Journal article
Cross‐scale integration of knowledge for predicting species ranges: a metamodelling framework
Global ecology and biogeography, Vol.25(2), pp.238-249
02/2016
Handle:
https://hdl.handle.net/2376/113283
PMID: 27499698
Abstract
Aim
Current interest in forecasting changes to species ranges has 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.
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Details
- Title
- Cross‐scale integration of knowledge for predicting species ranges: a metamodelling framework
- Creators
- Matthew V Talluto - CNRSIsabelle Boulangeat - Quebec Centre for Biodiversity ScienceAitor Ameztegui - Université du Québec à MontréalIsabelle Aubin - Canadian Forest ServiceDominique Berteaux - Université du Québec à RimouskiAlyssa Butler - Quebec Centre for Biodiversity ScienceFrédérik Doyon - Institut des Sciences de la Forêt Tempérée (ISFORT)C. Ronnie Drever - The Nature Conservancy CanadaMarie‐Josée Fortin - University of TorontoTony Franceschini - Département de biologie, Université du Québec à RimouskiJean Liénard - Washington State UniversityDan McKenney - Canadian Forest ServiceKevin A Solarik - Université du Québec à MontréalNikolay Strigul - Washington State UniversityWilfried Thuiller - CNRSDominique Gravel - Quebec Centre for Biodiversity Science
- Publication Details
- Global ecology and biogeography, Vol.25(2), pp.238-249
- Academic Unit
- Mathematics and Statistics, Department of
- Number of pages
- 12
- Grant note
- Quebec Centre for Biodiversity Science European Research Council (281422) NSERC strategic grant (430393‐12)
- Identifiers
- 99900547529201842
- Language
- English
- Resource Type
- Journal article