A functional model for characterizing long‐distance movement behaviour

Abstract

1. Advancements in wildlife telemetry techniques have made it possible to collect large data sets of highly accurate animal locations at a fine temporal resolution. These data sets have prompted the development of a number of statistical methodologies for modelling animal movement. 2. Telemetry data sets are often collected for purposes other than fine-scale movement analysis. These data sets may differ substantially from those that are collected with technologies suitable for fine-scale movement modelling and may consist of locations that are irregular in time, are temporally coarse or have large measurement error. These data sets are time-consuming and costly to collect but may still provide valuable information about movement behaviour. 3. We developed a Bayesian movement model that accounts for error from multiple data sources as well as movement behaviour at different temporal scales. The Bayesian framework allows us to calculate derived quantities that describe temporally varying movement behaviour, such as residence time, speed and persistence in direction. The model is flexible, easy to implement and computationally efficient. 4. We apply this model to data from Colorado Canada lynx (Lynx canadensis) and use derived quantities to identify changes in movement behaviour.

Modelling Animal Activity as Curves: An Approach Using Wavelet-Based Functional Data Analysis

“…They demonstrated that FPCA was an useful tool to assess spatio-temporal patterns in natural ecosystems and their study revealed the fine scale details of the interaction between environmental factors and prey behavior and predator foraging behavior. Just recently, [58] developed a Bayesian model to study animal movement patterns at different temporal scales within the context of functional data analysis. They applied this model to estimate movement paths and associated movement descriptors of the canadian lynx reintroduced to Colorado.…”

“…In this application, B-splines were used but the model was general enough to incorporate other basis functions such as Fourier series and wavelets. The approach by [58] seems extremely promising to reveal details of animal movements with important implications for population dynamics.…”

Guest Editor’s Introduction to the Special Issue on “Animal Movement Modeling”

“…Scharf et al (2017) applied what they refer to as "process imputation" to fit potential function models using a two-stage procedure. In the first stage, they interpolated the data (with uncertainty) using a simplified continuous-time movement model like the functional movement model of Buderman et al (2016) and then integrated over the posterior predictive track distribution when fitting the potential function model in the second stage of the procedure. They found that imputation approaches account for complicated measurement error processes and work remarkably well for recovering interpretable model parameters, but appear to show bias for other nuisance model parameters that are not usually used for inference.…”

Bayesian Inference for Multistate ‘Step and Turn’ Animal Movement in Continuous Time

“…This approach offers a flexible range of models, but the user is unable to associate behaviours directly with environmental information or identify the behavioural state of the animal at a specific point in time. The functional model of Buderman et al (2016) fits splines to infer movement in continuous time, offering much versatility. However, as the estimable quantities of this approach are parameters of splines, rather than mechanistic parameters such as a 'mean speed', the interpretation of these quantities is unclear.…”

“…An alternative approach to those above is given by Hanks et al (2015) in which movement is defined in discrete space, using a Markov chain to model location switches. The inference method they propose, however, requires imputing continuous-time movement paths via some other movement model [examples include Johnson et al (2008a) and Buderman et al (2016)], therefore inheriting such a model's associated assumptions and limitations.…”