Cross‐scale integration of knowledge for predicting species ranges: a metamodelling framework

Media type: E-Article
Title: Cross‐scale integration of knowledge for predicting species ranges: a metamodelling framework
Contributor: Talluto, Matthew V.; Boulangeat, Isabelle; Ameztegui, Aitor; Aubin, Isabelle; Berteaux, Dominique; Butler, Alyssa; Doyon, Frédérik; Drever, C. Ronnie; Fortin, Marie‐Josée; Franceschini, Tony; Liénard, Jean; McKenney, Dan; Solarik, Kevin A.; Strigul, Nikolay; Thuiller, Wilfried; Gravel, Dominique
imprint: Wiley, 2016
Published in: Global Ecology and Biogeography
Language: English
DOI: 10.1111/geb.12395
ISSN: 1466-822X; 1466-8238
Keywords: Ecology ; Ecology, Evolution, Behavior and Systematics ; Global and Planetary Change
Origination:
Footnote:
Description: <jats:title>Abstract</jats:title><jats:sec><jats:title>Aim</jats:title><jats:p>Current interest in forecasting changes to species ranges has resulted in a multitude of approaches to species distribution models (<jats:styled-content style="fixed-case">SDMs</jats:styled-content>). 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.</jats:p></jats:sec><jats:sec><jats:title>Location</jats:title><jats:p><jats:styled-content style="fixed-case">E</jats:styled-content>astern <jats:styled-content style="fixed-case">N</jats:styled-content>orth <jats:styled-content style="fixed-case">A</jats:styled-content>merica (as an example).</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>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 <jats:styled-content style="fixed-case">SDM</jats:styled-content> 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 (<jats:styled-content style="fixed-case"><jats:italic>A</jats:italic></jats:styled-content><jats:italic>cer saccharum</jats:italic>), an abundant tree native to eastern North America.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Main conclusions</jats:title><jats:p>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.</jats:p></jats:sec>

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