Alignment of threat, effort, and perceived success in North American conservation translocations
Notice bibliographique
Résumé
The use of conservation translocations to mitigate human effects on biodiversity is increasing, but how these efforts are allocated remains unclear. Based on a comprehensive literature review and online author survey, we sought to determine the goals of translocation efforts, whether they focus on species and regions with high threat and likelihood of perceived success, and how success might be improved. We systematically searched the ISI Web of Knowledge and Academic Search Complete databases to determine the species and regions of conservation translocations and found 1863 articles on conservation translocations in the United States, Canada, Mexico, Central America, and Caribbean published from 1974 to 2013. We questioned 330 relevant authors to determine the motivation for translocations, how translocations were evaluated, and obstacles encountered. Conservation translocations in North America were geographically widespread (in 21 countries), increased in frequency over time for all animal classes (from 1 in 1974 to 84 in 2013), and included 279 different species. Reintroductions and reinforcements were more common in the United States than in Canada and Mexico, Central America, or the Caribbean, and their prevalence was correlated with the number of species at risk at national and state or provincial levels. Translocated species had a higher threat status at state and provincial levels than globally (International Union for Conservation of Nature Red List categorization), suggesting that translocations may have been motivated by regional priorities rather than global risk. Our survey of authors was consistent with these results; most translocations were requested, supported, or funded by government agencies and downlisting species at national or state or provincial levels was the main goal. Nonetheless, downlisting was the least reported measure of success, whereas survival and reproduction of translocated individuals were the most reported. Reported barriers to success included biological factors such as animal mortality and nonbiological factors, such as financial constraints, which were less often considered in the selection of release sites. Our review thus highlights discrepancies between project goals and evaluation criteria and between risk factors considered and obstacles encountered, indicating room to further optimize translocation projects.
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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) | 0,001 | 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 ».