“ClusterApp”: A Shiny R application to guide cluster studies based on GPS data

TL;DR

This paper introduces ClusterApp, an R-based tool that helps researchers analyze GPS data to study animal behavior more efficiently.

Contribution

The novel contribution is the development of a user-friendly Shiny R application to standardize and streamline GPS cluster analysis in wildlife research.

Findings

ClusterApp provides a step-by-step interface for parametrizing and generating interactive maps of GPS activity clusters.

The application reduces data collection biases by using a predefined approach for cluster analysis.

ClusterApp was successfully demonstrated using GPS data from brown bears and gray wolves.

Abstract

The rapid evolution of GPS devices, and therefore, collection of GPS data can be used to investigate a wide variety of topics in wildlife research. The combination of remotely collected GPS data with on‐the‐ground field investigations is a powerful tool for exploring behavioral ecology. “GPS cluster studies” are aimed at pinpointing and investigating identified clusters in the field. Activity clusters can be based on various parameters (e.g., distance between GPS locations and the number of locations needed to establish a cluster), which are closely related to the set research questions. Variation in methods across years within the same study may result in data collection biases. Therefore, a streamlined method to parametrize, generate interactive maps, and extract activity cluster data using a predefined approach will limit biases, and make field work and data management straightforward for field technicians. We developed the “ClusterApp” Shiny application in the R software to facilitate a step‐by‐step guide to execute cluster analyses and data management of cluster studies on any species using GPS data. We illustrate the use of the “ClusterApp” with two location datasets constructed by data collected on brown bears (Ursus arctos) and gray wolves (Canis lupus).

The evolution of GPS devices has revolutionized wildlife research by enabling the combination of GPS data with field investigations to explore behavioral ecology. GPS cluster studies focus on identifying and investigating activity clusters in the field based on specific parameters related to research questions. To address potential data collection biases from varying methods, the “ClusterApp” Shiny application was developed in R to streamline cluster analyses and data management, which is demonstrated with data on brown bears and gray wolves.

Figures