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Enregistrement W3111272982 · doi:10.1002/wmon.1057

Best Management Practices for Trapping Furbearers in the United States

2020· article· en· W3111272982 sur OpenAlexaboutno aff
Herbert White, Gordon R. Batcheller, Edward K. Boggess, Clifford L. Brown, Joseph W. Butfiloski, Thomas Decker, John D. Erb, S. Michael Fall, David A. Hamilton, Tim L. Hiller, George F. Hubert, Matthew J. Lovallo, John F. Olson, Nathan Roberts

Notice bibliographique

RevueWildlife Monographs · 2020
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueWildlife Ecology and Conservation
Établissements canadiensnon disponible
Organismes subventionnairesAnimal and Plant Health Inspection Service
Mots-clésEuropean unionEndangered speciesSustainabilityBest practicePopulationTrap (plumbing)Political scienceBusinessGeographyEnvironmental planningHabitatEcologyBiologyInternational tradeEnvironmental healthLawMedicine

Résumé

récupéré en direct d'OpenAlex

ABSTRACT Humans have used wild furbearers for various purposes for thousands of years. Today, furbearers are sustainably used by the public for their pelts, leather, bones, glands, meat, or other purposes. In North America, contemporary harvest of furbearers has evolved along with trap technologies and societal concerns, and is now highly regulated and more closely coupled with harvest analysis and population monitoring. Traps and regulated trapping programs provide personal or cultural rewards that can also support conservation, and can assist with advancing ecological knowledge through research, protecting endangered species, restoring populations or habitats, protecting personal property, and enhancing public health and safety. However, animal welfare and trap selectivity remain important topics for furbearer management in North America, as they have for more than a century. A related international challenge to modern furbearer management came with the Wild Fur Regulation by the European Union, which passed in 1991. This regulation prohibited use of foothold traps in many European countries and the importation of furs and manufactured fur products to Europe from countries that allowed use of foothold traps or trapping methods that did not meet internationally agreed‐upon humane trapping standards. To address existing national concerns and requirements of the Wild Fur Regulation, the United States and European Union signed a non‐binding bilateral understanding that included a commitment by the United States to evaluate trap performance and advance the use of improved traps through development of best management practices (BMPs) for trapping. Our testing followed internationally accepted restraining‐trap standards for quantifying injuries and capture efficiency, and we established BMP pass‐fail thresholds for these metrics. We also quantified furbearer selectivity, and qualitatively assessed practicality and user safety for each trap, yielding overall species‐specific performance profiles for individual trap models. We present performance data for 84 models of restraining traps (6 cage traps, 68 foothold traps, 9 foot‐encapsulating traps, and 1 power‐activated footsnare) on 19 furbearing species, or 231 trap‐species combinations. We conducted post‐mortem examinations on 8,566 furbearers captured by trappers. Of the 231 trap model‐species combinations tested, we had sufficient data to evaluate 173 combinations, of which about 59% met all BMP criteria. Pooling species, cage traps produced the lowest average injury score (common injuries included tooth breakage), with minimal differences across other trap types; species‐specific patterns were generally similar, with the exception of raccoons ( Procyon lotor ) for which foot‐encapsulating traps performed better than other foot‐restraining trap types. Padded‐jaw foothold traps performed better than standard‐jaw models for many species, though often similar to and occasionally worse than offset‐ or laminated‐jaw models. Most traps we tested had high capture efficiency; only 5 (3%) failed BMP standards strictly because of poor efficiency. Average furbearer selectivity was high across all trap types we evaluated and was lowest for footsnares (88%) and highest for foot‐encapsulating traps (99%). Mortality from trap‐related injury in restraining traps we tested was very rare for furbearers (0.5% of animals). In over 230,000 trap‐nights across a 21‐year period, no individuals of a threatened or endangered species were captured. Of 9,589 total captures, 11% were non‐furbearers, of which 83% were alive upon trap inspection; nearly all non‐furbearer mortalities were birds, rabbits, or squirrels. Approximately 2% of total captures were feral or free‐ranging dogs ( Canis familiaris ), of which none died or were deemed in need of veterinary care by either our technicians or the owners (if located). Similarly, 3% of total captures were feral or free‐ranging cats ( Felis catus ); 2 were dead, and although locating potential owners was often impossible, none of the remaining cats were deemed in need of veterinary care by technicians or owners. Our results show that furbearer selectivity was high for all trap types evaluated, mortality or significant injury was very rare for domestic (or feral) animals, and the most potential for mortality or injury of non‐furbearers was with smaller animals, a majority of which were squirrels and rabbits. Our results suggest that injury scores for a given trap‐species combination are unlikely to vary significantly across states or regions of the United States, provided similar methods are employed. Our data also suggest that taxonomic affiliation and body‐size groupings are correlated with injury scores, presumably through morphological, physiological, or behavioral adaptations or responses that influence injury potential during restraint; higher injury scores in foot‐restraining trap types were more likely in smaller or more dexterous species, whereas injury scores were typically lowest for the felids we evaluated. For some species (e.g., American badger [ Taxidea taxus ], bobcat [ Lynx rufus ]), most restraining traps we tested met BMP standards, whereas few restraining traps we tested met standards for other species (e.g., muskrat [ Ondatra zibethicus ], striped skunk [ Mephitis mephitis ]). Comparison of our results with survey information collected during 2015 on trap use in the United States indicates that approximately 75% of all target furbearers harvested were taken in BMP‐compliant traps, with another 10% taken in traps yet to be tested on that species. Future trap testing and development should focus on commonly used traps not yet tested on a species, species for which few passing traps currently pass BMP criteria, and trap models and modifications most likely to minimize trap injuries given a species morphology, physiology, and behavior. Outreach efforts should focus on general BMP awareness, discouraging use of traps that fail BMP standards for a given species, and public outreach on trapping. Restraining (and other) traps have evolved substantially in recent decades and offer numerous benefits to individuals, conservation, and society. However, continuing to address societal concerns remains a critical component of modern regulated trapping and furbearer management. Published trapping BMPs are regularly updated online and may include additional approved restraining and killing traps that were evaluated as part of testing by Canada. We will periodically update the trap performance tables and figures we presented and make them available online at the Association of Fish and Wildlife Agencies website. Published 2020. This article is a U.S. Government work and is in the public domain in the USA. Wildlife Monographs published by Wiley Periodicals LLC on behalf of The Wildlife Society.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,532
Score d'incertitude au seuil0,346

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,045
Tête enseignante GPT0,269
Écart entre enseignants0,224 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations27
Publié2020
Routes d'admission1
Résumé présentoui

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