[en] Species distribution models (SDMs) relate species observations to mapped environmental variables to estimate the realized niche of species and predict their distribution. SDMs are key tools for projecting the impact of climate change on species and have been used in many biodiversity assessments. However, when fitted within spatial extents that do not encompass the whole species range (i.e. subrange), the estimated realized environmental niche can be truncated, which can lead to wrong or inaccurate predictions. A simple solution to this niche truncation consists in fitting SDMs at a spatial extent that encompasses the whole species range, but this often implies using a spatial resolution too coarse for local conservation assessments. To keep a fine resolution, a solution is to fit spatially nested SDMs (N-SDMs), where a whole range, coarse-grain SDM is combined with a subrange, fine-grain SDM. N-SDMs have demonstrated superior performance to subrange (truncated) SDMs in projecting species distributions under climate change and have accordingly regained considerable interest. Here, we review developments, applications and effectiveness of N-SDMs. We present and discuss existing methods and tools to fit N-SDMs, and assess when N-SDMs are not needed. We highlight strengths and weaknesses of N-SDMs, underline their importance in reducing niche truncation, and identify remaining challenges and future perspectives. Our review highlights that subrange SDMs most often lead to niche truncation and thus to incorrect spatial projections, a problem that can be overcome by using N-SDMs. We show that the various N-SDM methods come with their strengths and weaknesses and should be selected depending on the intended goal of the study. Synthesis. N-SDMs are key tools to develop untruncated regional climate change forecasts of species distributions at fine resolution over restricted extent. While several N-SDM approaches were proposed, there is currently no universal solution suggesting that further developments and testing are crucial if we are to derive robust future projections of species distributions, at least until SDMs can be applied for most species at high resolution over large geographic extents.
Disciplines :
Environmental sciences & ecology
Author, co-author :
Guisan, Antoine ; Department of Ecology & Evolution, University of Lausanne, Lausanne, Switzerland ; Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland ; Interdisciplinary Center for Mountain Research, University of Lausanne, Lausanne, Switzerland
Chevalier, Mathieu ; IFREMER, Centre de Bretagne, DYNECO, Laboratoire d'Ecologie Benthique Côtière (LEBCO), Plouzané, France
Adde, Antoine ; Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland ; Department of Aquatic Ecology, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
Zarzo-Arias, Alejandra ; Departamento de Biología, Universidad Autónoma de Madrid, Madrid, Spain ; Universidad de Oviedo, Oviedo, Spain
Goicolea, Teresa ; Departamento de Biología, Universidad Autónoma de Madrid, Madrid, Spain
Broennimann, Olivier; Department of Ecology & Evolution, University of Lausanne, Lausanne, Switzerland ; Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
Petitpierre, Blaise; Swiss Center for Floristic Data, Info Flora, Geneva, Switzerland
Scherrer, Daniel ; Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Rey, Pierre-Louis ; Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland ; Interdisciplinary Center for Mountain Research, University of Lausanne, Lausanne, Switzerland
Collart, Flavien ; Université de Liège - ULiège > Integrative Biological Sciences (InBioS) ; Department of Ecology & Evolution, University of Lausanne, Lausanne, Switzerland
Riva, Federico ; Environmental Geography Department, Institute for Environmental Studies, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
Steen, Bart ; Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland ; Sapienza University of Rome, Rome, Italy
Mateo, Rubén G. ; Departamento de Biología, Universidad Autónoma de Madrid, Madrid, Spain ; Centro de Investigación en Biodiversidad y Cambio Global (CIBC-UAM), Universidad Autónoma de Madrid, Madrid, Spain
SNF - Schweizerischer Nationalfonds zur Förderung der wissenschaftlichen Forschung AEI - Agencia Estatal de Investigación
Funding text :
A.G. received support from the Swiss Federal Office for the Environment (Valpar.CH project) funding A.A. and from the Swiss National Science Foundation (grants 310030L_197777, GEN4MIG project) funding F.C. R.G.M. was supported by project grants Connect2restore (TED2021\u2010129589B\u2010I00, funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR), and NextDive (PID2021\u2010124187NB\u2010I00, funded by MCIN/AEI/10.13039/501100011033 and by ERDF, a way of making Europe).
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