title: Benchmarking novel approaches for modelling species range dynamics.
abstract: Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species' range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species' response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species.
authors.affiliation: Dynamic Macroecology, Swiss Federal Research Institute WSL, Zürcherstrasse 111, CH-8903, Birmensdorf, Switzerland.
authors.forename: Damaris
authors.identifier: 0000-0002-4628-3558
authors.initials: D
authors.lastname: Zurell
authors.affiliation: Univ. Grenoble Alpes, Laboratoire d'Écologie Alpine (LECA), UMR-CNRS 5553 Université J. Fourier BP 53, F-38000, Grenoble, France.|CNRS, Laboratoire d'Écologie Alpine (LECA), UMR-CNRS 5553 Université J. Fourier BP 53, F-38000, Grenoble, France.
authors.forename: Wilfried
authors.initials: W
authors.lastname: Thuiller
authors.affiliation: Institute of Landscape and Plant Ecology, University of Hohenheim, August-v.Hartmann-Str. 3, D-70599, Stuttgart, Germany.
authors.forename: Jörn
authors.initials: J
authors.lastname: Pagel
authors.affiliation: Biodiversity, Macroecology and Conservation Biogeography, University Göttingen, Büsgenweg 2, D-37077, Goettingen, Germany.|Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv), Deutscher Platz 5e, D-04103, Leipzig, Germany.
authors.forename: Juliano S
authors.initials: JS
authors.lastname: Cabral
authors.affiliation: Univ. Grenoble Alpes, Laboratoire d'Écologie Alpine (LECA), UMR-CNRS 5553 Université J. Fourier BP 53, F-38000, Grenoble, France.|CNRS, Laboratoire d'Écologie Alpine (LECA), UMR-CNRS 5553 Université J. Fourier BP 53, F-38000, Grenoble, France.
authors.forename: Tamara
authors.initials: T
authors.lastname: Münkemüller
authors.affiliation: Université de Québec à Rimouski, 300 Allée des Ursulines, Rimouski, Canada, G5L 3A1.
authors.forename: Dominique
authors.initials: D
authors.lastname: Gravel
authors.affiliation: Department of Botany and Biodiversity Research, University of Vienna, Rennweg 14, 1030, Vienna, Austria.
authors.forename: Stefan
authors.initials: S
authors.lastname: Dullinger
authors.affiliation: Section for Ecoinformatics and Biodiversity, Department of Bioscience, Aarhus University, Ny Munkegade 114, DK-8000, Aarhus C, Denmark.
authors.forename: Signe
authors.initials: S
authors.lastname: Normand
authors.affiliation: Univ. Grenoble Alpes, Laboratoire d'Écologie Alpine (LECA), UMR-CNRS 5553 Université J. Fourier BP 53, F-38000, Grenoble, France.|CNRS, Laboratoire d'Écologie Alpine (LECA), UMR-CNRS 5553 Université J. Fourier BP 53, F-38000, Grenoble, France.|Senckenberg Biodiversity and Climate Research Centre (BiK-F), Georg Voigt-Straße 14-16, D-60325, Frankfurt (Main), Germany.
authors.forename: Katja H
authors.initials: KH
authors.lastname: Schiffers
authors.affiliation: Center for Population Biology, University of California, Davis, One Shields Avenue, Davis, CA, 95616, USA.
authors.forename: Kara A
authors.initials: KA
authors.lastname: Moore
authors.affiliation: Dynamic Macroecology, Swiss Federal Research Institute WSL, Zürcherstrasse 111, CH-8903, Birmensdorf, Switzerland.|Department of Environmental Systems Science, Swiss Federal Institute of Technology ETH, CH-8092, Zurich, Switzerland.
authors.forename: Niklaus E
authors.initials: NE
authors.lastname: Zimmermann
mesh_terms: D001499:Bayes Theorem; D019985:Benchmarking; D002980:Climate; D057231:Climate Change; D017753:Ecosystem; D008954:Models, Biological; D011157:Population Dynamics
keywords: climate change; demographic models; dispersal; population viability; prediction; simulated data; species distribution models; virtual ecologist approach