Assessing urban tree taxonomic diversity, composition and structure across public and private green space types: a community-based tree inventory
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Notice bibliographique
Résumé
The urban forest is a crucial component of the city landscape, providing communities with countless benefits we refer to as ecosystem services. Trees improve urban air quality, decrease city temperatures, provide spaces for recreation and promote mental wellbeing. To properly quantify the benefits the urban forest provides, we require a strong baseline understanding of forest structure, diversity, and composition. To date, fine-scale work considering urban forest diversity has been commonly limited to trees on public land, considering only one or two green space types. However, the governance of green spaces in cities means tree species composition is being influenced by management decisions at various levels, including by institutions, municipalities, and individual landowners responsible for their care. Using a mixed-method approach combining a traditional field-inventory and community science project, I inventoried the urban forest in the residential neighbourhood of Notre-Dame-de-Grȃce, Montreal. I assessed four green space types in the public and private domain: parks, institutions, street rights of way and private yards to quantify how tree diversity, composition and structure varies across multiple land management types at local scales. I additionally considered how patterns of service-traits (traits related to managers preference and ecosystem services) differed across green space types, with implications for the distribution of ecosystem services across the urban landscape. I found that green space types displayed meaningful differences in both tree diversity and structure. For example, the inclusion of private trees contributed an additional 52 species (30% of total species) not found in the local public tree inventory, and private land was dominated by smaller trees compared to the public domain. I found patterns of richness, size and abundance extend to differences in tree composition and service-traits at local-scales, particularly in the street right-of way and private yards. Composition varied considerably across street blocks; however, blocks were very similar in terms of mean service-based traits. Contrastingly, species composition was similar from yard to yard, however, yards differed significantly in mean service-trait values. Overall, my work emphasizes that public tree inventories are unlikely to be fully representative of urban forest composition and structure, with implications for urban forest management at larger spatial scales.
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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) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 découle