Numerical sediment quality guidelines (SQGs) for freshwater ecosystems have previously been developed using a variety of approaches. Each approach has certain advantages and limitations which influence their application in the sediment quality assessment process. In an effort to focus on the agreement among these various published SQGs, consensus-based SQGs were developed for 28 chemicals of concern in freshwater sediments (i.e., metals, polycyclic aromatic hydrocarbons, polychlorinated biphenyls, and pesticides). For each contaminant of concern, two SQGs were developed from the published SQGs, including a threshold effect concentration (TEC) and a probable effect concentration (PEC). The resultant SQGs for each chemical were evaluated for reliability using matching sediment chemistry and toxicity data from field studies conducted throughout the United States. The results of this evaluation indicated that most of the TECs (i.e., 21 of 28) provide an accurate basis for predicting the absence of sediment toxicity. Similarly, most of the PECs (i.e., 16 of 28) provide an accurate basis for predicting sediment toxicity. Mean PEC quotients were calculated to evaluate the combined effects of multiple contaminants in sediment. Results of the evaluation indicate that the incidence of toxicity is highly correlated to the mean PEC quotient (R(2) = 0.98 for 347 samples). It was concluded that the consensus-based SQGs provide a reliable basis for assessing sediment quality conditions in freshwater ecosystems.

: The weight-of-evidence approach to the development of sediment quality guidelines (SQGs) was modified to support the derivation of biological effects-based SQGs for Florida coastal waters. Numerical SQGs were derived for 34 substances, including nine trace metals, 13 individual polycyclic aromatic hydrocarbons (PAHs), three groups of PAHs, total polychlorinated biphenyls (PCBs), seven pesticides and one phthalate ester. For each substance, a threshold effects level (TEL) and a probable effects level (PEL) was calculated. These two values defined three ranges of chemical concentrations, including those that were (1) rarely, (2) occasionally or (3) frequently associated with adverse effects. The SQGs were then evaluated to determine their degree of agreement with other guidelines (an indicator of comparability) and the percent incidence of adverse effects within each concentration range (an indicator of reliability). The guidelines also were used to classify (using a dichotomous system: toxic, with one or more exceedances of the PELs or non-toxic, with no exceedances of the TELs) sediment samples collected from various locations in Florida and the Gulf of Mexico. The accuracy of these predictions was then evaluated using the results of the biological tests that were performed on the same sediment samples. The resultant SQGs were demonstrated to provide practical, reliable and predictive tools for assessing sediment quality in Florida and elsewhere in the southeastern portion of the United States.

Although per capita rates of increase (r) have been calculated by population biologists for decades, the inability to estimate uncertainty (variance) associated with r values has until recently precluded statistical comparisons of population growth rates. In this study, we used two computerintensive techniques, Jackknifing and Bootstrapping, to estimate bias, standard errors, and sampling distributions of r for real and hypothetical populations of cladocerans. Results generated using the two techniques, using data on laboratory cohorts of Daphnia pulex, were almost identical, as were results for a hypothetical D. pulex population whose sampling distribution was approximately normal. However, for another hypothetical population whose sampling distribution was negatively skewed due to high juvenile mortality, Bootstrap and full-sample estimates of r were negatively biased by 3.3 and 1.8%, respectively. A bias adjustment reduced the bias in the Bootstrap estimate and produced estimates of rand SE(r) almost identical to those of the Jackknife technique. In general, our simulations show that the Jackknife will provide more cost-effective point and interval estimates of r for cladoceran populations, except when juvenile mortality is high (at least > 25%). Coefficients of variation in the mean of r within laboratory cohorts of D. pulex were one-half to one-third the magnitude of the corresponding coel!!.cients of variation in the mean of total reproduction and in the mean day to death (range of values of cv[r] = 1.6 to 3.8%). This suggests that extremes in reproductive output and survival of individuals tend to be dampened at the population level, and that within-cohort variability in r is not explosive. Moreover, between-cohort variability in r can be much greater than within-cohort variability, as indicated by a statistically significant difference of30% (P < .01) between the high and low r values that were computed for four cohorts of D. pulex born during a 1-mo period from the same laboratory stock population. Based on variability in per capita rates of increase that have been estimated for several cladoceran species, we suggest that the precision for reporting r values should in most cases be limited to two significant figures.

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Fine-grained sediments contaminated with complex mixtures of organic and inorganic chemical contaminants can be toxic in laboratory tests and/or cause adverse impacts to resident benthic communities. Effects-based, sediment quality guidelines (SQGs) have been developed over the past 20 years to aid in the interpretation of the relationships between chemical contamination and measures of adverse biological effects. Mean sediment quality guideline quotients (mSQGQ) can be calculated by dividing the concentrations of chemicals in sediments by their respective SQGs and calculating the mean of the quotients for the individual chemicals. The resulting index provides a method of accounting for both the presence and the concentrations of multiple chemicals in sediments relative to their effects-based guidelines. Analyses of considerable amounts of data demonstrated that both the incidence and magnitude of toxicity in laboratory tests and the incidence of impairment to benthic communities increases incrementally with increasing mSQGQs. Such concentration/response relationships provide a basis for estimating toxicological risks to sediment-dwelling organisms associated with exposure to contaminated sediments with a known degree of accuracy. This sediment quality assessment tool has been used in numerous surveys and studies since 1994. Nevertheless, mean SQGQs have some important limitations and underlying assumptions that should be understood by sediment quality assessors. This paper provides an overview of the derivation methods and some of the principal advantages, assumptions, and limitations in the use of this sediment assessmenttool. Ideally, mean SQGQs should be included with other measures including results of toxicity tests and benthic community surveys to provide a weight of evidence when assessing the relative quality of contaminated sediments.