Using state-space models to predict within-range variation in responses of wood frogs to annual variation in weather
A pressing concern in ecology is the development of models that elucidate factors regulating species ranges to better understand mechanisms driving current population dynamics and to predict future changes. Dynamic models that are parameterized on annual changes in occurrence or abundance can better incorporate temporal effects of climate variability than typical static approaches. State-space models (SSMs) represent a useful approach to modeling organismal responses to climate when count data are collected due to their ability to separately model process variation and observation error. This involves predicting annual population growth rates as a function of density and environmental drivers while accounting for observational error. We will discuss the SSM approach as it relates to estimating climate-fitness relationships and illustrate approaches to examine population dynamics across a species’ range through hierarchical formulations of the SSM. We present an approach that can be used to model range-wide dynamics as a function of local population dynamics by incorporating spatial variation in mean climate and intra-species variation in responses to climate into predictions.
We use data on egg mass counts of wood frogs (Lithobates sylvaticus) to illustrate the SSM approach for predicting range-wide response to environmental change. Wood frogs occupy an extensive range, spanning the north/northeast USA, Canada and Alaska. The mean climatic conditions experienced by these animals therefore vary drastically across their range. We modeled a time-series (3-22 years) of wood frog egg mass counts from 920 sites in 22 study areas across the USA and Canada. Using this data we illustrate how SSMs can be used to predict how fitness-climate relationships vary across the species range and whether these relationships are concordant with predictions based on static climate envelope modeling.