A model to illustrate the potential pairing of animal biotelemetry with individual-based modeling
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nimal Biotelemetry Open Access
RESEARCH
A model to illustrate the potential pairing of animal biotelemetry with individual‑based modeling Ian G. Brosnan1* and David W. Welch2
Abstract Background: Animal biotelemetry and individual-based modeling (IBM) are natural complements, but there are few published examples where they are applied together to address fundamental or applied ecological questions. Existing studies are often found in the modeling literature and frequently re-use small datasets collected for purposes other than the model application. Animal biotelemetry can provide the robust measurements that capture relevant ecological patterns needed to parameterize, calibrate, and assess hypotheses in IBMs; together they could help meet demand for predictive modeling and decision-support in the face of environmental change. Results: We used an simple exemplar IBM that uses spatio-temporal movement patterns of 103 acoustic-tagged juvenile yearling Chinook salmon (Oncorhynchus tshawytscha), termed ‘smolts’, to quantitatively assess plausibility of two migratory strategies that smolts are hypothesized to use while migrating north through the plume of the Columbia River (United States of America). We find that model smolts that seek to maximize growth demonstrate movement patterns consistent with those of tagged smolts. Model smolts that seek to move quickly out of the plume region by seeking favorable currents do not reproduce the same patterns. Conclusions: Animal biotelemetry and individual-based modeling are maturing fields of inquiry. Our hope is that this model description and the basic analytical techniques will effectively illustrate individual-based models for the biotelemetry community, and perhaps inspire new collaborations between biotelemetry researchers and individualbased modelers. Keywords: Biotelemetry, Individual-based model, Acoustic tags, Salmon, Columbia River Background Individual-based models (IBMs), which are also referred to as agent-based models, track discrete, autonomous individuals with static or dynamic state variables, attributes, and behavior, simulate interactions between them and their environment, and record the emergent effects on populations and ecosystem ecology [1, 2]. IBMs have been used in a wide variety of applied and theoretical studies within ecology, as well fields such as *Correspondence: [email protected] 1 NASA Ames Research Center, Moffett Field, CA 94043, USA Full list of author information is available at the end of the article
epidemiology, where representing individual variation was required or desired [2, 3]. For example, IBMs have been used to explore the effects of conservation and management interventions on habitat connectivity of bighorn sheep [4], and elucidate factors that drive population dynamics of Antarctic salps [5]. They are also emerging as a promising tool for predictive modeling and decisionmaking in the face of environmental change [6–8]. IBMs do present some limits and drawbacks, which have been well described. Developing an IBM may
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