Incorporating environmental covariates into a Bayesian stock production model for the endangered Cumberland Sound beluga population
Bibliographic record
Abstract
The Cumberland Sound (CS) beluga (Delphinapterus leucas)population inhabits CS on eastern Baffin Island, Nunavut, Canada, and is listed as threatened under the Canadian Species at Risk Act. The population dynamics of CS beluga whales have been modelled in the past, but the effect of environmental covariates on these models has not previously been considered. An existing Bayesian population model fitted to CS beluga whale aerial survey data from 1990 to 2017 and harvest data from 1960 to 2017 was modified to include sea ice concentration (ICE) and sea surface temperature (SST). ICE and SST were extracted for all years from the CS study area in March and August, respectively, and incorporated into the state process component of the state-space model. The model resulted in a 2018 population estimate of 1245 (95% credible interval [CI] 564-2715) whales and an initial population estimate of 5147 (95% CI 1667-8779). Determining sustainable harvest by calculating the probability of population decline estimated 30% probability of decline after 10 yr with a harvest of ~15 whales annually. Compared to the previous model without environmental covariates, which followed a relatively linear trajectory, our model had more noticeable peaks and troughs in the population trend and wider CIs. The model suggested harvest levels be reduced by ~7 individuals for a management goal with a low risk of decline. The novelty of this approach for beluga whales provides an opportunity for further model development via the addition of various other abiotic and biotic variables related to beluga whale ecology.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".