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Relative Importance of Climate Variables to Population Vital Rates: A Quantitative Synthesis for the Lesser Prairie-Chicken

  • Julia E. Earl ,

    julia.earl@okstate.edu

    Affiliation Department of Natural Resource Ecology and Management, Oklahoma State University, Stillwater, Oklahoma, United States of America

  • Samuel D. Fuhlendorf

    Affiliation Department of Natural Resource Ecology and Management, Oklahoma State University, Stillwater, Oklahoma, United States of America

Abstract

Climate change is expected to affect temperature and precipitation means and extremes, which can affect population vital rates. With the added complexity of accounting for both means and extremes, it is important to understand whether one aspect is sufficient to predict a particular vital rate or if both are necessary. To compare the predictive ability of climate means and extremes with geographic, individual, and habitat variables, we performed a quantitative synthesis on the vital rates of lesser prairie-chickens (Tympanuchus pallidictinus) across their geographic range. We used an information theoretic approach to rank models predicting vital rates. We were able to rank climate models for three vital rates: clutch size, nest success, and subadult/adult seasonal survival. Of these three vital rates, a climate model was never the best predictor even when accounting for potentially different relationships between climate variables and vital rates between different ecoregions. Clutch size and nest success were both influenced by nesting attempt with larger clutches and greater success for first nesting attempts than second nesting attempts. Clutch size also increased with latitude for first nesting attempts but decreased with latitude for second nesting attempts. This resulted in similar clutch sizes for first and second nest attempts at southern latitudes but larger clutches for first nest attempts than second nest attempts at northern latitudes. Survival was greater for subadults than adults, but there were few estimates of subadult survival for comparison. Our results show that individual characteristics and geographic variables are better for predicting vital rates than climate variables. This may due to low samples sizes, which restricted our statistical power, or lack of precision in climate estimates relative to microclimates actually experienced by individuals. Alternatively, relationships between climate variables and vital rates may be constrained by time lags or local adaptation.

Introduction

Climate change is having large effects on populations, communities, and ecosystems [1, 2]. These effects are expected to continue into the future and increase in magnitude, involving both direct and indirect mechanisms [3, 4]. Populations, particularly, are expected to be affected by climate change through the alteration of vital rates [5], leading to changes in population cycles (e.g., [6]), declines (e.g., [7]), and even extinction [8]. These changes are of great concern for biodiversity conservation [1] and the maintenance of the current level of ecosystems services [9].

Climate change is expected to alter temperature and precipitation averages and variability and the frequency and magnitude of extreme events [10, 11]. Extreme events are characterized as events that are outside the 90th percentile of current climatic events [12]. Events include large storms like tornadoes and hurricanes but also heat waves, extremely cold periods, dry spells, and heavy rain. Extreme events can have large effects on populations [13] and can even drive geographic range shifts [14, 15]. For example, heat waves can cause catastrophic mortality in desert birds [16], whereas extreme ice events can have similar effects on reindeer [17].

Predictions for climate change are complicated by influences on the mean, variability, and extremes of both temperature and precipitation, so it is critical that population ecologists have a better understanding of the relative importance of these factors to species. Understanding the dominant factors driving population vital rates will be critical for understanding population dynamics and developing efficient conservation frameworks. Several recent empirical and theoretical studies have examined the relative importance of climate means and variability to vital rates and population dynamics (e.g., [18, 19, 20]). However, similar evaluations involving climate extremes are still lacking. With an increasing recognition of the likely increase in climate extremes [12], population biologists need to assess the importance of climate extremes to vital rates [21, 22].

Lesser prairie-chickens (Tympanuchus pallidicinctus) are a lekking species of conservation concern found in the southern plains of New Mexico, Texas, Oklahoma, Kansas, and Colorado. Lesser prairie-chickens are one species thought to be vulnerable to changes in climate due to their sensitivity to drought [23], their already vulnerable population status [24], and the predicted future climate change across their geographic distribution [10]. In the Southern High Plains of Texas and New Mexico, higher temperatures are expected to reduce lesser prairie-chicken nest survival through relationships with drought and temperatures exceeding the temperature threshold for egg viability [23]. Climate variables are also likely to affect lesser prairie-chicken vital rates through changes in vegetation, and prey and surface water availability with implications for bird physiology, behavior, and stress, which are all relevant to survival and potentially clutch size and nest initiation probability. These changes in nesting females may also alter nest attendance and nesting habitat selection with repercussions for eggs and chicks.

To compare the relative importance of climate means and extremes, we performed a quantitative synthesis of the vital rates of lesser prairie-chickens using an information-theoretic approach [25] that weighted vital rate estimates by their sample size. For vital rates with larger numbers of samples (N > 20; clutch size, nest success, subadult/adult survival), we took a two-step approach in which we first assessed the relative impact of factors known to affect vital rates, such as habitat, latitude, and individual characteristics by ranking models with AIC. We then used the best-fit model as a null model for comparison with climate models. We compared eleven climate models: null, average temperature, average precipitation, extreme temperature, extreme precipitation, drought, and the interactions between each climate variable and ecoregion (a group of five additional models). Models containing interactions between ecoregion and climate variables allowed us to assess the possibility that the effect of climate variables on vital rates may be different in different portions of the lesser prairie-chicken geographic range. We additionally report summarized estimates for all vital rates with lower sample sizes, including egg hatchability, chick survival, and probabilities of nest initiation, nest re-initiation, and brood success.

Materials and Methods

We located articles by searching Google Scholar, Web of Science, Biological Abstracts, BioOne Abstracts, and Ebsco Wildlife and Ecology Studies Worldwide using the search terms “lesser prairie-chicken” and “nest success,” “survival,” or “clutch.” We completed the final search on 10 December 2014 and included 25 papers in the analysis (Table 1). We included all published journal articles, book chapters, and theses found in this search that met our eligibility criteria, described as follows. Papers included in the quantitative synthesis needed to report a lesser prairie-chicken vital rate and the associated sample size, study site location, year(s) of data collection, and the method for data collection. Studies were excluded (listed in S1 File) if the data was repeated in multiple studies (e.g., [23, 26, 2731]), the information available was insufficient for a quantitative synthesis [32], the estimates combined data for both lesser and greater prairie-chickens [33], or information was combined for non-adjacent sites (e.g., New Mexico and Oklahoma [34]). We examined all studies that included data from the same study site during the same time period and eliminated any estimates that could include the same individuals or the same nests. For these cases, we chose to use the data that was most specific, such as using vital rate estimates separated for each year rather than summarized for multiple years. For each study, we extracted data on the following demographic rates: nest initiation probability, nest re-initiation probability (given failure of first nest), nest success (the probability that at least one egg hatches in a nest), egg hatchability (the probability that an egg hatches), clutch size, brood success (the probability that at least one chick in a brood survives a given time period), and seasonal survival for both sexes. For clutch size and nest success, we recorded whether estimates were for first nest attempts, second nest attempts, or a combination of first and second nest attempts. We additionally recorded the study site location, the year(s) of data collection, the time of year data was collected, the sex and age of the individuals, the sample size, the habitat type (sand sagebrush, sand shinnery oak, or a combination), and the estimation method for the vital rate. For almost all vital rates, estimates were made by monitoring individuals with radiotransmitters attached. Survival estimates using radiotransmitters appear relatively unbiased [30]. However, radiotransmitter estimates could bias estimates of nest initiation, nest reinitiation, or nest success if some nests failed before they were detected by researchers. During data extraction, two people assessed and recorded data from each paper independently. Where discrepancies occurred, discussion and rereading of text was used to generate consensus.

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Table 1. Studies included in quantitative synthesis of lesser prairie-chicken vital rates (note that additional vital rates may have been published in a study that was not included due to overlap with another study).

https://doi.org/10.1371/journal.pone.0163585.t001

We obtained monthly climate data from the National Oceanic and Atmospheric Administration (NOAA) for each year and site that we had vital rate estimates. Most site-level data were reported by county or a group of counties, so we obtained climate data spanning the appropriate years from a weather station closest to the geographic center of the county or counties. Data was checked for completeness and summarized for each demographic rate. We excluded any data from any month with more than 5 days of missing data. For clutch size, we examined the weather during the previous six months (October to March). For nest success, we examined weather during the nesting period (April, May, June). We analyzed chick survival separately from subadults and adults. We examined the daily survival rate for chicks and seasonal survival (i.e., all survival estimates converted to that for a three-month period) for subadults and adults and used weather data for the period survival was estimated. Subadult/adult survival was categorized by season into warm seasons for spring and summer (March to August) and cool seasons for fall and winter (September to February). For vital rate estimates from studies that averaged over multiple years, we used the median year and mean climate variables in the models.

We classified climate data into four groups: extreme temperature, average temperature, extreme precipitation, and average precipitation. Extreme temperature variables included the number of hot days (maximum temperature over 32.2°C), number of freezing days (maximum temperature below freezing), number of extremely cold nights (minimum temperature below -17.8°C), the extreme maximum temperature, and the extreme minimum temperature. Average temperature variables included the number of freezing nights, the mean daily maximum temperature, the mean daily minimum temperature, and the mean daily temperature. Extreme precipitation variables included the number of days with heavy rain (daily precipitation greater than 2.5 cm), the maximum daily precipitation, and the maximum snow depth. Average precipitation variables included the number of rainy days (precipitation greater than 0.25 cm), the total precipitation, and the total snowfall. For each data set, we first assessed the correlations among climatic variables within each group. For correlated variables (|r|> 0.7), we kept the variable most highly correlated with the vital rate of interest. For an indication of drought, we used the Palmer Drought Severity Index, where index values -4.0 or below indicate extreme drought and index values +4.0 or above indicate extreme wet conditions [60]. We used historical drought data from NOAA calculated for multi-county US Climate regions in each state. Our data covered two climate regions in Kansas (regions 4 and 7), one in Oklahoma (region 1), two in New Mexico (regions 3 and 7), and two in Texas (regions 1 and 2). For cases where a study site spanned two climatic regions, we calculated the average of the two regions for the time period.

For the analyses, we used the vital rate estimate as a response variable in general linear models. Note that because of the nature of the study, the data does not lend itself to calculating traditional effects sizes. As such, there is no established method for assessing publication bias. However, there is also less concern about potential bias relative to studies calculating traditional effect sizes, because there are not non-significant results that might be less likely to be published, and we also included graduate theses. One potential source of bias is a change in the established methods for estimating vital rates through time. To potentially account for this, we included a model with the year(s) that each vital rate was estimated as a predictor variable (see below).

For each analysis, we used one vital rate estimate as a replicate. Each replicate was weighted by the sample size to account for differences in precision among vital rate estimates [61]. We attempted to include the study as a random variable to account for possible non-independence between estimates made by the same researchers, but a strong relationship between study and climate variables prevented the inclusion of both factors in the models. As such, analyses did not include any random variables. We partitioned vital rates into three categories based on sample size and performed analyses accordingly. For vital rates with less than ten estimates, we report the weighted mean. For vital rates with 10–20 estimates, we restricted the model set to seven models (year, latitude, ecoregion, individual, habitat, nesting attempt [for nest success and clutch size]; null [intercept-only]; Table 2), which we will refer to as the limited model set. For ecoregion, we established two categories of ecoregion based on the four ecoregions specified in McDonald et al. [62]. The two categories were the sand shinnery oak prairie ecoregion (including New Mexico and the southern portion of the Texas pandhandle) and all other ecoregions (including northern Texas, Oklahoma, Kansas, and Colorado). These two areas are spatially separate portions of the lesser prairie-chicken geographic distribution, and there is evidence that there is no genetic or demographic connectivity between the two areas [63, 64]. We also included a time model for chick survival that included a parameter for the number of days over which survival was estimated. We further included post hoc models for clutch size containing the interaction between nesting attempt and other key variables, including year, latitude, ecoregion, age class, and habitat. For vital rates with greater than 20 estimates, we first ranked models in the limited model set (Table 2). We included variables with significant parameter estimates (i.e. 95% confidence intervals that did not include zero) found in the best and competing models in all of the climate models. We examined eleven climate models: null, drought, average temperature, average precipitation, extreme temperature, extreme precipitation, and five additional models with the interaction between each of the five types of climate variables and ecoregion (Table 2). We wanted to include the ecoregion-climate variable interaction models to account for the possibility that climate variables affected vital rates differently in the two different portions of the lesser prairie-chicken’s geographic distribution. For all climate models, we checked the variance inflation factors for all variables to determine the degree of multicollinearity. Variables in all models were included as fixed effects and had variance inflation factors less than four. Models were ranked using Akaike’s Information Criterion corrected for small samples sizes (AICc,), where the lowest value is considered to be the best model in the set of models and models within two units are competing models [65, 66]. We also computed Akaike weights to evaluate the relative support for each model. In cases with competing models, we used model averaging to make parameter estimates. All models were implemented in R [67] using standard functions and the AICcmodavg package. Nest initiation, chick survival, and adult/subadult survival were arcsine transformed, renest initiation was square root transformed, and nest success was log transformed to meet assumptions of normality and homoscedasticity.

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Table 2. Model sets used in the analysis of lesser prairie-chicken vital rates.

https://doi.org/10.1371/journal.pone.0163585.t002

Results

We found 44 relevant papers of which 25 contained sufficient data for the quantitative synthesis and met all of our eligibility requirements. Studies were conducted across the lesser prairie-chicken geographic range, except for Colorado (Table 1, data in S2 File). Egg hatchability and brood success had low sample sizes, and as such, we only present the weighted means (± standard error). Egg hatchability was estimated as 0.918 ± 0.043 (N = 5 estimates, 125 total nests) at three sites in Kansas, Oklahoma, and Texas, which included all three habitat types. Brood success was estimated at three sites in Kansas, New Mexico, and Texas, which also included all three habitat types. Brood success was estimated for time periods from 18 to 68 days, but the amount of time examined did not affect the estimate (p = 0.86). Brood success was estimated as 0.134 ± 0.072 (N = 5 estimates, 246 total broods).

Nest initiation, nest re-initiation, and chick survival had intermediate sample sizes. As such, we used the limited model set. The nest initiation probability was estimated at two sites in Kansas and Texas, and one site in New Mexico. The latitude model was the best model with the null and habitat models competing (Table 3; S1 Table for full AIC tables). However, only habitat parameters had confidence intervals that did not include zero. The nest initiation probability (N = 13 estimates, 370 females) was lower in mixed habitat (0.6381 ± 0.102) than sand sagebrush (0.936 ± 0.062) with sand shinnery oak intermediate (0.787 ± 0.112). Nest re-initiation was also estimated at two sites in Kansas and Texas, and one site in New Mexico. The best model for nest re-initiation was the ecoregion model with the null model and latitude model competing (Table 3 and S1 Table). However, model-averaging revealed that only the intercept had a confidence interval that did not include zero. Nest re-initiation was estimated at 0.213 ± 0.050 (N = 12 estimates, 204 females). Chick survival was estimated for only two sites in Kansas (both sand sagebrush; N = 13 estimates, 117 chicks). Because of the lack of variability in many of the parameters (habitat, latitude, sex), the model set for chick survival included only three models: the null, year, and time. The time model was the best (r2 = 0.36) with the null model competing (Table 3 and S1 Table). Daily chick survival increased with the number of days that survival was estimated [arcsine(survival) = 1.190 (± 0.045) + 0.003(± 0.0012)*days], where survival estimates were made over 14 to 60 days (Fig 1).

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Fig 1. Relationship between lesser prairie-chicken daily chick survival and the amount of time over which survival was estimated.

Circles indicate the relative sample size for each survival estimate.

https://doi.org/10.1371/journal.pone.0163585.g001

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Table 3. Model ranking for the limited model set for each vital rate, where K is the number of parameters and ωi is the model weight.

https://doi.org/10.1371/journal.pone.0163585.t003

We examined climate models for clutch size, nest success, and subadult/adult seasonal survival (i.e. for three-month periods). Clutch size was estimated in all four states (N = 31 estimates, 363 nests), including three sites in Kansas and two sites each in New Mexico, Oklahoma, and Texas. The best model from the limited model set was the nesting attempt latitude interaction model with a high model weight (0.99; Tables 3 and S1). As such, nesting attempt, latitude, and the interaction were used as the null model, which was the best out of the climate model set with no models competing (Tables 4 and S2). Clutch size increased with latitude for first nesting attempts, but decreased with latitude for second nesting attempts (r2 = 0.91; Fig 2). This relationship resulted in very little difference in clutch size between nesting attempts in the southern latitudes, but first nesting attempts had greater clutch sizes than second nesting attempts in the northern latitudes.

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Fig 2. Relationship between lesser prairie-chicken clutch size and latitude of the population.

Circles indicate the relative sample size for each clutch size estimate.

https://doi.org/10.1371/journal.pone.0163585.g002

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Table 4. Model ranking for climate models for each vital rate, where parameters included in all models for each vita rate are in parentheses, K is the number of parameters, and ωi is the model weight.

https://doi.org/10.1371/journal.pone.0163585.t004

Nest success was estimated in all four states (N = 32 estimates, 506 nests), including four sites in Texas, three sites in New Mexico, two sites in Kansas, and only one site in Oklahoma. The best model from the limited model set was the nesting attempt with a model weight of 0.93 (Table 3). We included nesting attempt in all of the climate models. Of the climate model set, the null model containing only nesting attempt was the best (model weight = 0.46; Table 4) with the drought model competing. However, the drought parameter had a confidence interval that overlapped zero. Second nesting attempts (0.091 ± 0.245) had lower nest success than first nesting attempts (0.632 ± 0.091), and estimates that included both first and second nesting attempts were intermediate (0.297 ± 0.091).

Seasonal subadult/adult survival was estimated at one site in Kansas, four sites in Texas, and one site in New Mexico (N = 32 estimates with a combined samples size of 1395 individuals). The best model from the limited model set was the individual model with no models competing (Table 3). Only the age class parameters had confidence intervals that did not include zero; these were incorporated into all climate models. This null model was the best climate model (r2 = 0.44, Table 4) with several climate models competing. However, all of the climate variable slopes had confidence intervals that included zero. Seasonal survival (i.e. over three months) was greater for mixed age classes (0.902 ± 0.035) than adults (0.733 ± 0.027) with intermediate survival for subadults (0.834 ± 0.063).

Discussion

We were able to compare the importance of climate means and extremes for three lesser prairie-chicken vital rates: clutch size, nest success, and subadult/adult seasonal survival. Climatic factors were never included in the best model. Previous work has examined climate extremes and averages separately, and it is clear that both can affect population vital rates (e.g., [16, 19]). Sometimes temperature averages and extremes can affect the same vital rate simultaneously, as was found for the greater sage-grouse, Centrocercus urophasianus [68], and recent reviews have highlighted the need to include extreme events in climate change experiments on communities and ecosystems [21, 22]. However, our results show that life history and geographic factors can be better predictors of vital rates than climate variables in some cases, though this result may be due to methodology.

The lack of importance of climate variables may have occurred for a number of reasons. First, our sample size may have been too low to account for relationships with climate variables, and our method of accounting for vital rate precision with sample size may have had low accuracy. Second, climate data from nearby weather stations may not accurately reflect the microclimates actually experienced by individuals. Different microhabitats are known to greatly affect the microclimates and operative temperatures experienced by individuals [69], and lesser prairie-chickens select microclimates that are cooler and more humid, which facilitates their survival [32] and is dependent on relative conditions [70]. If broader weather station data does not accurately reflect microclimates experienced by individuals, syntheses combining published data with off-study site climate data may have only a limited ability to detect trends. However, our results showing a lack of trends between climate and vital rates may also be due to time lags in climate variable effects or result from adaptations to different climatic regimes in different areas of the lesser prairie-chicken geographic distribution.

Previous work has found that climatic variables can affect lesser prairie-chicken vital rates, particularly in the Southern High Plains. For example, increases in winter temperature decrease daily nest survival [23]. There is also evidence that lesser prairie-chicken nest temperatures can exceed the threshold for egg viability during extreme heat waves [23]. These types of relationships may be different in other parts of the geographic distribution or may be stronger with different climate variables. While we did examine different relationships between climate variables and vital rates in different areas of the geographic range, our statistical power to evaluate these more complex relationships was limited.

Clutch size in temperate birds typically increases with latitude [71, 72]. Our data also shows this trend for first nesting attempts, which has been suggested previously for lesser prairie-chickens [52]. However, clutch size decreased with latitude for second nesting attempts, a trend that was based on a much smaller sample size than the first nesting attempt. This results in similar clutch sizes for first and second nest attempts at the southern end of the distribution, but much greater clutch sizes for first nest attempts than second nest attempts at the northern end of the distribution. Latitude integrates a number of climatic and habitat factors that may cause it to have a greater predictive power than any of those variables by themselves. However, previous work has found weak or no relationship between clutch size and climate in lesser prairie-chickens [23, 52], which is consistent with our results. Also, latitudinal changes in climate and habitat likely lead to greater fitness payoffs for different reproductive strategies along that gradient. Across many bird species, there is a tradeoff between clutch size and the number of nesting attempts that corresponds with latitude [71], which may be driven by latitudinal changes in the onset and duration of the breeding season [73]. Although the latitude model was a competing model for the nest re-intiation rate, the parameter estimate’s confidence interval included zero, which is not consistent with a tradeoff between clutch size and nest re-intiation rate. Some birds also trade off more clutches with fewer eggs per clutch in the face of higher predation risk as a bet-hedging strategy [74].

Lesser prairie-chicken nest success was greater for first nesting attempts than second nesting attempts. This is contrary to the closely related greater prairie-chicken (Tympanuchus cupido), which has greater nest success for second nesting attempts [75]. Additionally, sage-grouse display no difference in nest success between first and second nesting attempts [76]. Further modeling work will be necessary to understand how these changes affect recruitment and population growth rates across the lesser prairie-chicken geographic range.

Our modeling of lesser prairie-chicken subadult/adult seasonal survival (i.e. over three months) showed no effect of any climate variables. Because of the limited sample size and the vast number of different periods of time over which survival was estimated, our power to detect effects of climate was likely limited. Standardized time periods would likely increase the ability to detect overall trends in future studies. We did find that subadults had greater survival than adults overall. However, there were few estimates of subadult survival when compared with adult survival in the data set, and this likely skewed this result. A greater number of subadult survival estimates will be necessary to assess this trend.

Our results show that life history and geography are the primary factors affecting lesser prairie-chicken vital rates. We found no effects of climate. Other studies have found equal importance of climate averages and extremes [68], but few studies have compared climatic averages and extremes directly. Including both averages and extremes greatly complicates models predicting the current and future effects of climate change on population dynamics. As such, there is great value in performing additional studies comparing climate averages and extremes across a variety of species. With greater information, we can determine whether only one type of climate variable can result in robust population predictions or whether both or none are necessary. Expanding this type of work will help determine which species are most vulnerable to a changing climate and how managers may be able to mitigate that change.

Supporting Information

S1 File. List of reasons for excluding studies from quantitative synthesis.

https://doi.org/10.1371/journal.pone.0163585.s001

(DOCX)

S2 File. Data used in quantitative synthesis of lesser prairie-chicken vital rates.

https://doi.org/10.1371/journal.pone.0163585.s002

(CSV)

S3 File. PRISMA checklist showing compliance to standards for meta-analysis and quantitative reviews for the quantitative synthesis of lesser prairie-chicken vital rates.

https://doi.org/10.1371/journal.pone.0163585.s003

(DOC)

S4 File. PRISMA flow chart showing steps of gathering studies and assessing whether they met inclusion criteria for the quantitative synthesis of lesser prairie-chicken vital rates.

https://doi.org/10.1371/journal.pone.0163585.s004

(DOCX)

S1 Table. AICc tables for the limited model set predicting variability in lesser prairie-chicken vital rates, where K is the number of parameters and N is the sample size (number of estimates).

https://doi.org/10.1371/journal.pone.0163585.s005

(DOCX)

S2 Table. AICc tables for the climate model set predicting variability in lesser prairie-chicken vital rates, where K is the number of parameters and N is the sample size (number of estimates).

https://doi.org/10.1371/journal.pone.0163585.s006

(DOCX)

Acknowledgments

We thank Madison Aiken for assistance with data extraction. This material is based on work funded by the U.S. Geological Survey through the South Central Climate Science Center via cooperative agreement G12AC00002, which played no further role in the study.

Author Contributions

  1. Conceptualization: JEE SDF.
  2. Data curation: JEE.
  3. Formal analysis: JEE.
  4. Funding acquisition: SDF.
  5. Investigation: JEE.
  6. Methodology: JEE.
  7. Project administration: JEE.
  8. Supervision: JEE SDF.
  9. Validation: JEE.
  10. Visualization: JEE.
  11. Writing – original draft: JEE.
  12. Writing – review & editing: JEE SDF.

References

  1. 1. Grimm NB, Staudinger MD, Staudt A, Carter SL, Chapin FS III, Kareiva P, et al. Climate-change impacts on ecological systems: introduction to a US assessment. Front Ecol Env. 2013;11(9):456–64.
  2. 2. Staudinger MD, Carter SL, Cross MS, Dubois NS, Duffy JE, Enquist C, et al. Biodiversity in a changing climate. Front Ecol Env. 2013;11(9):465–73.
  3. 3. Blaustein AR, Walls SC, Bancroft BA, Lawler JJ, Searle CL, Gervasi SS. Direct and indirect effects of climate change on amphibian populations. Diversity. 2010;2(2):281–313.
  4. 4. Gilman SE, Urban MC, Tewksbury J, Gilchrist GW, Holt RD. A framework for community interactions under climate change. Trends Ecol Evol. 2010;25(6):325–31. pmid:20392517
  5. 5. Grosbois V, Gimenez O, Gaillard J-M, Pradel R, Barbraud C, Clobert J, et al. Assessing the impact of climate variation on survival in vertebrate populations. Biol Rev. 2008;83:357–99. pmid:18715402
  6. 6. Kausrud KL, Mysterud A, Steen H, Vik JO, Østbye E, Cazelles B, et al. Linking climate change to lemming cycles. Nature. 2008;456:93–7. pmid:18987742
  7. 7. Reed AW, Kaufman GA, Sandercock BK. Demographic response of a grassland rodent to environmental variability. J Mammal. 2007;88(4):982–8.
  8. 8. Cahill AE, Aiello-Lammens ME, Fisher-Reid MC, Hua X, Karanewsky CJ, Ryu HY, et al. How does climate change cause extinction? Proc R Soc Biol Sci Ser B. 2013;280:20121890.
  9. 9. Nelson EJ, Kareiva P, Ruckelshaus M, Arkema K, Geller G, Girvetz E, et al. Climate change's impact on key ecosystem services and the human well-being they support in the US. Front Ecol Env. 2013;11(9):483–93.
  10. 10. IPCC. Climate Change 2013: The Physical Science Basis. Cambridge: Cambridge University Press; 2013.
  11. 11. Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Tank AMGK, et al. Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res. 2006;11:D05109.
  12. 12. IPCC. Managing the risks of extreme events and disasters to advance climate change adaptation. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, et al., editors. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. New York, NY: Cambridge University Press; 2012. p. 1–19.
  13. 13. Parmesan C, Root TL, Willig MR. Impacts of extreme weather and climate on terrestrial biota. Bulletin of the American Meteorological Society. 2000;81(3):443–50.
  14. 14. Smale DA, Wernberg T. Extreme climatic event drives range contraction of a habitat-forming species. Proc R Soc Biol Sci Ser B. 2013;280:20122829. pmid:23325774
  15. 15. Wethey DS, Woodin SA, Hilbish TJ, Jones SJ, Lima FP, Brannock PM. Response of intertidal populations to climate: Effects of extreme events versus long term change. J Exp Mar Biol Ecol. 2011;400:132–44.
  16. 16. McKechnie AE, Wolf BO. Climate change increases the likelihood of catastrophic avian mortality events during extreme heat waves. Biol Lett. 2010;6:253–6. pmid:19793742
  17. 17. Chan K-S, Mysterud A, Øritsland NA, Severinsen T, Stenseth NC. Continuous and discrete extreme climatic events affecting the dynamics of a high-arctic reindeer population. Oecologia. 2005;145:556–63. pmid:16010537
  18. 18. Campbell RD, Nouvellet P, Newman C, McDonald DW, Rosell F. The influence of mean climate trends and climate variance on beaver survival and recruitment dynamics. Global Change Biol. 2012;18:2730–42. pmid:24501052
  19. 19. Jonzén N, Pople T, Knape J, Sköld M. Stochastic demography and population dynamics in the red kangaroo Macropus rufus. J Anim Ecol. 2010;79:109–16. pmid:19674179
  20. 20. García-Carrerras B, Reuman DC. Are Changes in the Mean or Variability of Climate Signals More Important for Long-Term Stochastic Growth Rate? PLOS ONE. 2013;8(5):e63974. pmid:23691131
  21. 21. Jentsch A, Kreyling J, Beierkuhnlein C. A new generation of climate-change experiments: events, not trends. Front Ecol Env. 2007;5(7):365–74.
  22. 22. Thompson RM, Beardall J, Beringer J, Grace MM, Sardina P. Means and extremes: building variability into community-level climate change experiments. Ecol Lett. 2013;16:799–806. pmid:23438320
  23. 23. Grisham BA, Boal CW, Haukos DA, Davis DM, Boydston KK, Dixon C, et al. The predicted influence of climate change on lesser prairie-chicken reproductive parameters. PLOS ONE. 2013;8(7):e68225. pmid:23874549
  24. 24. Hagen CA, Giesen KM. Lesser Prairie-Chicken (Tympanuchus pallidicinctus) Ithaca, New York, USA: Cornell Lab of Ornithology; 2005 Available: http://bna.birds.cornell.edu/review/species/364. Accessed 31 March 2016.
  25. 25. Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic approach. New York: Springer; 2002.
  26. 26. Pruett CL, Johnson JA, Larsson LC, Wolfe DH, Patten MA. Low effective population size and survivorship in a grassland grouse. Conserv Genet. 2011;12:1205–14.
  27. 27. Wisdom MJ. Nesting habitat of Lesser Prairie Chickens in eastern New Mexico. Las Cruces: New Mexico State University; 1980.
  28. 28. Robel RJ, Walker TL Jr., Hagen CA, Ridley RK, Kemp KE, Applegate RD. Helminth parasites of Lesser Prairie-Chicken Tympanuchus pallidicinctus in southwestern Kansas: incidence, burdens and effects. Wildl Biol. 2003;9(4):341–9.
  29. 29. Hagen CA, Pitman JC, Sandercock BK, Robel RJ, Applegate RD. Age-specific variation in apparent survival rates of male Lesser Prairie-Chickens. Condor. 2005;107(1):78–86.
  30. 30. Hagen CA, Sandercock BK, Pitman JC, Robel RJ, Applegate RD. Radiotelemetry survival estimates of Lesser Prairie-Chickens in Kansas: Are there transmitter biases? Wildl Soc Bull. 2006;34(4):1064–9.
  31. 31. Davis CA, Riley TZ, Suminski HR, Wisdom MJ. Habitat evaluation of Lesser Prairie Chickens in eastern Chaves Co., New Mexico. Final Report to BLM, Roswell, Contract YA-512-CT6-61. In: Management BoL, editor. Las Cruces NM: New Mexico State University; 1979.
  32. 32. Patten MA, Wolfe DH, Shochat E, Sherrod SK. Effects of microhabitat and microclimate selection on adult survivorship of the Lesser Prairie-Chicken. J Wildl Manag. 2005;69(3):1270–8.
  33. 33. Fields TL, White GC, Gilgert WC, Rodgers RD. Nest and brood survival of Lesser Prairie-Chickens in west central Kansas. J Wildl Manag. 2006;70(4):931–8.
  34. 34. Wolfe DH, Patten MA, Shochat E, Pruett CL, Sherrod SK. Causes and patterns of mortality in Lesser Prairie-chickens Tympanuchus pallidicinctus and implications for management. Wildl Biol. 2007;13(sp1):95–104.
  35. 35. Boal CW, Haukos DA, Grisham BA. FINAL REPORT–PHASE I: Understanding the Ecology, Habitat Use, Phenology and Thermal Tolerance of Nesting Lesser Prairie‐Chickens to Predict Population Level Influences of Climate Change. In: Service/GPLCC USFaW, editor. Albuquerque, NM: USFWS; 2010. p. 23.
  36. 36. Campbell H. A population study of lesser prairie-chickens in New Mexico. J Wildl Manag. 1972;36(3):689–99.
  37. 37. Copelin FF. The lesser prairie-chicken in Oklahoma. In: Department OC, editor. Oklahoma City, Oklahoma, USA: Oklahoma Conservation Department; 1963. p.
  38. 38. Davis DM. Nesting ecology and reproductive success of Lesser Prairie-Chickens in shinnery oak-dominated rangelands. Wilson J Ornithol. 2009;121(2):322–7.
  39. 39. Fields TL. Breeding season habitat use of Conservation Reserve Program (CRP) land by Lesser Prairie Chickens in west central Kansas. Fort Collins, CO: Colorado State University; 2004.
  40. 40. Grisham BA. The ecology of Lesser Prairie Chickens in shinnery oak-grassland communities in New Mexico and Texas with implications toward habitat management and future climate change. Lubbuck, TX: Texas Tech University; 2012.
  41. 41. Grisham BA, Borsdorf PK. Nesting ecology and nest survival of Lesser Prairie-Chickens on the southern high plains of Texas. J Wildl Manag. 2014;78(5):857–66.
  42. 42. Hagen CA, Jamison BE, Robel RJ, Applegate RD. Ring-Necked Pheasant parasitism of Lesser Prairie-Chicken nests in Kansas. Wilson Bull. 2002;114(4):522–4.
  43. 43. Hagen CA, Pitman JC, Sandercock BK, Robel RJ, Applegate RD. Age-specific survival and probable causes of mortality in female Lesser Prairie-Chickens. J Wildl Manag. 2007;71(2):518–25.
  44. 44. Holt RD. Breeding season demographics of a Lesser Prairie-Chicken (Tympanuchus pallidicinctus) population in the northeastern Texas panhandle. Lubbock: Texas Tech University; 2012.
  45. 45. Jamison BE. Lesser prairie-chicken chick survival, adult survival, and habitat selection and movement of males in fragmented rangelands of southwestern Kansas. Manhattan, Kansas, USA: Kansas State University; 2000.
  46. 46. Jones RS. Seasonal survival, reproduction, and use of wildfire areas by Lesser Prairie Chickens in the northeastern Texas panhandle. College Station: Texas A&M University; 2009.
  47. 47. Kukal CA. The over-winter ecology of Lesser Prairie-Chickens (Tympanuchus pallidicinctus) in the northeast Texas Panhandle. Lubbock: Texas Tech University; 2010.
  48. 48. Leonard JP. The effects of shinnery oak removal on lesser prairie chicken survival, movement, and reproduction. College Station: Texas A&M University; 2008.
  49. 49. Lyons EK, Collier BA, Silvy NJ, Lopez RR, Toole BE, Jones RS, et al. Breeding and non-breeding survival of Lesser Prairie-Chickens Tympanuchus pallidicinctus in Texas, USA. Wildl Biol. 2009;15:89–96.
  50. 50. Lyons EK, Jones RS, Leonard JP, Toole BE, McCleery RA, Lopez RR, et al. Regional variation in nesting success of lesser prairie-chickens. In: Sandercock BK, Martin K, Segelbacher G, editors. Ecology, conservation, and management of grouse. Studies in Avian Biology. Berkeley, CA: University of California Press; 2011. p. 223–31.
  51. 51. Merchant SS. Habitat-use, reproductive success, and survival of female Lesser Prairie Chickens in two years of contrasting weather. Las Cruces, NM: New Mexico State University; 1982.
  52. 52. Patten MA, Wolfe DH, Shochat E, Sherrod SK. Habitat fragmentation, rapid evolution and population persistence. Evol Ecol Res. 2005;7:235–49.
  53. 53. Pirius NE, Boal CW, Haukos DA, Wallace MC. Winter habitat use and survival of Lesser Prairie-Chickens in west Texas. Wildl Soc Bull. 2013;37(4):759–65.
  54. 54. Pitman JC. Lesser Prairie-Chicken nest site selection and nest success, juvenile gender determination and growth, and juvenile survival and dispersal in southwestern Kansas. Manhattan: Kansas State University; 2003.
  55. 55. Pitman JC, Hagen CA, Robel RJ, Loughlin TM, Applegate RD. Location and success of Lesser Prairie-Chicken nests in relation to vegetation and human disturbance. J Wildl Manag. 2005;69(3):1259–69.
  56. 56. Pitman JC, Hagen CA, Jamison BE, Robel RJ, Loughin TM. Survival of juvenile Lesser Prairie-Chickens in Kansas. Wildl Soc Bull. 2006;34(3):675–81.
  57. 57. Pitman JC, Jamison BE, Hagen CA, Robel RJ, Applegate RD. Brood break-up and juvenile dispersal of lesser prairie-chicken in Kansas. Prairie Nat. 2006;38(2):85–99.
  58. 58. Riley TZ, Davis CA, Ortiz M, Wisdom MJ. Vegetative characteristics of successful and unsuccessful nests of Lesser Prairie Chickens. J Wildl Manag. 1992;56(2):383–7.
  59. 59. Toole BE. Survival, seasonal movements, and cover use by Lesser Prairie Chickens in the Texas panhandle. College Station: Texas A&M University; 2005.
  60. 60. Palmer WC. Meteorological Drought. In: Climatology Oo, editor. Washington, DC: US Government Printing Office; 1965. p. 1–58.
  61. 61. Rosenberg MS, Rothstein HR, Gurevitch J. Effect sizes: conventional choices and calculations. In: Koricheva J, Gurevitch J, Mengersen K, editors. Handbook of Meta-Analysis in Ecology and Evolution. Princeton: Princeton University Press; 2013. p. 61–71.
  62. 62. McDonald L, Beauprez G, Gardner G, Griswold J, Hagen C, Hornsby F, et al. Range-wide population size of the lesser prairie-chicken: 2012 and 2013. Wildl Soc Bull. 2014;38(3):536–46.
  63. 63. Hagen CA, Pitman JC, Sandercock BK, Wolfe DH, Robel RJ, Applegate RD, et al. Regional variation in mtDNA of the lesser prairie-chicken. Condor. 2010;112(1):29–37.
  64. 64. Oyler-McCance SJ, DeYoung RW, Fike JA, Hagen CA, Johnson JA, Larsson LC, et al. Rangewide genetic analysis of lesser prairie-chicken reveals population structure, range expansion, and possible introgression. Conserv Genet. 2016;17(3):643–60.
  65. 65. Anderson DR, Burnham KP. Avoiding pitfalls when using information-theoretic methods. J Wildl Manag. 2002;66(3):912–8.
  66. 66. Burnham KP, Anderson DH, Huyvaert KP. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav Ecol Sociobiol. 2011;65:23–35.
  67. 67. R Core Team. R: A language and environment for statistical computing Vienna, Austria: R Foundation for Statistical Computing; 2015. URL https://www.R-project.org/:[
  68. 68. Anthony RG, Willis MJ. Survival rates of female Greater Sage-Grouse in autumn and winter in southeastern Oregon. J Wildl Manag. 2009;73(4):538–45.
  69. 69. Hovick TJ, Elmore RD, Allred BW, Fuhlendorf SD, Dahlgren DK. Landscapes as a moderator of thermal extremes: a case study from an imperiled grouse. Ecosphere. 2014;5(3):35.
  70. 70. Larsson LC, Pruett CL, Wolfe DH, Patten MA. Fine-scale selection of habitat by the lesser prairie-chicken. Southwest Nat. 2013;58(2):135–49.
  71. 71. Böhning-Gaese K, Halbe B, Lemoine N, . Factors influencing the clutch size, number of broods and annual fecundity of North American and European land birds. Evol Ecol Res. 2000;2:823–39.
  72. 72. Cody ML. A general theory of clutch size. Evolution. 1966;20(2):174–84.
  73. 73. McNew LB, Gregory AJ, Wisely SM, Sandercock BK. Reproductive biology of a southern population of Greater Prairie-Chickens. In: Sandercock BK, Martin K, Segelbacher G, editors. Ecology, Conservation, and Management of Grouse. Studies in Avian Biology. Berkeley, CA: University of California Press; 2011. p. 209–21.
  74. 74. Sandercock BK, Martin K, Hannon SJ. Life history strategies in extreme environments: comparative demography of arctic and alpine ptarmigan. Ecology. 2005;86(8):2176–86.
  75. 75. McNew LB, Gregory AJ, Wisely SM, Sandercock BK. Demography of greater prairie-chickens: Regional variation in vital rates, sensitivity values, and population dynamics. J Wildl Manag. 2012;76(5):987–1000.
  76. 76. Schroeder MA. Unusually high reproductive effort by Sage Grouse in a fragmented habitat in north-central Washington. Condor. 1997;99(4):933–41.