Summary
Published in PLOS ONE 11(3): e0152495. https://doi.org/10.1371/journal.pone.0152495
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.
Sector(s):
Forests
Categorie(s):
Scientific Article
Theme(s):
Ecosystems and Environment, Forestry Research, Forests
Departmental author(s):
Author(s)
CASAJUS, Nicolas, Catherine PÉRIÉ, Travis LOGAN, Marie-Claude LAMBERT, Sylvie DE BLOIS and Dominique BERTEAUX
Year of publication :
2016
Format :
PDF available upon request
How to get the publication :
Keywords :
scénario de changement climatique, modèle climatique, incertitude, prédiction d'ensemble, partitionnement par la méthode des K centroïdes, modèle de distribution d'espèce, écosystèmes et environnement, article scientifique de recherche forestière, ecosystems and environment, climate change scenario, climate model, coverage of uncertainty, ensemble forecasting, K-means clustering, species distribution model
