Research Paper

Vulnerability of birds to climate change in California's Sierra Nevada
 
Vulnérabilité des oiseaux aux changements climatiques dans la Sierra Nevada en Californie

• Abstract
• Introduction
• Methods
  • Study area
  • Assessing species vulnerability
  • Identifying habitats that host the most vulnerable species
• Results
  • Individual climate change vulnerability factors
  • Identifying the most vulnerable species
  • Variation in vulnerability rankings based on different climate models
  • Importance of habitat associations
• Discussion
  • Importance of habitat associations
  • Effect of range within the Sierra Nevada on vulnerability rankings
  • Sensitivity of results to different climate models
  • Previous climate change vulnerability assessments in California
  • Limitations of our approach
• Conclusion
• Responses to This Article
• Acknowledgments
• Literature Cited

INTRODUCTION

In a rapidly changing climate, effective management of bird populations and their habitats requires not only reliable data about the current status and vulnerability of species of conservation concern, but also credible projections of future status and vulnerability. Future projections may allow managers to address emerging threats proactively, and, where possible, preempt or reduce them through thoughtful and appropriate habitat management. Integrating spatially explicit climate projections with information about the current distribution and ecology of plant and animal species provides an approach for predicting how vulnerable different organisms are likely to be to climate change (Hansen and Hoffman 2011).

Anthropogenic climate change is already evident across mountainous regions of the western United States (Bonfils et al. 2008), and the rate of change is expected to increase in the coming decades (Hayhoe et al. 2004, IPCC 2007, Cayan et al. 2008, Thorne et al. 2012). In our study region, the Sierra Nevada mountains of California, USA, climate models uniformly project warming temperatures, with variation only in the magnitude of warming, while projected changes in precipitation and other moisture metrics vary more widely (Cayan et al. 2008, Thorne et al. 2012). Regardless of whether precipitation decreases greatly, warming is expected to result in more precipitation falling as rain rather than snow (Hayhoe et al. 2004), and greatly reduced end-of-winter snowpack because more runoff and flooding occurs during winter (Knowles and Cayan 2004, Dettinger et al. 2004). The magnitude of the reduction in snowpack and increases in winter runoff are expected to be highly variable across elevations and subregions (Dettinger et al. 2004, Maurer 2007), but will yield reduced spring and summer streamflows across the region (Knowles and Cayan 2002, Hayhoe et al. 2004). Throughout the 21st century, climate change is projected to decrease the area of montane and subalpine conifer forest in the Sierra Nevada, and increase the amount of grassland and oak/pine vegetation (Lenihan et al. 2008, PRBO Conservation Science 2011).

Long-term anthropogenic climate change may threaten bird populations through factors such as habitat change and emerging mismatches between bird life-cycle phenology and prey availability (Both et al. 2006, Huntley et al. 2008, Möller et al. 2008). In California, Stralberg et al. (2009) predicted that climate change is likely to yield large shifts in the distributions of many bird species by 2070, with as much as 57% of the state occupied by novel bird species assemblages. In contrast to other areas of California, however, Stralberg et al. (2009) predicted that bird species’ ranges in the Sierra Nevada will largely shift upslope in concert with one another, generally maintaining current species assemblages. Other evidence suggests that distributions of many bird (Tingley et al. 2009, Tingley and Beissinger 2013) as well as mammal (Moritz et al. 2008) species in the Sierra Nevada have already shifted in response to climate change during the past century, with some species shifting their ranges upslope in response to temperature changes and others shifting their ranges downslope in response to precipitation changes (Tingley et al. 2012).

The Climate Change Vulnerability Index (CCVI) was developed by NatureServe (Young et al. 2011) to predict vulnerability of plant and animal species to forecasted climate change, using a standardized method that has been applied across numerous taxa and regions (e.g., Young et al. 2009, Dubois et al. 2011, Walk et al. 2011, Anacker et al. 2013). The CCVI employs a scoring system to assess species-specific exposure and sensitivity to climate change within a defined geographic area, through the integration of spatially explicit climate change projections and detailed information about the distribution, ecology, and life-history of focal species (Young et al. 2009, 2011, 2012).

To investigate the potential impact of climate change on breeding bird populations in the Sierra Nevada, we used the CCVI to assess vulnerability of 168 bird species that breed regularly during summer in the region. To assess potential variation in response to uncertain future climate scenarios, we conducted the assessment under two different climate models with substantially divergent projections about future precipitation in the Sierra Nevada. Our primary goals were to identify individual bird species whose Sierra Nevada breeding populations are most likely to be jeopardized by climate change during the next half-century, and to assess patterns in the habitat associations of those species. We stress that we did not attempt to assess the overall vulnerability of individual species to all possible threats, but rather the likely vulnerability of each species to climate change during the next 40-50 years. Additionally, other nonclimate factors, such as habitat destruction and urban encroachment, or small population size, could also be important in driving overall vulnerability. Our results therefore will need to be considered in concert with other nonclimate factors for establishing conservation priorities and formulating management strategies (Ohlemüller et al. 2008, Young et al. 2011, Gardali et al. 2012, Anacker et al. 2013).

METHODS

Study area

We defined the Sierra Nevada according to biogeographical boundaries for California ecoregions established in Hickman (1993) and Davis and Stoms (2012). The 6.3 million ha region includes the Sierra Nevada foothills and highlands, as well as the Tehachapi Mountains at the southern extreme of the region (Fig. 1). Approximately 37% of lands in the Sierra Nevada are privately owned, with private holdings heavily concentrated at lower elevations. The remaining lands are publicly owned and largely concentrated in national forests (47%) and national parks (10%).

Assessing species vulnerability

We selected 168 bird species that breed or occur regularly during summer in the Sierra Nevada (Appendix 1). Although > 300 bird species are known to occur within the region (Lukas 2011, Beedy and Pandolfino 2013), many of these species are present only in winter, during northbound or southbound migration, or as rare vagrants. We restricted our list to species that regularly breed in the region, based on recent published sources (e.g., Siegel and DeSante 1999, Lukas 2011, Beedy and Pandolfino 2013) as well as personal experience.

For each species, our CCVI assessments included (a) projections of direct exposure to future climate change based on fine-scale, spatially explicit climate change projections, (b) four factors that assess indirect exposure to climate change, (c) 15 factors that assess sensitivity to climate change, and (d) four factors that incorporate any previously documented or modeled responses to climate change (Table 1). For sections (b) through (d), each factor was scored to one of five categories or “unknown,” based on how the factor may be expected to increase or decrease vulnerability to climate change (Table 2). Ordinal scores for each factor were determined based on published and unpublished information about the species in the assessment area. Space limitations preclude citing sources of information on natural history, ecology, and potential sensitivity to climate change for 168 species, but many of the works we consulted are provided in Poole (2005), Shuford and Gardali (2008), and Steel et al. (2012). We also relied on Jongsomjit et al. (2011) for scoring vulnerability based on modeled responses to climate change, and Tingley et al. (2012) for scoring vulnerability based on documented responses to climate change. Additional details about the data sets used, and our rationale for scoring the various factors are provided in Appendix 2.

For section (a), we indexed exposure to projected climate change as the proportion of each species’ current range within the Sierra Nevada, forecast to undergo different magnitudes of temperature and moisture change by the period 2040-2069. The CCVI guidelines (Young et al. 2011) recommend using the most accurate broad-scale range map polygons rather than finer point-scale range maps. We used broad-scale range maps of bird species’ breeding or summer ranges in California developed by Zeiner et al. (1990) and then digitized and for some species later updated by staff at the California Department of Fish and Game.

To assess climate change vulnerability factors related to current and projected climatic conditions, we used historical and projected future temperature, precipitation, and related environmental moisture metrics that were produced for California at a 270-meter grid-scale (Thorne et al. 2012) and clipped to the particular mapped breeding range of each species in the Sierra Nevada. These monthly climate data were developed using the Basin Characterization Model (BCM; Flint and Flint 2007), which uses a unique statistical downscaling technique that regresses historical trend on a per-grid cell basis, and accounts for the effects of topographic complexity to better approximate climatic conditions at a fine spatial resolution (Flint and Flint 2012). The climate data are then used in BCM on a per-grid cell basis to project climatic water deficit (CWD), a measure of unmet water demand by plants for metabolic purposes (Stephenson 1998), snowpack, runoff, and recharge. These outputs are from a mechanistic model, meaning they are derived from site characteristics (depth of soil and permeability of bedrock) as well as temperature and precipitation. The model has been calibrated through the use of 159 stream gauges in California (Flint et al. 2013). We used 30-year blocks to derive characteristic means of the values used: monthly mean temperature, precipitation, and climatic water deficit, for historic and future time periods (Thorne et al. 2012). We used the difference between recent historical values (from 1971-2000) and projections for 2040-2069 for our projections of climate change exposure (Figs. 2 and 3). Following CCVI guidelines (Young et al. 2011), we assessed projected changes in temperature directly, whereas for moisture we used projected changes in climatic water deficit (CWD). Although Young et al. (2011) provide explicit guidelines for using the Hamon ratio of actual evapotranspiration to potential evapotranspiration (Hamon 1961) as a moisture metric that is available for the entire United States (Hamon 1961), they caution that the metric is not ideal because it does not include components of habitat moisture retention such as water holding capacity, effect of snow pack on water availability, and different vegetation types, all of which are challenging to incorporate at a national scale. The authors therefore explicitly encourage the use of more detailed, locally-relevant models of moisture availability if they are available. We used climatic water deficit, the annual plant evaporative demand that exceeds available soil water, and integrates precipitation and temperature inputs to yield a single moisture index that can increase through increased evaporative demand, decreased water availability, or both factors (Thorne et al. 2012). CWD provides an ecologically meaningful index of available moisture and is increasingly used for predicting the effects of climate change on ecosystems (e.g., Crimmins et al. 2011), including Sierra Nevada ecosystems (van Mantgem and Stephenson 2007, Lutz et al. 2010). Using this alternative metric required that we develop a new set of threshold values for scoring species’ predicted exposure to changes in moisture across their ranges in the Sierra Nevada. Following the methods that Young et al. (2011) used in setting national threshold values for the Hamon Index, we used one and two positive and negative standard deviations from the mean projected CWD across the Sierra Nevada as our threshold values.

To capture uncertainty inherent in climate projections, we selected two models, the Geophysical Fluid Dynamics Laboratory (GFDL; Delworth et al. 2006, Stouffer et al. 2006) and Parallel Climate Model (PCM; Washington et al. 2000, Meehl et al. 2003), that accurately represent California climate in current time and provide divergent future projections whose implications for California’s climate have been well explored (Cayan et al. 2008). For both models we based future climate projections (Figs. 2 and 3) on a medium-high (A2) emissions scenario, resulting in medium-high global temperature increases (Hayhoe et al. 2004). Both models project warmer future temperatures but the GFDL model projects moisture deficit to be more pronounced in the Sierra Nevada than does the PCM model; e.g., GFDL shows substantial drying over the coming decades, whereas PCM projects relatively unchanged average precipitation (Thorne et al. 2012).

The CCVI also requires assessments of species’ sensitivity to changes in temperature and precipitation based on exposure to past climatic variation (Young et al. 2011). We quantified variation in temperature and precipitation across the Sierra Nevada Region between 1971 and 2000 (Fig. 4), and calculated the amount of variation within each species’ breeding range in the region. Calculations were based on year-round variation, even for migratory species not present year round in the Sierra Nevada, because habitats and food resources used by breeding birds are subject to year-round temperature variation (Young et al. 2011). To describe current temperature variation, we used the average annual difference between monthly mean July maximum temperatures and monthly mean January minimum temperatures within each 270-m grid cell, and for precipitation, the difference between the highest and lowest annual precipitation (mm) values for each cell during the 30-year period. Exposure to temporal variability in climate may not be an ideal proxy for sensitivity to climate change because some species may be able to tolerate wider variation in climatic conditions than they have experienced in the area and time span under consideration, but this approach nevertheless provides a standard that facilitates interstudy comparisons.

For calculating final vulnerability rankings, we used CCVI version 2.3. Young et al. (2011, 2012) provide detailed descriptions of the factor weighting scheme and the algorithm for calculation of the final vulnerability score. In brief, the index first combines information on exposure and sensitivity by summing subscores for each of the four indirect exposure and 15 species sensitivity factors (Sections B and C in Table 1), which receive values (3.0, 2.0, 1.0, 0, –1.0, and –2.0) that reflect the degree to which vulnerability is increased or decreased. The individual factor values are then weighted by exposure (Section A, Table 1) to calculate a subscore for exposure and sensitivity. For most factors, the exposure weighting is the product of weightings for temperature (0.5, 1.0, 1.5, or 2.0, depending on the projected temperature increase) and climatic water deficit (0.5, 1.0, 1.5, or 2.0, depending on the projected change in climatic water deficit). The thresholds for the index scores of Extremely Vulnerable, Highly Vulnerable, Moderately Vulnerable, Not Vulnerable/Presumed Stable, and Not Vulnerable/Increase Likely are 10.0, 7.0, 4.0, and –2.0, and correspond with possible scenarios of exposure and sensitivity. For example, the Extremely Vulnerable threshold is reached for species with high exposure and at least two indirect exposure or sensitivity factors with scores of 3.0, or with high exposure and three factors with scores of 2.0. The four vulnerability factors for the documented and/or modeled response subscore (Section D, Table 1) are scored in the same manner as the exposure and sensitivity factors, but are summed independently with no weighting, because exposure has presumably already been incorporated in the studies upon which the factors are based. The thresholds for these index scores are 6.0, 4.0, 2.0, and –1.0, a schema that accounts for the smaller number of response factors than are incorporated in the exposure and sensitivity subscore. The two subscores are averaged to yield the overall index of climate change vulnerability.

Identifying habitats that host the most vulnerable species

To identify major habitats in the Sierra Nevada that support the most vulnerable species, we assigned each species up to three of six habitat classifications we developed to capture the diversity of Sierra Nevada birds’ habitat associations with a minimal number of categories: foothill woodlands and foothill chaparral; montane conifer forest; montane chaparral and sagebrush; aquatic habitats including rivers, ponds, and lakes; marshes, meadows, and riparian vegetation; and high-elevation regions encompassing the subalpine and alpine zones. Classifications were based on published literature (e.g., Grinnell and Miller 1944, Beedy and Granholm 1985, Gaines 1992, Lukas 2011) as well as our own extensive experience in the region. We then assessed habitat associations in relation to CCVI rankings using a proportional odds ordinal logistic regression model (McCullagh 1980). This regression model interprets an ordinal response variable as an ordered series of binary logistic regressions with common regression parameters. A full model (i.e., all habitat and range variables) was run on both the GFDL and PCM CCVI rankings (ordinally ranked 1 for Increase Likely, 2 for Presumed Stable, 3 for Moderately Vulnerable, 4 for Highly Vulnerable, and 5 for Extremely Vulnerable), with the importance of habitat associations assessed by Wald Z statistics. Models were fit using maximum likelihood estimation. All analyses were run in R version 2.15.1 using the package ‘rms’ (Harrell 2012).

RESULTS

Individual climate change vulnerability factors

Our entire matrix of climate change vulnerability factor scores for all 168 species is provided in Appendix 3. Individual climate change vulnerability factors varied widely in the degree to which they differentiated risk among the species we assessed (Table 1). Projections of direct exposure to climate change varied substantially across species, with many species falling into one of several broad groupings. Species that breed primarily at low elevations on the Sierra Nevada’s west slope, e.g., California Thrasher, (Toxostoma redivivum; Fig. 5) have generally experienced relatively high temperature variation but low precipitation variation in the recent past, and are projected to be relatively less exposed, compared with other parts of the Sierra Nevada, to climate change in the next 50 years. Species that breed primarily on the east slope, e.g., Brewer’s Sparrow, (Spizella breweri; Fig. 6) have generally experienced relatively high temperature and precipitation variation in the recent past, and are projected to experience intermediate to high climate change exposure compared to other portions of the Sierra Nevada. Species restricted to breeding in alpine/subalpine areas, e.g., Gray-crowned Rosy-Finch, (Leucosticte tephrocotis; Fig. 7) have generally experienced intermediate temperature and precipitation variation in the recent past, and are projected to be exposed to high levels of climate change. Last, species with widespread breeding distributions across the low- and mid-elevation portions of the Sierra Nevada, e.g., Song Sparrow, (Melospiza melodia; Fig. 8) experienced intermediate (relative to the entire region) climate variation in the recent past and are projected to be exposed to intermediate levels of climate change in the coming decades.

Unlike direct exposure to climate change, vulnerability factors associated with indirect exposure to climate change (Table 1, Section B) did not vary greatly among species in our assessment. The 15 vulnerability factors associated with sensitivity to climate change (Table 1, Section C) collectively yielded substantially more variation across species, with especially diverse effects indicated for physiological or ecological need for moist habitats (factor C2bii) and responses to increased disturbance regimes (factor C2c). Vulnerability factors associated with documented responses to climate change (Table 1, Section D) yielded diverse scores across species for all four vulnerability factors.

Identifying the most vulnerable species

Of the five vulnerability rankings that the CCVI assigns, only one species, White-tailed Ptarmigan (Lagopus leucura), received the most vulnerable ranking, Extremely Vulnerable (Appendix 1); this ranking resulted using projections from both climate models. No species received the second-highest vulnerability ranking, Highly Vulnerable, using projections from either climate model. Sixteen species (Table 3) ranked as Moderately Vulnerable under at least one of the two climate scenarios (15 species using GFDL-based climate projections and 13 using PCM-based climate projections). The remaining 151 species were scored as Presumed Stable (110 species using GFDL-based projections and 125 using PCM-based projections), and Increase Likely (41 species using GFDL-based projections and 29 using PCM-based projections [Appendix 1]).

Variation in vulnerability rankings based on different climate models

Climate projections based on the GFDL climate model yielded fewer species ranked as Presumed Stable (110 species) than did projections based on the PCM model (125 species). The GFDL climate projections yielded more species ranked as Extremely Vulnerable or Moderately Vulnerable than did the PCM climate projections (16 species under the GFDL climate model versus 14 species under the PCM climate model), but also yielded more species ranked as Increase Likely (42 species versus under the GFDL model versus 29 species under the PCM model; Table 4).

Three species received more vulnerable rankings using climate projections from the GFDL model than from the PCM model, whereas 13 received more vulnerable rankings using projections from the PCM climate model than from the GFDL model (Appendix 1). The three species showing more vulnerable rankings with GFDL projections included two, Osprey (Pandion haliaetus) and American Dipper (Cinclus mexicanus), associated with aquatic habitats and one, Prairie Falcon (Falco mexicanus) associated with sagebrush or montane chaparral. All three of these species ranked as Moderately Vulnerable using GFDL-based climate projections and Presumed Stable using PCM projections. The 13 species receiving more vulnerable rankings under the PCM projections included 11 species associated with foothill woodland and chaparral (five species with this classification alone and six species with this and one other habitat classification), one species, Black-billed Magpie (Pica hudsonia), associated with montane chaparral and sagebrush and with marsh, meadow, and riparian habitats, and one species, Clark’s Nutcracker (Nucifraga columbiana) associated with both montane conifer forest and alpine/subalpine habitats. Twelve of these 13 species ranked Increase Likely with GFDL projections and Presumed Stable with PCM projections whereas one, Clark's Nutcracker, ranked as Presumed Stable with GFDL projections and Moderately Vulnerable with PCM projections.

Importance of habitat associations

Many species (109) were assigned just one primary habitat classification but two or three classifications were assigned to 59 species that occur extensively in multiple habitats or are associated with ecotones (Appendix 1). Among the 17 species ranked as moderately or extremely vulnerable under projections using at least one of the two climate models, seven species were associated with montane conifer forest, five with aquatic habitats, four with alpine/subalpine habitats, three with marsh, meadow, or riparian vegetation, one with sagebrush or montane chaparral, and none with foothill habitats (Table 3). Species associated with alpine/subalpine habitats were ranked significantly more vulnerable to climate change than species of other habitats, using projections based on both the GFDL (P = 0.049) and PCM (P = 0.007) climate models (Table 4). Species associated with aquatic habitats were also ranked as more vulnerable than species of other habitats, but this difference was significant only using rankings based on the GFDL climate model (P = 0.049; P = 0.380 for the PCM model). Species associated with foothill woodland and foothill chaparral, and with montane chaparral and sagebrush, were ranked as significantly less vulnerable than species of other habitat classifications using projections from both climate models (Foothill: P < 0.001 for GFDL, P = 0.007 for PCM; Montane: P = 0.026 for GFDL, P = 0.008 for PCM). Indeed, these species frequently scored as Increase Likely indicating that their ranges or populations within the Sierra Nevada could expand because of climate change. Species associated with marshes, meadows, and riparian vegetation were also ranked as less vulnerable than those of other habitats, a difference that was significant using projections based on the PCM model (P = 0.030) but marginal using the GFDL model projections (P = 0.050) projections.

DISCUSSION

The majority of species (65.5% using GFDL projections and 74.4% using PCM projections) are not projected to be jeopardized substantially or benefited by climate change in the region during the next 50 years; a moderate proportion of species (24.4% using GFDL projections and 17.3% using PCM projections) are projected to experience increases in range or population within the region because of climate change; and a minority of species (9.5% using GFDL projections and 8.3% using PCM projections) are ranked as moderately or extremely vulnerable to climate change. The relatively low percentage of species with vulnerable rankings is commensurate with other findings for bird species using the CCVI (Young et al. 2009, Dubois et al. 2011, Walk et al. 2011). Relative to other taxa, birds tend not to score as especially vulnerable because of their vagility and dispersal capacity, which may enhance their ability to shift their ranges in concert with appropriate environmental conditions as those conditions change and shift across the landscape. Nevertheless, the 17 species that our analysis identified as Extremely Vulnerable or Moderately Vulnerable (Table 3) should be considered in conservation planning efforts throughout the region.

Importance of habitat associations

Our analysis provided insight into which Sierra Nevada habitats host relatively more vulnerable bird species. Birds associated with alpine/subalpine and aquatic habitats ranked more vulnerable to climate change than birds associated with other habitats. In contrast, species of foothill, sagebrush, and chaparral habitats were ranked significantly less vulnerable to climate change.

Vulnerable alpine and subalpine species include White-tailed Ptarmigan, Clark’s Nutcracker, American Pipit (Anthus rubescens), and Gray-crowned Rosy-Finch. Species restricted to alpine/subalpine zones may be particularly vulnerable to climate change because they lack higher altitude habitats to which they can shift their ranges (Loarie et al. 2009). Moreover, potentially important climate-related changes in the structure of subalpine forests have already been detected in the Sierra Nevada (Millar et al. 2004, Thorne et al. 2008, Dolanc et al. 2013). However, it should not be assumed that climate change will uniformly shift bird species’ ranges upslope in response to increasing temperatures. Tingley et al. (2012) showed that avian range shifts during the past century occurred in response to changes in both temperature and precipitation, with changes in temperature clearly exerting upslope pressure on ranges, but changes in precipitation frequently exerting downslope pressure, and many species shifting their ranges downslope in response.

White-tailed Ptarmigan, which scored as the single most vulnerable species in our assessment, is not a native species in the Sierra Nevada or California (Braun et al. 1993). The entire Sierra population apparently stems from the deliberate release of 73 birds near Mono Pass in 1971 and 1972 (Frederick and Gutiérrez 1992). Conservation of the White-tailed Ptarmigan may therefore not be a high priority for most managers in the region, but our results may also have implications for the species within its native range elsewhere in North America.

Our analysis suggested that species associated with aquatic habitats are also significantly more vulnerable to climate change in the Sierra Nevada than other species, at least under the drier GFDL climate model. Species associated with aquatic habitats that ranked as Moderately Vulnerable to climate change based on projections from one or both climate models include Common Merganser (Mergus merganser), Osprey, Bald Eagle (Haliaeetus leucocephalus), Spotted Sandpiper (Actitis macularius), and American Dipper.

A somewhat surprising result of our analysis is that, under climate projections based on the PCM, but not the GFDL, model, species associated with marshes, meadows, and riparian vegetation were significantly less likely to show vulnerability to projected climate change than species associated with other habitats. A predominance of species associated with these habitats (40 of 52) ranked Presumed Stable, with only three species, Spotted Sandpiper, Great Gray Owl (Strix nebulosa), and Swainson’s Thrush (Catharus ustulatus) ranking Moderately Vulnerable. By contrast, nine species associated with these habitats are expected to increase under projections based on both climate models, most of them common and widespread species such as American Robin (Turdus migratorius), Song Sparrow, and Brewer’s Blackbird (Euphagus cyanocephalus). Seavy et al. (2009) suggest that intact riparian ecosystems may be particularly resilient to climate change, and our results appear to be consistent with this prediction.

Our analysis indicates that many species of foothill woodland and chaparral habitats are significantly less vulnerable to climate change in the region compared with other species, under projections based on both climate models we considered; many such species may undergo range expansions or population increases within the Sierra Nevada region. Twenty species (29.9%) associated with these habitats are predicted to benefit from climate change, perhaps through increased population size or range expansion, under both the GDFL and PCM climate models, 11 additional species (16.4%) are predicted to benefit under the more substantial climate change predicted by the GFDL model, and no species received rankings indicating vulnerability to climate change (Table 3). Rankings of Increase Likely for many of these foothill species reflect exposure to relatively broad temperature and precipitation variation in recent years, which are taken to suggest broad physiological and ecological tolerances to variation in temperature and moisture. Similar scoring contributed to rankings of Increase Likely for many montane chaparral and sagebrush species.

A large numbers of species expected to undergo range expansions or population increases in the region occupy drier and warmer foothill habitats, under climatic conditions and in vegetation types that are less likely to change dramatically and may expand upslope (Stralberg et al. 2009). The suggestion that these species might benefit from climate change assumes that species will be able to track appropriate climatic conditions as they shift across the region. Such tracking already appears to be occurring in numerous species (Tingley et al. 2009, 2012), although it is unclear how such shifts might affect regional population sizes over the long-term.

Over the near-term, J. Saracco, R. Siegel, S. Stock, R. Wilkerson, and D. DeSante (unpublished manuscript) corroborate that at least one climatic factor, reduced spring snowpack, may benefit some species in the region. During the period 1993-2010, a preponderance of bird species exhibited higher breeding productivity in Yosemite National Park during years with relatively less spring snowpack. Climate change is expected to dramatically reduce average spring snowpack over the coming decades, a phenomenon that may thus bolster many bird populations in the region. However, Saracco et al. caution that the effects of climate change are likely to be numerous and complex. Earlier snowmelt, for example, is likely to lead to drier midsummer meadow conditions, which might mean reduced food resources or other negative consequences for meadow-associated birds and it is unclear how such factors may weigh against one another to affect population trends.

Effect of range within the Sierra Nevada on vulnerability rankings

Species with ranges restricted to areas projected to undergo greater climate change tended to have higher overall vulnerability rankings than species restricted to areas where less climate change is projected. For example, Gray-crowned Rosy-Finch, whose breeding range is restricted to alpine/subalpine areas in the southeast portion of the Sierra Nevada Region (Fig. 7), ranked as Moderately Vulnerable using projections from either the GFDL or PCM climate models, whereas California Thrasher, whose range is restricted to lower-elevation areas along the western margin of the Sierra Nevada (Fig. 5), ranked as Increase Likely under both models. Other factors besides climate change exposure also contributed to these contrasting rankings, but within these two example species’ respective ranges, the distribution of land area projected to undergo various magnitudes of warming and especially drying (Fig. 9) differed substantially.

Sensitivity of results to different climate models

Although climate model selection clearly affected the vulnerability rankings for a minority of species (16 species, or 9.5% of all species assessed had different vulnerability rankings under the two climate models), no species differed by more than one ranking level under the two climate scenarios, e.g., no species ranked as Increase Likely under one climate scenario, but as Moderately Vulnerable under the other. Because the climate models we used are considered likely to bracket future conditions, we consider our vulnerability rankings to be relatively robust to uncertainties in the climate projections.

Previous climate change vulnerability assessments in California

Gardali et al. (2012) recently used fairly similar methods to assess climate change vulnerability for selected bird species across California as a whole. However, their analysis included many species associated with marine, desert, and other habitats not present in the Sierra Nevada, and subject to threats, e.g., sea level rise, that are not relevant to the Sierra Nevada region. Nevertheless, for 135 species that we assessed and that were also assessed by Gardali et al., the two sets of vulnerability rankings showed significant concordance using our GFDL-based assessments (proportional odds ordinal logistic regression comparing our CCVI rankings to the continuous vulnerability rankings of Gardali et al.; Wald Z-score = 2.31, P = 0.016) but no significant relationship using our PCM-based assessments (Wald Z-score = 1.41, P = 0.158).

The most important source of differences between our results and those of Gardali et al. (2012) is likely the spatial scales under consideration, i.e., the entire state of California by Gardali et al. (2012) and the Sierra Nevada in our analysis. Our study, like that of Gardali et al., evaluated vulnerability in a spatially explicit context; for species with broader distributions in California than just the Sierra Nevada, projected climate change exposure may vary substantially across the two areas of inference. Indeed, some projections indicate that climate change in other parts of California is likely to be considerably greater than in the Sierra Nevada (e.g., Ackerly et al. 2010). In some cases even indirect exposure to climate change as well as sensitivity to climate change may vary across regions. For these reasons, Gardali et al. noted that the state-wide scale of their assessment in California may not identify taxa vulnerable at smaller spatial scales. These differences in the geographic scope of the data considered and differences in the resulting vulnerability rankings provide a cautionary note about the importance of carefully and explicitly selecting the spatial domain for climate change vulnerability assessments. It should not be assumed that vulnerability to climate change always “scales up” to larger spatial domains, or “scales down” to smaller domains without important changes to species’ relative degree of exposure or perhaps even sensitivity to climate change.

Nevertheless, the smaller sets of Sierra Nevada species identified as vulnerable by both studies have substantial commonality, which helps validate the methods of both assessments, and also emphasizes the need to take seriously possible threats faced by the species ranked as vulnerable. Gardali et al. (2012) evaluated 14 of the 17 species that ranked as Moderately Vulnerable or Extremely Vulnerable in our analysis, and considered 6 of them to be climate-vulnerable across California as a whole: Osprey, Great Gray Owl, Black Swift (Cypseloides niger), Swainson’s Thrush, Gray-crowned Rosy-Finch, and Pine Grosbeak (Pinicola enucleator). Two more of the 17 species, Peregrine Falcon (Falco peregrinus) and American Dipper, received vulnerability scores only slightly below the cutoff that Gardali et al. (2012) used to separate taxa into vulnerable versus nonvulnerable.

Limitations of our approach

One limitation of the CCVI methodology is that it incorporates spatially explicit climate projections only within the current range of a given species. Species that are able to shift their ranges to track changing environmental conditions may be able to colonize newly suitable habitat outside their current ranges. The CCVI accounts for this to some degree, as many of the factors scored in Section B (indirect exposure to climate change), Section C (sensitivity to climate change) and Section D (documented response to climate change) are relevant to whether an individual species is likely to be able to shift its range to track appropriate environmental conditions (Table 1). Another limitation to our methodology is that we assessed the vulnerability of species to climate change and associated factors within breeding ranges only; for migratory species we did not consider climate-related threats on the wintering grounds or along migratory routes outside the region. Assessing climate-change vulnerability without explicitly using knowledge of migratory connectivity to incorporate vulnerability factors on the wintering grounds and migratory routes of migratory species has been criticized as an incomplete approach (Small-Lorenz et al. 2013), because many migratory species may face their biggest threats when they are away from their breeding range. Specific data on migratory connectivity are available for few if any Sierra Nevada populations of breeding migratory birds, and we encourage further study on this as well as evaluation of climate-change effects on migration and winter grounds of these species. Despite these limitations, we urge land managers to take seriously the apparent climate-related vulnerability of species ranked as Extremely or Moderately Vulnerable in our analysis, including species that have not yet shown evidence of population declines or range contractions in the region.

CONCLUSION

We recommend that managers gauge concern for individual bird species in the Sierra Nevada based on likely effects of climate change on those species and their habitats, rather than assuming that all species will be affected negatively. Our results suggest that species vary greatly in their vulnerability to climate change in the region. Interestingly, of the two climate model scenarios we used, the GFDL model projecting more-severe climate change yielded substantially more species predicted to benefit from climate change, but also slightly more species ranked as vulnerable to climate change, than did the PCM model, which projecting less severe climate change.

Our analysis identified 17 bird species that may be particularly vulnerable to climate change in the Sierra Nevada over the coming decades, and also suggests that two broader ecological groups of species, birds associated with subalpine or alpine habitats, and birds associated with aquatic habitats, appear to be more vulnerable than other groups. In contrast, birds associated with foothill habitats, as well as montane chaparral and sagebrush, appear to be less vulnerable and in some cases may benefit from climate change. These findings can help land managers set conservation priorities and develop strategies for bolstering resistance and resilience of the more vulnerable species and habitats. Balancing these efforts with the needs of species that are already jeopardized for other reasons may be a substantial challenge.

LITERATURE CITED

Ackerly, D. D., S. R. Loarie, W. K. Cornwell, S. B. Weiss, H. Hamilton, R. Branciforte, and N. J. B. Kraft. 2010. The geography of climate change: implications for conservation biogeography. Diversity and Distributions 16:476-487. http://dx.doi.org/10.1111/j.1472-4642.2010.00654.x

Anacker, B., M. Gogol-Prokurat, K. Leidholm, and S. Schoenig. 2013. Climate change vulnerability assessment of rare plants in California. Madroño 60(3):193-210. http://dx.doi.org/10.3120/0024-9637-60.3.193

Beedy, E. C., and S. L. Granholm. 1985. Discovering Sierra birds. Yosemite and Sequoia Natural History Association, Yosemite National Park, Yosemite, California, USA.

Beedy, E. C., and E. R. Pandolfino. 2013. Birds of the Sierra Nevada. University of California Press, Berkeley, California, USA.

Bonfils, C., B. D. Santer, D. W. Pierce, H. G. Hidalgo, G. Bala, T. Das, T. P. Barnett, D. R. Cayan, C. Doutriaux, A. W. Wood, A. Mirin, and T. Nozawa. 2008. Detection and attribution of temperature changes in the mountainous western United States. Journal of Climate 21:6404-6424. http://dx.doi.org/10.1175/2008JCLI2397.1

Both, C., S. Bouwhuis, C. M. Lessells, and M. E. Visser. 2006. Climate change and population declines in a long-distance migratory bird. Nature 441:81-83. http://dx.doi.org/10.1038/nature04539

Braun, C. E., K. Martin, and L. A. Robb. 1993. White-tailed Ptarmigan (Lagopus leucura). A. Poole, editor. The birds of North America online. Cornell Lab of Ornithology, Ithaca, New York, USA. [online] URL: http://bna.birds.cornell.edu/bna/species/068

Cayan, D. R., E. P. Maurer, M. D. Dettinger, M. Tyree, and K. Hayhoe. 2008. Climate change scenarios for the California region. Climatic Change 87(Suppl 1):S21-S42. http://dx.doi.org/10.1007/s10584-007-9377-6

Crimmins, S. M., S. Z. Dobrowski, J. A. Greenberg, J. T. Abatzoglou, and A. R. Mynsberge. 2011. Changes in climatic water balance drive downhill shifts in plant speciesʹ optimum elevations. Science 331:324-327. http://dx.doi.org/10.1126/science.1199040

Davis, F., and D. Stoms. 2012. Appendix SN. The Sierra Nevada region. University of California at Santa Barbara Biogeography Lab, Santa Barbara, California, USA. [online] URL: http://www.biogeog.ucsb.edu/projects/gap/report/sn_rep.html

Delworth, T. L., A. J. Broccoli, A. Rosati, R. J. Stouffer, V. Balaji, J. A. Beesley, W. F. Cooke, K. W. Dixon, J. Dunne, K. A. Dunne, J. W. Durachta, K. L. Findell, P. Ginoux, A. Gnanadesikan, C. T. Gordon, S. M. Griffies, R. Gudgel, M. J. Harrison, I. M. Held, R. S. Hemler, L. W. Horowitz, S. A. Klein, T. R. Knutson, P. K. Kushner, A. R. Langenhorst, H.-C. Lee, S.-J. Lin, J. Lu, S. L. Malyshev, P. C. D. Milly, V. Ramaswamy, J. Russell, M. D. Schwarzkopf, E. Shevlakova, J. J. Sirutis, M. J. Spelman, W. F. Stern, M. Winton, A. T. Wittenberg, B. Wyman, F. Zeng, and R. Zhang. 2006. GFDL’s CM2 global coupled climate models. Part I: formulation and simulation characteristics. Journal of Climate 19:643-674. http://dx.doi.org/10.1175/JCLI3629.1

Dettinger, M. D., D. R. Cayan, M. Meyer, and A. E. Jeton. 2004. Simulated hydrologic responses to climate variations and change in the Merced, Carson, and American River basins, Sierra Nevada, California, 1900-2099. Climatic Change 62:283-317. http://dx.doi.org/10.1023/B:CLIM.0000013683.13346.4f

Dolanc, C. R., J. H. Thorne, and H. D. Safford. 2013. Widespread shifts in the demographic structure of subalpine forests in the Sierra Nevada, California, 1934 to 2007. Global Ecology and Biogeography 22:264-276. http://dx.doi.org/10.1111/j.1466-8238.2011.00748.x

Dubois, N., A. Caldas, J. Boshoven, and A. Delach. 2011. Integrating climate change vulnerability assessments into adaptation planning: a case study using the NatureServe Climate Change Vulnerability Index to inform conservation planning for species in Florida. Final Report. Defenders of Wildlife, Washington, D.C., USA.

Flint, L. E., and A. L. Flint. 2007. Regional analysis of ground-water recharge. Pages 29-59 in D. A. Stonestrom, J. Constantz, T. P. A. Ferré, and S. A. Leake, editors. Ground-water recharge in the arid and semiarid southwestern United States. United States Geologic Survey Professional Paper 1703. USGS Information Services, Denver, Colorado, USA.

Flint, L. E., and A. L. Flint. 2012. Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis. Ecological Processes 1:2. http://dx.doi.org/10.1186/2192-1709-1-2

Flint, L. E., A. L. Flint, J. H. Thorne, and R. M. Boynton. 2013. Fine-scale hydrologic modeling for regional landscape applications: the California Basin characterization model development and performance. Ecological Processes 2:25. [online] URL: http://www.ecologicalprocesses.com/content/2/1/25

Frederick, G. F., and R. J. Gutiérrez. 1992. Habitat use and population characteristics of the White-tailed Ptarmigan in the Sierra Nevada, California. Condor 94:889-902. http://dx.doi.org/10.2307/1369286

Gaines, D. 1992. Birds of Yosemite and the East Slope. Second printing. Artemisia Press, Lee Vining, California, USA.

Gardali, T., N. E. Seavy, R. T. DiGaudio, and L. A. Comrack. 2012. A climate change vulnerability assessment of California’s at-risk birds. PLoS ONE 7(3):e29507. http://dx.doi.org/10.1371/journal.pone.0029507

Grinnell, J., and A. H. Miller. 1944. The distribution of the birds of California. Pacific Coast Avifauna 27:1-608.

Hamon, W. R. 1961. Estimating potential evapotranspiration. Journal of the Hydraulics Division, Proceedings of the American Society of Civil Engineers 87:107-120.

Hansen, L. J., and J. R. Hoffman. 2011. Climate savvy: adapting conservation and resource management to a changing world. Island Press, Washington, D.C., USA. http://dx.doi.org/10.5822/978-1-59726-988-9

Harrell, F. E., Jr. 2012. rms: Regression modeling strategies. R package version 3.5-0. The R Project for Statistical Computing, Vienna, Austria. [online] URL: http://CRAN.R-project.org/package=rms

Hayhoe, K., D. Cayan, C. B. Field, P. C. Frumhoff, E. P. Maurer, N. L. Miller, S. C. Moser, S. H. Schneider, K. N. Cahill, E,. E. Cleland, L. Dale, R. Drapek, R. M. Hanemann, L. S. Kalkstein, J. Lenihan, C. K. Lunch, R. P. Neilson, S. C. Sheridan, and J. H. Verville. 2004. Emissions pathways, climate change, and impacts on California. Proceedings of the National Academy of Sciences of the United States of America 101:12422-12427. http://dx.doi.org/10.1073/pnas.0404500101

Hickman, J. C., editor. 1993. Jepson Manual, vascular plants of California. Second edition. University of California Press, Berkeley, California, USA.

Huntley, B., Y. C. Collingham, S. G. Willis, and R. E. Green. 2008. Potential impacts of climatic change on European breeding birds. PLoS ONE 3:e1439. http://dx.doi.org/10.1371/journal.pone.0001439

Intergovernmental Panel on Climate Change (IPCC). 2007. Climate Change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Avery, M. Tignor, and H. L. Miller, editors. Cambridge University Press, Cambridge, UK.

Jongsomjit, D., S. Veloz, D. Stralberg, M. Fitzgibbon, C. Howell, D. Moody, S. Michaile, and G. Ballard. 2011. Modeling bird distribution responses to climate change: a mapping tool to assist land managers and scientists in California. Version 2. [Web application]. Petaluma, California, USA. [online] URL: http://data.prbo.org/cadc/tools/ccweb2/index.php

Knowles, N., and D. R. Cayan. 2002. Potential effects of global warming on the Sacramento/San Joaquin watershed and the San Francisco estuary. Geophysical Research Letters 29:38-1–38-4. http://dx.doi.org/10.1029/2001GL014339

Knowles, N., and D. R. Cayan. 2004. Elevational dependence of projected hydrologic changes in the San Francisco estuary and watershed. Climatic Change 62:319-336. http://dx.doi.org/10.1023/B:CLIM.0000013696.14308.b9

Lenihan, J. M., D. Bachelet, R. P. Neilson, and R. Drapek. 2008. Response of vegetation distribution, ecosystem productivity, and fire to climate change scenarios for California. Climatic Change 87:S215-S230. http://dx.doi.org/10.1007/s10584-007-9362-0

Loarie, S. R., P. B. Duffy, H. Hamilton, G. P. Asner, C. B. Field, and D. D. Ackerly. 2009. The velocity of climate change. Nature 462:1052-1055. http://dx.doi.org/10.1038/nature08649

Lukas, D. 2011. Sierra Nevada birds. Lukas Guides, Big Oak Flat, California, USA.

Lutz, J. A., J. W. van Wagtendonk, and J. F. Franklin. 2010. Climatic water deficit, tree species ranges, and climate change in Yosemite National Park. Journal of Biogeography 37:936-950. http://dx.doi.org/10.1111/j.1365-2699.2009.02268.x

Maurer, E. P. 2007. Uncertainty in hydrologic impacts of climate change in the Sierra Nevada, California, under two emissions scenarios. Climatic Change 82:309-325. http://dx.doi.org/10.1007/s10584-006-9180-9

McCullagh, P. 1980. Regression models for ordinal data (with discussion). Journal of the Royal Statistical Society, Series B 42:109-142.

Meehl, G. A., W. M. Washington, T. M. L. Wigley, J. M. Arblaster, and A. Dai. 2003. Solar and greenhouse gas forcing and climate response in the twentieth century. Journal of Climate 16:426-444. http://dx.doi.org/10.1175/1520-0442(2003)016<0426:SAGGFA>2.0.CO;2

Millar, C. I., R. D. Westfall, D. L. Delany, J. C. King, and L. J. Graumlich. 2004. Response of subalpine conifers in the Sierra Nevada, California, USA, to 20th-century warming and decadal climate variability. Arctic, Antarctic, and Alpine Research 36:181-200. http://dx.doi.org/10.1657/1523-0430(2004)036[0181:ROSCIT]2.0.CO;2

Möller, A. P., D. Rubolini, and E. Lehikoinen. 2008. Populations of migratory bird species that did not show a phenological response to climate change are declining. Proceedings of the National Academy of Sciences of the United States of America 105:16195-16200. http://dx.doi.org/10.1073/pnas.0803825105

Moritz, C., J. L. Patton, C. J. Conroy, J. L. Parra, G. C. White. and S. R. Beissinger. 2008. Impact of a century of climate change on small-mammal communities in Yosemite National Park, USA. Science 322(5899):261-264. http://dx.doi.org/10.1126/science.1163428

Ohlemüller, R., B. J. Anderson, M.B. Araújo, S. H. M. Butchart, O. Kudrna, R. S. Ridgely, and C. D. Thomas. 2008. The coincidence of climatic and species rarity: high risk to small-range species from climate change. Biology Letters 4:568-572. http://dx.doi.org/10.1098/rsbl.2008.0097

Poole, A., editor. 2005. The birds of North America online. Cornell Laboratory of Ornithology, Ithaca, New York, USA. [online] URL: http://bna.birds.cornell.edu/BNA/

PRBO Conservation Science. 2011. Projected effects of climate change in California: ecoregional summaries emphasizing consequences for wildlife. Version 1.0. PRBO Conservation Science, Petaluma, California, USA. [online] URL: http://data.prbo.org/apps/bssc/climatechange

Seavy, N. E., T. Gardali, G. H. Golet, F. T. Griggs, C. A. Howell, R. Kelsey, S. L. Small, J. H. Viers, and J. F. Weigand. 2009. Why climate change makes riparian restoration more important than ever: recommendations for practice and research. Ecological Restoration 27:330-338. http://dx.doi.org/10.3368/er.27.3.330

Shuford, W. D., and T. Gardali, editors. 2008. California bird species of special concern: a ranked assessment of species, subspecies, and distinct populations of birds of immediate conservation concern in California. Studies of Western Birds I. Western Field Ornithologists, Camarillo, California, and California Department of Fish and Game, Sacramento, California, USA.

Siegel, R. B., and D. F. DeSante. 1999. Draft avian conservation plan for the Sierra Nevada bioregion: a report to California Partners in Flight. The Institute for Bird Populations, Point Reyes Station, California, USA.

Small-Lorenz, S. L., L. A. Culp, T. B. Ryder, T. C. Will, and P. P. Marra. 2013. A blind spot in climate change vulnerability assessments. Nature Climate Change 3:91-93. http://dx.doi.org/10.1038/nclimate1810

Steel, Z. L., M. L. Bond, R. B. Siegel, and P. Pyle. 2012. Avifauna of Sierra Nevada Network parks: assessing distribution, abundance, stressors, and conservation opportunities for 145 bird species. Natural Resource Report NPS/SIEN/NRR—2012/506. National Park Service, Fort Collins, Colorado, USA.

Stephenson, N. L. 1998. Actual evapotranspiration and deficit: biologically meaningful correlates of vegetation distribution across spatial scales. Journal of Biogeography 25:855-870. http://dx.doi.org/10.1046/j.1365-2699.1998.00233.x

Stouffer, R. J., A. J. Broccoli, T. L. Delworth, K. W. Dixon, R. Gudgel, I. Held, R. Hemler, T. Knutson, H.-C. Lee, M. D. Schwarzkopf, B. Soden, M. J. Spelman, M. Winton, and F. Zeng. 2006. GFDL’s CM2 global coupled climate models. Part IV: Idealized climate response. Journal of Climate 19:723-740. http://dx.doi.org/10.1175/JCLI3632.1

Stralberg, D., D. Jongsomjit, C. A. Howell, M. A. Snyder, J. D. Alexander, A. Wiens, and T. L. Root. 2009. Re-shuffling of species with climate disruption: a no-analog future for California birds? PLoS ONE 4:e6825. http://dx.doi.org/10.1371/journal.pone.0006825

Thorne, J. H., R. Boynton, T. N. Le, A. Flint, and L. Flint. 2012. Development and application of downscaled hydroclimatic predictor variables for use in cross-sector climate vulnerability and assessment studies. CEC-500-2011-009. California Energy Commission, Sacramento, California, USA.

Thorne, J. H., B. J. Morgan, and J. A. Kennedy. 2008. Vegetation change over sixty years in the central Sierra Nevada, California, USA. Madroño 55:223-237. http://dx.doi.org/10.3120/0024-9637-55.3.223

Tingley, M. W. and S. R. Beissinger. 2013. Cryptic loss of montane avian richness and high community turnover over 100 years. Ecology 94:598-609. http://dx.doi.org/10.1890/12-0928.1

Tingley, M. W., M. S. Koo, C. Moritz, A. C. Rush, and S. R. Beissinger. 2012. The push and pull of climate change causes heterogeneous shifts in avian elevational ranges. Global Change Biology 18:3279-3290. http://dx.doi.org/10.1111/j.1365-2486.2012.02784.x

Tingley, M. W., W. B. Monahan, S. R. Beissinger, and C. Moritz. 2009. Birds track their Grinnellian niche through a century of climate change. Proceedings of the National Academy of Sciences of the United States of America 106:19637-19643. http://dx.doi.org/10.1073/pnas.0901562106

van Mantgem, P. J., and N. L. Stephenson. 2007. Apparent climatically induced increase of tree mortality rates in a temperate forest. Ecology Letters 10:909-916. http://dx.doi.org/10.1111/j.1461-0248.2007.01080.x

Walk, J., S. Hagen, and A. Lange. 2011. Adapting conservation to a changing climate: an update to the Illinois Wildlife Action Plan. Report to the Illinois Department of Natural Resources. Illinois Chapter of the Nature Conservancy, Peoria, Illinois, USA.

Washington, W. M., J. W. Weatherly, G. A. Meehl, A. J. Semtner, T. W. Bettge, A. P. Craig, W. G. Strand, J. Arblaster, V. B. Wayland, R. James, and Y. Zhang. 2000. Parallel climate model (PCM) control and transient simulations. Climate Dynamics 16:755-774. http://dx.doi.org/10.1007/s003820000079

Young, B. E., E. Byers, K. Gravuer, K. Hall, G. Hammerson, and A. Redder. 2011. NatureServe guidelines for using the NatureServe Climate Change Vulnerability Index (Release 2.1, 7 April 2011). NatureServe, Arlington, Virginia, USA.

Young, B. E., E. Byers, K. Gravuer, K. R. Hall, G. A. Hammerson, A. Redder, K. Szabo, and J. E. Newmark. 2009. Using the NatureServe Climate Change Vulnerability Index: a Nevada case study. NatureServe, Arlington, Virginia, USA.

Young, B. E., K. R. Hall, E. Byers, K. Gravuer, G. Hammerson, A. Redder, and K. Szabo. 2012. Rapid assessment of plant and animal vulnerability to climate change. Pages 129-152 in J. Brodie, E. Post, and D. Doak, editors. Conserving wildlife populations in a changing climate. University of Chicago Press, Chicago, Illinois, USA.

Zeiner, D. C., W. F. Laudenslayer, K. E. Mayer, and M. White. 1990. California’s wildlife, Volume II, birds. State of California Department of Fish and Game, Sacramento, California, USA.