This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.
Which camera-trap images to review? Estimating occupancy from a small part of a survey
Downloads
Authors
Abstract
Context. Camera-trap surveys produce more images than people can review. Computer-vision classifiers such as SpeciesNet label animals but miss some species, so people still review images.
Aims. To find how many and which images should be reviewed to reliably estimate species occupancy, and how a project can tell when it has reviewed enough.
Methods. On four fully labeled camera-trap surveys (in the USA, South Africa, and Malawi), I simulated partial reviews by choosing capture events in five ways: at random, with animals, spread across cameras, least certain first (as in active learning), and by confirming the suggestions an occupancy model counts. I then tested a two-step procedure for human-in-the-loop review: review two capture events per camera; then, in rounds, confirm the most confident suggestion of each unconfirmed species-site-week (a species at a site in a week), retraining a model of the project's species after each round.
Key results. SpeciesNet's suggestions alone gave occupancy within a mean absolute error (MAE) of 0.06 to 0.12 of a full review's where it knew the fauna, but missed Canada lynx and nyala; in Malawi, the MAE was 0.24. Reviewing 25% of images at random gave an MAE of 0.16 to 0.24 with only reviewed images counted as detections. Confirming suggestions came closest; reviewing the least certain first did little better than random. In the procedure, each capture event reviewed confirmed 0.6 to 1.0 new species-site-weeks until 92 to 98% were confirmed, then fewer than 0.2. Stopping there, after 15 to 27% of the images (1.1 to 1.8 capture events per species-site-week), occupancy, counting only reviewed images, had an MAE of 0.04 or less, lynx included.
Conclusions. Confirming suggestions approaches a full review's estimates because occupancy models need species-site-weeks, not images; reviewing the least certain images first spends effort where estimates gain least.
Implications. Projects can nearly match a full review's occupancy estimates by reviewing two capture events per camera, then confirming one suggestion per species-site-week until a round confirms fewer than one new species-site-week per five capture events. Researchers can expect to review about 1.5 capture events per species-site-week that the first step shows.
DOI
https://doi.org/10.32942/X20M63
Subjects
Ecology and Evolutionary Biology, Life Sciences
Keywords
Dates
Published: 2026-11-11 13:37
Last Updated: 2026-11-11 13:37
License
CC BY Attribution 4.0 International
Additional Metadata
Conflict of interest statement:
MT owns Quantitative Science Consulting, LLC, which develops WildObserve, the software used in this study.
Data and Code Availability Statement:
The four camera-trap surveys are public on LILA BC (https://lila.science) under the Community Data License Agreement (Permissive): Caltech Camera Traps (Beery et al. 2018), WSU Lynx (Thornton et al. 2026), the Snapshot Safari 2024 Expansion (Pardo et al. 2021), and Nkhotakota Camera Traps (Appel et al. 2025). The code that runs every experiment from the data exported from WildObserve, with the export script, is at https://github.com/QScienceC/occupancy-camtrap (MIT License). The exported data and the results are archived on Zenodo (https://doi.org/10.5281/zenodo.23734806). WildObserve is available from the developer (https://www.qsc.earth/WildObserve); its Community edition (version 0.9.0 or later), free for non-commercial use, includes the For occupancy list and the project model. Preliminary results for earlier versions of this analysis are presented in the preprint describing WildObserve (Tabak 2026).
Language:
English
Metrics
Views: 25
Downloads: 1
There are no comments or no comments have been made public for this article.