Using urban forest assessment tools to model bird habitat potential

Landscape and Urban Planning
By: , and 

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Abstract

The alteration of forest cover and the replacement of native vegetation with buildings, roads, exotic vegetation, and other urban features pose one of the greatest threats to global biodiversity. As more land becomes slated for urban development, identifying effective urban forest wildlife management tools becomes paramount to ensure the urban forest provides habitat to sustain bird and other wildlife populations. The primary goal of this study was to integrate wildlife suitability indices to an existing national urban forest assessment tool, i-Tree. We quantified available habitat characteristics of urban forests for ten northeastern U.S. cities, and summarized bird habitat relationships from the literature in terms of variables that were represented in the i-Tree datasets. With these data, we generated habitat suitability equations for nine bird species representing a range of life history traits and conservation status that predicts the habitat suitability based on i-Tree data. We applied these equations to the urban forest datasets to calculate the overall habitat suitability for each city and the habitat suitability for different types of land-use (e.g., residential, commercial, parkland) for each bird species. The proposed habitat models will help guide wildlife managers, urban planners, and landscape designers who require specific information such as desirable habitat conditions within an urban management project to help improve the suitability of urban forests for birds.

Publication type Article
Publication Subtype Journal Article
Title Using urban forest assessment tools to model bird habitat potential
Series title Landscape and Urban Planning
DOI 10.1016/j.landurbplan.2013.10.006
Volume 122
Year Published 2014
Language English
Publisher Elsevier
Contributing office(s) Coop Res Unit Leetown
Description 12 p.
First page 29
Last page 40
Online Only (Y/N) N
Additional Online Files (Y/N) N
Google Analytic Metrics Metrics page
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