{"refrec":{"BRefID":361808,"RR":"<b>Owens, H.L.; Rahbek, C.</b> (2023). voluModel: Modelling species distributions in three‐dimensional space. <i>Methods Ecol. Evol. 14(3)</i>: 841-847. <a href=\"https://dx.doi.org/10.1111/2041-210x.14064\" target=\"_blank\">https://dx.doi.org/10.1111/2041-210x.14064</a>","BEntID":359524,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Methods Ecol. Evol. 14(3)</i>: 841-847. <a href=\"https://dx.doi.org/10.1111/2041-210x.14064\" target=\"_blank\">https://dx.doi.org/10.1111/2041-210x.14064</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Owens, H.L.; Rahbek, C.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Owens, H.L.; Rahbek, C.","Englishabstract":"<li>Ecological niche modelling (ENM), species distribution modelling and related spatial analytical methods were first developed in two-dimensional (2-D)&nbsp;terrestrial systems; many common ENM workflows organize and analyse geographically structured occurrence and environmental data based on 2-D latitude and longitude coordinates. This may be suitable for most terrestrial organisms, but pelagic marine species are distributed not only horizontally but also vertically. Extracting environmental data for marine species based only on latitude and longitude coordinates may result in poorly trained ENMs and inaccurate prediction of species' geographical distributions, as water conditions may vary strikingly with depth.</li>                                             <li>We developed the <span style=\"font-family:monospace\">voluModel</span> R package to efficiently extract three-dimensional (3-D) environmental data for training ENMs (i.e. presences and absences/pseudoabsences/background). <span style=\"font-family:monospace\">voluModel</span> also provides tools for 3-D ENM projection visualization and estimation of model extrapolation risk.</li>                                             <li>We present the main features of the <span style=\"font-family:monospace\">voluModel</span> R package and provide a simple modelling workflow for Luminous Hake, <i>Steindachneria argentea</i>, as an example. We also compare results from 2-D and 3-D spatial models to demonstrate differences in how the modelling methods perform.</li>                                             <li>The use of 3-D environmental data generates more precise estimates of environmental conditions for training ENMs. This method also improves inference of species' suitable abiotic ecological niches and potential geographic ranges. 3-D niche modelling is important step forward for marine macroecology and biogeography, as it will yield more accurate estimates of ocean species richness and potential past and future changes in the horizontal and vertical dimensions of species' geographic ranges. The latter is particularly relevant considering ongoing climate change that may cause redistribution of species in environmental space (both in latitude and depth) over time.</li>","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"voluModel: Modelling species distributions in three‐dimensional space","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-06-10 01:32:48.010878","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":"3-D, ecological niche model, geographic range, marine, pelagic, R package, species distribution model, visualization","OtherDescriptors":null,"Notes":null,"AnaPub":2023,"MonPub":null,"DateUpdate":"2023-03-06","DateCreate":"2023-03-06","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000921670800001","VABBcode":null,"OpenAcc":1,"DOI":"10.1111/2041-210x.14064"},"refs":null,"anarec":{"AnaID":361808,"PubliDate":2023,"Pagination":"841-847","XtraPublOfAnaID":null,"ISBN":null,"Volume":"14","Issue":"3","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":205126,"SerRR":"Methods in Ecology and Evolution. 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