Thus, it is essential to understand the underpinnings of hydroclimatic variability in order to accurately predict effects of climate changes and effectively meet future water supply challenges.
To date, constraint-based models cannot predict effects of toxic intermediates and methods are needed that allow for prediction of concentrations within constraint-based models.
With sufficient resolution, the structure can be used to predict effects of mutations or sites of potential cross-links, and these predictions can be tested in molecular biological experiments.
Combining MRI and other data helps machine-learning systems predict effects of neurodegenerative disease.
However, standard engineering measures of the floor properties may not predict effects of the floor on cow behavior well.
Among the goals are: to characterize ecological feedbacks to climate change, to predict effects of global change on biodiversity, and to develop fundamental theory that predicts the structure of ecosystems across spatial scales.
