A classification‐occupancy model based on automatically identified species data DOI Creative Commons
Ryo Ogawa, Frédéric Gosselin, Kevin Darras

и другие.

Ecology, Год журнала: 2025, Номер 106(5)

Опубликована: Май 1, 2025

Occupancy models estimate a species' occupancy probability while accounting for imperfect detection, but often overlook the issue of false-positive detections. This problem false positives has gained attention recently with rapid advancement automated species detection tools using artificial intelligence (AI), which generate continuous confidence scores each detection. Novel have been introduced that integrate these to identify positives, require thorough assessments diagnosis and validation. Here, we propose new model based solely on AI-detected data. We conducted simulations examine inferential predictive accuracies known true parameters analyzed data test practical usefulness through goodness-of-fit tests evaluation external Our proposed mostly outperformed alternative ignore or error probabilities in terms accuracy simulation analyses case study, not discrimination metrics The aids understanding species-habitat relationships developing biodiversity monitoring workflows by both false-negative errors.

Язык: Английский

A classification‐occupancy model based on automatically identified species data DOI Creative Commons
Ryo Ogawa, Frédéric Gosselin, Kevin Darras

и другие.

Ecology, Год журнала: 2025, Номер 106(5)

Опубликована: Май 1, 2025

Occupancy models estimate a species' occupancy probability while accounting for imperfect detection, but often overlook the issue of false-positive detections. This problem false positives has gained attention recently with rapid advancement automated species detection tools using artificial intelligence (AI), which generate continuous confidence scores each detection. Novel have been introduced that integrate these to identify positives, require thorough assessments diagnosis and validation. Here, we propose new model based solely on AI-detected data. We conducted simulations examine inferential predictive accuracies known true parameters analyzed data test practical usefulness through goodness-of-fit tests evaluation external Our proposed mostly outperformed alternative ignore or error probabilities in terms accuracy simulation analyses case study, not discrimination metrics The aids understanding species-habitat relationships developing biodiversity monitoring workflows by both false-negative errors.

Язык: Английский

Процитировано

1