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Assessing social structure: a data-driven approach to define associations between individuals.

Sara B Tavares1, Hal Whitehead2, Thomas Doniol-Valcroze1

  • 1Cetacean Research Program, Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, Canada.

Mammalian Biology = Zeitschrift Fur Saugetierkunde
|February 1, 2023
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Summary

This study introduces a novel probability-based method for defining animal social associations using lagged identification rates. This data-driven approach offers a more objective way to analyze social structures, particularly in species with complex fission-fusion dynamics.

Keywords:
AssociationLagged identification rateNorthern resident killer whalesOrcinus orcaSocial structure

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Area of Science:

  • Ethology and Behavioral Ecology
  • Population Dynamics
  • Bioinformatics

Background:

  • Interpreting animal social structures relies on defining meaningful association criteria.
  • Arbitrary thresholds for social behavior can lead to overlooked impacts and biased interpretations.
  • Existing methods often use generalized preconceptions, lacking objective validation.

Purpose of the Study:

  • To develop and validate a probability-based method for defining biologically meaningful association thresholds.
  • To address the limitations of arbitrary association criteria in social structure analyses.
  • To provide a data-driven approach for studying animal societies, especially those with fission-fusion dynamics.

Main Methods:

  • Utilized lagged identification rates from photographic records of identifiable individuals.
  • Employed a simple emigration/immigration model to determine time-dependent lag values.
  • Defined association thresholds based on a 75% probability of close spatial proximity.

Main Results:

  • The probabilistic method maximized variation in association strengths, aligning with known social patterns in northern resident killer whales.
  • Compared to arbitrary thresholds, the data-driven approach revealed more nuanced social structure metrics.
  • Demonstrated the significant impact of arbitrary threshold choices on inferred social metrics.

Conclusions:

  • Data-driven association thresholds offer a promising, objective alternative to subjective field definitions.
  • The proposed method is applicable to various datasets with sequential identifications of associated individuals.
  • This technique enhances the accuracy of social structure analysis in populations with complex social dynamics.