Heterogeneity in the resource landscape encourages increased cognitive and perceptive capabilities in foragers

Richard Gibbs, Pietro Landi and Cang Hui

Ecological Modelling, 2024, vol. 492, issue C

Abstract: Foraging for resources is a fundamental animal activity. Successful and efficient foraging will ultimately lead to both indirect and direct selective advantages by providing animals with the time and resources needed to fulfil other life demands. The performance of a forager is ultimately determined by its behaviour and ability once placed within the context of its environment. As such, the environment of a forager is likely to have had a substantial effect on its perceptive and cognitive abilities. Here, we investigate the effect of various forms of heterogeneity (non-randomness) in resource distributions on the efficacy of forager cognisance (the degree to which it can learn its environment). To this end, several foraging strategies utilising different degrees of cognisance are assessed and compared across multiple simulated landscapes. It is shown that both increases in resource heterogeneity and broad-scale cognitive abilities (memory and learning) can lead to an improved foraging performance. Increased resource heterogeneity is also shown to favour the use of the broad-scale cognitive abilities. Importantly however, these results are dependent on the forager being able to first maintain an adequate perception of its local environment (via its sensory and short-term memory capabilities). If a forager's localised perception is limited, either increased resource heterogeneity or the use of broad-scale cognitive abilities may instead reduce the forager's performance. These observations ultimately call into question the use of advanced (and therefore expensive) cognisance in homogeneous environments or when the ability to form a localised perception is restricted.

Keywords: Foraging; Cognition; Learning; Agent-based model; Heterogeneous; Diet choice (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:492:y:2024:i:c:s0304380024000814

DOI: 10.1016/j.ecolmodel.2024.110693

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