Assessing the Effects of Natural and Anthropogenic Stressors in the Potomac Estuary: Implications for Long-Term Monitoring

Ecological conditions in the Potomac Estuary are affected by a variety of natural and anthropogenic stressors. Natural climatic factors combined with anthropogenic activities affect fluxes of material through Potomac River watersheds and cause changes in ecological conditions in the Potomac Estuary. A basic premise of this ongoing study is that effects of anthropogenic and natural stressors can be distinguished. The investigation involves: 1) analysis of existing data using time series methods, 2) retrospective modeling to link the response of estuarine water quality to changes in stressors, and 3) new measurements on sediment cores from the Potomac Estuary. Estuarine effects being considered include changes in the distribution and abundance of chlorophyll a, diatoms, dinoflagellates, ostracods, submerged aquatic vegetation, benthic fauna, dissolved oxygen, and foraminifera. Since current conditions may be due to the accumulation of effects over many years, our research considers variability and changes during the past century in the context of long-term changes during the past 500 years. The availability of large data sets from the past century, long-term information on variability in precipitation from tree ring data from the past 300 years, and paleoecological studies by other investigators in the Potomac Estuary and main stem of Chesapeake Bay make the Potomac Estuary an ideal place to develop methods to distinguish between effects of natural and anthropogenic stressors in estuaries, in the context of a varying, and perhaps changing, climate.     

State of the Estuaries in the Mid-Atlantic Region of the United States

The U.S. EPA has prepared a State of the Region Report for Mid-Atlantic Estuaries to increase knowledge of environmental condition for improved environmental management. Sources of information included the National Estuary Programs, the Chesapeake Bay Program, the state monitoring programs in Delaware, Maryland, and Virginia, Federal programs such as National Status & Trends, National Shellfish Register, National Wetlands Inventory, the Environmental Monitoring and Assessment Program, and other primary literature sources. The state of the estuarine environment was summarized using indicators for water and sediment quality, habitat change, condition of living resources, and aesthetic quality. Each indicator was briefly discussed relative to its importance in understanding estuarine condition. Wherever possible, data from multiple programs were used to depict condition. Finally, an overall evaluation of estuarine condition in the region was determined. The usefulness of monitoring programs that collect consistent information with a well-defined sampling design cannot be overemphasized.     

Can entropy maximization use functional traits to explain species abundances? A comprehensive evaluation

Entropy maximization (EM) is a method that can link functional traits and community composition by predicting relative abundances of each species in a community using limited trait information. We developed a complementary suite of tests to examine the strengths and limitations of EM and the community-aggregated traits (CATs; i.e., weighted averages) on which it depends that can be applied to virtually any plant community data set. We show that suites of CATs can be used to differentiate communities and that EM can address the classic problem of characterizing ecological niches by quantifying constraints (CATs) on complex trait relationships in local communities. EM outperformed null models and comparable regression models in communities with different levels of dominance, diversity, and trait similarity. EM predicted well the abundance of the dominant species that drive community-level traits; it typically identified rarer species as such, although it struggled to predict the abundances of the rarest species in some cases. Predictions were sensitive to choice of traits, were substantially improved by using informative priors based on null models, and were robust to variation in trait measurement due to intraspecific variability or measurement error. We demonstrate how similarity in species' traits confounds predictions and provide guidelines for applying EM.     

WATER QUALITY IN THE PAWTUXET RIVER: METAL MONITORING AND GEOCHEMISTRY1

ABSTRACT: The Pawtuxet River flows from a relatively rural area through some of the more highly industrialized sections of Rhode Island. During its journey, the river receives many municipal, industrial, and ground water sources of metal constituents. The present report is the first in a two part series in which the water quality of this urban river was evaluated by a chemical monitoring study of the sources, transport mechanisms, and fate of cadmium, chromium, copper, lead, and nickel in the river. The second paper will use the chemical data to derive and calibrate a steady-state water quality model for this river. The metal concentrations In the river tended to increase from the headwaters to the mouth with river stations nearest to point source outfalls showing elevated values. In some sections of the river, levels of a few of the metals could not be explained by the point sources; and other inputs, including sediment resuspension, axe proposed to make up this apparent unbalance. The ability of a municipal secondary treatment plant to remove metals was demonstrated, and the tie-in of the effluent from a major chemical company to the plant did not cause any observable deterioration in treatment efficiency.     

Building statistical models to analyze species distributions.

Models of the geographic distributions of species have wide application in ecology. But the nonspatial, single-level, regression models that ecologists have often employed do not deal with problems of irregular sampling intensity or spatial dependence, and do not adequately quantify uncertainty. We show here how to build statistical models that can handle these features of spatial prediction and provide richer, more powerful inference about species niche relations, distributions, and the effects of human disturbance. We begin with a familiar generalized linear model and build in additional features, including spatial random effects and hierarchical levels. Since these models are fully specified statistical models, we show that it is possible to add complexity without sacrificing interpretability. This step-by-step approach, together with attached code that implements a simple, spatially explicit, regression model, is structured to facilitate self-teaching. All models are developed in a Bayesian framework. We assess the performance of the models by using them to predict the distributions of two plant species (Proteaceae) from South Africa's Cape Floristic Region. We demonstrate that making distribution models spatially explicit can be essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Adding hierarchical levels to the models has further advantages in allowing human transformation of the landscape to be taken into account, as well as additional features of the sampling process.     

Catalyzing success in community-based conservation

Efforts to devolve rights and engage Indigenous Peoples and local communities in conservation have increased the demand for evidence of the efficacy of community-based conservation (CBC) and insights into what enables its success. We examined the human well-being and environmental outcomes of a diverse set of 128 CBC projects. Over 80% of CBC projects had some positive human well-being or environmental outcomes, although just 32% achieved positive outcomes for both (i.e., combined success). We coded 57 total national-, community-, and project-level variables and controls from this set, performed random forest classification to identify the variables most important to combined success, and calculated accumulated local effects to describe their individual influence on the probability of achieving it. The best predictors of combined success were 17 variables suggestive of various recommendations and opportunities for conservation practitioners related to national contexts, community characteristics, and the implementation of various strategies and interventions informed by existing CBC frameworks. Specifically, CBC projects had higher probabilities of combined success when they occurred in national contexts supportive of local governance, confronted challenges to collective action, promoted economic diversification, and invested in various capacity-building efforts. Our results provide important insights into how to encourage greater success in CBC.     

Protocol Development for Assessing Arsenic Background Concentrations in Florida Urban Soils

Knowledge of arsenic background concentrations in urban soils is important for making remediation decisions. The soil cleanup target level (SCTL) for arsenic in Florida lies within the range of arsenic background concentrations. The residential SCTL is also near the practical quantification limits using analytical procedures. Currently no standard protocols are available for determining arsenic background concentrations in urban soils, apart from site-specific cases. Therefore, a pilot study was conducted to develop and employ appropriate protocols to determine arsenic distribution in urban soils. This involved: site selection (e.g. size and sampling frame), sample collection (e.g. sampling technique), and statistical considerations (e.g. design). Factors such as ease of sample collection and maintaining anonymity of private properties were also considered as they influence the successful implementation of the study. Forty surface soil samples each were collected from five categories in three land use classes (residential-yard and right-of-way, commercial and public land-parks and public building), digested using EPA method 3051a and analysed using graphite furnace atomic absorption spectrometry. Experiences from the pilot study (e.g. complications during sample selection, digestion, data censoring etc.) were used in the development of the final protocol to be used in determining the distribution of arsenic in urban areas.