{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"Authorea, Inc."}],"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:42:08Z","timestamp":1772239328831,"version":"3.50.1"},"posted":{"date-parts":[[2023,10,19]]},"group-title":"Preprints","reference-count":0,"publisher":"Wiley","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2023,10,19]]},"abstract":"<jats:p id=\"p1\">Climate change affects biodiversity in diverse ways, necessitating the\nexploration of multiple climate dimensions using standardized metrics.\nHowever, existing methods for quantifying these metrics are scattered\nand tools for comparing alternative climate change metrics on the same\nfooting are lacking. To address this gap, we developed \u201cclimetrics\u201d\nwhich is an extensible and reproducible R package to spatially quantify\nand explore multiple dimensions of climate change through a unified\nprocedure. Six widely used climate change metrics are currently\nimplemented, including 1) Standardized Local Anomalies; 2) Changes in\nProbabilities of Local Climate Extremes; 3) Changes in Areas of\nAnalogous Climates; 4) Novel Climates; 5) Changes in Distances to\nAnalogous Climates; and 6) Climate Change Velocity. For climate change\nvelocity, three different algorithms are implemented and available\nwithin the package including; a) Distanced-based Velocity (\u201cdVe\u201d); b)\nThreshold-based Velocity (\u201cve\u201d); and c) Gradient-based Velocity\n(\u201cgVe\u201d). The package also provides additional tools to calculate the\nmonthly mean of climate variables over multiple years, to quantify and\nmap the temporal trend (slope) of a given climate variable at the pixel\nlevel, and to classify and map K\u00f6ppen-Geiger (KG) climate zones. The\nclimetrics R package is seamlessly integrated with the rts package for\nefficient handling of raster time-series data. The functions in\nclimetrics are designed to be user-friendly, making them suitable for\nless-experienced R users. Detailed comments and descriptions in their\nhelp pages and vignettes of the package facilitate further customization\nby advanced users. In summary, the climetrics R package offers a unified\nframework for quantifying various climate change metrics, making it a\nuseful tool for characterizing multiple dimensions of climate change and\nexploring their spatiotemporal patterns.<\/jats:p>","DOI":"10.22541\/au.169769525.52686463\/v1","type":"posted-content","created":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T02:01:05Z","timestamp":1697680865000},"source":"Crossref","is-referenced-by-count":0,"title":["climetrics: An R package to quantify multiple dimensions of climate change"],"prefix":"10.22541","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0303-3145","authenticated-orcid":true,"given":"Shirin","family":"Taheri","sequence":"first","affiliation":[{"name":"CSIC"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5431-2729","authenticated-orcid":true,"given":"Babak","family":"Naimi","sequence":"additional","affiliation":[{"name":"University of Utrecht Roosevelt Academy"}]},{"given":"Miguel","family":"Ara\u00fajo","sequence":"additional","affiliation":[{"name":"CSIC"}]}],"member":"311","container-title":[],"original-title":[],"deposited":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T02:01:05Z","timestamp":1697680865000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.authorea.com\/users\/674997\/articles\/673133-climetrics-an-r-package-to-quantify-multiple-dimensions-of-climate-change?commit=0f1355ea52a3d115f2ad2c9b4e2008bd4351b5df"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,19]]},"references-count":0,"URL":"https:\/\/doi.org\/10.22541\/au.169769525.52686463\/v1","relation":{"has-version":[{"id-type":"doi","id":"10.22541\/au.169769525.52686463\/v2","asserted-by":"object"}]},"subject":[],"published":{"date-parts":[[2023,10,19]]},"subtype":"preprint"}}