Data from: Animal behavior, cost-based corridor models, and real corridors

LaPoint, Scott D.; Gallery, Paul; Wikelski, Martin; Kays, Roland

doi:10.5441/001/1.2tp2j43g

Cited by 6 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To illustrate typical correlation in movement data, we will consider tracking data from fishers (Pekania pennanti) available through Movebank (Brown et al, 2012;LaPoint et al, 2013aLaPoint et al, , 2013b. The data, displayed in Figure 1a, consist of a total of 4,350 step lengths and turn angles of six individuals, resampled to an interval of 10 min with a tolerance of 1 min using the track_resample function in the R package amt (Signer et al, 2019).…”

Section: Motivating E X Amplementioning

confidence: 99%

“…We begin by providing a motivating example using GPS data collected on fishers (Pekania pennanti) from LaPoint et al (2013aLaPoint et al ( , 2013b. We expect most ecologists will be unfamiliar with copulae, so we follow this example with a short introduction to the basic theory (Section 3) before presenting new methods for producing circular-linear copulae suitable for animal movement data (Section 4).…”

Section: Introductionmentioning

confidence: 99%

“…Left: Scatter plot of step lengths (x, in meters) and turn angles ( ) of six fishers from(LaPoint et al, 2013a(LaPoint et al, , 2013b. Kernel density approximations of the marginal densities are plotted in red and blue next to the axes.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Circular–linear copulae for animal movement data

Hodel

Fieberg

2022

Methods Ecol Evol

View full text Add to dashboard Cite

Animal movement is often modelled in discrete time, formulated in terms of steps taken between successive locations at regular time intervals. Steps are characterized by the distance between successive locations (step lengths) and changes in direction (turn angles). Animals commonly exhibit a mix of directed movements with large step lengths and turn angles near 0 when travelling between habitat patches and more wandering movements with small step lengths and uniform turn angles when foraging. Thus, step lengths and turn angles will typically be cross‐correlated. Most models of animal movement assume that step lengths and turn angles are independent, likely due to a lack of available alternatives. Here, we show how the method of copulae can be used to fit multivariate distributions that allow for correlated step lengths and turn angles. We describe several newly developed copulae appropriate for modelling animal movement data and fit these distributions to data collected on fishers (Pekania pennanti). The copulae are able to capture the inherent correlation in the data and provide a better fit than a model that assumes independence. Further, we demonstrate via simulation that this correlation can impact movement patterns (e.g. rates of dispersion overtime). We see many opportunities to extend this framework (e.g. to consider autocorrelation in step attributes) and to integrate it into existing frameworks for modelling animal movement and habitat selection. For example, copulae could be used to more accurately sample available locations when conducting habitat‐selection analyses.

show abstract

Section: Motivating E X Amplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Circular–linear copulae for animal movement data

Hodel

Fieberg

2022

Methods Ecol Evol

View full text Add to dashboard Cite

show abstract

“…We begin by considering an ISSA using tracking data from three fishers (Pekania pennanti) available through Movebank (Brown et al, 2012;LaPoint et al, 2013aLaPoint et al, , 2013b. We resampled the data to a fix interval of 10 min with a tolerance of 1 min using the track_ resample() function in the R-package amt (Signer et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Using lineups to evaluate goodness of fit of animal movement models

Fieberg,

Freeman,

Signer

2024

Methods Ecol Evol

View full text Add to dashboard Cite

Movement models are frequently fit to animal location data to understand how individuals respond to and interact with local environmental features. Several open‐source software packages are available for analysing animal movements and can facilitate parameter estimation, yet there are relatively few methods available for evaluating model goodness of fit. We describe how a simple graphical technique, the lineup protocol, can be used to evaluate goodness of fit of integrated step‐selection analyses and hidden Markov models, but the method can be applied much more broadly. We leverage the ability to simulate data from fitted models and demonstrate the approach using both an integrated step‐selection analysis and a hidden Markov model applied to fisher (Pekania pennanti) data. A variety of responses and movement metrics can be used to evaluate models, and the lineup protocol can be tailored to focus on specific model assumptions or movement features that are of primary interest. Although it is possible to evaluate statistical significance using a formal hypothesis test, the method can also be used in a more exploratory fashion (e.g. to explore variability in model behaviour across stochastic simulations or to identify areas where the model could be improved). We provide coded examples and vignettes to demonstrate the flexibility of the approach. We encourage movement ecologists to consider how their models will be applied when choosing appropriate graphical responses for evaluating goodness of fit.

show abstract

“…To illustrate the lineup protocol, we first consider an ISSA fit to tracking data from 3 fishers (Pekania pennanti) available through Movebank (Brown et al 2012, LaPoint et al 2013a, LaPoint et al 2013b).…”

Section: Integrated Step-selection Analysis Of Fisher Datamentioning

confidence: 99%

Evaluating goodness-of-fit of animal movement models using lineups

Fieberg,

Freeman,

Signer

2023

Preprint

View full text Add to dashboard Cite

Models of animal movement are frequently fit to animal location data to understand how animals respond to and interact with local environmental features. Several open-source software packages are available for analyzing animal movements and can facilitate parameter estimation, yet there are relatively few methods available for evaluating model goodness-of-fit. We describe how a simple graphical technique, thelineup protocol, can be used to evaluate goodness-of-fit of integrated step-selection analyses and hidden Markov models, but the method can be applied much more broadly. We leverage the ability to simulate data from fitted models, and demonstrate the approach using both methods applied to fisher (Pekania pennanti) data. A variety of responses and movement metrics can be used to evaluate models, and the lineup protocol can be tailored to focus on specific model assumptions or movement features that are of primary interest. Although it is possible to evaluate goodness-of-fit using a formal hypothesis test, the method can also be used in a more exploratory fashion (e.g., to visualize variability in model behavior across stochastic simulations or identify areas where the model could be improved). We provide coded examples and two vignettes to demonstrate the flexibility of the approach and encourage movement ecologists to consider how their models will be applied when choosing appropriate graphical responses for evaluating goodness-of-fit.

show abstract

Data from: Animal behavior, cost-based corridor models, and real corridors

Cited by 6 publications

References 0 publications

Circular–linear copulae for animal movement data

Circular–linear copulae for animal movement data

Using lineups to evaluate goodness of fit of animal movement models

Evaluating goodness-of-fit of animal movement models using lineups

Contact Info

Product

Resources

About