Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: application to North American waterfowl.

ABSTRACT:

Background

Identifying animal behaviors, life history states, and movement patterns is a prerequisite for many animal behavior analyses and effective management of wildlife and habitats. Most approaches classify short-term movement patterns with high frequency location or accelerometry data. However, patterns reflecting life history across longer time scales can have greater relevance to species biology or management needs, especially when available in near real-time. Given limitations in collecting and using such data to accurately classify complex behaviors in the long-term, we used hourly GPS data from 5 waterfowl species to produce daily activity classifications with machine-learned models using "automated modelling pipelines".

Methods

Automated pipelines are computer-ge

SUBMITTER: Overton C 

PROVIDER: S-EPMC9109391 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature