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dc.contributor.authorFernández de la Hoz, Camino 
dc.contributor.authorRamos Manzano, Elvira
dc.contributor.authorPuente Trueba, Maria Araceli
dc.contributor.authorJuanes de la Peña, José A. 
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2024-03-14T15:44:33Z
dc.date.available2024-03-14T15:44:33Z
dc.date.issued2019-06
dc.identifier.issn0141-1136
dc.identifier.issn1879-0291
dc.identifier.otherCTM2015-68244-Res_ES
dc.identifier.urihttps://hdl.handle.net/10902/32249
dc.description.abstractThere are evidences of how climate change is affecting seaweeds distribution and the ecosystems services they provide. Therefore, it is necessary to consider these impacts when managing marine areas. One of the most applied tools in recent years to deal with this are species distribution models, however there are still some challenges to solve, such as the inclusion of hydrodynamic predictors and the application of effective, transferable and user-oriented methodologies. Five species (Saccorhiza polyschides, Gelidium spinosum, Sargassum muticum, Pelvetia canaliculata and Cystoseira baccata) in Europe and 15 variables were considered. Nine of them were projected to the RCPs 4.5 and 8.5 for the mid-term (2040?2069) and the long term (2070?2099). Algorithms for each species were applied to generate models that were assessed by comparison of probabilities and observations (area under the curve, true skill statistics, Boyce index, sensitivity, correct classification rate), niches overlap (Schoener's D, Hellinger's I), geographical similarity (interquartile range) and ecological realism. Models built demonstrated very good predictive accuracy and sensitivity, without overfitting risk. A medium overlap in the historical and RCPs environmental conditions were obtained, therefore the models can be considered transferable and results accurate because only some isolated points were detected as outliers, corresponding to low probabilities. The areas of S. polyschides and G. spinosum have been identified to be dramatically reduced, meanwhile S. muticum and C. baccata were predicted to expand their range. P. canaliculata was expected to keep its sites of presence but with a decrease in its probability of occurrence. For all species it was remarkable the importance of hydrodynamic variables and parameters representing extreme conditions. Spatially predictions of the potential species and areas at risk are decisive for defining management strategies and resource allocation. The performance and usefulness of the approach applied in this study have been demonstrated for algae with different ecological requirements (from upper littoral to subtidal) and distributional patterns (native and invasive), therefore results can be used by marine planners with different goals: marine protected areas designation, monitoring efforts guiding, invasions risk assessment or aquaculture facilities zonation.es_ES
dc.description.sponsorshipThis work was funded by the National Plan for Research in Science and Technological Innovation from the Spanish Government 2013-2016 (Ministry of Economy and Competitiveness) [grant number C3N project CTM2015-68244-R] and co-funded by the European Regional Development's funds (FEDER). Camino Fernández de la Hoz acknowledges financial support under a predoctoral grant from the Spanish Ministry of Economyes_ES
dc.format.extent11 p.es_ES
dc.language.isoenges_ES
dc.publisherElsevier Ltdes_ES
dc.rights© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 licensees_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceMarine Environmental Research 148 (2019) 1-11es_ES
dc.subject.otherAlgaees_ES
dc.subject.otherEcological nichees_ES
dc.subject.otherGlobal changees_ES
dc.subject.otherMarine ecologyes_ES
dc.subject.otherPhysiological thresholdses_ES
dc.subject.otherSpecies distribution modelses_ES
dc.titleClimate change induced range shifts in seaweeds distributions in Europees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.1016/j.marenvres.2019.04.012es_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOIj.marenvres.2019.04.012
dc.type.versionacceptedVersiones_ES


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© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 licenseExcepto si se señala otra cosa, la licencia del ítem se describe como © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license