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Using Approximate Bayesian Computation to infer sex ratios from acoustic data.

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A new Approximate Bayesian Computation (ABC) method infers population sex ratios from acoustic data, even with overlapping vocalizations. This non-invasive technique aids conservation efforts for species lacking external sex cues.

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Area of Science:

  • Ecology
  • Bioacoustics
  • Conservation Biology

Background:

  • Determining population sex ratios is crucial in ecology but difficult for species without obvious sexual dimorphism.
  • Acoustic sexing is often hindered by overlapping vocalization characteristics between males and females.

Purpose of the Study:

  • To develop a novel method for inferring population sex ratios from acoustic data, particularly when male and female vocalizations overlap.
  • To enable probabilistic sex assignment for individual acoustic signals and estimate acoustic parameter distributions.

Main Methods:

  • Developed an Approximate Bayesian Computation (ABC) approach to infer sex ratios and acoustic parameters (mean, standard deviation) from vocalizations.
  • Created a simpler exclusion method based on removing calls from the overlap zone.
  • Validated methods using simulated data and empirical echolocation calls from lesser horseshoe bats (Rhinolophus hipposideros).

Main Results:

  • The ABC method reliably inferred sex ratios and acoustic parameters in simulated data.
  • Both ABC and exclusion methods yielded sex ratios comparable to molecular sexing in lesser horseshoe bats.
  • The developed methods are non-invasive, low-cost, and time-efficient.

Conclusions:

  • The novel ABC and exclusion methods effectively determine sex ratios in species with overlapping acoustic signals.
  • These techniques offer valuable tools for evidence-based conservation and ecological research on sex-specific behaviors.
  • The provided R-script facilitates broad application across diverse biological systems and acoustic parameters.