Range position and climate sensitivity: The structure of among‐population demographic responses to climatic variation

Amburgey, Staci M.; Miller, David A. W.; Grant, Evan H. Campbell; Rittenhouse, Tracy A. G.; Benard, Michael F.; Richardson, Jonathan L.; Urban, Mark C.; Hughson, Ward; Brand, Adrianne B.; Davis, C. J.; Hardin, Carmen R.; Paton, Peter W. C.; Raithel, Christopher J.; Relyea, Rick A.; Scott, Anna; Skelly, David K.; Skidds, Dennis E.; Smith, Cambray; Werner, Earl E.

doi:10.1111/gcb.13817

Cited by 53 publications

(59 citation statements)

References 81 publications

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“…Our results can be compared with those recently published by Amburgey et al (2018) for a widespread species of amphibian. Our results can be compared with those recently published by Amburgey et al (2018) for a widespread species of amphibian.…”

Section: May 2018mentioning

confidence: 65%

“…1A-C; see also Amburgey et al 2018). In particular, we expect that those species that which will be most affected by long-term climate change will show a specific pattern of response across their range: in the hottest parts of their range, populations will decrease after warmer than average years, whereas in colder parts of the range, populations will increase after warmer than average years ( Fig.…”

Section: Introductionmentioning

confidence: 95%

“…Nevertheless, this approach has rarely been applied at the range-wide scale for broadly distributed species (but see Chen et al 2010, Amburgey et al 2018. This deficit can be attributed to the fact that long-term population data is usually collected at only a few locations and rarely across an entire species' distribution.…”

Section: Introductionmentioning

confidence: 99%

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The response of big sagebrush (Artemisia tridentata) to interannual climate variation changes across its range

Kleinhesselink

Adler

2018

Ecology

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Understanding how annual climate variation affects population growth rates across a species' range may help us anticipate the effects of climate change on species distribution and abundance. We predict that populations in warmer or wetter parts of a species' range should respond negatively to periods of above average temperature or precipitation, respectively, whereas populations in colder or drier areas should respond positively to periods of above average temperature or precipitation. To test this, we estimated the population sensitivity of a common shrub species, big sagebrush (Artemisia tridentata), to annual climate variation across its range. Our analysis includes 8,175 observations of year-to-year change in sagebrush cover or production from 131 monitoring sites in western North America. We coupled these observations with seasonal weather data for each site and analyzed the effects of spring through fall temperatures and fall through spring accumulated precipitation on annual changes in sagebrush abundance. Sensitivity to annual temperature variation supported our hypothesis: years with above average temperatures were beneficial to sagebrush in colder locations and detrimental to sagebrush in hotter locations. In contrast, sensitivity to precipitation did not change significantly across the distribution of sagebrush. This pattern of responses suggests that regional abundance of this species may be more limited by temperature than by precipitation. We also found important differences in how the ecologically distinct subspecies of sagebrush responded to the effects of precipitation and temperature. Our model predicts that a short-term temperature increase could produce an increase in sagebrush cover at the cold edge of its range and a decrease in cover at the warm edge of its range. This prediction is qualitatively consistent with predictions from species distribution models for sagebrush based on spatial occurrence data, but it provides new mechanistic insight and helps estimate how much and how fast sagebrush cover may change within its range.

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Section: May 2018mentioning

confidence: 65%

Section: Introductionmentioning

confidence: 95%

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The response of big sagebrush (Artemisia tridentata) to interannual climate variation changes across its range

Kleinhesselink

Adler

2018

Ecology

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“…Opportunities for data integration are not limited to traditional static species distribution models, but also could be utilized when distribution dynamics are of interest (Zipkin et al., ). Dynamic models range from simple abundance models that estimate local changes in occurrence or abundance across space and time (Amburgey et al., ; Miller et al, ) to more complex models that incorporate demographic parameters and life‐stage‐specific abundances (Davis, Hooten, Phillips, & Doherty, ; Zipkin et al., ). Changes in the distribution of species through time (expansion, contraction) are influenced by individual population processes governed by survival, reproduction, and movement.…”

Section: Creating a More Flexible And General Framework For Data Intementioning

confidence: 99%

The recent past and promising future for data integration methods to estimate species’ distributions

et al. 2019

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With the advance of methods for estimating species distribution models has come an interest in how to best combine datasets to improve estimates of species distributions. This has spurred the development of data integration methods that simultaneously harness information from multiple datasets while dealing with the specific strengths and weaknesses of each dataset. We outline the general principles that have guided data integration methods and review recent developments in the field. We then outline key areas that allow for a more general framework for integrating data and provide suggestions for improving sampling design and validation for integrated models. Key to recent advances has been using point‐process thinking to combine estimators developed for different data types. Extending this framework to new data types will further improve our inferences, as well as relaxing assumptions about how parameters are jointly estimated. These along with the better use of information regarding sampling effort and spatial autocorrelation will further improve our inferences. Recent developments form a strong foundation for implementation of data integration models. Wider adoption can improve our inferences about species distributions and the dynamic processes that lead to distributional shifts.

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“…However, estimates of demographic rates from such models may not always be reliable (Barker, Schofield, Link, & Sauer, ; Dennis, Morgan, & Ridout, ; Zipkin et al, ). Capture‐mark‐recapture (CMR) data provide additional information for modeling demographic rates, providing a more mechanistic link between population dynamics and climate than models based on occurrence or count data alone (Amburgey et al, ; Buckley et al, ; McMahon et al, ; Selwood, McGeoch, & Mac Nally, ). However, CMR data are less available and are relatively costly to obtain across large spatial extents.…”

Section: Introductionmentioning

confidence: 99%

Integrating broad‐scale data to assess demographic and climatic contributions to population change in a declining songbird

Saracco

Rubenstein

2020

Ecology and Evolution

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Climate variation and trends affect species distribution and abundance across large spatial extents. However, most studies that predict species response to climate are implemented at small spatial scales or are based on occurrence‐environment relationships that lack mechanistic detail. Here, we develop an integrated population model (IPM) for multi‐site count and capture‐recapture data for a declining migratory songbird, Wilson's warbler (Cardellina pusilla), in three genetically distinct breeding populations in western North America. We include climate covariates of vital rates, including spring temperatures on the breeding grounds, drought on the wintering range in northwest Mexico, and wind conditions during spring migration. Spring temperatures were positively related to productivity in Sierra Nevada and Pacific Northwest genetic groups, and annual changes in productivity were important predictors of changes in growth rate in these populations. Drought condition on the wintering grounds was a strong predictor of adult survival for coastal California and Sierra Nevada populations; however, adult survival played a relatively minor role in explaining annual variation in population change. A latent parameter representing a mixture of first‐year survival and immigration was the largest contributor to variation in population change; however, this parameter was estimated imprecisely, and its importance likely reflects, in part, differences in spatio‐temporal distribution of samples between count and capture‐recapture data sets. Our modeling approach represents a novel and flexible framework for linking broad‐scale multi‐site monitoring data sets. Our results highlight both the potential of the approach for extension to additional species and systems, as well as needs for additional data and/or model development.

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Range position and climate sensitivity: The structure of among‐population demographic responses to climatic variation

Cited by 53 publications

References 81 publications

The response of big sagebrush (Artemisia tridentata) to interannual climate variation changes across its range

The response of big sagebrush (Artemisia tridentata) to interannual climate variation changes across its range

The recent past and promising future for data integration methods to estimate species’ distributions

Integrating broad‐scale data to assess demographic and climatic contributions to population change in a declining songbird

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