Literature DB >> 25907267

What is the animal doing? Tools for exploring behavioural structure in animal movements.

Eliezer Gurarie1,2, Chloe Bracis3, Maria Delgado4,5, Trevor D Meckley6, Ilpo Kojola7, C Michael Wagner6.   

Abstract

Movement data provide a window - often our only window - into the cognitive, social and biological processes that underlie the behavioural ecology of animals in the wild. Robust methods for identifying and interpreting distinct modes of movement behaviour are of great importance, but complicated by the fact that movement data are complex, multivariate and dependent. Many different approaches to exploratory analysis of movement have been developed to answer similar questions, and practitioners are often at a loss for how to choose an appropriate tool for a specific question. We apply and compare four methodological approaches: first passage time (FPT), Bayesian partitioning of Markov models (BPMM), behavioural change point analysis (BCPA) and a fitted multistate random walk (MRW) to three simulated tracks and two animal trajectories - a sea lamprey (Petromyzon marinus) tracked for 12 h and a wolf (Canis lupus) tracked for 1 year. The simulations - in which, respectively, velocity, tortuosity and spatial bias change - highlight the sensitivity of all methods to model misspecification. Methods that do not account for autocorrelation in the movement variables lead to spurious change points, while methods that do not account for spatial bias completely miss changes in orientation. When applied to the animal data, the methods broadly agree on the structure of the movement behaviours. Important discrepancies, however, reflect differences in the assumptions and nature of the outputs. Important trade-offs are between the strength of the a priori assumptions (low in BCPA, high in MRW), complexity of output (high in the BCPA, low in the BPMM and MRW) and explanatory potential (highest in the MRW). The animal track analysis suggests some general principles for the exploratory analysis of movement data, including ways to exploit the strengths of the various methods. We argue for close and detailed exploratory analysis of movement before fitting complex movement models.
© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society.

Entities:  

Keywords:  behavioural change points; hidden Markov models; partitioning; segmentation; state space models; telemetry

Mesh:

Year:  2015        PMID: 25907267     DOI: 10.1111/1365-2656.12379

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


  35 in total

1.  A moving target--incorporating knowledge of the spatial ecology of fish into the assessment and management of freshwater fish populations.

Authors:  Steven J Cooke; Eduardo G Martins; Daniel P Struthers; Lee F G Gutowsky; Michael Power; Susan E Doka; John M Dettmers; David A Crook; Martyn C Lucas; Christopher M Holbrook; Charles C Krueger
Journal:  Environ Monit Assess       Date:  2016-03-22       Impact factor: 2.513

2.  Dynamic foraging of a top predator in a seasonal polar marine environment.

Authors:  Ben G Weinstein; Ari S Friedlaender
Journal:  Oecologia       Date:  2017-09-15       Impact factor: 3.225

3.  Detection of Velocity and Diffusion Coefficient Change Points in Single-Particle Trajectories.

Authors:  Shuhui Yin; Nancy Song; Haw Yang
Journal:  Biophys J       Date:  2017-12-11       Impact factor: 4.033

4.  Integrating direct observation and GPS tracking to monitor animal behavior for resource management.

Authors:  Chelsey Walden-Schreiner; Yu-Fai Leung; Tim Kuhn; Todd Newburger
Journal:  Environ Monit Assess       Date:  2018-01-10       Impact factor: 2.513

5.  Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data.

Authors:  Justin M Calabrese; Christen H Fleming; William F Fagan; Martin Rimmler; Petra Kaczensky; Sharon Bewick; Peter Leimgruber; Thomas Mueller
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2018-05-19       Impact factor: 6.237

Review 6.  Conceptual and methodological advances in habitat-selection modeling: guidelines for ecology and evolution.

Authors:  Joseph M Northrup; Eric Vander Wal; Maegwin Bonar; John Fieberg; Michel P Laforge; Martin Leclerc; Christina M Prokopenko; Brian D Gerber
Journal:  Ecol Appl       Date:  2021-11-28       Impact factor: 6.105

7.  Developing and Integrating Advanced Movement Features Improves Automated Classification of Ciliate Species.

Authors:  Ali Soleymani; Frank Pennekamp; Owen L Petchey; Robert Weibel
Journal:  PLoS One       Date:  2015-12-17       Impact factor: 3.240

8.  Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids.

Authors:  Eleanor R Dickinson; Joshua P Twining; Rory Wilson; Philip A Stephens; Jennie Westander; Nikki Marks; David M Scantlebury
Journal:  Mov Ecol       Date:  2021-06-07       Impact factor: 3.600

9.  Linking behavioral states to landscape features for improved conservation management.

Authors:  Maitreyi Sur; Brian Woodbridge; Todd C Esque; Jim R Belthoff; Peter H Bloom; Robert N Fisher; Kathleen Longshore; Kenneth E Nussear; Jeff A Tracey; Melissa A Braham; Todd E Katzner
Journal:  Ecol Evol       Date:  2021-05-25       Impact factor: 2.912

10.  Fine-scale movements and behaviors of coyotes (Canis latrans) during their reproductive period.

Authors:  Michael J Chamberlain; Bradley S Cohen; Patrick H Wightman; Emily Rushton; Joseph W Hinton
Journal:  Ecol Evol       Date:  2021-06-15       Impact factor: 2.912

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