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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

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A framework to evaluate whether to pool or separate behaviors in a multilayer network.

Annemarie van der Marel¹, Sanjay Prasher¹, Chelsea Carminito¹

¹Department of Biological Sciences, University of Cincinnati, Cincinnati, OH, 45221, USA.

Current Zoology

|March 3, 2021

Summary

This study developed a framework for analyzing monk parakeet behaviors using multilayer networks. Findings support pooling behaviors, offering a data-driven method for network analysis decisions.

Keywords:

Myiopsitta monachus behavioral interactions monk parakeet network analysis social context social relationships

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Area of Science:

Behavioral Ecology
Network Science
Animal Social Networks

Background:

Multilayer networks integrate diverse information from connected layers.
Selecting data for network analysis, especially behaviors, can be challenging.
Understanding how to pool or split behavioral data is crucial for accurate network properties.

Purpose of the Study:

To develop and test a framework for deciding whether to pool or split dyadic behavioral data in multilayer network analysis.
To investigate the impact of pooling behaviors on individual- and group-level social properties.
To provide researchers with a method for making informed decisions in network analysis.

Main Methods:

Utilized data from 2 agonistic behaviors in a captive monk parakeet population.
Developed a framework to assess the effects of pooling/splitting behaviors on network properties.
Created two reference models to compare observed data patterns with randomized interactions.

Main Results:

Initial analysis suggested behaviors should not be pooled due to conveying different social information.
After controlling for data sparsity and unequal behavior frequencies, results supported pooling the behaviors.
The developed framework effectively disentangles decision-making processes for behavior pooling.

Conclusions:

The study supports pooling the two agonistic behaviors in monk parakeets for multilayer network analysis.
Awareness of data properties, like sparsity and frequency, is essential when pooling measurements.
The proposed framework is versatile and applicable to various behaviors and research questions in network analysis.