Literature DB >> 31587257

Navigating through the r packages for movement.

Rocío Joo1, Matthew E Boone1, Thomas A Clay2, Samantha C Patrick2, Susana Clusella-Trullas3, Mathieu Basille1.   

Abstract

The advent of miniaturized biologging devices has provided ecologists with unprecedented opportunities to record animal movement across scales, and led to the collection of ever-increasing quantities of tracking data. In parallel, sophisticated tools have been developed to process, visualize and analyse tracking data; however, many of these tools have proliferated in isolation, making it challenging for users to select the most appropriate method for the question in hand. Indeed, within the r software alone, we listed 58 packages created to deal with tracking data or 'tracking packages'. Here, we reviewed and described each tracking package based on a workflow centred around tracking data (i.e. spatio-temporal locations (x, y, t)), broken down into three stages: pre-processing, post-processing and analysis, the latter consisting of data visualization, track description, path reconstruction, behavioural pattern identification, space use characterization, trajectory simulation and others. Supporting documentation is key to render a package accessible for users. Based on a user survey, we reviewed the quality of packages' documentation and identified 11 packages with good or excellent documentation. Links between packages were assessed through a network graph analysis. Although a large group of packages showed some degree of connectivity (either depending on functions or suggesting the use of another tracking package), one third of the packages worked in isolation, reflecting a fragmentation in the r movement-ecology programming community. Finally, we provide recommendations for users when choosing packages, and for developers to maximize the usefulness of their contribution and strengthen the links within the programming community.
© 2019 The Authors. Journal of Animal Ecology © 2019 British Ecological Society.

Keywords:  biologging; movement ecology; r project for statistical computing; spatial; tracking data

Year:  2019        PMID: 31587257     DOI: 10.1111/1365-2656.13116

Source DB:  PubMed          Journal:  J Anim Ecol        ISSN: 0021-8790            Impact factor:   5.091


  12 in total

1.  MoveApps: a serverless no-code analysis platform for animal tracking data.

Authors:  Andrea Kölzsch; Sarah C Davidson; Dominik Gauggel; Clemens Hahn; Julian Hirt; Roland Kays; Ilona Lang; Ashley Lohr; Benedict Russell; Anne K Scharf; Gabriel Schneider; Candace M Vinciguerra; Martin Wikelski; Kamran Safi
Journal:  Mov Ecol       Date:  2022-07-18       Impact factor: 5.253

Review 2.  Recent trends in movement ecology of animals and human mobility.

Authors:  Rocío Joo; Simona Picardi; Matthew E Boone; Thomas A Clay; Samantha C Patrick; Vilma S Romero-Romero; Mathieu Basille
Journal:  Mov Ecol       Date:  2022-05-25       Impact factor: 5.253

3.  Sexual segregation in juvenile Antarctic fur seals.

Authors:  Kayleigh A Jones; Norman Ratcliffe; Stephen C Votier; Simeon Lisovski; Anne-Sophie Bonnet-Lebrun; Iain J Staniland
Journal:  Oecologia       Date:  2021-07-26       Impact factor: 3.225

4.  A guide to pre-processing high-throughput animal tracking data.

Authors:  Pratik Rajan Gupte; Christine E Beardsworth; Orr Spiegel; Emmanuel Lourie; Sivan Toledo; Ran Nathan; Allert I Bijleveld
Journal:  J Anim Ecol       Date:  2021-11-16       Impact factor: 5.606

5.  Time-dependent memory and individual variation in Arctic brown bears (Ursus arctos).

Authors:  Peter R Thompson; Mark A Lewis; Mark A Edwards; Andrew E Derocher
Journal:  Mov Ecol       Date:  2022-04-11       Impact factor: 3.600

Review 6.  Non-Lethal Sampling Supports Integrative Movement Research in Freshwater Fish.

Authors:  Matt J Thorstensen; Carolyn A Vandervelde; William S Bugg; Sonya Michaleski; Linh Vo; Theresa E Mackey; Michael J Lawrence; Ken M Jeffries
Journal:  Front Genet       Date:  2022-04-25       Impact factor: 4.772

7.  Circular-linear copulae for animal movement data.

Authors:  Florian H Hodel; John R Fieberg
Journal:  Methods Ecol Evol       Date:  2022-03-01       Impact factor: 8.335

8.  Caution is warranted when using animal space-use and movement to infer behavioral states.

Authors:  Frances E Buderman; Tess M Gingery; Duane R Diefenbach; Laura C Gigliotti; Danielle Begley-Miller; Marc M McDill; Bret D Wallingford; Christopher S Rosenberry; Patrick J Drohan
Journal:  Mov Ecol       Date:  2021-06-11       Impact factor: 3.600

9.  Decision rules for determining terrestrial movement and the consequences for filtering high-resolution global positioning system tracks: a case study using the African lion (Panthera leo).

Authors:  Richard M Gunner; Rory P Wilson; Mark D Holton; Phil Hopkins; Stephen H Bell; Nikki J Marks; Nigel C Bennett; Sam Ferreira; Danny Govender; Pauli Viljoen; Angela Bruns; O Louis van Schalkwyk; Mads F Bertelsen; Carlos M Duarte; Martin C van Rooyen; Craig J Tambling; Aoife Göppert; Delmar Diesel; D Michael Scantlebury
Journal:  J R Soc Interface       Date:  2022-01-19       Impact factor: 4.293

10.  Deep inference of seabird dives from GPS-only records: Performance and generalization properties.

Authors:  Amédée Roy; Sophie Lanco Bertrand; Ronan Fablet
Journal:  PLoS Comput Biol       Date:  2022-03-11       Impact factor: 4.475

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