Minding the Data-Gap Trap: Exploring Dynamics of Abundant Dolphin Populations Under Uncertainty
Bibliographic record
Abstract
Preventing declines in common species is key to sustaining the structure and function of marine ecosystems. Yet for many common marine mammals, including oceanic dolphins, statistical power to detect declines remains low due to patchy distribution and large variability in group sizes. In this study, population viability analyses (PVA) were used to model the dynamics of four oceanic dolphin populations off the United States West Coast: eastern North Pacific long-beaked common dolphins ( Delphinus delphis capensis ), short-beaked common dolphins ( D. delphis delphis ), Pacific white-sided dolphins ( Lagenorhynchus obliquidens ), and “offshore” common bottlenose dolphins ( Tursiops truncatus ). We calibrated the PVA with life-history tables, studies on proxy species, and stock assessment reports. We explored the sensitivity of populations to demographic variation and projected how they may respond to changes in three sublethal threats (prey limitation, ocean noise, and chemical pollution) and one lethal threat (fisheries bycatch). We found the most serious projected declines in long-beaked common dolphins, which showed the lowest birth rate. Most threat scenarios resulted in declines that would not be detected by existing monitoring programs in the United States, which are among the most data-rich surveys of their kind. The cumulative effects of the three sublethal stressors exceeded the effect of the one lethal stressor (fisheries bycatch). To implement pro-active management and monitoring programs, anticipating which cetaceans are more at risk and which anthropogenic threats could cause declines is paramount. Our study highlights the value of model testing with PVA when monitoring data are poor, thereby identifying priorities for future research, monitoring, and management.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".