Essential tools but overlooked bias: Artificial intelligence and citizen science classification affect camera trap data

Abstract

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.