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Prediction of large whale distributions: A comparison of presence-absence and presence-only modeling techniques

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dc.contributor.authorFiedler, Paul C.
dc.contributor.authorRedfern, Jessica V.
dc.contributor.authorForney, Karin A.
dc.contributor.authorPalacios, Daniel M.
dc.contributor.authorSheredy, Corey
dc.contributor.authorRasmussen, Kristin
dc.contributor.authorGarcía-Godos, Ignacio A.
dc.contributor.authorSantillán, Luis
dc.contributor.authorTetley, Michael J.
dc.contributor.authorFélix, Fernando
dc.contributor.authorBallance, Lisa Taylor
dc.date.accessioned2018-12-06T20:03:25Z
dc.date.available2018-12-06T20:03:25Z
dc.date.issued2018-11-12
dc.description.abstractSpecies distribution models that predict species occurrence or density by quantifying relationships with environmental variables are used for a variety of scientific investigations and management applications. For endangered species, such as large whales, models help to understand the ecological factors influencing variability in distributions and to assess potential risk from shipping, fishing, and other human activities. Systematic surveys record species presence and absence, as well as the associated search effort, but are very expensive. Presence-only data consisting only of sightings can increase sample size, but may be biased in both geographical and niche space. We built generalized additive models (GAMs) using presence-absence sightings data and maximum entropy models (Maxent) using the same presence-absence sightings data, and also using presence-only sightings data, for four large whale species in the eastern tropical Pacific Ocean: humpback (Megaptera novaeangliae), blue (Balaenoptera musculus), Bryde's (Balaenoptera edeni), and sperm whales (Physeter macrocephalus). Environmental variables were surface temperature, surface salinity, thermocline depth, stratification index, and seafloor depth. We compared predicted distributions from each of the two model types. Maxent and GAM model predictions based on systematic survey data are very similar, when Maxent absences are selected from the survey trackline data. However, we show that spatial bias in presence-only Maxent predictions can be caused by using pseudo-absences instead of observed absences and by the sampling biases of both opportunistic data and stratified systematic survey data with uneven coverage between strata. Predictions of uncommon large whale distributions from Maxent or other presence-only techniques may be useful for science or management, but only if spatial bias in the observations is addressed in the derivation and interpretation of model predictions. © 2018 Fiedler, Redfern, Forney, Palacios, Sheredy, Rasmussen, García-Godos, Santillán, Tetley, Félix and Ballance.es_ES
dc.description.peer-reviewRevisado por pareses_ES
dc.formatapplication/pdfes_ES
dc.identifier.doi10.3389/fmars.2018.00419
dc.identifier.issn22967745
dc.identifier.journalFrontiers in Marine Sciencees_ES
dc.identifier.urihttps://hdl.handle.net/20.500.14005/4000
dc.identifier.urihttps://dx.doi.org/10.3389/fmars.2018.00419
dc.language.isospaes_ES
dc.publisherFrontiers Media S.A.es_ES
dc.rightsinfo:eu-repo/semantics/embargoedAccesses_ES
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.sourceUniversidad San Ignacio de Loyolaes_ES
dc.sourceRepositorio Institucional - USILes_ES
dc.subjectMaximum entropy
dc.subjectEastern tropical Pacifices_ES
dc.subjectSpecies distribution model
dc.subjectGeneralized additive modeles_ES
dc.titlePrediction of large whale distributions: A comparison of presence-absence and presence-only modeling techniqueses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dspace.entity.typePublication
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