Scientific publications

Essential tools but overlooked bias: artificial intelligence and citizen science
classification affect camera trap data
Simone Santoro, Santiago Gutiérrez-Zapata, Javier Calzada, Nuria Selva, Diego
Marín-Santos, Sara Beery, Kate Brandis, Paul Meek, Alessio Mortelliti, Eloy Revilla,
Jon Paul Rodríguez, Simone Tenan, Manuel Emilio Gegúndez. Methods in Ecology
and Evolution, Submitted


Keywords: artificial intelligence, deep learning, convolutional neuronal networks,
citizen science, camera trap, computer vision


Abstract: 1. Camera trapping, widely employed for wildlife monitoring, generates avast volume of images which must be classified to become analysable data. Yet, the factors influencing classification performance and their effects on ecological interpretation are often overlooked. The variability in classification performance may stem from different image classification methods, animal species, camera specifications, and environmental covariates.


2. This study assesses the performance of a citizen science project hosted in Zooniverse (CS-Zoo) and two artificial intelligence (AI) approaches in identifying diurnal and nocturnal images across six classes: empty, human, cervid, leporid, wild boar, and red fox. Using an out-of-sample (not used for AI training and validation) dataset of 51,588 images classified by experts, we evaluated for each class the accuracy of positive predictions (precision) and the ability to detect all positive instances (recall). Additionally, we conducted a single-season occupancy analysis to compare the
ecological inference derived from each classification system and class.


3. Citizen scientists demonstrated exceptional precision, though recall was more variable. AI outperformed citizen science in recall for some classes, such as leporid. However, both encountered challenges with nocturnal photos and experienced a significant loss of precision for empty images at night. AI data analyses produced positively biased occupancy predictions, whereas CS-Zoo data generated a slight negative bias, with both biases being noticeable only for taxa with low-to-moderate occupancy estimates, such as red fox and leporid. Notably, our AI model processed up to 1,150,000 images daily, over 500 times faster than CS-Zoo.


4. Our findings suggest that while CS-Zoo is preferable for minimizing classification errors, AI’s remarkable processing capacity is indispensable for studies needing rapid and large-scale data processing. However, the significant drop in performance for night photos and less common species in AI and CS warrants careful consideration, particularly regarding missed detections of nocturnal or conservation concern species. A regular comprehensive evaluation is crucial for understanding how classification performance impacts data and ecological inferences, including demographic and behavioural parameters like occupancy, abundance, community composition, and activity patterns. These inferences rely on the inherent classification process in camera
trap data studies.

Dog invasions in protected areas: A case study using camera trapping, citizen science and artificial intelligence.

Santiago Gutiérrez-Zapata, Simone Santoro, Manuel Emilio Gegundez-Arias, Nuria Selva, Javier Calzada. Global Ecology and Conservation, 07/2024.

https://doi.org/10.1016/j.gecco.2024.e03109.

Keywords: Machine learning; Canis familiaris; Citizen science; Invasive species; Protected Areas; Camera trapping

Abstract: Domestic dogs, Canis familiaris, wandering into natural habitats poses a grave threat to wildlife, increasing predation pressure and disease risk and disrupting the ecological balance within ecosystems. This study examines the presence of dogs in a European Protected Area (PA), Doñana National Park (SW Spain), where their access is strictly restricted, and explores how dog presence relates to potential access points. We utilised classifications provided by citizen science and artificial intelligence, subsequently validated by experts, to detect dogs within 5200,000 photos taken by 60 camera traps randomly deployed across the PA from October 2020 to January 2024. We discovered 33 dogs, primarily in groups of 2–5 individuals, recorded across 31 detection events at 22 camera locations. Dogs were detected ranging from 10 to 42 km2 (Minimum Convex Polygon) within the PA. The detection probability of dogs increased by 0.22 log odds per kilometre closer to a village (corresponding to an increase from 0.5 to approximately 0.55) bordering the PA and exceeded 0.9 near it. Our data revealed three types of dogs wandering within the PA: dogs accompanying poachers, free-roaming dogs living in nearby human settlements, and stray dogs, most likely relying on the PA resources. Urgent actions are needed in Doñana as dogs pose severe threats to endangered species like the Iberian lynx Lynx pardinus (six adult female lynx documented killed by dogs). We recommend raising awareness among local authorities of free-roaming dogs, particularly in settlements close to PAs, where their presence should be banned. Regularly monitoring dog presence within PAs is crucial to prevent invasions and their associated impacts. Our findings underscore the importance of using camera traps and integrating artificial intelligence with citizen science to monitor invasive species effectively.