A two-tier ecological risk assessment was conducted for pesticides monitored in sediment at 36 sampling sites in south Florida freshwater canals from 1990-2002. For tier 1, we identified the chemicals of potential ecological concern (COPECs) as DDT, DDD, DDE, chlordane and endosulfan based on their exceedence of sediment quality standards at 20 sites. For 12 sites with data on the fraction of organic carbon in sediments, whole sediment concentrations of COPECs were converted to pore water concentrations based on equilibrium partitioning. In tier 2, a probabilistic risk assessment compared distributions of pore water exposure concentrations of COPECs with effects distributions of freshwater arthropod response data from laboratory toxicity tests. Arthropod effects distributions included benthic and non-benthic arthropod species for chlordane (n = 9), DDD (n = 12), DDE (n = 5), DDT (n = 48), and endosulfan (n = 26). The overlap of predicted pore water concentrations and arthropod effects distributions was used as a measure of risk. DDE was the most frequently detected COPEC in sediment at the 12 sites. Chlordane was present at only one site. The mean 90th centile concentration for pore water exposure was highest for endosulfan and lowest for DDT. The estimated acute 10th centile concentration for effects was highest for chlordane and lowest for DDD. The probability of pore water exposures of COPECs exceeding the estimated 10th centile concentrations for species sensitivity distributions of arthropod acute toxicity data was between 0 and 1%. The estimated NOEC 10th centile concentration from arthropod chronic toxicity distributions was exceeded by the estimated 90th centile concentration for pore water distributions at three sites. Endosulfan had the highest potential chronic risk at S-178 in the C-111 canal system, based on the probability of pore water exposure concentrations exceeding the arthropod estimated chronic NOEC 10th centile at 41%. The COPEC with the next highest probability of exceeding the chronic NOEC 10th centile was DDD at 17. . DDT had minimal potential chronic risk. Uncertainties in exposure and effects analysis and risk characterization are discussed.

Decision science tools can be used in evaluating response options and making inferences on risks to ecosystem services (ES) from ecological disasters. Influence diagrams (IDs) are probabilistic networks that explicitly represent the decisions related to a problem and their influence on desired or undesired outcomes. To examine how IDs might be useful in probabilistic risk management for spill response efforts, an ID was constructed to display the potential interactions between exposure events and the trade-offs between costs and ES impacts from spilled oil and response decisions in the DWH spill event. Quantitative knowledge was not formally incorporated but an ID platform for doing this was examined. Probabilities were assigned for conditional relationships in the ID and scenarios examining the impact of different response actions on components of spilled oil were investigated in hypothetical scenarios. Given the structure of the ID, potential knowledge gaps included understanding of the movement of oil, the ecological risk of different spill-related stressors to key receptors (e.g., endangered species, fisheries), and the need for stakeholder valuation of the ES benefits that could be impacted by a spill. Framing the Deepwater Horizon problem domain in an ID conceptualized important variables and relationships that could be optimally accounted for in preparing and managing responses in future spills. These features of the developed IDs may assist in better investigating the uncertainty, costs, and the trade-offs if large-scale, deep ocean spills were to occur again.

This article is part of the special series "Applications of Bayesian Networks for Environmental Risk Assessment and Management" and was generated from a session on the use of Bayesian networks (BNs) in environmental modeling and assessment in 1 of 3 recent conferences:

Although the concept of ecosystem sustainability has a long-term focus, it is often viewed from a static system perspective. Because most ecosystems are dynamic, we explore sustainability assessments from three additional perspectives: resilient systems; systems where tipping points occur; and systems subject to episodic resetting. Whereas foundations of ecosystem resilience originated in ecology, recent discussions have focused on geophysical attributes, and it is recognized that dynamic system components may not return to their former state following perturbations. Tipping points emerge when chronic changes (typically anthropogenic, but sometimes natural) push ecosystems to thresholds that cause collapse of process and function and may become permanent. Ecosystem resetting occurs when episodic natural disasters breach thresholds with little or no warning, resulting in long-term changes to environmental attributes or ecosystem function. An example of sustainability assessment of ecosystem goods and services along the Gulf Coast (USA) demonstrates the need to include both the resilient and dynamic nature of biogeomorphic components. Mountain road development in northwest Yunnan, China, makes rivers and related habitat vulnerable to tipping points. Ecosystems reset by natural disasters are also presented, emphasizing the need to understand the magnitude frequency and interrelationships among major disturbances, as shown by (i) the 2011 Great East Japan Earthquake and resulting tsunami, including how unsustainable urban development exacerbates geodisaster propagation, and (ii) repeated major earthquakes and associated geomorphic and vegetation disturbances in Papua New Guinea. Although all of these ecosystem perturbations and shifts are individually recognized, they are not embraced in contemporary sustainable decision making.ecosystem stressors | complex system behavior | sustainability analysis | cascading effects | coastal zone management

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on valued ecological resources. These aspects are demonstrated through hypothetical problem scenarios that explore some major benefits of using Bayesian networks for reasoning and making inferences in evidence-based policy.