4.5 Article

A comparative gradient approach as a tool for understanding and managing urban ecosystems

期刊

URBAN ECOSYSTEMS
卷 15, 期 4, 页码 795-807

出版社

SPRINGER
DOI: 10.1007/s11252-012-0240-9

关键词

-

资金

  1. USDA [08-JV-11221632-260]
  2. National Science Foundation [BCS-1026865, BCS-0948749]
  3. Division Of Behavioral and Cognitive Sci
  4. Direct For Social, Behav & Economic Scie [0948749] Funding Source: National Science Foundation
  5. Division Of Behavioral and Cognitive Sci
  6. Direct For Social, Behav & Economic Scie [1229429] Funding Source: National Science Foundation
  7. Division Of Environmental Biology
  8. Direct For Biological Sciences [1026865] Funding Source: National Science Foundation

向作者/读者索取更多资源

To meet the grand challenges of the urban century-such as climate change, biodiversity loss, and persistent poverty-urban and ecological theory must contribute to integrated frameworks that treat social and ecological dynamics as interdependent. A socio-ecological framework that encapsulates theory from the social and ecological sciences will improve understanding of metropolitan dynamics and generate science for improved, sustainable management of urban ecosystems. To date, most urban ecological research has focused on single cities. A comparative approach that uses gradients within and between cities is a useful tool for building urban ecological theory. We offer five hypotheses that are testable using a comparative, gradient approach: (i) the current size, configuration, and function of larger metropolitan ecosystems predicts the potential trajectory of smaller urban areas; (ii) timing of growth explains the greatest variance in urban ecosystem structure and function; (iii) form and function of urban ecosystems are converging over time; (iv) urban ecosystems become more segregated and fragmented as populations increase; and (v) larger cities are more innovative than smaller cities in managing urban ecosystems.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据