Bad ecology leads to bad public policy

Bad ecology leads to bad public policy

POSTSCRIPT Bad ecology leads to bad public policy roblems of conservation have become prominent as human encroachments upon resources and habitat bec...

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Bad ecology leads to bad public policy roblems of conservation have become prominent as human encroachments upon resources and habitat become increasingly obvious and ominous. Ecologists have a new sense of mission, and ecological advice is now actively sought by decision-making bodies. There are dangers in accepting tasks for which we are ill-equipped. Similar developments in the earth sciences may provide useful lessons. The nuclear power industry is hampered by the necessity of dealing with nuclear wastes. Regulatory agencies have set up requirements that pose a new kind of problem for scientists: nuclear waste must be ‘disposed of’and we must be assured that we need take no further action for 10OOOyears.These requirements stand conventional scientific procedure on its head, since the outcome of the investigation is determined beforehand. Earth scientists and engineers have devised elaborate models that attempt to account for all of the various processes and outcomes that may be significant during the next 10000years. In order to provide appropriate assurances, these models must be ‘validated’ or ‘verified’. But true verification is impossible on both philosophical and pragmatic grounds: no scientific theory can be demonstrated to be true, and the models all require additional auxiliary hypotheses that are insufficiently supported by evidencelJ. The most that we can hope to provide is an assessment of the relative plausibilities of competing hypotheses. Scientists working on problems of conservation are attempting to provide predictions of the viability of species, communities and even whole ecosystems. Even the best ecologists have had great difficulties in understanding the distribution and abundance of the beststudied organisms: they are now tempted to adopt new methods in an attempt to extrapolate from poorly understood local phenomena involving a few species for a few years to grand scales over extended periods. The fact is that no theories can enable us to avoid making appropriate observations over appropriate time scales and properly analysing the results. Statistical methods have traditionally served as a brake on unwarranted inference: the more complex the problem, the more stringent are the requirements for proper inference. Experimenters know


that, to infer an effect. it is necessary

vary the experimental conditions. The larger the inherent fluctuations or inaccuracies in observations, the larger the variation in experimental conditions that is required to draw conclusions. However, our desire to avoid impending catastrophes tempts us to disregard proper scientific methods. Common sense is not a reliable guide when large numbers of explanatory variables are involved. Proper experimental designs must vary several factors independently. Such experiments may be extremely onerous or even impossible to perform, but they are indispensable if valid inferences are to be made. Proper statistical tools will provide warnings if data carrying insufficient information are used to draw such conclusion+. What is one to do if the appropriate experiments cannot be performed and uncontrolled observations are insufficiently informative? Logic and common sense dictate that we refrain from making inferences in such cases. Expediency may lead some scientists to use ‘best estimates’ or ‘expert judgment’ as a substitute for proper inference. Some earth scientists are following that course for the problem of nuclear waste management, and some ecologists are tempted to take that course in an attempt to ameliorate impending ecological catastrophes. Although there may be some benefits from such short-cuts in the short term, in the longer term scientists will lose their credibility and thereby squander whatever influence they might have on public policy. Do alternatives exist? An obvious alternative is to refuse to make insufficiently founded environmental assessments and instead to educate policy makers and the general public to have realistic expectations. Nobody can legislate scientific truths, although many have tried. Current requirements for population viability analyses are fantasies: legislators ask for something that no

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scientist can provide”. A proper representation of the results of scientific investigations is a balance of knowledge and ignorance. It requires that we abandon the notion of ‘acceptance’ and ‘rejection’ of statistical hypotheses, because those terms may mislead some to believe that we have actually proven something about nature. All that we can do is collect evidence that bears on the relative weights to be assigned to alternative models of the world. Such evidence can be displayed and communicated to decision makers. Instead of attempting to guarantee the effectiveness of conservation strategies, we can offer guidance regarding the probable consequences of various alternative actions. Instead of pretending to have knowledge about ‘minimal viable populations’, we can illustrate the increasing risks associated with habitat destruction. When legislators ask for assurances about the viability of populations, we can show how our ignorance forces us to impose more stringent restrictions upon encroachments in order to provide such assurances. We may identify critical areas of uncertainty and suggest experiments or observations that may reduce our ignorance, and thus enable us to relax those restrictions. In short, the more complicated the situation, the more circumspect we must be in admitting and displaying our ignorance and its implications for public policy. Acknowledgements I am indebted to Lee Gass for numerous suggestions. This work was supported by NSERCof Canada under grant A9239. Donald Ludwig Depts of Mathematics and Zoology, University of British Columbia, Vancouver, BC, Canada V6T lZ2

References 1 Oreskes, N., Shrader-Frechette. K. and Belitz, K. (1994) Science 263, 641-646 2 Amt. Mult (1994) Science 264,329-331 3 Larimore, W.E. and Mehra, R. (1985) Byte Magazine October, 16i-180 4 Rohlf, D.J. (1991) Cons. Biol. 5(3), 273-282


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