R.V. O'Neill’s research while affiliated with Oak Ridge National Laboratory and other places

Estimates of annual streamflow in connection with key natural and anthropogenic factors are necessary and important for different purposes, such as water resource planning and management, sediment and nutrient loading in streams and rivers, hydropower, and navigation. This study is an attempt to use the spatial statistical regression approach to develop regression models for mean annual streamflow at regional scale while adequately dealing with the common spatial dependency issue in input and output variables used in regression models. The proposed modeling approach is illustrated with a case study of the Upper Mississippi River Basin. The R-squared and the Nash–Sutcliffe model efficiency coefficient of the regional model were 0.993 and 0.985, respectively, while those of the sub-regional model were 0.995 and 0.990, respectively. Methodologically, the proposed model provided an effective way to utilize an extensive spatial dataset of various climatic, geomorphologic, and land cover variables for a large region like the Upper Mississippi River Basin to assess and compare the impact of various factors on mean annual streamflow at regional scale. Furthermore, the model was able to handle spatial dependency in data.

In this exploratory paper, we first make a case for considering the scarcity value of ecosystem services in the analyses of jointly determined ecological–economic systems. Next, we point out that insight into the scarcity value of an ecosystem service can be gained generally by examining the manner in which the state of an ecosystem responds to changes in environmental conditions. Following this, we specialize our discussion to the case of eutrophication in lakes. This leads us to pose and analyze a stochastic control problem of lake management in which ecological thresholds are salient. Finally, we show that this stochastic control theoretic framework can be used to obtain a numerical value that is closely related to the scarcity value of an ecosystem service provided by lakes.

We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems.

The paper presents a method for environmental vulnerability assessment with a case study of the Mid-Atlantic region. The method is based on the concept of “self-/peer-appraisal” of a watershed in term of vulnerability. The self-/peer-appraisal process is facilitated by two separate linear optimization programs. The analysis provided insights on the environmental conditions, in general, and the relative vulnerability pattern, in particular, of the Mid-Atlantic region. The suggested method offers a simple but effective and objective way to perform a regional environmental vulnerability assessment. Consequently the method can be used in various steps in environmental assessment and planning.

Density-independent and density-dependent Leslie models were investigated by Monte Carlo methods. Random values for parameters of striped bass (Morone saxatilis), white perch (Morone americanus), and tomcod (Microgadus tomcod) populations were selected from truncated normal distributions with standard deviations equal to 10% of the mean. Only total population size after 40 yr was considered. The error propagation properties of the density-independent models are strongly influenced by model assumptions (e.g. calculating egg to 1-yr-old survival to ensure an eigenvalue of 1.0) and by the way model parameters are estimated (e.g. reestimated from data each year). Prediction errors on total population size depend on the number of age-classes in a species, but become insensitive when the number of classes exceed 7. Under the very restrictive assumptions used here, there is little difference in the error propagating properties of alternative density-dependent models.Key words: matrix, population, striped bass, white perch, tomcod

Tropical deforestation often produces landscapes characterized by isolated patches of forest habitat surrounded by pasture, agriculture, or regrowth vegetation. Both the size and the distribution of these forest patches may influence the long-term persistence of faunal species. There is, therefore, a pressing need to understand faunal responses to patterns of forest fragmentation in tropical systems. The Biological Dynamics of Forest Fragments Project (BDFFP) provides a wealth of autecological information and spatially explicit data describing habitat use and movement of fauna between Amazonian forest fragments. Using data from the BDFFP and other studies in the Amazon Basin, this paper reviews the information available on tropical insects, frogs, birds, primates, and other mammals that can be used to identify and classify species most at risk for extirpation in fragmented forests.Key words: Amazonia, habitat fragmentation, rainforest, fauna, Biological Dynamics of Forest Fragmentation Project.

The paper presents a comprehensive analysis to explore the environmental vulnerability pattern of the Mid-Atlantic region. It is a combination of several methods – stressor–resource overlay, state-space analysis, and clustering analysis – at different steps of the analysis. In addition, a generalized distance measure was utilized in the state-space analysis to handle the interdependency among variables without reducing their dimensionality. Results from the analysis provided valuable insights on the environmental conditions, in general, and the relative vulnerability pattern, in particular, of the Mid-Atlantic region. The suggested method offers a simple but effective way to understand the complex spatial pattern of environmental vulnerability at regional scale.

The paper presents a method to determine the most influencing stressors and the most susceptible resources for complex assessment problems involving multiple stressors impacting multiple resources over a region. The method is based on the concept of limiting priorities in a square matrix which capture the transmission of influence along all paths between stressors and resources in the matrix. The proposed method allows the relationship between stressors and resources to be looked at in both univariate and multivariate fashion, taking into account the interactions among the variables. Hypothetical and case study examples are given for illustration purpose. It shows that the proposed method is suitable for the determination of the most important stressors and the most susceptible resources, a common (but often uneasy) task in integrated environmental assessment.

Environmental integrated assessments are often carried out via the aggregation of a set of environmental indicators. Aggregated indices derived from the same data set can differ substantially depending upon how the indicators are weighted and aggregated, which is often a subjective matter. This article presents a method of generating aggregated environmental indices in an objective manner via Monte Carlo simulation. Rankings derived from the aggregated indices within and between three Monte Carlo simulations were used to evaluate the overall environmental condition of the study area. Other insights, such as the distribution of good or bad values of indicators at a watershed and/or a subregion, were observed in the study.

Environmental indicators are often aggregated into a single index in environmental studies. Commonly, an aggregated index is derived in a specific weighting scheme imposed from the outside. The paper presents a novel approach by letting each unit under study choose a set of weights. It applies the concept of self- and cross-appraisal in generating various aggregated indices from two linear programming optimization models. The proposed method is illustrated via a case study of the Mid-Atlantic region. Results show that the derived aggregated indices reveal environmental conditions of the study area in an objective and robust fashion. The proposed method is a valuable tool for integrated environmental assessment.