Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing

Jonas J. Lembrechts; Jonathan Lenoir; Nina Roth; Tarek Hattab; Ann Milbau; Sylvia Haider; Loïc Pellissier; Aníbal Pauchard; Amanda Ratier Backes; Romina D. Dimarco; Martin A. Nuñez; Juha Aalto; Ivan Nijs

doi:10.1111/geb.12974

Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing

Research output: Journal contributions › Journal articles › Research › peer-review

Standard

Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. / Lembrechts, Jonas J.; Lenoir, Jonathan; Roth, Nina et al.
In: Global Ecology and Biogeography, Vol. 28, No. 11, 22.07.2019, p. 1578-1596.

Research output: Journal contributions › Journal articles › Research › peer-review

Harvard

Lembrechts, JJ, Lenoir, J, Roth, N, Hattab, T, Milbau, A, Haider, S, Pellissier, L, Pauchard, A, Ratier Backes, A, Dimarco, RD, Nuñez, MA, Aalto, J & Nijs, I 2019, 'Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing', Global Ecology and Biogeography, vol. 28, no. 11, pp. 1578-1596. https://doi.org/10.1111/geb.12974

APA

Lembrechts, J. J., Lenoir, J., Roth, N., Hattab, T., Milbau, A., Haider, S., Pellissier, L., Pauchard, A., Ratier Backes, A., Dimarco, R. D., Nuñez, M. A., Aalto, J., & Nijs, I. (2019). Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Global Ecology and Biogeography, 28(11), 1578-1596. https://doi.org/10.1111/geb.12974

Vancouver

Lembrechts JJ, Lenoir J, Roth N, Hattab T, Milbau A, Haider S et al. Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Global Ecology and Biogeography. 2019 Jul 22;28(11):1578-1596. doi: 10.1111/geb.12974

Bibtex

@article{d937f93011184d3883355212b64533dd,

title = "Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing",

abstract = "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.",

keywords = "Biology, bioclimatic envelope modelling, bioclimatic variables, climate change, growth forms, land surface temperature, microclimate, mountains, soil temperature, species distribution modelling",

author = "Lembrechts, {Jonas J.} and Jonathan Lenoir and Nina Roth and Tarek Hattab and Ann Milbau and Sylvia Haider and Lo{\"i}c Pellissier and An{\'i}bal Pauchard and {Ratier Backes}, Amanda and Dimarco, {Romina D.} and Nu{\~n}ez, {Martin A.} and Juha Aalto and Ivan Nijs",

note = "Publisher Copyright: {\textcopyright} 2019 John Wiley & Sons Ltd",

year = "2019",

month = jul,

day = "22",

doi = "10.1111/geb.12974",

language = "English",

volume = "28",

pages = "1578--1596",

journal = "Global Ecology and Biogeography",

issn = "1466-822X",

publisher = "Wiley-Blackwell Publishing Ltd.",

number = "11",

}

RIS

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

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 -

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DOI

https://doi.org/10.1111/geb.12974
Final published version

Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing

Standard

Harvard

APA

Vancouver

Bibtex

RIS

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