

northern Mexico, and on the western edge of the Great Plains, at elevations 

ranging from 500 to 2,200 m. The taxonomy of this wide-ranging species has

been debated; in our modeling, we did not include museum records in the 

current range of H. elegans. While vegetative habitat types vary, generally 

this small lizard is found in areas with sandy or loose gravel-covered soils

suitable for burrowing, in flats or grasslands. These small, short-lived diurnal

lizards are primarily insectivorous, but adults also may eat hatchling lizards. 

Mating occurs from May to July, and females typically lay one or two clutches

of four to six eggs each per year, with hatchlings emerging between July and 

early fall. The speciesis of conservation concern because of extensive habitat

loss that has occurred acrossits former range; in many areas this has been a 

result of habitat conversion following overgrazing by cattle and sheep.

Figure 11.17. Conceptual model of factors affecting suitable habitat for common lesser 

earless lizard and population dynamics, including explanatory variables used to model current 

suitable habitat for lesser earless lizard (in green and circled red). The relative strength of 

different effects is denoted by the width of connecting arrows.

Figure 11.16. Holbrookia maculate, 

photo by Erika Nowak, U.S.G.S. and 

Northern Arizona University
87

PROJECTIONS - 2099

Number of projections agreeing where there will be

suitable habitat in 2099.

Figure 11.18a. Illustration of Western United States 

showing predicted range in 2099, showing levels of 

agreement among predictions based on 5 different 

statistically downscaled global circulation models 

(GCM) plus a 6th representing an ensemble of 22 

GCMs. White denotes areas where all projections 

agree there will not be suitable habitat, whereas blue 

denotes areas where all models agree that suitable 

habitat will exist.

Figure 11.18b. Illustration of Western United 

States showing modeled areas (including 

vegetation variables) where projections of 

greater than or equal to five global circulation 

models predict there will be suitable habitat in 

2099, showing areas of habitat loss (red), habitat 

gain (green), and habitat persistence (blue) 

between 2010 and 2099.

Figure 11.18c. Illustration of Western United 

States showing modeled areas (excluding 

vegetation variables) where projections of 

greater than or equal to five global circulation 

models predict there will be suitable habitat in 

2099, showing areas of habitat loss (red), habitat 

gain (green), and habitat persistence (blue) 

between 2010 and 2099.
88

MODEL RESULTS 

Area Under the Curve (AUC) AUC with plant species ranges 0.92

AUC without plant species ranges 0.92

Summary of variable importance 

Figure 11.19. Graph 

showing percent 

contribution of variables 

retained in the best model 

of current range. Variable 

contributions should be 

interpreted with caution 

when the predictor variables 

are correlated.

Projected change in area of suitable habitat

 Unlimited dispersal 

No dispersal

 Figure 11.20. Measures of projected change in range at end of three different decades as a percent of current modeled 

range (year 2010). Green bars are results produced by the model that include the effect of plant species ranges; red bars 

are results produced by the model that does not include plant range effects. Values were calculated based on areas of 

agreement of at least five of the six climate projections.
89

COMMON CHUCKWALLA

Sauromalus ater

The Common Chuckwalla is primarily found acrossthe Mojave and Sonoran

deserts of the United States and Mexico, at elevations ranging from sea level

to 1,370 m. This large (125–180 mm) lizard is dorsoventrally flattened and 

has wrinkles on its belly and neck. Chuckwallas are strongly associated with

rock outcrops, lava flows, and boulder piles, and are well-known for their

defensive behavior of inflating their bodies to wedge themselvesin crevices.

They are herbivorous, eating the leaves, flowers, and shoots of annual and 

perennial plants in the Mojave desert, and primarily perennial plants in the

Sonoran desert. Preferred species of forage plants near Phoenix, Arizona, 

included mallows, desert lavender, America threefold, goldeneye, wolfberry, 

foothills palo verde, and ocotillo. Chuckwallas usually maintain active body 

temperatures between 35 and 40 degrees Celsius, yet wild individuals

apparently obtain all of their water for metabolic needs from eating plants.

Chuckwallas are long-lived and females may not breed every year. A clutch of 

5–16 eggs is laid in mid to late summer, and hatchlings emerge in the fall,

winter, or even the following spring. Although this species is common in many 

areas, in some locations excessive collecting and habitat destruction threaten

population.

B

Figure 11.21. Sauromalus ater, photo 

by Fish and Wildlife Service/fws.gov

Figure 11.22. Conceptual model of factors affecting suitable habitat for common chuckwalla and population 

dynamics,including explanatory variables used to model current suitable habitat for common chuckwalla (in 

green and circled red). The relative strength of different effects is denoted by the width of connecting arrows.
90

PROJECTIONS - 2099

Number of projections agreeing where there will be

suitable habitat in 2099.

Figure 11.23a. Illustration of Western United States 

showing predicted range in 2099, showing levels of 

agreement among predictions based on 5 different 

statistically downscaled global circulation models 

(GCM) plus a 6th representing an ensemble of 22 

GCMs. White denotes areas where all projections 

agree there will not be suitable habitat, whereas blue 

denotes areas where all models agree that suitable 

habitat will exist.

Figure 11.23b. Illustration of Western United 

States showing modeled areas (including 

vegetation variables) where projections of 

greater than or equal to five global circulation 

models predict there will be suitable habitat in 

2099, showing areas of habitat loss (red), habitat 

gain (green), and habitat persistence (blue) 

between 2010 and 2099.

Figure 11.23c. Illustration of Western United 

States showing modeled areas (excluding 

vegetation variables) where projections of 

greater than or equal to five global circulation 

models predict there will be suitable habitat in 

2099, showing areas of habitat loss (red), habitat 

gain (green), and habitat persistence (blue) 

between 2010 and 2099.
91

MODEL RESULTS 

Area Under the Curve (AUC) AUC with plant species ranges 0.97

AUC without plant species ranges 0.97

Summary of variable importance

Figure 11.24. Graph showing percent contribution of 

variables retained in the best model of current range. 

Variable contributions should be interpreted with caution 

when the predictor variables are correlated.

Projected change in area of suitable habitat

Unlimited dispersal

No dispersal

Figure 11.25. Measures of projected change in range at end of 

three different decades as a percent of current modeled range 

(year 2010). Green bars are results produced by the model that 

includes the effect of plant species ranges; red bars are results 

produced by the model that does not include plant range 

effects. Values were calculated based on areas of agreement of 

at least five of the six climate projections.
92

References Cited

Araújo, M.B., Thuiller, W., and Pearson, R.G., 2006, Climate warming and the decline of 

amphibians and reptiles in Europe: Journal of Biogeography, v. 33, p. 1712–1728. 

ArcGIS® Server 10. (http://cpbc.bio.nau.edu/CPBC_ArcGIS/rest/services[accessed 9/12/2013]). 

Arundel, S., 2005, Using special models to establish climate limiters of plant species?: 

Ecological Modelling v. 182, p. 159–181. 

Austin, M.P., 2002, Spatial prediction of species distribution—An interface between ecological 

theory and statistical modelling: Ecological Modelling, v. 157, p. 101–118. 

Austin, M.P., and Van Neil, K.P., 2011, Improving species distribution models for climate 

change studies: Variable selection and scale: Journal of Biogeography, v. 38, p. 1–8. 

Avian Knowledge Network (AKN) (http://www.avianknowledge.net)[accessed 9/12/2013] 

Balda, R.P., 2002, Pinyon Jay (Gymnorhinus cyanocephalus), in Pool, A., ed., 2002, The Birds 

of North America Online: Ithaca N.Y., Cornell Lab of Ornithology; accessed August 29, 2013, 

at http://bna.birds.cornell.edu/bna/species/605, doi:10.2173/bna.605. 

Barbet-Massin, M., Thuiller, W., and Jiguet, F., 2010, How much do we overestimate local 

extinction rates when restricting the range of occurrence data in climate suitability models?: 

Ecography, v. 33, p. 878–886. 

Barlow, J.C., Leckie, S.N., and Baril, C.T., 1999, Gray Vireo (Vireo vicinior). in Pool, A., ed., 

1999, The Birds of North America Online: Ithaca N.Y., Cornell Lab of Ornithology; accessed 

August 29, 2013 at http://bna.birds.cornell.edu/bna/species/447. doi:10.2173/bna.447

Barnett, T.P., Pierce, E.W., Hidalgo, H.G., Bonfils, C., Santer, B.D., Das, R., Bala, G., Wood, 

A.W., Nozawa, T., Mirin, A.A., Cayan, D.R., and Dettinger, M.D., 2008, Human-induced 

changes in the hydrology of the western United States: Science, v. 319, p. 1080–1083. 

Barry, R.G., and Chorley, R.J., 1976, Atmosphere, weather and climate (3d ed.): Methuen 

Publishing Ltd, London, U.K., 432 p. 

Barry, S., and Elith, J., 2006, Error and uncertainty in habitat models, Journal of Applied 

Ecology, v. 43, p. 413–423. 

Berry, K. H., Morafka, D.J., and Murphy, R.W., 2002, Defining the desert tortoise(s)—Our first 

priority for a coherent conservation strategy: Chelonian Conservation and Biology, v. 4, p. 

249–262. 

Berteaux, D., Humphries, M.M., Krebs, C.J., Lima, M., McAdam, A.G., Pettorelli, N., Reale, D., 

Saitoh, T., Tkadlec, E., Weladji, R.B., Stenseth, N.C, 2006, Constraints to projecting the 

effects of climate change on mammals: Climate Research, v. 32, p. 151–158. 

Brady, N C., and Weil, R.R., 2002, The Nature and properties of soils (13th ed.): Prentice Hall, 

Upper Saddle River, N.J., 960 p. 

Boykin, K.G., Thompson, B.C., Deitner, R.A., Schrupp, D., Bradford, D., O’Brien, L., Drost, C., 

Propeck-Gray, S., Rieth, W., Thomas, K., Kepner, W., Lowry, J., Cross, C., Jones, B., Hamer, 

T., Mettenbrink, C., Oakes, K.J., Prior-Magee, J., Schulz, K., Wynne, King, J.J.C., Puttere, 

Schrader, J.S., Schwenke, Z. 2007, Predicted animal habitat distributions and species richness, 

in Prior-Magee, J.S., and others (eds.), Southwest regional gap analysis final report: Moscow, 

Idaho, U.S. Geological Survey, Gap Analysis Program, p. 39–78. 

Burnham, K.P., and Anderson, D.R., 2002, Model selection and multimodel inference—A 

practical information, Theoretic approach, (2d ed.): Springer-Verlag, New York, 488 p. 

California Avian Data Center (http://data.prbo.org/cadc2/index.php?page=climate-changedistribution. [accessed 9/12/2013]). 
93

Centre National de Recherches Meteorologiques (CNRM) (http://wwwpcmdi.llnl.gov/ipcc/model_documentation/CNRM-CM3.htm [accessed 9/12/2013]). 

Christensen, N.S., and Lettenmaier, D.P., 2007, A multimodel ensemble approach to assessment 

of climate change impacts on the hydrology and water resources of the Colorado River basin: 

Hydrology and Earth Systems Science, v. 11, p. 1417–1434. 

Commonwealth Scientific and Industrial Research Organization (CSIRO). Models. 

(http://www.csiro.au/[accessed 9/12/2013]). 

Cole, K.L., Ironside, K., Arundel, S., Duffy, P., and Shaw, J., 2008, Modeling future plant 

distributions on the Colorado Plateau—An example using Pinus edulis, in, van Riper, III, C., 

and Sogge, M.K., eds., The Colorado Plateau III—Cultural, biological, and physical research: 

University of Arizona Press, Tucson, Arizona, p. 319–330. 

Collar, N.J., Wege, D.C., and Long, A.J., 1997, Patterns and causes of endangerment in the New 

World avifauna: Ornithological Monographs, v. 48 p. 237–260. 

Consortium of California Herbaria, (http://ucjeps.berkeley.edu/consortium/) [accessed 

9/12/2013]. 

Czech, B., and Krausman, P.R., 1997, Distribution and causation of species endangerment in the 

United States: Science, v. 277, p. 1116. 

Daszak, P., Cunnigham, A.A., and Hyatt, A.D., 2003, Infectious disease and amphibian 

population declines: Diversity and Distribution, v. 9, p. 141–150. 

De Leo, G., Dobson, A., and Goodman, A., 2002, Wildlife perspectives on the evolution of 

virulence, in Dieckmann, U., Metz, J.A.J., Sabelis, M.W., and Sigmund, K. (eds.), Adaptive 

dynamics of infectious diseases—In pursuit of virulence management: Cambridge University 

Press, Cambridge, U.K., p. 26–38. 

Diffenbaugh, N.S., Giorgi, F., and Pal, J.S., 2008, Climate change hotspots in the United States: 

Geophysical Research Letters, v. 35, p. L16709. 

Dobbs, R.C., Martin, T.E., and Conway, C.J., 1997, Williamson's sapsucker, in Poole, A. (ed.), 

The Birds of North America Online: Cornell Lab of Ornithology, Ithaca, N.Y., accessed 

August 29, 2013, at http://bna.birds.cornell.edu/bna/species/285

Dubayah, R., and Rich, P.M., 1995, Topographic solar radiation models for GIS: International 

Journal of Geographical Information Systems, v. 9, p. 405–419. 

Dudík, M., Phillips, S.J., and Schapire, R.E., 2007, Maximum entropy density estimation with 

generalized regularization and an application to species distribution modeling: Journal of 

Machine Learning Research, v. 8, p. 1217–1260. 

Egan, J.P., 1975, Signal detection theory and ROC analysis: Series in Cognition and Perception, 

Academic Press, New York, p. 277. 

Elith, J., Graham, C.H., Anderson, R.P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R.J., 

Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A., Manion, 

G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.M., Peterson, A.T., Phillips, S.J., 

Richardson, K., Scachetti-Pereira, R., Schapire, R.E., Soberon, J., Williams, S., Wisz, M.S., 

and Zimmermann, N.,E., 2006, Novel methods improve prediction of species' distributions 

from occurrence data: Ecography, v. 29, p.129–151. 

Elith, J., Kearney, M., and Phillips, S., 2010, The art of modeling range-shifting species: 

Methods in Ecology and Evolution, v. 1, p. 330–342. 

Elith, J., and Leathwick, J.R., 2009, Species distribution models—Ecological explanation and 

prediction across space and time: Annual Review of Ecology Evolution and Systematics, v. 40, 

p. 677–697. 
94

Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E., and Yates, C.J., 2011, A statistical 

explanation of MaxEnt for ecologists: Diversity and Distributions, v. 17, p. 43–57. 

Environmental Systems Research Institute (ESRI), 1992, Cell-based modeling with GRID, (2d 

ed.): Environmental Systems Research Institute, Redlands, California, 267 p. 

Ernst, C.H., and Lovich, J.E., 2009, Desert tortoise, Gopherus agassizii, in Turtles of the United 

States and Canada, (2d ed.): Johns Hopkins University Press, Baltimore, Md., p. 540–570 

Fawcett, R., 2003. ROC Graphs: Notes and Practical Considerations for Data Mining Research. 

Technical Report HPL-2003-4. HP Laboratories, Palo Alto, California 

Fielding, A.H., and Bell, J. F., 1997, A review of methods for the assessment of prediction errors 

in conservation presence/absence models: Environmental Conservation, v. 24, p. 38–49. 

Foley, J., Clifford, D., Castle, K., Cryan, P., and Ostfeld, R.S., 2011, Investigating and managing 

the rapid emergence of white-nose syndrome, a novel, fatal, infectious disease of hibernating 

bats: Conservation Biology, v. 25, p. 223–231. 

Freeman, E.A., and Moisen, G.G., 2008, A comparison of the performance of threshold criteria 

for binary classification in terms of predicted prevalence and kappa: Ecological Modeling, v. 

217, p. 48–58. 

Friend, M., McLean, R.G., and Dein, J., 2001, Disease emergence in birds—Challenges for the 

twenty-first century: The Auk, v. 118, p. 290–303. 

Garfin, G.M., Eischeid, J.K., Lenart, M.T., Cole, K.L., Ironside, K., and Cobb, N., 2010, 

Downscaling climate projections in topographically diverse landscapes of the Colorado Plateau 

in the arid southwestern United States, in van Riper, III, C., Wakeling, B.F., and Sisk T.D., 

(eds.), The Colorado Plateau IV—Shaping conservation through science and management: 

University of Arizona Press, Tucson, Ariz., p. 22–43. 

Global Biodiversity Information Facility (GBIF; Accessed through GBIF Data Portal, 

http://data.gbif.org) [accessed 9/12/2013].

Geographic Coordinate System, World Geodetic System, U.S. Department of Defense. 1972. 

GEOLocate ver. 3.21, (http://www.museum.tulane.edu/geolocate/[accessed 9/13/2013]).

Global Biodiversity Information Facility (http://www.gbif.org/), [accessed 9/12/2013].

Guisan, A., andThuiller, W., 2005, Predicting species distribution—offering more than simple 

habitat models: Ecology Letters, v. 8, p. 993–1009. 

Guisan, A., and Zimmermann, N.E., 2000, Predictive habitat distribution models in ecology. 

Ecological Modelling, v. 135, p. 147–186 

Gutiérrez, R.J., Cody, M., Courtney, S., and Franklin, A.B., 2007, The invasion of barred owls 

and its potential effect on the spotted owl—A conservation conundrum: Biological Invasions, 

v. 9, p. 181–196. 

Forest Inventory and Analysis (FIA) databases (http://apps.fs.fed.us/fiadbdownloads/datamart.html), [accessed 9/30/2013]. 

Hastie, T., Tibshirani, R., and Friedman, J.H., 2009, The elements of statistical learning—Data 

mining, inference, and prediction: Springer Verlag, New York, 763 p. 

Hayes, M. P., and Jennings, M.R., 1986, Decline of rand frog species in western North 

America—Are bullfrogs (Lithobates catesbeiana) responsible?: Journal of Herpetology, v. 20, 

p. 490–509. 

HerpNET data portal (http://www.herpnet.org) [accessed 9/12/2013]. 

Hilborn, R., and Mangel, M., 1997, The ecological detective—Confronting models with data: 

Princeton University Press, Princeton, N.J., p. 330. 
95

Hosmer, D.W., and Lemeshow, S., 1989, Applied logistic regression, (1st ed.): John Wiley & 

Sons, New York, p. 392. 

Huntley, B., Green, R.E., Collingham, Y.C., Hill, J.K., Willis, S.G., Bartlein, P.J., Cramer, W., 

Hagemeijer, J.M., and Thomas, C.J., 2004, The performance of models relating species 

geographical distributions to climate is independent of trophic level: Ecology Letters, v. 7, p. 

417–426. 

Ibáñez, I., Clark, J S., Dietze, M.C., Feeley, K., Hersh, M., LaDeau, S., McBride, A., Welch, 

N.E., and Wolosin, M.S., 2006, Predicting biodiversity change—Outside the climate envelope, 

beyond the species-area curve: Ecology, v. 87, p. 1896–1906. 

Jackson, S.T., Betancourt, J.L., Booth, R.K., and Gray, S.T., 2009, Ecology and the ratchet of 

events—Climate variability, niche dimensions, and species distributions: Proceedings of the 

National Academy of Sciences, v. 106, p.19685–19692 

Johnson, M.J., Van Riper, III, C., and Pearson, K.M. 2002, Black-throated sparrow, in Poole, A., 

ed., The Birds of North America Online: Cornell Lab of Ornithology, Ithaca, N.Y., accessed 

August 29, 2013, at: http://bna.birds.cornell.edu/bna/species/637. 

Kadmon, R., Farber, O., and Danin, A., 2003, A systematic analysis of factors affecting the 

performance of climatic envelope models: Ecological Applications, v., 13, p. 853–867. 

Kats, L.B., and Ferrer, R.P., 2003, Alien predators and amphibian declines—A review of two 

decades of science and transition to conservation: Diversity and Distributions, v. 9, p. 99–110. 

Keating, K.A., and Cherry, S., 2004, Use and interpretation of logistic regression in habitat 

selection studies: Journal of Wildlife Management, v. 68, p. 774–789. 

Kelly, E.G., Forsman, E.D., and Anthony, R.G., 2003, Are barred owls displacing spotted owls?: 

The Condor, v. 105, p. 45–53. 

Kingery, H.E., and Ghalambor, C.K., 2001, Pygmy nuthatch (Sitta pygmaea), in Poole, A., ed., 

The Birds of North America Online: Cornell Lab of Ornithology, Ithaca, N.Y., accessed 

August 29, 2013, at: http://bna.birds.cornell.edu/bna/species/567.doi:10.2173/bna.567. 

Kissling, W.D., Field, R., Korntheuer, H., Heyder, U., and Böhning-Gaese, K., 2010, Woody 

plants and the prediction of climate-change impacts on bird diversity: Philosophical 

Transactions of the Royal Society B, v. 365, p. 2035–2045. 

Lawler, J.J., Shafer, S.L., White, D., Kareiva, P., Maurer, E.P., Blaustein, A.R., and. Bartlein, 

P.J., 2009, Projected climate-induced faunal change in the Western Hemisphere: Ecology, 

v. 90, p. 588–597. 

Lawrence Livermore National Laboratory, Program for Climate Model Diagnosis and 

Intercomparison, accessed September 11, 2013, at http://www-pcmdi.llnl.gov. 

Legendre, P., 1993, Spatial autocorrelation: trouble or new paradigm?: Ecology, v. 74, p. 1659–

1673. 

Legendre, P., Dale, M.R.T., Fortin, M.-J., Gurevitch, J., Hohn, M., and Myers, D., 2002, The 

consequences of spatial structure for the design and analysis of ecological field experiments: 

Ecography, v. 25, p. 601–615. 

Liu, C.R., Berry, P.M., Dawson, T.P., and Pearson, R.G., 2005, Selecting thresholds of 

occurrence in the prediction of species distributions: Ecography, v. 28, p. 385–393. 

Lobo, J.M., Jiménez Valverde, A., and Real, R., 2008, AUC: a misleading measure of the 

performance of predictive distribution models: Global Ecology and Biogeography, v. 17, p. 

145–151. 

Longley, P.A., Goodchild, M.F., Maguire, D.J., and Rhind, D.W., 2005, Geographic information 

systems and science, (2d ed.): John Wiley and Sons, Hoboken, N.J., p. 517. 
96

Luoto, M., and Heikkinen, R.K., 2008, Disregarding topographical heterogeneity biases species 

turnover assessments based on bioclimatic models: Global Change Biology, v. 14, p. 483–494. 

MacKenzie, D.I., Nichols, J.D., Lachman, G.B., Droege, S., Andrew Royle, J., and Langtimm, 

C.A., 2002, Estimating site occupancy rates when detection probabilities are less than one: 

Ecology, v. 83, p. 2248–2255. 

Marzluff, J.M., and Balda, R.P., 2010, The Pinyon Jay—Behavioral ecology of a colonial and 

cooperative Corvid: T & AD Poyser, London, U.K., p. 344 

Maurer, E.P., Brekke, L., Pruitt, T., and Duffy, P.B., 2007, Fine-resolution climate projections 

enhance regional climate change impact studies: Eos, Transactions American Geophysical 

Union, v. 88, no. 47, p. 504. 

Maxent software for species habitat modeling. 3.3.3k version. 

http://www.cs.princeton.edu/~schapire/maxent/ [accessed 9/12/2013]. 

McMahon, S.M., Harrison, S.P., Armbruster, W.S., Bartlein, P.J., Beale, C.M., Edwards, M.E., 

Kattge, J., Midgley, G., Morin, X., and Prentice, I.C., 2011, Improving assessment and 

modelling of climate change impacts on global terrestrial biodiversity: Trends in Ecology and

Evolution, v. 26, no. 5, p. 248–259. 

Mearns, L.O., Gutowski, W., Jones, R., Leung, L.-Y., McGinnis, S., Nunes, A.M.B., and Qian, 

Y., 2009, A regional climate change assessment program for North America: Eos, Transactions 

American Geophysical Union, v. 90, no. 36, p. 311–312. 

Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Biodiversity 

synthesis: World Resources Institute, Washington D.C., 86 p. 

Muñoz, J., and Felicísimo, A.M., 2004, Comparison of statistical methods used in predictive 

modelling: Journal of Vegetation Science. v. 15, p. 285–292. 

Nature Serve online encyclopedia (http://www.natureserve.org/explorer/) [accessed 9/12/2013]. 

NHDPlus database (geo-spatial, hydrologic framework dataset) http://www.horizonsystems.com/nhdplus/ [accessed 9/12/2013]. 

North American Bird Conservation Initiative, U.S. Committee, 2010. The State of the Birds 2010 

Report on Climate Change, United States of America. U.S. Department of the Interior: 

Washington, D.C. 

Olson, C.R., and Martin, T.E., 1999, Virginia's warbler (Oreothlypis virginiae), in Poole, A., ed., 

The Birds of North America Online: Cornell Lab of Ornithology, Ithaca, N.Y., accessed 

August 29, 2013, at: http://bna.birds.cornell.edu/bna/species/477, doi:10.2173/bna.477. 

Paremsan, C., 2006, Ecological and evolutionary responses to recent climate change: Annual 

Review of Ecology, Evolution, and Systematics, v. 37, p. 637–669. 

Pearson, R.G., Thuiller, W., Araújo, M.B., Martinez-Mayer, E., Brotons, L., McClean, C., Miles, 

L., Seguardo, P., Dawson, T.P., and Less, D.C., 2006, Model-based uncertainty in species 

range prediction: Journal of Biogeography, v. 33, p. 1704–1711. 

Pearson, R.G., Raxworthy, C.J., Nakamura, M., and Peterson, A.T., 2007, Predicting species 

distributions from small numbers of occurrence records: a test case using cryptic geckos in 

Madagascar: Journal of Biogeography, v. 34, p. 102–117. 

Peduzzi, P., Concato, J., Kemper, E., Holford, T.R., and Feinstein, A.R., 1996, A simulation 

study of the number of events per variable in logistic regression analysis: Journal of Clinical 

Epidemiology, v. 49, p. 1373–1379. 

Persons, T.B., and Nowak, E.M., 2006a, Inventory of amphibians and reptiles at Death Valley 

National Park: U.S. Geological Survey Open-File Report 2006–1233, 36 p. 
97

Persons, T.B., and Nowak, E,M., 2006b, Inventory of amphibians and reptiles in southern 

Colorado Plateau National Parks—Final Report: U.S. Geological Survey, Southwest 

Biological Science Center, Flagstaff, Arizona, 73 p. 

Persons, T.B., Nowak, E.M., and Hillard, S., 2006, Inventory of amphibians and reptiles at 

Manzanar National Historic Site, California: U.S. Geological Survey Open-File Report 2006–

1232, 31 p. 

Persons, T.B., and Nowak, E.M., 2007, Inventory of amphibians and reptiles at Mojave National 

Preserve: U.S. Geological Survey Open-File Report 2007–1109. 

Phillips, S.J., Anderson, R.P., and Schapire, R.E., 2006, Maximum entropy modeling of species 

geographic distributions: Ecological Modelling, v. 190, p. 231–259. 

Phillips, S.J., and Dudík, M., 2008, Modeling of species distributions with Maxent—New 

extensions and a comprehensive evaluation: Ecography, v. 31, p. 161–175. 

Phillips, S.J., Dudík, M., Elith, J., Graham, C.H., Lehmann, A., Leathwick, J., and Ferrier, S., 

2009, Sample selection bias and presence-only distribution models—Implications for 

background and pseudo-absence data: Ecological Applications, v. 19, p. 181–197. 

PRISM Climate Group, Oregon State University, http://prism.oregonstate.edu [accessed 

9/12/2013]. 

ReGAP project (http://earth.gis.usu.edu/swgap/trainingsites.html) [accessed 9/12/2013].

Reisner, M., 1986, Cadillac desert—The American West and its disappearing water: Penguin 

Books, New York, 608 p. 

Reynolds, T.D., Rich, T.D., and Stephens, D.A., 1999, Sage Thrasher, in Poole, A., ed., The 

Birds of North America Online: Cornell Lab of Ornithology, Ithaca, N.Y., accessed August 29, 

2013, at: http://bna.birds.cornell.edu/bna/species/463. 

Rios, N.E., and Bart, H.L., 2010, GEOLocate (version 3.22), [Computer software]: Tulane 

University Museum of Natural History, Belle Chasse, Lousiana. 

Rowland, E.L., Davison, J.E., and Graumlich, L.J., 2011, Approaches to evaluating climate 

impacts on species—A guide to initiating the adaptive planning process: Environmental 

Management, v. 47, p. 322–337. 

Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and 

Tarantola, S., 2008, Global Sensitivity Analysis: The Primer, Chichester, England: John Wiley 

& Sons. 

Schofer, J.X., 2007, Movements, thermal biology, habitat use, and natural history of Crotalus 

cerberus in Northern Arizona, Master’s Thesis, Northern Arizona University, Flagstaff, 

Arizona. 

Schweiger, O., Settele, J., Kudrna, O., Klotz, S., and Kühn, I., 2008, Climate change can cause 

spatial mismatch of trophically interacting species: Ecology, v. 89, p. 3472–3479. 

Seager, R., Ting, M., Held, I., Kushnir, Y., Lu, J., Vecchi, G., Huang, H., Harnik, N., Leetma, 

A., Lau, N., Li, C., Velez, J., and Naik, N., 2007, Model projections of an imminent transition 

to a more arid climate in southwestern North America: Science, v. 316, p. 1181-1184. 

Shafroth, P.B., Stromberg, J.C., and Patton, D.T., 2002, Riparian vegetation response to altered 

disturbance and stress regimes: Ecological Applications, v. 12, p. 107–123. 

Sinclair, S.J., White, W.D., and Newell, G.R., 2010, How useful are species distribution models 

for managing biodiversity under future climates?: Ecology and Society, v. 15, no. 1, article 8, 

accessed August 30, 2013, at http://www.ecologyandsociety.org/vol15/iss1/art8/. 
98

Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K,B., Tignor, M., and Miller 

H.L., (eds.), 2007, Contribution of working group I to the Fourth Assessment Report of the 

Intergovernmental Panel on Climate Change, in Intergovernmental Panel on Climate Change, 

Climate change—The physical science basis, 2007: Cambridge University Press, Cambridge, 

United Kingdom and New York. 

Southwest Environmental Information Network (SEINet 

http://swbiodiversity.org/seinet/projects/index.php).[accessed 9/12/2013]. 

Southwest Plant Atlas, Northern Arizona University. 

http://www4.nau.edu/swplantatlas/endemic.html [accessed 9/12/2013] 

Stanton, J.C., Pearson, R.G., Horning, N., Ersts, P., and Akçakaya, H.R., 2012, Combining static 

and dynamic variables in species distribution models under climate change: Methods in 

Ecology and Evolution, v. 3, p. 349–357. 

Stiling, P., 1996, Ecology—Theory and applications (2d ed.): Prentice Hall, Upper Saddle River, 

N.J., 539 p. 

Story, M., and Congalton, R.G., 1986, Accuracy assessment—A user’s perspective: 

Photogrammatry Engineering and Remote Sensing: v. 52, p. 397–399. 

Thuiller, W., Brotons, L., Araújo, M.B., and Lavorel, S., 2004, Effects of restricting 

environmental range of data to project current and future species distributions: Ecography, v. 

27, p. 165–172. 

Thuiller, W., Albert, C., Araújo, M.B., Berry, P.M., Cabeza, M., Guisan, A., Hickler, T., 

Midgley, G.F., Paterson, J., Schurr, F.M., Sykes, M.T., and Zimmermann, N.E, 2008, 

Predicting global change impacts on plant species distributions—Future challenges: 

Perspectives in Plant Ecology, Evolution and Systematics, v. 9, p. 137–152. 

Tracy, C.R., Averill-Murray, R., Boarman, W.I., Delehanty, D., Heaton, J., McCoy, E., Morafka, 

D., Nussear, K., Hagerty, B., and Medica, P., 2004, Desert tortoise recovery plan assessment: 

DTRPAC Report prepared for the U.S. Fish and Wildlife Service, 254 p., accessed August 30, 

2013, at 

http://www.fws.gov/southwest/es/arizona/Documents/SpeciesDocs/DesertTortoise/DTRPACre

port.pdf. 

Tuft, Edward, 2013, Maps and Data Displays (http://www.edwardtufte.com/tufte/ [accessed 

9/12/2013]). 

U.S.D.A. Web Soil Survey, 2013, (http://websoilsurvey.sc.egov.usda.gov [accessed 

09/30/2013]). 

U.S. Forest Service FIA Data Mart: http://apps.fs.fed.us/fiadb-downloads/datamart.html . 

[accessed 9/11/2013]. 

U.S. Fish and Wildlife Service, 2008, Birds of Conservation Concern 2008: United States

 Department of Interior, Fish and Wildlife Service, Division of Migratory Bird Management,

 Arlington, Virginia, 85 p. [Online version available at http://www.fws.gov/migratorybirds/>].

U.S. Fish and Wildlife Service, 2010, Rising to the urgent challenge—Strategic plan for 

responding to accelerating climate change: U.S. Fish and Wildlife Service, Washington D.C., 

32 p. 

U.S. Fish and Wildlife Service, 2011, 2011 revised recovery plan for the Mojave population of 

the desert tortoise (Gopherus agassizii): U.S. Fish and Wildlife Service, Washington D.C., 227 

p., accessed August 30, 2013, at 

http://www.fws.gov/nevada/desert_tortoise/documents/recovery_plan/RRP%20for%20the%20

Mojave%20Desert%20Tortoise%20-%20May%202011.pdf. 
99

U.S. Geological Survey, GEOSS, http://rmgsc.cr.usgs.gov/ecosystems/usa.shtml [accessed 

9/12/2013]. 

U.S. Geological Survey GAP Analysis Program. Moscow, Idaho. 1999. 

(http://earth.gis.usu.edu/swgap/data/metadata/landform_albers.htm).[accessed 9/12/2013]. 

U.S. Geological Survey National Gap Analysis Program, 2004, Southwest Regional Gap 

Analysis Project Field Sample Database. Version 1.1. RS/GIS Laboratory, College of Natural 

Resources, Utah State University. http://earth.gis.usu.edu/swgap/trainingsites.html

U.S. Government. Data Catalog, (http://catalog.data.gov/dataset [accessed September 16, 2013]) 

VanDerWal, J., Shoo, L.P., Graham, C., and Williams, S.E., 2009, Selecting pseudo-absence 

data for presence-only distribution modeling—How far should you stray from what you 

know?: Ecological Modelling, v. 220, p. 589–594. 

Van Devender, T.R., 2002, The Sonoran Desert Tortoise—Natural history, biology, and 

conservation: University of Arizona Press and the Arizona-Sonora Desert Museum, Tucson, 

Arizona, 389 p. 

van Riper, C., III, van Riper, S.G., Goff, M.L., and Laird, M., 1986, The epizootiology and 

ecological significance of malaria in Hawaiian land birds: Ecological Monographs, v. 56, no. 

4, p. 327–344. 

Veloz, S.D., Williams, J.W., Blois, J.A., He, F., Otto-Bliesner, B., and Liu, Z., 2012, No-analog 

climates and shifting realized niches during the late Quaternary—Implications for 21st-Century 

predictions of species distribution models: Global Change Biology, v. 18, p. 1698–1713. 

Virkkala, R., Marmion, M., Heikkinen, R.K., Thuiller, W., and Luoto, M., 2010, Predicting 

range shifts of northern birds: Influence of modelling technique and topography: Acta 

Oecologica, v. 36, p. 269–281. 

Warren, D.L., Glor, R.E., and Turelli, M., 2010, ENMTools: a toolbox for comparative studies of 

environmental niche models. Ecography 33:607–611. 

Warren, D.L., and Seifert, S.N., 2011, Ecological niche modeling in Maxent—The importance of 

model complexity and the performance of model selection criteria: Ecological Applications, v. 

21, p. 335–342. 

Webb, R.H., and Leake, S.A., 2006, Ground-water surface-water interactions and long-term 

change in riverine riparian vegetation in the southwestern United States: Journal of Hydrology, 

v. 320. P. 302–323. 

Wicz, M.S., Hijmans, R.J., Li, J., Peterson, A.T., Graham, C.H., Guisan, A., and NCEAS 

Predicting Species Distributions Working Group, 2008, Effects of sample size on performance 

of species distribution models: Diversity and Distributions, v. 14, p. 763–773. 
100

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