eprintid: 10203386 rev_number: 11 eprint_status: archive userid: 699 dir: disk0/10/20/33/86 datestamp: 2025-01-15 10:43:00 lastmod: 2025-06-06 13:04:59 status_changed: 2025-01-15 10:43:00 type: article metadata_visibility: show sword_depositor: 699 creators_name: Jordan, Emily creators_name: Ewen, John creators_name: Hitchmough, Rod creators_name: Moran, Les creators_name: Murrell, David creators_name: Rowcliffe, Justin Marcus creators_name: Adams, Lynn title: Supplementing human observation with artificial intelligence impacts demographic estimates for a Critically Endangered lizard ispublished: pub divisions: UCL divisions: B02 divisions: C08 divisions: D09 divisions: F99 keywords: artificial intelligence, capture–recapture, individual identification, Kapitia skink, Oligosoma salmo, population estimation, reptile conservation, robust design note: © 2025 The Author(s). Ecological Solutions and Evidence published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). abstract: Species conservation relies heavily on population estimates derived from capture–recapture analyses, which are liable to produce biased results if individual animals are incorrectly identified. Captive and known-animal studies have shown that supplementing human observation with artificial intelligence (AI) has the potential to reduce these errors. However, no study has directly quantified the relationship between using AI for individual identification and the demographic estimates it produces for a threatened population in situ. We compared the demographic estimates produced by capture–recapture analyses of two distinct encounter histories constructed from the same survey data; one produced using individual identifications made by human observers alone (the ‘human-only data set’), and one produced using AI software to aid individual identification (the ‘AI-supplemented data set’). This approach enabled us to address two key questions: (i) does the use of artificial intelligence software for individual identification influence demographic estimates for an in situ conservation programme? (ii) How has the population of our case study species, the critically endangered Kapitia skink, responded following an extreme weather event, cyclone Fehi? We found that, without AI, human observers appeared prone to make reclassification or ‘splitting’ errors, in which a recaptured animal was wrongly assigned as a new individual. Analysis of the AI-supplemented data set consistently produced lower estimates of population abundance over time, relative to the same analysis of the human-only data set. This provides new evidence that wild species monitoring efforts may be prone to underestimating the extinction risk of populations if they are dependent on individual identification methodologies with high potential for human errors. Our case study species, the Kapitia skink, demonstrated a positive population trend in the period following cyclone Fehi. While promising, conservation intervention is recommended to address persistent threats. Practical implication. Supplementing human observation with AI software for individual identification could mitigate errors leading to the underestimation of extinction risk for endangered species. We encourage further development of AI software to increase its automation and accessibility and recommend that practitioners consider its use in population monitoring based on the identification of individuals in imagery. date: 2025-03 date_type: published publisher: Wiley official_url: https://doi.org/10.1002/2688-8319.70018 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2352856 doi: 10.1002/2688-8319.70018 lyricists_name: Rowcliffe, Justin Marcus lyricists_id: MROWC46 actors_name: Rowcliffe, Justin Marcus actors_id: MROWC46 actors_role: owner full_text_status: public publication: Ecological Solutions and Evidence volume: 6 number: 1 citation: Jordan, Emily; Ewen, John; Hitchmough, Rod; Moran, Les; Murrell, David; Rowcliffe, Justin Marcus; Adams, Lynn; (2025) Supplementing human observation with artificial intelligence impacts demographic estimates for a Critically Endangered lizard. Ecological Solutions and Evidence , 6 (1) 10.1002/2688-8319.70018 <https://doi.org/10.1002/2688-8319.70018>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10203386/7/Jordan_Supplementing%20human%20observation%20with%20artificial%20intelligence%20impacts%20demographic%20estimates%20for%20a%20Critically%20Endangered%20lizard_VoR.pdf