RESUMEN
Streamflow-duration assessment methods (SDAMs) are rapid, indicator-based tools for classifying streamflow duration (e.g., intermittent vs perennial flow) at the reach scale. Indicators are easily assessed stream properties used as surrogates of flow duration, which is too resource intensive to measure directly for many reaches. Invertebrates are commonly used as SDAM indicators because many are not highly mobile, and different species have life stages that require flow for different durations and times of the year. The objectives of this study were to 1) identify invertebrate taxa that can be used as SDAM indicators to distinguish between stream reaches having intermittent and perennial flow, 2) to compare indicator strength across different taxonomic and numeric resolutions, and 3) to assess the relative importance of season and habitat type on the ability of invertebrates to predict streamflow-duration class. We used 2 methods, random forest models and indicator species analysis, to analyze aquatic and terrestrial invertebrate data (presence/absence, density, and biomass) at the family and genus levels from 370 samples collected from both erosional and depositional habitats during both wet and dry seasons. In total, 36 intermittent and 53 perennial reaches were sampled along 31 forested headwater streams in 4 level II ecoregions across the United States. Random forest models for family- and genus-level datasets had stream classification accuracy ranging from 88.9 to 93.2%, with slightly higher accuracy for density than for presence/absence and biomass datasets. Season (wet/dry) tended to be a stronger predictor of streamflow-duration class than habitat (erosional/depositional). Many taxa at the family (58.8%) and genus level (61.6%) were collected from both intermittent and perennial reaches, and most taxa that were exclusive to 1 streamflow-duration class were rarely collected. However, 23 family-level or higher taxa (20 aquatic and 3 terrestrial) and 44 aquatic genera were identified as potential indicators of streamflow-duration class for forested headwater streams. The utility of the potential indicators varied across level II ecoregions in part because of representation of intermittent and perennial reaches in the dataset but also because of variable ecological responses to drying among species. Aquatic invertebrates have been an important field indicator of perennial reaches in existing SDAMs, but our findings highlight how including aquatic and terrestrial invertebrates as indicators of intermittent reaches can further maximize the data collected for streamflow-duration classifications.
RESUMEN
Tabulations of numerical concentration-based environmental benchmarks are commonly used to inform decisions on managing chemical exposures. Benchmarks are usually set at levels below which there is a low likelihood of adverse effects. Given the widespread use of tables of benchmarks, it is reasonable to expect that they are adequately reliable and fit for purpose. The degree to which a derived benchmark reflects an actual effect level or statistical randomness is critically important for the reliability of a numerical benchmark value. These expectations may not be met for commonly-used benchmarks examined in this study. Computer simulations of field sampling and toxicity testing reveal that small sample size and confounding from uncontrolled factors that affect the interpretation of toxic effects contribute to uncertainties that might go unrecognized when deriving benchmarks from data sets. The simulations of field data show that it is possible to derive a benchmark even when no toxicity is present. When toxicity is explicitly included in simulations, imposed effect threshold levels could not always be accurately determined. Simulations were also used to examine the influence of mixtures of chemicals on the determination of toxicity thresholds of chemicals within the mixtures. The simulations showed that data sets that appear large and robust can contain many smaller data sets associated with specific biota or chemicals. The sub-sets of data with small sample sizes can contribute to considerable statistical uncertainty in the determination of effects thresholds and can indicate that effects are present when they are absent. The simulations also show that less toxic chemicals may appear toxic when they are present in mixtures with more toxic chemicals. Because of confounding in the assignment of toxicity to individuals chemicals within mixtures, simulations showed that derived toxicity thresholds can be less than the actual toxicity thresholds. A set of best practices is put forward to guard against the potential problems identified by this work. These include conducting an adequate process of determining and implementing Data Quality Objectives (DQOs), evaluating implications of sample size, designing appropriate sampling and evaluation programs based on this information, using an appropriate tiered evaluation strategy that considers the uncertainties, and employing a weight of evidence approach to narrow the uncertainties to manageable and identified levels. The work underscores the importance of communicating the uncertainties associated with numerical values commonly included in tables for screening and risk assessment purposes to better inform decisions.