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dc.contributor.authorFollana-Berná, Guillermoes_ES
dc.contributor.authorPalmer, Miqueles_ES
dc.contributor.authorLekanda-Guarrotxena, Aitores_ES
dc.contributor.authorGrau, Amàliaes_ES
dc.contributor.authorArechavala-Lopez, Pabloes_ES
dc.date.accessioned2020-05-06T07:43:58Z-
dc.date.available2020-05-06T07:43:58Z-
dc.date.issued2020-06-
dc.identifier.citationJournal of Experimental Marine Biology and Ecology 527: 151376 (2019)es_ES
dc.identifier.issn0022-0981-
dc.identifier.urihttp://hdl.handle.net/10261/210525-
dc.description.abstractThe fast development of camera technologies opens a breakthrough opportunity for animal ecology, particularly at the marine realm where observing wildlife is challenging. These outstanding technological advances are meeting with the impressive capabilities of artificial intelligence for enabling automatic extraction of relevant information from videos and images. Altogether, this may be a unique opportunity for a qualitative jump in marine wildlife assessment but substantial strengthening of the links between theorists, empiricists and engineers is still required. Specifically, a recent theory proposes that animal density can be estimated from (1) the counted animals per frame, (2) the area surveyed by the camera and (3) the probability of detecting an animal that is actually within the area surveyed by the camera. However, a potential drawback for applying this theory to the real world is that environmental dependencies of camera's detection probability may lead to biased estimates of animal density. Therefore, here we propose a sampling protocol and a statistical model of general application for estimating (and accounting for) the environmental factors affecting fish detectability when estimating fish density with cameras. The method implies one calibration sampling with cameras and with the preferred reference method at the same time and place. The relevance of this method is that, once calibrated, it can be used to obtain unbiased estimates of fish density at new sites and moments using only cameras. Thus, fish density could be estimated at the temporal and the spatial scale needed, but with substantially less cost-effort than any other reference methods (e.g., underwater visual censuses). As a proof of concept, we evaluated the dependence of camera's detection probability on habitat complexity (e.g., cavities, rocks, seagrass, etc.) as a proxy for the hiding capability of a small serranid. In that specific case, probability of detection seems to be independent of habitat complexity. However, the sampling protocol and the statistical model provided here open the opportunity to estimate fish density using underwater cameras at wider temporal and/or spatial scales, which will help to better understanding the ultimate drivers of marine fish population dynamics and further development of science-based management.es_ES
dc.description.sponsorshipGFB were supported by a PhD fellowship (FPI-INIA) from the National Institute for Agricultural and Food Research and Technology (INIA). PAL was supported by a Juan de la Cierva Incorporación postdoctoral grant (IJCI-2015-25595). This work was funded by R + D project PHENOFISH (CTM2015-69126-C2-1-R; MINECO) and is a contribution of the Joint Research Unit IMEDEA-LIMIA.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationMINECO/ICTI2013-2016/IJCI-2015-25595es_ES
dc.relationMINECO/ICTI2013-2016/CTM2015-69126-C2-1-Res_ES
dc.relation.isversionofPostprint-
dc.rightsembargoedAccesses_ES
dc.subjectAnimal detectabilityes_ES
dc.subjectFish densityes_ES
dc.subjectHome rangees_ES
dc.subjectUnbaited vertical underwater camerases_ES
dc.subjectUnderwater visual censuses_ES
dc.titleFish density estimation using unbaited cameras: Accounting for environmental-dependent detectabilityes_ES
dc.typeartículoes_ES
dc.identifier.doihttp://dx.doi.org/10.1016/j.jembe.2020.151376-
dc.description.peerreviewedNoes_ES
dc.description.peerreviewedPeer reviewed-
dc.relation.publisherversionhttps://doi.org/10.1016/j.jembe.2020.151376es_ES
dc.contributor.funderMinisterio de Economía y Competitividad (España)es_ES
dc.contributor.funderInstituto Nacional de Investigación y Tecnología Agraria y Alimentaria (España)es_ES
dc.relation.csices_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/100007652es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100003329es_ES
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