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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".