Ecological Forecasts: An Emerging Imperative

Abstract

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. An 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 necessary because of the climate and societal controls on ecosystems, the feedbacks involving social change, and the decision-making relevance of forecasts.

Population Level Impacts of Cooling Water Withdrawals on Harvested Fish Stocks

“…The differences in the estimated values of %∆N A from this sensitivity analysis typically are of the same order of magnitude as those from uncertainty in the entrainment and impingement losses, but somewhat larger. Long term forecasts of dynamic ecological phenomena generally are highly uncertain, but forecasts that report useful information sometimes are possible (34). Because of the unavoidable data limitations and simplifying assumptions involved in this analysis, we have presented a range of estimates based on a variety of assumptions about key structural uncertainties (when in the life cycle density dependence operates) and parameter uncertainties (recruitment rates at low abundance, entrainment and impingement losses, and associated average harvest levels).…”

Elevated nonlinearity as an indicator of shifts in the dynamics of populations under stress

“…For example, in fisheries, increasing the complexity of a mechanistic model by including variables that are believed to be influential, such as temperature, can actually reduce predictability [1]. Thus, there is an urgent need for improved methods when it comes to forecasting ecological responses [4][5][6][7] especially under current levels of anthropogenic stress [8].…”

“…Coping with such fundamental sources of unpredictability requires alternative modelling strategies that go beyond traditional approaches [6,21]. One set of such strategies focuses on early warning signs that attempt to detect abrupt transitions based on the statistical properties of the observed time series without requiring any mechanistic model [12].…”

Using model‐data fusion to interpret past trends, and quantify uncertainties in future projections, of terrestrial ecosystem carbon cycling

“…The balance of these two numbers, net ecosystem exchange, drives the terrestrial carbon cycle and is tightly coupled to the growth rate of atmospheric CO 2 (Bosquet et al, 2000;Knorr et al, 2007). For policy makers, and many earth-system scientists, a major goal of global change research is therefore to understand the processes responsible for changes in terrestrial carbon cycling, and to project future states of ecosystems and climate at decadal, or even longer time scales (Clark et al, 2001;Luo et al, 2011).…”