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dc.contributor.authorHodges, Samuel
dc.contributor.authorErikstad, Kjell E.
dc.contributor.authorReiertsen, Tone Kristin
dc.coverage.spatialBarents Seaen_US
dc.identifier.citationEcological Solutions and Evidence. 2022, 3 (4), .en_US
dc.description.abstract1. The conservation of seabirds is increasingly important for their role as indicator species of ocean ecosystems, which are predicted to experience increasing levels of exploitation this century. Safeguarding these ecosystems will require predictive, spatial studies of seabird foraging hotspots. Current research on seabird foraging hotspots has established a significant relationship between probability of presence and several environmental variables, including Sea Surface Temperature (SST). However, inter-annual, basin-wide variation has the potential to invalidate these models, which depend on seasonal mesoscale variability. 2. In this study, we present a novel solution to predict presence from spatially and temporally variable environmental predictors, while reducing the influence of large-scale basin-wide variation. We model the Maximum Entropy (MaxENT) Model-derived relationship between Standardized Monthly SST (StdSST) and Habitat Suitability using Gaussian curve models, and then apply these models to independent StdSST data to produce heatmaps of predicted seabird presence. 3. In this study, we demonstrate StdSST to be a functional environmental predictor of seabird presence, within a Gaussian curve model framework. We demonstrate accurate predictions of the model’s training data and of independent seabird presence data to a high degree of accuracy (area under the receiver operator characteristic curve > 0.65) for four species of Auk: Common Guillemots (Uria aalge), Razorbills (Alca torda), Atlantic Puffins (Fratercula arctica) and Brunnich’s Guillemots (Uria lomvia). 4. We believe that the methodology we have developed and tested in this study can be used to guide ecosystem management practices by converting coupled-climate model predictions into predictions of future presence based on Habitat Suitability for the species, allowing us to consider the possible effects of climate change and yearly variation of SST on foraging seabird hotspots in the Barents Sea Atlantic Puffin, Barents Sea, Brunnich’s Guillemot, Common Guillemot, ecological modelling, MaxENT, Razorbill, spatial ecologyen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.subjectAtlantic Puffinen_US
dc.subjectBarents Seaen_US
dc.subjectBrunnich’s Guillemoten_US
dc.subjectCommon Guillemoten_US
dc.subjectecological modellingen_US
dc.subjectspatial ecologyen_US
dc.titlePredicting the foraging patterns of wintering Auks using a sea surface temperature model for the Barents Seaen_US
dc.title.alternativePredicting the foraging patterns of wintering Auks using a sea surface temperature model for the Barents Seaen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© 2022 The Authorsen_US
dc.subject.nsiVDP::Zoologiske og botaniske fag: 480en_US
dc.subject.nsiVDP::Zoology and botany: 480en_US
dc.source.journalEcological Solutions and Evidenceen_US
dc.relation.projectAndre: Office of Naval Researchen_US
dc.relation.projectAndre: National Ocean Partnership Programen_US
dc.relation.projectAndre: U.S. Navyen_US
dc.relation.projectEgen institusjon: Norwegian institute for nature research (NINA)en_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal