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      Sharks surf the slope: Current updrafts reduce energy expenditure for aggregating marine predators

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          Fish exploiting vortices decrease muscle activity.

          Fishes moving through turbulent flows or in formation are regularly exposed to vortices. Although animals living in fluid environments commonly capture energy from vortices, experimental data on the hydrodynamics and neural control of interactions between fish and vortices are lacking. We used quantitative flow visualization and electromyography to show that trout will adopt a novel mode of locomotion to slalom in between experimentally generated vortices by activating only their anterior axial muscles. Reduced muscle activity during vortex exploitation compared with the activity of fishes engaged in undulatory swimming suggests a decrease in the cost of locomotion and provides a mechanism to understand the patterns of fish distributions in schools and riverine environments.
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            Energy landscapes shape animal movement ecology.

            The metabolic costs of animal movement have been studied extensively under laboratory conditions, although frequently these are a poor approximation of the costs of operating in the natural, heterogeneous environment. Construction of "energy landscapes," which relate animal locality to the cost of transport, can clarify whether, to what extent, and how movement properties are attributable to environmental heterogeneity. Although behavioral responses to aspects of the energy landscape are well documented in some fields (notably, the selection of tailwinds by aerial migrants) and scales (typically large), the principles of the energy landscape extend across habitat types and spatial scales. We provide a brief synthesis of the mechanisms by which environmentally driven changes in the cost of transport can modulate the behavioral ecology of animal movement in different media, develop example cost functions for movement in heterogeneous environments, present methods for visualizing these energy landscapes, and derive specific predictions of expected outcomes from individual- to population- and species-level processes. Animals modulate a suite of movement parameters (e.g., route, speed, timing of movement, and tortuosity) in relation to the energy landscape, with the nature of their response being related to the energy savings available. Overall, variation in movement costs influences the quality of habitat patches and causes nonrandom movement of individuals between them. This can provide spatial and/or temporal structure to a range of population- and species-level processes, ultimately including gene flow. Advances in animal-attached technology and geographic information systems are opening up new avenues for measuring and mapping energy landscapes that are likely to provide new insight into their influence in animal ecology.
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              Estimation of short-term centers of activity from an array of omnidirectional hydrophones and its use in studying animal movements

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                Author and article information

                Contributors
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                Journal
                Journal of Animal Ecology
                J Anim Ecol
                Wiley
                0021-8790
                1365-2656
                June 13 2021
                Affiliations
                [1 ]Institute of the Environment Department of Biological Sciences Florida International University North Miami FL USA
                [2 ]Faculty of Aerospace EngineeringTechnion Haifa Israel
                [3 ]Division of Functional Morphology Department of Zoology Stockholm University Stockholm Sweden
                [4 ]College of Science and Engineering Flinders University Bedford Park South Australia Australia
                [5 ]MARBECUniv MontpellierCNRSIFREMERIRD Sète France
                [6 ]PSL Research UniversityEPHE‐UPVD‐CNRSUSR 3278 CRIOBE Perpignan France
                [7 ]Laboratoire d'Excellence “CORAIL” USR 3278 CNRS‐EPHE‐UPVD CRIOBE Perpignan France
                [8 ]Andromede Oceanology Carnon France
                Article
                10.1111/1365-2656.13536
                34121177
                76db7adc-ab97-4a35-b6db-08b40786071c
                © 2021

                http://onlinelibrary.wiley.com/termsAndConditions#vor

                http://doi.wiley.com/10.1002/tdm_license_1.1

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