5 years ago

Applying a biocomplexity approach to modelling farmer decision-making and land use impacts on wildlife

Christopher John Topping, Anna Malawska
1.The biocomplexity approach refers to a fully integrated social-ecological systems (SES) simulation that represents bidirectional feedbacks between social and ecological components. This method is essential to accurately assess impacts of economy and policy on SES such as agroecosystems, where feedbacks between the drivers and impacts of cropping changes need to be simulated. Here we exemplify the biocomplexity approach using energy maize, which is becoming an important source of bioenergy in Europe, and thus, might cause a significant change in land use with knock on-effects on wildlife. 2.The integrated simulation tool consisted of a farmer decision making agent-based model fully coupled to the Animal Landscape and Man Simulatin System (ALMaSS)an agent-based simulation system for predicting impacts of land use on a range of Danish wildlife species: the brown hare (Lepus europaeus), the grey partridge (Perdix perdix), the skylark (Alauda arvensis), a carabid beetle (Bembidion lampros), a linyphiid spider (Erigone atra), and the field vole (Microtus agrestis). This was used to assess the impacts of increasing demand on energy maize on six animal species. Two types of experimental scenarios were evaluated, with and without feedback between the social and ecological system. The assessment of species responses was based on changes in population size, abundance and occupancy. 3.The response to the cultivation of energy maize was negative for three vertebrate species (skylark, hare and field vole) and positive for partridge and the two invertebrate species. The feedback scenarios showed that the incorporation of information from ecological system to the farmer decision making affected both a trend in area cultivated with energy maize as well as the animal responses. 4.Synthesis and applications. Fully coupling agent-based decision making and environmental simulation allows a detailed representation and integration of both social and ecological components of agricultural systems at proper spatial and temporal scales, as well as of dynamic feedbacks between the two systems. By employing easy to interpret measures of changes in abundance and spatial occupancy of animal species, the simulation results could inform and simplify decision-making on expected impacts of economy and policy regulations on wildlife. This article is protected by copyright. All rights reserved.

Publisher URL: http://onlinelibrary.wiley.com/resolve/doi

DOI: 10.1111/1365-2664.13024

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