Using the Blanding's Turtle (Emydoidea blandingii) plastron as a 'fingerprint': photo identification of an endangered species
Bibliographic record
Abstract
The ability to uniquely identify individuals is critical to estimating and monitoring trends in population sizes, one of the key metrics used to evaluate a species’ conservation status and success of mitigation strategies. For freshwater turtles, shell notching and/or passive integrated transponder (PIT) tags are commonly used to mark individuals. However, because notch codes and PIT tags can be lost over time and require more invasive procedures, we explored if photographs offer an effective method to reliably identify individuals. The Blanding’s turtle (Emydoidea blandingii) is a globally endangered species with distinct black and yellow markings on its plastron. We used the I3 S Pattern software with custom parameters to classify patterns on Blanding’s turtle plastrons and to identify individuals. We analyzed 826 plastron images from 707 individual Blanding’s turtles taken between 1998 and 2019 from 12 study areas distributed throughout their Canadian range. When plastron photos were pooled across the sampled range (i.e., all study areas), there was an 84% probability of correctly identifying an individual turtle within the top 3 suggested matches, whereas when identifying Blanding’s turtles within a specific study area, identification accuracy was 82% in Central Ontario and 97% in Nova Scotia. Individual identification from plastron markings did not work well in areas where iron staining obscured the plastron pattern or for hatchlings and juveniles whose patterns changed over time. For example, the only misclassification in the Nova Scotia study area was for a turtle with photos through various life stages. In areas without iron staining, plastron photo identification offers a cost-effective, non-invasive method to identify individual adult Blanding’s turtles to support population monitoring and community science initiatives, and has the potential to assist with range-wide coordination to counteract illegal wildlife trade.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.093 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".