Predicting Animal Foraging Behaviour Under Climate Variability Using Advanced Ecological Simulation Models

Ramy Read Hossain, M. Faisal, Sami Najaf Bokhoor, H. Zainab, Sanjay Kumar

Journal of Animal Environment, 2025

Abstract

The changing animal foraging behavior due to climate variability caused by global environmental

change has significant implications for the persistence of the population and stability in the ecosystem.

The non-stationary and stochastic climatic forcing makes it difficult to predict these responses. The

given research builds a hybrid model, agent-based and machine learning (ABM-ML), in order to

simulate and predict the foraging behavior of animals in a climate fluctuation scenario. Animal agents

are a heterogeneous set of behavioral characteristics that control movement, patch choice, sensitivity

to risk, and energetic state, and the environment as a spatially explicit climate-driven resource field.

In order to enhance predictive accuracy in varying environmental conditions, machine learning is

employed to calibrate important behavioral parameters with the use of empirical movement and

foraging data. Benchmark dataset validation of the model with migratory birds and marine predators

shows high levels of performance with net energy intake coefficients of determination of 0.82 and

patch residence time coefficients of determination of 0.76. The models used to predict the future

climate show considerable drops in the foraging efficiency by the middle of the century and the

average falls of about 18 percent under the RCP 4.5 and up to 37 percent under the RCP 8.5. Findings

also indicate that behaviorally flexible foragers are more affected by efficiency losses to a…

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