Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling

Algorithms for inferring the structure of Bayesian networks from data have become an increasingly popular method for uncovering the direct and indirect influences among variables in complex systems. A Bayesian approach to structure learning uses posterior probabilities to quantify the strength with which the data and prior knowledge jointly support each possible graph feature. Existing Markov Chain Monte Carlo (MCMC) algorithms for estimating these posterior probabilities are slow in mixing and convergence, especially for large networks. We present a novel Markov blanket resampling (MBR) scheme that intermittently reconstructs the Markov blanket of nodes, thus allowing the sampler to more effectively traverse low-probability regions between local maxima. As we can derive the complementary forward and backward directions of the MBR proposal distribution, the Metropolis-Hastings algorithm can be used to account for any asymmetries in these proposals. Experiments across a range of network sizes show that the MBR scheme outperforms other state-of-the-art algorithms, both in terms of learning performance and convergence rate. In particular, MBR achieves better learning performance than the other algorithms when the number of observations is relatively small and faster convergence when the number of variables in the network is large

Eliciting density ratio classes

AbstractThe probability distributions of uncertain quantities needed for predictive modelling and decision support are frequently elicited from subject matter experts. However, experts are often uncertain about quantifying their beliefs using precise probability distributions. Therefore, it seems natural to describe their uncertain beliefs using sets of probability distributions. There are various possible structures, or classes, for defining set membership of continuous random variables. The Density Ratio Class has desirable properties, but there is no established procedure for eliciting this class. Thus, we propose a method for constructing Density Ratio Classes that builds on conventional quantile or probability elicitation, but allows the expert to state intervals for these quantities. Parametric shape functions, ideally also suggested by the expert, are then used to bound the nonparametric set of shapes of densities that belong to the class and are compatible with the stated intervals. This leads to a natural metric for the size of the class based on the ratio of the total areas under upper and lower bounding shape functions. This ratio will be determined by the characteristics of the shape functions, the scatter of the elicited values, and the explicit expert imprecision, as characterized by the width of the stated intervals. We provide some examples, both didactic and real, and conclude with recommendations for the further development and application of the Density Ratio Class

A novel deliberative multicriteria evaluation approach to ecosystem service valuation

Although efforts to address ecosystem services in decision making have advanced considerably in recent years, there remain challenges related to valuation. In particular, conventional economic approaches have been criticized for their inability to capture the collective nature of ecosystem services, for their emphasis on monetary metrics, and the difficulty of assessing the value of ecosystem services to future generations. We present a deliberative multicriteria evaluation (DMCE) method that combines the advantages of multicriteria decision analysis with a deliberation process that allows citizens and scientists to exchange knowledge and evaluate ecosystem services in a social context. Compared with previous applications we add the following: (i) a choice task that can be expected to lead to a more reliable assessment of trade-offs among ecosystem services, and (ii) an explicit consideration of the future by both presenting specific socioeconomic scenarios and asking participating citizens to serve as “trustees” for future generations. We implemented our DMCE framework with 11 panels of residents of the upper Merrimack River watershed in New Hampshire with the goal of assessing the relative value of 10 different ecosystem services in the form of trade-off weights. We found that after group deliberation and expert scientific input, all groups except one were able to reach internal consensus on the relative value of these ecosystem services. Additionally, the pattern of trade-off weights across groups was reasonably similar; there was no statistically significant effect of the specific future scenarios that were presented to the groups. Results of a survey given to participants after the deliberative process revealed that most felt that their opinion during the deliberation was heard by the others and that they were influential on the outcome. Further, the vast majority were satisfied with the outcome of the deliberation. We conclude by discussing the strengths and limitations of our framework at an operational level

Benthic and Pelagic Pathways of Methylmercury Bioaccumulation in Estuarine Food Webs of the Northeast United States

Methylmercury (MeHg) is a contaminant of global concern that bioaccumulates and bioamagnifies in marine food webs. Lower trophic level fauna are important conduits of MeHg from sediment and water to estuarine and coastal fish harvested for human consumption. However, the sources and pathways of MeHg to these coastal fisheries are poorly known particularly the potential for transfer of MeHg from the sediment to biotic compartments. Across a broad gradient of human land impacts, we analyzed MeHg concentrations in food webs at ten estuarine sites in the Northeast US (from the Hackensack Meadowlands, NJ to the Gulf of Maine). MeHg concentrations in water column particulate material, but not in sediments, were predictive of MeHg concentrations in fish (killifish and Atlantic silversides). Moreover, MeHg concentrations were higher in pelagic fauna than in benthic-feeding fauna suggesting that MeHg delivery to the water column from methylation sites from within or outside of the estuary may be an important driver of MeHg bioaccumulation in estuarine pelagic food webs. In contrast, bulk sediment MeHg concentrations were only predictive of concentrations of MeHg in the infaunal worms. Our results across a broad gradient of sites demonstrate that the pathways of MeHg to lower trophic level estuarine organisms are distinctly different between benthic deposit feeders and forage fish. Thus, even in systems with contaminated sediments, transfer of MeHg into estuarine food webs maybe driven more by the efficiency of processes that determine MeHg input and bioavailability in the water column

Transparent and feasible uncertainty assessment adds value to applied ecosystem services modeling

We introduce a special issue that aims to simultaneously motivate interest in uncertainty assessment (UA) and reduce the barriers practitioners face in conducting it. The issue, “Demonstrating transparent, feasible, and useful uncertainty assessment in ecosystem services modeling,” responds to findings from a 2016 workshop of academics and practitioners that identified challenges and potential solutions to enhance the practice of uncertainty assessment in the ES community. Participants identified that one important gap was the lack of a compelling set of cases showing that UA can be feasibly conducted at varying levels of sophistication, and that such assessment can usefully inform decision-relevant modeling conclusions. This article orients the reader to the 11 other articles that comprise the special issue, and which span multiple methods and application domains, all with an explicit consideration of uncertainty. We highlight the value of UA demonstrated in the articles, including changing decisions, facilitating transparency, and clarifying the nature of evidence. We conclude by suggesting ways to promote further adoption of uncertainty analysis in ecosystem service assessments. These include: Easing the analytic workflows involved in UA while guarding against rote analyses, applying multiple models to the same problem, and learning about the conduct and value of UA from other disciplines

A coupled terrestrial and aquatic biogeophysical model of the Upper Merrimack River watershed, New Hampshire, to inform ecosystem services evaluation and management under climate and land-cover change

Accurate quantification of ecosystem services (ES) at regional scales is increasingly important for making informed decisions in the face of environmental change. We linked terrestrial and aquatic ecosystem process models to simulate the spatial and temporal distribution of hydrological and water quality characteristics related to ecosystem services. The linked model integrates two existing models (a forest ecosystem model and a river network model) to establish consistent responses to changing drivers across climate, terrestrial, and aquatic domains. The linked model is spatially distributed, accounts for terrestrial–aquatic and upstream–downstream linkages, and operates on a daily time-step, all characteristics needed to understand regional responses. The model was applied to the diverse landscapes of the Upper Merrimack River watershed, New Hampshire, USA. Potential changes in future environmental functions were evaluated using statistically downscaled global climate model simulations (both a high and low emission scenario) coupled with scenarios of changing land cover (centralized vs. dispersed land development) for the time period of 1980–2099. Projections of climate, land cover, and water quality were translated into a suite of environmental indicators that represent conditions relevant to important ecosystem services and were designed to be readily understood by the public. Model projections show that climate will have a greater influence on future aquatic ecosystem services (flooding, drinking water, fish habitat, and nitrogen export) than plausible changes in land cover. Minimal changes in aquatic environmental indicators are predicted through 2050, after which the high emissions scenarios show intensifying impacts. The spatially distributed modeling approach indicates that heavily populated portions of the watershed will show the strongest responses. Management of land cover could attenuate some of the changes associated with climate change and should be considered in future planning for the region

Deliberative multiattribute valuation of ecosystem services across a range of regional land-use, socioeconomic, and climate scenarios for the upper Merrimack River watershed, New Hampshire, USA

We evaluate the relative desirability of alternative futures for the upper Merrimack River watershed in New Hampshire, USA based on the value of ecosystem services at the end of the 21st century as gauged by its present-day inhabitants. This evaluation is accomplished by integrating land-use and socioeconomic scenarios, downscaled climate projections, biogeophysical simulation models, and the results of a citizen-stakeholder deliberative multicriteria evaluation. We find that although there are some trade-offs between alternative plausible futures, for the most part, it can be expected that future inhabitants of the watershed will be most satisfied if land-use planning in the intervening years prioritizes water supply and flood protection as well as maintenance of existing farmland and forest cover. With respect to climate change, it is expected that future watershed inhabitants will be more negatively affected by the projected loss of snow cover than the anticipated increase in hot summer days. More important than the specific results for the upper Merrimack River watershed, this integrative assessment demonstrates the complex yet ultimately informative potential to link stakeholder engagement with scenario generation, ecosystem models, and multiattribute evaluation for informing regional-scale planning and decision making