FN Archimer Export Format
PT J
TI Comparing temperature data sources for use in species distribution models: From in‐situ logging to remote sensing
BT 
AF Lembrechts, Jonas J.
   Lenoir, Jonathan
   Roth, Nina
   Hattab, Tarek
   Milbau, Ann
   Haider, Sylvia
   Pellissier, Loïc
   Pauchard, Aníbal
   Ratier Backes, Amanda
   Dimarco, Romina D.
   Nuñez, Martin A.
   Aalto, Juha
   Nijs, Ivan
   Bates, Amanda
AS 1:1;2:2;3:3;4:2,4;5:5;6:6,7;7:8,9;8:10,11;9:6,7;10:12;11:13;12:14,15;13:1;14:;
FF 1:;2:;3:;4:PDG-RBE-MARBEC-LHM;5:;6:;7:;8:;9:;10:;11:;12:;13:;14:;
C1 Centre of Excellence Plants and Ecosystems (PLECO) University of Antwerp Wilrijk, Belgium
   UR “Ecologie et Dynamique des Systèmes Anthropisés” (EDYSAN, UMR 7058 CNRS‐UPJV) Université de Picardie Jules Verne Amiens Cedex 1 ,France
   Biogeography and Geomatics, Department of Physical Geography Stockholm University Stockholm ,Sweden
   MARBEC (IRD, Ifremer, Université de Montpellier, CNRS) Sète Cedex ,France
   Research Institute for Nature and Forest – INBO Brussels, Belgium
   Institute of Biology/Geobotany and Botanical Garden Martin Luther University Halle‐Wittenberg Halle (Saale) ,Germany
   German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig Leipzig, Germany
   Landscape Ecology, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science ETH Zürich Zürich ,Switzerland
   Swiss Federal Research Institute WSL Birmensdorf ,Switzerland
   Laboratorio de Invasiones Biológicas, Facultad de Ciencias Forestales Universidad de Concepción Concepción ,Chile
   Institute of Ecology and Biodiversity (IEB) Santiago, Chile
   Grupo de Ecología de Poblaciones de Insectos INTA‐CONICET Bariloche, Argentina
   Grupo de Ecología de Invasiones INIBIOMA, CONICET‐Universidad Nacional del Comahue Bariloche ,Argentina
   The Department of Geosciences and Geography FIN‐00014 University of Helsinki Helsinki, Finland
   Finnish Meteorological Institute Helsinki, Finland
C2 UNIV ANTWERP, BELGIUM
   UNIV PICARDIE JULES VERNE, FRANCE
   UNIV STOCKHOLM, SWEDEN
   IFREMER, FRANCE
   INBO, BELGIUM
   UNIV HALLE WITTENBERG, GERMANY
   IDIV, GERMANY
   ETH ZURICH, SWITZERLAND
   SWISS FED RES INST WSL, SWITZERLAND
   UNIV CONCEPCION, CHILE
   UNIV CHILE, CHILE
   INTA, ARGENTINA
   CONICET, ARGENTINA
   UNIV HELSINKI, FINLAND
   FINNISH METEOROL INST, FINLAND
SI SETE
SE PDG-RBE-MARBEC-LHM
UM MARBEC
IN WOS Ifremer UMR
   copubli-france
   copubli-europe
   copubli-univ-france
   copubli-int-hors-europe
   copubli-sud
IF 6.446
TC 90
UR https://archimer.ifremer.fr/doc/00508/61954/85718.pdf
LA English
DT Article
DE ;bioclimatic envelope modelling;bioclimatic variables;climate change;growth forms;land surface temperature;microclimate;mountains;soil temperature;species distribution modelling
AB Aim Although species distribution models (SDMs) traditionally link species occurrences to free‐air temperature data at coarse spatio‐temporal resolution, the distribution of organisms might instead be driven by temperatures more proximal to their habitats. Several solutions are currently available, such as downscaled or interpolated coarse‐grained free‐air temperatures, satellite‐measured land surface temperatures (LST) or in‐situ‐measured soil temperatures. A comprehensive comparison of temperature data sources and their performance in SDMs is, however, currently lacking. Location Northern Scandinavia. Time period 1970–2017. Major taxa studied Higher plants. Methods We evaluated different sources of temperature data (WorldClim, CHELSA, MODIS, E‐OBS, topoclimate and soil temperature from miniature data loggers), differing in spatial resolution (from 1″ to 0.1°), measurement focus (free‐air, ground‐surface or soil temperature) and temporal extent (year‐long versus long‐term averages), and used them to fit SDMs for 50 plant species with different growth forms in a high‐latitudinal mountain region. Results Differences between these temperature data sources originating from measurement focus and temporal extent overshadow the effects of temporal climatic differences and spatio‐temporal resolution, with elevational lapse rates ranging from −0.6°C per 100 m for long‐term free‐air temperature data to −0.2°C per 100 m for in‐situ soil temperatures. Most importantly, we found that the performance of the temperature data in SDMs depended on the growth forms of species. The use of in‐situ soil temperatures improved the explanatory power of our SDMs (R2 on average +16%), especially for forbs and graminoids (R2 +24 and +21% on average, respectively) compared with the other data sources. Main conclusions We suggest that future studies using SDMs should use the temperature dataset that best reflects the ecology of the species, rather than automatically using coarse‐grained data from WorldClim or CHELSA. 
PY 2019
PD NOV
SO Global Ecology And Biogeography
SN 1466-822X
PU Wiley
VL 28
IS 11
UT 000477231600001
BP 1578
EP 1596
DI 10.1111/geb.12974
ID 61954
ER

EF