This session invites papers from researchers addressing the challenges presented by data scarcity when developing mechanistic models simulating movement behaviours of organisms and validating emergent spatial population distributions. Success stories, case studies and methods of data integration into spatially-explicit simulation models will be presented. The focus will be on the role data plays in translating such models into reliable actions for improved species’ management or conservation, for example through forecasting and decision-support.
Mechanistic simulation-based approaches, such as agent-based modelling, are used to better understand the drivers of population dynamics and species interactions in biological systems. Although geospatial applications have been common in social simulations, such as pedestrian movement or traffic flow models, such dynamic spatial models are less prevalent in ecology (or at least tend to be confined to theoretical domains). One reason for this may be that parameterisation and validation data for spatially-explicit simulations driven by species’ behaviours is challenging to obtain. For instance, it isn't possible to interview non-human agents to understand their behaviours or motivations. In addition, the mass surveillance so common today for human pedestrian and vehicular traffic flow has little parallel when it comes to tracking movement of biological organisms.
The result is that many spatial simulation models of biological organisms with ‘real-world’ applications developed to date have relied on limited movement behaviour data and information or were based on theoretical assumptions. However, novel tracking and automated observation data, including population genetic techniques, radar or tracking devices, are now making relevant movement data available to ecological modellers. Such data offers the possibility for a step-change in the methods available to parameterize and validate spatially-explicit simulation models of complex biological systems, leading to increased systems understanding, predictive power and utility for decision-support.
Key topics: Individual-based models, Agent-based models, Movement ecology, Model-data fusion