Population abundance and apparent survival of the Vulnerable whale shark <i>Rhincodon typus</i> in the Seychelles aggregation
Bibliographic record
Abstract
Abstract Identifying individuals through time can provide information on population size, composition, survival and growth rates. Identification using photographs of distinctive physical characteristics has been used in many species to replace conventional marker tagging. We evaluated photographic records over 7 years of Vulnerable whale sharks Rhincodon typus, at an aggregation in the Seychelles, for estimation of population size and structure. We collected 11,681 photographs of which only 1,149 were suitable for comparison using semi-automated matching software (I3S) of individual spot patterns behind the gills. Photo-identification showed that there was considerable loss of marker tags and enabled an estimation of the rate of tag loss. The combination of photo-identification with marker tagging identified a total of 512 individual sharks over 2001–2007. Of these, there were 115 resightings in subsequent years with two sharks identified in 2001 resighted 5 years later in 2006 and another shark sighted in 2001 resighted in 2007. Estimates of abundance using conventional open mark–recapture models for 2004–2007 were 348–488 sharks (95% confidence interval), with a high level of entry into the population by itinerants. Annual apparent survival probability was 0.343–0.781 over 2004–2007, with an average annual recapture probability of 0.201. These results are the first to suggest a highly transient population of whale sharks around the Seychelles, indicating that international or at least regional-scale conservation approaches are required.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".