Modeling contemporary range retraction in Great Basin pikas (Ochotona princeps) using data on microclimate and microhabitat

Quaternary International 235 (2011) 77e88

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Modeling contemporary range retraction in Great Basin pikas (Ochotona princeps) using data on microclimate and microhabitat Jennifer L. Wilkening a, *, Chris Ray b, Erik A. Beever a, c, Peter F. Brussard a a

University of Nevada, Program in Ecology, Evolution, and Conservation Biology, Reno, NV 89557, United States University of Colorado, Department of Ecology and Evolutionary Biology, Boulder, CO 80309-0334, United States c 3115 Briarcliff Dr., Anchorage, AK 99508, United States b

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 31 May 2010

The American pika (Ochotona princeps) inhabits talus slopes on isolated mountaintops in the Great Basin, where the species is susceptible to localized extirpations. Previous studies from the region related pika extirpations to proxies for climate and habitat quality, or to relatively short datasets on microclimate. This study extends previous research by modeling extirpation using new data from microclimates and microhabitats, and by including data on the vegetation available to individual pikas. We re-surveyed 25 sites historically occupied by pikas, and collected microclimatic and vegetative-cover data from each site. Sites of pika extirpation experienced higher summer temperatures and higher frequency of extremely warm days during 2005e2007 than did sites of pika persistence. Several aspects of vegetative cover also differed between persistence and extirpation sites, and relative forb cover was positively related to pika persistence. Evaluation of competing models within an information-theoretic framework suggests strong support for recent mean summer temperature as the primary driver of extirpations in this dataset. In agreement with other modeling efforts, this result supports the hypothesis that extirpation results from chronic heat stress during the summer months when pikas must gather and store food for the winter. In contrast with previous studies, we found less support for the hypothesis that extirpation results from acute cold stress during the winter months, possibly due to several differences in analytical methods. Ó 2010 Elsevier Ltd and INQUA. All rights reserved.

1. Introduction There is now widespread consensus that global climate change in the form of rising temperatures, changing precipitation patterns, and increased frequency of extreme weather events is occurring at an unprecedented rate (IPCC, 2001, 2007; Climate Impact Groups, 2004; Cook et al., 2004; Leung et al., 2004; Parmesan and Galbraith, 2004; Service, 2004). Global mean surface temperature has increased 0.6
C since the late 1800s, and evidence is mounting that species distributions, phenology, and physiology are being affected worldwide (Hughes, 2000; Walther et al., 2002; Parmesan and Yohe, 2003; Root et al., 2003; IPCC, 2007). A global-scale meta-analysis has documented pole-ward shifts in species range and advances in the

* Corresponding author. EBIO, Ramaley N122, Campus Box 334/Univ. of Colorado, Boulder, CO 80309-0334. Tel.: þ1 303 638 9778; fax: þ1 303 492 8699 E-mail addresses: [email protected] (J.L. Wilkening), cray@ colorado.edu (C. Ray), [email protected] (E.A. Beever), brussard@biodiversity. unr.edu (P.F. Brussard). 1040-6182/$ e see front matter Ó 2010 Elsevier Ltd and INQUA. All rights reserved. doi:10.1016/j.quaint.2010.05.004

occurrence of spring events (Parmesan and Yohe, 2003). In addition to changes in species abundance and distribution, climate change already has been implicated in at least one species-level extinction (Pounds et al.,1999, 2006). Some researchers now believe that climate change will become the greatest threat to global ecological systems and biodiversity (Glick and VanPutten, 2002). Alpine and arctic species have been identified as being particularly vulnerable to global warming (Hughes, 2000; Moritz et al., 2008), in part because warming may occur more rapidly at higher elevations (Naftz et al., 2002; Beniston, 2003). Species adapted to higher elevations may also face changing patterns of precipitation. Water derived from snowmelt has decreased by 20% in the western US, and similar decreases along with changes in precipitation patterns will have far reaching consequences for humans and wildlife (IPCC, 2007). The flowering time of many alpine plants is determined by the date of winter snowmelt, and as spring snowmelt continues to occur much earlier, species abundance, community composition, and planteherbivore interactions will be affected (Inouye and McGuire, 1991).

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While there have been many studies relating climate to species distribution, the specific mechanisms influencing range shifts are poorly understood (Guralnick et al., in press). In order to understand direct and indirect effects of climate change on species distribution, detailed knowledge is needed about how individuals respond to climate in situ. Small mammals isolated on mountains within the Great Basin represent a relatively well-studied system in which range-shifts have been explained with reference to climate (Brown, 1971, 1978; Beever et al., 2003, 2010; Grayson, 2005), and represent an ideal system with which to test hypotheses relating local population persistence to microhabitat variables. The American pika (Ochotona princeps) is a small (125e175 g) lagomorph that occurs in rock slides or piles of broken rock (e.g., talus) throughout western North America. They are typically found in alpine, sub-alpine, montane and some steppe habitats and are considered to be well adapted to cold climates (Smith, 1974a). Pikas are generalized herbivores that do not hibernate and rely on vegetation stored in the form of “hay piles” to supply their nutritional needs throughout the winter. The idea that pika distribution is closely related to temperature, which generally decreases with increasing elevation and latitude, was first suggested by Grinnell (1917) and was later verified by Smith (1974a,b). In the northern part of its range, the species occurs at elevations ranging from sea level to 3000 m, while at the southern edge of the range (esp. in the Great Basin) pikas typically do not occur below 2500 m. Pikas are fairly abundant throughout the Rocky Mountains and Sierra Nevada Mountains, which have served as sources of colonists for populations in other locations (Hafner and Sullivan, 1995; Galbreath et al., 2009). In the Great Basin and other similarly arid areas, however, pika distribution is patchy and consists of isolated relict populations (Hall, 1946; Lawlor, 1998; Beever et al., 2003; Grayson, 2005). The fossil record for Ocohotona extends only to the last glacial period; however, genetic evidence suggests that the geographic range of O. princeps has expanded and contracted during glacial and interglacial periods, with its maximum extent occurring in the late Wisconsinan (Galbreath et al., 2009). During this time, pikas occurred at much lower elevations throughout the Western states, and populations were found 100 km south of their present distribution (Mead, 1987; Hafner, 1993; Grayson, 2005). The subsequent warming that occurred at the beginning of the Holocene (roughly 10,000 years ago) resulted in higher temperatures, forcing pikas to retreat to higher latitudes and elevations (Grayson, 1987; Hafner, 1993, 1994). By about 7500 years ago, the species’ distribution within the Great Basin consisted generally of relictual populations located on mountain ranges surrounded by large tracts of lower elevation desert. Isolated mountaintops in the Great Basin are “habitat islands” susceptible to the same biogeographic patterns of extirpation seen on oceanic islands (MacArthur and Wilson, 1963, 1967), especially for the most obligately alpine species (Lawlor, 1998; Rickart, 2001; Beever et al., 2010). Brown (1971) emphasized that small mammal populations residing on these mountaintops are in a phase of extirpations that are not balanced by recolonization events. Many researchers have used isolated montane faunas in the Great Basin to study biogeographic patterns in an attempt to identify factors most responsible for these extirpations (Brown, 1971, 1978; Johnson, 1975; Cutler, 1991; Grayson, 2006). The perceived importance of factors such as habitat area, quality, and especially isolation has shifted over time (Brown, 1978; Cutler, 1991; Grayson and Livingston, 1993; Lawlor, 1998; Grayson and Madsen, 2000). However, isolation is still considered an important factor contributing to extirpation dynamics of Great Basin pika populations, because the likelihood of successful dispersal in the current climate

across valley bottoms appears extremely low (Lawlor, 1998; Grayson, 2006). Recently it was discovered that several historical pika populations in the Great Basin became extirpated sometime during the 20th century (Beever et al., 2003; Fig. 1). Extirpations occurred at sites that were at lower elevations than sites with extant populations, and PRISM-modeled estimates of summer temperature and annual precipitation suggested that extirpation sites were hotter and drier than sites at which pikas remained extant (Beever et al., 2003). Various combinations of biogeographic, anthropogenic, and climatic factors were used to construct competing models to explain these extirpations. The best model of population persistence included positive effects of increasing habitat area, reduced access by road, and higher elevation of nearby habitat (i.e., maximum elevation of talus within the upper dispersal range of an individual pika). Analyses of the support for each variable suggested that the best predictor of pika persistence was the maximum elevation of nearby habitat. This result supported the hypothesis that climate change has driven pika extirpations, because high-elevation habitats may provide a refuge or source of recolonization for local populations impacted by warming temperatures. Contemporary climate change could affect Great Basin pika populations via numerous mechanisms (Beever et al., 2010). Elevated temperatures may cause acute or chronic heat stress in individuals that could result in local extirpations. Precipitation and snow cover have also been related to pika survival and occurrence (Smith, 1978; Hafner, 1993; Beever et al., 2003; Kruezer and Huntly, 2003; Morrison and Hik, 2007). Pika survival could decline as a result of reduced snowpack and increased exposure to extremely cold temperatures during the winter (Tapper, 1973; Smith, 1978; Morrison and Hik, 2007; Beever et al., 2010). Changes in precipitation patterns and elevated temperatures could impact the quantity and quality of forage available adjacent to talus areas, which may also influence pika survival (Huntly et al., 1986; Dearing, 1997a; Kreuzer and Huntley, 2003). A previous study attempting to explain patterns of loss in Great Basin pika populations found that persistence was well predicted by metrics of climate alone (Beever et al., 2010). That study used microclimatic data from 2005 to 2006 measured within pika habitats (taluses) to model persistence as a function of recent or longer-term microclimate at each of 25 sites within the Basin. Longer-term metrics of microclimate were modeled by relating temperatures measured within taluses with temperatures measured at Historical Climate Network weather stations during the period of study, and extrapolating these relationships using historical time series from the same weather stations. Chronic heat stress during the year of study and acute cold stress over the longer term were identified as the best predictors of persistence. The Beever et al. (2010) study represents a significant step toward mechanistic modeling of pika extinctions, by comparing the relative support for alternative mechanisms of thermal stress on pikas. However, it relied on microclimatic data from a single year, and did not examine the relative importance of microhabitat variables other than climate. Here, we explore the relative support for climatic vs. foragerelated predictors of pika persistence at two spatial scales. We predicted that patterns of persistence at either spatial scale could be explained by differences in available vegetation and microclimate, in accordance with existing hypotheses. For example, assuming pikas are subject to chronic or acute heat stress (Beever et al., 2010), we predicted that the mean summer temperature and the number of days above a temperature threshold would be higher where pikas no longer occur. Assuming that acute cold stress can adversely affect O. princeps (Beever et al., 2010), we

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Fig. 1. Patterns of extirpation and persistence of pikas (Ochotona princeps) within the hydrographic Great Basin.

predicted that pikas would be extirpated where temperatures more often dropped below a threshold. Our hypotheses relating pika persistence to the composition of available vegetation were based in part on studies suggesting that pikas reduce energy expenditures through selective foraging, feeding primarily on graminoids during the summer while caching mainly forbs for over-winter consumption (Huntly et al., 1986; Dearing, 1997a). Thus, climate change may indirectly cause extirpations by altering patterns of vegetation availability. It is also likely that both pikas and vegetation would respond to climate, and that local vegetation composition may be a reliable metric of the microclimate affecting pikas. In any case, we expected vegetation composition to predict pika persistence. Overall, we predicted that sites of extirpation would contain lower cover of forbs and graminoids than sites of persistence, and would be dominated by more xeric-adapted species such as graminoids. Combining hypotheses based on microclimate and vegetation, we predicted that pikas would no longer occur where mean summer temperatures were relatively high and the availability of either graminoids (summer forage) or forbs (cached during summer) was relatively low. We also

predicted that pikas would no longer occur where temperatures more often dropped below a threshold and relative forb cover was low, because lack of sufficient forb content in the hay pile (the winter food source) may exacerbate effects of acute cold stress. 2. Methods 2.1. Study area The hydrographic Great Basin of the United States covers an area of 520,000 square kilometers, roughly bounded by the Rocky Mountains on the east and the Sierra Nevada Mountains on the west (Fig. 1). The area is classified as a basin and range desert, characterized by numerous mountain ranges with peaks as high as 4342 m. Separating these ranges are wide valleys, which generally have elevations between 1220 and 1830 m (Grayson, 1993). Dominant vegetation at the lower elevations typically consists of sagebrush (Artemisia spp.) or saltbush (Atriplex spp.), while at higher elevations many alpine plant species are similar to those found in the Rocky Mountains and the Sierra Nevada Mountains.

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For the purpose of this study, sites in the Carson Range, Wasatch Front, and Warner Mountains were excluded due to their proximity to the Sierra Nevada, Rocky Mountains, and Cascades, respectively. Beever et al. (2003) compiled a list of historical records of pika sightings at 25 discrete sites in the Great Basin (Fig. 1, appseca1). The same 25 sites were used for this study, except that a site in the Hayes Canyon Range (Beever et al., 2008) was substituted for a site in the Ruby Mountain Range (Long Canyon) that was eliminated due to difficulties with access. 2.2. Site classification The 25 sites were divided into three groups, based on results from these and previous surveys (Beever et al., 2003, 2010): 1) “extirpation” sites where pikas no longer occurred within 3 km of the historically recorded location) “transitional” sites where pikas still occurred within 3 km of the historical location, but the lower limit of pika occupancy had moved at least 200 m upslope within the past decade and no longer overlapped the historical site location, and 3) “persistence” sites where pikas still occupied all previously documented areas and elevations. This classification resulted in eight extirpation sites, five transitional sites, and 12 persistence sites in each survey conducted during this study (2005e2007), as described below. 2.3. Survey methods During the years of 2005e2007, each study site was surveyed >5 times during the summer and fall months. Survey techniques for pikas were the same as those employed by Beever et al. (2003). Each study site consisted of all taluses (including other potentially suitable rock features) within a 3-km radius of the historical location. Each talus within each study site was searched for evidence of pikas during a site survey lasting approximately 8 h in total. At extirpation sites, additional taluses outside the 3-km radius were also searched in order to characterize any potential for recolonization. Locations of all pika sightings, calls, and hay piles were recorded with a handheld GPS unit equipped with WAAS correction. Surveys were scheduled to include dawn and/or dusk hours to maximize the likelihood of detecting pikas directly. Pika detection rates are typically quite high (MacArthur and Wang, 1974; Smith, 1974; Beever et al., 2003; Ray and Beever, 2007; Beever et al., 2008; Rodhouse et al., in press; Beever et al., 2010). 2.4. Temperature data collection At each site, five to eight Thermochron iButton temperature recorders (model DS1921G, Maxim Integrated Products, Sunnyvale, CA; hereafter, data loggers) were placed within taluses to record the range of temperatures in pika-relevant microhabitats. All data loggers were programmed to record a temperature reading in Celsius every 4 h, beginning at 2400, throughout the year. This resulted in a total of 2190 temperature readings per data logger per year, with the temperature being recorded at 2400, 0400, 0800, 1200, 1600, and 2000 every day, June 2005eNovember 2007. Data loggers were dispersed within each site to maximize site coverage, especially with respect to the elevational gradient and array of slope aspects represented by taluses currently or previously occupied by pikas. Distances between data loggers varied greatly among sites, depending on the distribution and abundance of talus habitat. At all sites, each data logger was placed within talus at a depth between 0.5 and 1.0 m below the surface, in a position completely shaded from direct sunlight. Surface topography varied with rock size, precluding precise measurements of depth. Logger position and elevation were recorded with a handheld GPS unit. Data used

in the current analysis were derived from loggers that were separated by at least 50 m (usually much more than 50 m) to ensure independent readings. At persistence sites, five data loggers were placed, each near a fresh hay pile, in locations stratified to cover the range of elevations currently occupied by pikas and within 3 km of the historical site location. Because talus habitat was relatively continuous within persistence sites, pikas at these sites were scattered across larger areas and a wide range of elevations. Each site was stratified into five different elevational bands, and a point was randomly selected within each band for data logger placement. Points were randomly selected from the list of pika/sign locations generated during our surveys. At extirpation sites, seven data loggers were distributed among available taluses. We chose to place seven loggers at extirpation sites and five at persistence sites, rather than six at each site type, because we were less certain where to place loggers at extirpation sites in order to characterize the microclimates most likely to be used by pikas. During surveys of taluses within each extirpation site, evidence of old pika scat or hay piles were recorded by location with a handheld GPS unit. Similar data from previous surveys (Beever et al., 2003) were merged with these data and all signs were grouped by talus patch. Each logger was randomly assigned to one of seven talus patches with old pika sign; specifically, each logger was placed at an old haypile or old scat station, or where pikas or fresh sign had been observed prior to extirpation. We did not stratify extirpation sites by elevational bands because taluses tended not to be spread over the large elevational range exhibited at persistence sites. At extirpation sites that lacked recent evidence of pikas or pika sign during this study (n ¼ 2), data loggers were placed in locations deemed most suitable for haypiles based on observations made at other sites. Various metrics were used to determine a likely haypile location, such as talus patch size, average rock size, talus depth, aspect, and proximity to vegetation. At extirpation sites, no data loggers were placed below the minimum elevation of historical records, to avoid inflation of estimates of temperature within the site. At transitional sites, eight data loggers were placed at each site, four within currently occupied taluses and four within previously occupied taluses. We placed more loggers within these sites to improve the characterization of both occupied and unoccupied portions of the site, and to improve statistical power for within-site comparisons. A talus patch was considered currently occupied if it contained a fresh haypile or at least one pika was seen or heard there during 2005 (when loggers were placed). Because unoccupied taluses within transitional sites had been occupied during surveys in the 1990s, we were able to place data loggers at coordinates where pikas or fresh sign had been observed during those surveys. From a list of points where pikas/sign had been detected, points were randomly selected for data-logger placement. At all of these locations, we observed abandoned haypiles or old scat during data-logger placement. 2.5. Vegetation surveys Vegetation surveys were conducted at all sites during the peak plant biomass months of July and August. Four to five vegetation survey locations were chosen at random from the list of temperature data logger locations at each site. This resulted in vegetation surveys at almost all of the data logger locations at persistence sites, and at roughly half of the data logger locations at extirpation and transitional sites. At transitional sites, vegetation surveys were divided equally between logger locations within occupied and unoccupied portions of each site. Each vegetation survey consisted of three, parallel, 50-m transects spaced 15 m apart. The central

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transect was centered on a data logger location (usually at a currently or previously active hay pile), and extended 25 m to either side of the data logger along an elevational isocline. The other two parallel transects were located upslope and down-slope of the middle transect. O. princeps is a central-place forager, foraging mainly in areas <25 m from its haypile (Huntly et al., 1986; Dearing, 1995). Haypiles are generally located within a few meters of a talusevegetation interface (Huntly et al., 1986), and talus margins are convoluted and patchy at relatively small scales in our study sites. The trio of transects used provided essential coverage of any gradient in available vegetation that may have occurred at locations with steep slopes, as well as good representation of potential foraging locations available to each pika. Due to the convoluted and patchy nature of the talusevegetation interface at these sites, almost all transects intersected both talus and vegetation areas. Relative cover of each vegetation type was used to standardize any differences in overall vegetation cover between sites. The lineepointeintercept method was used for quantifying vegetation at each meter along each 50m transect, resulting in 150 points per vegetation survey location and thus 600e750 points per site. Vegetation was classified into one of six different life forms: forbs (herbaceous, flowering plants, excluding cushion plants), graminoids (grasses and grass-like plants such as sedges and rushes), shrubs (woody plants without a central trunk), trees (woody plants with a central trunk), cushion plants (low, matforming plants), and non-vascular plants (mosses and lichens). All vegetation was identified in the field to species level when possible and to genus otherwise. For unidentified species, samples were collected and preserved in a standard plant press and later identified at the University of NevadaeReno Herbarium. 2.6. Analyses Variables calculated from each data logger included: mean summer temperature (JuneeSeptember 2006, 2007), number of days below 5
C and 10
C (October 2005eApril 2006, October 2006eApril 2007), and number of days above 26
C and 28
C (JuneeSeptember 2006, 2007). Values were averaged across all data loggers within each site or sub-site (scales defined below). For each vegetation survey, the relative cover (i.e., cover of a specific life form divided by total cover) of forbs and graminoids was calculated. Values were arithmetically averaged across vegetation surveys within each site or sub-site. Logistic regression was used to model persistence (1) vs. extirpation (0) as a function of predictor variables. The set of

Table 1 Potential predictor variables for modeling pika persistence during the years 2005e2007 among sites (N ¼ 25) in the hydrographic Great Basin. Each variable was selected a priori based on evaluation of research from the sources presented here.

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candidate predictor variables was chosen based upon the extensive literature on pikas (Table 1). Candidate models were formulated based on one or two predictors, as outlined below. The relative support for each model and each predictor was compared in an information-theoretic (I-T) framework using an information criterion corrected for small sample size (AICc, Burnham and Anderson, 2002). Analyses and plots were coded in R 2.6.2 (Ó2008 The R Foundation for Statistical Computing, http://www.rproject.org/). Prior to logistic-regression analyses, all data sets were checked for outliers, and no data were omitted. Three variables (number of days below 10
C, number of days above 28
C, and relative forb cover) were log-transformed prior to regression analyses, to reduce skew in these data. Persistence was modeled at the site level in an I-T analysis based on persistence/extirpation sites (N ¼ 20), and at the sub-site level in a separate I-T analysis based on transitional sites (N ¼ 5 site pairs). Within each transitional site, occupied sub-sites were located approximately 200 m higher than unoccupied sub-sites, and vegetation and temperature data associated with occupied sub-sites (response ¼ 1) were averaged separately from similar data from unoccupied sub-sites (response ¼ 0). Logistic-regression models of persistence within these transitional sites included “site” as a potential predictor variable, modeled as a random effect. We expected that persistence sites would have lower mean summer temperatures, fewer extreme temperatures (see thresholds in Table 1), higher forb and graminoid cover, and lower ratios of 1) mean summer temperature to forb cover, 2) mean summer temperature to graminoid cover, and 3) number of cold days (Table 1) to forb cover. To address these hypotheses, we constructed a set of logistic-regression models, each based on one of the three interactions above or one of the predictors in Table 1. We also predicted additive effects of mean summer temperature and number of either hot or cold days, additive effects of hot and cold days, and additive effects of forb and graminoid cover. These hypotheses were addressed in a set of multiple logistic-regression models. In total, we compared support for 13 models of site-level persistence and 18 models of persistence at the level of sub-sites, including a null (intercept-only) model in each analysis. Given the small sample of transitional sites, our analysis of sub-sites was admittedly exploratory. However, if site-level and sub-site analyses were to support similar drivers, it would suggest that the effects of these drivers were strong enough to counter the expected noise in a small dataset. In order to further characterize the variation in predictor variables, the mean value of each predictor variable was compared between sites of persistence and extirpation using a one-tailed Welch’s two-sample t-test with significance level set at P  0.05. Paired t-tests were also employed for analyses within sites. Prior to these analyses, several variables were log-transformed, as described above.

Predictor Variable

Source

3. Results

Predictors based on temperature Mean summer temperature Number of days above 26
C Number of days above 28
C Number of days below 5
C Number of days below 10
C

1 6,7 1, 4 1,2,3,5 1,2,3,5

Predictors based on vegetation Relative cover of forbs Relative cover of graminoids

8,9,10,11 8,12,13

Several of the potential predictor variables (Table 1) were highly correlated. The number of days below 5
C was positively correlated with the number of days below 10
C (r ¼ 0.93). We retained the latter for I-T analyses, because the former was slightly more correlated with several other predictors. Similarly, the number of days above 26
C was omitted from I-T analyses in favor of the number of days above 28
C (r ¼ 0.98 between these two variables). Because relative forb cover was negatively correlated with mean summer temperature (r ¼ 0.83), these variables appeared only in separate models or as an interaction effect.

Sources: 1) Beever et al., (2010), 2) Smith and Ivins (1983), 3) Smith (1978), 4) Hafner (1993), 5) Tapper (1973), 6) Smith (1974a), 7) MacArthur and Wang (1973), 8) Dearing (1995, 1996, 1997a) 9) Huntly et al., (1986), 10) Sundby (2002), 11) Ray and Beever (2007), 12) Dearing (1995, 1996), 13) Kreuzer and Huntly (2003).

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Table 2 Relative support for selected models of pika persistence at the site level (N ¼ 20 including 12 persistence sites and 8 extirpation sites) and at the sub-site-level (N ¼ 10 paired subsites from five transitional sites) in the hydrographic Great Basin. Models are ranked in order of increasing AICc values (Akaike’s information criterion, adjusted for small sample size). L denotes likelihood. K is the number of fitted parameters and DAICc is the difference between the indicated model and the best model (the model with lowest AICc). Models with DAICc > 3 are not shown. The generalized Akaike weight of each model (wi) indicates the relative strength of evidence in support of each model; wi ranges from 0 to 1 (Burnham and Anderson, 2002). The evidence ratio is w0/wi, where w0 is the generalized Akaike weight of the model with the lowest AICc. The evidence ratio is inverted here, such that smaller numbers indicate relatively weak support. Model: Predictor (effect sign)

2 X log(L)

K

AICC

DAICC

Akaike weight

Evidence ratio1

Site level Days below10
C (þ), mean summer temp () Mean summer temp/Relative forb cover () Mean summer temp ()

9.881 14.768 15.137

3 2 2

17.381 19.473 19.843

0 2.093 2.463

0.529 0.186 0.154

1.000 0.352 0.291

Sub-site level Null model (intercept-only) Days below10
C/Relative forb cover () Mean summer temp/Relative gram cover () Mean summer temp/Relative forb cover () Rel gram cover (þ) Rel forb cover (þ)

13.863 12.262 12.395 12.471 12.719 13.567

1 2 2 2 2 2

16.363 17.976 18.109 18.185 18.433 19.281

0 1.613 1.746 1.822 2.070 2.918

0.282 0.126 0.118 0.113 0.100 0.066

1.000 0.447 0.418 0.401 0.355 0.234

3.1. Site-level comparisons (persistence vs. extirpation) We observed no evidence of recolonization within extirpation sites identified by Beever et al. (2003). We did, however, find evidence for further extirpation: eight site-level populations were extirpated as of 2005 (and remained extirpated through 2007), as compared to the six extirpations reported by Beever et al. (2003). The best model of site-level persistence (Table 2) received far more support than the null model (DAICc ¼ 29.143), and no other model received similar support (all DAICc values were greater than 2). The predictors in the best model, mean summer temperature and number of days below 10
C, also garnered far more support than other predictors in the a priori model set (Table 3). As expected, summer temperature was always negatively related with pika persistence (Tables 2 and 3). In contrast, the relationship between persistence and the number of days below 10
C varied among models (Table 3), and was actually positive in the best model (Table 2). Support was also high for an interaction between

mean summer temperature and relative forb cover (Table 2), and the ratio of these two variables was, as expected, always negatively related to pika persistence (Table 3). Relative forb cover, inversely correlated with mean summer temperature, was always positively related to persistence. Number of days above 28
C and relative graminoid cover both received relatively weak support as predictors of site-level persistence, although both exhibited the expected, negative relationship with persistence in four out of five models. Fig. 2 displays the top two models of site-level persistence, both predicting persistence correctly (jpredicted eobservedj < 0.5) for 17 out of 20 sites. Both models failed to predict persistence at a site in the Hayes Canyon Range (Washoe County, Nevada) and extirpation at a site on Duffer Peak (Humboldt County, Nevada). Of those two sites, both were located in relatively small mountain ranges with limited amounts of talus habitat, but with no other obvious distinctions within our dataset. Among persistence sites, Hayes Canyon had the highest mean summer temperature; among extirpations sites, Duffer Peak had the lowest mean summer temperature. The best model failed to

Table 3 Relative support for predictors of pika persistence at the site level and pika occupancy at the sub-site level, calculated across all models in the a priori set. The generalized Akaike weight of each predictor indicates the strength of evidence for that particular predictor. The mean Akaike weight for each predictor variable is the sum of the Akaike weights from all models in which the predictor appeared, divided by the number of models in which it appeared. The sign of the fitted coefficient associated with each predictor indicates that predictor’s effect on pika persistence; for each predictor, we show the number of models in which the fitted coefficient was positive or negative. The evidence ratio for each predictor i is the highest mean Akaike weight/model (associated w/MeanSummerTemp) over the mean Akaike weight/model of predictor i, and can be interpreted as described in Table 2. Predictor

Akaike weight

Mean akaike wt/model

Sign of effect

Evidence ratio1

Site level Mean summer temp Mean summer temp/Rel forb cover Days below 10
C Rel forb cover Days above28
C Rel gram cover Days below 10
C/Rel forb cover Mean summer temp/Rel gram cover

0.722 0.186 0.536 0.066 0.058 0.016 0.004 0.000

0.241 0.186 0.179 0.033 0.019 0.008 0.004 0.000

Negative (3) Negative (1) Positive (2), Negative (1) Positive (2) Negative (2), Positive (1) Negative (2) Negative (1) Positive (1)

1.000 0.772 0.742 0.138 0.080 0.034 0.016 0.002

Sub-site level Days below 10
C/Rel forb cover Mean summer temp/Rel gram cover Mean summer temp/Rel forb cover Rel gram cover Rel forb cover Days above 28
C Mean summer temp Days below 10
C

0.126 0.118 0.113 0.100 0.066 0.075 0.071 0.071

0.126 0.118 0.113 0.050 0.033 0.019 0.018 0.018

Negative (1) Negative (1) Negative (1) Positive (2) Positive (2) Positive (4) Negative (4) Negative (4)

1.000 0.936 0.901 0.398 0.260 0.150 0.141 0.140

-5

0

5

10

Linear predictor: f(Mean summer temp., Days below -10°C)

1.0 0.6

0.8

H

0.4

0.6 0.4 0.2

D

b

0.2

H

83

PD

0.0

1.0

Persistence observed (dots) and modeled (line)

C

0.8

a

0.0

Persistence observed (dots) and modeled (line)

J.L. Wilkening et al. / Quaternary International 235 (2011) 77e88

-15

-10

-5

0

Linear predictor: f(Mean summer temp./Rel. forb cover)

Fig. 2. Observed persistence and modeled probability of persistence of pikas in the hydrographic Great Basin. (a) The best model (DAICc ¼ 0) of persistence at the site level. The linear predictor of this model was 15.33e1.18 m þ 2.35 n, where m ¼ mean summer temperature and n ¼ log-transformed number of days below 10
C. Labels indicate data from Cougar Peak (C), Hayes Canyon (H) and Duffer Peak (D), the only three sites which were not well predicted by this model (modeled persistence differed from observed by >0.5). (b) The second-best model (DAICc ¼ 2.09) of persistence at the site level. The linear predictor of this model was 4.43e0.65 (m/r), where m ¼ mean summer temperature and r ¼ logtransformed relative forb cover. Labels indicate data from Peterson Creek (P), Hayes Canyon (H) and Duffer Peak (D).

predict only one extirpation (at Duffer Peak), while the secondbest model missed two extirpations (Fig. 2). The best predictors based on our I-T analysis also differed significantly in value between persistence and extirpation sites (Table 4). Mean summer temperature, days above 26
C and 28
C, and the ratio of mean summer temperature to relative forb cover were all significantly higher at extirpation sites, as expected. Also as expected, relative forb cover was significantly lower at extirpation sites, and the ratio of days below 10
C to relative forb cover was marginally (P x 0.06) higher among extirpation sites (relative to among persistence sites). In contrast, there was no significant difference between persistence and extirpation sites in the number of days below 5
C and 10
C, relative graminoid cover, or the ratio of mean summer temperature to relative graminoid cover. The results of I-T analyses and t-tests differed dramatically in the case of two predictor variables. The (log-transformed) number of days below 10
C was highly supported as a (positive) predictor of pika persistence in our I-T analysis of site-level data (Tables 2 and 3), despite almost identical mean values at persistence and extirpation sites (Table 4). Conversely, although extirpation sites exhibiteddas expectedda somewhat higher ratio of days below 10
C to relative forb cover (Table 4), our I-T analysis of site-level data did not support this ratio as a predictor of persistence (Table 3). Here we also report selected differences in the data prior to log transformation for analyses (cf. Table 4, which reports only logtransformed data for these variables). Mean summer temperature was 5.7
C higher at extirpated than persistence sites, and the number of days above 26
C and 28
C was 15.1 and 9.19 days higher at sites of extirpation. Relative forb cover was significantly higher at sites of persistence (28.79%) than at sites of extirpation (8.61%), but relative graminoid cover was not. 3.2. Comparisons at the sub-site level (persistence within transitional sites) The best-supported models of persistence at the sub-site level (Table 2) included the null model and three others, each based on an interaction ratio (days below 10
C to relative forb cover,

mean summer temperature to relative graminoid cover, or mean summer temperature to relative forb cover). Models based on these interaction ratios were relatively well supported (all DAICc values were less than 2) and each of these predictors was negatively related to pika persistence, as expected (Table 3). We selected a cutoff of DAICc e3 to separate supported models from unsupported models, because the addition of one fitted parameter with zero explanatory power would increase AICc by 3.21 in this small dataset. No predictor variables differed significantly between occupied and unoccupied locations at the sub-site level (Table 4), as might be expected in such a small dataset. However, all three variables involving ratios exhibited marginally significant (Pe0.1) differences that were in the expected direction, and in keeping with the results of our I-T analysis of sub-site data. 4. Discussion Results from this study support some previous hypotheses and findings relating climate and pika extirpations in the Great Basin. Beever et al. (2003, 2010) concluded that both warmer temperatures and extremely cold days were contributing in some way to recent extirpations. Thermal predictors also appeared in all of the most plausible models for this study. Mean summer temperature was an extremely strong predictor of pika persistence, occurring in most of the supported models at both scales of analysis. Mean summer temperature was markedly higher at sites of extirpation than at sites of persistence, in agreement with the hypothesis (Beever et al., 2010) that extirpations may have resulted from chronic thermal stress. On warm days, pikas are relatively inactive, and their activity patterns become more crepuscular at the lower edge of their elevation and latitudinal limit (Smith, 1974a). Because they do not hibernate (Krear, 1965), pikas have annual energy demands that are somewhat higher than many other small mammals (Li et al., 2001). Pikas must maintain a high metabolic rate, and they spend a large part of their time during the summer feeding on fresh plants and gathering vegetation for winter consumption (Huntly et al., 1986; Dearing, 1997b). Reductions in pika foraging activity during warmer temperatures are well documented (Smith, 1974a; MacArthur and Wang, 1974).

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J.L. Wilkening et al. / Quaternary International 235 (2011) 77e88

Table 4 Mean values of predictor variables at sites of pika persistence vs. extirpation. Site-level statistics refer to situations in which pikas were either persisting (N ¼ 12) or extirpated (N ¼ 8) throughout the entire site. Statistics at the sub-site level refer to transitional sites (N ¼ 5) in which pikas were extirpated from a portion of each site. P-values (a ¼ 0.05) were obtained using one-tailed Welch two-sample t-tests (site-level data) or paired t-tests (sub-site data). Data were log-transformed where necessary (see asterisks). See text (Results) for means of untransformed data. Predictor variable

Persistence sites mean (SE)

Extirpation sites mean (SE)

P-value

Degrees of freedom

t

Site level Mean summer temperature
C *No. days above 26
C *No. days above 28
C *No. days below 5
C *No. days below 10
C *Relative forb cover Relative graminoid cover Mean summer temp./*Rel. forb cover Mean summer temp./Rel. gram. cover *No. days below 10
C/*Rel. forb cover

11.74(1.06) 1.17(0.31) 0.64 (0.17) 2.56 (0.46) 1.55 (0.28) 3.19 (0.20) 23.35 (3.88) 4.04 (0.57) 1.07 (0.55) 0.56 (0.12)

17.43(0.86) 2.34 (0.54) 1.84 (0.46) 3.16 (0.22) 1.56 (0.21) 1.87 (0.36) 25.46 (4.79) 14.66 (4.23) 0.92 (0.22) 1.39 (0.46)

0.0003 0.0436 0.0185 0.1327 0.4943 0.0041 0.6317 0.0204 0.5974 0.0599

18.00 11.51 9.02 15.20 17.95 11.33 15.06 7.26 14.24 7.99

4.17 1.87 2.44 1.16 0.01 þ3.20 0.34 2.49 0.25 1.74

Sub-site level Mean summer temperature
C *No. days above 26
C *No. days above 28
C No. days below 5
C No. days below 10
C Relative forb cover Relative graminoid cover Mean summer temp./*Rel. forb cover Mean summer temp./Rel. gram. cover *No. days below 10
C/*Rel. forb cover

15.75 1.99 0.96 52.45 10.52 19.14 23.79 1.04 0.69 0.68

15.88 1.46 0.77 53.09 10.84 15.85 19.72 2.06 0.94 1.63

0.3826 0.8450 0.7119 0.4717 0.4620 0.2548 0.1475 0.0902 0.1199 0.0950

(1.27) (0.16) (0.31) (6.76) (3.13) (4.25) (1.92) (0.29) (0.11) (0.24)

Higher summer temperatures could reduce foraging activity and the amount of vegetation stored in haypiles. This could lead to declines in health and body mass, which would negatively influence over-winter survival as well as female fecundity and successful recruitment of offspring in the following spring. Higher mean summer temperatures may also reduce dispersal rates. Females wean young in the early summer, and over-winter survival of juveniles depends on hay pile and territory establishment (Smith and Weston, 1990). As many as 25% of juveniles must disperse to obtain a territory (Smith and Weston, 1990). Rates of successful dispersal across non-talus habitat may be low (Smith, 1974b), and long-distance dispersal involving more than a day’s travel may be impeded or prevented as summer temperatures rise (Smith, 1974a). Aside from directly affecting individuals, mean summer temperature and other generalized climate metrics may also be predictive of a wide variety of processes affecting the pika (Loarie et al., submitted for publication). For example, disease may become more widespread with warmer temperatures, or pikas may become more susceptible to disease if their condition is already compromised from chronic heat stress. Little is known about disease in North American pikas, but they harbor 66 species of ectoparasites (Severaid, 1955), some of which may vector disease. Sylvatic plague, a flea-vectored bacterial disease endemic to Asia, is often closely linked with Asian pika populations (Biggins and Kosoy, 2001). Plague was introduced to western North America ca. 1900, dramatically altering mammal communities throughout this vast region (Biggins and Kosoy, 2001). Plague causes mortality in pikas and has been implicated in the recent and dramatic decline of one Asian pika species (Li and Smith, 2005). In addition to the influence of chronic thermal stress, acute heat stress may be playing an important role in pika extirpations. Data loggers were programmed to record temperature every 4 h, such that the precise amount of time above a specific temperature threshold was unknown. However, at extirpation sites, the mean number of days in which the temperature rose above 28
C was nearly a factor of 10 higher than at persistence sites. Pikas have

(1.18) (0.57) (0.49) (5.92) (2.71) (5.16) (3.61) (0.90) (0.19) (0.78)

4 4 4 4 4 4 4 4 4 4

0.32 þ1.16 þ0.61 0.08 0.10 þ0.72 þ1.20 1.62 1.38 1.58

a high resting body temperature (40.1
C; MacArthur and Wang, 1973, 1974; Smith, 1974a) that enables them to survive harsh winters without hibernating. They also have a relatively low upper lethal temperature (43.1
C; MacArthur and Wang, 1973, 1974; Smith, 1974a) and are unable to pant or engage in similar heatdissipating mechanisms even after exposure to ambient temperatures as high as 35
C (Yang, 1990). Experiments have shown that hyperthermia and death can occur in pikas that are even briefly constrained at moderately high temperatures (25.5e29.4
C; MacArthur and Wang, 1973, 1974; Smith, 1974a). Under natural conditions, pikas use the talus to thermoregulate behaviorally (Krear, 1965; MacArthur and Wang, 1974; Smith, 1974a) through passive heat dissipation (MacArthur and Wang, 1974). Temperatures deep (x1 m) within the talus are often several degrees lower than temperatures even in shaded locations near the talus surface (Krear, 1965; MacArthur and Wang, 1974; Millar and Westfall, 2010). However, without more detailed characterization of talus microclimates, it is not clear whether extirpated populations had access to summer temperatures suitably low for behavioral thermoregulation. Our results provide only some support for previous hypotheses regarding potential effects of acute cold stress on pikas. Although highly predictive, the number of extremely cold days (below 10
C) was often positively related to population persistence in multiple-regression models at the site level. This apparent discrepancy between the current results and results presented by Beever et al. (2010) deserves discussion. The two studies differ in both predictor and response variables. To summarize, Beever et al. (2010) classified each of 25 study sites as either extirpated or extant, resulting in 9 extirpations and 16 persistence sites which formed the response variable. In contrast, the current analysis is more conservative, classifying sites as either extirpated (n ¼ 8), transitional (n ¼ 5) or persisting (n ¼ 12). Predictor variables are modified in two ways, first by expanding the observed temperature database by one year, and second by omitting the use of modeled temperature histories. The two-year time series of observed temperature data analyzed

J.L. Wilkening et al. / Quaternary International 235 (2011) 77e88

here should better represent conditions experienced by these populations, but it still represents a very limited period of time relative to the several decades over which extirpations have occurred. Finally, the current analysis relates persistence to available vegetation in addition to metrics of temperature, altering the set of candidate models available for analysis. Given the scope and structure of the data analyzed here, we only cautiously suggest that recent exposure to very low temperatures may not be the dominant driver of pika losses in the Great Basin. Additional data relating in situ temperatures with individual survival would be very helpful for clarifying this issue. We would also emphasize results from this analysis and others that are in agreement with the finding of Beever et al. (2010), that exposure to low temperatures may explain extirpations. For example, the number of extremely cold days was negatively related to persistence in the single-regression models explored here. Also, the ratio of cold days to forb cover exhibited the expected relationship with persistence. Within sites, pikas persisted where there were fewer days below 10
C combined with relatively low-forb cover, supporting an early hypothesis (MacArthur and Wang, 1973) that pikas may be able to withstand cold snaps given a suitable food cache. Previous studies have documented the negative impacts of harsh winters on pika populations (Tapper, 1973; Smith, 1978; Smith and Ivins, 1983; Simpson, 2001; Morrison and Hik, 2007), suggesting that snow cover acts as a thermal blanket that insulates pikas during cold winters (Tapper, 1973; Smith, 1978). Demographic studies have documented a steep decline in the population density of pikas following a severely cold winter with a shallow snow pack (Smith and Ivins, 1983; Simpson, 2001). Pikas can also be adversely affected by an increased frequency of melterefreeze events, since this can result in an impenetrable barrier to food resources, or collapsed subnivean movement corridors (Morrison and Hik, 2007). Relative forb cover was much higher at persistence sites and garnered support as a predictor of pika persistence at both scales of analysis. However, it also was negatively correlated with mean summer temperature, thereby reducing our ability to investigate the potentially independent effects of these variables. The highly predictive ratio of mean summer temperature to relative forb cover may support our hypothesis that pikas are stressed during the summer especially when attempting to find and cache suitable forage in a low-forb community. Alternatively, this result may indicate a nonlinear impact of mean summer temperature (squaring mean summer temperature is similar to dividing it by a negatively correlated variable like relative forb cover). Also, relative forb cover may be a surrogate for long-term data on mean summer temperature, given that climate has strong effects on the structure of vegetation communities. In fact, the inverse relationship between summer temperature and forb cover observed here seems to agree with previous studies documenting an overall decline in forb abundance within increasing temperature in alpine communities (Harte and Shaw, 1995; Loik et al., 2000). Another possibility is that the foraging activity of pikas can have strong effects on the vegetation community in the vicinity of pika haypiles (Aho et al., 1998), such that the vegetation patterns we observed could be the result, rather than the cause, of pika extirpations. Although we admit this possibility, it seems unlikely given that a positive relationship between forb cover and pika occupancy has been observed at a wide range of spatial scales. Relative forb cover was much higher at persistence sites at both scales of analysis in this study, and was also higher at sites used by pikas in two smaller scale studies at the northern periphery of the Great Basin (Ray and Beever, 2007; Rodhouse et al., in press). At smaller scalesdespecially within the

85

dispersal distance of individualsdpatterns of occupancy should suggest the preferences rather than the impacts of pikas on vegetation, because dispersal to preferable sites should distribute impacts. At larger scales, if local populations cause a reduction of forb cover, then locations where pikas persist should exhibit lower forb cover than locations where pikas have been extirpated, especially where they have been extirpated for many years. Six of the eight extirpations reported here were reported in the early 1990s, and may have occurred decades earlier. Regardless, relationships between pikas and their associated vegetation communities are complex and multifaceted, and we do not yet understand how forb cover affects pikas. The quantity and quality of forage available adjacent to and within talus areas appears to influence pika survival (Huntly et al., 1986; Dearing, 1997a; Kreuzer and Huntley, 2003). Haypile composition and foraging patterns vary widely between populations in different geographical areas and even between individuals within the same population (Broadbooks, 1965; Krear, 1965; Conner, 1983; Beever et al., 2008). Pikas are generalist herbivores, and they tend to consume and cache plant species according to nutritional content and proportion of abundance. However, preference is somewhat determined by the size, distance, relative availability, and secondary chemistry of plants in the area (Huntly et al., 1986; Dearing, 1995, 1996,1997a,b; Sundby, 2002). Within pika habitats, forbs tend to be larger and have toxic secondary compounds for defending themselves against herbivory while graminoids tend to be smaller and less toxic (Freeland and Janzen, 1974; Huntly et al., 1986; Dearing, 1995, 1996, 1997a,b). Generally, it is believed that pikas consume graminoids immediately and store more chemically complex plants such as forbs and shrubs for winter consumption (Huntly et al., 1986; Dearing, 1995, 1997a,b; Sundby, 2002). Pikas are at greater risk to predation and heat stress when foraging outside the talus, and they have limited mobility and time available for foraging. Therefore, they tend to cache larger forbs rather than graminoids because grasses are smaller and require more time and energy to collect (Huntly et al., 1986). Additionally, grasses are more nutritious when consumed right away, but many forbs contain toxic chemicals that prohibit immediate consumption. These phenolics break down in the haypile over time and actually contribute to nutritional value throughout the winter (Dearing, 1995, 1997a). Although there have been various studies attempting to understand how haypiles relate to pika survival, the precise function of haypiles remains unclear (Millar and Zwickel, 1972; Conner, 1983; Dearing, 1997b). The significance of haypiles was first questioned by Millar and Zwickel (1972), and their study in the Canadian Rockies showed that most haypiles did not contain sufficient quantities of vegetation to provide an exclusive food source for the winter. They also experimentally removed haypiles in the fall and found that the over-winter survivorship of the owners of these haypiles did not differ from those of a control group. Contrary to this finding, Dearing (1997b) found that pika haypiles in the Colorado Rockies contained enough plant material to last 350 days, suggesting that pikas were harvesting much more vegetation than was needed to sustain them through the winter. Other studies have found that pikas forage outside of the haypile when winter conditions are favorable (Conner, 1983), and in some places they may not construct haypiles at all (Simpson, 2001; Ray and Beever, 2007). The most probable explanation appears to be that haypiles function as an adaptive response to environmental unpredictability. Thus, haypiles may act as an insurance policy during unusually harsh or long winters, when pikas are unable to forage above the talus and new vegetation growth is delayed (Millar and Zwickel, 1972; Conner, 1983).

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Because pikas more commonly cache forbs, identification of relative forb cover as an important predictor variable for pika persistence may indicate that haypiles are an important food resource for Great Basin pikas. Relative graminoid cover was not supported as a predictor of persistence at the site level, and it did not differ significantly between sites of persistence and extirpation. Together, these results suggest that the abundance of resources available for grazing and summer survival may not influence persistence as strongly as the abundance of resources available for haying and over-winter survival. There also may be essential nutrients found only in forbs that are necessary for Great Basin pikas. Additionally, forbs tend to have higher moisture content (Dearing, 1995), and this may be especially important for pika survival in more xeric environments such as the Great Basin. The amount of winter precipitation determines the relative moisture content of many Great Basin plants (Sharif and West, 1968) and winter precipitation patterns are changing throughout the western US (Service, 2004; Mote et al., 2005; IPCC, 2007). This study relies heavily on temperature data to predict pika persistence, while effects of precipitation have also been suggested (Tapper, 1973; Smith, 1978; Beever et al., 2003, 2010). Relative to temperature, spatially explicit data on precipitation are even harder to obtain and forecast in event-driven, arid and semi-arid systems such as the Great Basin. Climate data have shown that water derived from snowmelt has decreased in the Basin since 1950 (Mote et al., 2005). Warmer temperatures will increase the amount of precipitation occurring in the form of rain rather than snow, and this could lead to reduced levels of water content in vegetation (Cook et al., 2004; Epps et al., 2004). Warmer temperatures during spring can also create rain-on-snow events, further eroding the existing snowpack and directly impacting pika survival and fecundity (Morrison and Hik, 2007). Additionally, warmer temperatures and reduced winter precipitation may act synergistically to influence vegetation, such that isolating the specific effects of each is not easy. Additional studies that incorporate in situ precipitation data at sufficiently fine scales are needed to further clarify the influence of this variable on pika distribution and persistence.

5. Conclusion Approximately 5000e7000 years ago, during the middle Holocene, many areas of the Great Basin experienced a decrease in winter temperatures and precipitation and an increase in summer temperatures, perhaps as high as þ2
C. Grayson (1987, 1993, 2005) has argued that low elevation populations of pikas became extinct in the northern Great Basin during this time as a result of the climate changes. Our results indicate that both temperature and vegetation factors appear to be influencing pika persistence in the Great Basin. Modern pikas are being affected either directly by changes in temperature or indirectly by the resultant changes in vegetation or other processes affected by temperature. As climate change continues to affect Great Basin ecosystems, it seems likely that, increasingly pikas and other species within this area will again be faced with possible extirpation. Acknowledgements The Nevada Biodiversity Initiative and the Climate Change Program of the World Wildlife Fund provided funding for this study. A. Tiehm of the Northern Nevada Native Plant Society identified rare plants that were not otherwise identifiable.

Appendix I Study sites in the hydrographic Great Basin, United States. Each site consists of an area 3 km in radius, centered on the mean coordinates of historical pika records within this area. Extent of occupiable talus refers to the amount and configuration of pika habitat within the mountain range surrounding the site: Large ¼ many more than 5 major talus areas, each often vast and several contiguous, occupying many canyons, peaks or both; Medium ¼ more than 5 major talus areas, each often extensive but occupying only one or a few canyons or mountainsides; Small ¼ less than 5 major talus areas. The elevation range of temperature data loggers placed within each site is also given.

Location

Site classification

Mountain range

Mean latitude (
N)

Mean longitude (
W)

Extent of occupiable talus

Elevation range of data loggers (meters)

Big Indian Mountain Cougar Peak Crane Mountain Hayes Canyon Kiger Gorge Mustang Mountain Steels Creek South Twin River Stockade, Warner Creeks Thomas Creek Three Lakes Toby Canyon 20 miles NE Adel Current Mountain Duffer Peak Fort Bidwell Peterson Creek Smiths Creek Summit Lake Thomas Creek Ranger Station Arc Dome Green Monster Canyon Mohawk Canyon Mount Jefferson Pinchot Creek

Persistence Persistence Persistence Persistence Persistence Persistence Persistence Persistence Persistence Persistence Persistence Persistence Extirpation Extirpation Extirpation Extirpation Extirpation Extirpation Extirpation Extirpation Transitional Transitional Transitional Transitional Transitional

Wassuk Not in a range Warner Hayes Canyon Steens White East Humboldt Toiyabe Hart Ruby Ruby Desatoya Not in a range White Pine Pine Forest Not in a range Shoshone Desatoya Not in a range Not in a range Toiyabe Monitor Toiyabe Toquima White

38.49 42.31 42.08 41.34 42.77 37.90 40.90 38.83 42.42 40.62 40.59 39.39 42.40 38.91 41.65 41.90 39.19 39.35 41.61 42.40 38.83 38.75 38.98 38.75 37.92

118.79 120.63 120.24 119.95 118.58 118.31 115.11 117.31 118.58 115.40 115.39 117.75 119.64 115.40 118.74 119.96 118.30 117.68 119.07 120.60 117.35 116.57 117.33 116.72 118.29

Medium None Medium Small Medium Large Medium Large Large Large Large Medium None Small Small None Medium Medium None None Large Small Large Large Large

2994e3161 2234e2408 2357e2530 1920e1985 2447e2598 2885e3007 2622e2954 2505e2779 1872e2270 2704e3146 2953e3185 2540e2869 1791e1837 2681e2824 2447e2772 1662e1721 2427e2893 2218e2362 1876e2021 1714e1756 3100e3244 2509e3110 2405e2755 2902e3503 2365e2687

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