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USGS Data Series 423
National Water-Quality Assessment Program
During collection, processing, laboratory analysis, or data analysis, biological samples may have been deemed unusable because of errors in collection, documentation, preservation, or other unforeseen circumstances. These samples were removed for the biological group in question and noted with ND (no data) or NC (not calculated) for the metrics in the biological files. Prior to analysis, biological datasets were examined for errors and corrected for taxonomic ambiguities. All biological data files are organized and listed in table 4 (algal data, tables 4-A through 4-AC), table 5 (macroinvertebrate data, tables 5-A through 5-AE), and table 6 (fish data, tables 6-A through 6-C).
Taxonomic ambiguities arise when organisms from a particular sample or group of samples are not identified to the same taxonomic level. For example, an ambiguity occurs in a sample if some organisms are identified to genus (for example, Hydropsyche sp.) and some organisms are identified to species within the genus (for example, H. sparna, H. betteni). In this case, sparna and betteni are children of the ambiguous parent Hydropsyche. The occurrence of taxonomic ambiguities is a problem in determining taxa richness (for example, does the example presented here represent a taxa richness of 1, 2, or 3?) or in comparing the taxonomic composition of one or more samples by using techniques such as ordinations, cluster analysis, similarity indices, or diversity indices.
Ambiguities in the invertebrate data were resolved by using software specifically developed for use in the NAWQA Program—Invertebrate Data Analysis System (IDAS, version 3.9.5; Cuffney, 2003). Ambiguities in the RTH invertebrate samples were resolved by distributing the abundance of ambiguous parents among their children according to the relative abundance of each child (the distribute parents among children (DPAC) method, Cuffney and others, 2007). To create a comprehensive list of taxa present at each site, a qualitative richness dataset (QQ) was created for invertebrates and algae. This dataset consisted of a combination of all taxa found in the RTH and QMH samples for invertebrates and RTH and DTH samples for algae. Ambiguities in the QMH and QQ samples were handled by deleting the ambiguous parents (the remove parent keep child (RPKC) method, Cuffney and others, 2007) since the taxonomic information carried by ambiguous parents already resides in the children. Algal and fish data were identified consistently to species level. Consequently, there was very little ambiguity in these data. A very small number of individual fish were identified to a higher taxonomic level, and these fish were eliminated from the analysis.
Algal response at the study level was characterized in two ways. Ordination of algal data, in a multi-dimensional scaling (MDS) analysis, was used to derive site scores for the first and second axes that characterized overall assemblage structure. Algal metrics were used to characterize attributes of algal structure and function, and included composition, diversity, salinity indicators, trophic status indicators, pollution tolerance, oxygen tolerance, organic nitrogen index, pH preference, saprobity preference, motility of taxa, and biomass. These metrics are described in Porter (2008) as a comprehensive set of indicators for various changes that could occur with urbanization across the study areas. The metrics were calculated by using the Algal Data Analysis System (ADAS, version 2.4.5; T.F. Cuffney, U.S. Geological Survey, written commun., 2009). The ADAS program is a modification of IDAS for use with algae. The calculated metrics and community data for the algal samples can be found in tables 4-A through 4-AC.
Principal components analysis (PCA) was used to characterize environmental index variables associated with habitat, water chemistry, census, and a variety of watershed- and riparian-scale landscape characteristics (tables 7-A through 7-D). Spearman correlation analysis was used to determine the relative associations between algal response variables (the MDS site scores and algal metrics) and the MA-UII and between algal response variables and the environmental factors at both the watershed and the stream-reach scales (Coles and others, 2009).
Calculated metrics and community data for invertebrate RTH, QMH, and QQ samples are summarized in tables 5-A through 5-AE. Several measures of invertebrate response to urbanization were calculated, including metrics and ordination-based site scores. Metrics are individual variables or combinations of variables that emphasize specific characteristics of the assemblage and are used commonly in bioassessments to reduce the complex site-by-species matrix to a few variables that are thought to have significance ecologically and(or) are indicative of water-quality changes (Barbour and others, 1999). Metrics calculated from the invertebrate and algal data were based on measures of abundance, richness, functional groups, tolerance, and indices of diversity. Invertebrate traits used in the calculation of metrics are from Barbour and others (1999) and the North Carolina Department of Environment and Natural Resources (2001).
Assessment of invertebrates responses to urban intensity was based on regressions of nonparametric multi-dimensional scaling (nMDS) ordination scores against urban intensity (MA-NUII and NUII) or correlations (Spearman rank) between urban intensity (MA-NUII) and assemblage metrics (T.F. Cuffney, U.S. Geological Survey, written commun., 2009; tables 8-A through 8-C). The association between environmental variables and invertebrate responses were determined by correlating (Spearman rank) the MDS ordination site scores with environmental variables and urban intensity (tables 8-D through 8-F) using a subset of environmental variables selected for the invertebrate sampling sites. The correspondence between MDS and correspondence analysis (CA) ordination scores were also investigated to support the use of MDS instead of the more commonly used CA (table 8-G, 25 KB).
Fish responses to urban intensity were based on correlations of MDS scores, species richness, and ecological species traits with urban intensity (MA-NUII; Brown and others, 2009). MDS scores were obtained from nonmetric MDS analyses of species abundances. All calculated metrics and community data for fish are summarized in tables 6-A through 6-C. Species richness is the total number of taxa collected at a site. Each of 27 fish traits (Goldstein and Meador, 2004) was scored for each species collected at a sampling site. The traits are divided into six general categories: (1) substrate preference (bedrock, boulder, cobble–rubble, gravel, sand, mud, vegetation, and variable); (2) geomorphic preference (pool, riffle, run, backwater, and variable); (3) trophic ecology (herbivores, detritivores, planktivores, invertivores, and carnivores); (4) locomotion morphology (cruisers, accelerators, maneuverers, benthic high-velocity huggers, benthic low-velocity creepers, and specialists); (5) reproductive strategy (migratory, broadcaster, simple nester, complex nester–guarder, and bearer); and (6) stream-size preference (small streams, small rivers, medium rivers, and large rivers). Each trait then was weighted by the number of individuals with the trait at each site, and the relative abundance was determined for the trait at each site.
Associations between environmental variables, including urban intensity, and fish metrics and traits were examined with Spearman rank correlations. These correlations are arranged by metropolitan study area in tables 9-A through 9-I.
Associations among all urban, hydrology, habitat, stream chemistry, algae, invertebrate, and fish variables also were examined with Spearman rank correlations. These correlations are reported, by metropolitan study area, in table 10.