Busy Beavers in the Big City: An Analysis of Beaver Distribution, Foraging and Non-lethal Forage Management in Urban Riverine Forests
Notice bibliographique
Résumé
Where human and beaver populations overlap in cities, conflicts can arise over the beavers’ impact to woody vegetation and river valley infrastructure. One way that cities reduce beavers exploitation of limited shared resources is using non-lethal deterrents, like tree enclosures. Beaver foraging behaviour is well studied in natural systems, with decades of research describing their feeding behaviour and interactions with the woody vegetation community. In comparison, there is a poor understanding of beaver foraging behaviour in urban areas, and effectiveness of forage deterrents. My thesis helps to address this research gap. During late summer and fall of 2017, a survey of beaver lodge distribution and an inventory of riparian woody vegetation, as impacted by beavers, were completed on two reaches of the South Saskatchewan River. One river reach (24 km) passed through the City of Saskatoon where there is active beaver management; the second river reach (29 km) was the adjacent upstream conservation area where there is no beaver management. In City parks the effectiveness of a non-lethal forage deterrent use – tree enclosures - was assessed in May 2018. Results from the beaver activity surveys show that lodge complex density is 56% lower in the city reach; lodges active at the time of the inventory had a dispersed spatial distribution. The riparian woody vegetation community along the two river reaches is markedly different, with more than twice the species richness for both trees and shrubs in the city reach. Much of the enhanced plant diversity can be attributed to introduced woody species. Beaver prefer cottonwoods (Populus spp.), as evidenced by high foraging of this taxa in the unmanaged reach. But, in the managed reach, cottonwood trees are protected. Thus beavers shifted their foraging efforts to Manitoba maple and green ash. The City of Saskatoon is currently using four primary materials for construction tree enclosures. Wire-mesh in various gauges and patterns have an overall 80% effectiveness in deterring further beaver foraging, but chicken wire performs poorest as it girdles trees. Overall, this research contributes to the understanding of urban beaver foraging patterns and preferences within river valley forests. In addition, this research provides land and resource managers with evidence and suggestions regarding the appropriate use of wire-wrapping as a non-lethal beaver deterrent technique.
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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 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».