Ecological indicators of global climate change: A research framework

Abstract

ABSTRACT/ Large-scale environmental research efforts are conducted under frameworks that provide a common basis for evaluating research in many diverse fields. Such frameworks should be subjected to critical review to determine if they meet crucial expectations. In the case of the US Global Change Research Program, we perceive that most research follows a framework that we define as the predictive model framework. We believe this framework is insufficient for resolving the unprecedented predicaments posed by global change. We recommend a complementary framework, the system response framework, which directs research toward useful indicators of change rather than precise predictions. We further argue that, even if research is complementary and effective under the two frameworks, conclusive results prior to decisions should not be expected. The burden of proof must itself be a continuing topic of open discourse and inquiry.Public support for applied research is intended to provide a scientific basis for public policy (Committee on Earth and Environmental Sciences 1992). There is an implicit social contract: public funds support comprehensive research programs in return for research efforts that address matters of public importance. Toward this end, funds are directed toward some research activities more than others. This selectivity is based on what we define in this paper as a framework of assumptions and expectations. A research framework defines the overall rationale for a comprehensive research program. Expressed in program objectives, purpose statements, and overviews, a framework serves to mediate between public policy and a vast array of specialized research activities. It provides an overall rationale for public support of a wide range of research efforts.A framework focuses scientific inquiry so that proposals, assumptions, and claims are tested for practical public purpose as well as scientific credibility. At the same time, a framework establishes the scientific

Science and Management of Rocky Mountain Grizzly Bears

“…The problems of erratic or chronic bias and unequal error costs are potentially addressed through allocating burden of proof, determining acceptable risks, choosing which bound of certainty to use, and varying the emphasis on type I and type II errors (Shrader-Frechette 1991;Shrader-Frechette & McCoy 1993 whether society requires that agencies prove that proposed actions will or will not harm bears, or that managers operate on the basis of upper or lower confidence limits of, for example, an estimate of population growth rate (Bella et al 1994;Weaver et al, this issue). Managing for a high probability of grizzly bears surviving 1000 years will similarly be much different from managing for a modest probability of survival over 100 years (Mattson & Craighead 1994).…”

“…The amalgam of biophysical and social factors controlling grizzly bear mortality has defied scientifically reliable predictions and will likely continue to do so (Kravtsov 1993;Bella et al 1994;Funtowicz & Ravetz 1994). This is not to say that scientists will be unable to inform our insight or judgment by analyzing broad historic patterns of grizzly bear population survival or to identify indicators of various system attributes (e.g., grizzly bear population size and trend), or that we will be unable to characterize the system in ways that are directly helpful to management.…”

“…These epistemological problems have major implications for grizzly bear management and human social processes. An implicit and ftmdamental understanding common among grizzly bear researchers and managers is that given enough time and resources researchers will provide scien-tificaUy reliable answers to the managers' questions (Bella et al 1994). Accordingly, managers wait for delivery of their answers and, lacking data that indicate the harm of a proposed action to grizzly bears, understandably accom-modate the immediate, compelling sociopolitical demands that favor the creation of economic wealth from natural resources (Ludwig et al 1993;Primm 1993;Reading & Kelleft 1993;Mattson & Craighead 1994).…”

Uncertainty in River Restoration

“…In this type of situation, when science is used for public policy and decision making, scientists might wish to consider whether and to what extent they should be more comfortable with making conclusions based on the weight of evidence rather than based solely or primarily on high levels of confi dence, especially since public policy decisions are not based simply upon probabilistic considerations but rather involve making discrete and explicit choices among specifi c alternatives, including those with political, economic and ethical ramifi cations (Bella et al, 1994;Lemons et al, 1997). On the one hand, traditional scientifi c norms call for making conclusions on information about which there is a high degree of confi dence.…”

“…Some scientists and policy makers involved in environmental problem solving have argued for synthesizing analyses and alternatives to solutions of environmental resource problems (Lubchenco et al, 1991;Bella et al, 1994;Lemons and Brown, 1995;Caldwell, 1996). In practice, at least three levels of synthesis may be identifi ed.…”