Abstract Urbanization has a homogenizing effect on biodiversity and leads to communities with fewer native species and lower conservation value. However, few studies have explored whether or how land management by urban residents can ameliorate the deleterious effects of this homogenization on species composition. We tested the effects of local (land management) and neighborhood‐scale (impervious surface and tree canopy cover) features on breeding bird diversity in six US metropolitan areas that differ in regional species pools and climate. We used a Bayesian multiregion community model to assess differences in species richness, functional guild richness, community turnover, population vulnerability, and public interest in each bird community in six land management types: two natural area park types (separate and adjacent to residential areas), two yard types with conservation features (wildlife‐certified and water conservation) and two lawn‐dominated yard types (high‐ and low‐fertilizer application), and surrounding neighborhood‐scale features. Species richness was higher in yards compared with parks; however, parks supported communities with high conservation scores while yards supported species of high public interest. Bird communities in all land management types were composed of primarily native species. Within yard types, species richness was strongly and positively associated with neighborhood‐scale tree canopy cover and negatively associated with impervious surface. At a continental scale, community turnover between cities was lowest in yards and highest in parks. Within cities, however, turnover was lowest in high‐fertilizer yards and highest in wildlife‐certified yards and parks. Our results demonstrate that, across regions, preserving natural areas, minimizing impervious surfaces and increasing tree canopy are essential strategies to conserve regionally important species. However, yards, especially those managed for wildlife support diverse, heterogeneous bird communities with high public interest and potential to support species of conservation concern. Management approaches that include the preservation of protected parks, encourage wildlife‐friendly yards and acknowledge how public interest in local birds can advance successful conservation in American residential landscapes.
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Integrated urban land cover analysis using deep learning and post‐classification correction
Abstract The quantification of urban impervious area has important implications for the design and management of urban water and environmental infrastructure systems. This study proposes a deep learning model to classify 15‐cm aerial imagery of urban landscapes, coupled with a vector‐oriented post‐classification processing algorithm for automatically retrieving canopy‐covered impervious surfaces. In a case study in Corpus Christi, TX, deep learning classification covered an area of approximately 312 km2(or 14.86 billion 0.15‐m pixels), and the post‐classification effort led to the retrieval of over 4 km2(or 0.18 billion pixels) of additional impervious area. The results also suggest the underestimation of urban impervious area by existing methods that cannot consider the canopy‐covered impervious surfaces. By improving the identification and quantification of various impervious surfaces at the city scale, this study could directly benefit a variety of environmental and infrastructure management practices and enhance the reliability and accuracy of processed‐based models for urban hydrology and water infrastructure.
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- PAR ID:
- 10541849
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Computer-Aided Civil and Infrastructure Engineering
- ISSN:
- 1093-9687
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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