Dynamic Models of Animal Movement with Spatial Point Process Interactions

TL;DR

This paper introduces a flexible Bayesian modeling framework for animal movement that incorporates interactions between individuals using spatial point process functions, demonstrated on guppy movement data.

Contribution

It develops a novel approach combining dynamic movement models with spatial point process interactions, along with a double Metropolis-Hastings algorithm for inference.

Findings

Successfully modeled guppy movement with interaction effects

Demonstrated the flexibility of independent modeling of movement and interactions

Provided a Bayesian inference method for complex intractable models

Abstract

When analyzing animal movement, it is important to account for interactions between individuals. However, statistical models for incorporating interaction behavior in movement models are limited. We propose an approach that models dependent movement by augmenting a dynamic marginal movement model with a spatial point process interaction function within a weighted distribution framework. The approach is flexible, as marginal movement behavior and interaction behavior can be modeled independently. Inference for model parameters is complicated by intractable normalizing constants. We develop a double Metropolis-Hastings algorithm to perform Bayesian inference. We illustrate our approach through the analysis of movement tracks of guppies (Poecilia reticulata)