2007 NORTHEASTERN NATURALIST 14(2):225–234 Correlation Between Unionid Mussel Density and EPA Habitat-assessment Parameters Laura Nicklin1 and Michael T. Balas1,* Abstract - Freshwater mussels (Bivalvia: Unionidae) are sensitive to pollution of stream habitats. However, there has been no analysis of whether mussel density is correlated with measurements from commonly used rapid water assessment protocols. This study tested which water quality parameters are correlated with the density of freshwater mussels found in selected locations of the middle Allegheny River, PA. No correlation was found between mussel density and either temperature or chemical water quality parameters. However, there was a strong positive correlation between mussel density and the modified EPA physical habitat parameters tested. These results suggest that the physical habitat variables are a useful tool to assess the suitability of stream habitat for unionid mussels, and should help ecosystem managers make informed decisions about the maintenance or restoration of the ecosystem function that these mussels perform. Introduction Unionid freshwater mussels (Bivalvia: Unionidae; hereafter “mussels”) are pollution- and habitat-sensitive water quality indicators (Fuller 1974). Previous studies have shown that mussel density is linked to such factors as chemical pollutants (Fuller 1974, Stansbery 1970), variation in water chemistry (McMahon and Bogan 2001), and variation in the physical characteristics of streams (Strayer 1983, 1987, 1993, 1999). Several components of water chemistry influence mussel density. For example, low pH (< 5.6), high ammonia, high calcium, and low dissolved oxygen concentrations (< 3–6 ppm) all have been correlated with either the absence or reduced density of freshwater mussels (Buddensiek et al. 1993, Fuller 1974, Strayer 1993). The influence of phosphate is more equivocal; Buddensiek et al. (1993) concluded that high concentration of phosphate results in a higher mortality rate in adults, while Fuller (1974) found insufficient evidence to support any correlation between mussel density and phosphates. The density of mussels has also been correlated with several physical characteristics of streams (e.g., Aldridge et al. 1987; Buddensiek et al. 1993; Holland-Bartels 1990; Strayer 1981, 1983, 1993, 1999; Strayer and Ralley 1993). In particular, stream size and surface geology features (Strayer 1983, 1993), tidal influence (Strayer 1993), stream velocity and variation in velocity (Holland-Bartels 1990, Strayer and Ralley 1993), and sediment size (Holland-Bartels 1990) have been shown to influence 1Thiel College, 75 College Avenue, Greenville, PA 16125. *Corresponding author - mbalas@thiel.edu. 226 Northeastern Naturalist Vol. 14, No. 2 mussel density and location, but no consensus has developed regarding definitive, predictive relationships among any of these factors and mussel density in streams. There is interest in the development of a rapid assessment protocol by which experts and laypeople alike can determine the current suitability of a site for mussels, or understand the parameters required for mussels if a habitat improvement plan is to be implemented (Nightingale et al. 2004). Freshwater mussels perform important ecosystem functions such as consuming phytoplankton, clarifying water, and accelerating sediment deposition (reviewed in McMahon and Bogan 2001). The US Environmental Protection Agency has developed a rapid bioassessment protocol for streams and wadeable rivers for the purpose of both providing “guidance on cost-effective approaches to problem identification and trend assessment” and “to accelerate the development and application of promising biological monitoring techniques” (Barbour et al. 1999). To our knowledge, this protocol has not yet been tested for correlations with mussel density. The purpose of this study was to assess whether mussel density was correlated with rapidly obtained measures of stream habitat quality. We examined physicochemical variables such as pH, dissolved oxygen (DO) concentration, and the concentration of several inorganic compounds, as well as habitat variables such as stream velocity and depth combinations, embeddedness, instream cover, and sediment deposition, to test the hypothesis that these parameters were correlated with mussel density. Materials and Methods Field site description Our study was conducted during the summer of 2005 in the middle Allegheny River, a 7th-order river in Northwestern Pennsylvania (White et al. 2005). We sampled a 90-kilometer length of the river between Tidioute, PA and Kennerdell, PA (Fig. 1). The river meanders throughout most of this length, and features a wide variety of in-stream habitat conditions (Tables 1 and 2). The river is not dammed in the study area, but stream flow is influenced by the Kinzua Dam on the upper Allegheny River near Warren, PA, approximately 45 km northeast of the study reach. Sixteen locations within the study length were sampled. These were not chosen randomly, but on the basis of accessibility and the inclusion of a diverse selection of potential mussel habitats. Sampling methods At each sampling location, a 3- x 9-m area with a water depth between 0.75 and 1.4 m was located within wading distance of shore. The sampling area was marked off into 75 quadrats, and the mussels counted in situ using snorkeling gear. In situ sampling diminishes the disruption to the mussels and their 2007 L. Nicklin and M.T. Balas 227 surroundings (McMahon and Bogan 2001). To minimize the disturbance of mussels, no attempt was made to distinguish individual unionid species. We collected the following physicochemical parameters to estimate water quality: water temperature was taken at each site immediately prior to counting of mussels; the pH of the water was measured on-site using a portable, calibrated pH meter; and a Hach® water-pollution test kit was used in the field to test the concentrations of ammonium, nitrate, nitrite, sulfate, calcium, and phosphate. To measure dissolve concentrations in the laboratory with a YSI multi-meter, water samples from each site were collected and transported in gas-tight sampling containers. Qualitative ranks of the habitat at each sampling location were assigned using a habitat-assessment field-data sheet modified from the US Environmental Protection Agency (Barbour et al. 1999) specifically for use in Pennsylvania (Pennsylvania Department of Environmental Protection 2004; see Table 1). The four components analyzed were: 1) instream cover, 2) embeddedness, 3) velocity/depth regimes, and 4) sediment deposition. See Table 1 for a detailed description of each component. Each habitat parameter was scored on a scale from 0–20, with 20 signifying an optimal habitat. Results Measured physicochemical and habitat parameters and mussel density at each sampling location are detailed in Table 2. Because the ammonium and nitrite measurements were below detectable limits for all of the sites, they were excluded from further analyses. The temperature range during our study was 21.0 to 27.2 °C. Nitrate concentration varied from 10 to 15 parts Figure 1. Map of sampling locations from the middle Allegheny River, PA. Numbered circles correspond to site numbers listed in Table 2. 228 Northeastern Naturalist Vol. 14, No. 2 Table 1. Field-data ranks used to assess the four habitat parameters of the Allegheny River addressed in this study. Modified from Pennsylvania Department of Environmental Protection (2004). Habitat parameter Optimal (16–20) Suboptimal (11–15) Marginal (6–10) Poor (0–5) Instream cover Greater than 50% mix of 30–50% mix of boulder, 10–30% mix of boulder, Less than 10% mix of boulder, cobble, submerged cobble, or other stable or other stable habitat; boulder, cobble, or other logs, undercut banks, or habitat; adequate habitat. habitat availability less stable habitat; lack of other stable habitat. than desirable. habitat obvious. Embeddedness Gravel, cobble, and Gravel, cobble, and boulder Gravel, cobble, and boulder Gravel, cobble, and boulder particles are 0–25% particles are 25–50% particles are 50–75% boulder particles are surrounded by fine surrounded by fine sediment. surrounded by fine sediment. more than 75% sediment. surrounded by fine sediment. Velocity/depth All four velocity/depth Only 3 of the 4 regimes Only 2 of the 4 regimes Dominated by 1 regimes regimes present (slow- present (if fast-shallow is present (if fast-shallow or velocity/depth regime deep, slow-shallow, fast- missing, score lower than if slow-shallow are missing, (usually slow-deep). deep, fast-shallow). missing other regimes). score low). Sediment Little or no enlargement Some new increase in bar Moderate deposition of Heavy deposits of the deposition of islands or point bars and formation, mostly from new gravel, coarse sand on material, increased bar less than 5% of the bottom coarse gravel; 5–30% of the old and new bars; 30–50% development; more than affected by sediment bottom affected; slight of the bottom affected; 50% of the bottom deposition. deposition in pools. sediment deposits at changing frequently; obstructions, constrictions, polls almost absent due and bends; moderate to substantial sediment deposition of pools deposition. prevalent. 2007 L. Nicklin and M.T. Balas 229 Table 2. Chemical, physical, and habitat-assessment data measured at each of the seventeen sampling locations of the Allegheny River between Tidioute and Kennerdell, PA during the summer of 2005. Values for instream cover, embeddedness, velocity/depth, and sediment deposits are rank values assigned to the habitat according to the protocol of Barbour et al. (1999: see Table 1). Dissolved Mussel Site number Temperature Nitrate Sulfate Calcium Phosphate oxygen Instream Velocity/ Sediment density and name (Celsius) (ppm) (ppm) (ppm) (ppm) (ppm) pH cover Embeddedness depth deposits (per m2) 1. Tidioute 22.78 10 50 700 100 6.7 7.2 7 5 5 1 0.0 2. West Hickory 24.44 10 50 200 10 6.9 7.2 15 18 12 17 0.3 3. Tionesta 24.44 15 50 800 75 7.0 6.6 6 5 5 5 0.0 4. Hunter’s Station 27.22 15 100 150 75 9.8 7.8 1 2 2 0 0.0 5. Eagle Rock 21.67 10 100 700 75 8.6 7.9 20 20 18 19 11.0 6. Reno-A 22.13 10 50 200 50 9.7 7.3 12 20 6 20 0.0 7. Reno-B 26.11 10 50 150 75 6.5 7.5 0 1 0 0 0.0 8. Bike Trail 22.03 15 50 150 75 7.0 7.3 10 8 10 10 0.8 9. Sewage Plant 21.95 10 100 200 75 6.9 6.5 1 0 1 0 0.0 10. Third Street 21.52 10 50 150 50 7.9 6.9 12 10 6 11 0.6 11. Riverfront 21.02 15 50 150 50 7.3 7.1 19 15 11 15 1.5 12. Franklin 23.76 10 100 150 50 6.3 7.5 17 19 18 20 2.0 13. Lower Belmar 26.20 10 100 200 100 6.8 7.2 2 0 0 2 0.0 14. Belmar 25.23 10 50 150 75 6.9 8.4 17 20 15 14 1.2 15. Cove-A 24.44 10 100 150 100 6.9 7.5 18 17 14 18 2.7 16. Cove-B 24.44 10 250 150 100 7.5 8.4 11 0 8 0 0.0 230 Northeastern Naturalist Vol. 14, No. 2 per million (ppm), while sulfate and phosphate concentrations varied from 50 to 100 ppm at most sites. The calcium concentration was relatively high (700–800 ppm) at three sites, two of which lacked mussels and the third Table 3: Spearman correlation coefficients for the paired comparisons of each of the chemical, physical, and habitat variables measured with the density of mussels in each sampling location. n.s. = not significant; ** = p < 0.01; *** = p < 0.001. Spearman Variables coefficient Significance Chemical and physical Temperature -0.4004 n.s. Nitrates -0.0334 n.s. Sulfates 0.0438 n.s. Calcium -0.2989 n.s. Phosphates -0.2517 n.s. Dissolved oxygen -0.0016 n.s. pH 0.2722 n.s. Habitat Instream cover 0.8635 *** Embeddedness 0.6974 ** Velocity/depth 0.8720 *** Sediment deposits 0.7119 ** Figure 2. Scatterplots of significant correlations between mussel density and habitat score for four different habitat components: a) instream cover; b) embeddedness; c) velocity/depth; and d) sediment deposits. Spearman coefficient values for each habitat component are given in Table 3. 2007 L. Nicklin and M.T. Balas 231 having the highest mussel density. The pH ranged from 6.5 to 8.4. Dissolved oxygen concentration ranged from 6.3 to 9.8 ppm. Mussel density was not correlated with any of these variables (Table 3). Rank values varied widely, from optimal to poor for each of the four habitat parameters (Table 2). There were highly significant positive correlations between mussel density and each of the four habitat-assessment categories (Table 3, Fig. 2). In addition, the four habitat-assessment variables were strongly correlated with each other (Table 4). Discussion The lack of significant correlations between mussel density and the measured physicochemical parameters is consistent with previous studies that reported the lack of such correlations (summarized in McMahon and Bogan 2001, but see Nightingale et al. 2004). Most of our measurements of physicochemical parameters fell within previously established mussel survival ranges, especially for pH (McMahon and Bogan 2001) and dissolved oxygen (Buddensiek et al. 1993, Fuller 1974). Thus, we would not expect significant correlations between mussel density and these parameters. We also acknowledge that physicochemical parameters may fluctuate over time, so that our “snapshot” measurements may not reflect the full range of conditions that influence mussel distribution and abundance. The finding that mussel density is positively correlated with broad measures of habitat condition that include sedimentation is at first glance unsurprising, considering that sediment conditions have often been implicated in the distribution of unionid mussels (Buddensiek et al. 1993, Holland- Bartels 1990, Strayer 1999, Strayer and Ralley 1993). However, these studies did not reach a consensus as to the predictive relation between mussel density and sedimentation. Unionid mussels demonstrate a wide range of speciesspecific microhabitat preferences and include some species that prefer fine sediments (e.g., Holland-Bartels 1990, Strayer and Ralley 1993). This argues against finding overall correlations between mussel density and large-scale measures of sedimentation condition, especially because the scale we used designated conditions with low sedimentation as optimal. Thus, our finding of positive correlations between mussel density and measures of habitat condition is interesting. It may be explained by an absence of mussels that prefer fine sediments, but because we did not identify mussels to species, we lack Table 4. Spearman correlation coefficients for comparisons of the four physical habitat parameters used in this study. *** = p < 0.001. Instream cover Embeddedness Velocity/depth Embeddedness 0.8042*** Velocity/depth 0.9157*** 0.8003*** Sediment deposits 0.8277*** 0.9030*** 0.7666*** 232 Northeastern Naturalist Vol. 14, No. 2 information to test this. It is also possible that the generally high habitat quality indicated by the strong correlations among the habitat variables (Table 4) is responsible for this pattern. Alternatively, these results may indicate the presence of flow refuges for mussels from floods. Strayer (1999) noted that previous studies that focused on microhabitat condition often failed to predict mussel bed locations, and suggested that apparently favorable microhabitats for mussels might be subject to periodic severe flooding that may exclude them from these locations. Strayer (1999) suggested that flow refuges from hydraulic stress associated with floods are often occupied by a large number of mussels. Thus, it is possible that the areas of high mussel density and coarse substrate that we found in the Allegheny River, such as at the Eagle Rock site (Table 2), were flow refuges. The findings of this study should be useful to conservation managers interested in maintaining ecological processes provided by unionid mussels. In Pennsylvania, there is great interest in the protection and restoration of freshwater mussel communities. Thus, there is a need to acquire information regarding both mussel species richness and ecological function (Nightingale et al. 2004). Although our study did not address species richness and would not be useful to manage individual or endangered species, it does suggest that mussel density can be predicted by the EPA habitat assessment protocol. Managers using a coarse-filter approach (Meffe et al. 2002), i.e., managing for overall mussel number rather than for the protection of individual species, may find that improvement of habitat conditions allows the cumulative number of unionid mussels to rise or allow for restoration of a mussel community and its associated ecosystem functions. The rapid assessment protocol may allow a large number of sites to be assessed in a short period, reducing time in the field and thus costs. There are three caveats to our recommendation of the utility of this study. First, our sampling method was designed to test a wide variety of habitats, and habitats for inclusion were not chosen randomly. This may have introduced an unintentional bias by over-sampling rare habitats that were intentionally included. A larger, random sample of a stream is recommended to confirm these results for a different study site. Second, while mussel density was positively correlated with habitat quality, there is no evidence that mussels had a direct and positive impact on the overall health of the ecosystem, as some studies have suggested (reviewed in MacMahon and Bogan 2001). It remains to be demonstrated if mussel density directly affects ecosystem quality in the Allegheny River. Third, while we found no significant correlation between mussel density and physicochemical parameters, we suggest that long-term monitoring of these parameters at high-quality habitat sites is essential to ensure that 2007 L. Nicklin and M.T. Balas 233 the physicochemical parameters remain within the tolerance range of the mussel community present (Nightingale et al. 2004). 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