A science and management partnership to restore coregonine diversity to the Laurentian Great Lakes
Bibliographic record
Abstract
Similar to many freshwater ecosystems, the Laurentian Great Lakes of North America have undergone numerous anthropogenic stressors resulting in considerable loss of biodiversity and habitat. Among Great Lakes fishes, the coregonine sub-family has endured the most extensive declines, including extinction of several species ( Coregonus johannae, C. alpenae, and C. kiyi orientalis) and at least 10 instances of local extirpations of other species ( C. nigripinnis, C. reighardi, C. zenithicus, C. hoyi, and C. artedi) across all 5 lakes, much of which occurred prior to the 1960s owing to overfishing, interactions with non-indigenous species, and habitat loss. Despite these declines, no federal-, provincial-, or state-mandated actions were ever implemented to conserve coregonine diversity, potentially because so much of the coregonine declines occurred prior to the enactment of federal conservation legislation. Possible explanations for inaction since enactment of that legislation include insufficient data on biological vulnerability or threats, unresolved taxonomy, and limited support from the fishery management agencies and their stakeholders prior to the 2000s. In recent decades, however, several fishery management agencies have undertaken efforts to re-introduce coregonine diversity. These efforts helped lead to development of a science-based framework to restore coregonines that was universally endorsed by fishery managers representing eight U.S. states, four U.S. tribal organizations, and the province of ON, Canada, in May 2018. The basin-wide framework is based on principles of conservation biology and adaptive management. We describe details of its key steps, including planning, restoring, and evaluating, while also describing recent implementation efforts to develop methods, improve available resources, and enhance coordination across the basin. Although our paper describes a regional effort to restore native coregonines, our adaptive-management approach could be used by other multi-agency stakeholders seeking to conserve or restore native fishes.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".