An Integrated Population Model for St. Lawrence Estuary Belugas (Delphinapterus leucas)
Bibliographic record
Abstract
St. Lawrence Estuary (SLE) belugas are a relict population from the Wisconsin glaciation that probably numbered 10,000 or more in the mid-1800s. As a result of intensive harvests starting in the 1700s, combined with environmental degradation, the population was greatly reduced by the 1980s, leading to their classification as ‘Endangered’ by the Committee on the Status of Endangered Wildlife in Canada in 1983 and to the implementation of a series of programs to monitor the health, demography and dynamics of the population. SLE beluga are exposed to multiple stressors, including environmental contaminants, prey reductions, toxic algal blooms, vessel noise, and various other risk factors. We compiled available data sets on abundance trends, mortality patterns, cause of death and age structure, and used these data to develop and fit an integrative population model (IPM) with the aim of assessing the current status and trends of the SLE beluga population, estimating the risk of quasi-extinction under different scenarios, and identifying new recovery targets for this endangered population. The process model consisted of a stage-structured projection matrix model that incorporated age and sex differences in survival and reproduction. We used Bayesian hierarchical methods to estimate time and density varying vital rates by simultaneously fitting to aerial survey data (from visual and photo-based methods), data on the age structure of the living population and the death assemblage (based on age estimation of stranded carcasses) and cause-of-death analyses of the relative frequencies of natural mortality, harvest mortality, and dystocia/postpartum mortality of adult reproductive females. We found evidence for increased density-dependent (DD) and density independent (DiD) mortality over the 20th century that prevented rebound of the population after the cessation of human harvest in 1979. Our model results showed complex patterns of age and sex specific mortality trends over recent decades: for example, DiD hazards affecting older animals declined between 2010-2018, possibly reflecting reduced cancer rates associated with lower contaminant exposure, resulting in an increasing trend in abundance. In contrast, DD mortality of calves increased over that period, along with dystocia/postpartum mortality of pregnant females, with the result that the proportion of young animals in the living population decreased while the proportion of young animals in the death assemblage increased. These demographic shifts, combined with an uptick in DiD hazards after 2018, caused abundance trends to stabilize after 2018 and possibly begin to decline, although the higher level of uncertainty typical of the end of a time series makes the current trend uncertain.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".