RT Journal Article
T1 Essential tools but overlooked bias: Artificial intelligence and citizen science classification affect camera trap data
A1 Santoro, Simone
A1 Gutiérrez Zapata, Santiago
A1 Calzada Samperio, Javier
A1 Selva Fernández, Nuria
A1 Marín Santos, Diego
A1 Fernández de Viana González, Iñaki Josep
A1 Straková, Lenka
A1 Gegúndez Arias, Manuel Emilio
AB Camera trapping generates vast image datasets requiring classification before downstream ecological inference, yet the influence of classification errors on subsequent analyses is often overlooked. Classification performance can vary widely depending on the classification method (e.g. citizen science vs. artificial intelligence [AI]), species, illumination conditions (diurnal vs. nocturnal) and other contextual  factors.We compared a citizen science classification method to two AI classifiers (EfficientNet and DeepFaune) using an expert-labelled hold-out of 51,588 images across seven classes (‘empty’, ‘human’, ‘cervid’, ‘wild boar’, ‘red fox’, ‘leporid’ and ‘European badger’) captured day and night. For each class and method, we quantified precision (accuracy of positive predictions) and recall (ability to detect all positive instances), then fitted single-season occupancy models to the classified data and compared estimates against expert-derived benchmarks. Finally, we conducted a large-scale simulation to investigate how true occupancy, detection probability and classification performance (recall and precision) collectively influence the accuracy (root mean square error [RMSE]) of occupancy estimates.Citizen scientists exhibited consistently high precision but more variable recall. The AI classifiers outperformed the citizen science method in recall for several species, including wild boar, leporid and European badger. Both approaches performed worse on nocturnal images and showed reduced precision for night-time ‘empty’ images. Bias in occupancy estimates differed across species, methods and space—the AI-based estimates were generally more biased, with both the magnitude and direction of bias varying spatially, especially for rarer species such as leporids. In our simulation study, precision emerged as the strongest predictor of occupancy model accuracy, with lower precision substantially increasing RMSE. Lower occupancy rates increased RMSE, and precision regulated the impact of detection probability: at low precision, higher detection probability worsened errors; at high precision, RMSE remained low—or even decreased—as detection probability rose.Although AI classifiers offer unmatched processing speed, our findings show that citizen science can reduce classification errors. Moreover, low precision and poor recall, especially for rare or nocturnal species, can substantially bias occupancy models. Based on our results, we recommend improving precision and accounting for classification quality and uncertainty to ensure robust inference from camera trap data.
PB Wiley
SN 2041-210X (electrónico)
YR 2025
FD 2025
LK https://hdl.handle.net/10272/27191
UL https://hdl.handle.net/10272/27191
LA eng
NO Santoro, S., Gutiérrez‐Zapata, S., Calzada, J., Selva, N., Marín‐Santos, D., Beery, S., Brandis, K., Fernández de Viana, I., Meek, P., Mortelliti, A., Revilla, E., Rodríguez, J. P., Straková, L., Tenan, S., & Gegúndez, M. E. (2025). Essential tools but overlooked bias: Artificial intelligence and citizen science classification affect camera trap data. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.70132
NO Biodiversa+, Grant/Award Number: 101052342; Fundación Biodiversidad; Universidad de Huelva, Grant/Award Number: FEDER UHU -202028; Agencia Estatal de Investigación, Grant/Award Number: PCI2023-145963- 2; National Science Centre, Grant/Award Number: 2023/05/Y/NZ8/00104; Research Council of Norway; German Research Foundation; Fundación Biodiversidad, Ministerio para la Transición Ecológica y el Reto Demográfico
DS Repositorio Institucional de la Universidad de Huelva
RD 30 may 2026