Ecosystem-based fisheries management is attainable, affordable, and should be viewed as a long-term commitment: Experiences from Lake Vättern, Sweden
Bibliographic record
Abstract
Lake Vättern is Sweden’s second largest lake and faces multiple environmental challenges such as climate change, pollutants and invasive alien species. Since its foundation in 1957, the Lake Vättern Society of Water Conservation (LVSWC) has been a local actor aiming to manage a broad range of environmental issues concerning the lake and its catchment. Stakeholders can be members of LVSWC, which also organises a fisheries co-management group mainly dealing with fisheries issues. Ecosystem-based fisheries management (EBFM) is a widely desired framework shaped to focus on ecosystems, involve stakeholders, and address environmental issues in an adaptive, integrated and holistic manner while also including societal aspects. We have investigated how the management of Lake Vättern relates to 15 central principles for EBFM. The study found that LVSWC and the fisheries co-management group work along and towards the EBFM principles in a locally adapted way. Several environmental improvements have been observed concurrently with LVSWC activity, such as increased water clarity, and a strengthened stock of the Arctic char (Salvelinus alpinus). Long-term commitment by European, national and regional authorities was seen as essential to maintain and improve EBFM functions for Lake Vättern, to ensure continuity in the work, rather than having to depend on short projects. Becasue literature examples of EBFM in lakes are scarce worldwide, this study provides a unique example of the pursuit of EBFM in lake ecosystem management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".