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      Navigating uncertain waters: a critical review of inferring foraging behaviour from location and dive data in pinnipeds

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          Abstract

          In the last thirty years, the emergence and progression of biologging technology has led to great advances in marine predator ecology. Large databases of location and dive observations from biologging devices have been compiled for an increasing number of diving predator species (such as pinnipeds, sea turtles, seabirds and cetaceans), enabling complex questions about animal activity budgets and habitat use to be addressed. Central to answering these questions is our ability to correctly identify and quantify the frequency of essential behaviours, such as foraging. Despite technological advances that have increased the quality and resolution of location and dive data, accurately interpreting behaviour from such data remains a challenge, and analytical methods are only beginning to unlock the full potential of existing datasets. This review evaluates both traditional and emerging methods and presents a starting platform of options for future studies of marine predator foraging ecology, particularly from location and two-dimensional (time-depth) dive data. We outline the different devices and data types available, discuss the limitations and advantages of commonly-used analytical techniques, and highlight key areas for future research. We focus our review on pinnipeds - one of the most studied taxa of marine predators - but offer insights that will be applicable to other air-breathing marine predator tracking studies. We highlight that traditionally-used methods for inferring foraging from location and dive data, such as first-passage time and dive shape analysis, have important caveats and limitations depending on the nature of the data and the research question. We suggest that more holistic statistical techniques, such as state-space models, which can synthesise multiple track, dive and environmental metrics whilst simultaneously accounting for measurement error, offer more robust alternatives. Finally, we identify a need for more research to elucidate the role of physical oceanography, device effects, study animal selection, and developmental stages in predator behaviour and data interpretation.

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          The online version of this article (doi:10.1186/s40462-016-0090-9) contains supplementary material, which is available to authorized users.

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          Most cited references162

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          State-space models of individual animal movement.

          Detailed observation of the movement of individual animals offers the potential to understand spatial population processes as the ultimate consequence of individual behaviour, physiological constraints and fine-scale environmental influences. However, movement data from individuals are intrinsically stochastic and often subject to severe observation error. Linking such complex data to dynamical models of movement is a major challenge for animal ecology. Here, we review a statistical approach, state-space modelling, which involves changing how we analyse movement data and draw inferences about the behaviours that shape it. The statistical robustness and predictive ability of state-space models make them the most promising avenue towards a new type of movement ecology that fuses insights from the study of animal behaviour, biogeography and spatial population dynamics.
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            Biotelemetry: a mechanistic approach to ecology.

            Remote measurement of the physiology, behaviour and energetic status of free-living animals is made possible by a variety of techniques that we refer to collectively as 'biotelemetry'. This set of tools ranges from transmitters that send their signals to receivers up to a few kilometers away to those that send data to orbiting satellites and, more frequently, to devices that log data. They enable researchers to document, for long uninterrupted periods, how undisturbed organisms interact with each other and their environment in real time. In spite of advances enabling the monitoring of many physiological and behavioural variables across a range of taxa of various sizes, these devices have yet to be embraced widely by the ecological community. Our review suggests that this technology has immense potential for research in basic and applied animal ecology. Efforts to incorporate biotelemetry into broader ecological research programs should yield novel information that has been challenging to collect historically from free-ranging animals in their natural environments. Examples of research that would benefit from biotelemetry include the assessment of animal responses to different anthropogenic perturbations and the development of life-time energy budgets.
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              EXTRACTING MORE OUT OF RELOCATION DATA: BUILDING MOVEMENT MODELS AS MIXTURES OF RANDOM WALKS

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                Author and article information

                Contributors
                matthew.carter@plymouth.ac.uk
                k.bennett@abertay.ac.uk
                clare.embling@plymouth.ac.uk
                phil.hosegood@plymouth.ac.uk
                dr60@st-andrews.ac.uk
                Journal
                Mov Ecol
                Mov Ecol
                Movement Ecology
                BioMed Central (London )
                2051-3933
                26 October 2016
                26 October 2016
                2016
                : 4
                : 25
                Affiliations
                [1 ]Marine Biology & Ecology Research Centre, School of Marine Science & Engineering, Plymouth University, PL4 8AA Plymouth, UK
                [2 ]School of Science, Engineering & Technology, Abertay University, DD1 1HG Dundee, UK
                [3 ]Centre for Coast and Ocean Science & Engineering, School of Marine Science & Engineering, Plymouth University, PL4 8AA Plymouth, UK
                [4 ]Sea Mammal Research Unit, University of St. Andrews, KY16 8LB St. Andrews, UK
                [5 ]Centre for Research into Ecological and Environmental Modelling, University of St. Andrews, KY16 9LZ St. Andrews, UK
                Article
                90
                10.1186/s40462-016-0090-9
                5080796
                26753094
                de727e85-e24a-40fe-ab53-67fcd7940565
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

                History
                : 25 May 2016
                : 17 October 2016
                Funding
                Funded by: Natural Environment Research Council (GB)
                Award ID: SMRU1001
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000270, Natural Environment Research Council;
                Award ID: PhD Studentship Co-Funder
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002286, Plymouth University;
                Award ID: PhD Studentship Co-Funder
                Award Recipient :
                Categories
                Review
                Custom metadata
                © The Author(s) 2016

                movement ecology,area-restricted search,satellite telemetry,gps,argos,tdr,animal tracking,marine mammals,seals

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