4.5 Article

Estimates of wildlife species richness, occupancy, and habitat preference in a residential landscape in New York State

Journal

URBAN ECOSYSTEMS
Volume 26, Issue 3, Pages 689-700

Publisher

SPRINGER
DOI: 10.1007/s11252-022-01318-4

Keywords

Camera traps; Biodiversity; Human-impacted; Site-occupancy model

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Studies on using camera traps to characterize wildlife communities in residential areas in North America are lacking. In this study conducted in Dutchess County, NY, camera traps were placed in 22 residential neighborhoods over three seasons. Bayesian site-occupancy models were applied to account for imperfect detection. Results showed a range of mammal and non-passerine bird species detected, with small cryptic species being less reliably captured. Factors such as impervious surface and forest cover influenced species occupancy. These findings contribute to the understanding of how residential landscapes affect wildlife communities.
Despite the widespread adoption of motion-triggered camera traps, studies using camera traps to characterize wildlife communities in residential areas in North America are limited. To fill this data gap, we placed camera traps over three seasons in 22 residential neighborhoods within Dutchess County, NY. To account for imperfect detection, we applied individual-level and community-level Bayesian site-occupancy models to these data. Overall, we captured 64,639 independent detections over 17,820 camera trap days. We detected between 17 and 22 mammal and non-passerine bird species in each of the seasons of data collection, while our community models estimated between 24 and 33 mammal and non-passerine bird species in each season. Small, cryptic species were not reliably captured by camera traps, limiting our ability to model their occupancy. We identified five species: raccoons (Procyon lotor), eastern gray squirrels (Sciurus carolinensis), red foxes (Vulpes vulpes), Virginia opossums (Didelphis virginiana), and white-tailed deer (Odocoileus virginianus) found in all neighborhoods. The most common covariate included in our final occupancy models was the percent of area within each neighborhood that was an impervious surface, which positively affected occupancy for some species, and negatively affected occupancy for others. Forest cover, the second most common variable in our final models, negatively affected occupancy for all species. Our estimates characterize a baseline for quantifying species richness and composition in residential areas of Dutchess County, NY and surrounding regions, and offer a comparison to similar studies in natural areas. Overall, the results improve understanding of how residential landscapes affect individual species and communities.

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