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dc.contributor.authorPérez-Jorge, Sergi
dc.contributor.authorOliveira, Cláudia
dc.contributor.authorRivas, Esteban Iglesias
dc.contributor.authorPrieto, Rui
dc.contributor.authorCascão, Irma
dc.contributor.authorWensveen, Paul J.
dc.contributor.authorMiller, Patrick J. O.
dc.contributor.authorSilva, Mónica A.
dc.date.accessioned2023-06-09T15:33:55Z
dc.date.available2023-06-09T15:33:55Z
dc.date.issued2023-06-08
dc.identifier.citationPérez-Jorge , S , Oliveira , C , Rivas , E I , Prieto , R , Cascão , I , Wensveen , P J , Miller , P J O & Silva , M A 2023 , ' Predictive model of sperm whale prey capture attempts from time-depth data ' , Movement Ecology , vol. 11 , 33 . https://doi.org/10.1186/s40462-023-00393-2en
dc.identifier.issn2051-3933
dc.identifier.otherPURE: 287456166
dc.identifier.otherPURE UUID: 3609d32d-b82b-4639-b788-50703e18b369
dc.identifier.otherRIS: urn:24EDE46319E9EAAF04A25669AC4B6AC5
dc.identifier.otherRIS: Pérez-Jorge2023
dc.identifier.otherScopus: 85161278777
dc.identifier.urihttps://hdl.handle.net/10023/27767
dc.descriptionFunding: Research was supported by the Portuguese Science & Technology Foundation (FCT), the Azorean Science & Technology Fund (FRCT), and the EU through research projects WATCH IT-Acores-01-0145-FEDER-000057, FCT-IF/00943/2013/CP1199/CT0001, META-FA_06_2017_017, and SUMMER-H2020 GA 817806, co-funded by FEDER, COMPETE, QREN, POPH, ESF, PO AZORES 2020, Portuguese Ministry for Science and Education, and individual contracts/grants to CO (WATCH IT-Acores-01-0145-FEDER-000057, 3/SRMCT/DRAM/2019 under RAGES-SUB/ENV.C.2-GA 110661, and INTERTAGUA-MAC2/1.1a/385), SPJ (SUMMER-H2020 GA 817806), IC (FCT-IP Project UIDP/05634/2020). PJW is funded by RANNÍS Icelandic Research Fund grant 207081. RP and MAS are co-financed by AZORES2020, through the EU Fund 01-0145-FEDER-000140 “MarAZ Researchers: Consolidate a body of researchers in Marine Sciences in the Azores”. Okeanos is funded by FCT (UIDB/05634/2020) and by the Regional Government of the Azores through the initiative to support the Research Centers of the University of the Azores (M1.1.A/REEQ.CIENTÍFICO UI&D/2021/010).en
dc.description.abstractBackground High-resolution sound and movement recording tags offer unprecedented insights into the fine-scale foraging behaviour of cetaceans, especially echolocating odontocetes, enabling the estimation of a series of foraging metrics. However, these tags are expensive, making them inaccessible to most researchers. Time-Depth Recorders (TDRs), which have been widely used to study diving and foraging behaviour of marine mammals, offer a more affordable alternative. Unfortunately, data collected by TDRs are bi-dimensional (time and depth only), so quantifying foraging effort from those data is challenging. Methods A predictive model of the foraging effort of sperm whales (Physeter macrocephalus) was developed to identify prey capture attempts (PCAs) from time-depth data. Data from high-resolution acoustic and movement recording tags deployed on 12 sperm whales were downsampled to 1 Hz to match the typical TDR sampling resolution and used to predict the number of buzzes (i.e., rapid series of echolocation clicks indicative of PCAs). Generalized linear mixed models were built for dive segments of different durations (30, 60, 180 and 300 s) using multiple dive metrics as potential predictors of PCAs. Results Average depth, variance of depth and variance of vertical velocity were the best predictors of the number of buzzes. Sensitivity analysis showed that models with segments of 180 s had the best overall predictive performance, with a good area under the curve value (0.78 ± 0.05), high sensitivity (0.93 ± 0.06) and high specificity (0.64 ± 0.14). Models using 180 s segments had a small difference between observed and predicted number of buzzes per dive, with a median of 4 buzzes, representing a difference in predicted buzzes of 30%. Conclusions These results demonstrate that it is possible to obtain a fine-scale, accurate index of sperm whale PCAs from time-depth data alone. This work helps leveraging the potential of time-depth data for studying the foraging ecology of sperm whales and the possibility of applying this approach to a wide range of echolocating cetaceans. The development of accurate foraging indices from low-cost, easily accessible TDR data would contribute to democratize this type of research, promote long-term studies of various species in several locations, and enable analyses of historical datasets to investigate changes in cetacean foraging activity.
dc.format.extent13
dc.language.isoeng
dc.relation.ispartofMovement Ecologyen
dc.rightsCopyright © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.en
dc.subjectPhyseter macrocephalusen
dc.subjectVertical movementen
dc.subjectBuzzesen
dc.subjectForaging behaviouren
dc.subjectLow-resolution dataen
dc.subjectTime-depth recordersen
dc.subjectQL Zoologyen
dc.subjectNDASen
dc.subjectSDG 14 - Life Below Wateren
dc.subjectMCCen
dc.subject.lccQLen
dc.titlePredictive model of sperm whale prey capture attempts from time-depth dataen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Sea Mammal Research Uniten
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Institute of Behavioural and Neural Sciencesen
dc.contributor.institutionUniversity of St Andrews. Centre for Social Learning & Cognitive Evolutionen
dc.contributor.institutionUniversity of St Andrews. Bioacoustics groupen
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.identifier.doihttps://doi.org/10.1186/s40462-023-00393-2
dc.description.statusPeer revieweden


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