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Incorporating physiological knowledge into correlative species distribution models minimizes bias introduced by the choice of calibration area.

2024-05Journal article Open access
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dc.contributor.authorZhang, Zhixin
dc.contributor.authorZhou, Jinxin
dc.contributor.authorGarcía Molinos, Jorge
dc.contributor.authorMammola, Stefano
dc.contributor.authorBede-Fazekas, Ákos
dc.contributor.authorFeng, Xiao
dc.contributor.authorKitazawa, Daisuke
dc.contributor.authorQiu, Tianlong
dc.contributor.authorLin, Qiang
dc.contributor.authorAssis, Jorge
dc.date.accessioned2024-12-02T14:25:26Z
dc.date.available2024-12-02T14:25:26Z
dc.date.issued2024-05
dc.description.abstractCorrelative species distribution models (SDMs) are important tools to estimate species' geographic distribution across space and time, but their reliability heavily relies on the availability and quality of occurrence data. Estimations can be biased when occurrences do not fully represent the environmental requirement of a species. We tested to what extent species' physiological knowledge might influence SDM estimations. Focusing on the Japanese sea cucumber within the coastal ocean of East Asia, we compiled a comprehensive dataset of occurrence records. We then explored the importance of incorporating physiological knowledge into SDMs by calibrating two types of correlative SDMs: a naïve model that solely depends on environmental correlates, and a physiologically informed model that further incorporates physiological information as priors. We further tested the models' sensitivity to calibration area choices by fitting them with different buffered areas around known presences. Compared with naïve models, the physiologically informed models successfully captured the negative influence of high temperature on and were less sensitive to the choice of calibration area. The naïve models resulted in more optimistic prediction of the changes of potential distributions under climate change (i.e., larger range expansion and less contraction) than the physiologically informed models. Our findings highlight benefits from incorporating physiological information into correlative SDMs, namely mitigating the uncertainties associated with the choice of calibration area. Given these promising features, we encourage future SDM studies to consider species physiological information where available.eng
dc.description.abstractThe online version contains supplementary material available at 10.1007/s42995-024-00226-0.eng
dc.description.sponsorshipPTDC/BIA-CBI/6515/2020
dc.identifier.doi10.1007/s42995-024-00226-0
dc.identifier.eissn2662-1746
dc.identifier.other38827135
dc.identifier.urihttp://hdl.handle.net/10400.1/26377
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relationAlgarve Centre for Marine Sciences
dc.relationAlgarve Centre for Marine Sciences
dc.relationCentre for Marine and Environmental Research
dc.relationClimate-informed prioritization of marine biodiversity hotspots to support the implementation of the post-2020 biodiversity framework
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectBayesian approach
dc.subjectClimate change
dc.subjectHabitat suitability
dc.subjectPhysiological knowledge
dc.subjectSpecies distribution model
dc.titleIncorporating physiological knowledge into correlative species distribution models minimizes bias introduced by the choice of calibration area.eng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleAlgarve Centre for Marine Sciences
oaire.awardTitleAlgarve Centre for Marine Sciences
oaire.awardTitleCentre for Marine and Environmental Research
oaire.awardTitleClimate-informed prioritization of marine biodiversity hotspots to support the implementation of the post-2020 biodiversity framework
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04326%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04326%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0101%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FBIA-CBI%2F6515%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/CEEC IND5ed/2022.00861.CEECIND%2FCP1729%2FCT0003/PT
oaire.citation.endPage362
oaire.citation.issue2
oaire.citation.startPage349
oaire.citation.titleMarine life science & technology
oaire.citation.volume6
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream3599-PPCDT
oaire.fundingStreamCEEC IND5ed
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameAssis
person.givenNameJorge
person.identifier.ciencia-id5C1D-05B6-29F7
person.identifier.orcid0000-0002-6624-4820
person.identifier.ridG-9688-2012
person.identifier.scopus-author-id53463298700
project.funder.identifierhttp://doi.org/10.13039/501100001871
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project.funder.nameFundação para a Ciência e a Tecnologia
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project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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