Integrating Spatial Behavioral Ecology in Agent-Based Models for Species Conservation

Abstract

Anthropogenic activities are causing highly influential impacts on species persistence. The sustained environmental change wildlife are experiencing may surpass the capacity of developmental, genetic, and demographic mechanisms that populations have evolved to deal with these alterations. Undeniably, habitat fragmentation, habitat loss, and human disturbance are causing a decline in species numbers on a global scale, with shifts or reductions occurring in species-distribution ranges. The knowledge of species distribution is a vital component in wildlife conservation and management. Such information aids in quantifying animal–habitat relationships, describing and predicting differential space use by animals, and ultimately identifying habitat that is important to an animal (Beyer et al. 2010). The field of species distribution modeling (SDM) as a means of quantifying species– environment relationships has been extensively developed since the first formal definition of differential habitat selection theory by Fretwell and Lucas in 1969. It has since produced a variety of numerical tools that combine observations of species occurrence or abundance with environmental estimates based on statistically or theoretically derived response surfaces (Guisan and Zimmermann 2000). These models include presence/absence models, dispersal/migration models, disturbance models, and abundance models; they are now widely used across terrestrial, freshwater, and marine realms. SDMs are used to determine the suitability of the organisms’ habitat, relying on density/abundance measures or the ratio between used and available habitats to infer habitat quality. These models use spatial environmental data to make inferences on species’ range limits (Kearney and Porter 2009). Most approaches are correlative in that they statistically link spatial data (typically geographic information systems data) to species distribution records. Despite the prevalence of SDMs in applied ecology, a review of recent papers cautions using a statistical description that implicitly captures these “habitat use” processes as they are statistically associated with the predictor variables, but may not be so biologically. Firstly, habitat use does not necessarily equate with high quality habitat, range requirements, nor resultant increased wildlife fitness because biotic and abiotic cues can cause animals to choose habitats that do not provide the necessary resources to ensure high fitness returns (Jonzen 2008; Perot and Villard 2009). Secondly, SDMs are frequently applied for predicting potential future distributions of range-shifting species, despite these models’ assumptions that (1) species are at equilibrium with the environments, and (2) the data used to train (fit) the models are representative of conditions to which the models are already