A multistate capture-recapture model to estimate reproduction of North Atlantic right whales
Bibliographic record
Abstract
Abstract The recent steep decline of critically endangered North Atlantic right whales ( Eubalaena glacialis ) can be attributed to high mortality combined with low reproduction. While the former is a clear result of anthro-pogenic activity, the latter involves more complexity. Evidence suggests that both short-term fluctuations in prey availability and long-term decline in health are responsible for depressed right whale calving rates. To facilitate an assessment of extinction risk, we developed a multistate capture-recapture model that estimated the probability of calving using extensive sightings data from 1990-2019. The model estimated sub-lethal effects of severe injury on calving probability, and modeled temporal variability in calving as related to indices of prey availability ( Calanus spp. biomass) and an apparent regime shift. The average annual probability of calving for known-breeding females, given average prey conditions, decreased from 0.217 [0.162, 0.281] to 0.142 [0.067, 0.252] after the 2010 regime shift. The model indicated strong evidence of a relationship between calving probability and the prey index from the eastern Gulf of Maine, though this relationship effectively disappeared after 2010; moderate evidence for a relationship with prey from the southwest Gulf of St. Lawrence remained. Weak evidence of reduced calving probability due to severe injury resulted from low sample sizes, given increased mortality for individuals observed with severe injuries. The regime effect is hypothesized to be capturing a long-term decline in health due to a combination of decreasing habitat quality resulting from climate change and potentially chronic sublethal injuries (e.g., entanglements). Our reproduction model provides demographic parameter estimates that can be used in population projections for North Atlantic right whales, though uncertainty remains in the mechanisms responsible for recent declines in calving.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".