Matthew V Talluto1, Isabelle Boulangeat2, Aitor Ameztegui3, Isabelle Aubin4, Dominique Berteaux5, Alyssa Butler2, Frédérik Doyon6, C Ronnie Drever7, Marie-Josée Fortin8, Tony Franceschini9, Jean Liénard10, Dan McKenney4, Kevin A Solarik11, Nikolay Strigul10, Wilfried Thuiller12, Dominique Gravel2. 1. Département de biologie, Université du Québec à Rimouski, Rimouski, Quebec, Canada; Quebec Centre for Biodiversity Science, Montreal, Quebec, Canada; Université Grenoble Alpes, Laboratoire d'Ecologie Alpine (LECA), F-38000 Grenoble, France; CNRS, Laboratoire d'Ecologie Alpine (LECA), F-38000 Grenoble, France. 2. Département de biologie, Université du Québec à Rimouski, Rimouski, Quebec, Canada; Quebec Centre for Biodiversity Science, Montreal, Quebec, Canada. 3. Centre d'Étude de la Forêt, Département des sciences biologiques, Université du Québec à Montréal, Montreal, Quebec, Canada. 4. Great Lakes Forestry Centre, Canadian Forest Service, Natural Resources Canada, Sault Ste Marie, Ontario, Canada. 5. Département de biologie, Université du Québec à Rimouski, Rimouski, Quebec, Canada; Quebec Centre for Biodiversity Science, Montreal, Quebec, Canada; Centre for Northern Studies, Université du Québec à Rimouski, Rimouski, Quebec, Canada. 6. Université du Québec en Outaouais, Gatineau, Quebec, Canada; Institut des Sciences de la Forêt Tempérée (ISFORT), Ripon, Quebec, Canada. 7. The Nature Conservancy Canada, Ottawa, Ontario, Canada. 8. Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada. 9. Département de biologie, Université du Québec à Rimouski, Rimouski, Quebec, Canada. 10. Department of Mathematics, Washington State University, Vancouver, Washington, USA. 11. Quebec Centre for Biodiversity Science, Montreal, Quebec, Canada; Université Grenoble Alpes, Laboratoire d'Ecologie Alpine (LECA), F-38000 Grenoble, France. 12. Université Grenoble Alpes, Laboratoire d'Ecologie Alpine (LECA), F-38000 Grenoble, France; CNRS, Laboratoire d'Ecologie Alpine (LECA), F-38000 Grenoble, France.
Abstract
AIM: Current interest in forecasting changes to species ranges have resulted in a multitude of approaches to species distribution models (SDMs). However, most approaches include only a small subset of the available information, and many ignore smaller-scale processes such as growth, fecundity, and dispersal. Furthermore, different approaches often produce divergent predictions with no simple method to reconcile them. Here, we present a flexible framework for integrating models at multiple scales using hierarchical Bayesian methods. LOCATION: Eastern North America (as an example). METHODS: Our framework builds a metamodel that is constrained by the results of multiple sub-models and provides probabilistic estimates of species presence. We applied our approach to a simulated dataset to demonstrate the integration of a correlative SDM with a theoretical model. In a second example, we built an integrated model combining the results of a physiological model with presence-absence data for sugar maple (Acer saccharum), an abundant tree native to eastern North America. RESULTS: For both examples, the integrated models successfully included information from all data sources and substantially improved the characterization of uncertainty. For the second example, the integrated model outperformed the source models with respect to uncertainty when modelling the present range of the species. When projecting into the future, the model provided a consensus view of two models that differed substantially in their predictions. Uncertainty was reduced where the models agreed and was greater where they diverged, providing a more realistic view of the state of knowledge than either source model. MAIN CONCLUSIONS: We conclude by discussing the potential applications of our method and its accessibility to applied ecologists. In ideal cases, our framework can be easily implemented using off-the-shelf software. The framework has wide potential for use in species distribution modelling and can drive better integration of multi-source and multi-scale data into ecological decision-making.
AIM: Current interest in forecasting changes to species ranges have resulted in a multitude of approaches to species distribution models (SDMs). However, most approaches include only a small subset of the available information, and many ignore smaller-scale processes such as growth, fecundity, and dispersal. Furthermore, different approaches often produce divergent predictions with no simple method to reconcile them. Here, we present a flexible framework for integrating models at multiple scales using hierarchical Bayesian methods. LOCATION: Eastern North America (as an example). METHODS: Our framework builds a metamodel that is constrained by the results of multiple sub-models and provides probabilistic estimates of species presence. We applied our approach to a simulated dataset to demonstrate the integration of a correlative SDM with a theoretical model. In a second example, we built an integrated model combining the results of a physiological model with presence-absence data for sugar maple (Acer saccharum), an abundant tree native to eastern North America. RESULTS: For both examples, the integrated models successfully included information from all data sources and substantially improved the characterization of uncertainty. For the second example, the integrated model outperformed the source models with respect to uncertainty when modelling the present range of the species. When projecting into the future, the model provided a consensus view of two models that differed substantially in their predictions. Uncertainty was reduced where the models agreed and was greater where they diverged, providing a more realistic view of the state of knowledge than either source model. MAIN CONCLUSIONS: We conclude by discussing the potential applications of our method and its accessibility to applied ecologists. In ideal cases, our framework can be easily implemented using off-the-shelf software. The framework has wide potential for use in species distribution modelling and can drive better integration of multi-source and multi-scale data into ecological decision-making.
Entities:
Keywords:
Climate change; decision making; patterns and processes; range dynamics; scaling; spatial ecology; species distribution modeling; uncertainty
Authors: Sean M McMahon; Sandy P Harrison; W Scott Armbruster; Patrick J Bartlein; Colin M Beale; Mary E Edwards; Jens Kattge; Guy Midgley; Xavier Morin; I Colin Prentice Journal: Trends Ecol Evol Date: 2011-04-07 Impact factor: 17.712