Integrating least-cost models with agent-based simulations: example hedgehog responses to fragmented landscapes
Understanding a landscape in terms of its functional connectivity requires a shift in focus from the individual properties of habitat fragments to larger-scale landscape-wide dynamics. Least-cost modelling provides capacity to evaluate the impact of landscape composition on the dispersal dynamics of a species by constructing a species-specic model of functional landscape connectivity. Model validation, however, is rarely achieved due to diffculties in collecting dispersal data. Agent-based modelling provides a new and alternative approach to modelling ecological systems by simulating the behaviour of individual animals. Observed and empirical knowledge of individual behaviours, as well as spatial information, can be incorporated and integrated to improve the ability to understand the link between individual-level mechanisms and system-level behaviour. This study presents a novel analysis of an agent-based model of hedgehog movements integrated with a least-cost model of hedgehog dispersal and validated in landscapes with a varying degree of habitat fragmentation. A comparison of the fitness of individual agents reveals that incorporating a simple rule into individual agents, to better mimic movement choices by real hedgehogs, dramatically affects the relationship between individual fitness and fragmentation. More realistic individuals show an increase in fitness in more fragmented landscapes due to their ability to occupy multiple habitats. Least-cost models appear sensitive to the selection of empirical data sets. This study highlights the importance of understanding individual-level movement decisions and supports agent-based modelling as a valid choice for exploring ecological systems. Integrating models of functional connectivity with those of individual behaviours captures and combines the advantages of both modelling techniques and provides a unique way of applying model outcomes to direct conservation action.
Life sciences simulation: Ecology
Algorithms and computational methods: Agents
Simulation software: NetLogo