Effects of Weather on Survival in Populations of Boreal ... - BioOne

Journal of Herpetology, Vol. 42, No. 3, pp. 508–517, 2008 Copyright 2008 Society for the Study of Amphibians and Reptiles

Effects of Weather on Survival in Populations of Boreal Toads in Colorado RICK D. SCHERER,1,2 ERIN MUTHS,3 1

AND

BRAD A. LAMBERT4

Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523-1474, USA; E-mail: [email protected] 3 U.S. Geological Survey Fort Collins Science Center, Fort Collins, Colorado 80526-8118, USA 4 Colorado Natural Heritage Program, Colorado State University, Fort Collins, Colorado 80523, USA

ABSTRACT.—Understanding the relationships between animal population demography and the abiotic and biotic elements of the environments in which they live is a central objective in population ecology. For example, correlations between weather variables and the probability of survival in populations of temperate zone amphibians may be broadly applicable to several species if such correlations can be validated for multiple situations. This study focuses on the probability of survival and evaluates hypotheses based on six weather variables in three populations of Boreal Toads (Bufo boreas) from central Colorado over eight years. In addition to suggesting a relationship between some weather variables and survival probability in Boreal Toad populations, this study uses robust methods and highlights the need for demographic estimates that are precise and have minimal bias. Capture-recapture methods were used to collect the data, and the Cormack-Jolly-Seber model in program MARK was used for analysis. The top models included minimum daily winter air temperature, and the sum of the model weights for these models was 0.956. Weaker support was found for the importance of snow depth and the amount of environmental moisture in winter in modeling survival probability. Minimum daily winter air temperature was positively correlated with the probability of survival in Boreal Toads at other sites in Colorado and has been identified as an important covariate in studies in other parts of the world. If air temperatures are an important component of survival for Boreal Toads or other amphibians, changes in climate may have profound impacts on populations.

A central objective of population ecology is to understand relationships between animal population demography and the biotic and abiotic elements of environments in which they are present. Anthropogenic alterations of environments have put populations of animals at risk (Crooks, 2002; Crooks et al., 2004). By striving to understand ecological relationships, population ecologists have the opportunity to contribute information directly relevant to the management and conservation of populations. Ecologists are trained to observe and gather information on a system of interest, generate testable hypotheses, and collect data to evaluate those hypotheses. After assessing the hypotheses in one context, ecologists are encouraged to assess the generality of the hypotheses by testing them again using temporally and spatially unique data on the same phenomenon (Johnson, 2002). The overarching goal is to determine whether the hypothesis is broadly applicable (e.g., across taxa, time, or space) or specific to the original case. Unfortunately, the lack of replication of studies over time and space has limited the identification of general patterns in population ecology (Belovsky et al., 2004). Studies in population ecology are often conducted over 2

Corresponding Author.

short periods of time and small geographic extents, making inference beyond the specific study difficult. The global decline in amphibian populations (Houlahan et al., 2000) is one example of a problem in need of reliable information from population ecologists (Schmidt et al., 2002; Beebee and Griffiths, 2005). Recent papers have encouraged increased use of more recently developed, robust methods of estimation and inference in herpetological studies (Schmidt et al., 2002; Mazerolle, 2006; Mazerolle et al., 2007). In particular, these authors argue that the use of capture-recapture methods within an information-theoretic framework can lead to more reliable inferences and rapid progress in our understanding of relationships between demographic parameters and biotic and abiotic factors (Schmidt et al., 2002; Mazerolle, 2006). We agree and point out that a number of herpetological papers have been published in which these methods were used (e.g., Anholt et al., 2003; Bailey et al., 2004; Fretey et al., 2004; Schmidt et al., 2005). Recently, Scherer et al. (2005) identified correlations between weather variables and the probability of survival in populations of Boreal Toads (Bufo boreas) using information theory and robust survival estimation methods. They

SURVIVAL OF BOREAL TOADS evaluated models of hypothesized relationships between 13 weather variables and the probability of survival in two populations of Boreal Toads in northern Colorado and found evidence of lower probabilities of survival in years with lower minimum daily winter temperatures and shorter growing seasons. However, the degree to which these relationships can be generalized to other Boreal Toad populations is unknown, because their importance has not been evaluated using data from other populations. In this paper, we use the information-theoretic framework to analyze capture-recapture data collected over eight years from three populations of Boreal Toads in central Colorado. The primary objective of this project was to use data from different populations to validate the observed relationships between weather variables and survival probability in Boreal Toads reported in Scherer et al. (2005). Because the Boreal Toad is declining rapidly over portions of its geographic range (Carey et al., 2005), a broader understanding of Boreal Toad life-history traits may facilitate management and conservation planning. For example, elucidation of relationships between demographic parameters and weather variables may be useful in predicting and planning for population changes induced by climate change. MATERIALS AND METHODS Study Sites and Collection of CaptureRecapture Data.—Capture data were collected from 1998 to 2005 at three meadow ponds in the Collegiate Peaks area of central Colorado: Denny Creek, Collegiate, and South Cottonwood (Fig. 1). Ponds are between 2,951- and 3,238-m elevations and located approximately 4–10 km from one another. Populations of toads at each site were sampled during multiple capture sessions that were conducted during or shortly after the breeding season (early May to early June). Throughout this paper, we refer to the period of time over which all capture sessions were conducted each year as the sampling period. The average length of annual sampling periods was 13 days (range: 1–22 days) and from 1–7 capture sessions were conducted in each sampling period. During capture sessions, 1–4 workers searched sites (edges of all bodies of water and adjacent wetlands and terrestrial areas) and captured toads by hand. As issues of diseases in amphibian populations became a concern (e.g., Batrachochytrium dendrobatidis), field hygiene practices recommended by the Declining Amphibian Populations Task Force (DAPTF) were implemented. These practices included the use

509

of disposable gloves by fieldworkers as they captured toads and storage of captured individuals in separate containers prior to processing. Each captured adult toad was checked for an existing passive integrated transponder (PIT) tag using an AVIDH reader, and the toad’s identification number was recorded (Gibbons and Andrews, 2004). A PIT tag was inserted on the dorsal surface if the animal had not been captured previously. Because of small numbers of captures for females, only male toads were used in the analyses. Collection of Weather Data.—Data from the Porphyry Creek weather station (Natural Resources Conservation Service in Colorado [http://www.co.nrcs.usda.gov]) were used to represent weather conditions at the three study sites. The Porphyry Creek weather station is approximately 23 km from the study sites and at a similar elevation (3,280 m; Fig. 1). All values of weather covariates were scaled to between zero and one to aid the numerical optimization algorithm in finding the correct parameter estimates. Every covariate value was divided by 100, except for active season length which was divided by 200. Capture-Recapture Model.—The Cormack-JollySeber (CJS) model in Program MARK (Lebreton et al., 1992; White and Burnham, 1999) was used for analysis of the capture-recapture data. The CJS model contains two parameters: apparent survival and capture probability. Apparent survival over interval i, denoted Wi, is defined as the probability that a marked individual in the population during the sampling period at time i survives and remains in the population until the sampling period at time i + 1 (Williams et al., 2002). This parameter is referred to as apparent survival, because individuals that emigrate permanently from the sampled populations cannot be distinguished from animals that die (Williams et al., 2002; Schmidt et al., 2007). Unless the species under study shows high site fidelity, apparent survival equals 1 2 (mortality rate + rate of permanent emigration). Capturerecapture studies for many species of pondbreeding amphibians, including B. boreas, suggest that site fidelity in adults is high (i.e., rate of permanent emigration % 0 [Olson, 1992; Muths et al., 2006]). Thus, we consider Wi to represent true survival for Boreal Toads. The second parameter in the CJS model is capture probability. Capture probability at period i, denoted pi, is defined as the probability that a marked individual in the population is captured during sampling period i. Hypotheses: Capture Probability.—Although we were primarily interested in identifying covariates that were associated with the probability of survival in Boreal Toads, effective modeling of

510

R. D. SCHERER ET AL.

FIG. 1. Map of study sites in Colorado. Porphyry Creek (denoted by a circle) is the location of the weather station. Triangles denote the study sites, and the town of Buena Vista provides a regional reference.

capture probability is necessary to avoid bias and imprecision in estimates of survival probability (Lebreton et al., 1992). We evaluated models of capture probability that represented seven hypotheses: (1) p is constant across years and populations (i.e., a null model); (2) p varies across years but does not differ between populations; (3) p varies across years and populations; (4) variation in p over years is different between populations (i.e., an interactive effect of year and population on capture probability); (5) p varies with the number of capture sessions in a year (we predicted that p

would increase as the number of capture sessions increased); (6) p varies across populations and by the number of capture sessions in a year; and (7) variation in p over years is correlated with the number of capture sessions conducted in a year, but the relationship varies between populations (i.e., an interactive effect of site and the number of capture sessions on capture probability). Hypotheses: Survival.—Boreal Toads occur at elevations over 2,286 m. They form breeding aggregations in the spring just after snowmelt, are active throughout summer and into the fall,

SURVIVAL OF BOREAL TOADS and hibernate underground to avoid freezing air temperatures (Hammerson, 1999). We drew on earlier studies and general knowledge of amphibian biology to develop the hypotheses for the probability of survival which include effects of site, year, and six weather covariates. A brief description of each effect and weather covariate follows. Site (s): We modeled the probability of survival using a site effect that tested the hypothesis that survival differed between the three populations of Boreal Toads. The site effect is denoted by an ‘‘s’’ in the model description. Year (t): We also tested the hypothesis that survival varies across years. This hypothesis is similar to the hypotheses that include weather covariates, because it allows for the possibility that the probability of survival is different each year. However, it makes no attempt to ascribe that variation to an explanatory variable such as a weather variable. The year effect was denoted by a ‘‘t’’ in the model description. Winter Air Temperature (WinTmin7): During colder winters, the probability of survival in populations of temperate amphibians may be reduced (Anholt et al., 2003; Scherer et al., 2005). To evaluate the hypothesis that the probability of survival in Boreal Toad populations is positively associated with air temperature, we calculated the average minimum daily temperature over the coldest period of seven consecutive days during the winter (1 December through 28 February) of each year. Winter Precipitation (Snow) and Ground Water (WinWater): Snow cover and flowing ground water may protect hibernating Boreal Toads from freezing and desiccation (Campbell, 1970). We developed two covariates, Snow and WinWater, to test the hypothesis that greater snow cover and groundwater increase the probability of survival in populations of Boreal Toads. Snow is the depth of the snowpack on 1 January and represents the insulating capacity of the snowpack. The depth of the snowpack is measured as the snow water equivalent (SWE; the water content of the snowpack). A year with a high SWE is expected to have a deeper snowpack and greater insulating capacity than a year with a low SWE, although characteristics of the snow affect the relationship. WinWater is the cumulative precipitation over the 12 months preceding the start of the winter hibernation period, which we defined as starting on 1 November for toads in Colorado. Available Moisture during the Active Season (ActiveMoist): Amphibians have permeable skin that is important in hydration and gas exchange (Boutilier et al., 1992). Xeric conditions could promote dehydration or impair gas exchange thereby reducing survival. We developed one

511

covariate, ActiveMoist, to examine the possibility that the probability of survival is correlated with the amount of available moisture in the environment during the active season. ActiveMoist is the cumulative precipitation over the 12 months preceding the start of the active season (defined as 1 June) in a year. We selected the year previous to the active season because precipitation during the active season is limited and is likely to have less impact on the availability of moisture in the environment. Precipitation in the mountains of Colorado falls predominantly in the late winter and spring with snow melt occurring during May and June (Pielke et al., 2003). Air Temperature over the Active Season (ActiveTmax7): High summer air temperatures are unlikely to threaten adult amphibians (Rome et al., 1992), but high temperatures combined with xeric conditions (i.e., measured by the variable ActiveMoist) might reduce the probability of survival by promoting dehydration or impairing gas exchange. We developed a covariate to examine the potential impact of air temperature on survival, ActiveTmax7. ActiveTmax7 is the average maximum daily temperature over the warmest period of seven consecutive days during the active season for Boreal Toads in Colorado (1 June to September 30). Active Season Length (ActiveLength): Between leaving hibernation in the spring and entering hibernation in the fall, toads need to acquire sufficient energetic resources to sustain basic metabolism, grow, reproduce, and survive the next hibernation period (Carey et al., 2005). The duration of the active season constrains the acquisition of energy by toads. For example, prey species may be less active during a cool active season and a short active season might reduce opportunities to catch prey. We developed a covariate, ActiveLength, to examine the possibility that the probability of survival is affected by the length of the active season. The Natural Resources Conservation Service considers temperatures at or below 24uF (24.44uC) sufficient to produce a killing frost (http:// www.wcc.nrcs.usda.gov). ActiveLength is the number of days between the last day the minimum daily temperature was at or below 24.44uC in the spring of a year and the first day the minimum daily temperature was at or below 24.44uC in the fall of the same year. Because additive effects and interactions are likely to be important in natural systems, we evaluated models with multiple predictor variables as well as single predictor variables. For example, we evaluated 20 hypotheses with additive and interactive effects of site and the weather variables. We also evaluated six models with effects that are common to most capturerecapture analysis (e.g., constant probability of

512


survival, time-dependent probability of survival). Models that represented hypotheses with an additive effect included a ‘‘+’’ in the model description, and models with an interactive effect included a ‘‘*’’ in the model description. Development of Mathematical Models from Hypotheses.—Thirty-two mathematical models, which represented each of the hypotheses of capture probability and survival, were specified. Initially, each mathematical model was specified in the form of a linear model (Lebreton et al., 1992). Because estimates of capture probability and survival cannot be constrained to between zero and one using a linear model, we constrained these estimates to between zero and one by linking each linear model to the parameter of interest using the logit link function (Lebreton et al., 1992), a common practice in analyses of capture-recapture data. Evaluation of Models and Predictor Variables.— We used DQAICc-values, Di, and Akaike weights, wi, to determine which model or set of models had the most support in the data. QAICc is a modification of AIC that accounts for small sample size and overdispersion in the data (Burnham and Anderson, 2002). Both Di and wi quantify the strength of evidence in support of a particular model as the best model in the candidate set of models (Burnham and Anderson, 2002). Relative to other models under consideration, the best model will provide good fit to the data using the fewest parameters (Burnham and Anderson, 2002). The Di of any model i in the candidate set is computed as QAICc(i) 2 QAICc(best model). As Di increases, the strength of evidence for model i decreases. The Akaike weight of a particular model can be interpreted as the probability that the model is the best model (of those models in the candidate set) given the sampling situation (Burnham and Anderson, 2002). Our evaluation of models based on the Di and wi suggested that there was considerable uncertainty regarding the best model in the candidate set. Consequently, we used model-averaged estimates of regression coefficients to evaluate the importance of the weather variables and quantify the magnitude of their effect on the probability of survival (Burnham and Anderson, 2002; Mazerolle, 2006). For each of the weather variables, we estimated a regression coefficient and its variance by model-averaging across all models in the candidate set, including models that did not contain the weather variable. For the models that did not contain the weather variable of interest, we set the value of the regression coefficient from that model to zero (Burnham and Anderson, 2002). We also examined the 95% confidence interval for each estimate of the regression coefficients to

determine whether they included zero. We considered regression coefficients with confidence intervals that did not include zero to be more important to the modeling of the probability of survival. We used the ‘‘step-down’’ approach recommended by Lebreton et al. (1992) to evaluate the candidate models of pi and Wi. We evaluated the models in three steps. First, we paired each model of pi with the model of Wi that had the most parameters (i.e., W[s * t]). We used Di and wi to determine which model or set of models of pi best represented the data. After selecting the top model of pi, we paired each of the candidate models of Wi with the top model of pi and used Di and wi to determine the strength of evidence for each model of Wi. Finally, the candidate models of pi were evaluated a second time after the top model of Wi was identified. Each model of pi was paired with the top model of Wi, and Di-values and wi were used to determine which model of pi had the most support in the data. Goodness-of-Fit Testing and Parameter Estimation.—We tested the fit of the capture-recapture data to the global CJS model using TESTS 2 and 3 in program RELEASE (Lebreton et al., 1992), which is incorporated into Program MARK. Fisher’s method of maximum likelihood was used to estimate parameters and associated standard errors (Lebreton et al., 1992). RESULTS Summary of Raw Data and Goodness-ofFit Test.—From 1998–2005, we recorded 428 captures of 173 toads at Denny Creek, 290 captures of 120 toads at Collegiate, and 453 captures of 205 toads at South Cottonwood (Table 1). Results of the goodness-of-fit test provided evidence of overdispersion in the data (total x2test statistic 5 49.68 [df 5 39]). Thus, we used a variance inflation factor, cˆ, of 1.27 (x2/df). Model Selection Results and Parameter Estimates: Capture Probability.—The first evaluation of the models of capture probability provided strong evidence that the model representing hypothesis 7 (an interactive effect of site and the number of capture occasions) was the top model. Variation in pi over years was correlated with the number of capture sessions conducted in a year, but the relationship varied between sites. The notation used to represent this model hereafter is p(s * # of capture occasions). The wi of this model was 0.99, and the Di of the second-ranked model was .9. Based on these results, we concluded that the other six models had little or no support in the data. The results of the second evaluation of the candidate models of p were nearly identical to the results

SURVIVAL OF BOREAL TOADS

513

TABLE 1. Summaries of the capture-recapture data for (A) Denny Creek, (B) Collegiate, and (C) South Cottonwood using mij arrays, where Ri 5 number of individuals captured and released during sampling period i and mij 5 number of individuals captured during sampling period i that are next captured at sampling period j. Sample occasion, i

R(i)

(A) Denny Creek 1998 40 1999 78 2000 59 2001 82 2002 46 2003 48 2004 37 (B) Collegiate 1998 35 1999 36 2000 3 2001 15 2002 26 2003 28 2004 25 (C) South Cottonwood 1998 37 1999 44 2000 54 2001 71 2002 44 2003 72 2004 54

j 5 1999

2000

2001

2002

2003

2004

2005

Total

26

1 39

3 13 40

0 0 3 37

0 1 0 12 22

0 0 0 2 9 21

0 0 0 1 1 6 18

30 53 43 52 32 27 18

20

0 2

0 6 2

0 0 0 9

0 0 0 0 16

0 0 0 0 0 15

0 0 0 0 0 1 22

20 8 2 9 16 16 22

31

2 29

0 5 31

0 0 1 32

0 0 0 4 34

0 0 0 0 0 32

0 0 0 1 0 5 41

33 34 32 37 34 34 41

from the first evaluation, and the model p(s * # of capture occasions) was used in the assessment of the models of survival probability. Selection of the model p(s * # of capture occasions) was caused by a low capture probability at Collegiate in 2000. Only three toads were captured during two capture sessions that year. Estimates of capture probability increased from 0.12 (SE 5 0.09) in 2000 to greater than 0.95 in all other years when at least four capture sessions were conducted at Collegiate. At Denny Creek and South Cottonwood, the results suggest no relationship between p and the number of capture sessions. In these populations, capture probabilities during years with only one and two capture sessions were similar to those in years with as many as six capture sessions.

Model Selection Results and Parameter Estimates: Survival.—The Di and wi for the candidate set of models of the probability of survival suggest that three models have relatively strong support in the data, whereas two other models are supported but to a much smaller degree (Table 2). WinTmin7 appears in each of the top models, which suggests it is important to the modeling of survival probability. Two other weather variables have weaker support in the data. Snow appears in the firstand third-ranked models and WinWater appears in the third- and fourth-ranked models (Table 2). In addition to examining model selection results, the relative importance of the weather variables can be evaluated using the modelaveraged estimates of their regression coeffi-

TABLE 2. The top models of the probability of survival. All other models of survival had no support in the data (Di . 12) and are not shown. The model of capture probability was p(s * # capture occasions) in every case. The number of estimable parameters for each model is in the column labeled K, and QDeviance is 2[2 * (logˆ ))/cˆ]. likelihood(W Model

W(s W(s W(s W(s W(s

+ WinTmin7 + Snow) * WinTmin7) + WinTmin7 + Snow + WinWater) + WinTmin7 + WinWater) + WinTmin7)

QAICc

DQAICc

wi

K

QDeviance

1297.363 1297.557 1299.010 1303.175 1303.382

0 0.194 1.647 5.812 6.019

0.407 0.370 0.179 0.022 ,0.001

11 12 12 11 10

250.93 249.08 250.53 256.75 259.00

514


TABLE 3. Model-averaged estimates of the regression coefficients and the 95% confidence intervals for all weather variables (logit scale). Regression coefficients for WinTmin7 are shown for each population (DC 5 Denny Creek; C 5 Collegiate; SC 5 South Cottonwood) because of the strong evidence that the effect of WinTmin7 on survival probability varies among populations. Weather variable

Site

WinTmin7 WinTmin7 WinTmin7 Snow WinWater ActiveTmax7 ActiveMoist ActiveLength

DC C SC

Estimate of regression coefficient

95% CI of regression coefficient

0.34 0.33 0.59 0.05 20.002 20.00006 20.000007 0.0000001

0.05 to 0.63 0.008 to 0.644 0.32 to 0.86 20.006 to 0.10 20.006 to 0.003 20.0002 to 0.00006 20.0002 to 0.000009 20.0000002 to 0.0000004

cients (regression coefficients are symbolized using b with the name of the weather variable in the subscript and their estimates are on the logit scale; Table 3). The strong support for the second-ranked model (Table 2) suggests that the effect of WinTmin7 on survival probability varies between the three populations. Therefore, we derived model-averaged estimates of bWinTmin7 for each population. The model-averaged estimate of bWinTmin7 at Collegiate was 0.33 (95% CI 5 0.008–0.644); at Denny Creek 0.34 (95% CI 5 0.05–0.63); and South Cottonwood 0.59 (95% CI 5 0.32–0.86; Table 3). The point estimates for these three regression coefficients and the fact that their 95% confidence intervals do not overlap zero suggest that the probability of survival is higher at all sites in years when the seven-day minimum temperature is higher. The model-averaged regression coefficient for the other weather covariates that had support in the data was 0.049 (bSnow) and 20.002 (bWinWater). The 95% confidence intervals for the modelaveraged estimates of bSnow only slightly overlapped zero (approximately 20.006 to 0.10), whereas the 95% confidence interval for the model-averaged estimates of bWinWater considerably overlapped 0 (20.006 to 0.003; Table 3). In addition to model-averaging regression coefficients, we also model-averaged estimates of the probability of survival (model-averaged estimates ranged from 0.46–0.86, Fig. 2). Modelaveraged estimates of Wi follow the same temporal pattern at all three sites, but differences among sites were also apparent. For example, estimates of Wi at South Cottonwood are within the 95% confidence interval of estimates at Denny Creek in all years, except 2001 and 2003 (Fig. 2). Estimates of survival at Collegiate were generally lower than at Denny Creek and South Cottonwood. They were below the lower confidence limit at Denny Creek every year except 2004 and below the lower confidence limit at

South Cottonwood in four of the seven years (Fig. 2). DISCUSSION Various aspects of weather and climate change have been noted to affect some amphibian populations (Ovaska, 1997; Corn and Muths, 2002; Pounds et al., 2006), and earlier studies on temperate zone amphibians have evaluated the effects of weather variables on survival probability. For example, Anholt et al. (2003) reported lower probabilities of overwinter survival in years with extremely low and variable winter temperatures for two ranid species in northern Switzerland. Additionally, Bradford (1983) found that low temperatures adversely affect survival in Mountain Yellow-Legged Frogs (Rana muscosa). Determining the reliability and the broader-scale applicability of such information, specifically about the relationships between the biotic and abiotic elements of an animal’s environment and demography, is important to management and conservation. We found that minimum winter air temperature (WinTmin7) is positively correlated with the probability of survival in Boreal Toads in central Colorado. In a previous paper, Scherer et al. (2005) reported a similar correlation between winter air temperature and the probability of survival in two populations of Boreal Toads from northern Colorado (approximately 200 km from the current study sites). Thus, this relationship may be broadly applicable in Boreal Toad populations across space and time. The estimated regression coefficients for WinTmin7 from the current analysis ranged from 0.33–0.59, and none of the 95% confidence intervals overlapped zero such that these estimates provide evidence for the importance of WinTmin7 in modeling survival probability in Boreal Toad populations. The analysis by

SURVIVAL OF BOREAL TOADS

515

FIG. 2. Model-averaged estimates of survival probability with 95% confidence intervals for three sites in central Colorado: Denny Creek, Collegiate, and South Cottonwood.

Scherer et al. (2005) also provided evidence that WinTmin7 was important to modeling survival probability, although the estimated regression coefficient for WinTmin7 from Scherer et al. (2005) was much larger. When the temperature data were scaled to facilitate numerical optimization using the same conversion as in this analysis (i.e., divided by 100), the estimate of bWinTmin7 from the 2005 paper was 2.80 (95% confidence interval from 20.5 to 6.2; Scherer et al., 2005). Although both analyses provide evidence of a correlation between survival probability and WinTmin7, the model selection results and estimated regression coefficients for WinTmin7 suggest the relationship differs between populations. The estimate of bWinTmin7 in the 2005 analysis of northern Colorado Boreal Toad populations is much larger than those from the current analysis. In addition, the secondranked (and strongly supported) model in the current analysis contains an interactive effect of site and WinTmin7 (Table 2), suggesting differences in the relationship between the probability of survival and WinTmin7 at Denny Creek, Collegiate, and South Cottonwood. In fact, the estimated regression coefficient for WinTmin7 at South Cottonwood was nearly twice as large as the estimated regression coefficients at

Collegiate and Denny Creek. Site and population characteristics may play an important role in determining the magnitude of the effect of minimum winter temperature on survival. For example, the amphibian chytrid fungus, Batrachochytrium dendrobatidis, was known to be present in the populations of Boreal Toads from northern Colorado (Scherer et al., 2005) but has not been observed at the sites in central Colorado. Interactions between ambient temperature and growth in B. dendrobatidis may alter the relationship between temperature and survival rates in amphibian populations (Pounds et al., 2006). In addition, differences in water depth, soil characteristics, and availability and quality of hibernacula among sites may affect the relationship between survival and winter temperature. In addition to differences in the effect of minimum winter air temperature on Boreal Toads among populations, our results suggest that the probability of survival varies among the populations in central Colorado. The site effect is present in all of the top models (Table 2) and model-averaged estimates of the probability of survival are different between sites in some years (Fig. 2). Differences in the proximity of each of the sites to human traffic and to adjacent wetlands may be responsible for these differences.

516


The Collegiate site (lowest probabilities of survival; Fig. 2) is near a high traffic road and campground, whereas the other two sites are at least 1.5 km from a paved road. In addition, the South Cottonwood and Denny Creek sites are adjacent to extensive wetlands where Boreal Toads can be found throughout the summer. These wetland areas may be important for foraging. The Collegiate site is not near wetlands, and toads may have to travel farther to access associated resources. With respect to the other weather variables we evaluated in this analysis and the previous analysis by Scherer et al. (2005), the results are inconsistent. In the current analysis, we found weak evidence that the probability of survival in Boreal Toads was positively correlated with snow depth and negatively correlated with precipitation prior to winter (Tables 2 and 3). The analysis in Scherer et al. (2005) found no support for these relationships. Scherer et al. (2005) reported a positive correlation between the length of the active season and survival probability and argued that this relationship may be facilitated by increased energy acquisition by toads over longer growing seasons. Contrary to this result, the current analysis provides no evidence for this relationship (Tables 2 and 3). There are numerous possible causes for these inconsistencies including variation in attributes across sites and differences in weather conditions over the time periods of the two studies. The results of our analysis are useful from two perspectives. First, estimates of demographic parameters and their variances are essential elements of various modeling exercises (e.g., sensitivity analyses; Biek et al., 2002) and our results provide additional robust estimates of the probability of survival for Boreal Toads that extends the spatial and temporal scale over which this demographic parameter has been estimated. Consequently, we have a better understanding of the variation in the probability of annual survival. However, it is important to remember that our estimates were derived from data on male Boreal Toads only. Second, our results offer insights into possible effects of changes in climate on populations of Boreal Toads and other temperate, montane amphibians. Pounds et al. (2006) discuss the interaction between infection by B. dendrobatidis, changes in cloud cover regimes, and large-scale warming in the Neotropics, suggesting that changes in air temperatures may affect disease dynamics in amphibian populations. For example, B. dendrobatidis, which has been implicated in declines in Boreal Toad populations, grows over a wide range of temperatures (4–25uC; Piotrowski et al., 2004). Temperatures above and below this range may halt growth in the fungus

or cause death (Piotrowski et al., 2004). Campbell (1970) reported temperatures in Boreal Toad hibernacula of nearly 0uC during winter months. Thus, cold winter temperatures may aid toads in resisting or clearing the fungus. Warmer winter temperatures could increase the susceptibility of toads to the disease, if they result in fewer opportunities for toads to experience temperatures near freezing. We suggest that the elucidation of associations between demographic rates and environmental factors, using robust analytical methods, will contribute substantially to an understanding of the ecological context within which we work. In this study we have attempted to determine whether patterns identified for one set of data are common to other data sets collected from different populations of the same organism within the region and are, thus, broadly applicable. The identification of common relationships provides insights that may be useful across the region. For example, minimum winter temperature appears to impact survival of boreal toads and appears to be a factor that is common across several populations of Boreal Toads in the Southern Rocky Mountains. This knowledge has the potential to contribute to an assessment of which boreal toad populations may benefit or be most at risk given the predictions of various climate change scenarios (e.g., Barnett et al., 2004). Acknowledgments.—We thank N. Chelgren and A. Yackel Adams for valuable feedback on an earlier draft of this paper. Funding for this work was provided by the Amphibian Research and Monitoring Initiative (ARMI) of the U.S. Geological Survey and the Colorado Division of Wildlife. The methods of handling and marking toads in this study were approved by Colorado State University’s Animal Care and Use Committee (01-018A-04). LITERATURE CITED ANHOLT, B. R., H. HOTZ, G.-D. GUEX, AND R. D. SEMLITSCH. 2003. Overwinter survival of Rana lessonae and its hemiclonal associate Rana esculenta. Ecology 84:391–397. BAILEY, L. L., W. L. KENDALL, D. R. CHURCH, AND H. M. WILBUR. 2004. Estimating survival and breeding probability for pond-breeding amphibians: a modified robust design. Ecology 85:2456–2466. BARNETT, T., R. MALONE, W. PENNELL, D. STAMMER, B. SEMTNER, AND W. WASHINGTON. 2004. The effects of climate change on water resources in the west: introduction and overview. Climatic Change 62:1–11. BEEBEE, T. J. C., AND R. A. GRIFFITHS. 2005. The amphibian decline crisis: a watershed for conservation biology? Biological Conservation 125:271–285.

SURVIVAL OF BOREAL TOADS BELOVSKY, G. E., D. B. BOTKIN, T. A. CROWL, K. W. CUMMINS, J. F. FRANKLIN, M. L. HUNTER JR, A. JOERN, D. B. LINDENMAYER, J. A. MACMAHON, C. R. MARGULES, AND J. M. SCOTT. 2004. Ten suggestions to strengthen the science of ecology. BioScience 54:345–351. BIEK, R., W. C. FUNK, B. A. MAXELL, AND L. S. MILLS. 2002. What is missing in amphibian decline research: insights from ecological sensitivity analysis. Conservation Biology 16:728–734. BOUTILIER, R. G., D. F. STIFFLER, AND D. P. TOEWS. 1992. Exchange of respiratory gases, ions, and water in amphibious and aquatic amphibians. In M. E. Feder and W. W. Burggren (eds.), Environmental Physiology of the Amphibians, pp. 81–124. University of Chicago Press, Chicago. BRADFORD, D. F. 1983. Winterkill, oxygen relations, and energy metabolism of a submerged dormant amphibian, Rana muscosa. Ecology 64:1171–1183. BURNHAM, K. P., AND D. R. ANDERSON. 2002. Model Selection and Inference: A Practical InformationTheoretic Approach. Springer–Verlag, New York. CAMPBELL, J. B. 1970. Hibernacula of a population of Bufo boreas boreas in the Colorado Front Range. Herpetologica 26:278–282. CAREY, C., P. S. CORN, M. S. JONES, L. J. LIVO, E. MUTHS, AND C. W. LOEFFLER. 2005. Environmental and life history factors that limit recovery in southern Rocky Mountain populations of Boreal Toads (Bufo boreas). In M. J. Lannoo (ed.), Amphibian Declines: the Conservation Status of United States Species, pp. 222–236. University of California Press, Berkeley. CORN, P. S., AND E. MUTHS. 2002. Variable breeding phenology affects the exposure of amphibian embryos to ultraviolet radiation. Ecology 83: 2958–2963. CROOKS, K. R. 2002. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conservation Biology 16:488–502. CROOKS, K. R., A. V. SUAREZ, AND D. T. BOLGER. 2004. Avian communities along a gradient of urbanization impact in a highly fragmented landscape. Biological Conservation 115:451–562. FRETEY, T., E. CAM, B. LEGARFF, AND J. MONNAT. 2004. Adult survival and temporary emigration in the Common Toad. Canadian Journal of Zoology 82: 859–872. GIBBONS, J. W., AND K. M. ANDREWS. 2004. PIT tagging: simple technology at its best. Bioscience 54:447–454. HAMMERSON, G. A. 1999. Amphibians and Reptiles in Colorado. University Press of Colorado, Boulder. HOULAHAN, J. E., C. S. FINDLAY, B. R. SCHMIDT, A. H. MEYER, AND S. L. KUZMIN. 2000. Quantitative evidence for global amphibian declines. Nature 404:752–755. JOHNSON, D. H. 2002. The importance of replication in wildlife research. Journal of Wildlife Management 66:919–932. LEBRETON, J. D., K. P. BURNHAM, J. CLOBERT, AND D. R. ANDERSON. 1992. Modeling survival and testing biological hypotheses using marked animals: a unified approach with case studies. Ecological Monographs 62:67–118. MAZEROLLE, M. J. 2006. Improving data analysis in herpetology: using Akaike’s Information Criterion (AIC) to assess the strength of biological hypotheses. Amphibia-Reptilia 27:169–180.

517

MAZEROLLE, M. J., L. L. BAILEY, W. L. KENDALL, J. A. ROYLE, S. J. CONVERSE, AND J. D. NICHOLS. 2007. Making great leaps forward: accounting for detectability in herpetological field studies. Journal of Herpetology 41:672–689. MUTHS, E., R. D. SCHERER, P. S. CORN, AND B. A. LAMBERT. 2006. Estimation of temporary emigration in male toads. Ecology 87:1048–1096. OLSON, D. H. 1992. Ecological susceptibility of amphibians to population declines. In R. R. Harris and D. E. Erman (eds.), Proceedings of the Symposium on Biodiversity of Northwestern California, Report 2, pp. 55–62. University of California Wildland Resources Center, Davis, CA. OVASKA, K. 1997. The vulnerability of amphibians in Canada to global warming and increased solar ultraviolet radiation. In D. M. Green (ed.), Amphibians in Decline: Canadian Studies of a Global Problem, pp. 206–226. Society for the Study of Amphibians and Reptiles, St. Louis, MO. PIELKE, R. A., N. DOESKEN, AND O. BLISS. 2003. Climate of Colorado. Climatology Report 60. Colorado State University, Fort Collins. PIOTROWSKI, J. S., S. L. ANNIS, AND J. E. LONGCORE. 2004. Physiology of Batrachochytrium dendrobatidis, a chytrid pathogen of amphibians. Mycologia 96:9–15. POUNDS, J. A., M. R. BUSTAMANTE, L. A. COLOMA, M. P. CONSUEGRA, M. P. L. FOGDEN, P. N. FOSTER, E. LAMARCA, K. L. MASTERS, A. MERINO-VITERI, R. PUSCHENDORF, S. R. RON, G. A. SANCHEZ-AZOFEIFA, C. J. STILL, AND B. E. YOUNG. 2006. Widespread amphibian extinctions from epidemic disease driven by global warming. Nature 439:161–167. ROME, L. C., E. D. STEVENS, AND H. B. JOHN-ADLER. 1992. The influence of temperature and thermal acclimation on physiological function. In M. E. Feder and W. W. Burggren (eds.), Environmental Physiology of the Amphibians, pp. 183–205. University of Chicago Press, Chicago. SCHERER, R. D., E. MUTHS, B. NOON, AND P. S. CORN. 2005. An evaluation of weather and disease as causes of decline in two populations of Boreal Toads (Bufo boreas). Ecological Applications 15:2150–2160. SCHMIDT, B. R., M. SCHAUB, AND B. R. ANHOLT. 2002. Why you should use capture-recapture methods when estimating survival and breeding probabilities: on bias, temporary emigration, overdispersion, and common toads. Amphibia-Reptilia 23:375–388. SCHMIDT, B. R., R. FELDMANN, AND M. SCHAUB. 2005. Demographic processes underlying population growth and decline in Salamandra salamandra. Conservation Biology 19:1149–1156. SCHMIDT, B. R., M. SCHAUB, AND S. STEINFARTZ. 2007. Apparent survival of the salamander Salamandra salamandra is low because of high migratory activity. Frontiers in Zoology 4:19, www.frontiersinzoology. com. Accessed 9 January 2007. WHITE, G. C., AND K. P. BURNHAM. 1999. Program MARK: survival estimation from populations of marked animals. Bird Study 46(Suppl.):120–130. WILLIAMS, B. K., J. D. NICHOLS, AND M. J. CONROY. 2002. Analysis and Management of Animal Populations. Academic Press, San Diego, CA. Accepted: 25 January 2008.