Keywords
Stochastic Patch Occupancy Models, Genetic Algorithms, Climate Change Adaptation, Species Distributions.
Location
Session G1: Using Simulation Models to Improve Understanding of Environmental Systems
Start Date
16-6-2014 3:40 PM
End Date
16-6-2014 5:20 PM
Abstract
Standard approaches to modelling the effect of climate change on species distributions model a direct link between climatic and other biophysical variables and species occupancy. Though these provide a reasonable estimate for the effects of climate change on species distributions in the future, there are a number of issues with these approaches that fail to account for dynamic landscape interactions. For example, the mass occupancy effect means that species may be observed in unsuitable habitat patches surrounding a well-populated area of highly suitable patches. Conversely, a highly suitable area may be too disconnected from other suitable patches to allow long-term species occupancy. The degree of isolation, however, is not fixed but depends on landscape dynamics. The dynamics of patch occupancy can be modelled using tools such as stochastic patch occupancy models, which use a habitat variable to represent the suitability of each patch for species occupancy, and include the computation of landscape habitat connectivity in determining whether a patch is occupied. Fitting biophysical and climatic variables to habitat suitability and using stochastic patch occupancy models to model the distributions offers a means to account for issues such as the mass occupancy effect, which can be partly responsible for autocorrelated error in purely statistical approaches, and also allows us to account for the effect of the rate and variability with which climatic variables change when modelling future species distributions. However, the method poses a more significant computing challenge to finding the fitting parameters. We report and reflect on preliminary work using genetic algorithms to search for these parameters.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
Using Genetic Algorithms to Fit Species and Habitat Parameters for Modelling the Effect of Climate Change on Species Distributions with Stochastic Patch Occupancy Models
Session G1: Using Simulation Models to Improve Understanding of Environmental Systems
Standard approaches to modelling the effect of climate change on species distributions model a direct link between climatic and other biophysical variables and species occupancy. Though these provide a reasonable estimate for the effects of climate change on species distributions in the future, there are a number of issues with these approaches that fail to account for dynamic landscape interactions. For example, the mass occupancy effect means that species may be observed in unsuitable habitat patches surrounding a well-populated area of highly suitable patches. Conversely, a highly suitable area may be too disconnected from other suitable patches to allow long-term species occupancy. The degree of isolation, however, is not fixed but depends on landscape dynamics. The dynamics of patch occupancy can be modelled using tools such as stochastic patch occupancy models, which use a habitat variable to represent the suitability of each patch for species occupancy, and include the computation of landscape habitat connectivity in determining whether a patch is occupied. Fitting biophysical and climatic variables to habitat suitability and using stochastic patch occupancy models to model the distributions offers a means to account for issues such as the mass occupancy effect, which can be partly responsible for autocorrelated error in purely statistical approaches, and also allows us to account for the effect of the rate and variability with which climatic variables change when modelling future species distributions. However, the method poses a more significant computing challenge to finding the fitting parameters. We report and reflect on preliminary work using genetic algorithms to search for these parameters.
