. "Status of Ecological Knowledge Related to Policy Decision-Making Needs in the Area of." Linking Science and Technology to Society's Environmental Goals. Washington, DC: The National Academies Press, 1996.
The following HTML text is provided to enhance online
readability. Many aspects of typography translate only awkwardly to HTML.
Please use the page image
as the authoritative form to ensure accuracy.
Linking Science and Technology to Society's Environmental Goals
Current ecological knowledge can help minimize risks from the introduction of transgenic species. For example, risks are clearly higher in regions where wild relatives of the species are present.
These five examples demonstrate that substantial knowledge gaps exist that prevent a clear assessment of the ecological effects of any particular pressure or perturbation on a system. For certain types of well-studied perturbations, such as the application of the pesticide DDT, scientists can make detailed predictions of consequences. For certain well-studied species, such as deer, we can also make precise assessments of the impact of any change in the population of that species. But, by and large, unless a particular system has been the subject of intense research and monitoring, the current status of ecological knowledge only enables us to identify a list of potential consequences of a given action and to identify the set of data or experiments that would enable that uncertainty to be reduced.
Ecological knowledge helps to define the types of management practices and policy tools available for resource management. Current management practices have been influenced by advances in ecological knowledge, particularly increased understanding of (i) the natural variability of ecological systems; (ii) the non-linear nature of many ecological interactions; and (iii) the site-specific nature of many ecological interactions and processes (Ludwig et al. 1993).
For decades, wildlife and fisheries managers relied on demographic models that assumed far less natural variability in populations and more linear population responses than is now recognized to generally be the case. The knowledge available at the time led to the design of management techniques like calculating the maximum sustainable yield for a population and then setting harvest levels based on those estimates. Now, we recognize that the non-linear aspects of the population dynamics of most species place insurmountable limits to the precision of population projections. As a consequence, managers increasingly set goals for population management based on a safe minimum standard rather than a prediction of the optimum sustained yield.
Natural variability of ecological systems also makes it exceedingly difficult to evaluate the consequences of any particular management intervention. Adaptive management practices have been developed to reduce uncertainty and improve resource management through time. Adaptive management is a technique for managing biological systems so as to simultaneously reduce uncertainty about the functioning of the systems and respond to the changing social, biological, and physical environment. The principal elements of adaptive management are (i) management interventions are made in an experimental manner so that the outcome of the intervention can be used to reduce uncertainty about the system; (ii) sufficient monitoring prior to and during the intervention enables detection of the results of the management intervention and thereby allows managers to learn