Barn owl breeding in agricultural landscapes of Great Britain
Notice bibliographique
Résumé
Habitat loss and fragmentation associated with agricultural intensification have affected farmland biodiversity worldwide. Large tracts of heterogeneous natural habitats are transformed into homogenous agricultural lands thereby resulting in a decline in farmland bird populations. The resultant decline in farmland bird populations can be associated with unsuitable foraging habitats, a decline in prey resources and an increase in chemical pollutants such as pesticides associated with agriculture. In this thesis, I use the widely studied and monitored farmland raptor species, the barn owl (Tyto alba) to examine the effects of agricultural landscape composition of different crop types, and the pesticides used in the cultivation of cereal crops, the most dominant crop type in Great Britain, on barn owl brood size (a proxy for barn owl productivity) and nestling body mass (a proxy for nestling body condition). In addition, I also explore the impact of the most dominant crop type, cereal crops, on the diet of the barn owl in Great Britain. \n \nPrevious studies on barn owl breeding success in relation to land use in the South Midlands and South East of Great Britain have shown that barn owl breeding is independent of land use. However, these studies are local and use broad habitat types. In this novel study (Chapter 2), the effects of agricultural landscape composition of different crop types on barn owl brood size and nestling body mass across a national level and a regional level between three regions of Great Britain, namely the Midlands, the South East and the South West, with varying degrees of agricultural intensification, are examined. Among all crop types, fruit/forage crops have a positive impact on barn owl brood size, whereas, cereal crops have a negative impact on barn owl productivity, with a greater total area of cereal crops predicting smaller brood sizes. I build on using the landscape composition of cereal crops to further explore the impacts on aspects of barn owl reproduction in Chapter 3, where the effects of the landscape composition of cereal crops on maternal barn owl body condition and consequently the impact on barn owl brood size and nestling body mass is determined. Here I show that the perimeter:area ratio (a proxy for habitat complexity) of cereal crop fields has a positive impact on the maternal body condition of barn owls with a greater perimeter:area ratio of cereal crops predicting larger brood sizes. Building on the results of both Chapter 2 and Chapter 3, the impact of four commonly used pesticides by weight, in the cultivation of cereal crops namely, fungicides (chlorothalonil and diflufenican) and herbicides (glyphosate and flufenacet), on barn owl brood size and nestling body mass is investigated. An increase in the use of the herbicide flufenacet has a negative impact on barn owl brood size in Great Britain. Finally, in Chapter 5, the impact of the landscape composition of cereal crops on the diet of the barn owl in the Midlands and South East, the two regions that showed differential responses in the impact of cereal crops on brood sizes in Chapter 2 and Chapter 4, is determined. The number of prey items recovered in the diet of the barn owl decreased with an increase in the total area of cereal crops. \n \nThe findings of this study demonstrate that barn owl reproduction is not influenced by agricultural landscape alone, but by a series of knock-on effects of agricultural landscape composition and management practices on life-history traits such as maternal body condition of breeding barn owls, and on prey availability around barn owl nest boxes. Finally, Chapter 6 offers recommendations to improve the quality of life for barn owls, with implications for the conservation of all farmland species.
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) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».