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Machine Learning Quantifies Fine-Scale Hairiness in Shore Flies (Diptera: Ephydridae).

Shawn M Abraham1, Marcos Rodriguez1, Victoria Hristova1

  • 1Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada.

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Summary

This study introduces a machine-learning workflow to efficiently quantify insect microtrichia (hairs), aiding morphological analysis. The method accurately counts hairs and assesses coverage, offering a valuable tool for biological research.

Keywords:
adaptationelectron microscopymicrotrichiapixel classificationsemiaquatic

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Area of Science:

  • Entomology
  • Morphometrics
  • Bioinformatics

Background:

  • Traditional insect fine structure analysis is time-consuming and limited.
  • Automated methods are needed for efficient morphological studies.

Purpose of the Study:

  • To develop and validate a machine-learning workflow for quantifying microtrichia in shore flies (Ephydridae).
  • To compare microtrichia abundance and coverage between different Ephydridae species.

Main Methods:

  • Utilized a pixel classification machine-learning workflow with Ilastik and Fiji.
  • Employed scanning electron micrographs to semi-automate microtrichia counting and percent coverage determination.
  • Validated results against manual counts.

Main Results:

  • The workflow accurately quantified microtrichia, consistent with manual counts.
  • Hot spring-tolerant Paracoenia species showed higher microtrichia counts than less aquatic Parydra.
  • Microtrichia length varied, and percent coverage per unit area did not consistently differentiate species.

Conclusions:

  • The developed workflow offers an efficient, semi-automated method for microtrichia quantification.
  • This approach is adaptable to other taxonomic groups and morphological features.
  • Efficient morphological workflows are crucial for complementing multi-omics data in biological inferences.