Abstract

Understanding why animals move as they do when searching for resources is a central question in ecology, and a prerequisite for the development of predictive process-based models for conservation and management. Many species are central-place foragers (CPF). While several models for CPFs have been proposed, they often assume well-defined return rules to the focal point (like breeding). For some CPFs, however, the decisions to return to central sites are governed by multiple interactions between environmental and physiological factors.We present AgentSeal, a behaviour- and physiology-based, spatially explicit, agent-based model. We use harbour seals, a marine CPF, as a case study and focus on individuals outside their breeding and moulting seasons to capture general fine- and large-scale movements and drivers behind CPF. We model movement decisions based on optimal foraging strategy, cognitive and physiological processes in a realistic landscape, coupled with realistic prey distribution and tuned to a range of behavioural and physiological patterns observed at different scales and levels of organisation (pattern-orientated modelling, POM).The model can reproduce energetic, movement and other behavioural patterns such as net energy balance, at-sea and on land site fidelity, daily activity budgets and trip extents. The model reveals the crucial elements needed to model return-trips of CPFs including movement characteristics that vary as a function of local environmental conditions, cognitive mapping of foraging areas as points of attraction in subsequent foraging trips, and physiological requirements defining switches between resting and foraging.We discuss potential applications and extensions of the model, including investigations of fundamental questions in foraging ecology: how spatial distribution and aggregation of resources affect movement of marine CPFs; what are the main drivers behind their at-sea site-fidelity to foraging patches? We also discuss applied objectives such as improving our understanding of population-level consequences of anthropogenic disturbances and ultimately evolving AgentSeal into a practical management tool.

Highlights

  • Understanding why animals move as they do when searching for resources is a central question in ecology, and a prerequisite for the development of predictive process-based movement models for conser­ vation and management

  • We model movement decisions based on optimal foraging strategy, cognitive and physiological processes in a realistic landscape, coupled with realistic prey distribution and tuned to a range of behavioural and physiological patterns observed at different scales and levels of organisation

  • The objective of mseals is to maximise their net energy intake, while taking their digestive constraints and need to periodically haul out into account. Mseals increase their chance of finding fish by spending more time in good quality areas and returning to these if previous visits resulted in high food intake, while they will transit through areas of low quality

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Summary

Introduction

Understanding why animals move as they do when searching for resources is a central question in ecology, and a prerequisite for the development of predictive process-based movement models for conser­ vation and management (for example Boult et al, 2018). A predictive process-driven movement model should be able to establish a direct link between individual movement decisions and future distributional, energetic and fitness consequences - and vice versa If, such models are spatially explicit, animal move­ ment can be modelled based on a realistic landscape and realistic management scenarios, which allows for modelling of specific areas and populations of interest. Models from which multiple empirically observed patterns emerge are likely to include the key processes responsible for animal movement decisions Examples of such patterns are the spatial distribution of ani­ mals, foraging trip extents and duration, changes in time energy bud­ gets, and movement characteristics in relation to food availability in a variety of scenarios (Grimm et al, 2005)

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