Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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in: Global Ecology and Biogeography, Jahrgang 28, Nr. 11, 22.07.2019, S. 1578-1596.
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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TY - JOUR
T1 - Comparing temperature data sources for use in species distribution models
T2 - From in-situ logging to remote sensing
AU - Lembrechts, Jonas J.
AU - Lenoir, Jonathan
AU - Roth, Nina
AU - Hattab, Tarek
AU - Milbau, Ann
AU - Haider, Sylvia
AU - Pellissier, Loïc
AU - Pauchard, Aníbal
AU - Ratier Backes, Amanda
AU - Dimarco, Romina D.
AU - Nuñez, Martin A.
AU - Aalto, Juha
AU - Nijs, Ivan
N1 - Publisher Copyright: © 2019 John Wiley & Sons Ltd
PY - 2019/7/22
Y1 - 2019/7/22
N2 - 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.
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.
KW - Biology
KW - bioclimatic envelope modelling
KW - bioclimatic variables
KW - climate change
KW - growth forms
KW - land surface temperature
KW - microclimate
KW - mountains
KW - soil temperature
KW - species distribution modelling
UR - http://www.scopus.com/inward/record.url?scp=85069720022&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/e1ca0554-7964-36d3-bbac-53cb22a04551/
U2 - 10.1111/geb.12974
DO - 10.1111/geb.12974
M3 - Journal articles
AN - SCOPUS:85069720022
VL - 28
SP - 1578
EP - 1596
JO - Global Ecology and Biogeography
JF - Global Ecology and Biogeography
SN - 1466-822X
IS - 11
ER -