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Quantifying leaf herbivory: A guide to methodological trade-offs and best practices.

Tatiana Cornelissen1, Gisele M Mendes1, Fernando A O Silveira1

  • 1Centro de Síntese Ecológica e Conservação, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil.

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Summary

Researchers compared methods for estimating leaf herbivory. Visual estimation is faster but less accurate than digital image analysis, while AI shows mixed results, highlighting the need for proper training and tool selection for accurate herbivory quantification.

Keywords:
digital methodsfolivoryherbivory intensityherbivory protocolherbivory quantificationleaf damage

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Area of Science:

  • Ecology
  • Plant Science
  • Computational Biology

Background:

  • Leaf herbivory is a widespread ecological interaction with variable intensity.
  • Accurate quantification of leaf damage is vital for ecological research.
  • Existing methods for damage estimation lack comparative evaluation.

Purpose of the Study:

  • To compare the accuracy and precision of visual estimation, digital image analysis, and artificial intelligence for leaf herbivory quantification.
  • To provide a practical guide for herbivory estimation methods.

Main Methods:

  • Utilized a phylogenetically diverse group of tropical plants.
  • Compared visual estimation, digital image analysis (ImageJ), and deep-learning algorithms.
  • Evaluated accuracy and precision of each method.

Main Results:

  • Visual estimation overestimated herbivory but was significantly faster and improved with training.
  • Deep learning underestimated marginal herbivory compared to ImageJ but was comparable for internal damage.
  • Digital methods, particularly image analysis, are crucial for precise quantification.

Conclusions:

  • Visual methods are suitable for rapid, large-scale assessments and detecting broad patterns.
  • Image analysis offers superior accuracy and precision for herbivory quantification.
  • Optimizing training and tool selection can mitigate disadvantages of each method for effective herbivory estimation.