Identification of camera trap images by artificial intelligence and human experts produces similar multi‐species occupancy models

Abstract The use of camera traps in ecology and conservation has expanded rapidly, but the time spent to accurately identify species in camera trap images remains a fundamental challenge that limits project scope and impact. Although artificial intelligence (AI) is often used to speed up image processing, a human review step is still standard practice to arrive at final species identifications. A potentially transformative next step is thus to remove humans entirely from the analysis chain and still produce accurate statistical models for subsequent inference across a wide diversity of species and regions. Here, we compare the output of Bayesian multi‐species occupancy models derived from a complete AI workflow (no human review of images) with a general species classifier (SpeciesNet) to those from an expert (human) workflow, using large‐scale camera datasets from three study areas and two distinct and diverse mid‐large mammal assemblages. We apply several pre‐ and post‐processing steps to the AI workflow to improve model agreement. We perform a comprehensive model comparison, assessing agreement in identified species–environment relationships and rates of occupancy and detection, and similarity of spatial projections of occupancy. We found that for most species of mammal, AI based models were remarkably similar to expert‐based models, with some variability based on post‐processing decisions. This agreement was robust, holding across multiple metrics of comparison (e.g. parameter estimates, precision, occupancy and detection rates), multiple study sites and at species and community levels. Similarity in model output occurred even in the presence of misclassification errors, suggesting our approach was resilient to some level of false negatives and positives. Substantial divergence in model output and subsequent inference, while rare, was most prevalent for rarely detected species. Synthesis and applications . The use of a global AI classifier to identify species and reproducible pre‐ and post‐processing decisions makes our approach broadly applicable and particularly beneficial for national and international monitoring programs that collect large amounts of photo data on threatened, at risk, or management sensitive species and wildlife communities. A fully automated workflow will allow such programs to progress more rapidly from photo collection to analysis, inference and decision‐making. Read the free Plain Language Summary for this article on the journal's blog .