Estimated population abundance and probability of population decline for Northern Hudson Bay narwhal (Monodon monoceros)
Bibliographic record
Abstract
Narwhal are an important subsistence harvest species for Inuit communities. The Northern Hudson Bay (NHB) population is spatially and genetically distinct from other populations of narwhal in Canada and Greenland. The NHB narwhal population has been assessed through periodic aerial surveys from 1981 – 2018. Survey estimates in 1982 and 2000 were negatively biased, as they did not factor in perception bias and were conducted and analyzed using different protocols. The 2011 and 2018 surveys addressed these biases. As a result, the 1982 and 2000 survey estimates were adjusted prior to population trend analysis using ratios that were calculated by re-analysing the 2011 survey data using methods similar to those applied in 1982 and 2000. To estimate the population trajectory and predict future population trends under various harvest scenarios, a Bayesian population model was fit to these four fully-adjusted aerial survey estimates and harvest data from 1951 – 2018. The preferred model resulted in a 2019 population estimate of 14,377 (95% CI 10,265 – 20,370), and an estimated starting population of 7,164 (95% CI 1,447 – 19,155) in 1951. Using a risk-based model approach to estimate the probability of population decline over 10 years, the model predicted a 0%, 20%, 40%, 50%, 60%, 80%, and 100% probability of decline with annual harvest quotas of 0, 63, 83, 93, 108, 173, and 450 narwhal per year. The incorporation of the updated 2018 survey estimate and the adjustment of past survey estimates provides greater confidence in the model results and, thus, in the risk-based approach for assessing harvest levels. The 2019 modelled estimated abundance was robust to input parameters in a series of model runs. Potential Biological Removal (PBR) was also calculated for the 2019 abundance estimate from the model and resulted in a removal threshold of 188 whales annually and a Total Allowable Landed Catch (TALC) of 151 narwhal. The risk-based approach considers the probability of population decline at different harvest levels, while the PBR inherently aims to keep the population at the maximum net productivity level (MNPL), which may result in a population decline if the population is already at or above MNPL. Management goals should be well defined to determine whether a PBR or a risk-based model approach is more appropriate for the NHB narwhal population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".