Species distribution models for sustainable ecosystem management
Van Echelpoel, W.; Boets, P.; Landuyt, D.; Gobeyn, S.; Everaert, G.; Bennetsen, E.; Mouton, A.; Goethals, P.L.M. (2015). Species distribution models for sustainable ecosystem management, in: Park, Y.-S. et al. Advanced modelling techniques studying global changes in environmental sciences. Developments in environmental modelling, 27: pp. 115-134. https://dx.doi.org/10.1016/b978-0-444-63536-5.00008-9
In: Park, Y.-S. et al. (Ed.) (2015). Advanced modelling techniques studying global changes in environmental sciences. Developments in environmental modelling, 27. Elsevier: [s.l.]. ISBN 978-0-444-63536-5. xviii, 361 pp., more
In: Developments in environmental modelling. ISSN 0167-8892, more
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| Author keywords |
Species distribution; Decision tree; GLM; ANN; Fuzzy logic; BBN |
| Abstract |
Reactions to ongoing loss of biodiversity include a variety of restoration actions and are characterised by high costs and uncertainty. Related decision-making can be supported by developing species distribution models (SDMs) that link predictors (both abiotic and biotic) with biotic response variables (e.g., abundance, occurrence, etc.). SDMs can fill in the gaps of current ecological knowledge and predict the potential impact of environmental (including climate) change on species distributions. As climate change already resulted in species shifting their range and an increased risk of extinction, invasion, and disease propagation, SDMs can act as a valuable tool to estimate future species distributions and their effects on ecosystem functioning and related services. Among the variety of modelling techniques used to predict future species distributions, five modelling techniques are selected: decision trees, generalised linear models, artificial neural networks, fuzzy logic, and Bayesian belief networks. The unique advantages of each modelling technique allow the modeller to choose the most appropriate technique in each particular situation. In turn, each modelling technique is characterised by specific drawbacks and is restricted by the limited ecological knowledge related to biotic interactions. Gathering additional ecological knowledge provides the possibility to go beyond simple pattern recognition and to establish more ecologically sound models. |
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