More Tools Than Users: A Scoping Review of Image‐Processing Automation in Mammal Camera Trapping

Introduction Camera trapping has become a cornerstone of mammal monitoring, yet the volume of images frequently exceeds the capacity for manual review. Automated image‐processing tools—like AI‐based species classifiers and on‐device alerting systems—offer scalable solutions, but uptake in applied research (field‐based ecological studies) remains poorly characterised. Aims We conducted a scoping review of AI in mammal camera‐trapping studies (2014–2026), complemented by a corresponding‐author survey, to assess adoption and synthesise the critical domains advancing the field. Methods We screened 984 records from a stratified random sample of 8725 records identified through Web of Science and Scopus, retaining 654 publications, and surveyed the corresponding authors of 121 applied studies. Results AI‐methods publications have outnumbered applied studies each year since 2023, yet only 9.1% (11 of 121) of surveyed researchers reported using AI in their applied work. Adoption emerged only after 2022 and was restricted to blank‐frame filtering alone (9.1%) or paired with species classification (90.9%). Other AI‐enabled tasks such as individual re‐identification, behaviour classification and real‐time alerting were absent from all 288 applied papers reviewed and survey responses. Their prevalence in applied work is unlikely to exceed 1.04% (97.5% upper bound). Synthesis and Outlook Most image‐processing tools, blank filtering, species classification and real‐time alerting are well developed; adoption in applied research lags, as expected for emerging technology. Implementation also hinges on two still‐underdeveloped capacities: propagating classifier uncertainty into population estimates and automated camera‐to‐animal distance measurement for density estimation of unmarked populations. Bridging these gaps requires standardised reporting, interdisciplinary collaboration and uncertainty‐aware workflows.