Questions behind the evidence

No. Partial bodies, low light, unfamiliar species and close look-alikes can defeat a model. Some systems only detect animals rather than name species. Keep an unknown category, retain the original image and send consequential records for appropriate review. A forced label is not better evidence than an honest unresolved record. Read the recognition guide.

Not necessarily. A score may rank predictions usefully without being a calibrated probability. Performance can change with region, equipment or species composition. Test score thresholds on independent local examples, examine errors by class and avoid presenting a high score as proof that the animal has been correctly identified.

Not by themselves. More working camera-days, a different trail position, seasonal movement or a changed classification threshold can increase the record count. Distinguish events, individuals and estimated population quantities. A trend claim needs comparable sampling and an appropriate analysis of detection. Explore population inference.

Sometimes. Distinctive markings can support identification when the relevant body region is visible and suitable reference images exist. Reliability differs among species and image conditions. False matches and missed matches can bias capture–recapture estimates, so document uncertainty, review candidate identities and do not assume every species is individually recognisable.

There is no universal percentage. Set review effort around the monitoring question, local validation and the consequences of mistakes. A possible first record of an endangered species deserves different scrutiny from routine archive sorting. Review some apparently negative records too, and use independent second review where disagreements could materially affect conclusions.

Compare the tasks you need: detection, classification, individual matching, data management or operational alerts. Then test local performance, review tools, security, exports, support and total costs. Include field maintenance and staff time. We do not provide a universal ranking or imply that an organisation named here endorses this site.

Yes, many surveys collect images or recordings offline and process them later. Near-real-time alerts require connectivity, power and staff able to verify and act on the alert. Decide whether immediacy is necessary before adding those dependencies. Plan a sustainable field workflow.

Only after reviewing ecological sensitivity, permissions and the risk of inference from combined records. Location may be exposed through embedded metadata, filenames or scenery even when coordinates are absent. Restrict source data where needed and describe any generalisation in public datasets. Read the data-care guide.

Yes. A model that finds more difficult animals can create an apparent rise in detections. Retain model versions and evaluate old and new workflows on a fixed test set. Where appropriate, reprocess historical data consistently. Report remaining comparability limits rather than assuming that a software update has no analytical effect.

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