New abundance and survival estimates for the critically endangered Taiwanese white dolphin indicate no signs of recovery
Bibliographic record
Abstract
Abstract Small populations of cetaceans with restricted ranges are particularly vulnerable to anthropogenic disturbances. The assessment of demographic parameters for these populations can provide meaningful insight into the ways in which they are being impacted by different threats. The Taiwanese white dolphin (TWD), Sousa chinensis taiwanensis, is a subspecies endemic to Taiwan and is classified as Critically Endangered. Multistate robust design mark–recapture models were fitted under a Bayesian framework to estimate demographic parameters and trends for the population from 2010 to 2018. Differences in these parameters by age category and the dolphins' use of their habitat were also explored. Abundance estimates varied from a maximum of 72 in 2010 (95% credible interval (CI) [65, 78]) to a minimum of 61 (95% CI [58, 64]) in 2017. In the oldest age category, abundance estimates varied from 10 (95% CI [10, 11]) in 2013 to 13 (95% CI [10, 13]) in 2016. Modelling showed that, over time, the abundance of dolphins in the oldest age group remained stable, while the remainder of the population, which includes reproductive individuals, experienced a steady decline. This decline is bound to affect the demographic recovery of TWD. Spatial heterogeneity indicated that some areas (i.e. with consistent high occurrences) likely represent important areas for biological functions of the TWD. This suggests that the recovery of the population may be enhanced with immediate and focused actions (such as the ban of gillnet fisheries) in these areas. However, with a small and declining population, reducing threats throughout their entire range is needed, including the new offshore windfarm development being constructed in and adjacent to the dolphin's range. Concerted actions to address known threats, continued monitoring of the population, and education of stakeholders are needed to prevent the extinction of the TWDs.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".