Understanding the factors that determine a species’ geographic range is a fundamental goal of ecology, yet this knowledge is lacking for many populations. At the geographic scale, climate plays a dominant role in shaping species’ ranges because of its influence on physiological tolerances and control on ecological communities (Waite and Strickland 2006, Levin 2012). As a result, future climatic changes are expected to alter the ranges of many taxa in the coming decades (McDonald et al. 2012, Auer and King 2014, Merker and Chandler 2020). These impacts may be especially strong in trailing-edge populations, despite the propensity for these populations to contain greater genetic diversity than those within contemporary core areas (Hampe and Petit 2005, Ferrari et al. 2018, Merker and Chandler 2020). However, information on the basic biology and status of many of these populations is greatly lacking despite their importance to the overall metapopulation structure (Angert et al. 2011, Cahill et al. 2014). Additionally, because of their isolated nature these populations are often at a greater risk of localized extinction (Cahill et al. 2014) and may require immediate conservation activities in coming years.
The Dusky Grouse (Dendragapus obscurus) is a cold-adapted species associated with coniferous forests with a range that extends from Alaska southward through the Rocky Mountains to Arizona and New Mexico (Zwickel and Bendell 2004). With a lower thermal limit (-5 to -20 °C) than the Rock Ptarmigan (Lagopus muta; Pekins et al. 1992) and a distinct winter diet of conifer needles, the Dusky Grouse represents a unique example of a boreal-adapted species that occurs at low southern latitudes. Genetic data confirmed that the Dusky Grouse is distinct from the Sooty Grouse (D. fulignosus), which occurs along the Pacific coast from Alaska through the Sierra Nevada to southern California; together these forms were formerly regarded as “Blue Grouse” (Brooks 1929, Barrowclough et al. 2004, Banks et al. 2006). The Dusky Grouse consists of four subspecies, of which D. o. obscurus is the southernmost. Dendragapus o. obscurus occurs in the Wasatch and Uinta mountains (extreme southeastern Idaho, Utah, and extreme southwestern Wyoming), the Southern Rocky Mountains (southeastern Wyoming, Colorado, and north-central New Mexico), and isolated mountain ranges of the American Southwest in Arizona and New Mexico (Zwickel and Bendell 2004, Clements et al. 2021). However, specimens from northern New Mexico (San Miguel and Rio Arriba counties) were genetically isolated and distinct compared to specimens of D. o. obscurus from northern Colorado (Routt Co.) and the Wasatch Mountains in Utah (Utah Co.; Barrowclough et al. 2004). Barrowclough et al. (2004) suggested that the clade represented by the specimens from north-central New Mexico was nearly monophyletic and may represent a distinct species, a hypothesis that may be supported by unique tail plumage within these populations, although they recommended further study before making taxonomic conclusions. No specimens from isolated mountain ranges further south in Arizona or New Mexico were included in the study and hence their phylogenetic relations remain unknown.
Coniferous forests have a relatively expansive and continuous distribution throughout the Southern Rocky Mountains. However, south and west of this region in Arizona and New Mexico, coniferous forests occur as Pleistocene relicts stranded on isolated or semi-isolated mountain ranges where high elevations have retained relatively cool, mesic climate (Lomolino et al. 1989). Given limited dispersal ability, populations of Dusky Grouse in this region likely also exist as Pleistocene relicts on mountaintop islands of habitat large enough, or interconnected enough, to support persisting populations (Waltari et al. 2007). However, the current distribution and status of Dusky Grouse on these isolated mountaintop forest islands is poorly known, especially in New Mexico where there have been no published studies focused on this species. In Arizona, studies on Dusky Grouse have addressed seasonal food and habitat use (LeCount 1970, Severson 1986) and have suggested a positive relationship between precipitation and population size (Brown and Smith 1980). Further, the general lack of information regarding Dusky Grouse within this region is despite its status as a protected game species. Currently, both Arizona and New Mexico manage annual fall hunting seasons with harvest limits of three grouse per day (New Mexico Department of Game and Fish 2021, Arizona Game and Fish Department 2022). Recent management efforts to translocate Dusky Grouse to establish new populations have often failed, with no explanation as to the cause (Zwickel and Bendell 2004).
Self-sustaining populations of grouse require large areas of interconnected habitat (Storch 2007). Relict populations of Dusky Grouse in the American Southwest are likely facing serious and ongoing conservation issues and the small, isolated nature of their populations likely exacerbate these concerns. By 1928 the Dusky Grouse was considered greatly reduced in numbers in New Mexico and was considered extirpated from Mount Taylor, the Zuni Mountains, and the Black Range (Ligon 1927, Bailey 1928). By the 1960s populations of the Dusky Grouse in New Mexico outside the north-central mountains were thought to persist in only four mountain ranges in the southwestern part of the state (Mogollon, Tularosa, San Francisco, and San Mateo; Ligon 1961). Furthermore, Merrill (1967) concluded that they could disappear entirely from this region. Even in their core range at high elevations in the Mogollon Mountains they were considered uncommon (Hubbard 1965). Reasons provided for the apparent decline included hunting, logging, drought, and especially overgrazing by livestock (Bailey 1911, Ligon 1927, Merrill 1967). Dusky Grouse require grass and forb ground cover during the breeding season to conceal nests and thus excessive livestock grazing was blamed for the near lack of recruitment during some years, as well as their overall scarcity (Ligon 1927, Bailey 1928). Those observations led to a call to protect Dusky Grouse and to end poor grazing management in New Mexico (Bailey 1928). Subsequent research has confirmed that grazing and drought can result in lowered abundance of Dusky Grouse through reduced herbaceous vegetation (Mussehl 1963).
More recently, the Dusky Grouse was recognized as a “climate threatened” species by the National Audubon Society (National Audubon Society 2013, 2015, Wilsey et al. 2019a). Climate models predicted substantial reductions in areas of suitable “climate refuge” for the Dusky Grouse relative to current conditions (National Audubon Society 2013, Wilsey et al. 2019a). In part, this vulnerability might be explained by the species’ dependence on high elevation coniferous forests during the winter because these forests are highly susceptible to degradation and loss due to future climatic change (Thorne et al. 2018, Maxwell and Scheller 2020). Further, decreasing annual precipitation, decreased snowmelt, and increasing temperatures associated with climate change are expected to facilitate larger and more severe wildfires in the American Southwest in the coming century (Overpeck et al. 2013). Already, the largest wildfires on record in Arizona and New Mexico recently occurred in the only two mountain ranges in the southern part of the region where Dusky Grouse are known to persist, including the 2011 Wallow Fire in the White Mountains, Arizona, which burned 2178 km² (Kennedy and Johnson 2014) and the 2012 Whitewater-Baldy Complex Fire in the Mogollon Mountains, New Mexico, which burned 1204 km² (Burned Area Emergency Response 2012). These wildfire events could further reduce or fragment already small grouse populations in the region and are a major cause of conservation concern.
Understanding the environmental drivers that define a species’ geographic range is an essential goal of management and conservation, particularly for trailing edge populations along southern range limits that may be particularly susceptible to future changes. Species distribution models (SDMs) use species’ occurrence records along with environmental variables to produce models of the species’ distribution that can also be interpreted as habitat suitability (Guisan and Zimmerman 2000, Guisan et al. 2017). SDMs have been used to identify environmental variables that influence a species’ distribution, predict the effects of climate change on species’ ranges, pinpoint areas of core habitat for endangered species, and predict new areas where a species may occur (Guisan and Zimmerman 2000, Guisan et al. 2013, Goljani Amirkhiz et al. 2018). The objectives of our study were to (1) create a SDM that predicts the distribution and habitat suitability of the Dusky Grouse at the southern extent of the species’ geographic range, (2) identify environmental variables that limit the geographic range of the Dusky Grouse, (3) predict future habitat for Dusky Grouse under climate change scenarios, (4) predict loss of winter habitat for Dusky Grouse due to recent wildfires, and (5) recommend strategies for the effective conservation and management of this species in the region.
Habitat selection occurs at multiple scales (Johnson 1980), and consequently it is important to correctly match methods and sources of data to the scale of investigation. Our goal was to create a SDM that predicts the geographic range of the Dusky Grouse in the American Southwest, which is equivalent to first order selection sensu Johnson (1980). To accomplish this, we used spatial environmental data in a geographic information system (GIS), which we analyzed using Maxent version 3.4.1.k (Phillips et al. 2006, 2017). Maxent is a program that uses a machine learning algorithm to model the distribution of a species based on occurrence locations and spatial data layers. Maxent does not require absence points, which was useful for our study because no formal presence-absence survey data exist for Dusky Grouse in our study area. In addition, it allowed us to use museum records and observational data as sources of data for modeling (Elith et al. 2011). Maxent is commonly used to construct SDMs in part because it performs better than other presence-only methods, especially when sample sizes are small (Elith et al. 2006, 2011). However, Maxent is prone to overfitting and only performs well if its assumptions are met and its settings are tuned (Merow et al. 2013, Morales et al. 2017). Consequently, our methods were based on the recommendations of Merow et al. (2013) and were designed to reduce potential sources of introduced bias in our SDM. Model outputs are a relative likelihood of occurrence of the species, which can be interpreted as habitat suitability (Guillera-Arroita et al. 2015).
The study area was the states of Arizona and New Mexico, which represent the southernmost range limits of the Dusky Grouse. This region has diverse topography, ranging in elevation from 21 m to 4013 m, which results in different vegetation communities occurring along elevational gradients (Brown 1994). Within the study area, Dusky Grouse are primarily associated with subalpine coniferous forest and upper mixed coniferous forest. Subalpine coniferous forest is dominated by Engelmann spruce (Picea engelmannii) and subalpine fir (Abies lasiocarpa) and it is the highest elevation forest type, generally occurring above 2500 m to treeline (~3300 m; Brown 1994). The cold, mesic upper mixed coniferous forest is dominated by Douglas-fir (Pseudotsuga menziesii), white fir (Abies concolor), blue spruce (Picea pungens), and quaking aspen (Populus tremuloides; Brown 1994). These coniferous forests, particularly the subalpine coniferous forest, which only occur on the highest peaks, are considered essential winter habitat for Dusky Grouse (Ligon 1927, Brown 1989). Further, montane and subalpine meadows that occur in openings within these forests are important for nesting and foraging during the breeding season (Zwickel and Bendell 2004). Vegetation types at elevations below the upper mixed coniferous forest zone—which become progressively warmer and more arid—include coniferous woodlands, grasslands, and deserts, which likely limit the potential for occurrence or dispersal by Dusky Grouse (Zwickel and Bendell 2018).
We compiled occurrence records of Dusky Grouse within the study area from museums, observational databases, credible observations made by knowledgeable professionals, and the literature. We searched for museum specimen records using Vertnet (http://www.vertnet.org) and Arctos (https://arctos.database.museum/). We also searched for specimens in the New Mexico State University Vertebrate Museum, National Museum of Natural History, and the University of Arizona Museum of Natural History. We searched for observation records in the New Mexico Ornithological Society Observation Database (http://www.nmbirds.org), Arizona Field Ornithologists Sightings Database (http://www.azfo.org), eBird (Sullivan et al. 2009; https://www.ebird.org), and iNaturalist (https://www.inaturalist.org). We georeferenced occurrence records lacking global positioning system coordinates using DeLorme Topo USA Version 5.0 (DeLorme 2004, Yarmouth, Maine, USA). We followed Frey et al. (2013) to assign each record to six precision classes (H: < 30 m, I: 30–500 m, J: 500–1000 m, K: 1000–2000 m, L: 2000–3000 m, and M: > 3000 m) that represented the likely deviation of the coordinate relative to the actual location and to four reliability classes (A: museum specimen or photo evidence, B: expert observation, C: non-expert observation, and X: erroneous record). We excluded records in precision class K, L, and M, reliability class X, and translocations outside the historical range. We excluded records in reliability class C that were below 2316 m elevation north of latitude 35°N and below 2499 m elevation south of latitude 35°N, which we considered not suitable and therefore likely misidentifications. Additionally, we removed records that were outside the temporal range of our environmental predictor layers. Finally, we mapped the remaining occurrence records using ArcGIS version 10.8 software (Environmental Systems Research Institute 2011, Redlands, California, USA) and used them to create the SDM.
We developed hypotheses about environmental factors that might structure the distribution of the Dusky Grouse in our study area and selected bioclimatic and biophysical environmental layers to represent those hypotheses for testing (see Appendix 1 for the hypotheses, rationales, and variable definitions). We did not consider environmental variables that were highly correlated (r > 0.9) to reduce redundancy. The bioclimatic variables were obtained from WorldClim Version 2.1 (https://www.worldclim.org) representing years 1970–2000 and depicting measures of annual trends, seasonality, and limiting factors (Hijmans et al. 2005, Fick and Hijmans 2017). We obtained landcover data from the Southwest Regional GAP Analysis (https://www.swregap.org), but combined some landcover types that were similar (e.g., Rocky Mountain subalpine mesic meadow and Southern Rocky Mountain montane-subalpine grassland) to reduce the number of classes (see Appendix 2 for the reclassified landcover types). We included topographical variables (heat load index and terrain ruggedness index) because of their possible influence on local environment and vegetative conditions. We did not include elevation because it directly influences climate and we aimed to model climate variables directly without this influence. We generated heat load index using the ArcGIS Geomorphology and Gradient Metrics toolbox version 2.0 (Evans et al. 2014) to calculate the relationship between slope and aspect ratios following the methods of McCune and Keon (2002). We generated terrain ruggedness index, a measure of landscape heterogeneity, in ArcGIS Version 10.8 by subtracting the elevation layer from nearby grid cells following the methods of Riley et al. (1999). We included annual normalized difference vegetation index (NDVI) because it indicates plant “greenness” or productivity and canopy cover, and models including this variable can outperform those without it (Amaral et al. 2007). For NDVI we averaged one year of 14-day survey window data from the Moderate Resolution Imaging Spectroradiometer database (https://modis.gsfc.nasa.gov/) for 2017 to represent annual conditions across the study area. Environmental data layers were generated at a 1-km² spatial resolution.
Prior to model construction, we restricted our occurrences to the temporal window of our environmental predictor layers (1970–2017). Next, to reduce the effect of sampling bias and spatial autocorrelation, we rarefied occurrence records to 1 km. These steps allowed us to pair the occurrences with the temporal and spatial resolution of our environmental predictor layers. Maxent compares environmental conditions at the occurrence locations with those at random background locations to predict the species’ relative likelihood of occurrence (Phillips et al. 2006, Merow et al. 2013). We restricted the background extent to only areas where Dusky Grouse could occur by creating a buffer around each occurrence point using the maximum dispersal distance of the species (35 km; Barrowclough et al. 2004).
Maxent is capable of making overly complex models, which may be less interpretable, more sensitive to sampling bias, and prone to falsely inflated model evaluation metrics (Merow et al. 2013). Thus, to prevent these issues we controlled model complexity by reducing multicollinearity, selecting the best subset of covariates, and tuning the regularization parameter (β multiplier), which is a penalty coefficient to reduce overfitting (Anderson and Gonzalez 2011, Warren et al. 2014). We accomplished this via a series of steps following the methods of Warren et al. (2014). We began the modeling process with an initial candidate pool of 14 variables (4 biophysical, 10 climate) that we hypothesized may be important in determining the geographic range of the species within the study area (Appendix 1). First, we generated a set of models using the full variable suite where we manipulated the β multiplier from 0 to 15 at an increment of 0.1 (Warren et al. 2014). Next, we used ENMTools (Warren et al. 2010) to select the best supported model based on the lowest AICC value (Akaike’s 1974 information criteria corrected for small sample size; Burnham and Anderson 2002). From this starting model, we removed all variables with < 5% contribution and designated the highest contributing variable as the most important variable. If the next highest contributing variable was correlated at |r| > 0.7 with the most important variable, it was discarded. If it was not correlated, this variable was retained along with the remaining variables that contributed over 5% to this preliminary model. We then re-ran the model, continually retaining the most important variable and removing correlated variables (|r| > 0.7) or variables with < 5% contribution. We continued this process until all variables were uncorrelated and contributed greater than 5%; this suite of variables became the final variable set. Although these methods were designed in part to reduce collinearity, recent research suggests that Maxent is robust against these effects (Feng et al. 2019). Finally, we validated our models by calculating the AUC value for the top model at each step of the variable selection process, following the recommendations of Vignali et al. (2020), to document how the reduction of variables improved the predictive power of each subsequent model within this framework.
After developing the final variable set, we re-tuned the model by generating models where we adjusted the β multiplier from 0 to 15 at increments of 0.1 and used ENMTools to select the model with the lowest AICC value. Using this final suite of variables and the adjusted β multiplier, we ran the final model with the default settings for convergence threshold, maximum iterations, and 20 bootstrap model replicates. The bootstrap replicated run type was selected because of the small number of occurrence locations (Wu et al. 2009). To maintain a larger sample size, test data were not separated from training data. Maxent models assume that species are at equilibrium with the environmental predictor layers used to fit the model and consequently extrapolation outside the range of variation of the environmental layers can lead to misinterpretation of model results (Pearson et al. 2006, Buisson et al. 2010). Thus, the environmental predictor layers for our model were generated in the raster format in order to perform a multivariate environmental similarity surface (MESS) analysis, as suggested by Elith et al. (2010), by using the MESS analysis setting in Maxent. The MESS analysis allowed us to compare the similarity of environmental predictors at the occurrence locations and the projection dataset, which was used to identify areas where conditions were outside of the range of the training data. Model predictions outside of this range may be unreliable.
It has been recommended that multiple evaluation metrics be used for evaluating Maxent models (Fourcade et al. 2018). Therefore, we evaluated our model using the area under the receiving operator characteristic curve (AUC), sensitivity, specificity, and true skill statistic (TSS; Landis and Kock 1977). The use of AUC is important for evaluating the model’s ability to determine which records were likely accurate (1.0), compared to those records that represented false occurrences or models with limited predictive power (0.5; Tingley and Herman 2009). We calculated AUC values using a block-partitioned k-fold cross-validation using the package “ENMeval” (Muscarella et al. 2014) in Program R (R Core Team 2018), withholding 75% of the occurrence records for training and using the remaining 25% for testing. The block-partitioned method partitions occurrence records by latitude and longitude into k bins of approximately equal size; and we defined k = 4 to determine our training and testing samples. This method is recommended for situations where models are transferred across time, particularly under scenarios where non-analog conditions may occur (Muscarella et al. 2014). The TSS represents matches and mismatches between occurrences and model predictions and ranges from -1 to +1, where +1 indicates perfect model agreement and values < 0 represent random predictions (Fielding and Bell 1997). We used TSS to define model performance as: poor (0–0.3), moderate (0.31–0.6), and substantial (> 0.6; Allouche et al. 2006). We evaluated the variable response curves to understand the relationship between the relative likelihood of occurrence and individual environmental predictors. We displayed our model as a map in logistic output format, which describes the relative likelihood of occurrence of the species (i.e., which can be interpreted as habitat suitability) as a continuous variable from a range of 0 (no likelihood of occurrence) to 1 (occurrence extremely likely), which we interpreted as habitat suitability (Phillips and Dudík 2008). We compared the map created from the model with occurrence records of Dusky Grouse to confirm if the model accurately portrayed the species distribution. For interpretation, we also created a map that displays five habitat suitability classes based on the relative likelihood of occurrence: 0–0.2 no/very low suitability; 0.21–0.4 low suitability; 0.41–0.6 moderate suitability; 0.61–0.8 high suitability; 0.81–1.0 very high suitability.
To predict the geographic range of an organism in future climates, it requires a SDM based on current climate variables, which represents the predicted “climate envelope” of the species within the study area (Thomas et al. 2004, Ibáñez et al. 2006). The climate envelope model (CEM) describes the species’ niche and consequently the relationships are assumed to remain constant through time as they are based on the species’ biology and evolutionary history (Pearson and Dawson 2003, Fitzpatrick et al. 2008). To predict the species’ geographic range at a future time, the CEM is projected onto data layers that represent the climate in the future time (Araújo and Peterson 2012, Watling et al. 2013a, b).
We were unable to use our SDM for the climate predictions because it was created with 1-km spatial resolution data, while the future climate data were available at 4.5-km spatial resolution (https://www.worldclim.org). Therefore, we created a new model to serve as the CEM based on an initial candidate pool of 12 climate variables that we hypothesized may be important to the species distribution in the study area; the 12 variables represented current climate (1970–2000) at 4.5-km spatial resolution (Appendix 1). We followed the same procedures for constructing, evaluating, and interpreting the CEM as we did for the SDM. Changes in coarseness of climate layers can produce different model results (Franklin et al. 2013). Consequently, to ensure that the SDM and the CEM made similar predictions despite a difference in resolution, we measured the degree of similarity between the habitat suitability values of both models using the niche overlap feature in ENMTools. This analysis measures model overlap using Schoener’s D (Schoener 1968), I statistic (Warren et al. 2008), and relative rank (Warren and Seifert 2011), where each value ranges from 0 (no agreement) to 1 (full agreement). The D and I statistics standardize output cells from the models so that they sum to 1 across the model extent and the difference between relative likelihood of occurrence cell values from competing models are compared. The relative rank estimates the probability that the relative likelihood of occurrence for a grouping of cells is similar for competing models, rather than at individual cells (Warren et al. 2010, Warren and Seifert 2011).
To predict how the geographic range of the Dusky Grouse might change in the future, we projected the CEM onto data layers representing future climate conditions. For the climate projections we used the Coupled Model Intercomparison Project phase 6 (CNRM-CM6-1) global climate model calibrated for WorldClim data (Eyring et al. 2016, Voldoire et al. 2019). We chose the CNRM-CM6-1 global climate model because it has been shown to not over or under predict future changes in seasonal precipitation or extended dry periods within the American Southwest (Srivastava et al. 2020, Akinsanola et al. 2020a, 2020b). We projected the CEM onto GIS layers representing climate at two time periods, 2041–2060 and 2081–2100, and based on two climate change scenarios: an optimistic low CO2 emission scenario (Shared Socioeconomic Pathways [SSP 2–4.5]) and a pessimistic high CO2 emission scenario (SSP 5–8.5). The SSP 2–4.5 scenario represents warming of ~3 °C by the year 2100, with an increase in CO2 emissions until 2060 followed by a gradual decrease in emissions through the year 2100 (Riahi et al. 2017). In the SSP 5–8.5 scenario, reliance on fossil fuels continues to drive economic development, resulting in projected warming of ~5 °C by the end of 2100 (Riahi et al. 2017). We performed a MESS analysis for the CEM, which allowed us to compare similar and novel (negative values) environmental conditions across all future climate scenarios.
To understand how the geographic range of the Dusky Grouse is predicted to change under future climate scenarios, we first converted the continuous logistic outputs of the CEM and the future projections into five habitat suitability classes: 0–0.2 no/very low suitability; 0.21–0.4 low suitability; 0.41–0.6 moderate suitability; 0.61–0.8 high suitability; 0.81–1.0 very high suitability. Within each class (except no and low suitability, which are unlikely to be used by Dusky Grouse), and for each future climate scenario, we calculated the area of predicted habitat and the cumulative percent loss of habitat relative to current conditions (i.e., the CEM). We made these calculations for the entire study area as well as within the northern and southern regions of the study area divided at 35°N latitude.
Although our SDM and CSM sought to understand factors that shape the geographic range of the Dusky Grouse in the study area, we also were interested in understanding if recent wildfires had caused loss of habitat for the Dusky Grouse at smaller orders of selection, specifically winter roosting and foraging habitat. We considered the mixed coniferous and subalpine coniferous forests to represent winter habitat for Dusky Grouse. To understand how recent wildfires may have affected winter habitat for the Dusky Grouse, we obtained burn severity data from the Monitoring Trends in Burn Severity (MTBS) program (https://www.mtbs.gov) for all fires that occurred in the study area for the years 2000 to 2017. We selected this temporal extent because it captures the most extensive recent fire history and includes “megafires” (Stephens et al. 2014, Mueller et al. 2020), which are an increasing threat across this region. We extracted the Moderate and Severe burn severity categories for each fire that overlapped habitat for the Dusky Grouse as predicted by our SDM. We defined Dusky Grouse habitat as habitat suitability classes 0.4–1.0 in the SDM. We used the Moderate and Severe burn severity categories because these classes reflect widespread mortality and damage to conifer trees in mixed coniferous and subalpine coniferous forests (USDA Forest Service 2010). The Moderate burn severity class indicates that the tree canopy was mostly scorched or consumed, while the Severe burn severity class indicates that the canopy foliage was completely consumed (USDA Forest Service 2010). These conditions would largely or completely eliminate winter roosting and foraging habitat for Dusky Grouse.
To assess the extent of loss of winter habitat for the Dusky Grouse due to wildfire, we calculated the area that the Moderate and Severe burn severity classes overlapped Dusky Grouse predicted habitat and expressed the totals as percent of available habitat. To understand regional differences, we calculated the percent loss for each state, for the northern and southern regions (divided at 35° latitude), and for the Gila National Forest subregion of southwestern New Mexico (i.e., Catron, Grant, and Sierra counties). We excluded areas where the SDM predicted habitat outside of the historical range of the Dusky Grouse (e.g., Pinaleño Mountains, AZ, Mogollon Rim, AZ, Sacramento Mountains complex, NM) to provide a realistic assessment for managers on the impacts of fire on potential grouse habitat.
We found 783 occurrence records representing 430 unique locations for Dusky Grouse in Arizona and New Mexico (Appendix 3 and 4), of which 137 were used for modeling (Fig. 1). Following the variable selection procedure, the final SDM included five variables: precipitation of warmest quarter, mean diurnal range, annual mean temperature, precipitation of driest month, and mean temperature of wettest quarter (Table 1). The SDM with five variables and based on 137 occurrence records met the rule of thumb of 10 occurrence records per variable, which reduces the risk of overfitting the model (Warren et al. 2014). The SDM used a β multiplier of 2.7 (Table 2) and feature classes: hinge, product, linear, threshold, and quadratic. The SDM had good model performance based on AUC metrics and the TSS indicated substantial support (Table 2). The map depicting the continuous likelihood distribution (0–1) based on the SDM showed overlap of known grouse occurrences (Appendix 4) with areas of high relative likelihood of occurrence (Fig. 2). The model improved at each stage of the validation process, from a starting point of AUC = 0.899 and an ending value of AUC = 0.909 (Appendix 5). The variable with the highest percent contribution to the model was precipitation of warmest quarter (39.2%) and exhibited an optimum range that peaked at ~330 mm (Fig. 3). The next highest contribution was mean diurnal range (19.8%), which was negatively associated with relative likelihood of occurrence of the Dusky Grouse (Fig. 3). The three remaining variables were: annual mean temperature (14.0%), which had an optimum at ~2 °C and precipitously declined with higher temperatures; precipitation of driest month (13.5%), which did not display a relationship with relative likelihood of occurrence in the marginal response curve; and mean temperature of wettest quarter (13.4%), which had a peak at ~1 °C and then declined to a relatively low relative likelihood of occurrence (0.3) above 20 °C.
Areas predicted to have the highest relative likelihood of occurrence for the Dusky Grouse included the Kaibab Plateau, San Francisco Peaks, White Mountains, and Pinaleño Mountains in Arizona, and the San Juan Mountains, Sangre de Cristo Mountains, Jemez Mountains, Mount Taylor, Mogollon Mountains, and Sacramento Mountains Complex (Capitan, White, and Sacramento mountains) in New Mexico (Fig. 2). Historical records of grouse were available from several mountain ranges that were not included in our modeling (Appendix 3 and 4), yet those ranges were predicted to have moderate likelihood of occurrence (e.g., Chuska Mountains, Sandia Mountains, Magdalena Mountains, San Mateo Mountains, Black Range). The MESS analysis indicated that the following regions had variables outside the range of the training data and thus may not predict well: Mojave and Sonoran desert regions of extreme western and southwestern Arizona, Lake Powell region in northcentral Arizona, high elevations of south-central Arizona, the Carrizo Mountains in northeastern Arizona, the Chuska Mountains in northeastern Arizona and northwestern New Mexico, and the Sacramento Mountains complex and Guadalupe Mountains in south-central New Mexico (Appendix 6).
The CEM had good model performance based on AUC metrics and the TSS, which indicated substantial support (Table 2). The CEM improved at each stage of the validation process, beginning at AUC = 0.873 and concluding with AUC = 0.899 (Appendix 5). The CEM included three variables: maximum temperature of warmest month (69.4% contribution), precipitation of wettest quarter (22.1% contribution), and isothermality (8.5% contribution). Relative likelihood of occurrence of the Dusky Grouse exhibited a positive association with precipitation of wettest quarter and a negative association with maximum temperature of the warmest month and isothermality (Appendix 7). The spatial patterns of predicted grouse habitat were visually similar between the SDM and CEM (Fig. 2 and Appendix 7) and the two models exhibited high similarity based on all three metrics: D (0.68); I (0.91); and relative rank (0.89).
Based on projections of the CEM onto data layers representing future climate, the geographic range of the Dusky Grouse within the study area is predicted to shrink over time, with the magnitude dependent on the carbon emissions scenario and region (Fig. 4; Table 3). The highest quality habitat (> 0.6) was predicted to be completely lost in both time periods and according to both emission scenario, with exception of a relatively small area remaining under the SSP2-4.5 scenario in the 2041–2060 time period. Areas of moderate habitat suitability were predicted to be mostly lost by the 2041–2060 time period, and completely lost by the 2081–2100 time period under the SSP 5–8.5 emission scenario. The southern region was predicted to experience more rapid loss of habitat than the northern region, with many patches of habitat completely eliminated by the 2041–2060 time period (Fig. 4; Table 3).
Across the study area, 4.0% of Dusky Grouse winter habitat experienced moderate to severe burn severity during 2000–2017, although extent varied by region (Table 4; Appendix 8). In the northern region 2.3% of winter habitat was predicted to have been lost, whereas in the southern region 18.9% of winter habitat was predicted to have been lost. In the Gila subregion of southwestern New Mexico, 69.9% of winter habitat was predicted to have been lost because of effects of recent wildfires.
Our SDM suggests that climate is the primary limiting factor structuring the geographic range of the Dusky Grouse at its southern limits in the American Southwest; other variables representing vegetation and topography were not significant in our analysis. This finding supports the hypothesis that climate is the primary driver defining the distribution of trailing-edge populations (Cahill et al. 2014, Lynch et al. 2014, Coristine and Kerr 2015). At warm range boundaries, climate can structure distribution directly by limiting physiological processes (Cahill et al. 2014) or indirectly through influences on vegetation (Brovkin 2002). Although there currently is little information about the physiology of Dusky Grouse that relates directly to climate, such as its upper critical temperature threshold or the role of snow (or snow quality) in thermoregulation during winter, our model supported general hypotheses about the potential influence of the climate variables on the species’ physiology and required vegetation.
Organisms may select different aspects of the environment at different scales of habitat selection (Johnson 1980). The SDM and CEM were designed to evaluate the broadest (first order) scale of selection-selection of the geographic range. Consequently the models likely predict more habitat than actually exists because Dusky Grouse also require specific habitat conditions at finer scales of selection. For instance, at finer scales of selection Dusky Grouse rely on Douglas-fir needles and other parts of conifer trees as their winter food, primarily selecting younger needles from the upper canopy of large (mean 49 cm dbh) and old (mean 235 years old) trees (Remington and Hoffman 1996). During winter, Dusky Grouse roost in the largest Douglas-fir or subalpine fir trees that have a structure providing a thermally protected microclimate (Severson 1986, Cade and Hoffman 1990, Pekins et al. 1989). During spring through fall, male Dusky Grouse may continue to use conifer forests, again selecting the largest Douglas-firs for roosts, while females use bunchgrass meadows and other open areas with adequate herbaceous cover for nesting and rearing young (Mussehl 1963). These finer scale habitat requirements are nested within the higher order selection of the geographic range; the species cannot persist without both. The climate variables in our models likely have direct or indirect influence on the availability of these finer scale resources. For instance, climate is the major factor that determines the biotic communities selected for the establishment of grouse home ranges (Brown 1994). The conifer trees used by Dusky Grouse require cold winter temperatures to prepare for summer growth (Ford et al. 2016) and to resist bark beetles and other pests (Raffa et al. 2015); thus, winter roost sites and food resources may be limited by warm temperatures. Subalpine and montane meadows require adequate precipitation for lush growth; thus, low precipitation could increase depredation rates and decrease nest success (Brown and Smith 1980, Zwickel and Bendell 2004).
The SDM indicated that the Dusky Grouse is associated with relatively cool, mesic conditions. For instance, the most important variable in the SDM was precipitation of warmest quarter of the year, which not unexpectedly was correlated (r = 0.784) with elevation and hence reflects the adiabatic cooling and increased precipitation of increasing elevation. At the coarser 4.5-km scale of the CEM, maximum temperature of the warmest month was the most influential variable. These relationships provide independent support for the premise that populations of Dusky Grouse in the American Southwest are Pleistocene relicts effectively trapped on mountaintops that have retained suitable cool, mesic climate.
Unlike most other birds, Dusky Grouse have relatively limited dispersal capabilities that may reduce or prevent movements among mountain ranges given current climate and vegetation patterns (Zwickel and Bendell 2018). Thus, similar to other organisms with restricted dispersal capabilities such as small mammals, current distribution is likely a product of vicariance, wherein the species achieved a broader distribution during Pleistocene glacial periods but then fragmented with retraction of its range to mountaintop refugia upon onset of Holocene warming (Frey et al. 2007). Therefore, limited dispersal may prevent rescue of declining populations. In such a “relaxation system” where intermontane dispersal is largely lacking, extinction becomes the overriding factor shaping contemporary distribution patterns and it results in a distribution pattern whereby only the largest patches of habitat remain occupied and populations on smaller patches have the highest extinction risk (Brown 1971). Populations may only exist in small patches of habitat if they are located near core areas or corridors exist to connect small patches of habitat to core areas (Lomolino et al. 2016). Another potential consequence of this relictual relaxation process is an erosion of genetic variation within isolated populations (Ditto and Frey 2007). Thus, the most important consideration for maintaining populations of Dusky Grouse in the American Southwest is maintaining the overall size of habitat area, with additional focus on maintaining connectivity of smaller peripheral patches of habitat.
The SDM predicted high habitat suitability of Dusky Grouse in some mountain ranges where it is not known to occur, including the Pinaleño Mountains in southeastern Arizona and the Sacramento Mountains complex in southeastern New Mexico. However, the MESS analysis indicated that at least one environmental variable in these areas was outside the range of the training data and hence predictions may be unreliable. Conversely, contemporary absence of the Dusky Grouse from these mountain ranges could be due to their biogeographic history. These ranges are highly isolated from source populations in the Rocky Mountains, and consequently have depauperate Rocky Mountain faunas (Frey et al. 2007). Absence of the Dusky Grouse from these mountain ranges is consistent with the biogeographic pattern for several terrestrial small mammals typical of the Southern Rocky Mountains such as the water shrew (Sorex navigator), southern red-backed vole (Myodes gapperi), and montane cottontail (Sylvilagus nuttallii; e.g., Frey et al. 2007).
The SDM provides insight into the historical distribution of the Dusky Grouse in the American Southwest. For instance, the historical presence of Dusky Grouse in the San Francisco Peaks area of northern Arizona is based on anecdotal information (Merriam 1890). Our model indicates that this mountain has high suitability habitat, lending support to the historical presence of Dusky Grouse in this area. Similarly, the SDM predicted moderate habitat suitability in some mountain ranges such as the Sandia, San Mateo, and Magdalena mountains in New Mexico, which have records of Dusky Grouse, but were not included in the modeling. This lends support to the veracity of the SDM and to the legitimacy of the infrequent reports of the species in those ranges. However, the moderate habitat suitability in these ranges suggests these populations could be vulnerable to extirpation. Lastly, our model predicted very low to moderate habitat suitability in several mountain ranges in New Mexico where the Dusky Grouse is thought to have historically occurred, but is currently considered extirpated, including the Zuni Mountains and several outlying ranges of the Mogollon Plateau, such as Escondido Mountain, Gallo Mountains, and Elk Mountains (Ligon 1927, Bailey 1928). It is noteworthy that these suspected extirpations occurred in areas lacking nearby core areas of high habitat suitability.
Dusky Grouse have been translocated to five mountain ranges in the American Southwest (Appendix 9) and our SDM helps explain the fate of these translocation efforts. The only apparently successful translocation was 36 birds released to San Francisco Peaks in the 1970s. The San Francisco Peaks has a small area of high and very high habitat suitability and Dusky Grouse were reported to occur in this range historically. The only other translocation to an area where Dusky Grouse might have historically occurred, was 11 birds released to Mount Taylor in 1969. We are not aware of any observations of grouse in this range since 1979 suggesting they did not persist, despite the range containing moderate to high habitat suitability. There have been translocations to three ranges outside of the species’ historical range (Fig. 1). A total of 59 birds (of which only 13 were adults, all female) were translocated to Sierra Blanca in the Sacramento Mountains Complex from 1960 to 1963, but no grouse have been observed in this range since. Although Sierra Blanca is predicted to have very high habitat suitability, this is an area where the model cautioned interpretation because of variables outside the reference range. A total of 4 birds (1 hen and 3 chicks) were translocated to the Pinaleño Mountains in 2012, but this translocation is thought to have failed. This range has a small area ranked as very high suitability, but it also is an area where the model cautioned interpretation because of variables outside the reference range. Lastly, in 2008, 32 birds were translocated to the Mogollon Rim in Arizona in an effort to expand the distribution of the species in Arizona amid concerns of future range retractions. However, our SDM indicates that this region has low habitat suitability (< 0.3) and therefore is unlikely to support a population of Dusky Grouse. The lack of subsequent reports of grouse in this area provides further support for our model.
Given that climate exerts a strong control on the geographic range of Dusky Grouse within trailing-edge populations at its southern range limit, it is unsurprising that future climate change is predicted to cause a reduction in the species’ distribution. The CEM predicted substantial contractions in the geographic range of the Dusky Grouse, regardless of the region, emissions scenario, or time frame. The species is predicted to lose nearly 100% of its current high suitability habitat by the end of the 21st century under both emissions scenarios. These losses were predicted to be most rapid and severe in the southern subregion, where only moderate suitability habitat is predicted to remain in the White Mountains, Pinaleño Mountains, and Sacramento Mountains complex, although this too is predicted to be completely eliminated by the 2081–2100 time frame under the high emissions scenario. Additionally, the only large areas of suitable climate predicted to be retained in the southern subregion where Dusky Grouse are known to currently occur, is within the White Mountains, Arizona. In the Northern subregion, the Chuska (including Lukachukai) Mountains and Mount Taylor are predicted to lose their entire climate envelope under each of the future climate scenarios, while the San Francisco Peaks are predicted to lose a large proportion of suitable climate areas under the models for the 2041–2060 time frame and are predicted to have no suitable climate by the later time period. Only the San Juan Mountains, Sangre de Cristo Mountains, and Jemez Mountains are predicted to retain a large proportion of the current climate envelope under all but the latest time frame under the high emissions scenario.
The extent of loss of suitable climate predicted by our CEM was greater than predicted in prior studies by the National Audubon Society for the combined range of the Dusky Grouse and Sooty Grouse, which predicted a range-wide 60% loss of suitable climate (National Audubon Society 2013; https://climate.audubon.org/birds/blugrs/dusky-sooty-grouse), and for the Dusky Grouse, which predicted an overall 81% loss of the species range and virtually complete loss of suitable climate from all areas of the American Southwest except the San Francisco Peaks, San Juan Mountains, and Sangre de Cristo Mountains (Wilsey et al. 2019a; https://www.audubon.org/field-guide/bird/dusky-grouse). However, the National Audubon Society models used different sources for occurrence records, spatial extent for occurrence records, methodology, and emission scenarios (National Audubon Society 2015, Wilsey et al. 2019a). Therefore, the models are not directly comparative. Regardless, each model strengthens the overriding conclusion that climate change will have profound impact to habitat for the Dusky Grouse in the American Southwest, particularly for populations in the southern subregion.
Climate change can cause deleterious impacts to wildlife on a variety of spatio-temporal scales, such as direct mortality or sublethal effects due to extreme weather events (e.g., McKechnie and Wolf 2010, Martin et al. 2017) or gradual loss of montane habitat due to upslope retraction of vegetation zones (e.g., Sekercioglu et al. 2008). Wilsey et al. (2019b) considered the chief threats of climate change to Dusky Grouse to be an increase in spring heat waves under modest warming scenarios (+1.5 °C) with the addition of weather that promotes wildfire under more severe warming scenarios (+3.0 °C). They concluded that spring heat waves could negatively affect nesting success, while wildfire could cause loss of habitat, particularly if coniferous forests fail to regenerate. However, our results suggest that recent wildfires have already caused substantial losses of habitat for Dusky Grouse in the American Southwest, particularly in the southern subregion. Singleton et al. (2019) found that the numbers, area, and severity of wildfires in Arizona and New Mexico have increased over the last three decades, with increased proportions of area burned severely in mixed coniferous forest types. In addition, they found a dramatic shift in uncharacteristic fires post-2000, which they postulated was linked to a dramatic shift to warmer and drier climate and weather (Singleton et al. 2019). Such uncharacteristic fires are projected to increase with continued climate change (Kent 2015).
Research has suggested beneficial effects of fire and other disturbances such as logging on habitat for Dusky Grouse via creating patches of early successional herbaceous or shrubby vegetation, which is necessary for successful nesting (e.g., Martinka 1972, Hutto and Patterson 2016). However, those findings were from more northern regions with extensive dense mesic coniferous forest, limited nesting habitat, and relatively small areas of disturbance, and hence are likely not relevant to the current or predicted trends for wildfires in the Southwest. In contrast, coniferous forests in the Southwest tend to be more xeric and are naturally heterogeneous and fragmented. The large size and severity of recent wildfires has eliminated a majority of the subalpine and mixed coniferous forest from some mountain ranges, such as the Mogollon Mountains in the Gila subregion. Mounting research indicates that conifers may fail to regenerate following wildfire under current and future climates, particularly in xeric environments or where repeated burns have occurred, which can result in sustained non-forest conditions (Rother and Veblen 2016, Stoddard et al. 2018). In the American Southwest, impacts of recent wildfire on Dusky Grouse habitat have been most extreme in the southern subregion eliminating majorities of subalpine and mesic mixed coniferous forest in some key regions such as the Mogollon Mountains, where the substantial loss of habitat may dramatically increase the risk of extirpation. Given the likely metapopulation structure of Dusky Grouse in the semi-isolated mountains surrounding the Mogollon Mountains, this loss of habitat could also cause collapse of the metapopulation if the core population is no longer able to produce migrants that sustain the peripheral populations. The loss of habitat due to wildfire in the southern subregion of the Southwest are likely to continue with future climate change and provides an example of what may happen in the future to more northerly populations.
Habitat for the Dusky Grouse in Arizona and New Mexico is highly fragmented such that most populations are restricted to small and isolated (or semi-isolated) mountaintop refugia, which can result in unique genetic signatures as well as heightened risk of extirpations due to habitat loss. Historical occurrence records suggest that extirpations have already occurred in some mountain ranges. However, despite the Dusky Grouse being managed as a game species, there has been little (Arizona) or no (New Mexico) published research to determine the distribution, habitat use, population sizes, demography, or other information necessary to assure sustainable populations. Recent wildfires have caused substantial loss of habitat in the southern subregion, which could jeopardize persistence of those populations. Translocations moved individuals to locations outside their natural historic range without appropriate information about the amount of available habitat or genetic makeup of source or resident birds, which can waste resources or disrupt genetic makeups. However, as trailing-edge populations may contain higher genetic diversity than populations within a species’ core range (Hampe and Petit 2005, Ferrari et al. 2018, Merker and Chandler 2020), grouse within the American Southwest may possess unique traits that could allow them to persist under future conditions. Therefore, Dusky Grouse from these areas may be viable source populations for future assisted migration efforts that seek to introduce this diversity into more northern populations that will begin to experience similar climates to those within the American Southwest in future decades.
Despite the challenges we outline for Dusky Grouse within the American Southwest, northern populations within this region (i.e., Sangre de Cristo Mountains, San Francisco Peaks) retain seemingly robust populations of grouse and may provide refugia against moderate future climatic changes. Our future predictions do not take into account potential climate-related resiliency that may occur within populations at southern range edges (Herrero and Zamora 2014). Potential unexplored behavioral, habitat use, or genetic differences within these populations could mitigate some of the effects of a warming climate, although this needs further study. Immediate research and conservation measures are necessary to manage Dusky Grouse habitat for resiliency and to assure the long-term persistence of Dusky Grouse within Arizona and New Mexico. Research investigating the genetic health, phylogeography, and taxonomy of Southwest populations is necessary to inform conservation, especially to identify appropriate units for management. Field studies are needed to assess the current distribution, habitat use, and demography of trailing-edge Dusky Grouse populations to inform scientifically defensible management strategies, including sustainability of any harvests. Additionally, immediate attention is required to assess the conservation status of populations in southern New Mexico. Research is needed on effective translocation methods and potential for establishing climate refuges for phylogenetically distinct populations that might be at risk of extinction. Lastly, research is necessary on forest management that can promote more resilient habitat for the Dusky Grouse in the face of climate change and wildfire risk, as well as research on firefighting methods that can protect patches of habitat necessary to sustain populations of Dusky Grouse.
As this article went to press, Arizona and New Mexico were experiencing an unprecedented fire season with several megafires burning in Dusky Grouse habitat. Two of these fires in New Mexico became the largest in the state’s history. The Hermits Peak-Calf Canyon Fire started 6 April 2022 and as of 23 June 2022 had burnt > 1380 km² in the Sangre de Cristo Mountains; the fire footprint is within some of the highest suitability habitat for Dusky Grouse within the study area. The Black Fire started 13 May 2022 and as of 23 June 2022 had burnt 1316 km² in the Black Range of the Gila National Forest. Other smaller megafires during this period also effected habitat on the San Francisco Mountains in Arizona and the Jemez and San Mateo Mountains in New Mexico. These fires support conclusions of this research that wildfire is outpacing the loss of habitat due to climate change within this region.
JY - data curation, formal analysis, visualization, writing original draft, writing review and editing (co-lead).
RG - formal analysis (assist), methodology (assist), writing review and editing (assist).
JKF - conceptualization, methodology, supervision, writing review and editing (co-lead).
We would like to thank the users of eBird, iNaturalist, and New Mexico Ornithological Society Field Notes Database for submitting observations and making them available for research. We are grateful for the institutions in the VertNet and Arctos portals, University of Arizona Museum of Natural History, Peter Houde from the New Mexico State University Vertebrate Museum, and Chris Milensky from the National Museum of Natural History for providing specimen data. We thank Scott Carleton, Jerry Monzingo, Mitch East, Robert Parmenter, Dan Bastion, and Brian Long for observation data. We also thank the anonymous reviewers and editor for their helpful comments that improved an earlier version of the manuscript.
Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19:716-723. https://doi.org/10.1109/TAC.1974.1100705
Akinsanola, A. A., G. J. Kooperman, A. G. Pendergrass, W. M. Hannah, and K. A. Reed. 2020a. Seasonal representation of extreme precipitation indices over the United States in CMIP6 present-day simulations. Environmental Research Letters 15:094003. https://doi.org/10.1088/1748-9326/ab92c1
Akinsanola, A. A., G. J. Kooperman, K. A. Reed, A. G. Pendergrass, and W. M. Hannah. 2020b. Projected changes in seasonal precipitation extremes over the United States in CMIP6 simulations. Environmental Research Letters 15:104078. https://doi.org/10.1088/1748-9326/abb397
Allouche, O., A. Tsoar, and R. Kadmon. 2006. Assessing the accuracy of species distribution models: prevalence, kappa, and the true skill statistic (TSS). Journal of Applied Ecology 43:1223-1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x
Amaral, S., C. B. Costa, and C. D. Rennó. 2007. Normalized difference vegetation index (NDVI) improving species distribution models: an example with the neotropical genus Coccocypselum (Rubiaceae). Anais XIII Simpósio Brasileiro de Sensoriamento Remoto 2275-2282.
Anderson, R. P., and I. Gonzalez Jr. 2011. Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. Ecological Modeling 222:2796-2811. https://doi.org/10.1016/j.ecolmodel.2011.04.011
Angert, A. L., L. G. Crozier, L. J. Rissler, S. E. Gilman, J. J. Tewksbury, and A. J. Chunco. 2011. Do species’ traits predict recent shifts at expanding range edges? Ecology Letters 14:677-689. https://doi.org/10.1111/j.1461-0248.2011.01620.x
Araújo, M. B., and A. T. Peterson. 2012. Uses and misuses of bioclimatic envelope modeling. Ecology 93:1527-1539. https://doi.org/10.1890/11-1930.1
Arizona Game and Fish Department. 2022. 2022-23 Arizona hunting regulations. Arizona Game and Fish Commission, Phoenix, Arizona, USA. https://www.azgfd.com/Hunting/Regulations/
Auer, S. K., and D. I. King. 2014. Ecological and life-history traits explain recent boundary shifts in elevation and latitude of western North American songbirds. Global Ecology and Biogeography 23:867-875. https://doi.org/10.1111/geb.12174
Bailey, F. M. 1911. A drop of four thousand feet. Auk 28:219-225. https://doi.org/10.2307/4071437
Bailey, F. M. 1928. Birds of New Mexico. New Mexico Department of Game and Fish in cooperation with the State Game Protective Association and the Bureau of Biological Survey.
Banks, R. C., C. Cicero, J. L. Dunn, A. W. Kratter, P. C. Rasmussem, J. V. Remsen Jr., J. D. Rising, and D. F. Stotz. 2006. Forty-seventh supplement to the American Ornithologists’ Union Check-list of North American Birds. Auk 123:926-936. https://doi.org/10.1093/auk/123.3.926
Barrowclough, G. F., J. G. Groth, L. A. Mertz, and R. J. Gutiérrez. 2004. Phylogeographic structure, gene flow and species status in Blue Grouse (Dendragapus obscurus). Molecular Ecology 13:1911-1922. https://doi.org/10.1111/j.1365-294X.2004.02215.x
Brooks, A. 1929. On Dendragapus obscurus obscurus. Auk 46:111-113. https://doi.org/10.2307/4075798
Brovkin, V. 2002. Climate-vegetation interaction. Journal de Physique (Proceedings) 12:52-72. https://doi.org/10.1051/jp4:20020452
Brown, D. E. 1989. Arizona game birds. The University of Arizona Press and Arizona Game and Fish Department, Tucson, Arizona, USA.
Brown, D. E. 1994. Biotic communities: Southwestern United States and Northwestern Mexico. University of Utah Press, Salt Lake City, Utah, USA.
Brown, D. E., and R. H. Smith. 1980. Winter-spring precipitation and population levels of Blue Grouse in Arizona. Wildlife Society Bulletin 8:136-141.
Brown, J. H. 1971. Mammals on mountaintops: nonequilibrium insular biogeography. American Naturalist 105:467-478. https://doi.org/10.1086/282738
Buisson, L., W. Thuiller, N. Casajus, S. Lek, and G. Grenouillet. 2010. Uncertainty in ensemble forecasting of species distributions. Global Change Biology 16:1145-1157. https://doi.org/10.1111/j.1365-2486.2009.02000.x
Burned Area Emergency Response. 2012. Whitewater Baldy Complex: burned area emergency response (BAER) team executive summary. Gila National Forest, Silver City, New Mexico, USA.
Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. Springer, New York, New York, USA.
Cade, B. S., and R. W. Hoffman. 1990. Winter use of Douglas-fir forests by Blue Grouse in Colorado. Journal of Wildlife Management 54:471-479. https://doi.org/10.2307/3809661
Cahill, A. E., M. E. Aiello-Lammens, M. C. Fisher-Reid, X. Hua, C. J. Karanewsky, H. Y. Ryu, G. C. Sbeglia, F. Spagnolo, J. B. Waldron, and J. J. Wiens. 2014. Causes of warm-edge range limits: systematic review, proximate factors and implications for climate change. Journal of Biogeography 41:429-442. https://doi.org/10.1111/jbi.12231
Clements, J. F., T. S. Schulenberg, M. J. Iliff, S. M. Billerman, T. A. Fredericks, J. A. Gerbracht, D. Lepage, B. L. Sullivan, and C. L. Wood. 2021. The eBird/Clements checklist of Birds of the World: v2021. https://www.birds.cornell.edu/clementschecklist/download/
Coristine, L. E., and J. T. Kerr. 2015. Temperature-related geographical shifts among passerines: contrasting processes along poleward and equatorward range margins. Ecology and Evolution 5:5162-5176. https://doi.org/10.1002/ece3.1683
DeLorme. 2004. DeLorme Topo USA for Windows, Version 5.0. DeLorme, Yarmouth, Maine, USA.
Ditto, A. M., and J. K. Frey. 2007. Effects of ecogeographic variables on genetic variation in montane mammals: implications for conservation in a global warming scenario. Journal of Biogeography 34:1136-1149. https://doi.org/10.1111/j.1365-2699.2007.01700.x
Elith, J., C. H. Graham, R. P. Anderson, M. Dudík, S. Ferrier, A. Guisan, R. J. Hijmans, F. Huettmann, J. R. Leathwick, A. Lehmann, J. Li et al. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129-151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
Elith, J., M. Kearney, and S. Phillips. 2010. The art of modelling range-shifting species. Methods in Ecology and Evolution 1:330-342. https://doi.org/10.1111/j.2041-210X.2010.00036.x
Elith, J., S. J. Phillips, T. Hastie, M. Dudik, Y. E. Chee, and C. J. Yates. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17:43-57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
Environmental Systems Research Institute (ESRI). 2011. ArcGIS Desktop for Windows. Release 10. Environmental Systems Research Institute, Redlands, California, USA.
Evans, J. S., J. Oakleaf, S. A. Cushman, and D. Theobald. 2014. An ArcGIS toolbox for surface gradient and geomorphometric modeling, version 2.0-0. https://evansmurphy.wixsite.com/evansspatial/arcgis-gradient-metrics-toolbox
Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor. 2016. Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development 9:1937-1958. https://doi.org/10.5194/gmd-9-1937-2016
Feng, X., D. S. Park, Y. Liang, R. Pandey, and M. Papeş. 2019. Collinearity in ecological niche modeling: confusions and challenges. Ecology and Evolution 9:10365-10376. https://doi.org/10.1002/ece3.5555
Ferrari, B. A., B. M. Shamblin, R. B. Chandler, H. R. Tumas, S. Hache, L. Reitsma, and C. J. Nairn. 2018. Canada Warbler (Cardellina canadensis): novel molecular markers and a preliminary analysis of genetic diversity and structure. Avian Conservation and Ecology 13(1):8. https://doi.org/10.5751/ACE-01176-130108
Fick, S. E., and R. J. Hijmans. 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37:4302-4315. https://doi.org/10.1002/joc.5086
Fielding, A. H., and J. F. Bell. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24:38-49. https://doi.org/10.1017/S0376892997000088
Fitzpatrick, M. C., A. D. Gove, N. J. Sanders, and R. R. Dunn. 2008. Climate change, plant migration, and range collapse in a global biodiversity hotspot: the Banksia (Proteaceae) of Western Australia. Global Change Biology 14:1337-1352. https://doi.org/10.1111/j.1365-2486.2008.01559.x
Ford, K. R., C. A. Harrington, S. Bansal, P. J. Gould, and J. B. St. Clair. 2016. Will changes in phenology track climate change? A study of growth initiation timing in coast Douglas fir. Global Change Biology 22:3712-3723. https://doi.org/10.1111/gcb.13328
Fourcade, Y., A. G. Besnard, and J. Secondi. 2018. Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics. Global Ecology and Biogeography 27:245-256. https://doi.org/10.1111/geb.12684
Franklin, J., F. W. Davis, M. Ikegami, A. D. Syphard, L. E. Flint, A. L. Flint, and L. Hannah. 2013. Modeling plant species distributions under future climates: how fine scale do climate projections need to be? Global Change Biology 19:473-483. https://doi.org/10.1111/gcb.12051
Frey, J. K., M. A. Bogan, and T. L. Yates. 2007. Mountaintop island age determines species richness of boreal mammals in the American Southwest. Ecography 30:231-240. https://doi.org/10.1111/j.2007.0906-7590.04721.x
Frey, J., J. Lewis, R. Guy, and J. Stuart. 2013. Use of anecdotal occurrence data in species distribution models: an example based on the White-Nosed Coati (Nasua narica) in the American Southwest. Animals 3:327-348. https://doi.org/10.3390/ani3020327
Goljani Amirkhiz, R., J. K. Frey, J. W. Cain III, S. W. Breck, and D. L. Bergman. 2018. Predicting spatial factors associated with cattle depredations by the Mexican Wolf (Canis lupus baileyi) with recommendations for depredation risk modeling. Biological Conservation 224:327-335. https://doi.org/10.1016/j.biocon.2018.06.013
Guillera-Arroita, G., J. J. Lahoz-Monfort, J. Elith, A. Gordon, H. Kujala, P. E. Lentini, M. A. McCarthy, R. Tingley, and B. A. Wintle. 2015. Is my species distribution model fit for purpose? Matching data and models to applications. Global Ecology and Biogeography 24:276-292. https://doi.org/10.1111/geb.12268
Guisan, A., W. Thuiller, and N. E. Zimmermann. 2017. Habitat suitability and distribution models: with applications in R. Ecology, Biodiversity, and Conservation. Cambridge University Press, Cambridge, UK. https://doi.org/10.1017/9781139028271
Guisan, A., R. Tingley, J. B. Baumgartner, I. Naujokaitis-Lewis, P. R. Sutcliffe, A. I. T. Tulloch, T. J. Regan, L. Brotons, E. McDonald-Madden, C. Mantyka-Pringle, et al. 2013. Predicting species distributions for conservation decisions. Ecology Letters 16:1424-1435. https://doi.org/10.1111/ele.12189
Guisan, A., and N. E. Zimmerman. 2000. Predictive habitat distribution models in ecology. Ecological Modeling 135:147-186. https://doi.org/10.1016/S0304-3800(00)00354-9
Hampe, A., and R. J. Petit. 2005. Conserving biodiversity under climate change: the rear edge matters. Ecology Letters 8:461-467. https://doi.org/10.1111/j.1461-0248.2005.00739.x
Herrero, A., and R. Zamora. 2014. Plant responses to extreme climatic events: a field test of resilience capacity at the southern range edge. PLoS ONE 9:e87842. https://doi.org/10.1371/journal.pone.0087842
Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25:1965-1978. https://doi.org/10.1002/joc.1276
Hubbard, J. P. 1965. The summer birds of the forests of the Mogollon Mountains, New Mexico. Condor 67:404-415. https://doi.org/10.2307/1365633
Hutto, R. L., and D. A. Patterson. 2016. Positive effects of fire on birds may appear only under narrow combinations of fire severity and time-since-fire. International Journal of Wildland Fire 25:1074-1085. https://doi.org/10.1071/WF15228
Ibáñez, I., J. S. Clark, M. C. Dietze, K. Feeley, M. Hersh, S. LaDeau, A. McBride, N. E. Welch, and M. S. Wolosin. 2006. Predicting biodiversity change: outside the climate envelope, beyond the species-area curve. Ecology 87:1896-1906. https://doi.org/10.1890/0012-9658(2006)87[1896:PBCOTC]2.0.CO;2
Johnson, D. H. 1980. The comparison of usage and availability measurements for evaluating resource preference. Ecology 61:65-71. https://doi.org/10.2307/1937156
Kennedy, M. C., and M. C. Johnson. 2014. Fuel treatment prescriptions alter spatial patterns of fire severity around the wildland-urban interface during the Wallow Fire, Arizona, USA. Forest Ecology and Management 318:122-132. https://doi.org/10.1016/j.foreco.2014.01.014
Kent, L. Y. 2015. Climate Change and fire in the Southwest. ERI Working Paper No. 34. Ecological Restoration Institute and Southwest Fire Science Consortium, Northern Arizona University, Flagstaff, Arizona, USA.
Landis, J. R., and G. G. Koch. 1977. The measurement of observer agreement for categorical data. Biometrics 33:159-174. https://doi.org/10.2307/2529310
LeCount, A. 1970. Fall food preferences of Blue Grouse in the White Mountains of Arizona. Thesis. University of Arizona, Tucson, Arizona, USA.
Levin, D. A. 2012. Mating system shifts on the trailing edge. Annals of Botany 109:613-620. https://doi.org/10.1093/aob/mcr159
Ligon, J. S. 1927. Wild life of New Mexico: its conservation and management. State Game Commission, New Mexico Department of Game and Fish, Santa Fe, New Mexico, USA.
Ligon, J. S. 1961. New Mexico birds and where to find them. University of New Mexico Press, Albuquerque, New Mexico, USA.
Lomolino, M. V., J. H. Brown, and R. Davis. 1989. Island biogeography of montane forest mammals in the American Southwest. Ecology 70:180-194. https://doi.org/10.2307/1938425
Lomolino, M. V., B. R. Riddle, and R. J. Whittaker. 2016. Biogeography: biological diversity across space and time. Sinauer Associates, Inc., Sunderland, Massachusetts, USA.
Lynch, H. J., M. Rhainds, J. M. Calabrese, S. Cantrell, C. Cosner, and W. F. Fagan. 2014. How climate extremes—not means—define a species’ geographic range boundary via a demographic tipping point. Ecological Monographs 84:131-149. https://doi.org/10.1890/12-2235.1
Martin, K., S. Wilson, E. C. MacDonald, A. F. Camfield, M. Martin, and S. A. Trefry. 2017. Effects of severe weather on reproduction for sympatric songbirds in an alpine environment: interactions of climate extremes influence nesting success. Auk 134:696-709. https://doi.org/10.1642/AUK-16-271.1
Martinka, R. R. 1972. Structural characteristics of Blue Grouse territories in southwestern Montana. Journal of Wildlife Management 36:498-510. https://doi.org/10.2307/3799081
Maxwell, C. J., and R. M. Scheller. 2020. Identifying habitat holdouts for high elevation tree species under climate change. Frontiers in Forest and Global Change 2:94. https://doi.org/10.3389/ffgc.2019.00094
McCune, B., and D. Keon. 2002. Equations for potential annual direct incident radiation and heat load. Journal of Vegetation Science 13:603-606. https://doi.org/10.1111/j.1654-1103.2002.tb02087.x
McDonald, K. W., C. J. W. McClure, B. W. Rolek, and G. E. Hill. 2012. Diversity of birds in eastern North America shifts north with global warming. Ecology and Evolution 2:3052-3060. https://doi.org/10.1002/ece3.410
McKechnie, A. E., and B. O. Wolf. 2010. Climate change increases the likelihood of catastrophic avian mortality events during extreme heat waves. Biology Letters 6:253-256. https://doi.org/10.1098/rsbl.2009.0702
Merker, S. A., and R. B. Chandler. 2020. Identifying global hotspots of avian trailing-edge population diversity. Global Ecology and Conservation 22:e00915. https://doi.org/10.1016/j.gecco.2020.e00915
Merow, C., M. J. Smith, and J. A. Silander. 2013. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36:1058-1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x
Merriam, C. H. 1890. Results of a biological survey of the San Francisco Mountains region and desert of the Little Colorado in Arizona. Department of Agriculture, Div. Ornithology and Mammalogy. North American Fauna 3:1-136. https://doi.org/10.5962/bhl.title.86972
Merrill, G. W. 1967. Dusky Grouse. New Mexico wildlife management. New Mexico Department of Game and Fish, Santa Fe, New Mexico, USA.
Morales, N. S., I. C. Fernández, and V. Baca-González. 2017. MaxEnt’s parameter configuration and small samples: are we paying attention to recommendations? A systematic review. PeerJ 5:e3093. https://doi.org/10.7717/peerj.3093
Mueller, S. E., A. E. Thode, E. Q. Margolis, L. L. Yocom, J. D. Young, and J. M. Iniguez. 2020. Climate relationships with increasing wildfire in the southwestern US from 1984 to 2015. Forest Ecology and Management 460:117861. https://doi.org/10.1016/j.foreco.2019.117861
Muscarella, R., P. J. Galante, M. Soley-Guardia, R. A. Boria, J. M. Kass, M. Uriarte, and R. P. Anderson. 2014. ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in Ecology and Evolution 5:1199-1205. https://doi.org/10.1111/2041-210X.12261
Mussehl, T. W. 1963. Blue Grouse brood cover selection and land-use implications. Journal of Wildlife Management 27:546-555. https://doi.org/10.2307/3798464
National Audubon Society. 2013. Developing a management model of the effects of future climate change on species: a tool for the landscape conservation cooperatives. Unpublished report prepared for the U.S. Fish and Wildlife Service.
National Audubon Society. 2015. Audubon’s birds and climate change report: a primer for practitioners, Version 1.3. National Audubon Society, New York, New York, USA.
New Mexico Department of Game and Fish. 2021. 2021-2022 New Mexico upland game hunting rules and info. New Mexico Department of Game and Fish, Santa Fe, New Mexico, USA. http://www.wildlife.state.nm.us/home/publications/
Overpeck, J., G. Garfin, A. Jardine, D. E. Busch, D. Cayan, M. Dettinger, E. Fleishman, A. Gershunov, G. MacDonald, K. T. Redmond, et al. 2013. Summary for decision makers. Pagers 1-20 in G. Garfin, A. Jardine, R. Merideth, M. Black, S. LeRoy, editors. Assessment of climate change in the Southwest United States: a report prepared for the National Climate Assessment. A report by the Southwest Climate Alliance. Island Press, Washington, D.C., USA. https://doi.org/10.5822/978-1-61091-484-0_1
Pearson, R. G., and T. P. Dawson. 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecology and Biogeography 12:361-371. https://doi.org/10.1046/j.1466-822X.2003.00042.x
Pearson, R. G., W. Thuiller, M. B. Araújo, E. Martinez-Meyer, L. Brotons, C. McClean, L. Miles, P. Segurado, T. P. Dawson, and D. C. Lees. 2006. Model-based uncertainty in species range prediction. Journal of Biogeography 33:1704-1711. https://doi.org/10.1111/j.1365-2699.2006.01460.x
Pekins, P. J., F. G. Lindzey, J. A. Robertson, G. McDaniel, and R. Berger. 1989. Winter habitats and foods of Blue Grouse in the Sheeprock Mountains, Utah. Great Basin Naturalist 49:229-232.
Pekins, P. J., J. A. Gessaman, and F. G. Lindzey. 1992. Winter energy requirements for Blue Grouse. Canadian Journal of Zoology 70:22-24. https://doi.org/10.1139/z92-003
Phillips, S. J., R. P. Anderson, M. Dudík, R. E. Schapire, and M. E. Blair. 2017. Opening the black box: an open-source release on Maxent. Ecography 40:887-893. https://doi.org/10.1111/ecog.03049
Phillips, S. J., R. P. Anderson, and R. E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modeling 190:231-259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Phillips, S. J. and M. Dudík. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161-175. https://doi.org/10.1111/j.0906-7590.2008.5203.x
R Core Team. 2018. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Raffa, K. F., B. H. Aukema, B. J. Bentz, A. L. Carroll, J. A. Hicke, and T. E. Kolb. 2015. Responses of tree-killing bark beetles to a changing climate. Chapter 10 in C. Björkman and P. Niemelä, editors. Climate change and insect pests. CAB International, Wallingford, UK. https://doi.org/10.1079/9781780643786.0173
Remington, T. E., and R. W. Hoffman. 1996. Food habitat and preferences of Blue Grouse during winter. Journal of Wildlife Management 60:808-817. https://doi.org/10.2307/3802381
Riahi, K., D. P. van Vuuren, E. Kriegler, J. Edmonds, B. C. O’Neill, S. Fujimori, N. Bauer, K. Calvin, R. Dellink, O. Fricko, et al. 2017. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Global Environmental Change 42:153-168. https://doi.org/10.1016/j.gloenvcha.2016.05.009
Riley, S. J., S. D. DeGloria, and R. Elliot. 1999. A terrain ruggedness index that quantifies topographic heterogeneity. Intermountain Journal of Sciences 5:23-27.
Rother, M. T., and T. T. Veblen. 2016. Limited conifer regeneration following wildfire in dry ponderosa pine forests of the Colorado Front Range. Ecosphere 7:e01594 https://doi.org/10.1002/ecs2.1594
Schoener, T. W. 1968. The Anolis lizards of Bimini: resource partitioning in a complex fauna. Ecology 49:704-726. https://doi.org/10.2307/1935534
Sekercioglu, C. H., S. H. Schneider, J. P. Fay, and S. R. Loarie. 2008. Climate change, elevation range shifts, and bird extinctions. Conservation Biology 22:140-150. https://doi.org/10.1111/j.1523-1739.2007.00852.x
Severson, K. E. 1986. Spring and early summer habits and foods of male Blue Grouse in Arizona. Journal of the Arizona-Nevada Academy of Science 21:13-18.
Singleton, M. P., A. E. Thode, A. J. Sanchez Meador, and J. M. Inguez. 2019. Increasing trends in high-severity fire in the southwestern USA from 1984 to 2015. Forest Ecology and Management 433:709-719. https://doi.org/10.1016/j.foreco.2018.11.039
Srivastava, A., R. Grotjahn, and P. A. Ullrich. 2020. Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions. Weather and Climate Extremes 29:100268. https://doi.org/10.1016/j.wace.2020.100268
Stephens, S. L., N. Burrows, A. Buyantuyev, R. W. Gray, R. E. Keane, R. Kubian, S. Liu, F. Seijo, L. Shu, K. G. Tolhurst, and J. W. van Wagtendonk. 2014. Temperate and boreal forest mega-fires: characteristics and challenges. Frontiers in Ecology and the Environment 12:115-122. https://doi.org/10.1890/120332
Stoddard, M. T., D. W. Huffman, P. Z. Fule, J. E. Crouse, and A. J. Sánchez Meador. 2018. Forest structure and regeneration responses 15 years after wildfire in a ponderosa pine and mixed-conifer ecotone, Arizona, USA. Fire Ecology 14:12. https://doi.org/10.1186/s42408-018-0011-y
Storch, I. 2007. Grouse: status survey and conservation action plan 2006-2010. International Union for Conservation of Nature, World Pheasant Association, Gland, Switzerland.
Sullivan, B. L., C. L. Wood, M. J. Iliff, R. E. Bonney, D. Fink, and S. Kelling. 2009. eBird: A citizen-based bird observation network in the biological sciences. Biological Conservation 142:2282-2292. https://doi.org/10.1016/j.biocon.2009.05.006
Thomas, C. D., A. Cameron, R. E. Green, M. Bakkenes, L. J. Beaumont, Y. C. Collingham, B. F. N. Erasmus, M. F. Siqueira, A. Grainger, L. Hannah, et al. 2004. Extinction risk from climate change. Nature 427:145-148. https://doi.org/10.1038/nature02121
Thorne, J. H., H. Choe, P. A. Stine, J. C. Chambers, A. Holguin, A. C. Kerr, and M. W. Schwartz. 2018. Climate change vulnerability assessment of forests in the Southwest USA. Climatic Change 148:387-402. https://doi.org/10.1007/s10584-017-2010-4
Tingley, R., and T. B. Herman. 2009. Land-cover data improve bioclimatic models for anurans and turtles at a regional scale. Journal of Biogeography 36:1656-1672. https://doi.org/10.1111/j.1365-2699.2009.02117.x
United States Department of Agriculture, U.S. Forest Service. 2010. Field guide for mapping post-fire soil burn severity. Rocky Mountain Research Station, Ft. Collins, Colorado, USA.
Vignali, S., A. G. Barras, R. Arlettaz, and V. Braunisch. 2020. SDMtune: an R package to tune and evaluate species distribution models. Ecology and Evolution 10:11488-11506. https://doi.org/10.1002/ece3.6786
Voldoire, A., D. Saint-Martin, S. Sénési, B. Decharme, A. Alias, M. Chevallier, J. Colin, J. F. Guérémy, M. Michou, M. P. Moine, et al. 2019. Evaluation of CMIP6 DECK experiments with CNRM-CM6-1. Journal of Advances in Modeling Earth Systems 11:2177-2213. https://doi.org/10.1029/2019MS001683
Waite, T. A., and D. Strickland. 2006. Climate change and the demographic demise of a hoarding bird living on the edge. Proceedings of the Royal Society Biological Sciences 273:2809-2813. https://doi.org/10.1098/rspb.2006.3667
Waltari, E., R. J. Hijmans, A. T. Peterson, Á. S. Nyári, , S. L. Perkins, and R. P. Guralnick. 2007. Locating Pleistocene refugia: comparing phylogeographic and ecological niche model predictions. PLoS ONE 2:e563. https://doi.org/10.1371/journal.pone.0000563
Warren, D. L., R. E. Glor, and M. Turelli. 2008. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62:2868-2883. https://doi.org/10.1111/j.1558-5646.2008.00482.x
Warren, D. L., R. E. Glor, and M. Turelli. 2010. ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 33:607-611. https://doi.org/10.1111/j.1600-0587.2009.06142.x
Warren, D. L., and S. N. Seifert. 2011. Ecological modelling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications 21:335-342. https://doi.org/10.1890/10-1171.1
Warren, D. L., A. N. Wright, S. N. Seifert, and H. B. Shaffer. 2014. Incorporating model complexity and spatial sampling bias into ecological niche models of climate change risks faced by 90 California vertebrate species of concern. Diversity and Distributions 20:334-343. https://doi.org/10.1111/ddi.12160
Watling J. I., L. A. Brandt, F. J. Mazzotti, and S. S. Romañach. 2013a. Use and interpretation of climate envelope models: a practical guide. University of Florida, Gainesville, Florida, USA.
Watling, J. I., D. N. Bucklin, C. Speroterra, L. A. Brandt, F. J. Mazzotti, and S. S. Romañach. 2013b. Validating predictions from climate envelope models. PLoS ONE 8:e63600. https://doi.org/10.1371/journal.pone.0063600
Wilsey, C., B. Bateman, L. Taylor, J. X. Wu, G. LeBaron, R. Shepherd, C. Koseff, S. Friedman, and R. Stone. 2019a. Survival by degrees: 389 bird species on the brink. National Audubon Society, New York, New York, USA.
Wilsey, C., L. Taylor, B. Bateman, C. Jensen, N. Michel, A. Panjabi, and G. Langham. 2019b. Climate policy action needed to reduce vulnerability of conservation-reliant grassland birds in North America. Conservation Science and Practice: 1(4):e21. https://doi.org/10.1111/csp2.21
Wu, D., W. S. Lee, N. Ye, and H. L. Chieu. 2009. Domain adaptive bootstrapping for named entity recognition. Pages 1523-1532 in EMNLP '09: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. Vol. 3. Association for Computational Linguistics, Singapore. https://doi.org/10.3115/1699648.1699699
Zwickel, F. C., and J. F. Bendell. 2004. Blue Grouse: their biology and natural history. National Research Council Research Press Canada, Ottawa, Ontario, Canada.
Zwickel, F. C., and J. F. Bendell. 2018. Blue Grouse (Dendragapus obscurus), version 2.1. The birds of North America. P. G. Rodewald, editor. Cornell Lab of Ornithology, Ithaca, New York, USA. https://doi.org/10.2173/bna.dusgro.02.1