Applying a biocomplexity approach to modelling farmer decision-making and land use impacts on wildlife
Authored by Christopher J Topping, Anna Malawska
Date Published: 2018
DOI: 10.1111/1365-2664.13024
Sponsors:
No sponsors listed
Platforms:
C++
Model Documentation:
Other Narrative
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Model Code URLs:
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Abstract
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 for 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 the 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.
Tags
Agent-based model
agent-based simulation
Management
Dynamics
Diversity
Landscape
ALMaSS
Biodiversity
Pesticides
Impact assessment
population
Social-ecological systems
Occupancy
Agricultural systems
Abundance
Agroecology
Biocomplexity
Energy crop
Hare
Skylark