2017
DOI: 10.1111/jbi.13029
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Implications and alternatives of assigning climate data to geographical centroids

Abstract: Aim When precise coordinate data for training species distribution models (SDMs) are lacking, climatic variables are often assigned to centroids of geopolitically defined regions, frequently counties. This is problematic because approximations using centroids may not be representative of the regional climate or the locality from where species actually occur, thus leading to spurious conclusions. We evaluated county centroid climate versus simple alternatives for assigning climate to species observations in the… Show more

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Cited by 51 publications
(64 citation statements)
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“…To examine relationships between climate and herbivory, we extracted various temperature predictors from the PRISM Climate Group, gridded data [http://www.prism.oregonstate.edu], all of which were county‐level means corresponding to each specimen collection date (Park & Davis, ). To broadly represent temperatures during overwintering and spring development of insect herbivores, we extracted mean temperatures from January to March during the year when a specimen was collected.…”
Section: Methodsmentioning
confidence: 99%
“…To examine relationships between climate and herbivory, we extracted various temperature predictors from the PRISM Climate Group, gridded data [http://www.prism.oregonstate.edu], all of which were county‐level means corresponding to each specimen collection date (Park & Davis, ). To broadly represent temperatures during overwintering and spring development of insect herbivores, we extracted mean temperatures from January to March during the year when a specimen was collected.…”
Section: Methodsmentioning
confidence: 99%
“…Label information on specimen locations can be at coarse geographical scales (e.g., at the county level in the United States), or is not provided, especially for specimens collected long ago. This is further complicated by the fact that such specimens may be inaccurately georeferenced, which can propagate through subsequent analyses especially if climate data is linked to such data (Park and Davis ). Because we cannot go back in time and collect more fine‐scale location data, its absence may prevent researchers from using specimens to address certain hypotheses.…”
Section: Herbaria As Novel Data Sources: Limitations and Challengesmentioning
confidence: 99%
“…To quantify the diversity of biomes for which we have data, we extracted mean annual temperature and precipitation from CHELSA climate layers at 30 arc-second resolution (Karger et al., 2017) and took the median environmental variable per polygon (Park & Davis, 2017) to represent each site's climatic niche. To quantify the diversity of biomes for which we have data, we extracted mean annual temperature and precipitation from CHELSA climate layers at 30 arc-second resolution (Karger et al., 2017) and took the median environmental variable per polygon (Park & Davis, 2017) to represent each site's climatic niche.…”
Section: Biomesmentioning
confidence: 99%