{"refrec":{"BRefID":358984,"RR":"<b>Chevalier, M.</b> (2022). <i>crestr</i>: an R package to perform probabilistic climate reconstructions from palaeoecological datasets. <i>Clim. Past 18(4)</i>: 821-844. <a href=\"https://dx.doi.org/10.5194/cp-18-821-2022\" target=\"_blank\">https://dx.doi.org/10.5194/cp-18-821-2022</a>","BEntID":356699,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Clim. Past 18(4)</i>: 821-844. <a href=\"https://dx.doi.org/10.5194/cp-18-821-2022\" target=\"_blank\">https://dx.doi.org/10.5194/cp-18-821-2022</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Chevalier, M.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Chevalier, M.","Englishabstract":"Statistical climate reconstruction techniques are fundamental tools to study past climate variability from fossil proxy data. In particular, the methods based on probability density functions (or PDFs) can be used in various environments and with different climate proxies because they rely on elementary calibration data (i.e. modern geolocalised presence data). However, the difficulty of accessing and curating these calibration data and the complexity of interpreting probabilistic results have often limited their use in palaeoclimatological studies. Here, I introduce a new R package (crestr) to apply the PDF-based method CREST (Climate REconstruction SofTware) on diverse palaeoecological datasets and address these problems. crestr includes a globally curated calibration dataset for six common climate proxies (i.e. plants, beetles, chironomids, rodents, foraminifera, and dinoflagellate cysts) associated with an extensive range of climate variables (20 terrestrial and 19 marine variables) that enables its use in most terrestrial and marine environments. Private data collections can also be used instead of, or in combination with, the provided calibration dataset. The package includes a suite of graphical diagnostic tools to represent the data at each step of the reconstruction process and provide insights into the effect of the different modelling assumptions and external factors that underlie a reconstruction. With this R package, the CREST method can now be used in a scriptable environment and thus be more easily integrated with existing workflows. It is hoped that crestr will be used to produce the much-needed quantified climate reconstructions from the many regions where they are currently lacking, despite the availability of suitable fossil records. To support this development, the use of the package is illustrated with a step-by-step replication of a 790 000-year-long mean annual temperature reconstruction based on a pollen record from southeastern Africa.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"<i>crestr</i>: an R package to perform probabilistic climate reconstructions from palaeoecological datasets","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-06-14 01:32:07.239690","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":null,"OtherDescriptors":null,"Notes":null,"AnaPub":2022,"MonPub":null,"DateUpdate":"2022-10-31","DateCreate":"2022-10-31","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000783499300001","VABBcode":null,"OpenAcc":1,"DOI":"10.5194/cp-18-821-2022"},"refs":null,"anarec":{"AnaID":358984,"PubliDate":2022,"Pagination":"821-844","XtraPublOfAnaID":null,"ISBN":null,"Volume":"18","Issue":"4","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":220505,"SerRR":"Climate of the Past. 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