| Predicting Occurrence of Area-sensitive Forest Birds Contributor(s): Dey, Amanda (Author), Burger, Joanna (Author), Niles, Lawrence J. (Author) |
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ISBN: 363901538X ISBN-13: 9783639015386 Publisher: VDM Verlag Dr. Mueller E.K. OUR PRICE: $50.27 Product Type: Paperback Published: July 2008 Annotation: Forest fragmentation is well studied from the perspective of avian reproductive and pairing success, species richness, abundance and occurrence. It is less clear how non-forest habitats affect bird occurrence and appropriate spatial scales (spatial extent) to measure such effects. Few studies examine how birds respond to landscape configuration at spatial scales useful for conservation and land-use planning; i.e., to identify important habitats and assess impacts of land-use activities. To understand how area-sensitive forest birds respond to non-forest habitats at various spatial scales, we quantified bird abundance in large areas of northern and southern New Jersey and used multivariate logistic regression to identify significant habitat variables at four spatial scales for six species breeding in both regions (48 predictive models). |
| Additional Information |
| BISAC Categories: - Science | Life Sciences - Biology - Science | Life Sciences - Ecology |
| Physical Information: 0.22" H x 6" W x 9" (0.34 lbs) 108 pages |
| Themes: - Topical - Ecology |
| Descriptions, Reviews, Etc. |
| Publisher Description: Forest fragmentation is well studied from the perspective of avian reproductive and pairing success, species richness, abundance and occurrence. It is less clear how non-forest habitats affect bird occurrence and appropriate spatial scales (spatial extent) to measure such effects. Few studies examine how birds respond to landscape configuration at spatial scales useful for conservation and land-use planning; i.e., to identify important habitats and assess impacts of land-use activities. To understand how area-sensitive forest birds res-pond to non-forest habitats at various spatial scales, we quantified bird abundance in large areas of northern and southern New Jersey and used multivariate logistic regression to identify significant habitat variables at four spatial scales for six species breeding in both regions (48 predictive models). |