The relevance of a right scale for sampling when studying high-resolution behavioral dynamics

• Published in: Scientific reports Vol. 13; no. 1; p. 13291
• Main Authors: Barberis, L., Simian, C., Marin, R. H., Kembro, J. M.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 16-08-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/553/2393; 631/553/2703; 631/601/18; Behavior; Geographical distribution; Humanities and Social Sciences; Long term memory; Metabolic rate; multidisciplinary; Sampling; Science; Science (multidisciplinary); Vigilance
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-39295-z

Many species used in behavioral studies are small vertebrates with high metabolic rates and potentially enhanced temporal resolution of perception. Nevertheless, the selection of an appropriate scales to evaluate behavioral dynamics has received little attention. Herein, we studied the temporal organization of behaviors at fine-grain (i.e. sampling interval ≤1s) to gain insight into dynamics and to rethink how behavioral events are defined. We statistically explored high-resolution Japanese quail (
Coturnix japonica
) datasets encompassing 17 defined behaviors. We show that for the majority of these behaviors, events last predominately <300ms and can be shorter than 70ms. Insufficient sampling resolution, even in the order of 1s, of behaviors that involve spatial displacement (e.g. walking) yields distorted probability distributions of event durations and overestimation of event durations. Contrarily, behaviors without spatial displacement (e.g. vigilance) maintain non-Gaussian, power-law-type distributions indicative of long-term memory, independently of the sampling resolution evaluated. Since data probability distributions reflect underlying biological processes, our results highlight the importance of quantification of behavioral dynamics based on the temporal scale pertinent to the species, and data distribution. We propose a hierarchical model that links diverse types of behavioral definitions and distributions, and paves the way towards a statistical framework for defining behaviors.