Habitat selection of breeding riparian birds in an urban environment: untangling the relative importance of biophysical elements and spatial scale

Abstract

AbstractAim Urbanization is a leading threat to global biodiversity, yet little is known about how the spatial arrangement and composition of biophysical elements – buildings and vegetation – within a metropolitan area influence habitat selection. Here, we ask: what is the relative importance of the structure and composition of these elements on bird species across multiple spatial scales?Location The temperate metropolitan area of Cincinnati, Ohio, USA.Methods We surveyed breeding birds on 71 plots along an urban gradient. We modelled relative density for 48 bird species in relation to local woody vegetation composition and structure and to tree cover, grass cover and building density within 50–1000 m of each plot. We used an information‐theoretic approach to compare models and variables.Results At the proximate scale, native tree and understory stem frequency were the most important vegetation variables explaining bird distributions. Species’ responses to landscape biophysical features and spatial scales varied. Most native species responded positively to vegetation measures and negatively to building density. Models combining both local vegetation and landscape information represented best or competitive models for the majority of species, while models containing only local vegetation characteristics were rarely competitive. Smaller spatial scales (≤ 500 m) were most important for 36 species, and eight species had best models at larger scales (> 500 m); however, several species had competitive models across multiple scales.Main conclusions Habitat selection by birds within the urban matrix is the result of a combination of factors operating at both proximate and broader spatial scales. Efforts to manage and design urban areas to benefit native birds require both fine‐scale (e.g., individual landowners and landscape design) and larger landscape actions (e.g., regional comprehensive planning).