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Remington, M.

Publications and source records attributed to Remington, M..

2 recordsLinked to original sources

Thermal Dynamics of Ectotherm Behavioral Strategies: Insights from Agent-Based Simulations

Terrestrial ectotherms use dynamic behavioral thermoregulation strategies that typically rely on finding thermal refugia to avoid unfavorable conditions. Traditional analytic approaches rely on analysis of thermal indices to describe thermal conditions organisms may or may not choose, though computational simulations could potentially be used to analyze thermoregulatory behavior explicitly. Here, we leverage a novel simulation framework that integrates data from operative thermal models deployed in microhabitats available to ectotherms to showcase the trade-offs between two basic thermoregulatory strategies: maintaining a thermal optimum point or attempting to keep their body temperature within a preferred thermal range. We assess the output from simulations of these behavioral strategies to understand their influence on body temperature regulation and occupancy of thermal refugia, and then compare our findings from an empirical case study. Results from our analysis suggest that these two general strategies do not impact thermoregulation as strongly as previously assumed. We also found that occupancy of thermal refugia appeared to be driven by environmental conditions rather than the thermoregulation strategy. Although our case study showed promise in providing insights into predicting use of thermal refugia, understanding microhabitat occupancy from thermal data alone may require finer scale temporal data, or the integration of other factors such as foraging dynamics or predation avoidance. HighlightsO_LIAgent-Based simulation framework which explicitly models ectotherm behavioral thermoregulation. C_LIO_LIAnalysis shows minimal differences between varying thermoregulation strategies. C_LIO_LISimulations identify cathermal activity patterns regardless of behavioral strategy. C_LIO_LIResults show promise of empirical applications. C_LI

ecology↗

Uumarrty: Agent Based Simulation Model of Predator Prey Interactions with a Game Theoretical Framework

This paper introduces a new simulation framework for testing hypotheses relating to behavior strategies in predator-prey systems. To this end, we present two tools for simulating and analyzing behavioral trait dynamics: The Nash Score, a novel metric for evaluating evolutionary stability, and Uumarrty, an agent-based framework for simulating predator-prey interactions using game theory. These tools provide an approach for assessing the temporal co-evolution of behavioral traits within agent-based models, with a particular focus on predator-prey dynamics, though the framework is generalizable to other ecological interactions. The Nash Score functions as an analog to the Evolutionarily Stable Strategy (ESS) from classical game theory, offering a quantitative index to assess the relative stability and resilience of behavioral traits under selection. We demonstrate the utility of these tools through a case study on the microhabitat preferences of kangaroo rats and rattlesnakes. Specifically, we explore the emergence and stability of optimal strategies across scenarios with: (1) heterogeneous energy yields among microhabitats, (2) differential strike success rates by microhabitat, and (3) the presence of a specialist predator. Our results highlight how microhabitat specialist predators can drive other predators in the system to specialize due to outcompeting generalists at a given population frequency; leading to behavioral strategy stability in the system. Our case studies also show how behavioral trait dynamics can greatly vary depending on if you treat the trait as a pure strategy versus a mixed strategy. Collectively, this framework enhances our ability to explore ecological and evolutionary responses to environmental change, supporting more robust and comparable simulation-based research in eco-evolutionary dynamics.

evolutionary biology↗