Cumulative effects assessment for West Coast transient population of Bigg’s Killer Whale in the Northeast Pacific
Bibliographic record
Abstract
The West Coast Transient (WCT) population of Bigg’s Killer Whales (BKW) was listed as Threatened under the Species at Risk Act in 2003 and is vulnerable to the cumulative effects of human threats. The current understanding of threats (reduced prey availability, acoustic and physical disturbance, and contaminants), threat interactions, and potential impacts to fecundity and mortality were summarized in a Pathways of Effects (PoE) model that includes 16 pathway linkages from threats and threat interactions to effects on WCT fecundity and mortality. Sufficient knowledge and data were available to quantify eight of the pathway linkages in the PoE model and include them in a population viability analysis (PVA) model. Using the most recent available data, the PVA model quantified impacts of threats on the population parameters of the Canadian Salish Sea subset of the WCT. The impacts of individual and cumulative threat scenarios on the modelled population were compared to the observed population demographics to define a model that best captured real-world dynamics. The cumulative model incorporating all threats simulated a population abundance trend that included the observed abundance within the 90% distribution of model simulations. The final model included impacts of prey availability on the population carrying capacity, vessel noise masking of prey sounds, vessel strikes, and PCB contamination. Sensitivity analysis demonstrated the importance of prey availability for the model results, as a result of the direct effect on carrying capacity and interactions with noise and contamination. The cumulative effects PVA model projected continued increase in the modelled population to the carrying capacity, with the rate depending on the trend in the prey populations, indicating that the current threat levels are unlikely to limit population growth. There are knowledge gaps in our understanding of the WCT population of Bigg’s killer whales compared to the better studied fish-eating resident killer whale populations. Building a quantitative population model for the WCT population required higher effort in scoping, population data analysis, and threat quantification than for resident killer whales (RKW). In particular, the impact of acoustic disturbance remains a significant knowledge gap for WCT. The PoE model and the quantitative sub-model explicitly identified knowledge and data gaps and needs for future research that can inform the next iteration and reduce uncertainty. Uncertainties and assumptions inherent in the conceptual PoE model and quantitative PVA model have been described in the document. The cumulative effects model for WCT advances the field and provides a valuable tool that can be used to examine scenarios of mitigation and management for the continued recovery of the 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".