Interpreting outputs of agent-based models using abundance-occupancy relationships

a b s t r a c t Reliable assessments of how human activities affect wild populations are essential for effective natural resource management. Agent-based models provide a powerful tool for integration of multiple drivers of ecological systems, but selecting and interpreting their output is often challenging. Here, we develop an indicator (the AOR-index) based on the abundance-occupancy relationship to facilitate the interpre- tation of agent-based model outputs. The AOR-index is based on the distribution of individuals in the landscape translated into the number of individuals in each cell of a regular grid. The proportion of grid cells with at least one individual is used to quantify occupancy and the mean number of individuals in occupied cells is used to quantify abundance. The AOR-index is a two-dimensional index giving the rela- tive change in abundance and occupancy in response to a scenario (e.g. a change in land use or climate). We systematically modify a digital version of a real landscape to produce a set of artificial landscapes differing only in the degree of landscape fragmentation. We test how these different landscapes affect the AOR-index of six model animal species in four different land use scenarios using an agent-based model framework (ALMaSS). Our results suggest that the AOR-index is a sensitive tool to demonstrate how dif- ferent species respond to particular land-use scenarios. The bird and mammal species generally showed larger responses than the invertebrates and changes in abundance and occupancy were often of different magnitude. The different responses are caused by species-specific habitat requirements and dispersal abilities, but the importance of such life history traits depend on landscape structure. Hence, predic- tions of species-specific responses to land-use changes in terms of abundance and occupancy are greatly improved by incorporation in a model framework taking spatial and temporal dynamics into account. The AOR-index facilitates the evaluation of multiple scenarios and allows for multi-species assessments. Its use, however, still requires identified management goals in order to evaluate scenario responses. © 2012 Elsevier Ltd. All rights reserved.

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