Peder S Engelstad
Catherine S. Jarnevich
Helen Sofaer
Wesley M. Daniel
Richard A. Erickson
2022
<div id="abstracts" class="Abstracts u-font-gulliver text-s"><div id="d1e271" class="abstract author"><div id="d1e274"><p id="d1e275"><span>Climate matching allows comparisons of climatic conditions between different locations to understand location and species range climatic suitability. The approach may be used as part of horizon scanning exercises such as those conducted for invasive species. We implemented the CLIMATCH algorithm into an R package, </span><span class="monospace">climatchR</span>. The package allows automated and scripted climate matching exercises across all steps from downloading data to summarizing species climate matches. We also show how<span> </span><span class="monospace">climatchR</span><span> </span>may be used with high-throughput computing to process many species. For example, we were able to calculate climate scores for over 8,000 species in less than 3 days using this package. This automation allows high-throughput processing of species data, a new development for improving the efficiency and speed of climate matching and horizon scanning.</p></div></div></div>
application/pdf
10.1016/j.envsoft.2022.105510
en
Elsevier
Climate matching with the climatchR R package
article