2013
DOI: 10.1371/journal.pone.0057640
|View full text |Cite
|
Sign up to set email alerts
|

Effects of Temporal Resolution on an Inferential Model of Animal Movement

Abstract: Recently, there has been much interest in describing the behaviour of animals by fitting various movement models to tracking data. Despite this interest, little is known about how the temporal ‘grain’ of movement trajectories affects the outputs of such models, and how behaviours classified at one timescale may differ from those classified at other scales. Here, we present a study in which random-walk state-space models were fit both to nightly geospatial lifelines of common brushtail possums (Trichosurus vulp… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

3
47
1

Year Published

2014
2014
2022
2022

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 39 publications
(51 citation statements)
references
References 31 publications
3
47
1
Order By: Relevance
“…Three features of the trajectory were estimated: (i) the mean distance between two successive locations; (ii) the mean net square displacement between each location [40] and the barycentre of all locations; and (iii) the mean turning angle between two successive segments and its standard error, providing an index of path sinuosity, with low values corresponding to a more linear path. At the home range scale, only the first two of these metrics were calculated, as path sinuosity with inter-fix intervals of 4 or 6 h is not appropriate [41]. Furthermore, at this scale, the mean distance between two successive locations and the mean distance to the barycentre were strongly dependent on winter home range size.…”
Section: (Ii) Mobilitymentioning
confidence: 99%
“…Three features of the trajectory were estimated: (i) the mean distance between two successive locations; (ii) the mean net square displacement between each location [40] and the barycentre of all locations; and (iii) the mean turning angle between two successive segments and its standard error, providing an index of path sinuosity, with low values corresponding to a more linear path. At the home range scale, only the first two of these metrics were calculated, as path sinuosity with inter-fix intervals of 4 or 6 h is not appropriate [41]. Furthermore, at this scale, the mean distance between two successive locations and the mean distance to the barycentre were strongly dependent on winter home range size.…”
Section: (Ii) Mobilitymentioning
confidence: 99%
“…Attempting to detect individuals almost continuously within an array should improve the accuracy and precision of position estimates via triangulation (e.g., Ward et al. ) and improve the ability of statistical models to successfully distinguish between behavioral modes (e.g., stopover vs. active migration; Postlethwaite and Dennis ).…”
Section: Discussionmentioning
confidence: 99%
“…In addition to observational errors, differences between/among behavior state movement metric distributions may influence state‐space model classification accuracies (Codling and Hill , Beyer et al . , Postlethwaite and Dennis , Homburger et al . ).…”
Section: Discussionmentioning
confidence: 99%
“…Although previous research has evaluated state‐space model behavior classifications, studies applicable to free‐ranging animals have been restricted to simulated data sets that did not incorporate observation error (Beyer et al . , Postlethwaite and Dennis ). Thus, an evaluation of state‐space model behavior classifications with empirical data would provide researchers, managers, and policy‐makers with novel information to improve scientific investigations and conservation policy decisions.…”
mentioning
confidence: 99%