2025/10/01 by Alexis Mendoza‐Lozana, Rubén Ortega‐Álvarez, Adolfo G. Navarro‐Sigüenza +2
Environmental Science · Psychology · #Species Distribution and Climate Change #Wildlife Ecology and Conservation #Animal and Plant Science Education
paper · pdf · doi:10.1002/ece3.72237
The rise of participatory ornithology projects in the Global North has spurred studies aimed at optimizing the quality of data generated by these initiatives to support conservation efforts. However, in Latin America-where these projects are typically developed in collaboration with rural and indigenous communities-evaluations addressing this issue remain scarce. This study analyzes a community-based bird monitoring project in southwestern Mexico and highlights key aspects for improving data quality. First, five members of a rural community were trained in bird identification and counting. Subsequently, using data generated by professional ornithologists as a reference, the accuracy and bias in the monitoring data were examined to assess bird species richness and community structure across forest and human-altered habitats. In addition, hierarchical clustering analyses were employed to identify biological traits of species that affect data quality. The community monitoring data demonstrated sufficient quality to detect changes in bird communities resulting from anthropogenic impacts, though there were identifiable biases associated with forest habitat species, migratory species, and species belonging to the families Trochilidae and Tyrannidae. The results suggest that scientific endeavors in community projects should focus on developing adaptive training strategies to enhance monitors' skills in identifying birds and recording their abundance. The evaluation framework proposed in this study offers a valuable collaborative perspective for improving data quality in community monitoring initiatives across Latin America.