Donald F. Charles

Dam Removal: Challenges and Opportunities for Ecological Research and River Restoration

W flow is a “master variable” (sensu Power et al. 1995) that governs the fundamental nature of streams and rivers (Poff et al. 1997, Hart and Finelli 1999), so it should come as no surprise that the modification of flow caused by dams alters the structure and function of river ecosystems. Much has been learned during the last several decades about the adverse effects of dams on the physical, chemical, and biological characteristics of rivers (Ward and Stanford 1979, Petts 1984, Poff et al. 1997, Poff and Hart 2002). Increasing concerns about these impacts, together with related social and economic forces, have led to a growing call for the restoration of rivers by removing dams (AR/FE/TU 1999, Pejchar and Warner 2001). For the purposes of this paper, we define restoration broadly as an effort to compensate for the negative effects of human activities on ecological systems by facilitating the establishment of natural components and regenerative processes, although we acknowledge that these efforts rarely eliminate all human impacts (see Williams et al. 1997 for alternative definitions). Interest in dam removal as a means of river restoration has focused attention on important new challenges for watershed management and simultaneously created opportunities for advancing the science of ecology. One challenge lies in determining the magnitude, timing, and range of physical, chemical, and biological responses that can be expected following dam removal. This information is needed to decide whether and how dam removals should be performed to achieve specific restoration objectives (Babbitt 2002). Opportunities for advancing ecological research also exist because dam removal represents a major, but partially controllable, perturbation that can help scientists test and refine models of complex ecosystems. In contrast to the small-scale experiments that traditionally have been employed in stream and river ecology, the unusually large magnitude and spatial extent of dam removal WE DEVELOP A RISK ASSESSMENT FRAME-

Quantifying species indicator values for trophic diatom indices : a comparison of approaches

This study compares two approaches for constructing diatom-based indices for monitoring river eutrophication. The first approach is based on weighted averaging of species indicator values with the underlying assumption that species have symmetrical unimodal distributions along the nutrient gradient, and their distributions are sufficiently described by a single indicator value per species. The second approach uses multiple indicator values for individual taxa and is based on the possibility that species have complex asymmetrical response curves. Multiple indicator values represent relative probabilities that a species would be found within certain ranges of nutrient concentration. We used 155 benthic diatom samples collected from rivers in the Northern Piedmont ecoregion (Northeastern U.S.A.) to construct two datasets: one used for developing models and indices, and another for testing them. To characterize the shape of species response curves we analyzed changes in the relative abundance of 118 diatom taxa common in this dataset along the total phosphorus (TP) gradient by fitting parametric and non-parametric regression models. We found that only 34 diatoms had symmetrical unimodal response to TP. Among several indices that use a single indicator value for each species, the best was the weighted averaging partial least square (WA-PLS) inference model. The correlation coefficient between observed and inferred TP in the test dataset was 0.67. The best index that employed multiple indicator values for each species had approximately the same predictive power as the WA-PLS based index, but in addition, this index provided a sample-specific measure of uncertainty for the TP estimation.

Choice of substrate in algae-based water-quality assessment

Abstract Our study investigated whether algae-based water-quality assessments are affected by differences between algal assemblages on hard substrates (rocks, wood) and soft substrates (fine-grained sediments). We analyzed a US Geological Survey National Water-Quality Assessment (NAWQA) program data set that consisted of 1048 pairs of samples collected from hard and soft substrates at 551 river sampling locations throughout the US. Biovolume and diversity of algal assemblages, biovolume of major taxonomic groups, and abundance of motile diatoms differed significantly between samples collected from hard and soft substrates at the same sites. Ordinations of assemblages from hard and soft substrates were highly concordant and provided similar information on environmental gradients underlying species patterns. The strengths of relationships between composition of algal assemblages and water chemistry parameters (conductivity, pH, total P, and total N) did not differ consistently between substrate types. Performance of weighted averaging (WA) inference models did not differ between models based on assemblages from hard and soft substrates. Moreover, the predictive power of inference models developed from single-substrate data sets was not reduced when these models were applied to samples collected from other substrates. We concluded that the choice of substrate to sample should depend on the assessment indicators to be used. If indicators based on the autecologies of many algal taxa (e.g., inference models or autecological indices) are used, restricting samples to a single type of substrate is unnecessary. If algal diversity, total algal biovolume, or abundance of specific algal taxa is used, samples should be collected from a single type of substrate.

Effects of removal of a small dam on downstream macroinvertebrate and algal assemblages in a Pennsylvania stream

Abstract Dam removal is often proposed as way to restore ecological integrity to rivers and streams, but ecological responses to dam removals are poorly understood, especially for downstream benthic communities. We examined the responses of benthic macroinvertebrate and algal assemblages in downstream reaches to the removal of a small, run-of-river dam on Manatawny Creek, Pennsylvania. Benthic macroinvertebrates, algae, and habitat characteristics were monitored upstream and downstream of the dam for 4 mo before removal, 3 mo after partial removal (i.e., when the impoundment was largely eliminated but sediment remained trapped behind the remaining structure), and 12 mo after complete dam removal. Macroinvertebrate density, algal biomass, and diatom species richness declined significantly downstream of the dam following complete dam removal, but overall assemblage structure (as indicated by Nonmetric Multidimensional Scaling ordinations) downstream remained similar to upstream control sites throughout the study for both invertebrates and diatoms. Downstream impacts occurred only after the dam structure had been completely removed and sediments had been transported downstream from the former impoundment by high flows. Biotic impacts persisted for the duration of the study (12 mo after complete removal). Our results and other studies of dam removal suggest that downstream sedimentation following dam removal can reduce densities of macroinvertebrates and benthic algae and may reduce benthic diversity, but for small dams such impacts may be relatively minor and will usually be temporary.

Historical Trends in Atmospheric Sulfur Deposition and Methods for Assessing Long-Term Trends in Surface Water Chemistry

This chapter presents historical sulfur emission and deposition trends for regions in the United States and describes methods for assessing changes in water chemistry based on current spatial patterns, ion ratios and empirical models, and paleolimnological approaches. Reconstruction of sulfur deposition trends shows that current deposition to case study regions ranges from a factor of about 1 (Upper Midwest) to a factor of 10 (Catskills) above natural background. Deposition in the Northeast was high during the 1920s, 1940s, and 1960s, and has declined significantly since 1970. Sulfur deposition in the Southeast was low before the 1950s, but has increased significantly since then. Change in surface water chemistry can be assessed using simple empirical models, ion ratios, and analysis of current spatial patterns of chemistry. Many assumptions are implicit in these methods, so results should be interpreted carefully. Paleolimnological reconstructions of chemistry and biota from lake sediment records provide more direct evidence of past change than other approaches. Quantitative analyses of diatom and chrysophyte assemblages can be used to reconstruct past lakewater pH with a mean standard error of about ± 0.25 pH units.

Influence of organic acids on model projections of lake acidification

We employed three mathematical models to make quantitative estimates of the pH of 33 statistically-selected lakes in the Adirondack mountains, New York (USA) prior to the Industrial Revolution (1840). The models included 1) the MAGIC watershed acidification model, 2) a paleolimnological model of diatom-inferred pH, and 3) the MAGIC model modified to incorporate an empirically-based model of natural organic acidity. Application of approaches 2) and 3) yielded consistent estimates of pre-industrial Adirondack lakewater pH. However, when the organic acid model was not included, MAGIC calculations and diatom-inferred values showed poor agreement. MAGIC projections of lakewater pH 50 years into the future, under differing atmospheric deposition scenarios, were also sensitive to inclusion of the organic acid model. MAGIC predicted greater recovery in response to reduced deposition when organic acids were not considered. These results suggest that failure to consider the pH buffering of naturally-occurring organic acidity will often result in biased projections which overemphasize the response of lakewater pH to changes in atmospheric inputs of strong acid.

Large-scale regional variation in diatom-water chemistry relationships: rivers of the eastern United States

We analyzed diatom and water chemistry data collected by The Academy of Natural Sciences from 47 rivers throughout the eastern United States to address several ecological questions. How does the composition of diatom assemblages vary over large regional scales? What are the most important environmental factors affecting assemblage composition and how does their influence vary among regions and with spatial scale? How do distributions and autecological characteristics of individual taxa vary spatially? What are the implications of answers to these questions for use of diatoms as water quality indicators? Data for 186 samples at 116 sites were collected from 1951 to 1991 onmoderate- to large-sized rivers ranging fromMaine to Texas as part of Academy monitoring and survey programs, most initiated and implemented by Dr. Ruth Patrick. Several sites were highly impaired by point and non-point source pollution. Diatomassemblages grouped into four main categories, based on multivariate analyses. Group membership correlated equally well with intermediate-scale geographic regions and water chemistry: (1) Northeastern US rivers with lower alkalinity and hardness, and pH 6.5–7.8; (2) Primarily dilute coastal plain rivers in the southeastern United States with the lowest average pH (5.5–7.3) of all sites and some with high DOC; (3) Rivers within and west of the AppalachianMountains, generally having higherpH(>7.5) than those in other regions, but with relatively low chloride concentrations; and (4) Gulf Coast rivers with the highest chloride (>100 mg 1−1), hardness (>250 mg 1−1), and pH of rivers in all the groups. Hardness, pH, alkalinity, and Cl explained most of the variation among diatom assemblages, based on ordination analysis. Factors related to water quality problems, such as BOD, P, NH4, and turbidity explained much less variability at the eastern US scale, but were more important in the four intermediate-scale regions. Diatom taxa abundance-weighted mean values for water chemistry characteristics varied among the four intermediate-scale regions, often greatly, and in proportion to the average measured values for each region. Design of calibration data sets for development of water quality indicators should account for spatial scale in relation to species dispersal, regional geochemistry and habitat types, and human-influenced water chemistry characteristics.

Total phosphorus inference models and indices for coastal plain streams based on benthic diatom assemblages from artificial substrates

We investigated the potential for using diatoms to monitor and assess nutrient enrichment in coastal plain streams using weighted-averaging inference models and diatom trophic indices. Samples were collected from low-gradient, clay- to sand-bottom streams in New Jersey (NJ), USA, using artificial substrates (diatometers). Multivariate analysis showed that conductivity was overall the most important explanatory variable. Total phosphorus (TP) explained a significant proportion of the variation in diatom species composition. There was statistical justification for development of inference models for TP but not for total nitrogen (TN). We developed and tested models for inferring TP using weighted-averaging (WA) and weighted-averaging partial least squares (WA-PLS) regression and calibration techniques. We also created a diatom TP index by rescaling the inferred TP values. WA-PLS provided the best model (nxa0=xa038), which showed moderate predictive ability (rboot2xa0=xa00.43; RMSEPbootxa0=xa00.30 log10xa0μgxa0l−1 TP); it performed best at lower TP concentrations and tended to underestimate values above 100xa0μg l−1. The TP index performed well; it assigned the majority of the index scores to the correct nutrient category. TP models and indices developed for the Coastal Plain had lower predictive ability than those developed for northern NJ and streams in other comparable geographic regions of the US. This lower performance can be attributed primarily to a data gap in the TP gradient in the calibration dataset (lack of sites with TP concentrations between 240 and 560xa0μgxa0l−1), and a smaller number of samples. We conclude that diatom-based TP inference models and artificial substrate sampling are useful for assessing and monitoring nutrient enrichment in coastal plain streams. Given the worldwide distribution of streams similar to those in this study, these tools should be widely applicable.

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.

Relationship Between Landscape Characteristics, History, and Lakewater Acidification in the Adirondack Mountains, New York

Interactions between acidic deposition and watershed characteristics were evaluated for a group of lakes in the Adirondack Mountains, New York. Landscape characteristics were compiled and examined relative to paleolimnological inferences of historical acidification. Results of estimates of acidification using the Model of Acidification of Groundwater in Catchments (MAGIC) and paleolimnological analysis were compared to physical, biological, and landscape change data, including such factors as watershed disturbance, logging, fire, and windthrow, to evaluate if inclusion of additional processes could improve model estimates. Results of bivariate and multivariate analysis confirmed that lakes that have experienced historical acidification tend to be those that receive relatively high amounts of precipitation and have short hydraulic residence times. These variables explained 58% of the diatom-inferred acidification. A combined model of long-term precipitation amount, hydraulic residence time, and recent blowdown accounted for 71% of the historic acidification in the Adirondacks. Lakes that have increased in pH since pre-industrial times tend to be those subject to substantial human disturbance and those that burned during major fires recorded after 1900. The magnitude of the discrepancy between MAGIC model and diatom-inferred hindcasts of acidification was not significantly correlated with any of the landscape change variables, suggesting that additional modifications to the MAGIC model to take into account landscape change are not likely to appreciably improve model performance.