Attempts to evaluate stream water quality using macrobenthic invertebrate indices

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1 154 Attempts to evaluate stream water quality using macrobenthic invertebrate indices Rob Birdsall! INTRODUCTION Through the 1996 Fann Bill, the Environmental Quality Incentive Program (EQIP) was established to assist crop and livestock producers in dealing with environmental and conservation improvements on the fann (USDA, 1996), the main objective being to improve surface water quality. In addition to other incentive programs, EQIP best management practices (BMPs) have been instituted on 20 sites throughout the northern portion of Otego Lake's watershed (Figure 1) utilizing matching funds provided by the Otsego County Conservation Association (Poulette,1999). Land treatment using BMPs is believed to be the most effective approach to control agricultural pollution sources (Meals, 1993). A variety of practices exist, which when implemented can maintain high surface water quality (Daniel, 1994). Examples of such practices include crop rotation, nutrient management, the construction ofbarnyard water management systems and manure storage facilities (Poulette, 1999). A goal of this study is to develop a measurement of the effectiveness of these practices by evaluating the macrobenthic invertebrate communities occupying the streams draining this area. Collections ofbenthic macroinvertebrates were taken from four Otsego Lake tributaries during the summer of 1998 (Vanderlip, 1999). Cripple Creek, Hayden Creek, Shadow Brook, and White Creek were all sampled extensively at various sites to detennine their macrobenthic populations (see Figure 1). Macrobenthos can be utilized to indicate water quality due to their varying sensitivities to pollutants. The abundance or absence of particular species can characterize the quality of a watershed, in addition to identifying the possible contaminants. Using the acquired samples ofmacrobenthos as indicator species for assessment of water quality on these streams would be extremely beneficial in that it would provide a tool for the evaluation ofthe effectiveness ofthe recently established BMPs. The data collected here could be compared to those collected in 1995 (Fahey, 1996), prior to the completion ofmany BMPs. The use of macrobenthos to assess water quality is well known. The two basic methods in using macrobenthos to rate water quality are surveillance and compliance (Rosenberg and Resh, 1993). Surveillance includes macrobenthic surveys completed prior to and after the establishment of a certain project. For example, surveillance can provide infonnation for the evaluation of current or emerging environmental problems in relation to water quality, and the point and/or nonpoint pollutants affecting a particular watershed (Rosenberg and Resh, 1993). Conversely, it could indicate improving water quality following mitigative practices. Compliance refers to the use of macrobenthic samples as indicator species to assure that particular environmental standards and requirements, involving water quality, are being met effectively (Rosenberg and Resh, 1993). 1 Paper submitted to fulfill requirement for BioI Present affiliation: SUNY Oneonta

2 155 SCAlE IN J]LOHETElS o 1 I 3 I 4 I Figure 1. Map of the five major tributaries to Otsego Lake showing the sampling stations. Symbols represent agricultural best management projects, funded through EQIP, completed by summer 1999 (modified from Poulette, 1999).

3 156 With the significance of the Otsego Lake watershed to the greater Cooperstown community, the importance ofwater quality assessment is paramount. For this reason the use of various water monitoring techniques are essential. It is expected that this study would compliment physical and chemical monitoring conducted over the past several summers at the same sites (Heavey, 1996; Hewett, 1997; Miller, 1998; Poulette, 1999; Collins and Albright, 2000; Miner, 2001). The wide range of species present offer a spectrum ofresponses to many different environmental pollutants. Given th~ relatively long life cycles of some taxa, benthic macroinvertebrates allow for continuing analysis of the fresh water environment they inhibit (Rosenberg and Resh, 1993). RESULTS With the macrobenthic collections ofthe four main tributaries from 1998 on record (Vanderlip, 1999), it is now a matter of converting the numerical/population values into a useful form to assess the tributaries water quality. This conversion of data is completed by means of macroinvertebrate community indices (Bode et al., 1996). There are seven indices in all, but for our purposes four specific indices seem appropriate. The indices include: species richness, EPT richness, Hilsenhoffbiotic index, and percent model affinity. Each index uses the macrobenthic samples in a slightly different manner. However, the 1998 macrobenthic database, as it now stands, does not comply with the requirements of the indices for water quality assessment. Namely, refmement of the taxonomic identifications of the quantitative samples to generic level (at least) is necessary, as well as enumerations at that level. To date, most taxa have only been identified to family. INDEX INFORMAnON/COMPLETION REQUIREMENTS 1. Species Richness: The total number of species found in a sample (Bode et al., 1996). A high species richness value indicates the probability of clean water conditions (Bode et al., 1996). This index is to be taken at each individual site on each individual stream. This value is best obtained by determining the abundance of each species, rather than some higher taxa, and presenting the data tabularly. This task of accounting for each species needs to be done for the HilsenhoffBiotic Index as well (see Table 1). 2. EPT Richness: The total number of three particular orders to a 100-organism subsample. These three orders include: Ephemeroptera (mayflies), Plecoptera (stoneflies), and Trichoptera (caddisflies). Because pollution-intolerant organisms generally represent these orders, a high EPT value is a good indicator of clean water (Bode et al., 1996). The methods to determine the 100 organism subsample need to be formally established (see Table 1). 3. HilsenhoffBiotic Index: (HBI) This index is calculated by multiplying the number of individuals of each species by its assigned tolerance value, summing these products, and dividing by the total number of individuals. On a 0-10 scale, tolerance values range from intolerant (0) to tolerant (10) (for tolerance values check Hilsenhoff, 1987). High HBI values are indicative of organic sewage pollution, while low values indicate clean-water conditions (Bode et al., 1996). The tolerance scale that includes values for all macroinvertebrates needs to be acquired. The

4 157 SPECIES RICHNESS spp>35 replace sppdat with 10 spp>26 replace sppdat with (((spp-26)/9)* 25)+75 spp>18 replace sppdat with (((spp-18)/8.5)* 2.5)+5 spp>io replace sppdat with (((spp-1o)/8.5)*2.5)+2.5 spp<5 replace sppdat with 0 spp<11 replace sppdat with ((spp-5)/5.5)*2.5 EPT RICHNESS ept> 15 replace eptdat with 10 ept> 10 replace eptdat with (((ept-10)/5)*2.5)+7.5 ept>5 replace eptdat with (((ept-5)/5.5)*2.5)+5 ept>1 replace eptdat with (((ept-i )/4.5)*2.5)+2.5 ept<2. if ept = I replace eptdat with 1.25 ifept = 0 replace eptdat with 0 flllsenhoff BIOTIC INDEX hbi<2 replace hbidat with 10 hbi <4.51 replace hbidat with 10-(hbi-2) hbi <6.51 replace hbidat with 7.5-(((hbi-4.5)/2)*2.5) hbi <8.51 replace hbidat with 5-(((hbi-6.5)/2)*2.5) hbi >8.50 replace hbidat with 2.5-(((hbi-8.5)/l.5)*25) PERCENT MODEL AFFINITY pma >90 replace pmadat with 10 pma :>64 replace pmadat with (((pma-64)/26)*2.5)+7.5 pma>49 replace pmadat with (((pma-49)1i5.5)*2.5)+5 pma >34 replace pmadat with (((pma-34)1i5.5)*2.5)+2.5 pma <20 replace pmadat with 0 pma <35 replace pmadat with ((pma-20)1i4.5)*2.5 Table 1. Fonnulae used to convert macroinvertebrate community composition to the indices. See Bode et al. for explanation of index applications.

5 158 classification methods in the 1998 collection need to be refined to the generic or, preferable, specific level (see Table 1). 4. Percent Model Affinity: (PMA) This index takes the percentage abundance ofseven major groups of macroinvertebrates in each sample and compare those percentages to a nonimpacted percentage model of the same seven groups. These groups include: Ephemeroptera, Plecoptera, Trichoptera, Coleoptera, Chironomidae, Oligochaeta, and "others". A high PMA value is indicative of clean water conditions (Bode et al., 1996). For this index, one needs to refine the classification further as stated earlier. The Otsego Lake Watershed database meets the criteria for this index, with the exception ofvalues for Chironomidae (see Table 1). DISCUSSION With the proper components in place, in terms ofthe indices, this study can proceed following the refinement ofthe collections. It will then be possible to determine the effect and location of a particular pollution source, or the reduction of existing pollution. In addition, this method ofwater quality assessment, along with water chemistry monitoring, will help evaluate the effects the agricultural land management techniques are having around the watershed. As a result, it is anticipated that a regular water assessment program will be established for these tributaries along with others throughout the Otsego Lake watershed. The acquisition and proper use of these data, pertaining to macrobenthic invertebrates, to assess the water quality ofthe Otsego Lake watershed will prove to be a valuable tool in evaluating water quality. REFERENCES Bode, R.N., M.A. Novak and L.E. Abele Quality assurance work plan for biological stream monitoring in New York. NYS Department of Environmental Conservation. Albany, NY. Collins, E. and M.F. Albright Water quality monitoring in the Otsego Lake watershed. In 32 nd Ann. Rept. (1999). SUNY Bio. Fld. Sta., SUNY Oneonta. Daniel, T.C., A.N. Sharpley, D.R. Edwards, R. Wedepohl and J.L Lemunyon Minimizing surface water eutrophication from agriculture by phosphorus management. Journal of Soil and Water Conservation. 49 (2): Fahey, J Benthic survey of Otsego Lake tributaries. In 28 th Ann. Rept. (1995). SUNY Bio. Fld. Sta., SUNY Oneonta. Heavey, K.F Water quality monitoring in the Otsego Lake watershed. In 28 th Ann. Rept. (1995). SUNY Bio. Fld. Sta., SUNY Oneonta. Hewett, B.L Water quality monitoring and benthic community in the Otsego Lake Watershed.. In 29 th Ann. Rept. (1996). SUNY Bio. Fld. Sta., SUNY Oneonta.

6 159 Meals, D.W Assessing nonpoint phosphorus control in the LaPlatte River watershed. Lake and Reservoir Management. 7 (2): Miller, C Water quality monitoring in the Otsego Lake watershed. In 30 th (1997). SUNY Bio. Fld. Sta., SUNY Oneonta. Ann. Rept. Miner, M.M Water quality monitoring of five major tributaries in the Otsego Lake watershed, summer In 33 rd Ann. Rept. (2000). SUNY Bio. Fld. Sta., SUNY Oneonta. Poulette, Water quality monitoring of five major tributaries in the Otsego Lake watershed, summer In 31 st Ann. Rept. (1998). SUNY Bio. Fld. Sta., SUNY Oneonta. Rosenburg, D.N. and V.H. Resh Freshwater biomonitoring and benthic macroinvertebrates. Chapman and Hall. New York, NY. United States Department ofagriculture The Federal Agricultural Improvement Act of Accessed 24 July Vanderlip, A Macroinvertebrate study of the northern tributaries of Otsego Lake, summer In 31 st Ann. Rept. (1998). SUNY Bio. Fld. Sta., SUNY Oneonta.