A New Perspective to Fish Trajectory Imputation: A Methodology for Spatiotemporal Modeling of Acoustically Tagged Fish Data

TL;DR

This paper introduces a spatiotemporal modeling methodology for fish trajectory imputation using acoustic data, employing simulation-based Markov chain and random-walk techniques to improve movement pattern analysis.

Contribution

It presents a novel simulation-based imputation strategy for fish movement data collected by static acoustic receivers, addressing data gaps due to detection limitations.

Findings

Effective trajectory imputation using Markov chain and random walk methods.

Enhanced understanding of fish movement patterns from incomplete acoustic data.

Method applicable across different fish species and migration behaviors.

Abstract

The focus of this paper is a key component of a methodology for understanding, interpolating, and predicting fish movement patterns based on spatiotemporal data recorded by spatially static acoustic receivers. Unlike GPS trackers which emit satellite signals from the animal's location, acoustic receivers are akin to stationary motion sensors that record movements within their detection range. Thus, for periods of time, fish may be far from the receivers, resulting in the absence of observations. The lack of information on the fish's location for extended time periods poses challenges to the understanding of fish movement patterns, and hence, the identification of proper statistical inference frameworks for modeling the trajectories. As the initial step in our methodology, in this paper, we devise and implement a simulation-based imputation strategy that relies on both Markov chain and random-walk principles to enhance our dataset over time. This methodology will be generalizable and applicable to all fish species with similar migration patterns or data with similar structures due to the use of static acoustic receivers.