FN Archimer Export Format
PT J
TI Accounting for niche truncation to improve spatial and temporal predictions of species distributions
BT 
AF Chevalier, Mathieu
   Zarzo-Arias, Alejandra
   Guélat, Jérôme
   Mateo, Rubén G.
   Guisan, Antoine
AS 1:1,2;2:3,4;3:5;4:6;5:2,7;
FF 1:PDG-ODE-DYNECO-LEBCO;2:;3:;4:;5:;
C1 Centre de Bretagne, DYNECO, Laboratoire d’Ecologie Benthique Côtière (LEBCO), IFREMER, Plouzané, France
   Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
   Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales (MNCN-CSIC), Madrid, Spain
   Department of Organisms and Systems Biology, Universidad de Oviedo, Oviedo, Spain
   Swiss Ornithological Institute, Sempach, Switzerland
   Department of Biology, Universidad Autónoma de Madrid, Madrid, Spain
   Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
C2 IFREMER, FRANCE
   UNIV LAUSANNE, SWITZERLAND
   CSIC, SPAIN
   UNIV OVIEDO, SPAIN
   SWISS ORNITHOLOGICAL INSTA, SWITZERLAND
   UNIV AUTONOMA MADRID, SPAIN
   UNIV LAUSANNE, SWITZERLAND
SI BREST
SE PDG-ODE-DYNECO-LEBCO
IN WOS Ifremer UPR
   DOAJ
   copubli-europe
   copubli-int-hors-europe
IF 3
TC 19
UR https://archimer.ifremer.fr/doc/00788/89994/95549.pdf
   https://archimer.ifremer.fr/doc/00788/89994/95550.docx
LA English
DT Article
DE ;birds;climate change;predictions;range;spatial niche truncation;species distribution model (SDM);data integration
AB Species Distribution Models (SDMs) are essential tools for predicting climate change impact on species’ distributions and are commonly employed as an informative tool on which to base management and conservation actions. Focusing only on a part of the entire distribution of a species for fitting SDMs is a common approach. Yet, geographically restricting their range can result in considering only a subset of the species’ ecological niche (i.e., niche truncation) which could lead to biased spatial predictions of future climate change effects, particularly if future conditions belong to those parts of the species ecological niche that have been excluded for model fitting. The integration of large-scale distribution data encompassing the whole species range with more regional data can improve future predictions but comes along with challenges owing to the broader scale and/or lower quality usually associated with these data. Here, we compare future predictions obtained from a traditional SDM fitted on a regional dataset (Switzerland) to predictions obtained from data integration methods that combine regional and European datasets for several bird species breeding in Switzerland. Three models were fitted: a traditional SDM based only on regional data and thus not accounting for niche truncation, a data pooling model where the two datasets are merged without considering differences in extent or resolution, and a downscaling hierarchical approach that accounts for differences in extent and resolution. Results show that the traditional model leads to much larger predicted range changes (either positively or negatively) under climate change than both data integration methods. The traditional model also identified different variables as main drivers of species’ distribution compared to data-integration models. Differences between models regarding predicted range changes were larger for species where future conditions were outside the range of conditions existing in the regional dataset (i.e., when future conditions implied extrapolation). In conclusion, we showed that (i) models calibrated on a geographically restricted dataset provide markedly different predictions than data integration models and (ii) that these differences are at least partly explained by niche truncation. This suggests that using data integration methods could lead to more accurate predictions and more nuanced range changes than regional SDMs through a better characterization of species’ entire realized niches. 
PY 2022
PD AUG
SO Frontiers In Ecology And Evolution
SN 2296-701X
PU Frontiers Media SA
VL 10
UT 000843064000001
DI 10.3389/fevo.2022.944116
ID 89994
ER

EF