Capturing the rhythm: methodological choices shape inference from circadian activity data

Authors

Anderson, A. M., McNamara, K. B., Jones, T. M.

Abstract

Abstract 1. Statistical tools and analytical decisions for analysing animal activity data are often limited and poorly justified, particularly with respect to model choice, sampling design and temporal aggregation. These shortcomings can constrain our ability to draw accurate and biologically meaningful inferences about behaviour. 2. We interrogated 24 h activity data taken from individual males and females derived from five Drosophila melanogaster populations to generate a general framework for collecting and analysing activity data that systematically compares common model classes, continuous verses fragmented sampling strategies, and alternative temporal aggregation choices (time-bin sizes). 3. Our analyses identified generalised additive mixed models (GAMMs) applied to high-frequency continuous data with small-moderate bin sizes (1-5 minutes) as providing the most robust and interpretable inference for our complex, high-frequency activity data. 4. However, our interrogation of the data set confirmed that analytical and sampling decisions need to be tailored to the study species, the timing of activities and the nature of the behaviour of interest. Accordingly, polynomial and cosinor generalised linear models, increased but biologically targeted snapshot sampling, and bin sizes smaller than one hour yielded adequate analyses with similar biological conclusions when appropriately matched to the system. 5. Our approach provides an accessible decision-making framework for activity analyses that allows researchers across behavioural, ecological and evolutionary studies to justify and optimise choices about data collection and modelling. Such an approach improves the biological relevance of inference and supporting the use of modern tracking and analytical technologies.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 31 Oct 2026.

Stats

1-terrible, 9-excellent. How would you rate this preprint? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.