Ecological Forecasts: an Merging Imperative James S
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Ecological Forecasts: An merging Imperative James S. Clark,'* Steven R. Carpenter: Mary Barber: Scott Collins: Andy Dobs~n,~Jonathan A. F~ley,~ David M. L~dge,~Mercedes Pascual: Roger Pielke Jr.,' William Pizer,1° Cathy Pringle," Walter V. Reid," Kenneth A. Rose,13 Osvaldo Sala,14 William H. ~chlesinger,'~Diana H. David wear" Planning and decision-making can be improved by access to reliable for which uncertainty can be reduced to the forecasts of ecosystem state, ecosystem services, and natural capital. point where a forecast reports a useful Availability of new data sets, together with progress in computation and amount of information. Information content statistics, will increase our ability to forecast ecosystem change. An is affected by all sources of stochasticity. agenda that would lead toward a capacity to produce, evaluate, and communicate forecasts of critical ecosystem services requires a process that engages scientists and decision-makers. Interdisciplinary linkages are 'Department of Biology, Duke University, Durham, necessary because of the climate and societal controis on ecosystems, the NC 27708 USA. 'University of Wisconsin Center for feedbacks involving social change, and the decision-making relevance of Limnology, Madison, WI 53706, USA. 3Ecological So- ciety of America, 1707 H Street NW, Suite 400, forecasts. Washington, DC 20006, USA. 'Division of Biology, Kansas State University. Manhattan, KS 66506, USA. Scientists and policy-makers can agree that ecosystem services, and natural capital, with SDepartment of Ecology and Evolutionary Biology, success in dealing with environmental change fully specified uncertainties, and is contin- Princeton University, Princeton, N] 08544, USA. Ten- ter for Sustainability and the Global Environment, rests with a capacity,to anticipate. Rapid gent on explicit scenarios for climate, land University of Wisconsin, Madison, Wl 53706, USA. change in climate and chemical cycles, de- use, human population, technologies, and 'Department of Biological Sciences, University of pletion of the natural resources that support economic activity. The spatial extent ranges Notre Dame, Notre Dame, IN 46556, USA BDepart- regional economies, proliferation of exotic from small plots to regions to continents to ment of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA. 'Environmen- species, spread of disease, and deterioration the globe. The time horizon can extend up to tal and Societal Impacts GrouplNational Center for of air, waters, and soils pose unprecedented 50 years. The information content of a fore- Atmospheric Research. 3250 Mitchell Lane. Boulder, threats to human civilization. Continued cast is inversely proportional to forecast un- CO 80301. USA. loResources for the Future, 1616 P food, fiber, and freshwater supplies and the certainty (2). A 'wide confidence envelope Street NW, Washington, DC 20036, USA. "Depart- maintenance of human health depend on our indicates low information content. A scenario ment of Ecology, University of Georgia, Athens, GA 30602. USA. '' Millennium Ecosystem Assessment. ability to anticipate and prepare for the un- assumes changes in "possible future bound- 731 North 79th Street, Seattle, WA 98103, USA. certain future (1). Anticipating many of the ary conditions (e.g., emissions scenarios). 13Coastal Fisheries Institute and Department of environmental challenges of coming decades For the decision maker, scenarios provide an Oceanography and Coastal Sciences, Louisiana State requires improved scientific understanding. indication of possibilities, but not definitive University, Baton Rouge, !A 70803, USA. "Depart- ment of Ecology, Faculty of Agronomy-IFEVA, Uni- An evolving science of ecological forecasting probabilities" (3). Scenarios can be the basis for versity of Buenos Aires-CONICET, Buenos Aires 1417, is beginning to emerge and could have an projections, which apply the tools of ecological Argentina. lSNicholas School of Environment and expanding role in policy and management. forecasting to specific scenarios. Earth Sciences, Duke University, Durham, NC 27708, An initiative in ecological forecasting USA. 16Natural Resource Ecology Laboratory, Colo- rado State University, Fort Collins, CO 80523, USA. must define the appropriate role of science in What Is Forecastable? l7United States Department of Agriculture Forest Ser- the decision-making process and the research Accurate estimation and communication of vice, Post Office Box 12254, Research Triangle Park, that is required to develop the capability. information content will determine the suc- NC 27709. USA. Ecological forecasting is defined here as the cess of an ecological forecasting initiative. *To whom correspondence should be addressed. E- process of predicting the state of ecosystems, "Forecastable" ecosystem attributes are ones mail: [email protected] www.sciencernag.org SCIENCE VOL 293 27JULY 2001 657 ECOLOGY THROUGH TIME Low information content can result because plant composition and reduced carbon stor- predictable from fine-grained studies (23, drivers (and, thus, model structures) are un- age potential in tallgrass prairie soils (15). 24). The feedbacks from vegetation to cli- certain, parameters are uncertain, and un- Knowledge of fertilizer and irrigation effects mate become important only when the spa- known human responses to ecosystem change on carbon storage in agroecosystems can be tial extent of a study exceeds a critical (or to forecasts of ecosystem change) affect used to forecast how managed ecosystems threshold. Factorial, whole-ecosystem ex- outcomes. Many sources of stochasticity are will contribute to or stem the future rise of periments with CO,, temperature, moisture, typically ignored in ecological models. When CO, in Earth's atmosphere (16). and nutrients may be the only way to de- reported at all, prediction uncertainties are Analysis of projections can help anticipate termine forest responses to global change typically confined to estimation error (4, 5), change, even where forecasts are uninforma- (25). For example, free-air CO, enrichment which is reduced by sampling and is often tive. Although forecasts of population migra- (FACE) studies show that the water stress overwhelmed by other sources of uncertainty. tion rates will typically have low information expected from studies of individual plants Most daunting is the "inherent" uncertain- content, analysis shows that productive re- may not be realized in an intact stand (26). ty that results from strong nonlinearities and search will focus on factors affecting inva- Data networks can provide a baseline for stochasticity. For example, the inherent un- sion potential, such as the mechanisms of forecasting. Missing variables, low resolu- certainty involved in extinction risks leads long-distance dispersal and propagule pro- tion, inadequate duration, temporal and spa- ecologists to disagree on the value of predic- duction, as opposed to precise estimation of tial gaps, and declining coverage are perva- tions from population viability models (6). long-distance dispersal (8). Rates will remain sive limitations. Due to abandonment of pre- Extinction forecasts are highly sensitive to uncertain, but we may improve our ability to cipitation, stream-height, and discharge gaug- poorly constrained assumptions (7). Inherent predict introduced species that can success- es, the capacity to forecast droughts and uncertainty will always limit informative fully invade (17). floods was greater 30 years ago than it is forecasts of spread velocity for invasive The developing capacity for prediction today. Countries with the poorest hydrologi- plants with high reproductive rates. Even pre- requires careful model evaluation, which can cal networks (e.g., sub-Saharan Africa, arid cise knowledge of parameters that might be involve model selection, m+l averaging, or regions of the former Soviet Union) have the estimated, for example, through detailed both. Model selection methods are routinely most pressing water needs (27). The problem study of long-distance dispersal, would do used in ecological applications. Because the is not restricted to developing and transitional little to increase forecast information (8). models themselves are often uncertain, eco- economies. There is an average density of Large inherent uncertainty does not nec- logical forecasting may eventually rely more one stream gauge per 1024 km2 in the lower essarily neutralize efforts to anticipate heavily on model averaging. Techniques for 48 states of the United States (28). Since change. Forecasting will improve as ecolo- model evaluation developed in econometrics, 197 1 there has been a 22% decline in gauging gists identify the "slow" variables that fore- finance, and meteorology make use of hind stations that record flow on small U.S. rivers. warn of consequences years in advance. casting (la), including the ability to identify Sustained monitoring is needed that can Whereas deterministic weather forecasts con- turning points and events (12). dovetail with forecasts in an adaptive feed- front an approximate 2-week limit, probabi- Failing to accommodate the important back design. listic climate prediction makes use of the sources of stochasticity makes for a forecast The ability to anticipate exotic invasions system memory represented by sea-surface that contains less information than it purports would benefit from historic records of species temperatures. The limitations imposed on a (confidence intervals are misleadingly