Simple Bagged Movement Models for Telemetry Data

Simple Bagged Movement Models for Telemetry Data | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Ecology and Evolution This is a preprint and has not been peer reviewed. Data may be preliminary. 15 August 2025 V1 Latest version Share on Simple Bagged Movement Models for Telemetry Data Authors : Andrew Whetten 0000-0002-5694-8541 [email protected] , Trevor Hefley , David Haukos , and Dustin Brewer Authors Info & Affiliations https://doi.org/10.22541/au.175524779.90955410/v1 Published Ecology and Evolution Version of record Peer review timeline 235 views 202 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Determining which statistical methods are appropriate for data is both user and data dependent and prone to change as new methodology becomes available. This process encompasses model ideation, model selection, and determining appropriate use of statistical methods. Literature on models for animal movement emerging in the past two decades has yielded a rich collection of statistical methods garnering much deserved positive attention. Among such efforts, there is limited investigation of the broader place for simple machine learning methodology in animal movement modeling. We propose a bagged (i.e., bootstrap aggregated) animal movement model using simple, off-the-shelf machine learning algorithms. The model is intuitive, retains statistical inference about characteristics of animal movement (i.e., estimated from model-based summary statistics), and only requires knowledge of elementary statistical and machine learning analysis to understand. We show by simulation that our model can provide unbiased estimates of pertinent characteristics of animal movement (e.g., daily displacement) in the presence of large and realistic location error. We believe that increasing accessible literature on simple machine learning animal movement models provides valuable pedagogical and practical support for researchers using statistical models to study animal movement. Supplementary Material File (manuscript_eco_evo.pdf) Download 2.39 MB Information & Authors Information Version history V1 Version 1 15 August 2025 Peer review timeline Published Ecology and Evolution Version of Record 7 Sep 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Ecology and Evolution Keywords evolutionary ecology method development multiple vertebrate Authors Affiliations Andrew Whetten 0000-0002-5694-8541 [email protected] Cornell University Cornell Laboratory of Ornithology View all articles by this author Trevor Hefley Kansas State University View all articles by this author David Haukos U.S. Geological Survey, Kansas Cooperative Fish and Wildlife Research Unit View all articles by this author Dustin Brewer Central Michigan University View all articles by this author Metrics & Citations Metrics Article Usage 235 views 202 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Andrew Whetten, Trevor Hefley, David Haukos, et al. Simple Bagged Movement Models for Telemetry Data. Authorea . 15 August 2025. DOI: https://doi.org/10.22541/au.175524779.90955410/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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