From Catastrophe to Recovery: Stories of Fishery Management Success
Notice bibliographique
Résumé
<i>Abstract</i>.â€â€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 inâ€â€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 manipulationâ€â€whether in Lake Erie (Canada and USA), the Vindeln River in northern Sweden, an Adirondack Mountain lake of New York (USA), or Sea Lamprey <i>Petromyzon marinus</i> along the Atlantic coast (USA). Fish need acceptable water quality and habitat for living: simply stated and obviousâ€â€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 <i>Oncorhynchus nerka</i>. 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 <i>Salvelinus fontinalis</i>, 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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».