From Catastrophe to Recovery: Stories of Fishery Management Success
Bibliographic record
Abstract
Abstract.—Success achieving fishery management goals is possible but often requires concurrent strategies addressing ecology, politics, and public communication combined with some level of good fortune. As an introduction to this book, we identify several themes consistently highlighted among the fish management stories that follow, regardless of species, their life history, habitat needs, or type of waters they live in—streams, lakes, or ocean. In almost every case, success of management relied first and foremost on the abilities of professionals to restore the quality and quantity of a fish’s habitat. The success of these efforts varied in magnitude but was accomplished by a combination of effective environmental regulation, substantial public and private investment, and direct habitat manipulation—whether in Lake Erie (Canada and USA), the Vindeln River in northern Sweden, an Adirondack Mountain lake of New York (USA), or Sea Lamprey Petromyzon marinus along the Atlantic coast (USA). Fish need acceptable water quality and habitat for living: simply stated and obvious—fish need water! When water and fish habitat are restored, fish populations can naturally recover through colonization from remnant populations, as was experienced in the Scioto River, Ohio. In some cases, populations were restored by stocking fish, using careful genetic considerations, such as told for Snake River Sockeye Salmon Oncorhynchus nerka. Public engagement was a common theme among case studies presented in this text. Public support for management yielded the political will to provide funding, regulation, and enforcement. Public involvement was a critical component of stories told about Great Smoky Mountains Brook Trout Salvelinus fontinalis, Pacific salmon in British Columbia and Idaho, and Tonle Sap fisheries of Cambodia. Consistently, management success came when goals were clearly articulated and combined with an effective consensus-built management plan that had the long-term commitment of personnel and support of their agencies. These attributes yielded programs where actions were taken and long-term monitoring and assessment were implemented to gauge success. Assessment information allowed programs to be adaptive over time to changes in the ecological system and society and thereby helped address new, as well as ongoing, challenges the fish and fishery were experiencing. The stories in this text provide incontrovertible evidence that good things can happen with the development and implementation of effective fish management programs, demonstrating the value of our profession and providing clear evidence that success is not an impossible allusion but rather an achievable event. These success stories of restored fish and fisheries throughout the world should be celebrated within fishery science.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.023 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".