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      Links between the three-dimensional movements of whale sharks ( Rhincodon typus) and the bio-physical environment off a coral reef

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          Abstract

          Background

          Measuring coastal-pelagic prey fields at scales relevant to the movements of marine predators is challenging due to the dynamic and ephemeral nature of these environments. Whale sharks ( Rhincodon typus) are thought to aggregate in nearshore tropical waters due to seasonally enhanced foraging opportunities. This implies that the three-dimensional movements of these animals may be associated with bio-physical properties that enhance prey availability. To date, few studies have tested this hypothesis.

          Methods

          Here, we conducted ship-based acoustic surveys, net tows and water column profiling (salinity, temperature, chlorophyll fluorescence) to determine the volumetric density, distribution and community composition of mesozooplankton (predominantly euphausiids and copepods) and oceanographic properties of the water column in the vicinity of whale sharks that were tracked simultaneously using satellite-linked tags at Ningaloo Reef, Western Australia. Generalised linear mixed effect models were used to explore relationships between the 3-dimensional movement behaviours of tracked sharks and surrounding prey fields at a spatial scale of ~ 1 km.

          Results

          We identified prey density as a significant driver of horizontal space use, with sharks occupying areas along the reef edge where densities were highest. These areas were characterised by complex bathymetry such as reef gutters and pinnacles. Temperature and salinity profiles revealed a well-mixed water column above the height of the bathymetry (top 40 m of the water column). Regions of stronger stratification were associated with reef gutters and pinnacles that concentrated prey near the seabed, and entrained productivity at local scales (~ 1 km). We found no quantitative relationship between the depth use of sharks and vertical distributions of horizontally averaged prey density. Whale sharks repeatedly dove to depths where spatially averaged prey concentration was highest but did not extend the time spent at these depth layers.

          Conclusions

          Our work reveals previously unrecognized complexity in interactions between whale sharks and their zooplankton prey.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s40462-024-00452-2.

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          Most cited references92

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          Tracking apex marine predator movements in a dynamic ocean.

          Pelagic marine predators face unprecedented challenges and uncertain futures. Overexploitation and climate variability impact the abundance and distribution of top predators in ocean ecosystems. Improved understanding of ecological patterns, evolutionary constraints and ecosystem function is critical for preventing extinctions, loss of biodiversity and disruption of ecosystem services. Recent advances in electronic tagging techniques have provided the capacity to observe the movements and long-distance migrations of animals in relation to ocean processes across a range of ecological scales. Tagging of Pacific Predators, a field programme of the Census of Marine Life, deployed 4,306 tags on 23 species in the North Pacific Ocean, resulting in a tracking data set of unprecedented scale and species diversity that covers 265,386 tracking days from 2000 to 2009. Here we report migration pathways, link ocean features to multispecies hotspots and illustrate niche partitioning within and among congener guilds. Our results indicate that the California Current large marine ecosystem and the North Pacific transition zone attract and retain a diverse assemblage of marine vertebrates. Within the California Current large marine ecosystem, several predator guilds seasonally undertake north-south migrations that may be driven by oceanic processes, species-specific thermal tolerances and shifts in prey distributions. We identify critical habitats across multinational boundaries and show that top predators exploit their environment in predictable ways, providing the foundation for spatial management of large marine ecosystems. ©2011 Macmillan Publishers Limited. All rights reserved
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            The package “adehabitat” for the R software: A tool for the analysis of space and habitat use by animals

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              AIC model selection using Akaike weights.

              The Akaike information criterion (AIC; Akaike, 1973) is a popular method for comparing the adequacy of multiple, possibly nonnested models. Current practice in cognitive psychology is to accept a single model on the basis of only the "raw" AIC values, making it difficult to unambiguously interpret the observed AIC differences in terms of a continuous measure such as probability. Here we demonstrate that AIC values can be easily transformed to so-called Akaike weights (e.g., Akaike, 1978, 1979; Bozdogan, 1987; Burnham & Anderson, 2002), which can be directly interpreted as conditional probabilities for each model. We show by example how these Akaike weights can greatly facilitate the interpretation of the results of AIC model comparison procedures.
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                Author and article information

                Contributors
                Ben.dantonio@research.uwa.edu.au
                Journal
                Mov Ecol
                Mov Ecol
                Movement Ecology
                BioMed Central (London )
                2051-3933
                31 January 2024
                31 January 2024
                2024
                : 12
                : 10
                Affiliations
                [1 ]Oceans Graduate School and the UWA Oceans Institute, The University of Western Australia, ( https://ror.org/047272k79) 35 Stirling Highway, Perth, WA 6009 Australia
                [2 ]GRID grid.1012.2, ISNI 0000 0004 1936 7910, Australian Institute of Marine Science, Indian Ocean Marine Research Centre, , University of Western Australia, ; Perth, WA Australia
                [3 ]GRID grid.1012.2, ISNI 0000 0004 1936 7910, The Oceans Institute, , University of Western Australia, ; Perth, WA Australia
                [4 ]Marine Biological Association of the United Kingdom, The Laboratory, Citadel Hill, ( https://ror.org/0431sk359) Plymouth, PL1 2PB UK
                [5 ]Institute for Marine and Antarctic Studies, University of Tasmania, ( https://ror.org/01nfmeh72) Hobart, TAS Australia
                [6 ]CSIRO Environment, Battery Point, TAS 7004 Australia
                [7 ]Pelagic Ecology Research Group, Scottish Oceans Institute, Gatty Marine Laboratory, School of Biology, University of St. Andrews, ( https://ror.org/02wn5qz54) St Andrews, KY16 8LB Scotland, UK
                [8 ]GRID grid.1001.0, ISNI 0000 0001 2180 7477, Research School of Biology, Division of Ecology and Evolution, , The Australian National University, ; 46 Sullivans Creek Road, Canberra, ACT 2600 Australia
                Author information
                http://orcid.org/0000-0002-1664-1860
                http://orcid.org/0000-0001-6755-2799
                http://orcid.org/0000-0002-3067-9427
                http://orcid.org/0000-0002-4833-9594
                http://orcid.org/0000-0002-9870-1256
                http://orcid.org/0000-0003-2229-6183
                http://orcid.org/0000-0002-6438-6892
                http://orcid.org/0000-0001-6906-799X
                http://orcid.org/0000-0002-8669-8440
                Article
                452
                10.1186/s40462-024-00452-2
                10829290
                38297368
                dcc69ad7-945e-4d18-bbaa-629119bfa0cf
                © The Author(s) 2024

                Open Access 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. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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.

                History
                : 15 August 2023
                : 17 January 2024
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                marine megafauna,predator–prey,foraging ecology,bio-physical drivers,zooplankton,habitat use,oceanography,3d utilisation distribution

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