Harvest Distribution and Derivation of Atlantic Flyway Canada Geese
Notice bibliographique
Résumé
Abstract Harvest management of Canada geese Branta canadensis is complicated by the fact that temperate- and subarctic-breeding geese occur in many of the same areas during fall and winter hunting seasons. These populations cannot readily be distinguished, thereby complicating efforts to estimate population-specific harvest and evaluate harvest strategies. In the Atlantic Flyway, annual banding and population monitoring programs are in place for subarctic-breeding (North Atlantic Population, Southern James Bay Population, and Atlantic Population) and temperate-breeding (Atlantic Flyway Resident Population [AFRP]) Canada geese. We used a combination of direct band recoveries and estimated population sizes to determine the distribution and derivation of the harvest of those four populations during the 2004–2005 through 2008–2009 hunting seasons. Most AFRP geese were harvested during the special September season (42%) and regular season (54%) and were primarily taken in the state or province in which they were banded. Nearly all of the special season harvest was AFRP birds: 98% during September seasons and 89% during late seasons. The regular season harvest in Atlantic Flyway states was also primarily AFRP geese (62%), followed in importance by the Atlantic Population (33%). In contrast, harvest in eastern Canada consisted mainly of subarctic geese (42% Atlantic Population, 17% North Atlantic Population, and 6% Southern James Bay Population), with temperate-breeding geese making up the rest. Spring and summer harvest was difficult to characterize because band reporting rates for subsistence hunters are poorly understood; consequently, we were unable to determine the magnitude of subsistence harvest definitively. A better understanding of subsistence hunting is needed because this activity may account for a substantial proportion of the total harvest of subarctic populations. Our results indicate that special September and late seasons in the United States were highly effective in targeting AFRP geese without significantly increasing harvest of subarctic populations. However, it is evident that AFRP geese still are not being harvested at levels high enough to reduce their numbers to the breeding population goal of 700,000.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 ».