Using the risk of spatial extrapolation by machine-learning models to assess the reliability of model predictions for conservation-Reference-Cited by-同舟云学术

Using the risk of spatial extrapolation by machine-learning models to assess the reliability of model predictions for conservation

Published:2023-04-10 Issue:6 Volume:38 Page:1363-1372
ISSN:0921-2973
Container-title:Landscape Ecology
language:en
Short-container-title:Landsc Ecol

Author:

Gutzwiller Kevin J.^ORCID,Serno Kimberly M.

Publisher

Springer Science and Business Media LLC

Subject

Nature and Landscape Conservation,Ecology,Geography, Planning and Development

Link

https://link.springer.com/content/pdf/10.1007/s10980-023-01651-9.pdf

Reference39 articles.

1. Baltensperger AP, Dixon MD, Swanson DL (2020) Implications of future climate- and land-change scenarios on grassland bird abundance and biodiversity in the Upper Missouri River Basin. Landsc Ecol 35:1757–1773

2. Bartley ML, Hanks EM, Schliep EM, Soranno PA, Wagner T (2019) Identifying and characterizing extrapolation in multivariate response data. PLoS ONE 14:e0225715

3. BirdLife International and Handbook of the Birds of the World (2018) Bird species distribution maps of the world. Version 2018.1. http://datazone.birdlife.org/species/requestdis.

4. Booker DJ, Whitehead AL (2018) Inside or outside: quantifying extrapolation across river networks. Water Resour Res. https://doi.org/10.1029/2018WR023378

5. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

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