Jan Baetens

Individual biology influences environment‐dependent population dynamics through life history. Population models that consider individual physiology are therefore popular for modelling dynamics under various environments. In recent years, a quantitative framework integrating metabolic theory (dynamic energy budget theory) into individual‐based models (DEB‐IBMs) has emerged to investigate the link from individual physiology to demography. However, this link shows substantial variation, some of which may be explained by individual behaviours operating in local environments. Current DEB‐IBMs focusing on population modelling do not consider individual‐scale behaviours and instead resort to imposed population‐level relationships. We therefore propose to extend the DEB‐IBM approach to consider the role of individual‐scale behaviour by replacing the functional response – a population‐averaged phenomenological relationship – with individual‐scale foraging mechanisms in a spatially heterogeneous environment. Using this model, we simulate consumer dynamics in a consumer‐resource system for different individual behaviours across a range of temperature, resource carrying capacity and individual variability values. We further illustrate the model in a case study by comparing simulated population dynamics with both the classical DEB‐IBM and experimental data for a laboratory Daphnia magna population. Simulations reveal that temperature‐ and resource‐dependent consumer extinction probability patterns change with individual behaviour. Moreover, simulations agree with experimental data on D. magna populations: dynamics after the initial growth peak were better captured under random walk movement behaviour compared to the classical DEB‐IBM. Both the simulation and case study showed how fine‐scale behaviour mediates the metabolism's impact on population dynamics by allowing for the emergence of different functional responses. Our model thus provides a link between metabolism, life history and population dynamics by centring behavioural mechanisms and environmental heterogeneity at the individual scale. This expansion of the modelling toolbox for physiologically structured populations can boost theory development by bridging various fields in ecology, and contribute to our understanding of environment‐dependent ecological patterns.