DOI: 10.5061/dryad.bvq83bkkh
title: Data from: Supplementing human observation with artificial intelligence impacts demographic estimates for a Critically Endangered lizard.

description: 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. Whilst 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.
description: This data includes encounter histories saved as text .inp files
 for use in ProgramMARK. The collapsed encounter histories were used in
 goodness of fit tests (TESTS 2+3 in ProgramMARK). The full histories were
 used for Robust Design capture-recapture analysis (Huggin's p's
 and c's in ProgramMARK).
description: # Supplementing human observation with artificial intelligence impacts
 demographic estimates for a Critically Endangered lizard.
 [https://doi.org/10.5061/dryad.bvq83bkkh](https://doi.org/10.5061/dryad.bvq83bkkh) ## Description of the data and file structure These files contain two sets of encounter histories constructed by alternative individual identification methods, and were used in our paper to highlight the practical impact individual identification methods can have on real-world conservation scenarios. The collapsed encounter histories in these sets were used for goodness of fit testing whilst the full encounter histories formed the primary date for our analysis; using a robust design method in ProgramMARK. ### Files and variables The encounter history files are provided in .inp format; these can be opened and viewed in software capable of reading .txt files, such as notepad. In the 'collapsed' files, presence and absence of an individual is recorded for each of the 5 primary capture occasions, e.g. /* 211 */ 11000 1; where /* 211 */ indicates to ProgramMARK that 211 is the identifier for the individual the encounter history belongs to, 11000 indicates that this individual was captured within the first and second primary occasions, but not the following three, and 1; indicates that this is the capture pattern for one individual and that all information following will relate to a new individual.  Full encounter history files  are formatted in the same way but record presences and absences across both primary and secondary occasions, running to a total of 52 occasions.  Further information regarding data formatting and analysis with ProgramMARK can be found here: [Program MARK](http://www.phidot.org/software/mark/index.html) #### File: AI\_SupplementedEncounterHistory.inp **Description:** Encounter history in .inp format suitable for use with ProgramMARK. Used for Robust Design Analysis of the AI-Supplemented data set. #### File: AI\_SupplementedEncounterHistory\_Collapsed.inp **Description:** Collapsed encounter history in .inp format suitable for use with ProgramMARK. Used for goodness of fit testing of the AI-Supplemented data set. #### File: HumanOnlyEncounterHistory.inp **Description:** Encounter history in .inp format suitable for use with ProgramMARK. Used for Robust Design Analysis of the Human-Only data set. #### File: HumanOnlyEncounterHistory\_Collapsed.inp **Description:** Collapsed encounter history in .inp format suitable for use with ProgramMARK. Used for goodness of fit testing of the Human-Only data set. ## Code/software Data is formatted for analysis in ProgramMARK.