A diatom-based biological condition gradient (BCG) approach for assessing impairment and developing nutrient criteria for streams.

Over-enrichment leading to excess algal growth is a major problem in rivers and streams. Regulations to protect streams typically incorporate nutrient criteria, concentrations of phosphorus and nitrogen that should not be exceeded in order to protect biological communities. A major challenge has been to develop an approach for both categorizing streams based on their biological conditions and determining scientifically defensible nutrient criteria to protect the biotic integrity of streams in those categories. To address this challenge, we applied the Biological Condition Gradient (BCG) approach to stream diatom assemblages to develop a system for categorizing sites by level of impairment, and then examined the related nutrient concentrations to identify potential nutrient criteria. The six levels of the BCG represent a range of ecological conditions from natural (1) to highly disturbed (6). A group of diatom experts developed a set of rules and a model to assign sites to these levels based on their diatom assemblages. To identify potential numeric nutrient criteria, we explored the relation of assigned BCG levels to nutrient concentrations, other anthropogenic stressors, and possible confounding variables using data for stream sites in New Jersey (n=42) and in surrounding Mid-Atlantic states, USA (n=1443). In both data sets, BCG levels correlated most strongly with total phosphorus and the percentage of forest in the watershed, but were independent of pH. We applied Threshold Indicator Taxa Analysis (TITAN) to determine change-points in the diatom assemblages along the BCG gradient. In both data sets, statistically significant diatom changes occurred between BCG levels 3 and 4. Sites with BCG levels 1 to 3 were dominated by species that grow attached to surfaces, while sites with BCG scores of 4 and above were characterized by motile diatoms. The diatom change-point corresponded with a total phosphorus concentration of about 50µg/L.

Source apportionment of polychlorinated biphenyls in the tidal Delaware River.

The Delaware River, similar to many surface water bodies throughout the United States, is impaired due to polychlorinated biphenyl (PCB) concentrations that exceed the federal water quality standard. A total maximum daily load (TMDL) for PCBs in the Delaware was promulgated in 2003 after construction of a detailed water quality model that relied upon estimated PCB loadings from sources such as wastewater treatment plant effluents, contaminated sites, and tributaries. The purpose of this project was to corroborate this loading estimate by analyzing ambient water column data on PCB concentrations in the Delaware River via positive matrix factorization (PMF) in order to identify covarying congener patterns that are, in theory, associated with sources. The PMF program identified six factors (covarying congener patterns) that appear to be associated with sources, such as sediment resuspension, contaminated sites, and wastewater effluents and combined sewer overflows (CSOs). PCB 11 was found to be a useful tracer for CSO/ wastewater inputs despite the fact that no known dye manufacturers exist in the watershed. This analysis generally corroborates the PCB loading estimate used in the water quality model.

Managing troubled data: coastal data partnerships smooth data integration.

Understanding the ecology, condition, and changes of coastal areas requires data from many sources. Broad-scale and long-term ecological questions, such as global climate change, biodiversity, and cumulative impacts of human activities, must be addressed with databases that integrate data from several different research and monitoring programs. Various barriers, including widely differing data formats, codes, directories, systems, and metadata used by individual programs, make such integration troublesome. Coastal data partnerships, by helping overcome technical, social, and organizational barriers, can lead to a better understanding of environmental issues, and may enable better management decisions. Characteristics of successful data partnerships include a common need for shared data, strong collaborative leadership, committed partners willing to invest in the partnership, and clear agreements on data standards and data policy. Emerging data and metadata standards that become widely accepted are crucial. New information technology is making it easier to exchange and integrate data. Data partnerships allow us to create broader databases than would be possible for any one organization to create by itself.