Skip to Main Content
Conserving biodiversity using risk management: hoax or hope?Author(s): Susan Hummel; Geoffrey H. Donovan; Thomas A. Spies; Miles A. Hemstrom
Source: Frontiers in ecology and the environment. 6. DOI: 10.1890/070111.
Publication Series: Scientific Journal (JRNL)
PDF: Download Publication (1.38 MB)
DescriptionBiodiversity has been called a form of ecosystem insurance. According to the "insurance hypothesis", the presence of many species protects against system decline, because built-in redundancies guarantee that some species will maintain key functions even if others fail. The hypothesis might have merit, but calling it "insurance" promotes an ambiguous understanding of risk management strategies and underlying theories of risk. Instead, such redundancy suggests a strategy of diversification. A clearer understanding of risk includes comprehending the important distinction between risk assessment and risk management, acknowledging the existence of undiversifiable risk, and recognizing that risk and uncertainty are not synonymous. A better grasp of risk management will help anyone interested in assessing the merits of different biodiversity conservation strategies. At stake is the adequacy of conservation strategies for mitigating human-caused biodiversity losses.
- Visit PNW's Publication Request Page to request a hard copy of this publication.
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationHummel, Susan; Donovan, Geoffrey H.; Spies, Thomas A.; Hemstrom, Miles A. 2008. Conserving biodiversity using risk management: hoax or hope?. Frontiers in ecology and the environment. 6. DOI: 10.1890/070111.
- Implications of climate change for managing urban green infrastructure: an Indiana, US case study
- Theory and practice to conserve freshwater biodiversity in the Anthropocene
XML: View XML