"ClusterApp": A Shiny R application to guide cluster studies based on GPS data.

Area of Science:

• Wildlife ecology
• Behavioral ecology
• Spatial ecology

Background:

• Global Positioning System (GPS) devices provide valuable data for wildlife research.
• Combining GPS data with field studies enhances understanding of animal behavior.
• Existing methods for analyzing GPS-derived activity clusters can be inconsistent, leading to biases.

Purpose of the Study:

• To develop a standardized method for analyzing GPS data to identify animal activity clusters.
• To create a user-friendly application for parametrizing, mapping, and managing cluster data.
• To reduce data collection biases and simplify fieldwork for technicians.

Main Methods:

• Development of the "ClusterApp" Shiny application in R software.
• The application provides a step-by-step guide for cluster analysis and data management.
• Illustrative use cases with GPS data from brown bears (Ursus arctos) and gray wolves (Canis lupus).

Main Results:

• ClusterApp offers a streamlined approach to GPS data analysis for wildlife studies.
• The application facilitates the generation of interactive maps and extraction of cluster data.
• Demonstrated effectiveness using datasets from two distinct species.

Conclusions:

• ClusterApp provides a robust and efficient tool for GPS-based wildlife activity cluster analysis.
• Standardizing methods with ClusterApp minimizes biases and improves data management.
• This application supports diverse wildlife research applications across various species.