DURHAM, N.C. -- A broad consortium of scientists has proposed a concerted effort by researchers and policymakers to develop the ability to forecast ecological change in areas ranging from small plots to the entire globe.
The scientists say advances in science and technology could enable forecasts guiding policy to forewarn of invasions of exotic species and disease epidemics, protect the ecology of lakes, rivers and estuaries, and predict ecological impacts of global warming.
In an article in the July 27, 2001 Science, the consortium wrote: "Planning and decision making can be improved by access to reliable forecasts of ecosystem state, ecosystem services, and natural capital. Availability of new data sets, together with progress in computation and statistics, will increase our ability to forecast ecosystem change."
Thus, the consortium called for an initiative in which policymakers would work with ecologists and other scientists to define ecological systems in which it would be both useful and possible to make forecasts.
"This paper is a response to a problem that many ecologists perceive in making our work more relevant to societal needs," said first author James Clark of Duke University. "We devote much effort to understanding the biosphere, and we communicate our findings to the scientific community. But society faces a great number of environmental problems, and if it can't come to us for help and knowledge, there is no place else to go. The federal agencies by themselves can't provide much of the basic scientific understanding of these issues that people need."
However, Clark, who is H. L. Blomquist Professor of Biology at Duke, pointed out that he and his colleagues are emphasizing the necessity of a collaborative effort with decision makers. "If we as ecologists just begin making forecasts without understanding what will be useful to policymakers, governments will pay little attention," he said. "Thus, this paper represents the beginning of a discussion on how to make our science useful to policymakers."
In the Science paper, the authors emphasize the difficulty of making forecasts, given the inherent uncertainties and complexity of ecosystems.
Many ecosystem properties are inherently uncertain because they are very sensitive to things that cannot be known precisely, or there are too many things that simply can't be known. On the other hand, identification of "slow" variables that can dependably forewarn of consequences years in advance can, in some cases, lead to improved forecasting. In other cases, techniques of averaging the results of ecological models will increase the usefulness of such forecasts.
Said Clark, "Many ecologists are leery of prediction, because they feel that there is much uncertainty about their models, and that most predictions are going to be wrong. But we have to weigh this uncertainty against the costs of not trying to anticipate. We also need to examine why traditional modeling strategies can fail, and we must be willing to exploit indicators and slow variables that can be related to ecosystem change in rather simple ways. Moreover, forecasting efforts can help us identify the possibilities for change, if not to actually 'predict' it," he said.
"Someone is going to make forecasts, and if the scientists won't do it, then other groups will. We'll likely do a better job, and so we should be involved, using our understanding to anticipate possible change in a critical way. Otherwise they will be done in a less critical way, possibly producing misguided policies."
The authors wrote that ecological forecasting depends on large-scale ecological studies, extensive gathering of ecological data and effective use of technologies such as satellite remote sensing. Unfortunately, they wrote, ecological data are often inadequate.
"Due to abandonment of precipitation, stream-height and discharge gauges, the capacity to forecast droughts and floods was greater 30 years ago than it is today," the authors said. "Countries with the poorest hydrological networks (e.g. sub-Saharan Africa, arid regions of the former Soviet Union) have the most pressing water needs."
Of the need for large-scale ecological studies, Clark said: "There has been a tradition in ecology of rather fine-scale studies. But it is hard to use that fine-scale information to make predictions on a regional scale because there are different processes that operate at these coarser scales. So, these larger and longer-term data sets will prove invaluable."
"There are also advances in both computer sciences and in statistics that we ecologists have not yet cashed in on. In computer sciences, there are new faster algorithms that allow us to simulate a process on a broader scale, as well as better statistical methods for dealing with uncertainty. These new methods allow us to incorporate scientific uncertainty about ecological processes in ways that still enable us to create useful predictive models."
According to the paper, the next steps for launching an ecological forecasting initiative include defining priorities for forecasting and the scientific research needed to better understand ecological systems and uncertainties underlying them. "Focus should be on the problems for which forecasts are now possible, and those that are not presently forecastable but could become forecastable within a decade," the scientists said.
Clark said that in developing a forecasting ability ecologists can take a lesson from the problems that climatologists have encountered in predicting climate change. "There have been some significant difficulties in communicating what a forecast really means," he said. "For example, when climatologists run models to predict future climates based on expected increases in atmospheric carbon dioxide, they don't necessarily view any of those models as predictions. Rather, they are explorations of what could happen, based on a particular scenario.
In contrast, a weather forecast is intended to be the best estimate of what the weather will be tomorrow or the next day. Unfortunately, there has been miscommunication in how scientists view the outputs of such models versus how the general public or decision makers see them. Those miscommunications have in some cases resulted in confusion, a situation we hope to minimize by working closely with a broad range of experts, including the policymaking community."
In addition to Clark, the authors are Stephen Carpenter of the University of Wisconsin, Mary Barber of the Ecological Society of America, Scott Collins of Kansas State University, Andy Dobson of Princeton University, Jonathan Foley of the University of Wisconsin, David Lodge of the University of Notre Dame, Mercedes Pascual of the University of Notre Dame, Roger Peilke Jr. of the National Center on Atmospheric Research, William Pizer of Resources for the Future, Cathy Pringle of the University of Georgia, Walter Reid of Millennium Ecosystem Assessment, Kenneth Rose of Louisiana State University, Osvaldo Sala of the University of Buenos Aires-CONICET, William Schlesinger of Duke, Diana Wall of Colorado State University and David Wear of the USDA Forest Service.
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