A perspective on successful implementation of ecosystem-based approaches to management and conservation in the Laurentian Great Lakes
Bibliographic record
Abstract
Abstract To celebrate the 1972 Great Lakes Water Quality Agreement, a conference was held on the evolution of the Ecosystem Approach during the past half-century to learn how to enhance successful implementation of ecosystem-based approaches for resource management, conservation, and societal problems worldwide. Among several conference workshops, one focused on the origins and history of ecosystem approaches, which was attended by 14 researchers with global expertise in conservation biology, ecology, economics, ecosystem modeling, limnology, resource and ecosystem management, policymaking, political science, and social science. This paper presents insights gleaned from this workshop on key needs for and challenges to effective implementation of these approaches. We identified six categories of needs and challenges, spanning from the initial phases of Ecosystem Approach development (e.g. setting clear goals; fostering stakeholder buy-in) to the final ones (e.g. adapting to change; maintaining program support). Setting clear goals aligned with a shared vision was identified as most critical to successful implementation and offered the fewest barriers. By contrast, 1) accounting for poorly understood governance structures and navigating administrative constraints, 2) sustaining support, and 3) gaining stakeholder buy-in were viewed as the biggest three challenges. Overcoming these challenges was viewed as critical to success, thus helping us understand why effective implementation of ecosystem approaches has remained difficult globally. Sound science (and overcoming associated hurdles; e.g. breaking down disciplinary silos) and effective communication were also mentioned by some. Using these findings, we assess the state of ecosystem approaches in the Laurentian Great Lakes Basin, concluding with recommendations on how to promote their successful implementation inside and outside of the Basin.
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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 | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".