Bayesian estimation of heterogeneous environments from animal movement data

Svetlana V Tishkovskaya,Paul G Blackwell

doi:10.1002/env.2679

Svetlana V Tishkovskaya, Paul G Blackwell

Open Access

https://doi.org/10.1002/env.2679

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Abstract

AbstractWe describe a flexible class of stochastic models that aim to capture key features of realistic patterns of animal movements observed in radio‐tracking and global positioning system telemetry studies. In the model, movements are represented as a diffusion‐based process evolving differently in heterogeneous regions. In this article, we extend the process of inference for heterogeneous movement models to the case in which boundaries of habitat regions are unknown and need to be estimated. Data augmentation is used in reconstructing the partition of the heterogeneous environment. The augmentation helps to diminish the impact of uncertainty about when and where the animal crosses habitat boundaries, and allows the extraction of additional information from the given observations. The approach to inference is Bayesian, using Markov chain Monte Carlo methods, allowing us to estimate both the parameters of the diffusion processes and the unknown boundaries. The suggested methodology is illustrated on simulated data and applied to real movement data from a radio‐tracking experiment on ibex. Some model checking and model choice issues are also discussed.

Highlights

Recent technological advances in animal tracking systems have made complex, spatially explicit, high-frequency datasets of wildlife behavior and movement available, such as those derived from the global positioning system (GPS)
We have presented a method for modeling individual animal movement in a heterogeneous environment, while simultaneously learning about that environment
The approach involves applying diffusion models to account for observed movement and using a data augmentation technique to reconstruct animal locations between collected observations

Summary

Introduction

Recent technological advances in animal tracking systems have made complex, spatially explicit, high-frequency datasets of wildlife behavior and movement available, such as those derived from the global positioning system (GPS). GPS telemetry technology provides nearly continuous, systematically scheduled datasets of locations that allow the details of an animal’s movement to be characterized and related to its environment. The availability of new data sources provides opportunities for a more complete and complex analysis of movement processes than was previously possible. Developments in tracking technologies have advanced the study of animal movement and motivated the development of new theoretical frameworks and novel data analysis tools (Hooten et al, 2017).

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