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Topping C.J.,University of Aarhus | Topping C.J.,Center for Integrated Population Ecology | Hoye T.T.,University of Aarhus | Hoye T.T.,Center for Integrated Population Ecology | And 2 more authors.
Ecological Modelling | Year: 2010

A number of wildlife species including the grey partridge (Perdix perdix) have shown dramatic post-war population declines. Multiple drivers have been proposed as reasons for the declines, for example agrochemical use and intensification of agricultural practices, climate, predation, and changes in landscape structure. These drivers may interact in non-linear ways and are inherently spatio-temporal in nature. Therefore models used to investigate mechanisms should be spatio-temporal, of proper scale, and have a high degree of biological realism. Here we describe the development and testing of an agent-based model (ABM) of grey partridge using a well documented pre-decline historical data set in conjunction with a pattern-oriented modelling (POM) approach. Model development was an iterative process of defining performance criteria, testing model behaviour, and reformulating as necessary to emulate system properties whilst ensuring that internal mechanisms were biologically realistic. The model was documented using ODdox, a new protocol for describing large agent-based models. Parameter fitting in the model was achieved to within ±2% accuracy for 15 out of 17 field data patterns used, and within 5% for the remaining two. Tests of interactions between input parameters showed that 62% of parameter pairs tested had significant interactions underlining the complex nature of the model structure. Sensitivity analysis identified chick mortality as being the most sensitive factor, followed by adult losses to hunting and adult overwinter mortality, agreeing in general with previous partridge models. However, the ABM used here could separate individual drivers, providing a better understanding of the underlying mechanisms behind population regulation, and allowing factors to be compared directly. The ABM used is rich in output signals allowing detailed testing and refinement of the model. This approach is particularly suited to systems such as the partridge system where data for comparison to model outputs is readily available. Despite the accurate fit between historical data and model output, making use of the predictive power of the approach the model requires further calibration and testing under modern field conditions. © 2009 Elsevier B.V. All rights reserved.

Hoye T.T.,University of Aarhus | Hoye T.T.,Center for Integrated Population Ecology | Skov F.,University of Aarhus | Topping C.J.,University of Aarhus | Topping C.J.,Center for Integrated Population Ecology
Ecological Indicators | Year: 2012

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 interpretation 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 relative 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 different 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, predictions 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.

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