Weitao Zhang

TL;DR: This study outlines how to attain realistic predictive links between management actions and ecosystem response through a probabilistic framework that accommodates rigorous uncertainty analysis of a variety of error sources.

TL;DR: A generalized likelihood uncertainty estimation approach combined with a simple Monte Carlo sampling scheme and a Bayesian methodological framework along with Markov Chain Monte Carlo simulations is illustrated, for elucidating the propagation of uncertainty in the high-dimensional parameter spaces of mechanistic eutrophication models.

TL;DR: The main objective is to attain effective model calibration and rigorous uncertainty assessment by integrating environmental mathematical modeling with Bayesian analysis and underscores the lack of perfect simulators of natural system dynamics using a statistical formulation that explicitly accounts for the discrepancy between mathematical models and environmental systems.

TL;DR: In this article, the authors evaluate the relative importance of the causal connection between exogenous total phosphorus loading and internal nutrient recycling with the water quality conditions in the Bay of Quinte, Ontario, Canada.

TL;DR: The results suggest that the Bayesian hierarchical approach allows overcoming problems of insufficient local data by “borrowing strength” from well-studied sites and this feature will be highly relevant to conservation practices of regions with a high number of freshwater resources for which complete data could never be practically collected.