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      Recent trends in movement ecology of animals and human mobility

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          Abstract

          Movement is fundamental to life, shaping population dynamics, biodiversity patterns, and ecosystem structure. In 2008, the movement ecology framework (MEF Nathan et al. in PNAS 105(49):19052–19059, 2008) introduced an integrative theory of organismal movement—linking internal state, motion capacity, and navigation capacity to external factors—which has been recognized as a milestone in the field. Since then, the study of movement experienced a technological boom, which provided massive quantities of tracking data of both animal and human movement globally and at ever finer spatio-temporal resolutions. In this work, we provide a quantitative assessment of the state of research within the MEF, focusing on animal movement, including humans and invertebrates, and excluding movement of plants and microorganisms. Using a text mining approach, we digitally scanned the contents of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$>8000$$\end{document} papers from 2009 to 2018 available online, identified tools and methods used, and assessed linkages between all components of the MEF. Over the past decade, the publication rate has increased considerably, along with major technological changes, such as an increased use of GPS devices and accelerometers and a majority of studies now using the R software environment for statistical computing. However, animal movement research still largely focuses on the effect of environmental factors on movement, with motion and navigation continuing to receive little attention. A search of topics based on words featured in abstracts revealed a clustering of papers among marine and terrestrial realms, as well as applications and methods across taxa. We discuss the potential for technological and methodological advances in the field to lead to more integrated and interdisciplinary research and an increased exploration of key movement processes such as navigation, as well as the evolutionary, physiological, and life-history consequences of movement.

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            Finding scientific topics.

            A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying "hot topics" by examining temporal dynamics and tagging abstracts to illustrate semantic content.
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              Graphical Models, Exponential Families, and Variational Inference

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                Author and article information

                Contributors
                rocio.joo@globalfishingwatch.org
                simona.picardi@usu.edu
                tclay@ucsc.edu
                Samantha.Patrick@liverpool.ac.uk
                VROMERO@ulima.edu.pe
                mathieu@basille.org
                Journal
                Mov Ecol
                Mov Ecol
                Movement Ecology
                BioMed Central (London )
                2051-3933
                25 May 2022
                25 May 2022
                2022
                : 10
                : 26
                Affiliations
                [1 ]GRID grid.15276.37, ISNI 0000 0004 1936 8091, Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, , University of Florida, ; Fort Lauderdale, FL USA
                [2 ]GRID grid.512016.1, Global Fishing Watch, ; Washington DC, USA
                [3 ]GRID grid.53857.3c, ISNI 0000 0001 2185 8768, Jack H. Berryman Institute and Department of Wildland Resources, S.J. & Jessie E. Quinney College of Natural Resources, , Utah State University, ; Logan, UT USA
                [4 ]GRID grid.10025.36, ISNI 0000 0004 1936 8470, School of Environmental Sciences, , University of Liverpool, ; Liverpool, UK
                [5 ]GRID grid.205975.c, ISNI 0000 0001 0740 6917, Institute of Marine Sciences, , University of California Santa Cruz, ; Santa Cruz, CA USA
                [6 ]GRID grid.441813.b, ISNI 0000 0001 2154 1816, Systems Engineering, Faculty of Engineering and Architecture, , University of Lima, ; Lima, Peru
                Author information
                http://orcid.org/0000-0003-0319-4210
                http://orcid.org/0000-0002-2623-6623
                http://orcid.org/0000-0001-9366-7127
                Article
                322
                10.1186/s40462-022-00322-9
                9134608
                35614458
                15bdbe88-8401-47ef-8892-906747d49cc0
                © The Author(s) 2022

                Open AccessThis 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
                : 29 December 2021
                : 7 April 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100004412, Human Frontier Science Program;
                Award ID: RGY0072/2017
                Award ID: RGY0072/2017
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100004412, Human Frontier Science Program;
                Award ID: RGY0072/2017
                Award ID: RGY0072/2017
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100004412, Human Frontier Science Program;
                Award ID: RGY0072/2017
                Award Recipient :
                Categories
                Review
                Custom metadata
                © The Author(s) 2022

                biologging,movement ecology framework,tracking technology,text mining,interdisciplinarity

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