Towards a better characterisation of deep-diving whales’ distributions by using prey distribution model outputs?

Type Article
Date 2021-08
Language English
Author(s) Virgili AurianeORCID1, Hedon Laura1, Authier Matthieu1, Calmettes Beatriz2, Claridge Diane3, Cole Tim4, Corkeron Peter4, Dorémus Ghislain1, Dunn Charlotte3, Dunn Tim E.5, Laran Sophie1, Lehodey Patrick2, Lewis Mark4, Louzao Maite6, Mannocci Laura7, Martínez-Cedeira José8, Monestiez Pascal9, Palka Debra4, Pettex EmelineORCID10, Roberts Jason J.11, Ruiz LeireORCID12, Saavedra Camilo13, Santos M. BegoñaORCID13, Van Canneyt Olivier1, Bonales José Antonio Vázquez14, Ridoux Vincent1, 15
Affiliation(s) 1 : Observatoire PELAGIS, UMS 3462 CNRS—La Rochelle Université, La Rochelle, France
2 : Space Oceanography Division, CLS, Ramonville, France
3 : Bahamas Marine Mammal Research Organisation, Marsh Harbour, Abaco, Bahamas
4 : Protected Species Branch, NOAA Fisheries Northeast Fisheries Science, Woods Hole, Massachusetts, United States of America
5 : Joint Nature Conservation Committee, Inverdee House, Aberdeen, United Kingdom
6 : AZTI, Marine Research, Basque Research and Technology Alliance (BRTA), Pasaia, Spain
7 : MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Sète, France
8 : CEMMA, Pontevedra, Spain
9 : BioSP, INRA, Avignon, France, Centre d’Etudes Biologiques de Chizé - La Rochelle, UMR 7372 CNRS—La Rochelle Université, Villiers-en-Bois, France
10 : ADERA, Pessac Cedex, Pessac, France, Cohabys—ADERA, La Rochelle Université, La Rochelle, France
11 : Marine Geospatial Ecology Laboratory, Duke University, Durham, North Carolina, United States of America
12 : AMBAR Elkartea Organisation, Bizkaia, Spain
13 : Instituto Español de Oceanografía, Centro Oceanográfico de Vigo, Vigo, Spain
14 : Alnilam Research and Conservation, Madrid, Spain
15 : Centre d’Etudes Biologiques de Chizé - La Rochelle, UMR 7372 CNRS—La Rochelle Université, Villiers-en-Bois, France
Source Plos One (1932-6203) (Public Library of Science (PLoS)), 2021-08 , Vol. 16 , N. 8 , P. e0255667 (21p.)
DOI 10.1371/journal.pone.0255667
WOS© Times Cited 8
Abstract

In habitat modelling, environmental variables are assumed to be proxies of lower trophic levels distribution and by extension, of marine top predator distributions. More proximal variables, such as potential prey fields, could refine relationships between top predator distributions and their environment. In situ data on prey distributions are not available over large spatial scales but, a numerical model, the Spatial Ecosystem And POpulation DYnamics Model (SEAPODYM), provides simulations of the biomass and production of zooplankton and six functional groups of micronekton at the global scale. Here, we explored whether generalised additive models fitted to simulated prey distribution data better predicted deep-diver densities (here beaked whales Ziphiidae and sperm whales Physeter macrocephalus) than models fitted to environmental variables. We assessed whether the combination of environmental and prey distribution data would further improve model fit by comparing their explanatory power. For both taxa, results were suggestive of a preference for habitats associated with topographic features and thermal fronts but also for habitats with an extended euphotic zone and with large prey of the lower mesopelagic layer. For beaked whales, no SEAPODYM variable was selected in the best model that combined the two types of variables, possibly because SEAPODYM does not accurately simulate the organisms on which beaked whales feed on. For sperm whales, the increase model performance was only marginal. SEAPODYM outputs were at best weakly correlated with sightings of deep-diving cetaceans, suggesting SEAPODYM may not accurately predict the prey fields of these taxa. This study was a first investigation and mostly highlighted the importance of the physiographic variables to understand mechanisms that influence the distribution of deep-diving cetaceans. A more systematic use of SEAPODYM could allow to better define the limits of its use and a development of the model that would simulate larger prey beyond 1,000 m would probably better characterise the prey of deep-diving cetaceans.

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Publisher's official version 21 2 MB Open access
S1 Appendix. Details of surveys used in the analyses. Total effort represents the total length of transects of each survey (without removing the transects with a Beaufort sea-state > 4). NE-ATL: ... 3 212 KB Open access
S2 Appendix. Average conditions of the static, oceanographic and SEAPODYM variables over the entire period (from 1998 to 2015). Base map from https://www.gebco.net/. 2 922 KB Open access
S3 Appendix. Correlations between environmental and SEAPODYM variables and model outputs for beaked and sperm whales. 5 588 KB Open access
S4 Appendix. Uncertainty maps representing the standard error associated with the predicted relative density of beaked (BW) and sperm (SW) whales. Black areas represent extrapolation where we did... 5 MB Open access
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Virgili Auriane, Hedon Laura, Authier Matthieu, Calmettes Beatriz, Claridge Diane, Cole Tim, Corkeron Peter, Dorémus Ghislain, Dunn Charlotte, Dunn Tim E., Laran Sophie, Lehodey Patrick, Lewis Mark, Louzao Maite, Mannocci Laura, Martínez-Cedeira José, Monestiez Pascal, Palka Debra, Pettex Emeline, Roberts Jason J., Ruiz Leire, Saavedra Camilo, Santos M. Begoña, Van Canneyt Olivier, Bonales José Antonio Vázquez, Ridoux Vincent (2021). Towards a better characterisation of deep-diving whales’ distributions by using prey distribution model outputs? Plos One, 16(8), e0255667 (21p.). Publisher's official version : https://doi.org/10.1371/journal.pone.0255667 , Open Access version : https://archimer.ifremer.fr/doc/00710/82202/