{"refrec":{"BRefID":291117,"RR":"<b>Bosch, S.; Tyberghein, L.; Deneudt, K.; Hernandez, F.; De Clerck, O.</b> (2018). In search of relevant predictors for marine species distribution modelling using the MarineSPEED benchmark dataset. <i>Diversity Distrib. 24(2)</i>: 144-157. <a href=\"https://dx.doi.org/10.1111/ddi.12668\" target=\"_blank\">https://dx.doi.org/10.1111/ddi.12668</a>","BEntID":283160,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":0,"RefStringPartII":". <i>Diversity Distrib. 24(2)</i>: 144-157. <a href=\"https://dx.doi.org/10.1111/ddi.12668\" target=\"_blank\">https://dx.doi.org/10.1111/ddi.12668</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Bosch, S.; Tyberghein, L.; Deneudt, K.; Hernandez, F.; De Clerck, O.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Bosch, S. <i>et al.</i>","Englishabstract":"Aim: Ideally, datasets for species distribution modelling (SDM) contain evenly sampled records covering the entire distribution of the species, confirmed absences and auxiliary ecophysiological data allowing informed decisions on relevant predictors. Unfortunately, these criteria are rarely met for marine organisms for which distributions are too often only scantly characterized and absences generally not recorded. Here, we investigate predictor relevance as a function of modelling algorithms and settings for a global dataset of marine species.Location: Global marine.Methods: We selected well-studied and identifiable species from all major marine taxonomic groups. Distribution records were compiled from public sources (e.g., OBIS, GBIF, Reef Life Survey) and linked to environmental data from Bio-ORACLE and MARSPEC. Using this dataset, predictor relevance was analysed under different variations of modelling algorithms, numbers of predictor variables, cross-validation strategies, sampling bias mitigation methods, evaluation methods and ranking methods. SDMs for all combinations of predictors from eight correlation groups were fitted and ranked, from which the top five predictors were selected as the most relevant. Results: We collected two million distribution records from 514 species across 18 phyla. Mean sea surface temperature and calcite are, respectively, the most relevant and irrelevant predictors. A less clear pattern was derived from the other predictors. The biggest differences in predictor relevance were induced by varying the number of predictors, the modelling algorithm and the sample selection bias correction. The distribution data and associated environmental data are made available through the R package marinespeed and at http://marinespeed.org.Main conclusions: While temperature is a relevant predictor of global marine species distributions, considerable variation in predictor relevance is linked to the SDM set-up. We promote the usage of a standardized benchmark dataset (MarineSPEED) for methodological SDM studies.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"In search of relevant predictors for marine species distribution modelling using the MarineSPEED benchmark dataset","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-04-20 01:32:24.060886","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":"benchmark dataset; ecological niche modelling; spatial cross-validation; species distribution modelling; variable importance ","OtherDescriptors":null,"Notes":null,"AnaPub":2018,"MonPub":null,"DateUpdate":"2019-09-06","DateCreate":"2017-11-28","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000419339800002","VABBcode":null,"OpenAcc":1,"DOI":"10.1111/ddi.12668"},"refs":null,"anarec":{"AnaID":291117,"PubliDate":2018,"Pagination":"144-157","XtraPublOfAnaID":null,"ISBN":null,"Volume":"24","Issue":"2","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":99812,"SerRR":"Diversity and Distributions. 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