Is my species distribution model fit for purpose? Matching data and models to applications

Species distribution models (SDMs) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underestimate the strong links between data type, model output and suitability for end-use. We synthesize current knowledge and provide a simple framework...

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Autores: Guillera-Arroita, Gurutzeta, Lahoz-Monfort, José J., Elith, Jane, Gordon, Ascelin, Kujala, Heini, Lentini, Pia E., McCarthy, Michael A., Tingley, Reid, Wintle, Brendan A.
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2015
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/378684
Acceso en línea:http://hdl.handle.net/10261/378684
https://api.elsevier.com/content/abstract/scopus_id/84922503905
Access Level:acceso abierto
Palabra clave:Ecological niche model
Habitat model
Imperfect detection
Presence-absence
Presence-background
Prevalence
Sampling bias
Presence-only
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spelling Is my species distribution model fit for purpose? Matching data and models to applicationsGuillera-Arroita, GurutzetaLahoz-Monfort, José J.Elith, JaneGordon, AscelinKujala, HeiniLentini, Pia E.McCarthy, Michael A.Tingley, ReidWintle, Brendan A.Ecological niche modelHabitat modelImperfect detectionPresence-absencePresence-backgroundPrevalenceSampling biasPresence-onlySpecies distribution models (SDMs) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underestimate the strong links between data type, model output and suitability for end-use. We synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process (i.e. imperfect detection and sampling bias) determine the quantity that is estimated by a SDM. We then draw upon the published literature and simulations to illustrate and evaluate the information needs of the most common ecological, biogeographical and conservation applications of SDM outputs. We find that, while predictions of models fitted to the most commonly available observational data (presence records) suffice for some applications, others require estimates of occurrence probabilities, which are unattainable without reliable absence records. Our literature review and simulations reveal that, while converting continuous SDM outputs into categories of assumed presence or absence is common practice, it is seldom clearly justified by the application's objective and it usually degrades inference. Matching SDMs to the needs of particular applications is critical to avoid poor scientific inference and management outcomes. This paper aims to help modellers and users assess whether their intended SDM outputs are indeed fit for purpose.This work was supported by the Australian Research Council (ARC) Centre of Excellence for Environmental Decisions, the National Environment Research Program (NERP) Environmental Decisions Hub, and ARC Future Fellowships to J.E., M.M. and B.W. The authors thank Laura Pollock for helpful discussion regarding species distribution modelling for phylogenetic studies, and Janet Franklin, an anonymous referee and the editors for comments that improved the quality of the manuscript.Peer reviewedJohn Wiley & SonsAustralian Research CouncilGuillera-Arroita, Gurutzeta [0000-0002-8387-5739]Lahoz-Monfort, José J. [0000-0002-0845-7035]Elith, Jane [0000-0002-8706-0326]Wintle, Brendan A. [0000-0002-4234-5950]202520252015info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_dcae04bcPostprintinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttp://hdl.handle.net/10261/378684https://api.elsevier.com/content/abstract/scopus_id/84922503905reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.1111/geb.12268Noinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3786842026-05-22T06:33:51Z
dc.title.none.fl_str_mv Is my species distribution model fit for purpose? Matching data and models to applications
title Is my species distribution model fit for purpose? Matching data and models to applications
spellingShingle Is my species distribution model fit for purpose? Matching data and models to applications
Guillera-Arroita, Gurutzeta
Ecological niche model
Habitat model
Imperfect detection
Presence-absence
Presence-background
Prevalence
Sampling bias
Presence-only
title_short Is my species distribution model fit for purpose? Matching data and models to applications
title_full Is my species distribution model fit for purpose? Matching data and models to applications
title_fullStr Is my species distribution model fit for purpose? Matching data and models to applications
title_full_unstemmed Is my species distribution model fit for purpose? Matching data and models to applications
title_sort Is my species distribution model fit for purpose? Matching data and models to applications
dc.creator.none.fl_str_mv Guillera-Arroita, Gurutzeta
Lahoz-Monfort, José J.
Elith, Jane
Gordon, Ascelin
Kujala, Heini
Lentini, Pia E.
McCarthy, Michael A.
Tingley, Reid
Wintle, Brendan A.
author Guillera-Arroita, Gurutzeta
author_facet Guillera-Arroita, Gurutzeta
Lahoz-Monfort, José J.
Elith, Jane
Gordon, Ascelin
Kujala, Heini
Lentini, Pia E.
McCarthy, Michael A.
Tingley, Reid
Wintle, Brendan A.
author_role author
author2 Lahoz-Monfort, José J.
Elith, Jane
Gordon, Ascelin
Kujala, Heini
Lentini, Pia E.
McCarthy, Michael A.
Tingley, Reid
Wintle, Brendan A.
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Australian Research Council
Guillera-Arroita, Gurutzeta [0000-0002-8387-5739]
Lahoz-Monfort, José J. [0000-0002-0845-7035]
Elith, Jane [0000-0002-8706-0326]
Wintle, Brendan A. [0000-0002-4234-5950]
dc.subject.none.fl_str_mv Ecological niche model
Habitat model
Imperfect detection
Presence-absence
Presence-background
Prevalence
Sampling bias
Presence-only
topic Ecological niche model
Habitat model
Imperfect detection
Presence-absence
Presence-background
Prevalence
Sampling bias
Presence-only
description Species distribution models (SDMs) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underestimate the strong links between data type, model output and suitability for end-use. We synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process (i.e. imperfect detection and sampling bias) determine the quantity that is estimated by a SDM. We then draw upon the published literature and simulations to illustrate and evaluate the information needs of the most common ecological, biogeographical and conservation applications of SDM outputs. We find that, while predictions of models fitted to the most commonly available observational data (presence records) suffice for some applications, others require estimates of occurrence probabilities, which are unattainable without reliable absence records. Our literature review and simulations reveal that, while converting continuous SDM outputs into categories of assumed presence or absence is common practice, it is seldom clearly justified by the application's objective and it usually degrades inference. Matching SDMs to the needs of particular applications is critical to avoid poor scientific inference and management outcomes. This paper aims to help modellers and users assess whether their intended SDM outputs are indeed fit for purpose.
publishDate 2015
dc.date.none.fl_str_mv 2015
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_dcae04bc
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/378684
https://api.elsevier.com/content/abstract/scopus_id/84922503905
url http://hdl.handle.net/10261/378684
https://api.elsevier.com/content/abstract/scopus_id/84922503905
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1111/geb.12268
No
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv John Wiley & Sons
publisher.none.fl_str_mv John Wiley & Sons
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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