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Aipedia: Ecologists and environmental consultants
Machine-learning tools can sort camera trap footage and identify species, and even individual animals, far faster than manual review. The risk for ecologists is a false absence or an identification nobody can verify, presented as evidence at planning or licensing stage.
Part of: AI in Ecologists and environmental consultants
In short
Image recognition is used to filter empty frames, label species in camera trap footage and match individuals, for example badgers or great crested newts from photographs. CIEEM’s 2025 Summer Conference covered machine-learning identification of both. The main risks are false absences when a model filters out a frame that contained the target species, misidentification, and models whose training and accuracy cannot be explained. Under the CIEEM Code you must be competent, accurate and state the limits of information relied on. Where results support a planning or licensing decision, weak evidence can cause delay and lead to a claim.
Camera traps on mammal surveys can produce thousands of images, most of them empty or showing non-target species. AI tools now remove blank frames, label species and, in research and some consultancy settings, recognise individuals from markings. CIEEM’s 2025 Summer Conference included sessions on machine-learning identification of individual badgers and great crested newts.
For consultants the appeal is cost and speed. The difficulty is that the most important result in ecology is often an absence: "no evidence of the species was recorded". An automated filter that wrongly discards a frame turns a presence into an apparent absence, and nothing in the output shows it happened.
The CIEEM Code does not mention AI, but clause 3 (competence), clause 4 (accuracy and regard to published guidance) and clause 10 (responsibility for decisions) apply to how you use these tools. The supplementary notes require you to state the limitations of information relied on and to record evidence for any departure from published guidance. If standard methods call for manual review, replacing it with an unvalidated model is a departure you should be able to justify.
Where results feed into an EIA, ISEP’s advice note expects outputs to be checked against the evidence, an audit trail to be kept and the tool to be declared. See CIEEM, ISEP and Planning Inspectorate guidance.
Illustrative scenario. An ecologist sets camera traps at possible sett entrances on a development site and runs the footage through a species filter. The filter flags no badger activity and the report says the setts are disused. Groundworks start, active use is later confirmed, works pause and the developer has to revise its programme and layout. On review, several frames showing a badger had been discarded as empty. The developer claims its delay costs.
A second risk is evidential. If a local planning authority, a statutory body or an objector asks how a species or individual was identified, "the model said so" is weak. Where you cannot explain the model’s accuracy for the species and conditions, the evidence may be given little weight, and further survey may be required. The same pattern affects bat call auto-ID and preliminary ecological appraisals built on automated outputs.
A claim that survey evidence was negligently gathered or interpreted is a professional negligence claim, and PI commonly responds subject to the policy terms. Insurers and experts will look at what a competent ecologist would have done: whether the frames were spot-checked, whether the limits were stated, and whether the conclusion went further than the evidence. A report that clearly states the limits of automated review is usually easier to defend. The guide to whether ecologists need PI explains the basics.
Before relying on image recognition for a conclusion that matters, consider these checks:
Apex arranges PI for ecologists running mammal, amphibian and other protected species surveys. A broker reviews your cover and how your policy treats survey work that uses automated image analysis. The AI in ecology consultancy overview links related guides.
Often not for a decision that matters. Under the CIEEM Code you should be able to explain how an identification was reached and state its limits. Manual verification of key frames gives evidence more weight with planning authorities and licensing bodies.
A false absence is a survey result that records a species as absent when it is present. With AI filtering it can happen when frames containing the species are discarded as empty or mislabelled, so the absence comes from the tool rather than the site.
Usually yes, if the claim alleges negligence in your professional work, subject to the policy terms. Check that your activities description covers the surveys you do and answer any technology questions on the proposal form accurately.
Machine-learning identification of individual badgers and great crested newts was discussed at CIEEM’s 2025 Summer Conference. If you rely on it for population or movement conclusions, be ready to explain the method, its validation and its limits, and verify matches that drive your recommendations.
Tell us about your firm and the work you do. A broker will review how your current PI treats AI-assisted work, or arrange new cover. No fee for a quote. Or call 0117 325 0027.
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