NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping

Area of Science:

• Ecology
• Ethology
• Image Analysis

Background:

• Animal grouping is a complex behavior influenced by various mechanisms across species.
• Understanding spatial distribution and aggregation patterns is crucial in ecological and behavioral studies.

Purpose of the Study:

• To introduce NEIGHBOUR-IN, a novel image processing software for analyzing animal grouping.
• To provide statistical tools for discriminating aggregates based on spatial localization.

Main Methods:

• Development of the NEIGHBOUR-IN software for coordinate analysis of individuals in up to three groups.
• Implementation of statistical analyses and specific indexes to quantify aggregation.
• Validation using artificial patterns and case studies on woodlouse spatial distribution.

Main Results:

• The NEIGHBOUR-IN software effectively analyzes spatial data to identify and differentiate animal aggregates.
• Statistical indexes provide quantitative measures for discriminating between different grouping patterns.
• Case studies demonstrate the software's applicability in real-world ecological scenarios.

Conclusions:

• NEIGHBOUR-IN offers a robust solution for studying animal aggregation dynamics.
• The software and its associated methods enhance the understanding of spatial behaviors in various species.
• This approach provides valuable insights into the mechanisms driving animal grouping.