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Aipedia: Ecologists and environmental consultants
Auto-identification software speeds up analysis of bat echolocation recordings, but the ecologist remains responsible for the species identified. A wrong or missed species can change licensing and planning outcomes and cost a client a survey season.
Part of: AI in Ecologists and environmental consultants
In short
Ecologists may run static and transect detector recordings through auto-identification software that assigns calls to species. The Bat Conservation Trust’s 4th edition guidelines say ecologists need new skills to use this technology and warn that field learning is essential before relying on automated tools. Under the CIEEM Code you must be competent in the methods you use and accurate in what you report. If auto-ID misses or mislabels a species and that only emerges later, the client may face re-surveys in the next season, redesign or licensing delay, and may claim the cost from the ecologist.
Bat surveys generate large volumes of recordings. Auto-identification software classifies calls by species or genus and flags likely social calls, so an ecologist can focus manual checking on a subset. Automated species identification for passive acoustic monitoring featured at CIEEM’s 2025 Summer Conference, which shows the approach is attracting professional attention.
The weak points are familiar to experienced bat workers: species with overlapping call parameters, poor quality recordings, noise and clutter, and a tool trained on data that may not match the site. Generative AI can add a second layer when it is used to summarise the results into the report text.
The Bat Conservation Trust guidelines (4th edition, 2023) are the industry standard for bat survey work. The foreword says ecologists need new skills to use auto-identification technology, and section 2.5.2 notes concern that automation may reduce fieldcraft and says field learning is essential before relying on automated tools.
Under the CIEEM Code, clause 3 requires competence in the work you take on, clause 4 requires accuracy and regard to published technical guidance, and clause 6 requires staff to be trained and supervised. The supplementary notes ask you to state the limitations of information relied on, which includes the confidence of automated identifications. Our guide to CIEEM and ISEP expectations covers the wider framework.
Illustrative scenario. A consultancy runs a season of static detector data through auto-ID. A junior ecologist accepts the classifications without manual verification of uncertain calls. The report concludes that only common species use the site and that no further roost surveys are needed. During demolition a roost of a different species is found. Works stop, a licence has to be sought and the next survey window is months away. The developer claims for delay, redesign and the cost of repeating surveys.
The core allegation would not be "the software was wrong" but that a competent ecologist should have checked the uncertain calls, recognised the tool’s limits and reported them. The quality of the file matters: which calls were vetted, by whom, against what reference. Similar problems arise with camera trap image recognition and feed into protected species licence applications.
A claim that survey analysis was negligent is a standard professional negligence claim, and PI for ecologists commonly responds regardless of whether software was involved, subject to the policy terms. Disputes tend to centre on causation and loss: whether the species would have been found with proper checking, and whether delay costs would have arisen anyway once the roost was known about. Policies vary in how they treat contractual promises such as guaranteed survey outcomes, so avoid giving them. The PI guide for ecologists and arboriculturists explains typical cover.
Notify your insurer as soon as you become aware of circumstances that might lead to a claim, such as a roost found during works that your survey did not identify. Late notification can create problems under many policies, and early advice can help limit the client’s losses.
A defensible auto-ID workflow usually includes the following steps:
Apex arranges PI for ecologists who carry out bat and other protected species surveys. A broker reviews your cover and how your policy treats survey analysis that relies on automated identification. See the AI in ecology consultancy overview for related guides.
You can use them, but the Bat Conservation Trust guidelines stress field learning before relying on automated tools, and the CIEEM Code requires competence and accuracy. In practice that means verifying uncertain or significant identifications manually and stating the limits of automated results in the report.
Describing your methods, including automated identification and how results were verified, is part of stating the limitations of information relied on under the CIEEM Code supplementary notes. It also helps if your conclusions are later questioned.
If the client claims that your negligence forced a re-survey, PI commonly responds to the claim, subject to the terms and excess. The cost of redoing your own work without a claim, or to protect your reputation, is often not covered, so check the wording.
As far as your client is concerned, you are. Clause 10 of the CIEEM Code makes you responsible for your professional decisions. You may have rights against the software supplier, but those depend on its licence terms, which often limit liability, and they do not stop a client claiming against you.
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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