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      Positioning of aquatic animals based on time-of-arrival and random walk models using YAPS (Yet Another Positioning Solver)

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          Abstract

          Aquatic positional telemetry offers vast opportunities to study in vivo behaviour of wild animals, but there is room for improvement in the data quality provided by current procedures for estimating positions. Here we present a novel positioning method called YAPS (Yet Another Positioning Solver), involving Maximum Likelihood analysis of a state-space model applied directly to time of arrival (TOA) data in combination with a movement model. YAPS avoids the sequential positioning-filtering-approach applied in alternative tools by using all available data in a single model, and offers better accuracy and error control. Feasibility and performance of YAPS was rigorously tested in a simulation study and by applying YAPS to data from an acoustic transmitter towed in a receiver array. Performance was compared to an alternative positioning model and proprietary software. The simulation study and field test revealed that YAPS performance was better and more consistent than alternatives. We conclude that YAPS outperformed the compared alternative methods, and that YAPS constitute a vast improvement to currently available positioning software in acoustic telemetry. Additionally, in contrast to vendor-supplied solutions, YAPS is transparent, flexible and can easily be adapted and extended for further improvements or to meet study specific requirements such as three-dimensional positioning.

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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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            TMB: Automatic Differentiation and Laplace Approximation

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              Diffusion and Ecological Problems: Modern Perspectives

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                Author and article information

                Contributors
                hba@aqua.dtu.dk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                30 October 2017
                30 October 2017
                2017
                : 7
                : 14294
                Affiliations
                [1 ]ISNI 0000 0001 2181 8870, GRID grid.5170.3, National Institute of Aquatic Resources, Technical University of Denmark, ; 8600 Silkeborg, Denmark
                [2 ]ISNI 0000 0001 2107 519X, GRID grid.420127.2, Norwegian Institute of Nature Research, ; 9007 Tromsø, Norway
                [3 ]ISNI 0000 0001 2107 519X, GRID grid.420127.2, Norwegian Institute of Nature Research, ; 7485 Trondheim, Norway
                [4 ]ISNI 0000 0001 2181 8870, GRID grid.5170.3, National Institute of Aquatic Resources, Technical University of Denmark, ; 2800 Lyngby, Denmark
                Author information
                http://orcid.org/0000-0002-3644-4960
                http://orcid.org/0000-0003-4036-4207
                Article
                14278
                10.1038/s41598-017-14278-z
                5662720
                29084968
                637608fa-b801-40bd-9f18-ac7f01442767
                © The Author(s) 2017

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 12 June 2017
                : 6 October 2017
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