Impacts of urban land-cover on plant community structure and biodiversity in a multi-use landscape
Notice bibliographique
Résumé
While research and policy alike have recognized the importance of conserving biodiversity, the rapid and continued expansion of urban areas hinders many conservation efforts, particularly as many high-value conservation areas are found in landscapes already modified by human use. Research into the impact of landscape mosaics—their composition and configuration in particular—is important to understanding the impact that human induced land-use change may have on biodiversity, biotic communities, and thus the ecological processes within these areas. The objectives of this research paper are to determine the impacts of the landscape composition surrounding conservation forests has on the plant communities of the forest understory communities. We also seek to outline the possible mechanisms by which the landscape can indirectly impact plant communities, and in so doing, provide a deeper understanding of how natural areas within mosaic landscapes may sustain biodiversity. Using plant community measures from the Credit Valley Conservation Authority in Ontario, Canada, and open-sourced spatial data on Canada’s landcover, we calculated the land cover composition of urban and natural lands surrounding each forest site, and the biodiversity of the understory community in each forest. We used both individual species richness and abundance (NMDS, TITAN), as well as aggregate biodiversity measures (linear regression) to test for significant relationships between the plant community metrics and the composition of the surrounding landscape. Natural land cover, urban land cover, and continuous forest size were all significantly associated with species changes in the NMDS at all scales, and the direction of the urban cover vector was nearly opposite of the natural cover vector in the NMDS space. The output of the TITAN analysis identified both positive and negative responses of individual species to land cover composition at the three scales considered, indicating that indicator species had strong responses to changes in the land cover, with different species being associated with urban vs. natural land cover. The TITAN and NMDS both showed that many more species were positively associated with natural land cover. Only a few species responded positively to high urban cover, and those forests had much lower populations. A series of linear regressions revealed a negative relationship between urban land cover and plant diversity metrics, and positive relationships between natural land cover and plant biodiversity at all scales. Both species richness and species abundance changed significantly with the surrounding land cover composition, but species richness (that is the total number of species present in a community) had the most consistent and statistically significant response—indicating that an areas ability to sustain a certain number of species is affected by the surrounding landscape. The significant findings of both species-level and community level changes associated with land cover confirm our expectations that land cover in mosaic landscapes does indeed have significant impact on plant communities, and can impact forest’s potential to support biodiversity, even when the changes are indirect changes. Forest understory vegetation shows a significant relationship to surrounding land cover composition, with changes associated with urban and natural land cover being consistently significant at 1 km, 2 km, and 5 km scales. This indicates that the forest understory communities of the CVC are not random assemblages, but communities found in predictable patterns that are associated with the composition of the landscape around each site.
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 | 0,000 |
| 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) | 0,001 | 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 ».