Assessing marine species vulnerability to projected warming on the Scotian Shelf, Canada
Bibliographic record
Abstract
Abstract Resource managers need climate adaptation tools. We build on a popular tool, the climate change vulnerability assessment (CCVA), to identify vulnerable marine species. Only warming was considered, as warming is expected to have earlier impacts in the offshore than other climate drivers, and projections of other climate drivers are not well developed. For this reason, we coin our generalized, semi-quantitative method the “Vulnerability to Projected Warming Assessment” (VPWA) as opposed to using the broader term, CCVA. We refine the typical “exposure” component to be a function of gain/loss of thermal habitat at multiple life stages. We also build on the traditional logic approach of CCVAs. We produce scores for each species, and use a null distribution through Monte Carlo simulations to identify the most vulnerable species. We evaluate the vulnerability of 33 fish and invertebrate species, on the scale of the Scotian Shelf, Canada, to two warming scenarios, mild and severe, based on regional trends and projections. At smaller spatial scales, we evaluate populations of a subset of these species. Populations in the southwest portion of the domain are found to be more vulnerable than those in the northeast. Overall, our results indicate that 45% of populations may be vulnerable under a severe (+3°C) warming scenario, including currently endangered, threatened, and commercial populations (e.g. southwestern Atlantic cod, Smooth skate, Snow crab), while only one species has a relatively high vulnerability score under the mild (+0.7°C) scenario (Moustache sculpin). Populations triaged by relative vulnerability to regional warming should help managers prioritize resources and identify knowledge gaps. For this reason, and for its biological and ecological underpinnings, our method has broad relevance within the marine science and management field. As more information become available, our VPWA can be used as a stepping-stone in the continued development of CCVA methods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".