Ecological Factors Driving Uptake of Anticoagulant Rodenticides in Predators (Abstract)
Notice bibliographique
Résumé
In recent years, anticoagulant rodenticides have emerged as an important factor reducing the survival of many birds of prey and some predatory mammals (Berny and Gaillet 2008, Jacquot et al. 2013, Poessel et al. 2015, Serieys et al. 2015, Murray 2017). Understanding the ecological factors driving the exposure of predators is a key component in assessing the risk posed by anticoagulant rodenticides. We have reviewed the literature to better understand and synthesize the ecological factors driving AR exposure in predators, focusing on landscape and environmental management, traits of the exposed predators and the most common exposure pathways. On a global scale, the large output of ARs, in particular the more toxic and persistent second generation ARs into urban and agricultural settings and the relatively large footprint of these landscapes, has led to widespread AR exposure of many species, ranging from insects to large carnivores (Dowding et al. 2010, Sánchez-Barbudo et al. 2012, Serieys et al. 2015, Alomar et al. 2018). The methods of applying ARs vary widely, and can range from fastening bait stations to the outside or inside perimeter of buildings, to placing AR bait in underground burrows in fields, to mass application of ARs in agricultural fields and orchards (Corrigan 2001, Rattner et al. 2014). The scale of AR field application varies from a couple of hectares to mass application of ARs on a regional scale (3,000 - 10,000 km²) to control rodent outbreaks (Jacquot et al. 2013, Baldwin et al. 2014). General inferences can be made with regards to the traits of the most affected predators. We determined that at-risk predators tend to be nocturnal opportunistic predators for which rodents are a key dietary component, seasonally or year-round (Birks 1998, Jacquot et al. 2013, Hindmarch and Elliott 2015, Serieys et al. 2015). They also tend to be non-migratory and occupy habitats within, or in close proximity to landscapes that are heavily influenced by human activities such as intensive agriculture or urban areas (Birks 1998, Way et al. 2006, Elmeros et al. 2011, Christensen et al. 2012, Cypher et al. 2014, Nogeire et al. 2015, Poessel et al. 2015, Serieys et al. 2015, Hindmarch et al. 2017). Predators that consume rats in urban environments are disproportionately affected by ARs (Lambert et al. 1981, Hindmarch and Elliott 2014, 2015). As our understanding of how ARs are transferred up the food-chain is still limited, there is a need to further comprehend the extent to which non-target prey are being exposed to ARs in different landscapes, as we are frequently documenting AR residues in predators that do not typically prey on rodents (Dowding et al. 2010, Ruiz-Suárez et al. 2014, López-Perea et al. 2015). We recommend a focus on urban landscapes, where to date no exposure data has been collected on non-target prey. We also have a very limited understanding of non-target prey exposure in the urban-wildland/agricultural interface where opportunistic predators are known to hunt both habitat types interchangeably. Finally, we need to decipher whether the mounting evidence of exposure in predators translates into any sub-lethal and population levels effects. For a more in-depth review of this topic, we refer to the chapter “Ecological Factors Driving Uptake of Anticoagulant Rodenticides in Wildlife" (Hindmarch and Elliott 2018) in the book Anticoagulant Rodenticides and Wildlife (van den Brink et al. 2018).
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 | 0,001 |
| 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) | 0,004 | 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 ».