Orange County Water Authority Water Quality Biomonitoring Project Summary Report for Years

Transcription

1 Orange County Water Authority Water Quality Biomonitoring Project Summary Report for Years Prepared by Watershed Assessment Associates, LLC For Prime Consultant: Basin River Watch Doug Reed, Executive Director 3570 Route 29 East Greenwich, NY December 2008

2 Table of Contents Introduction... 5 Summary of Key Findings... 9 Watersheds Delaware Wallkill Ramapo Recommendations Model Macroinvertebrate Community for Orange County Targeted Water Quality Assessment References Appendix I: Reference community genera for the Orange County Model Community (OCMC) metric Appendix II: Summary Table of Monitoring Findings Appendix III: Field data summaries

3 List of Tables List of Figures Table 1. Summary of water quality findings for all stations assessed in Orange County Table 2. Model affinity results and the water quality condition obtained by the Orange County Model Community metric and the NY DEC PMA metric Table 3. Diatom Assessment Profile scores, the Biological Assessment Profile scores, and the mean water quality category and scores for fifteen stations assessed in the Moodna Creek watershed during 2005 and Table 4. The Nutrient Biotic Index scores for nitrates and total phosphorus (NBI N and NBI P) and the respective category of impairment for fifteen stations assessed in the Moodna Creek watershed during 2005 and Figure 1. Locations were benthic samples were collected within Orange County, NY during Figure 2. Distribution of scores for all stations collected in rivers and streams in Orange County. This graph includes all stations sampled from , including multiple year samples taken at certain stations Figure 3. Water quality condition of Orange County streams assessed in and stream condition assessed by NY DEC pre Figure 4. Mean scores ( ) for Orange County Delaware Watershed stream stations (circles). Lines indicate the standard deviation of the scores for Squares represent stations sampled once during Figure 5. Mean scores ( ) for Orange County Wallkill Watershed stream stations (circles). Lines indicate the standard deviation of the scores for Squares represent stations sampled once during Figure 6. Mean scores ( ) for Orange County Ramapo Watershed stream stations (circles). Lines indicate the standard deviation of the scores for Squares represent stations sampled once during Figure 7. Mean scores ( ) for Orange County Watershed stream stations (circles). Lines indicate the standard deviation of the scores for Squares represent stations sampled once during Figure 8. Mean scores (circles with standard deviation bars) (including all stations) for each of the four major watersheds and percent land cover. Black horizontal lines indicate minimum and maximum scores within each watershed

4 Figure 9. Box plots of Genera Level values for the nine subsamples (100 organism subsamples) for each site from the Smith and Bode (2004) work. Circles represent the mean, solid lines represent the median, and bars represent standard deviation

5 Introduction This report summarizes the results of an ongoing assessment of Orange County, NY, streams performed for the Orange County Water Authority (OCWA). The assessment uses macroinvertebrate specimens as indicators of water quality. For a complete project overview, history, rational, background, project goals, methods, key terminology and detailed interpretation of 2004 through 2007 data, see the OCWA Water Quality Biomonitoring Project Summary Report for Years available from the OCWA website ( and the 2007 Water Quality Biomonitoring Project Orange County Benthic Samples Report. Data was collected at 180 unique stations from (Figure 1). Most stations were sampled more than once during the study period. Unique stations are defined as distinct monitoring stations (unique geographic location); the term is used to distinguish between samples collected at distinct stations and the total number of stations sampled, which includes samples taken at the same station (geographic location) in multiple years. Approximately 26 sites were selected as reference stations (by the project coordinator, based on land use characteristics or protected areas with very high water quality) and were used to develop an Orange County model community structure (described in detail in the OCWA Water Quality Biomonitoring Project Summary Report for Years ). Total number of stations assessed from : 236 Number of unique stations assessed: 180 Number of stations assessed in more than one year: 72 Total number of stations with insufficient data for assessment: 4 Macroinvertebrate community metric results indicated that the water quality of most stream stations (124 of the 236) in Orange County is slightly impacted; water quality at 58 stations is moderately impacted, and water quality at 7 stations is severely impacted. The remaining 47 stations are non-impacted (Table 1). Headwater environments and upstream impoundments influence the macroinvertebrate community structure, such that erroneous assessments of impacted water quality may occur (Bode et al. 2002). The NYS DEC has developed criteria that consider these influences and has established correction factors to account for these influences. Corrections were made to the final water quality category for 26 stations (slightly impacted changed to non-impacted; 5

6 moderately impacted changed to slightly impacted), as these stations were located at headwaters or were influenced by an impoundment. Table 1. The table summarizes the water quality findings for all stations assessed in Orange County during Water quality level of impact Orange County Non impacted 47 Slightly impacted 124 Moderately impacted 58 Severely impacted 7 Total 236 Impact source determination (ISD) values indicate that the majority of stations are likely impacted by non-point source nutrients, impoundments, municipal/industrial inputs, organic inputs, or complex combinations of these sources. The remaining stations are influenced by one or more of the following: organic/sewage effluent, complex municipal/industrial inputs, toxins, siltation or impoundment effects. Urbanization, with resultant point and nonpoint source effects, is likely the strongest influence on the integrity of these systems. Non-point sources are insidious and less obvious than point sources (e.g., municipal wastewater and industrial discharges). Potential non-point sources within Orange County are the amount and configuration of impervious surfaces (roads, parking areas, roof tops), inadequate riparian buffer areas, poor or inadequately enforced best management practices (BMPs) during construction projects, leaky septic systems (improper installation or maintenance), fertilizer application on lawns and farms, application of de-icing materials on roads and parking areas, automotive chemicals, and pet waste. At a small number of the monitoring stations (particularly at severely impacted stations) sufficient information may be available to conclude that a specific point-source discharge nearby the station is impacting water quality. It is beyond the scope of this project to conduct statistical analysis to measure or demonstrate specific relationships between benthic macroinvertebrate community metrics, stream water quality, and the source of degradation for each stream station. However, these results can help Orange County prioritize remediation actions, focus public outreach programming, and protect high quality streams. The data also provides a detailed baseline assessment of water quality around the County, which will facilitate monitoring trends and changes in water quality over time. 6

7 Figure 1. Locations were benthic samples were collected within Orange County, NY during

8 Figure 2. Distribution of scores for all stations collected in rivers and streams in Orange County. This graph includes all stations sampled from , including multiple year samples taken at certain stations. Non impacted Slightly impacted Moderately impacted Severely impacted Water Quality Score 8

9 Summary of Key Findings Note: These findings describe water quality data for all stations sampled from , which includes stations that were sampled in multiple years, the percentages below reflect all of this aggregated data. Figure 3. Water quality condition of Orange County streams assessed in and stream condition assessed by NY DEC pre % 26% Orange County 3% 53% 20% 2002 Orange County 2% 28% Non Slight Moderate Severe Non Slight Moderate Severe The majority (53%) of stream stations sampled were slightly impacted, exhibiting a macroinvertebrate community significantly altered from the pristine state. Slightly impacted water is still considered good water quality and does not limit fish survival, but may limit fish propagation. Twenty five percent of stations sampled exhibited poor water quality (moderately impacted), which is often limiting to fish propagation, but usually not to fish survival. Twenty percent of stream stations sampled were nonimpacted, indicating very good water quality that will not limit fish survival or propagation. 44% Assessed by NYDEC Three percent of stations sampled were severely impacted, indicative of very poor water quality, which impedes both fish propagation and survival. Orange County water quality was assessed at 57 stations starting in the mid 1980 s through 2002 by NY DEC (Bode et al. 2004). The following stream water quality was found at that time: 28% nonimpacted, 44% slightly impacted, 26% moderately impacted, and 2% severely impacted. The shift from non impacted to slightly impacted is the most remarkable water quality change; from 28% in pre 2004 to 20% in Bode et al. (2004) found similar results throughout NY State. 9

10 Consistent with the OCWA findings, the 2002 DEC assessment of NY State concluded that the most likely cause of impact at the majority of stations, as shown by Impact Source Determination, was nonpoint source nutrient enrichment. Watersheds A detailed review of findings for all 236 station assessments is beyond the scope of this report. For a detailed assessment, please see previous reports. This section highlights some of the most notable findings for the four watersheds located within Orange County. Although remotely sensed land use data may be an indication of stream community integrity, direct assessment is critical to reporting actual water quality and the most likely sources of impairment. The land use values cited below were calculated, in ArcMap, from the 2001 National Land Cover Dataset (NLCD) downloaded from the Environmental Protection Agency ( html). Delaware Benthic samples were collected at 10 stations in the Delaware Watershed within Orange County. Four stations were sampled multiple times (Figure 4); the mean scores from of these four stations indicate non-impacted water quality. The six stations that were sampled once, during , indicate slightly impaired water quality. The majority of land use within the Delaware watershed is forested (83%). Figure 4. Mean scores ( ) for Orange County Delaware Watershed stream stations (circles). Lines indicate the standard deviation of the scores for Squares represent stations sampled once during

11 Wallkill Samples were collected at 77 stations in the Wallkill Watershed; 19 stations were sampled more than once. The mean score for stations sampled during indicate that water quality ranges from non- to severely impacted. The majority of streams are slightly or moderately impacted (Figure 5). Stations assessed once during are similar. The Wallkill watershed has evenly mixed land coverage within Orange County: developed (13%), forested (36%), agricultural (35%), and other (16%). The two severely impacted stations are located on the Rio Grand. Figure 5. Mean scores ( ) for Orange County Wallkill Watershed stream stations (circles). Lines indicate the standard deviation of the scores for Squares represent stations sampled once during

12 Ramapo Benthic macroinvertebrate samples were collected at 17 stations in the Ramapo Watershed; 8 stations were sampled multiple times. Water quality within this watershed ranges from slightly to moderately impacted (mean scores, years ), although the moderately impacted stations are on the high end of the category, closer to slightly impacted (Figure 6). Stations sampled once showed non- to slightly impacted water quality. The majority of land cover in the Ramapo watershed is forested (73%) with approximately 12% developed land cover; the majority of moderately impacted stations were located within the developed land areas. Figure 6. Mean scores ( ) for Orange County Ramapo Watershed stream stations (circles). Lines indicate the standard deviation of the scores for Squares represent stations sampled once during

13 Samples were collected at 74 stations within the Watershed; 26 stations were assessed multiple times over multiple years. The mean scores for the majority of these streams fell within the slightly impacted category. The Watershed is approximately 50% forested, 19% developed, 15% agricultural, 13% wetland, and 3% other. The non-impacted stations were located in primarily within forested areas. Figure 7. Mean scores ( ) for Orange County Watershed stream stations (circles). Lines indicate the standard deviation of the scores for Squares represent stations sampled once during According to 2001 NLCD, Orange County is dominated by forested land cover and mean scores for all stations within each watershed indicate that Orange County water quality is slightly impacted (Figure 8). Delaware Watershed mean score is on the high end of the slightly impacted category and the minimum and maximum scores fall solidly within slight to non-impacted categories. Wallkill Watershed score is on the low end of the slightly impacted category and the minimum and maximum scores greatly vary from severely impacted to nonimpacted. 13

14 Figure 8. Mean scores (circles with standard deviation bars) (including all stations) for each of the four major watersheds and percent land cover. Black horizontal lines indicate minimum and maximum scores within each watershed. Recommendations The following is a summary of recommendations for consideration. Work with individual municipalities to address public health and water quality issues at specific stations where significant impacts have been found. There are public health concerns associated with bacterial or other microbial contamination and with specific conductance as an indicator of chloride/salt levels at particular stations in Orange County. There are significant water quality concerns related to environmental quality and recreational use of streams and rivers detected at a few stations. In some cases, there are downstream communities that should be notified of these findings. Establish an ongoing monitoring program to revisit selected stations, with a goal of re-sampling stations at least once every five years. Refine the existing network of stations to determine which ones may be of lower priority for various reasons. Maintain the current process of station selection, using the station selection criteria developed for this project with some 14

15 additional refinements, to identify additional priority stations for monitoring to augment and complete the existing network of stations. Consider the implementation of probability-based methods of station selection. Coordinate these monitoring efforts with the New York State DEC Stream Biomonitoring Unit to eliminate duplication and maximize program efficiency. Produce yearly water quality assessment reports and link these reports, and other aspects of the program, to assisting the MS4 communities, including Orange County itself, to effectively meet their regulatory requirements and responsibilities. Use the stream monitoring program as a key vehicle for meeting specific MS4 requirements including public participation, illicit discharge detection and elimination, and post-construction monitoring of stormwater management facilities. Explore creative partnerships and funding sources for ongoing monitoring, including the potential to work with MS4 communities, water districts, wastewater districts, stormwater districts, and other municipal entities to collaborate on funding, public education and participation, and other aspects of an ongoing, comprehensive monitoring program. Test the potential of using trained volunteers to collect samples, thereby reducing costs, while using professional analysts for sample analysis and reporting to maintain quality control and comparability with NYS DEC data. Work with community groups and other stakeholders to develop stream visual assessment initiatives to complement and help guide biomonitoring work. Continue to build partnerships with academic, research, and education organizations including the Black Rock Forest Consortium, colleges and schools in the region, the River Estuary Program, the Highlands Environmental Research Institute, the CUNY Institute for Sustainable Cities, SUNY colleges at New Paltz and Syracuse, Mount Saint Mary College, and others. Explore how to utilize the findings of this study to support improved land use planning and development policies in Orange County. Explore ways to use the findings and overall approach of this project to support using low-impact development practices for new development. 15

16 Explore the benefits of participating in the Lotic Scene Investigation program or similar program, which matches college interns with a professional stream monitoring assessment project to provide intensive field experience and classroom study. This approach can leverage OCWA and other local funding sources by attracting outside funding based on the educational value while still maintaining professional level water quality assessments. Consider the potential benefits of a county-wide volunteer stream monitoring and public outreach coordinator position. Through this position or other mechanisms, provide annual training and regular outreach programs targeted for municipal officials, watershed groups, recreational and sporting organizations, consulting planners and engineers, landowners, and other audiences about the general principles of stream ecology, how streams are affected by land use and human activities, and about utilizing the findings of stream biomonitoring studies for decision-making. Model Macroinvertebrate Community for Orange County NYS DEC macroinvertebrate community metrics include a metric [Percent Model Affinity (PMA)] that compares the macroinvertebrate community sampled (at each stream station) to an ideal model community structure (a community structure indicative of non-impacted water quality). The NYS PMA model uses macroinvertebrates identified to Order and is based on macroinvertebrate communities located throughout New York State. The US EPA and others recommend using local and regionally tailored model communities, which may improve water quality analysis. This project included the establishment of a genera level model community for Orange County. A genera level model community has been shown to provide a more robust indication of impairment than model communities based on Order or Family level taxonomy, especially within more degraded systems (Barton 1996; Karr and Chu 1999). Smith and Bode (2004) assessed the variability of the Biological Assessment Profile () category relative to the level of taxonomic identification (family vs. species) for each of the four metrics used in the. They found species level identification was more accurate (100 percent of the time) at properly assigning the water quality category than family level identification (92 percent of the time). To test the benefit of genera level taxonomic resolution the Smith and Bode (2004) data was evaluated to an all genera level of taxonomy. The Hilsenhoff Biotic Index and Species Richness scales were revised to reflect these changes. The recalculated score for the all genera level of taxonomy placed water 16

17 quality in the proper category 100 percent of the time (Figure 9) when compared to the species level identification (J. K. Nolan, personal communication, December 1, 2008). Figure 9. Box plots of Genera Level values for the nine subsamples (100 organism subsamples) for each site from the Smith and Bode (2004) work. Circles represent the mean, solid lines represent the median, and bars represent standard deviation. The same level of accuracy is achieved by all genera or species level identification because an all species level of taxonomy is actually a combination of species and genera level identification (less than 50% of the samples identified were to the species level in the Smith and Bode (2004) work). This is due to the physical condition of the specimens being identified (missing body parts, immature specimens, or damaged during collection), the lack of species level keys necessary to identify specimens, and the level of effort required (time needed to attempt identification of the specimen); any of these conditions or combination of conditions prevents identifying the specimen to the species level. Due to the physical condition of the specimen, level of effort, and the lack of working species level keys, a prudent fiscal approach for biological monitoring programs within NYS would be to adopt an all genera level of taxonomy to categorize water quality. The Orange County genera model community was developed using 26 reference stations from the benthic samples, which were located within parks or protected areas with high water quality (Appendix I). The methods used to develop the Orange County Model Community (OCMC) followed methods outlined in Novak and Bode (1992) and Barton (1996). 17

18 Six stations from the assessments were randomly selected to test the OCMC. The OCMC metric results, when compared to the NYS PMA metric results, placed the water quality in the same or a lower water quality category (Table 2). The OCMC can be used to augment analysis of water quality conditions of streams in Orange County; it is not meant to replace the NYS PMA metric used to assess water quality. Station ID Table 2. Model affinity results and the water quality condition obtained by the Orange County Model Community metric and the NY DEC PMA metric. PMA (water quality condition) OCMC (water quality condition) 5089_ % (slightly) 35% (moderately) 3089_ % (severely) 8% (severely) 4800_ % (moderately) 39% (moderately) 3089_ % (slightly) 45% (moderately) 1300_ % (slightly) 43% (moderately) 4289_ % (severely) 13% (severely) Targeted Water Quality Assessment This project included additional water quality analysis on 15 stations located within the Moodna Creek watershed that were selected by the project coordinator (Table 3). Diatoms collected from the 15 stations were analyzed following procedures outlined in the Stream Biomonitoring in New York Using Periphytic Diatoms document (Passy 2000). Diatoms are an earlier and more sensitive indicator of excessive nutrient loading in water bodies compared to benthic macroinvertebrates. The method provides a Diatom Assessment Profile (DAP) score using three indices: Pollution Tolerance Index (PTI), Trophic Index (TRI), and Diatom Model Affinity (DMA). The DAP score is based on the method of assessment in that multi-metrics are used to assess the diatom community structure. The diatom metric scores are converted to a common scale and are then averaged. The averaged score is the DAP score. As in the, the DAP categorizes water quality in one of the four water quality categories used in NYS (non, slightly, moderately, or severely impacted). When used in combination with benthic macroinvertebrate analysis, the and DAP scores are averaged to determine the station s water quality category. This is a provisional method of assessment in NYS (Bode et al. 2000). Results of the DAP and scores are show in Table 3. 18

19 Table 3. Diatom Assessment Profile scores, the Biological Assessment Profile scores, and the mean water quality category and scores for fifteen stations assessed in the Moodna Creek watershed during 2005 and Station ID Date DAP Mean WQS (DAP, ) Category of impact 2489_002 7/10/ Slightly 4800_012 7/10/ Slightly 4800_012 7/21/ Slightly 4800_003 7/21/ Slightly 4800_005 7/21/ Slightly 5889_007 8/6/ Moderately 5889_007 7/6/ Slightly 2489_003 8/6/ Slightly 2489_008 8/6/ Slightly 2489_008 7/16/ Slightly 2489_010 7/10/ Slightly 2489_010 7/21/ Slightly 2089_004 8/7/ Slightly 5889_009 8/6/ Slightly 5889_009 7/16/ Slightly The targeted water quality assessment also includes the application of a new metric that uses macroinvertebrate data. This new metric categorizes a station s trophic state (i.e. the amount of nutrients--total phosphorus and nitrate) that may be entering the system and causing water quality impairment. The Nutrient Biotic Index (NBI) was developed by Smith et al. (2007). The index provides a numeric score based on the benthic macroinvertebrate community structure and the score places water quality into one of three tiered categories of impairment: oligotrophic (the least impaired), mesotrophic (a transitional zone of impairment), and eutrophic (the highest level of impairment). The index was applied to the 15 stations in the Moodna Creek watershed, the same sites used in the diatom DAP analysis. The NBI metric analysis determines the station s nitrate and total phosphorus (NBI-N and NBI-P) impairment category. NBI results are provided in Table 4. The information from these additional analyses can provide useful data for water quality planning and protection purposes. 19

20 Station ID Table 4. The Nutrient Biotic Index scores for nitrates and total phosphorus (NBI N and NBI P) and the respective category of impairment for fifteen stations assessed in the Moodna Creek watershed during 2005 and Date NBI N score NBI N Category NBI P score NBI P Category 2489_002 7/10/ Eutrophic 7.23 Eutrophic 4800_012 7/10/ Eutrophic 7.54 Eutrophic 4800_012 7/21/ Mesotrophic 6.42 Eutrophic 4800_003 7/21/ Mesotrophic 5.97 Mesotrophic 4800_005 7/21/ Mesotrophic 6.39 Eutrophic 5889_007 8/6/ Eutrophic 6.73 Eutrophic 5889_007 7/6/ Eutrophic 7.28 Eutrophic 2489_003 8/6/ Eutrophic 5.72 Mesotrophic 2489_008 8/6/ Oligotrophic 5.70 Mesotrophic 2489_008 7/16/ Mesotrophic 5.49 Oligotrophic 2489_010 7/10/ Mesotrophic 6.96 Eutrophic 2489_010 7/21/ Mesotrophic 6.33 Eutrophic 2089_004 8/7/ Eutrophic 7.90 Eutrophic 5889_009 8/6/ Mesotrophic 5.24 Oligotrophic 5889_009 7/16/ Mesotrophic 5.57 Mesotrophic 20

21 References Barton, D.R The use of Percent Model Affinity to assess the effects of agriculture on benthic invertebrate communities in headwater streams of southern Ontario, Canada. Freshwater Biology 36: Bode, R. W., M.A. Novak, L.E. Abele, D.L. Heitzman, and A.J. Smith Quality Assurance work plan for biological stream monitoring in New York State. NYS DEC technical report. Bode, R. W., M.A. Novak, L.E. Abele, D.L. Heitzman, and A.J. Smith year trends in water quality of rivers and streams in New York State based on macroinvertebrate data NYS DEC technical report. Bode, R. W., M.A. Novak, L.E. Abele, D.L. Heitzman, and P. I. Passy Biological Stream Assessment West Branch Delaware River. NYS DEC technical report. Karr, J.R. and E.W. Chu Restoring Life in Running Waters Better Biological Monitoring. Island Press, Covelo, CA Novak, M. A. and Bode, R. W Percent model affinity: a new measure of macroinvertebrate community composition. Journal of the North American Benthological Society. 11(1): Orange County Comprehensive Plan Orange County Department of Planning. Technical report adopted April 11, Passy S.I Stream Biomonitoring in New York Using Periphytic Diatoms. New York State Department of Environmental Conservation, Technical Report, 14 pages. Smith, A.J. and R.W. Bode Analysis of variability in New York State benthic macroinvertebrate samples. New York State Department of Environmental Conservation, Technical Report, 43 pages. Smith, A.J., R.W. Bode, G. S. Kleppel, A nutrient biotic index (NBI) fo use with benthic macroinvertebrate communities. Ecological Indicators. 7:

22 Appendix I: Reference community genera for the Orange County Model Community (OCMC) metric Genera Percent Genera Percent Genera Percent Acentrella sp 0.42 Helicopsyche sp 0.04 Polycentropus sp 0.19 Acroneuria sp 5.00 Hemerodromia sp 0.04 Polypedilum sp 3.31 Agnetina sp 0.15 Heterocloeon sp 0.42 Prionocera sp 0.12 Antocha sp 0.42 Heterotrissocladius gr 0.04 Procloeon sp 0.08 Atherix sp 0.19 Promoresia sp 0.65 Hexatoma sp 1.65 Baetis sp 4.73 Prostoma sp 0.12 Hydropsyche sp 9.31 Boyeria sp 0.04 Psephenus sp 1.46 Isonychia sp 6.77 Brachycentrus sp 0.31 Pteronarcys sp 0.92 Isoperla sp 0.35 Brillia sp 0.08 Pycnopsyche sp 0.08 Lanthus sp 0.27 Caecidotea sp 0.15 Rheocricotopus sp 0.15 Lepidostoma sp 1.15 Caenis sp 0.08 Rheotanytarsus sp 0.69 Leucotrichia sp 0.04 Calopteryx sp 0.08 Rhyacophila sp 1.38 Leucrocuta sp 1.42 Cardiocladius sp 0.04 Serratella sp 0.50 Leuctra sp 1.73 Centroptilum sp 0.12 Sialis sp 0.08 Lype sp 0.12 Ceraclea sp 0.04 Simulium sp 0.73 Macrostemum sp 0.04 Chelifera sp 0.15 Sphaerium sp 0.12 Malirekus sp 0.58 Stenacron sp 0.08 Cheumatopsyche sp 5.12 Micrasema sp 0.15 Stenelmis sp 3.65 Chimarra sp 4.08 Micropsectra sp 0.12 Stenochironomus sp 0.04 Corydalus sp 0.27 Microtendipes sp 0.19 Stenonema sp 5.04 Crangonyx sp 0.04 Molanna sp 0.04 Stylogomphus sp 0.27 Cricotopus sp 0.23 Neophylax sp 0.04 Sweltsa sp 1.62 Cricotopus/Orthocladius 0.04 Neureclipsis sp 0.04 Tallaperla sp 2.12 Diamesa sp 0.15 Nigronia sp 1.42 Tanytarsus sp 0.96 Dicranota sp 1.08 Ophiogomphus sp 0.15 Dineutus sp 0.04 Optioservus sp 0.88 Thienemanniella sp 0.19 Diphetor sp 0.04 Orthocladius sp 0.04 Thienemannimyia gr 1.23 Diplectrona sp 4.77 Oulimnius sp 0.04 Tipula sp 1.04 Dolophilodes sp 3.35 Pagastia 0.04 Turbellaria 0.15 Ectopria sp 0.85 Parachaetocladius sp 0.12 Tvetena sp 0.04 Epeorus sp 1.19 Tvetenia sp 0.96 Ephemera sp 0.19 Paragnetina sp 0.65 Wormaldia sp 0.08 Ephemerella sp 0.54 Paraleptophlebia sp 0.15 Yugus sp 0.27 Ephoron sp 0.19 Eukiefferiella sp 0.27 Parametriocnemus sp 0.73 Other 6.19 Eurylophella sp 0.62 Paraphaenocladius sp 0.04 Ferrissia sp 0.04 Pedicia sp 0.12 Gammarus sp 0.38 Perlesta sp 0.19 Glossosoma sp 0.46 Physella sp 0.23 Goera sp 0.04 Plauditus sp

23 Watershed STATION ID Appendix II: Summary Table of Monitoring Findings Latitude Longitude mean score Delaware 2800_ insufficient data Delaware 2800_ Delaware 4489_ Delaware 1300_ Delaware 2800_ Delaware 2800_ Delaware 2800_ Delaware 2800_ Delaware 2800_ Delaware 1300_ _ Moodna 4800_ Ramapo 4005_ Ramapo 5089_ Ramapo 4089_ Ramapo 5089_ Ramapo 4089_ Ramapo 4089_ Ramapo 4089_ Ramapo 5089_ Ramapo 5089_ Ramapo 5089_ Ramapo 5489_ Ramapo 4089_ Ramapo 4089_ Ramapo 5089_ Ramapo 5089_ Ramapo 5089_ Ramapo 5089_ _ insufficient data 4800_ insufficient data 2089_ _ _ _ _ st dev

24 Watershed STATION ID Latitude Longitude mean score 2289_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ st dev 24

25 Watershed STATION ID Latitude Longitude mean score st dev 2089_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

26 Watershed STATION ID Latitude Longitude mean score st dev 2489_ _ _ _ _ _ _ _ _ _ Wallkill 4401_ insufficient data Wallkill 5600_ Wallkill 5200_ Wallkill 3889_ Wallkill 4289_ Wallkill 3200_ Wallkill 5489_ Wallkill 5489_ Wallkill 5200_ Wallkill 5200_ Wallkill 5200_ Wallkill 3889_ Wallkill 2600_ Wallkill 2600_ Wallkill 4489_ Wallkill 3889_ Wallkill 5489_ Wallkill 5489_ Wallkill 5489_ Wallkill 3089_ Wallkill 5200_ Wallkill 5200_ Wallkill 5489_

27 Watershed STATION Latitude Longitude mean score st dev ID Wallkill 3889_ Wallkill 5600_ Wallkill 5489_ Wallkill 4489_ Wallkill 0900_ Wallkill 5489_ Wallkill 0900_ Wallkill 5489_ Wallkill 5200_ Wallkill 3889_ Wallkill 3889_ Wallkill 5200_ Wallkill 4289_ Wallkill 5489_ Wallkill 2600_ Wallkill 4289_ Wallkill 4205_ Wallkill 0900_ Wallkill 4205_ Wallkill 4205_ Wallkill 5889_ Wallkill 3089_ Wallkill 5600_ Wallkill 4205_ Wallkill 4289_ Wallkill 5489_ Wallkill 5489_ Wallkill 0900_ Wallkill 5600_ Wallkill 2600_ Wallkill 0900_ Wallkill 4489_ Wallkill 2600_ Wallkill 5200_ Wallkill 4489_ Wallkill 5489_ Wallkill 5200_ Wallkill 3089_ Wallkill 3089_ Wallkill 4289_ Wallkill 3089_

28 Watershed STATION ID Latitude Longitude mean score Wallkill 5489_ Wallkill 5489_ Wallkill 4289_ Wallkill 4289_ Wallkill 5489_ Wallkill 5489_ Wallkill 2600_ st dev 28

29 Appendix III: Field data summaries. 29