Quantifying leaf herbivory: A guide to methodological trade‐offs and best practices
Leaf herbivory is a ubiquitous ecological interaction that varies significantly in intensity across species, habitats, and biogeographic regions. Although quantification of leaf damage is crucial for understanding many ecological processes, the accuracy and precision of various damage estimation methods used by researchers, including visual estimation, digital image analysis, and artificial intelligence, have not been evaluated and compared. We use a phylogenetically diverse group of tropical plants to compare the accuracy and precision of damage estimation methods and use the results to provide a guide to herbivory estimation that balances the advantages and disadvantages of each method. We found that visual estimation tended to overestimate herbivory levels compared to digital methods but was 15 times faster and improved in accuracy and speed with training. Conversely, deep-learning algorithms underestimated herbivory relative to image analysis with ImageJ when it was on the margin, but showed similar accuracy for damage inside of leaf margins. Our results indicate that while visual methods allow for rapid assessment of large sample sizes and are suitable for detecting broad patterns of damage, image analysis is crucial for accurate and precise quantification. The disadvantages of each method, however, can be minimized through proper training and efficient use of each tool, and we therefore provide a guide of practical approaches to herbivory estimation.
Authors
- Gustavo Q. Romero (ORCID: https://orcid.org/0000-0003-3736-4759)
- Marcílio Fagundes
- Letícia Franscisca Ramos
- Elenir Aparecida Queiroz (ORCID: https://orcid.org/0000-0001-5261-0714)
- Gisele Mendes (ORCID: https://orcid.org/0000-0002-1643-0342)
- Gabriela Zorzal
- Tatiana Cornelissen (ORCID: https://orcid.org/0000-0002-2721-7010)
- Geraldo Wilson Fernandes (ORCID: https://orcid.org/0000-0003-1559-6049)
- Lucas N. Paolucci (ORCID: https://orcid.org/0000-0001-6403-5200)
- Juliana Kuchenbecker (ORCID: https://orcid.org/0000-0003-4602-9557)
- Jhonathan Silva
- Heraldo Vasconcelos
- Tiago Vinícius Fernandes (ORCID: https://orcid.org/0000-0002-7462-6036)
- Tathiana G. Sobrinho (ORCID: https://orcid.org/0000-0003-1567-0122)
- Ramiro Aguilar (ORCID: https://orcid.org/0000-0003-4741-2611)
- Wesley Dáttilo (ORCID: https://orcid.org/0000-0002-4758-4379)
- Sérvio Pontes Ribeiro (ORCID: https://orcid.org/0000-0002-0191-8759)
- William C. Wetzel (ORCID: https://orcid.org/0000-0001-5390-6824)
- C. V. Oliveira
- Thiago Gechel Kloss (ORCID: https://orcid.org/0000-0003-0958-1748)
- Inácio José Melo Teles Gomes
- Cássio Cardoso Pereira
- Leandro Maracahipes
- Frederico Neves
- Ricardo Solar
- Guilherme Ramos Demetrio
- Fernando A O Silveira
- Roger Guevara
- Ricardo Campos
- Rafael de Paiva Farias
- Ek del Val
- Maria Gabriela Boaventura
Institutions
- Universidade Federal de Ouro Preto (BR)
- Universidade Federal de Minas Gerais (BR)
- Universidade Federal da Bahia (BR)
- Universidade Federal de Viçosa (BR)
- Universidade Estadual de Campinas (UNICAMP) (BR)
- Montana State University (US)
- Yale University (US)
- Universidade Federal dos Vales do Jequitinhonha e Mucuri (BR)
- Instituto Multidisciplinario de Biología Vegetal (AR)
- Instituto de Ecología (MX)
- Universidade Estadual de Montes Claros (BR)
- Universidade Federal do Espírito Santo (BR)
- Universidade Federal do Vale do São Francisco (BR)
- Universidade Federal de Alagoas (BR)
- Universidade Federal de Uberlândia (BR)
- Universidad Nacional Autónoma de México (MX)
Publication Details
- Journal
- Ecology
- Published
- 2026-02-01
- DOI
- https://doi.org/10.1002/ecy.70308
- Primary Topic
- Ecology and Vegetation Dynamics Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00