Photographic Capture-Recapture Analysis Reveals a Large Population of Indo-Pacific Bottlenose Dolphins (Tursiops aduncus) With Low Site Fidelity off the North West Cape, Western Australia
Bibliographic record
Abstract
Little is known about the ecology of Indo-Pacific bottlenose dolphins (Tursiops aduncus) inhabiting the coastal waters of tropical north-western Australia. We used photoidentification data collected between 2013 and 2015, site fidelity indexes and capturerecapture models to estimate the abundance and site fidelity patterns of Indo-Pacific bottlenose dolphins (T. aduncus) inhabiting the coastal waters off the North West Cape (NWC), Western Australia. A standardized site fidelity index (SSFI) indicated low site fidelity (SSFI = 0.019) at the population level to the 130 km2 study area. Agglomerative hierarchical clustering (AHC) of individual re-sight rates classified 58% of individuals as “transients.” Open POPAN modeling estimated a super-population size of 311 (95% CI: 249–373) individuals over the study period. A maximum likelihood transient model which considers both resident and non-resident individuals in a population, estimated a resident population of 141 (95% CI: 121–161) individuals and a super-population of 370 (95% CI: 333–407) individuals. These models indicate that a large population of Indo- Pacific bottlenose dolphins of relatively high density (an average of 2.4–2.8 bottlenose dolphins per km2) inhabits the waters off the NWC. The large number of both resident and non-resident bottlenose dolphins found throughout the coastal waters off the NWC suggest this area, as well as neighboring waters outside our study area, are of high importance to this species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".