Conference Contribution Details
Mandatory Fields
Holloway, P.
Annual Meeting of the AAG
Incorporating movement-related resource dynamics in species distribution models
Boston, MA
Oral Presentation
2017
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0
Optional Fields
05-APR-17
09-APR-17
Understanding the current and future distributions of species is a critical facet of biodiversity conservation, and species distribution models (SDMs) are an important framework for achieving this. Despite the vast potential of SDMs to address an array of pressing biodiversity questions, they are subject to a number of conceptual and methodological uncertainties, including the role of animal movement processes in determining geographic ranges. Movement processes have only recently been incorporated within SDMs, with movement predominantly conceptualized as broad-scale processes (e.g., dispersal), while finer-scale ambulatory movements of mobile animals (e.g., foraging) have been omitted. In this research, spatial simulation was used to model the dynamic relationship between movement and biotic resources (e.g. food sources) for oilbirds in Venezuela, in order to generate a new environmental variable for use in model calibration. This environmental layer represented the sustainability of a neighborhood, based on connectivity, accessibility, and viability of biotic resources, and this dynamic variable greatly improved the accuracy and ecological realism of the SDM projection compared to other commonly applied SDM scenarios. Furthermore, the explicit simulation of movement represented by individual movement paths in SDM was novel. No previous research has integrated a Lagrangian (individual-level; movement paths) approach of movement in SDM, and the use of step-selection functions to parameterize the biased-correlated random walk identified a successful empirical framework for achieving realistic movement trajectories. The results of this research should help foster discussion across a multitude of disciplines, including ecological modelling, movement ecology and computational movement analysis.