Urban wildlife species detection, species richness, and community shifts along a gradient of urbanization in an agriculturally embedded landscape using passive and active methods
Notice bibliographique
Résumé
Urbanization is a major contributor to global biodiversity loss, and there are knowledge gaps on how urban wildlife responds to urbanization in prairie cities. I analyzed how species richness of birds and mammals responded to a gradient of urbanization, measured by impervious cover, in Regina, Saskatchewan, Canada. Regina is a prairie city with a large amount of green space of planted trees, a lake, and is surrounded largely by monoculture annual crops. I deployed trail cameras and automated recording units to assess mammal and bird richness, respectively. Equipment was deployed for 30 d seasonally between October 2021 and May 2024. Bird species richness was highest (53 species) at low to intermediate levels (0-20%) of impervious cover and is likely due to edge effects in suburban habitats. Mammal species richness did not respond to any variable, which may be due to the generalist life history of most mammals detected. Most bird species that decreased in response to urbanization were associated with wetlands or grasslands, which generally decline in response to urbanization. Two bird species responded positively to impervious cover but were likely responding to increased tree cover in the city, not increased impervious cover. Individual species of mammals tended to decrease with increasing impervious cover, except for Fox Squirrels (Sciurus niger), which are likely responding to increased tree cover. My research highlights the need to understand species specific responses, taxa specific responses, and how city characteristics influence wildlife’s responses to urbanization. Ecologists use many methods to detect species and determine species richness. The validation of these methods generally has occurred in natural environments rather than urban environments. I analysed two methods, trail cameras and a community science project, to determine factors that influence probability of species detection, understand differences in species detected, and assess how many trail cameras or community science routes are needed to detect target species. Both survey methods were implemented between April and November 2023 in Regina, Saskatchewan, Canada. Trail camera data were collected from previously defined urban and suburban locations. The community science project had survey routes along walking trails and sidewalks. The target species were White-tailed deer (Odocoileus virginianus), White-tailed jackrabbit (Lepus townsendii), American Robin (Turdus migratorius), Rock pigeon (Columba livia), and Fox squirrel. The method and species were the most important factors influencing detection probability, but time was not important for detection probability. This may be due to target species being common and easy to detect, making shorter surveys or deployments just as effective as longer surveys at detecting the species. Trail cameras and community science volunteers detected most species at different proportions, but no differences were significant. Fewer trail cameras, but with long deployment periods, were needed to detect all species compared with community science routes. The two methods detected species differently due to species’ behaviour. The trail cameras detected 57 species compared to community scientists detected 10, but there were species that only community scientists detected. I suggest that researchers would need to use less time and resources to assess species richness with trail cameras but would potentially miss a few species. However, combining trail cameras and community science is a potential solution to maximize species detections without increasing cost and effort in comparison to hiring professional biologists. Researchers conducting long term studies could benefit the most from implementing community science to offset the potentially large up-front investments associated with community science. My study highlights the value of comparing research methods to detect species in an urban setting and how complementary methods can improve detections and measures of species richness in urban environments.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| 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 source (Gemma direct ou Codex distillé), 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 ».