Running on empty: Recharge dynamics from animal movement data
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The field of animal movement modeling has exploded with options for statistical models using telemetry data. Such models have provided a way to obtain inference for dynamic resource selection, behavioral clustering of trajectories, and interactions among individuals. New modeling approaches for animal trajectories to infer space use patterns and behavior are also starting to accommodate additional data sources, such as depth/altitude and accelerometer data. These methods allow researchers to investigate the relationships among movement and true behavioral processes. Auxiliary data may not be available in many ongoing studies where movement processes contain signals corresponding to recharge dynamics (e.g., energetics, memory, thermal retention). In the context of food webs and trophic cascades, ecological energetics has been a vibrant area of research during the past several decades. Similarly, critical vital rates such as survival and recruitment have always been important in the study of population and community ecology. While it is commonly known that vital rates and population-level characteristics are tied with individual-level animal movement, most statistical models for telemetry data are not equipped to provide inference about these relationships because they lack the explicit, mechanistic connection to recharge dynamics. We present an individual-based framework for modeling telemetry data that explicitly includes a recharge process associated with movement in heterogeneous environments. We show how our approach can be formulated in continuous-time to provide direct inference about vital gains and losses associated with movement. Our approach can also be extended to accommodate auxiliary data when available. We demonstrate our model with simulated data and apply it to infer mountain lion (Puma concolor) recharge dynamics in Colorado, USA.
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