Climate Adapt Wildlife Model for Predicting Animal Responses to Temperature and Habitat Variability

Climate Adapt Wildlife Model for Predicting Animal Responses to Temperature and Habitat Variability

Authors

  • Mukesh Sharma, Anil Kumar Singh, Manvi Pant

Keywords:

Climate adaptation, Wildlife behavior, Physiological tolerance, Habitat variability, Species distribution shift, Thermal ecology, Human-wildlife conflict.

Abstract

Changes in temperature and more unpredictable habitat are changing the distribution, activity patterns, and tolerance of animals to the environment beyond the range to which genetically adapted. Most of the work documenting these changes has been for single species or one particular response (e.g. distribution shift or physiological tolerance), making it difficult for wildlife managers to predict how a particular species or population will react when changes in temperature and changes in habitat occur simultaneously. This paper presents the Climate Adapt Wildlife Model, a conceptual approach to predicting behavioral and physiological responses of wildlife populations to combined temperature and habitat variability, which consists of three linked components; a component for temperature exposure characterization that predicts the temperature a population will experience; a component for behavioral response that predicts shifts in wildlife habitats use, activity timing, and movement; and a component for physiological tolerance that estimates how much, or how little, is being added to a population's adaptive capacity from projected conditions. The model is informed by recent research reporting distributional shifts of marine predators, development of adaptive habitat in amphibians, and when adaptive responses to climate change can be predicted, as well as genetic evidence of climate adaptation in songbirds, evidence of drought refuge use by fish, and the increasing importance of climate change in human-wildlife conflict. The model, which has yet to be tested against observations of the field, could provide wildlife managers with guidance to predict which populations will be more likely to adapt successfully, which may shift range or behavior, and those that are at higher risk of going beyond the physiological limits.

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Published

2026-08-08

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Section

Articles

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