title: An Objective Approach to Select Climate Scenarios when Projecting Species Distribution under Climate Change.
abstract: An impressive number of new climate change scenarios have recently become available to assess the ecological impacts of climate change. Among these impacts, shifts in species range analyzed with species distribution models are the most widely studied. Whereas it is widely recognized that the uncertainty in future climatic conditions must be taken into account in impact studies, many assessments of species range shifts still rely on just a few climate change scenarios, often selected arbitrarily. We describe a method to select objectively a subset of climate change scenarios among a large ensemble of available ones. Our k-means clustering approach reduces the number of climate change scenarios needed to project species distributions, while retaining the coverage of uncertainty in future climate conditions. We first show, for three biologically-relevant climatic variables, that a reduced number of six climate change scenarios generates average climatic conditions very close to those obtained from a set of 27 scenarios available before reduction. A case study on potential gains and losses of habitat by three northeastern American tree species shows that potential future species distributions projected from the selected six climate change scenarios are very similar to those obtained from the full set of 27, although with some spatial discrepancies at the edges of species distributions. In contrast, projections based on just a few climate models vary strongly according to the initial choice of climate models. We give clear guidance on how to reduce the number of climate change scenarios while retaining the central tendencies and coverage of uncertainty in future climatic conditions. This should be particularly useful during future climate change impact studies as more than twice as many climate models were reported in the fifth assessment report of IPCC compared to the previous one.
authors.affiliation: Canada Research Chair on Northern Biodiversity and Centre d'Études Nordiques, Université du Québec à Rimouski, 300 allée des Ursulines, Rimouski, QC, G5L 3A1, Canada.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Casajus
authors.affiliation: Direction de la recherche forestière, Ministère des Forêts, de la Faune et des Parcs, Québec, QC, G1P 3W8, Canada.
authors.forename: Catherine
authors.initials: C
authors.lastname: Périé
authors.affiliation: Ouranos Consortium on Regional Climatology and Adaptation to Climate Change, Montréal, QC, H3A 1B9, Canada.
authors.forename: Travis
authors.initials: T
authors.lastname: Logan
authors.affiliation: Direction de la recherche forestière, Ministère des Forêts, de la Faune et des Parcs, Québec, QC, G1P 3W8, Canada.
authors.forename: Marie-Claude
authors.initials: MC
authors.lastname: Lambert
authors.affiliation: Department of Plant Science and McGill School of Environment, Macdonald Campus, McGill University, 21111 Lakeshore Road, Ste-Anne-de-Bellevue, QC, H9X 3V9, Canada.
authors.forename: Sylvie
authors.initials: S
authors.lastname: de Blois
authors.affiliation: Canada Research Chair on Northern Biodiversity and Centre d'Études Nordiques, Université du Québec à Rimouski, 300 allée des Ursulines, Rimouski, QC, G5L 3A1, Canada.
authors.forename: Dominique
authors.initials: D
authors.lastname: Berteaux
mesh_terms: D044822:Biodiversity; D002980:Climate; D057231:Climate Change; D016000:Cluster Analysis; D004463:Ecology; D017753:Ecosystem; D005843:Geography; D015233:Models, Statistical; D011157:Population Dynamics; D011792:Quebec; D013045:Species Specificity; D013696:Temperature; D014197:Trees / Q000502:physiology*