4.8 Article

An invasive foundation species enhances multifunctionality in a coastal ecosystem

出版社

NATL ACAD SCIENCES
DOI: 10.1073/pnas.1700353114

关键词

exotic plant; ecosystem engineer; novel facilitation; conservation; biodiversity

资金

  1. National Science Foundation (CAREER) [1056980]
  2. Duke University
  3. Stolarz Foundation
  4. University of North Carolina Wilmington
  5. Royal Society of New Zealand [13-UOC-106]
  6. National Science Foundation [1459384]
  7. Directorate For Geosciences
  8. Division Of Ocean Sciences [1459384, 1056980] Funding Source: National Science Foundation

向作者/读者索取更多资源

While invasive species often threaten biodiversity and human wellbeing, their potential to enhance functioning by offsetting the loss of native habitat has rarely been considered. We manipulated the abundance of the nonnative, habitat-forming seaweed Gracilaria vermiculophylla in large plots (25 m(2)) on southeastern US intertidal landscapes to assess impacts on multiple ecosystem functions underlying coastal ecosystem services. We document that in the absence of native habitat formers, this invasion has an overall positive, density-dependent impact across a diverse set of ecosystem processes (e.g., abundance and richness of nursery taxa, flow attenuation). Manipulation of invader abundance revealed both thresholds and saturations in the provisioning of ecosystem functions. Taken together, these findings call into question the focus of traditional invasion research and management that assumes negative effects of nonnatives, and emphasize the need to consider context-dependence and integrative measurements when assessing the impact of an invader, including density dependence, multifunctionality, and the status of native habitat formers. This work supports discussion of the idea that where native foundation species have been lost, invasive habitat formers may be considered as sources of valuable ecosystem functions.

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

4.8
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