RESEARCH TO UNDERSTAND SURFACE WATER CONDITIONS IN AREAS OVERLYING SHALE GAS RESOURCES IN SOUTHWEST NEW BRUNSWICK

FINAL REPORT November 14, 2017

Prepared by: Jennifer Lento1, Michelle Gray1, Adam Chateauvert1, Eva Walker1, Allen Curry1, Kerry MacQuarrie1, Olivier Clarisse2, Martin Boissonault1, Courtney Johnson1, and Allison Ferguson1 1 Canadian Rivers Institute, University of New Brunswick 2 Université de Moncton

Executive Summary

The New Brunswick Energy Institute (NBEI) sought research to support a better understanding of the surface water monitoring relevant to shale gas development in New Brunswick and asked for an evaluation of appropriate methods to assess environmental conditions of streams and for establishing baseline conditions in targeted extraction regions. The Canadian Rivers Institute (CRI) at the University of New Brunswick (UNB), in partnership with the Université de Moncton (UdeM), designed and conducted a two-year research program that focused on the baseline characterization of the chemical, physical, and biological conditions in surface waters pre- development and that supports the ability of provincial and federal regulators to assess and detect changes of concern during or post-development.

The Frederick Brook Shale is part of the Lower Carboniferous Albert Formation, which underlies large areas of southeast New Brunswick. The Frederick Brook shale is organic rich and represents a source rock for oil and gas in the Sussex and Moncton region. The upper Kennebecasis and Pollett River watersheds and a subset of their tributaries were selected for the current study, because their catchments overlie the Albert Formation Early Carboniferous bedrock. The overall research program was split into five distinct sub-projects, or components, including assessment of groundwater inflows and stream temperature (A), characterization of water quality and biological community structure (B), characterization of sediment geochemistry (C), development of a method to assess dissolved methane in streams (D), and development of an online data portal for publicly accessible data (E).

A. Assessment of groundwater inflows to streams and stream temperature. Using airborne thermal infrared (IR) and optical cameras, five rivers were surveyed (Kennebecasis, Pollett, South Branch, Smith Creek, and Trout Creek) in early August 2015. Analysis of georeferenced images resulted in 268 thermal anomalies (cold water inflows). Of these, 219 were likely groundwater driven, and 49 were likely surface water driven. The Pollet River had the highest density of groundwater driven thermal anomalies (2.8 anomalies/km) followed by Trout Creek (1.0 anomalies/km). Lateral seeps were the most common, accounting for 45% of all anomalies but only 16% of anomaly area, followed by tributary confluence plumes (18% of anomalies covering 30% of anomaly area). Thermal anomaly temperatures ranged from 8.9 °C to 23.3 °C. Overall, hydrograph separation indicated that the Kennebecasis River, at Apohaqui, is dominated by groundwater. Peaks in the contribution of baseflow to total discharge occurred during winter (February) when precipitation falls as snow and during the drier summer period (August).

Mean summer water temperatures on the mainstem Kennebecasis were similar (12.4 to 15.6 °C) from the headwaters (KB1) to the valley bottom (KB5). Downstream of KB5, a gradual warming was observed to KB6 and KB7 (up to 19.3 °C) due to decreased riparian cover (shading) in this area dominated by agricultural land-use. In the Kennebecasis tributaries, Calamingo Brook (CB1) had the lowest summer mean water temperature in 2015 (9.8 °C) and in 2016 (9.1 °C). Summer discharge in Calamingo Brook is almost entirely derived from groundwater and this accounts for the low observed water temperatures. Mean summer water temperatures in the Pollett watershed were greater than those observed in the upper Kennebecasis watershed.

The airborne IR surveys indicated that most thermal anomalies indicating groundwater inflow were located at the stream margins and were likely missed during in-stream surveys where temperatures were measured in the thalweg when possible. However, areas with significant groundwater inflow or cold tributary inflow were detected by the in-stream longitudinal temperature surveys. In addition, the in-stream surveys were conducted from June to mid-July while water temperatures and flow conditions were not optimal for detecting thermal anomalies.

Airborne IR surveys, in-stream longitudinal surveys, and in-stream logger surveys provided valuable information about the natural variability of water temperature and the presence of groundwater inflows in the study area, which can be used to relate to trends observed in biological communities. Reported baseline temperature and groundwater inflow data can inform management and mitigation plans relating to the cumulative effects of potential future development in the study area. Finally, the results obtained using these methods can be used to inform future monitoring programs in this region and others.

B. Baseline characterization of water quality and biological community structure

An extensive spatial survey of biotic and abiotic ecosystem components was conducted to characterize community structure and environmental drivers in the Kenebecasis and Pollet River watersheds. 28 stream sampling stations were selected throughout the study area to represent the range of natural conditions, with 20 sites located in the Upper Kennebecasis watershed and eight sites in the Pollett River watershed. Sample stations were located on the mainstem and on tributaries to ensure a range of system sizes was included in the survey. Stations were primarily located on Early and Late Carboniferous bedrock, with additional stations sampled in areas with older bedrock age classes (Devonian-Carboniferous and Neoproterozoic). Stream sampling for water quality, benthic macroinvertebrates, and fish primarily took place in late summer 2015, with supplementary data (benthic algae and fish health) collected in spring/summer 2016. Contemporary water quality data collected in 2014 and 2015 by the New Brunswick Department of Environment and Local Government (NBDELG) and the Kennebecasis Watershed Restoration Committee (KWRC) were available for an additional 15 stations in the area, and were used for a more extensive spatial analysis of environmental drivers in the region. Historical water quality and fish data were available for a number of stations, allowing for a temporal analysis of trends and quantification of changes that have occurred in the abiotic environment and biotic communities of this region in recent years.

Water Quality

Unfiltered grab water samples were collected once in late summer 2015 for each CRI site, and were analyzed for broad-scale patterns with additional water quality data collected by NBDELG and KWRC in late summer of 2014 or 2015. Where available, historical water quality data from the NBDELG and KWRC were also used in the data analyses. Water quality analysis revealed a distinct separation among stations that reflected a strong gradient of ions, nutrients, and metals. There was a clear pattern of higher levels of conductivity and ions (especially calcium, chloride, sodium, and sulphate) in stations underlain by Early Carboniferous bedrock, with the lowest levels of ions in stations underlain by older classes of bedrock. Nutrients and metals differentiated between Late Carboniferous stations and stations on older classes of bedrock. Together, these results indicated the presence of natural geology-driven gradients in water chemistry among the study sites. Assessment of physical habitat data also supported the importance of geology in the study area, with both surficial geology and bedrock geology playing a dominant role in discriminating stations. However, streams were also characterized by strong

ii differences in temperature (correlated with bedrock geology), stream size, substrate size, and surrounding landuse.

There were few exceedances of water quality guidelines for most measured water chemistry parameters. Temporal analysis of water quality revealed decreasing trends (1999-2015), for most ions (significant for calcium, chloride, sodium, sulphate) and for alkalinity, conductivity, and hardness, each of which is controlled by ionic composition. In contrast, there was evidence of significant increasing trends for aluminum, colour (a proxy for dissolved organic carbon), and nitrite/nitrate. Temporal trends were most apparent when they were analyzed for July, indicating that the long-term decreases in ions were driven by declining trends in mid-summer.

Benthic Community

Benthic chlorophyll a, as a measure of algal biomass, was sampled from rocks at 20 stations in July 2016, with a subset of 15 stations sampled for three consecutive months (July-September) to look at temporal changes. Chlorophyll a levels were variable among stations and among sampling dates, with generally higher levels of chlorophyll a in August and September that may have reflected seasonal shifts in nutrients and flows. Future monitoring of benthic chlorophyll a would be beneficial to assess the primary producer response to any changes in nutrient levels.

Using the national Canadian Aquatic Biomonitoring Network (CABIN) protocol, benthic macroinvertebrate (BMI) communities were characterized across 26 CRI stations. BMI communities in the study area were taxonomically rich, with a 110 genera identified in 44 families of insects and 13 families/classes of non-insects. The most diverse groups were Diptera (true flies), Ephemeroptera (mayflies), Plecoptera (stoneflies), and Trichoptera (caddisflies), whereas other groups such as Odonata (dragonflies and damselflies) were low in diversity.

Water chemistry was strongly related to macroinvertebrate community composition, and benthic communities were distinguished on the basis of underlying bedrock geology age. Abundance of benthic macroinvertebrates was significantly higher at Early and Late Carboniferous stations than at Older Class stations. Genus richness was significantly higher in Early Carboniferous stations than in either Late Carboniferous or Older Class stations. There was a shift in dominance among geology age classes, with higher proportions of Chironomidae (midges) in Carboniferous stations and higher proportions of Ephemeroptera, Plecoptera, and Trichoptera in Older Class stations, indicating natural differences in community composition that were related to geological control of water chemistry. Habitat variables were less directly associated with the underlying gradients in the community data than was water quality, but communities were differentiated on the basis of bedrock geology, land cover, catchment area, and substrate size.

Conductivity was the strongest overall predictor of benthic macroinvertebrate diversity and abundance. Both conductivity and substrate size drove the percent composition of Ephemeroptera, Plecoptera, and Trichoptera, whereas the percent Chironomidae was negatively related to maximum summer temperatures at the stations. Taxonomic richness was positively related to conductivity, likely reflecting the presence of more non-insects in glacial veneer (high conductivity) stations. Overall abundance related to TOC, catchment area (indicating larger streams), and conductivity. Assessment of stream health using macroinvertebrate community richness indicated that over half the stations were outside the normal range for reference condition (samples had fewer taxa than expected), but many of the stations deviated from normal by only a small amount. Stations appeared closer to reference condition with respect to taxonomic evenness, as only four stations were below normal.

iii Fish

Fish community monitoring in 27 CRI stations resulted in the collection of 14 different species, with Brook Trout and Slimy Sculpin captured at the most stations (22 and 21 stations, respectively). Other species including Atlantic Salmon, Blacknose Shiner, and Golden Shiner were more rare and only collected at 1-2 stations. Total CPUE at a station was variable, ranging from 1.39 to 36.53. Richness was low across many stations, and ranged from one species per station (at PL2) to a maximum of nine species at a station (at KB6, KB7, and PL4). Fish CPUE and richness differed among bedrock geology age classes, but composition of Brook Trout and Slimy Sculpin and overall diversity were more related to catchment size. Community data appeared to separate the stations into three distinct groups that were not related to bedrock geology age. The primary separation distinguished the warmer stations (including PL1, PL2, PL3, PL6, and PL7) from the remaining (cooler) stations. Warmer stations were associated with Blacknose Dace, American Eel, White Sucker, and Common Shiner, and cooler stations were associated with Slimy Sculpin and Brook Trout. This gradient of stations was also associated with a gradient in total organic carbon (TOC) and aluminum, which were negatively correlated with Slimy Sculpin and Atlantic Salmon. Along the secondary separation of stations, Brook Trout relative abundance was associated with high conductivity and total phosphorus (TP). Habitat and GIS variables provided a better fit to the fish community data than was obtained with water quality data. Primary separation of stations was most strongly associated with the relative area of intrusive bedrock, which was strongly correlated with summer maximum water temperatures. Blacknose Dace and American Eel were the species most strongly associated with high area of intrusive bedrock and warm temperatures.

The percent Brook Trout had an extremely strong negative relationship with wetted width of the stream as well as a positive relationship with median substrate size; both relationships reflected brook trout life history characteristics and species preferences. The percent Slimy Sculpin was driven by average summer temperature, reflecting temperature preferences of this species. Both taxonomic evenness and richness of fish were positively related to conductivity, though richness appeared to have a threshold beyond which increasing conductivity caused a decline in the number of species. CPUE, a measure of standardized measure of total abundance, was strongly negatively related to aluminum levels.

Male and female slimy sculpin showed significant differences in relative gonad size, relative liver size and condition factor. In general, females had higher relative liver size and relative gonad size than males, whereas males generally had higher condition factor. The gonadosomatic index for females appeared to differ as a function of temperature, with an increase in the index evident with higher variability in winter temperatures.

Community concordance

There was evidence that benthic macroinvertebrate communities and fish communities provided similar characterizations of stream stations. This concordance in community structure was evident despite differences in the primary driving factors for each group of organisms, which indicated that the combined assessment of both benthic macroinvertebrates and fish may allow for detection of a wider range of potential impacts than focusing on one group alone.

Overall, the strong association between water chemistry and benthic macroinvertebrate communities indicated the importance of considering underlying geology when selecting study sites for assessment in areas of potential gas exploration. Both water chemistry and BMI

iv structure differed significantly across geology age classes, with the strongest differences evident between areas of shale gas potentential (Early Carboniferous bedrock) and surrounding areas on older bedrock age classes with little resource potential. Although fish communities appeared to be driven more strongly by temperature and system size, there was still an apparent response to geology age that underlined the importance of site classification for biomonitoring in resource extraction areas.

C. Baseline characterization of sediment and water geochemistry Characterization of sediment geochemistry and evaluation of radionuclides in water were conducted to assess whether there was evidence of the accumulation of toxic and radioactive elements in soil or stream sediments near shale gas exploitation sites. Except for two samples, all others were in compliance with the Canadian Council of Ministers of the Environment (CCME) water guidelines for the protection of aquatic life, revealing no major pollution of the selected rivers. Two samples (MP1 and ST2) present an iron concentration slightly above the CCME guideline value of 300 μg/L. However, at these two particular sites, most of the iron is bound to particles which limits its bioavailability for aquatic life. Long life radionuclides (209Bi, 232Th, 235U and 238U) and radionuclides that are products of radioactive decay (daughter elements; 46Ti, 124Te, 133Cs, 192Pt) were measured as potential indicators of shale gas exploitation impact on water quality. There was no evidence of differences in total or dissolved concentrations in rivers flowing through the McCully gas field or their nearby upstream tributaries.

Except for chromium (Cr), all sediment samples were in compliance with CCME guidelines, revealing no major pollution of the selected rivers. For the Kennebecasis River, Stoney Creek and McLeod Brook, uranium (U) concentration in sediment were systematically higher in sites downstream producing gas wells. However, it would be a hasty conclusion that the shale gas industry is responsible for this apparent enrichment: the number of sampled sites downstream gas well remained extremely limited (three in Kennebecasis River, one in Stoney Creek and one in McLeod Brook) and the range of natural background U for these rivers is undetermined. A larger survey focusing on the Kennebecasis River should be conducted adding multiple sampling sites upstream and downstream of existing gas well locations. Alternatively, if a depositional zone is clearly identified in the Kennebecasis River adjacent to McCully gas field area, a sediment core may be sampled, sliced, dated by radiometric method (i.e. 210Pb) and U concentration profile with depth measured. If the enrichment U in the fine particles of the dated sediment core starts at the same period as shale gas exploitation started in the area, contamination and impact of this industry would be demonstrated.

The instruments and methodologies used to detect metals from hydraulic fracturing may influence detection and interpretation of results. We suggest the use of a complete digestion of samples to highlight any abnormalities in the sediment metallic composition potentially related to shale gas activities as well as the use of precise instruments devoted to isotopic signature such as a multi-collector ICP-MS.

D. Methods development: Dissolved methane in streams Monitoring of dissolved methane in gaining streams (those receiving groundwater inflow) was recently proposed as a means to estimate dissolved methane concentrations in groundwater discharge and as an approach to detect changes in groundwater methane concentrations related to shale gas extraction. The objectives of this component of the study were to determine regional baseline methane concentrations for selected streams in the Kennebecasis and Pollett River watersheds, and to conduct an in-stream tracer test to assess methane transport and

v losses from a small stream. The research was intended to provide a basis for deciding whether the monitoring of stream methane is a viable approach for assessing stray gas migration in areas of natural gas development.

A reconnaissance survey produced baseline methane data for 18 streams in the Kennebecasis and Pollett River watersheds; 10 of the 18 had dissolved methane concentrations above the detection limit of 0.001 mg/L, with the highest observed concentration being 0.018 mg/L. Dissolved ethane and propane, which are more commonly associated with thermogenic natural gas, were not detected in any of the streams sampled during the reconnaissance survey.

The synoptic surveys of three streams, and in particular Parsons Brook, provided a good overview of the spatial variability of both temperature and methane concentrations, as well as allowing for selection of a site to perform a tracer test. Parsons Brook displayed a temperature profile and stream discharge results indicative of a gaining stream over a reach of approximately 1.5 km. Spatial changes in dissolved methane in Parsons Brook corresponded with changes in water temperature, suggesting the methane may have been delivered to the stream by groundwater discharge.

The results of the in-stream methane tracer test in Parsons Brook were inconclusive because of the limited downstream movement of the injected methane. The very shallow water depth, and low discharge, in the brook during the tracer test may have limited the ability to detect the injected methane downstream of the injection location. In the context of monitoring stream methane to detect changes as a result of natural gas development, more effort would be required to locate streams that would be both large enough to retain methane for a significant distance, yet small enough to have a relatively large component of groundwater discharge. Should suitable gaining reaches of streams be located downgradient of shale gas development/production areas, stream methane monitoring should be considered in conjunction with more established groundwater monitoring methods such as sampling of monitoring wells

E. Publicly accessible data: ArcGIS online data portal An online data portal was explored as a means to increase visibility of the results of this study, and to increase stakeholder awareness of the project by making results more easily accessible. ArcGIS Online is a secure cloud platform for creating maps, analytics, sharing and storing data in various formats. For this Surface Water Monitoring project, particular apps were utilized to visually engage the public, share information and present data. The Story Map application was particularly applicable to display project content such as project studies, themes, information, data, maps and photographs.

The style of the Story Map is particularly effective in guiding the user to explore the data layers and helps the user to understand the context of the available data. The app can incorporate embedded media (e.g., pictures, videos, websites) within its side panel making it more visually appealing than plain text. ‘Actions’ are also available in the panel, allowing an interactive aspect where users can click on words to zoom in to specific areas or content within the map. The Story map outlines the purpose of the project and displays data collected in the Kennebecasis and Pollett rivers watersheds. For this research program, the basemap and location remain consistent and the data and descriptions change as you scroll through the side panel. Data can be displayed visually in a variety of ways to highlight the spatial differences within the watershed.

Recommendations for Future Monitoring

vi Results of the assessment of baseline conditions in streams in the Kennebecasis and Pollett watersheds led to a number of recommendations for effective future monitoring: • Temperature logging should continue in the area as a low-cost option to monitor changes in water temperature (which was an important driver of stream communities). o Selection of logging stations should maximize variability across the river and stream temperature gradients, incorporating identified thermal anomalies. • Selection of stations for water quality monitoring must take into account underlying bedrock geology age, as this was a large driver of chemical composition. Baseline conditions and regular monitoring should be established at minimum in a number of reference sites across Early Carboniferous bedrock areas to ensure test sites are compared with appropriate reference conditions. o Water samples should be analyzed for both total and dissolved metals portions to characterize their bioavailability. • Long-term monitoring of water quality should be expanded with a regular rotation of stations to maximize spatial coverage of temporal data. Selection of stations should build upon the historical data from NBDELG and KWRC. • The age of sediments should be determined using sediment cores to measure the timing of U enrichment in the area of shale gas exploitation and characterize the natural background levels of U upstream and downstream of operating wells. • Regular monitoring of chlorophyll a should be incorporated as a low-cost option to assess algal biomass and trophic status of stream systems. • Benthic macroinvertebrate monitoring should continue to follow CABIN protocols, with identification of organisms to the level of family, and stations should be selected to ensure sufficient representation of reference and test sites in Early Carboniferous bedrock areas. • Fish monitoring should continue on a regular basis (1-2 years) using the rapid assessment method of single-pass electrofishing, but should be completed on a longer time interval (e.g., 3-5 years) at the three stations with historical monitoring data using more intensive depletion electrofishing methods to allow for temporal assessment of fish community data collected in a similar manner. Site selection for fish monitoring must include a range of stream sizes (small, medium, and large catchments) within the Early Carboniferous bedrock area, as both system size and geology were found to be important. • Slimy sculpin should continue to be monitored as a measure of fish health within this region as a fairly ubiquitous and abundant fish species that reflects local conditions. • Additional in-stream dissolved methane tracer tests should be conducted in some of the larger streams in the area, or during periods of higher discharge, so that methane transport and losses are better quantified. • The isotopic signature of stream methane (stable isotopes of C and H) should be investigated to assess whether the observed low concentrations of methane are of thermogenic or biogenic origin. To provide effective monitoring of the streams within the Kennebecasis watershed, it is necessary that site selection includes careful evaluation of underlying bedrock geology age, habitat conditions, temperature, and water quality at all potential stations to ensure the full gradient is captured. Water quality and biotic community structure were closely linked to variability across the landscape, and effective monitoring should take that into account to ensure the range of natural variability is captured.

vii Establishment of long-term monitoring that builds upon the data collected for this report and the historical data collected by government and watershed associations will ensure a strong foundation for future assessments of ecosystem health in these stream systems.

viii Disclaimer The data and any related information presented in this report are not to be published without written permission from Dr. Michelle Gray. The information provided by the authors is accurate to the best of the authors’ knowledge, however, while every effort has been made to provide accurate information, the authors do no warrant that the information is free from error. Furthermore, some data presented in this report are from third-party providers. For more information or guidance on proper and improper uses of the data from this report, please contact the authors. The authors will not be liable for the non-permitted, improper or incorrect use of any data from this study.

Corresponding author: Dr. Michelle Gray, Faculty of Forestry and Environmental Management, UNB, 28 Dineen Drive, Fredericton, NB E3B5A3. [email protected]

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Table of Contents

Executive Summary ...... i Disclaimer ...... ix Table of Contents ...... i List of Tables ...... iv List of Figures ...... vii 1 Background ...... 1 2 Objectives ...... 3 3 Study Area ...... 5 4 Project A: Assessment of groundwater inflows to streams and stream temperature ...... 9 4.1 Introduction ...... 9 4.2 Methods ...... 10 4.2.1 Study design ...... 10 4.2.2 Airborne infrared (IR) imagery ...... 12 4.2.3 In-stream temperature surveys ...... 14 4.3 Results ...... 15 4.3.1 Airborne IR surveys: Thermal anomalies and groundwater inflow ...... 15 4.3.2 Longitudinal stream temperature profiles (Thermal IR Surveys) ...... 22 4.3.3 In-stream surveys ...... 22 4.4 Conclusions ...... 35 5 Project B: Baseline characterization of water quality and biological community structure ..36 5.1 Introduction ...... 36 5.2 Methods ...... 37 5.2.1 Study design ...... 37 5.2.2 Water quality and physical habitat ...... 42 5.2.3 Biological community ...... 52 5.3 Results and Discussion ...... 59 5.3.1 Water Quality and Physical Habitat ...... 59 5.3.2 Biological community ...... 71 5.4 Conclusions ...... 102 6 Project C: Sediment and Water Geochemistry ...... 104 6.1 Introduction ...... 104 6.2 Methods ...... 104 6.2.1 River water sample collection, treatment and preparation ...... 106 6.2.2 Sediment sample collection, treatment and preparation ...... 106 6.2.3 Instrumentation ...... 107 6.3 Results and Discussion ...... 107 6.3.1 Water samples ...... 107 6.3.2 Sediment samples ...... 112 6.4 Conclusions ...... 117 7 Project D: An assessment of a stream-based methane monitoring method ...... 118 7.1 Introduction ...... 118 7.2 Methods ...... 118 7.2.1 Reconnaissance Dissolved Methane Survey ...... 118 7.2.2 Synoptic Surveys ...... 120 7.2.3 Temperature profiling ...... 120 7.2.4 Methane sampling ...... 121 7.2.5 Discharge ...... 123 7.2.6 In-stream Tracer Test ...... 124 7.3 Results and Discussion ...... 126 7.3.1 Reconnaissance Survey ...... 126 7.3.2 Synoptic Survey ...... 127 7.4 Limitations and recommendations for future work ...... 135 7.5 Conclusions ...... 136 8 Project E: ArcGIS online and Story Mapping ...... 137 8.1 Next steps and future utility ...... 141 9 Recommendations for Future Monitoring ...... 142 9.1 Water Temperature and Source ...... 142 9.1.1 Temperature logging ...... 142 9.1.2 Temperature profiling ...... 143 9.2 Water and Sediment Quality ...... 143 9.2.1 Water quality characterization ...... 143 9.2.2 Spatial and temporal water quality sampling ...... 144 9.2.3 Sediment quality ...... 144 9.3 Biotic communities ...... 144 9.3.1 Chlorophyll a ...... 144 9.3.2 Benthic macroinvertebrates ...... 145 9.3.3 Fish ...... 145 9.4 Stream Methane ...... 146 9.5 Conclusions ...... 146

ii 10 Acknowledgments ...... 148 11 References ...... 149 Appendix A: Location, area, and classification of thermal anomalies detected in southeast New Brunswick rivers, August 2015...... 156 Appendix B: Water and sediment geochemistry project: Technical information for laboratory analyses (Clarisse Lab; U de Moncton)...... 167 Appendix C: Dissolved methane in surface waters, reconnaissance and tracer studies. .... 170

iii List of Tables Table 4-1. Summary of longitudinal temperature surveys, 2015...... 10 Table 4-2. Thermal anomaly classification and probable water source (modified from Dugdale et al. 2013)...... 13 Table 4-3. Groundwater driven thermal anomalies in southeast New Brunswick rivers, August 2015...... 16 Table 4-4. Surface water driven thermal anomalies in southeast New Brunswick rivers, August 2015...... 16 Table 5-1 Stations sampled as part of the 2015-2016 CRI monitoring program, including station codes, stream names, coordinates (latitude and longitude in decimal degrees), upstream catchment area (area, km2), dominant bedrock geology age in a 1 km upstream buffer, and a record of water quality, physical habitat, chlorophyll a (chl a), benthic macroinvertebrate (BMI), and fish data from each station (X = sample collected)...... 39 Table 5-2 Details about additional water quality stations in the assessment, including stations monitored by the New Brunswick Department of Environment and Local Government (NBDELG) and the Kennebecasis Watershed Restoration Committee (NWRC), with stream names, coordinates (latitude and longitude in decimal degrees), and the years of water quality data that were used for assessment. All listed stations were included in the analysis of broad-scale water quality (using average August-September data from 2015 or 2014, whichever was available, to be comparable with CRI data collection), but only those stations with multiple years of data indicated in the table were included in the assessment of long-term trends...... 42 Table 5-3 Chemistry parameters, organized by test group, measured across the CRI, NBDELG, and KWRC water quality sample stations, including abbreviated parameter names, units of measurement, and limits of quantitation (LOQs). Included are parameters measured across the full range of stations. All parameters, with the exception of pH, were log10-transformed prior to use in analysis...... 46 Table 5-4 Physical habitat descriptors, organized by group, estimated for the CRI study sites by CRI field crew, including abbreviated parameter names, units of measurement, and data transformation. Included are the variables that were considered for the analysis...... 47 Table 5-5 Physical habitat descriptors, organized by group, estimated in ArcGIS for the CRI study sites, including abbreviated parameter names, units of measurement, data transformation, and data source. Included are the variables that were considered for the analysis...... 48 Table 5-6 Mean (± standard error) conductivity (μS/cm), alkalinity (mg/L), and concentrations of major ions (mg/L) for stream stations grouped by geological age class determined by the dominant age class within a 1 km upstream catchment buffer (Early Carboniferous, Late Carboniferous, or Older Classes, which includes Neoproterozoic and Devonian- Carboniferous). Values below LOQ were included as half the LOQ...... 60 Table 5-7 Summary table of average (± SE) chemistry values for the 41 water quality stations sampled by CRI, NBDELG, and KWRC in 2014 and 2015, with average (± SE) for stations within Early Carboniferous (n=25), Late Carboniferous (n=9), and Older Classes of bedrock (n=5). Two Older Class stations were omitted from geology-specific summary statistics because they appeared to be influenced by Early Carboniferous bedrock in the local vicinity. Values below LOQ were included as half the LOQ and results are reported only for parameters that were measured above the LOQ for at least one station...... 64 Table 5-8 The number of water quality samples (collected at CRI, NBDELG, and KWRC stations) that exceed the CCME Water Quality Guidelines for the Protection of Aquatic Life (Freshwater) in 2014 and 2015...... 66

iv Table 5-9 Results of the seasonal Kendall test to evaluate water quality trends over time (incorporating data from May, July, September, and November in each year) for three NBDELG water quality stations with the longest records. All parameters showed homogeneous long-term patterns among the four sampling months in preliminary testing. Colours and directional arrows indicate the direction and significance of any long-term monotonic trends, with dark colours and arrows for significant trends and light colours and arrows for non-significant trends (see legend for details)...... 67 Table 5-10 Results of the homogeneity of trend test to compare water quality patterns across all long-term trend stations (see Table 5-2 for site details) for parameters that showed evidence of trends in July or September in the three NBDELG water quality stations. All parameters showed homogeneous patterns across the stations, but colours and directional arrows indicate the direction and significance of any long-term monotonic trends, with dark colours and arrows for significant trends and light colours and arrows for non-significant trends (see legend for details)...... 70 Table 5-11 Classifications of benthic macroinvertebrates collected in the 26 CRI study sites in 2015, indicating the number of stations in which each family (subfamily for Chironomidae) was found, and the taxonomic abbreviation used in analysis...... 73 Table 5-12 Diversity metrics calculated for benthic macroinvertebrates sampled at each CRI station in 2015, including macroinvertebrate abundance, richness (calculated at subfamily level for Chironomidae and family level for remaining taxa), diversity (Simpson’s 1-D Index, which ranges from 0 to 1), and the percent of the sample composed of Chironomidae (midges) and Ephemeroptera, Plecoptera, and Trichoptera (EPT; mayflies, stoneflies, and caddisflies)...... 74 Table 5-13 Results of the least-squares linear regression analysis of benthic macroinvertebate metrics and environmental drivers (water quality and physical/habitat drivers) for 26 CRI study sites, showing the models that had the lowest AICc value and explained the greatest amount of variation in the response metric. Reported statistics include the standardized regression coefficient for each driver, the p value for each driver, the residual mean square for model (RMS; a measure of error around the estimate), the p value for each model, and the R2 (or adjusted R2 for multiple regression models)...... 80 Table 5-14 Summary of fish species collected in 27 CRI stations sampled in 2015, with taxonomic codes used in figures, the number of stations at which each species was collected, and the CPUE (/100 s) averaged across stations at which each taxon was found...... 85 Table 5-15 Biological metrics summarizing fish communities collected at 27 CRI stations in 2015, including the total number of fish collected, the catch per unit effort (CPUE; standardardized per 100 s), species richness, Simpson's Index (1-D), percent Brook Trout and percent Slimy Sculpin...... 86 Table 5-16 Results of the least-squares linear regression analysis of fish metrics and environmental drivers (water quality, physical/habitat drivers, and temperature) for the CRI study sites, showing the models that had the lowest AICc value and explained the greatest amount of variation in the response metric. Reported statistics include the standardized regression coefficient for each driver, the p value for each driver, the residual mean square for model (RMS; a measure of error around the estimate), the p value for each model, and the R2 (or adjusted R2 for multiple regression models). Where the AICc and R2 indicated that models provided similar fits to the data, both models are included...... 93 Table 5-17 Presence of fish species at three stream stations sampled in 1996-1998, 2008-2009, and 2015. An X indicates that the species was collected during sampling events in the indicated year...... 96 Table 6-1. Abbreviations and their elements measured in overlaying water and sediments in the Kennebecasis and Pollett Rivers baseline characterization project...... 105

v Table 6-2. Long lived radionuclides and short lived radionuclide decay products measured (shaded) in this project...... 105 Table 6-3. Total concentrations of Li, Na, Mg, Al, K, Ca, Sc, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Ag, Cd, Sb, Te, Cs, Ba, Pt, Pb, Bi, Th and U measured by ICP-MS in water at the different sampling locations on the Kennebecasis and Pollett Rivers. Limit Of Detection (LOD) and Limit Of Quantification (LOQ) also indicated for each element (list of abbreviations in Table 6-1).Values exceeding CCME guidelines for aqatic life are highlighted in green...... 108 Table 6-4. Dissolved concentrations of various elements measured by ICP-MS in water after filtration at the different sampling locations on the Kennebecasis and Pollett Rivers. Limit Of Detection (LOD) and Limit Of Quantification (LOQ) also indicated for each element (list of abbreviations in Table 6-1)...... 110 Table 6-5. Concentration of Li, Be, Na, Mg, Al, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Zn, As, Rb, Sr, Ag, Cd, Sb, Cs, Ba, La, Ce, Eu, Pt, Pb, Bi, Th and U measured by ICP-MS in sediment at the different sampling locations on the Kennebecasis and Pollett Rivers. Limit Of Detection (LOD) and Limit Of Quantification (LOQ) also indicated for each element (list of abbreviations in Table 6-1)...... 114 Table 7-1. Stream methane concentrations from the reconnaissance survey, July 2015,. McLeod Brook Spring denotes a spring found during sampling which was also sampled . 126 Table 7-2: Average water quality parameters, for a 24-hr period, at a location 25 m upstream of the tracer injection location, Parsons Brook...... 132 Table 7-3: Discharge results, Parsons Brook, September 7 2016...... 133 Table 7-4: Comparison of stream methane concentrations in New Brunswick 2015 (18 streams and one near stream spring) and in Pennsylvania (16 streams; Heilweil et al. 2014)...... 134 Table 7-5: Gas transfer velocities and key stream characteristics for several streams in which gas evasion studies have been conducted ...... 135

vi List of Figures

Figure 3-1. The study area in the Kennebecasis and Pollett river watersheds, southeast New Brunswick, showing bedrock geology age for the region (indicative of shale gas potential) and location of exisiting wells...... 6 Figure 3-2. Mean monthly temperature and precipitation at Sussex, NB (1981-2006; Environment Canada 2016a)...... 7 Figure 3-3. Mean monthly discharge for the Kennebecasis River at Apohaqui (1961-2013; Environment Canada 2016b)...... 7 Figure 4-1. Airborne infrared temperature survey pathways flown by helicopter, August 2015. Inset figure shows the Hammond River which was included as part of a secondary research study for the Atlantic Salmon Conservation Foundation (ASCF), and will not be reported here...... 11 Figure 4-2. In-stream temperature survey pathways and continuous temperature logger locations...... 11 Figure 4-3. Kennebecasis River, thermal anomaly temperature and location estimated from Airborne IR survey, August 2015...... 17 Figure 4-4. South Branch, thermal anomaly temperature and location estimated from Airborne IR survey, August 2015...... 18 Figure 4-5. Smiths Creek, thermal anomaly temperature and location estimated from Airborne IR survey, August 2015...... 19 Figure 4-6. Trout Creek, thermal anomaly temperature and location estimated from Airborne IR survey, August 2015...... 20 Figure 4-7. Pollet River, thermal anomaly temperature and location estimated from Airborne IR survey, August 2015...... 21 Figure 4-8. Kennebecasis River, longitudinal stream temperature profile, measured via an in- stream survey, June 2015...... 23 Figure 4-9. Calamingo Brook, longitudinal stream temperature profile, measured via an in- stream survey, June 2015...... 24 Figure 4-10. Negro Brook (tributary of the South Branch River), longitudinal stream temperature profile, measured via an in-stream survey, June 2015...... 25 Figure 4-11. South Branch, longitudinal stream temperature profile, measured via an in-stream survey, June 2015...... 26 Figure 4-12. Stone Brook, longitudinal stream temperature profile, measured via an in-stream survey, July 2015...... 27 Figure 4-13. McLeod Brook, longitudinal stream temperature profile, measured via an in-stream survey, July 2015...... 28 Figure 4-14. Mean monthly runoff, baseflow, and baseflow index (BFI) for the Kennebecasis River at Apohaqui using six hydrograph separation techniques...... 30 Figure 4-15. Summer water temperatures at sites in the Upper Kennebecasis and Pollett River watersheds, 2015-2016...... 33 Figure 4-16. Fall 2015, Winter 2015-2016, and Spring 2016 water temperatures at sites in the Upper Kennebecasis and Pollett River watersheds...... 34 Figure 5-1 Stations where CRI baseline characterization surveys were conducted in 2015/2016, including collection of chemical/physical data, chlorophyll a, benthic macroinvertebrates, fish community, and sculpin data. Symbols indicate which suite of parameters was collected at each station (see Table 5-1 for full details)...... 40 Figure 5-2 Stations where CRI baseline characterization surveys were conducted in 2015/2016 for the collection of chemical/physical data, chlorophyll a, benthic macroinvertebrates, fish

vii community, and sculpin data, with underlying bedrock geology age indicated. Stations were classified based on the dominant bedrock geology age in a 1km upstream buffer...... 41 Figure 5-3 Stations where water quality samples were collected by CRI, NBDELG, and/or KWRC in 2014 or 2015. Stations with historical records that were sufficient for inclusion in the temporal trend analysis include the three NBDELG stations (KR-5Points, SB- Penobsquis, and KV-18) as well as KV-06, KV-07, KV-10, KV-11, KV-14, and KV-28 (see Table 5-2 for details of years of sampling)...... 43 Figure 5-4 Stations where water quality samples were collected by CRI, NBDELG, and/or KWRC in 2014 or 2015, with bedrock geology age indicated. Stations were classified by the dominant geological age class in a 1 km upstream catchment buffer...... 44 Figure 5-5 (a) Surficial geology and (b) bedrock geology underlying the 41 water quality stations sampled by CRI, NBDELG, and KWRC in 2014 or 2015. Geology layers are national layers obtained from Geogratis, with surficial and national bedrock class names from Geological Survey of Canada...... 49 Figure 5-6 Water chemistry data summarized for each of the three geological age classes (Early Carboniferous, Late Carboniferous, or Older Classes, which include Neoproterozoic and Devonian-Carboniferous), including (A) conductivity (μS/cm), (B) total organic carbon (mg/L), (C) nitrate (mg/L), and (D) total phosphorous (mg/L). Lower-case letters on each plot indicate significant differences (at α = 0.05) among geological age classes, as determined by Tukey tests adjusted for unequal sample sizes. In each box plot, the median (central horizontal line) is in a box bounded by the 25th and 75th percentiles, with whiskers indicating 10th and 90th percentiles and points for statistical outliers...... 60 Figure 5-7 Principal Components Analysis (PCA) biplot of the 26 CRI water quality stations sampled in 2015, with station symbols indicating the geological age of the bedrock within a 1 km upstream buffer. Water quality variable abbreviations are listed in Table 5-3...... 61 Figure 5-8 Principal Components Analysis (PCA) biplot of the 41 water quality stations sampled by CRI, NBDELG, and KWRC in 2014 and 2015, with station symbols indicating the geological age of the bedrock within a 1 km upstream buffer. Water quality variable abbreviations are listed inTable 5-3...... 62 Figure 5-9 Principal Components Analysis (PCA) biplot of habitat variables collected on site and estimated through GIS for the 26 water quality stations sampled by CRI in 2015, with station symbols indicating the dominant bedrock geology age in a 1 km upstream catchment buffer. Habitat variable abbreviations are listed in Table 5-4 and GIS variable abbreviations are listed in Table 5-5...... 65 Figure 5-10 Temporal trends in conductivity (uS/cm) with data plotted for annual samples collected in May, July, September, and November from NBDELG stations (a) KV-18 and (b) KR-5Points and SB-Penobsquis...... 69 Figure 5-11 Chlorophyll a results (mg/m2) for samples taken in July, August, and September 2016, with TP results (line plot) from the same stations in 2015. Samples were collected from 20 CRI stations in July 2016, and from 15 stations in August and September 2016. ..71 Figure 5-12 Benthic macroinvertebrate community metric values summarized for each of the three geological age classes (with geology classified by geological age as Early Carboniferous, Late Carboniferous, or Older Classes, which include Neoproterozoic and Devonian- Carboniferous), including (A) abundance, (B) genus richness, (C) percent Chironomidae, and (D) percent EPT. Lower-case letters on each plot indicate significant differences (at α = 0.05) among geological age classes, as determined by Tukey tests adjusted for unequal sample sizes. In each box plot, the median (central horizontal line) is in a box bounded by the 25th and 75th percentiles, with whiskers indicating 10th and 90th percentiles and points for statistical outliers...... 75 Figure 5-13 Principal Components Analysis (PCA) biplot based on relative abundances of benthic macroinvertebrate taxa collected at the 26 stations sampled by CRI in 2015, with

viii station symbols indicating dominant bedrock geology age in a 1 km upstream catchment buffer. Due to the high diversity among stations, taxonomic associations have been summarized by highlighting particular taxa and their approximate location on the ordination. Taxonomic abbreviations can be found in Table 5-11...... 76 Figure 5-14 Redundancy Analysis (RDA) biplot of benthic macroinvertebrate relative abundance constrained by water quality data for 26 CRI study sites sampled in 2015, with station symbols indicating dominant bedrock geology age in a 1 km upstream catchment buffer. Water quality abbreviations can be found in Table 5-3...... 78 Figure 5-15 Redundancy Analysis (RDA) biplot of benthic macroinvertebrate relative abundance constrained by habitat and GIS data for 26 CRI study sites sampled in 2015, with station symbols indicating dominant bedrock geology age in a 1 km upstream catchment buffer. Habitat abbreviations can be found in Table 5-4 and GIS variable abbreviations can be found in Table 5-5...... 79 Figure 5-16 Relationship between log10 taxonomic richness of benthic macroinvertebrates (at subfamily for Chironomidae, family for other taxa) and log10 conductivity at the 26 stations sampled by CRI in 2015. A linear regression line is fit to the data...... 81 Figure 5-17 Relationship of the log10-transformed percent Ephemeroptera, Plecoptera, and Trichoptera (EPT) with (a) log10 conductivity and (b) log10 D50 (average particle size) at the 26 stations sampled by CRI in 2015. A linear regression line is fit to each to visualize direct relationships in the data the data...... 82 Figure 5-18 Observed/Expected (O/E) richness of benthic macroinvertebrate data from 26 CRI stations that were assessed using the Atlantic Reference Model. The upper dashed line indicates the breakpoint between normal samples (> 0.95) and divergent samples (0.47- 0.95). The lower limit of the divergent class and upper limit of the highly divergent class is indicated by the lower dashed line. Samples that fall within the divergent range are coloured in orange...... 83 Figure 5-19 Map of CRI benthic macroinvertebrate sampling locations indicating samples that were within normal range for reference sites with respect to taxonomic richness (blue circles) and samples that were divergent from reference condition (red squares)...... 83 Figure 5-20 Observed/Expected (O/E) evenness (Simpson’s Index) of benthic macroinvertebrate data from 26 CRI stations that were assessed using the Atlantic Reference Model. The upper dashed line indicates the breakpoint between normal samples (> 0.96) and divergent samples (0.48-0.96). The lower limit of the divergent class and upper limit of the highly divergent class is indicated by the lower dashed line. Samples that fall within the divergent range are coloured in orange...... 84 Figure 5-21 Map of CRI benthic macroinvertebrate sampling locations indicating samples that were within normal range for reference sites with respect to taxonomic evenness calculated as Simpson’s Index (blue circles) and samples that were divergent from reference condition (red squares)...... 85 Figure 5-22 Fish community metric values summarized for each of the three catchment size classes (small, medium, and large), including (A) percent Brook Trout, (B) percent Slimy Sculpin, and (C) diversity measured by Simpson’s (1-D) Index. Lower-case letters on each plot indicate significant differences (at α = 0.05) among geological age classes, as determined by Tukey tests adjusted for unequal sample sizes. In each box plot, the median (central horizontal line) is in a box bounded by the 25th and 75th percentiles, with whiskers indicating 10th and 90th percentiles and points for statistical outliers...... 87 Figure 5-23 Fish community metric values summarized for each of the three geological age classes (with geology classified by geological age as Early Carboniferous, Late Carboniferous, or Older Classes, which include Neoproterozoic and Devonian- Carboniferous), including (A) species richness, and (B) catch per unit effort (CPUE) per 100s. Lower-case letters on each plot indicate significant differences (at α = 0.05) among

ix geological age classes, as determined by Tukey tests adjusted for unequal sample sizes. In each box plot, the median (central horizontal line) is in a box bounded by the 25th and 75th percentiles, with whiskers indicating 10th and 90th percentiles and points for statistical outliers...... 88 Figure 5-24 Principal Components Analysis (PCA) biplot based on fish relative abundance for species collected at the 27 stations sampled by CRI in 2015, with station symbols indicating underlying surficial geology. Taxonomic abbreviations can be found in Table 5-14. Stations PL7 (glacial blanket) and PL4A (undifferentiated bedrock) are stations where only fish data were collected, but are included in this analysis to increase geospatial coverage of the assessment...... 89 Figure 5-25 Redundancy Analysis (RDA) biplot of fish relative abundance data constrained to water chemistry variables collected at the 25 CRI stations with both fish and water quality data collection in 2015, with station symbols indicating dominant bedrock geology age in a 1 km upstream catchment buffer. Taxonomic abbreviations can be found in Table 5-14 and water quality abbreviations can be found in Table 5-3...... 91 Figure 5-26 Redundancy Analysis (RDA) biplot of fish relative abundance data constrained to habitat variables estimated from the field and from GIS at the 25 CRI stations with both fish and habitat data collection in 2015, with station symbols indicating dominant bedrock geology age in a 1 km upstream catchment buffer. Taxonomic abbreviations can be found in Table 5-14 and habitat variable abbreviations can be found inTable 5-4 and Table 5-5. 92 Figure 5-27 Relationship between the log10 percent Brook Trout at a station and (a) log10 wetted width (m) and (b) log10 D50 (a measure of substrate size) for the 25 CRI stations with both fish sampling and habitat description in 2015. A linear regression line is fit to the data...... 94 Figure 5-28 Relationship between log10 CPUE and log10 aluminum (mg/L) at 25 CRI stations with both fish and water quality sample collection in 2015. A linear regression line is fit to the data...... 95 Figure 5-29 Relationship between log10 fish species richness and log10 conductivity (μS/cm) at the 25 CRI stations with fish and water quality sampling in 2015. A second-order polynomial regression line is fit to the data, fitting the model log10Richness = log10Conductivity + 2 (log10Conductivity) ...... 95 Figure 5-30 (a) The log10 liver weight and (b) log10 gonad weight of Slimy Sculpin as a function of log10 carcass weight, with different regression lines fit to males and females...... 98 Figure 5-31 The relationship between log10 carcass weight and log10 length among male and female Slimy Sculpin, with separate regression lines fit to males and females...... 99 Figure 5-32 Boxplots of differences in gonadosomatic index for female (solid fill) and male (no fill) Slimy Sculpin among stations ordered from highest to lowest winter water temperature coefficient of variation (see section 4). Letters indicate significant differences among stations (Tukey’s post hoc test)...... 99 Figure 5-33 Boxplots of differences in liversomatic index for female (solid fill) and male (no fill) Slimy Sculpin among stations ordered from highest to lowest winter water temperature coefficient of variation (see section 4). Letters indicate significant differences among stations (Tukey’s post hoc test)...... 100 Figure 5-34 Residuals from Procrustes Analysis of fish (target) and benthic macroinvertabrate (rotational) PCA ordinations, indicating the residual distance between a site’s location in the target ordination and its location in the rotational ordination after translation and rotation. The solid and dotted horizontal lines represent type 7 quartiles of the residuals...... 101 Figure 6-1. Sampled sediment classification according to their granulometry characteristics (ISO 14688-2)...... 112 Figure 6-2 Average enrichment factor for Ni, Cs and U monitored in the sampled sediment from the Kennebecasis and Pollett Rivers. Error bars represent the enrichment factor standard deviation measured for the 26 samples...... 113

x Figure 6-3 a) Gas well locations and sediment sampling sites map from the Canadian Rivers Institute’ online data portal developed as part of this program and b) U concentration in sediment fine particles for each river system. Reference sites were defined as upstream locations from existing and active gas well...... 116 Figure 6-4 Isotopic signatures for a) U (238U/235U) and b) Ti (46Ti/47Ti) in sediment from the selected rivers. Reference sites were defined as upstream locations from existing and active gas well...... 116 Figure 7-1. Reconnaissance survey sample locations within New Brunswick (insert). Green triangles indicate the reconnaissance survey stream sampling locations, while the shaded pink area is the approximate extent of the McCully Gas Field...... 119 Figure 7-2. Map of locations of interest, Parsons Brook (see Figure 7-1 for location). Water in the brook flows from south to north. The temperature profiling was conducted on July 15, 2015; methane sampling was conducted in July, 2015, and in May, 2016...... 121 Figure 7-3. Map of locations of interest, McLeod Brook (see Figure 7-1 for location). Water in the brook flows from south to north. The temperature profiling was conducted on July 16- 17, 2015; methane sampling was conducted in August, 2015...... 122 Figure 7-4. Map of locations of interest, Shannon Brook. (see Figure 7-1 for location). Water in the brook flows from east to west. The temperature profiling was conducted in July, 2015; methane sampling was conducted in October, 2015...... 123 Figure 7-5. Methane injection equipment including silicone tubing (left) and methane gas source and controls (right), September 2016...... 124 Figure 7-6. Parsons Brook below the tracer injection location, September 2016...... 125 Figure 7-7. Temperature profiles for Parsons Brook, McLeod Brook, and Shannon Brook (July 2015). The downstream flow direction is to the left of the plot. Maps showing the locations of the temperature profiles are shown in Figure 7-2, Figure 7-3, and Figure 7-4...... 127 Figure 7-8: Methane concentrations (July 2015, June 2016) and temperature profile (July 2015) for Parsons Brook. The downstream flow direction the left of the plot. The locations of the methane sampling sites are shown in Figure 7-2...... 128 Figure 7-9: Methane concentrations (August 2015), and temperature profile (July 2015) for McLeod Brook. The downstream flow direction is toward lower chainage. The locations of the methane sampling sites are shown in Figure 7-3...... 129 Figure 7-10: Methane concentrations (October 2015) and temperature profile (July 2015) for Shannon Brook. The downstream flow direction is to the left of the plot. The locations of the methane sampling sites are shown in Figure 7-4...... 129 Figure 7-11: Parsons Brook discharge, June 2016. The downstream flow direction is to the left of the plot. The locations of the stream discharge measurements are shown in Figure 7-2...... 130 Figure 7-12: Parson Brook bromide concentrations, pre- and 24 hours post-tracer test, September 6-7, 2016. The downstream flow direction is to the right of the plot...... 131 Figure 7-13: Temporal variability of methane concentration 25 m upstream of injection site on Parsons Brook (September 6-7, 2016) ...... 131 Figure 7-14: Methane concentrations, Parsons Brook September 6-7 2016...... 133 Figure 8-1. Screen shot of the NBEI Surface Water Monitoring program Story Map introduction with descriptive text in the side panel and larger map. Full current web address is: http://arcg.is/1YnSOKx...... 138 Figure 8-2. Panel showing overall land-use within the upper Kennebecasis and Pollett River watersheds...... 138 Figure 8-3. Landuses within the watershed of site MP1 are displayed when the "MP1 Watershed Landuse" text is clicked on by the user...... 139 Figure 8-4. Differences in temperature displayed by colour scale within the study area (A), and differences in conductivity displayed by the size of the circle (B)...... 139

xi Figure 8-5. Substrate types displayed in pie charts when the user clicks on each site with results for site KB1 shown...... 140 Figure 8-6. Sliding bar used to show temporal differences in methane concentrations in Parson's Brook (2015-2016)...... 141

xii 1 Background

Natural gas production from oil shale deposits through hydraulic fracturing (unconventional gas) has gained in popularity over the last 15 years as a potential alternative to conventional oil and gas deposits (Vidic et al. 2013, Rivard et al. 2014). Shale gas production is the extraction of natural gas from fine-grained sedimentary bedrock containing high amounts of organic matter (Dyni 2003), and it is considered an unconventional source because the low permeability of the bedrock requires the injection of hydraulic fracturing to allow the gas to flow (Vidic et al. 2013). Potential environmental concerns to water resources from shale gas production include contamination of groundwater from hydrologic connectivity to fractured areas and improper handling/disposal of fracturing fluids and wastewater, which could lead to spills in nearby waterways (Vengosh et al. 2013, Vidic et al. 2013, Vengosh et al. 2014). Despite the potential for environmental impacts, there remains a lack of information to support environmental monitoring of these activities, (Vengosh et al. 2013, Vidic et al. 2013, Vengosh et al. 2014, Leger et al. 2016a, b). Environmental monitoring of water chemistry and biological community structure will be required in most jurisdictions where shale gas extraction occurs, but there is insufficient information about the abiotic and biotic characteristics of freshwater systems underlain by the distinctive bedrock geology of areas with high shale gas potential. Such knowledge is important for meaningful management and assessment planning for fresh water systems in these cases of geology-specific and driven resource development, i.e., understanding in-river variability as it relates to surface and underlying geology and thus establishing spatially appropriate metrics and monitoring programs.

New Brunswick has the potential for intensive unconventional gas development in the Frederick Brook shale, a Carboniferous bedrock area in the southeast region of the province. Concern for surface water quality and a quantity has been expressed among the public. This concern is a legitimate issue across all jurisdictions where shale gas is being developed, as reported by the Council of Canadian Academies (2014):

“Reliable and timely information, including characterization, underpins the implementation of a risk management framework. Although monitoring is no substitute for effective prevention practices, it is the means by which environmental and human health impacts are identified, making it possible for mitigation measures to be designed and implemented.”

The NB Energy Institute (NBEI) sought research to support better understanding of the potential impacts of this unconventional gas development on surface waters in the province. The NBEI asked for an evaluation of appropriate methods to assess environmental conditions of streams and establish baseline conditions in targeted extraction regions. The Canadian Rivers Institute (CRI) at the University of New Brunswick (UNB), in partnership with the Université de Moncton (UdeM), designed a two-year research program to focus on the baseline characterization of the chemical, physical, and biological conditions in surface waters pre-development to support the ability of provincial and federal regulators to assess and detect changes of concern during or post-development.

The overall research program was led by the Canadian Rivers Institute at the University of New Brunswick (CRI/UNB). The project deliverables were achieved using a university-based

1 research approach involving graduate students, research associates, and research staff. Projects were developed and completed both as the final report as presented here, in partial fulfillment of three graduate degrees (Masters level), and multiple peer-reviewed scientific journal articles. In addition to producing results from a well-established research team and environment, the research program trained and prepared several young professionals to join the emerging resource development, monitoring, and regulatory sectors in NB and beyond.

2 2 Objectives

The overriding goal of this research program and report was to inform management discussions and decisions regarding the cumulative effects of future development in this area.

The overall research program was split into 5 distinct sub-projects, or components. A. Assessment of groundwater inflows to streams and stream temperature. B. Baseline characterization of water quality and biological community structure C. Baseline characterization of sediment geochemistry D. Methods development: Dissolved methane in streams E. Publicly accessible data: ArcGIS online data portal

Project A: Assessment of groundwater inflows to streams and stream temperature

For smaller stream systems of NB, groundwater and/or other cool-water inputs can be detected by remote sensing using airborne thermal infrared (IR) imagery (Dugdale et al. 2013, Monk et al. 2013). The objective was to use airborne thermal IR imagery to locate and identify thermal anomalies during low-flow (summer) conditions along 250+ km of low order streams (2 to 6) of southcentral NB. Stream temperatures were also monitored using in-stream continuous data loggers to monitor patterns and summarize standardized temperature metrics during the research program.

Project B: Baseline characterization of water quality and biological community structure

Monitoring of water quality and biotic community structure are necessary tools for meaningful assessments of potential impacts on freshwater ecosystems. Determining how freshwater quality varies across natural gradients and in relation to pre-existing perturbations is particularly important when planning land use activities that are spatially predetermined, for example, mining and energy resource development that will be focused on areas with specific surface deposits and underlying bedrock formations (Dyni 2003, Rivard et al. 2014). The potential shale gas development ‘zone of interest’ covers a spatial area that lies within two watersheds: upper Kennebecasis and upper Petitcodiac. Within these watersheds are areas of Early Carboniferous bedrock (within the potential resource extraction zone), Late Carboniferous bedrock, and older classes of bedrock including Devonian and Neoproterozoic. Geologic differences such as those found within these watersheds may play a large role in driving the water chemistry of freshwater systems (Johnson et al. 1997, Dow et al. 2006), which may in turn affect the composition of biotic communities (Cannan and Armitage 1999, Leland and Porter 2000, Esselman et al. 2006, Kratzer et al. 2006, Neff and Jackson 2011), resulting in a natural gradient in ecosystem structure that may overshadow differences due to anthropogenic effects. The utility and success of a future monitoring program requires support from baseline characterization of the aquatic environment in these areas.

The purpose of this project was to characterize baseline conditions in water quality, physical habitat, benthic macroinvertebrate communities, fish communities, and fish health within the study region and across bedrock geology age classes. The objectives of the baseline data collection included selecting a set of stations representing a variety of geological and geomorphological characteristics of the area, and working in consultation with the regulatory agency (NB Department of Environment and Local Government; NB DELG) and local community organizations (e.g., Kennebecasis Watershed Restoration Committee; KWRC) to enhance their current surface water-monitoring program. Assessment focused on establishing

3 current conditions in abiotic and biotic ecosystem components in stream reaches underlain by Early and Late Carboniferous bedrock and older classes of sedimentary and non-sedimentary bedrock. In addition to assessing current levels of ecosystem health within the region, these data serve as a baseline to inform future monitoring and to allow the assessment of future shifts within these systems.

Project C: Baseline characterization of sediment geochemistry

As above, in order to characterize change over time, an understanding of past and present conditions necessitated a survey of stations to establish a current baseline. Streambed sediments and water samples taken directly above the streambed were collected and analyzed for a suite of metals (total and dissolved), and select radionuclides to establish presence/absence and baseline conditions. Sediment geochemistry characterization followed standardized sampling methods of the US Geological Service (Shelton and Capel 1994) and others (Levitan et al. 2014). The data were related to the guidelines of the Canadian Council of Ministers of the Environment (Canadian Council of Ministers of the Environment 1999).

Project D: Methods development: Dissolved methane in streams

Recent research conducted by the US Geological Survey (USGS) suggested that dissolved methane in streams could be monitored and, by establishing a baseline for streams near hydraulic fracturing and/or gas producing sites, changes in stream methane concentrations may be useful indicators of impacts from shale gas development (Heilweil et al. 2013). A benefit of monitoring methane in the gaining reaches of streams (i.e., reaches receiving groundwater discharge) is that the measurements may provide an integrated measure of groundwater quality in relatively large up-gradient source areas.

The objectives of this component of the study were twofold. First, given that there have been no previous investigations of dissolved methane in streams in southern New Brunswick, regional baseline concentrations for selected streams were to be established. Second, an in-stream tracer test and data on physical characteristics were to be used to assess methane losses from a reach of a small stream in the study area. The research was intended to provide a better basis for deciding whether the monitoring of stream methane is a viable approach for assessing stray gas migration in areas of natural gas development.

Project E: Publicly accessible data: ArcGIS online data portal

An important objective of this research project was to develop and advance tools that allow for data to be publicly accessible, both in terms of availability and understanding. For scientific research projects that collect data across various spatial and temporal scales, it is generally difficult to release data in a timely manner but also in a way and format that stakeholders and managers, including the public, can access, understand, and use. The final objective of this project was to support the development of the CRI online data portal using ArcGIS online tools to present project-specific data in spatially and visually relevant formats.

4 3 Study Area

The upper Kennebecasis and Pollett Rivers and a subset of their tributaries were selected for the current study because their catchments overlie bedrock geology that has potential for shale gas extraction. The catchments are located in the Caledonian Highlands area of the Appalachians physiographic region (Rampton 1984, Bostock 2014). The Caledonian Highlands are largely composed of the central plateau, containing the headwaters of the Kennebecasis and Pollett rivers, and the Anagance Ridges, containing the study rivers’ mid and lower reaches (Rampton 1984). The headwaters and of the Kennebecasis and Pollett rivers are located in the Caledonia ecodistrict (ecozone 3, Central Uplands; Zelazny 2007). The mid- and lower reaches of the Kennebecasis River is located in the Anagance and Kingston ecodistricts (ecozone 5, Valley Lowlands; Zelazny 2007). The mid-reaches of the Pollett River are also located in the Anagance ecodistrict, and its lower reaches are located in the Petitcodiac ecodistrict (ecozone 6, Eastern Lowlands; Zelazny 2007).

The study area is located in the Moncton geological sub-basin and the Caledonia Uplift outcrop of the Upper Paleozoic Maritimes Basin (Wilson and White 2006). Bedrock geology is sedimentary in the mid- to lower reaches of the Kennebecasis and Pollett rivers (within the Moncton sub-basin), and volcanic or intrusive in the headwater areas that fall on the Caledonia Uplift outcrop to the south (Geological Survey of Canada). The western part of the study area is characterized by Early Carboniferous sedimentary bedrock (Mississippian era), with deposits in the Mabou, Sussex, and Windsor groups (Wilson and White 2006; Figure 3-1). The Lower Carboniferous Albert formation underlies this Early Carboniferous bedrock, and contains the Hiram Brook and Frederick Brook members, which have potential for conventional natural gas and unconventional (shale gas) extraction, respectively (Macauley et al. 1984, Macauley et al. 1985, Leblanc et al. 2011).

The Frederick Brook shale is an organic-rich rock unit that was deposited in an ancient lake setting approximately 340 million years ago. It is a source rock for oil and gas deposits and in the Sussex and Moncton areas it occurs at variable depths, from the surface down to greater than 2000 m. The McCully gas field, which was the first commercial location for shale gas extraction in the province (Leblanc et al. 2011), is located in this area, and houses a number of wells for natural gas production and exploration (Wilson and White 2006, Leblanc et al. 2011; Figure 3-1); however, a moratorium on hydraulic fracturing that was issued by the Government of New Brunswick in December 2014 has halted the fracturing activities required to stimulate production in these wells.

In the eastern part of the study area there is Late Carboniferous sedimentary bedrock (Pennsylvanian era), with deposits in the Cumberland and Pictou groups. This typically coal- producing bedrock differs from oil shale bedrock with respect to mineral content and composition of organic material (Dyni 2003). Along the southern boundary of the study area are older classes of bedrock, including sedimentary Devonian-Carboniferous bedrock and volcanic and intrusive bedrock from the Neoproterozoic era (Geological Survey of Canada).

Surficial geology in the area is complex and is summarized here after Rampton (1984). The headwaters of the Kennebecasis and Pollett Rivers are composed of morrainal and colluvial deposits with the Kennebecasis having a greater sand content. The mid-reaches of the Kennebecasis flow largely through the alluvial deposits of the Sussex area. The lower reaches

5

Figure 3-1. The study area in the Kennebecasis and Pollett river watersheds, southeast New Brunswick, showing bedrock geology age for the region (indicative of shale gas potential) and location of exisiting wells.

of the Kennebecasis were not included in this study. The mid-reaches of the Pollett River flows through an area of morrainal and glaciofluvial deposits before transitioning to alluvial deposits in its lower reaches.

The higher elevation and proximity to the coast makes the Caledonia ecodistrict wet and cool compared to the drier and warmer climates of the lower lying Anagance, Kingston, and Petitcodiac ecodistricts. Steep gorges at the margins of the Caledonia ecodistrict mark the transition to the Anagance ecodistrict that lead to the broad valleys of the Kingston and Petitcodiac ecodistricts.

July and August are the warmest months with mean daily temperatures of ~19°C, and mean daily minimums of ~13°C and maximums of ~25°C (1981 to 2006 at Sussex, NB; Figure 3-2; Environment Canada 2016a). Annual precipitation is ~1170 mm, of which ~20% falls as snow. Most precipitation occurs during the fall, though there is also a peak during May (Figure 3-2). August is the driest month (74 mm; Environment Canada 2016a).

6

Figure 3-2. Mean monthly temperature and precipitation at Sussex, NB (1981-2006; Environment Canada 2016a).

Figure 3-3. Mean monthly discharge for the Kennebecasis River at Apohaqui (1961-2013; Environment Canada 2016b).

7 A gauging station on the Kennebecasis River near Apohaqui, just downstream of the study area, gives a general idea of the annual hydrograph (1961-2013; Figure 3-3). Peak monthly discharge occurs in April and flows decline through the summer months, with August and September having the lowest discharge. Autumn precipitation results in a second peak in discharge (Figure 3-3).

The upper Kennebecasis and Pollett River watersheds are 90% and 93% forested, respectively (Service New Brunswick-GeoNB), though some of this area is classified as a forest harvest area and is subject to logging. Agriculture accounts for 7% and 5% of landuse in the upper Kennebecasis and Pollett River watersheds, respectively, with settlement, industry, infrastructure (e.g., roads), recreation, and wetlands composing the remaining landuses.

8 4 Project A: Assessment of groundwater inflows to streams and stream temperature 4.1 Introduction Groundwater and surface water are interdependent components of the hydrologic system and their interactions are dependent upon topography, geology, and climate (Sophocleous 2002) Groundwater moves along flow paths depending on the hydraulic conductivity of soil/rock, water table elevation, and recharge (precipitation). Where these flow paths intersect in a stream, groundwater maybe discharged to surface water or groundwater may be recharged by surface water depending on current water table elevations (Winter et al. 1998).

Baseflow is the portion of water flowing in a stream that originates from persistent groundwater sources (i.e., maintains streamflow during low flow conditions). Zhang et al. (2013) found baseflow accounted for greater than 60% of annual discharge in a central New Brunswick river, and Curry et al. (2002) described groundwater as ‘dominant’ in the Kennebecasis River. Beck et al. (2013) produced a global map of estimates for BaseFlow Index (BFI) and obtained results similar to those reported by Zhang et al. (2013) using similar methods. Along the Kennebecasis River, the BFI estimates of Beck et al. (2013) indicated that baseflow accounted for 50% to 71% of annual discharge depending on the estimation method.

Groundwater inflows modify the thermal regime of streams by moderating seasonal and daily temperature fluctuations. During winter, groundwater is a warming influence and during the summer, groundwater is a cooling influence (Caissie 2006). Perhaps more importantly, areas of substantial groundwater inflow may cause abrupt changes in temperature (anomalies; Webb et al. 2010). Thermal anomalies are referred to as thermal refuges when they are exploited by temperature sensitive species during periods of elevated stream temperature (e.g., salmonids avoiding heat stress; Breau et al. 2007).

Development and landuse impact the interaction between groundwater and surface water (reviewed in Winter et al. 1998). Essentially, water use (i.e., groundwater or surface water withdrawal) and landscape augmentation (e.g., dams or dykes, recontouring, deforestation, drainage) can affect where, when, and in which direction groundwater and surface water interact, while point sources of contamination (e.g., wastewater discharge) and nonpoint sources (e.g., fertilizer) may affect groundwater and/or surface water chemistry (see examples in Brunke et al. 1997, Winter et al. 1998).

Groundwater withdrawals may lower the water table and decrease groundwater inflows to streams or induce seepage from streams into the groundwater system (induced groundwater recharge), decreasing baseflow (Sophocleous 2002). If groundwater withdrawals occur in areas with contaminated surface water (point or nonpoint sources), groundwater contamination may occur through induced groundwater recharge (Winter et al. 1998).

The large quantities of water potentially required for hydraulic fracturing imply the potential to affect groundwater and surface water interactions whether the water is acquired from groundwater or surface water or both (U.S. EPA 2015). Thus, an understanding of groundwater inflows to streams in the area of potential development is required to document baseline conditions. Further, the locations of groundwater inflow are important habitats for highly valued

9 (e.g., Brook Trout) and endangered (e.g., Atlantic Salmon) species, and knowledge of these locations will inform management and development decisions.

4.2 Methods 4.2.1 Study design Airborne infrared (IR) imagery surveys were conducted to identify areas of groundwater inflow and provide a snapshot of the stream’s longitudinal temperature profile (Table 4-1; Figure 4-2). IR imagery surveys were conducted during low-flow conditions (early August 2015), a time when mean stream water temperatures were expected to be greatest, thereby maximizing the temperature differential between stream water and groundwater and maximizing the probability that groundwater inflows would be detected. Baseflow (groundwater discharge) was estimated from a long-term discharge data set collected at Apohaqui (station code 01AP004) using six hydrograph separation models.

Table 4-1. Summary of longitudinal temperature surveys, 2015.

Watercourse Airborne Infrared Surveys In-stream Surveys

Date km Date km

Kennebecasis River 06 Aug 70.3 04-05 Jun 32.4

Calamingo Brook - - 15 Jun 2.5

South Branch 06 Aug 14.9 16-17 Jun 10.7

Negro Brook - - 17 Jun 1.8

Stone Brook - - 07 Jul 6.2

McLeod Brook - - 16-17 Jul 10.1

Millpond Brook - - 08 Jul 6.1

Trout Creek 08 Aug 22.9 - -

Smiths Creek 08 Aug 35.1 - -

Pollett River 07 Aug 53.5 - -

A set of in-stream longitudinal surveys was conducted along the length of streams to identify areas of groundwater inflow during early June to mid-July 2015 (Table 4-1). Most of these streams were too small to be surveyed using airborne IR imagery, though there was some overlap which could allow for a future comparison of the efficacy of the two approaches for identifying areas of groundwater inflow. In-stream temperature loggers were also deployed in a variety of mainstem, tributary, and groundwater inflow areas to record fine scale temperature trends (May 2015-October 2016; Figure 4-2).

10

Figure 4-1. Airborne infrared temperature survey pathways flown by helicopter, August 2015. Inset figure shows the Hammond River which was included as part of a secondary research study for the Atlantic Salmon Conservation Foundation (ASCF), and will not be reported here.

Figure 4-2. In-stream temperature survey pathways and continuous temperature logger locations.

11 4.2.2 Airborne infrared (IR) imagery Airborne IR surveys were completed via helicopter (R44 Robinson) equipped with a thermal IR and optical camera as described by Dugdale et al. (2013). A FLIR SC660 uncooled microbolometer thermal IR camera captured images using 7.5–13 μm wavelengths with 640 × 480 pixel resolution and was able to distinguish temperature differences less than -273.12°C with an accuracy of ±1°C. A Canon EOS 550D digital SLR camera with 5184 × 3456 pixel resolution was used to capture optical images. The thermal IR camera and optical camera captured images with similar nadir ground footprint dimensions using 38 mm and 50 mm lenses, respectively. The cameras were mounted to a pan-tilt unit (Directed Perception PTU-D48) housed in a luggage pod (Simplex Helipod II) with a 35 cm × 35 cm cutout in the base through which imagery was acquired. Range of movement for the pan-tilt unit was restricted to ± 5° from vertical. A Garmin GPS76 CSx GPS unit connected to a high-gain antenna provided positioning data.

Airborne IR surveys were flown at a speed of approximately 70 km/h at an altitude of approximately 300 m above ground level resulting in nadir ground footprints of approximately 120 m × 90 m with pixel sizes of 18.7 cm2 and 2.6 cm2 for the thermal and optical images, respectively. A laptop computer ran a custom-made MATLAB (MathWorks 2009) program that recorded coordinates, triggered the cameras simultaneously (0.5 Hz), and provided/recorded a continuous readout of groundspeed and altitude above ground calculated from a pre-loaded digital elevation model (DEM) of the survey area. FLIR ThermaCAM Researcher Professional and Canon EOS Utility software packages were used to transfer the images to the laptop.

The thermal IR radiance values were converted to thermal IR temperatures (TIR) with the FLIR ThermaCAM Researcher Professional software. TIR may deviate from kinetic temperature (Tk) due to atmospheric distortion (e.g., air temperature, humidity), observation distance (i.e., altitude), and water surface roughness (Torgersen et al. 2001). In addition, infrared images do not discriminate between energy emitted by the target (i.e., water) and that reflected by the nearby landscape (e.g., vegetation or stream banks), atmosphere (sky or clouds), or solar radiation (Torgersen et al. 2001). A MODTRAN radiative transfer model was used to correct for atmospheric distortion (data acquired from local meteorological stations) and observation distance. The wavelengths detected by the thermal IR camera (7.5-13 m) minimize the effect of solar radiation (Torgersen et al. 2001). Thermal images were visually interpreted and pixels used to derive TIR were selected manually to avoid deviation from Tk due to reflectance from nearby objects. Further, reflectance from nearby objects is low at view angles <30° from vertical (Dugdale 2016).

Since thermal IR imagery measures the temperature of the top 0.1 mm of the water surface, the measurements are valid only if the surface water temperature is indicative of the water column temperature (i.e., the water column is well mixed; Dugdale 2016). Turbulent flow typically characterizes water movement in rivers resulting in a well-mixed water column, though exceptions may occur in very slow moving reaches such as pools or backwaters.

Locations of groundwater inflow were identified manually using a graphical user interface created in MATLAB (MathWorks 2009) that displayed sequentially-located thermal and optical images on a split screen. Temperature anomalies were defined as regions with a water temperature at least 0.5°C below background stream temperature. To avoid false positives from noise in the IR images, thermal anomalies were required to cover at least 2 ✕ 2 pixels (0.14 m2 at target altitude) and occur in at least two successive IR images. Thermal anomalies were

12 further classified following Dugdale et al. (2013) and each anomaly type was assigned a probable water source (Table 4-2).

Optical images were georeferenced in ArcGIS (ArcMap 10.3) or QGIS (2.8.1 or 2.14.1) to create a map of the study streams consisting of high-resolution images. The ground-control points used for the optical images were then applied to the thermal IR images to create a map of water temperatures for the study streams. A simplified longitudinal profile of stream temperature was also created by manually selecting a series of five pixels located along the stream’s thalweg in each thermal IR image.

Table 4-2. Thermal anomaly classification and probable water source (modified from Dugdale et al. 2013).

Type Description Water Source

Tributary Thermal plumes created prior to mixing where a cold tributary Surface confluence discharges into the (warmer) main river channel water plumes

Lateral seeps Elongated bank side filaments of cold water inflow observed Groundwater when the active river channel intersects zones of groundwater flow (often in steep terraces or valleys)

Springbrooks Cold water channels flowing from springs, marshland or Groundwater depressions adjacent to the channel. Often associated with abandoned channels

Cold side Cold secondary channels flowing in ephemeral flood Groundwater channels pathways normally only completely wetted during periods of high flow

Cold alcoves Zones of cold water found at the downstream edge of a bar Groundwater often associated with emergence of an abandoned channel or formed when groundwater pathways converge and accumulate in a backwater

Hyporheic Resurgence of hyporheic flow from the streambed found at Groundwater upwelling the downstream ends of gravel bars, mid channel islands or in sequence with pool-riffle bedforms

Wall-base Runoff-fed channels emerging from terraces and then flowing Surface channels over the immediate floodplain into the river channel, often water through relict meander scars

Note: Classification type and description from Dugdale et al. 2013

13 4.2.3 In-stream temperature surveys 4.2.3.1 Longitudinal surveys Longitudinal temperature surveys of select study streams (Figure 4-2) were completed walking in an upstream direction. Duplicate measurements were logged using temperature loggers (Onset HOBO U24-001) set to record data every 10 seconds. Logger accuracy, resolution, and response rates were 0.1°C, 0.01°C, and 1 second (to 90% of change), respectively. The loggers were fastened to a lanyard and pulled through the middle of the stream, just below the water surface, by two surveyors walking within 2 m to 20 m of one another. A GPS (Garmin GPS 60x or GPSMAP 78) carried by one of the surveyors logged coordinates every 10 seconds. In areas of deep water, the loggers were pulled as close to the middle as was safe. In areas of shallow water, the thalweg (deepest part of the channel) was sampled. The GPS time was recorded at the beginning of the survey to allow the syncing of temperature and conductivity measurements with GPS locations. Deep-water (>2 m) sections of the upper Kennebecasis River required that this survey be conducted from canoes towing the loggers in an upstream to downstream direction. Otherwise, the survey was conducted as described above.

Since the surveyed streams were small, and the riparian vegetation dense, the loggers were often removed from the water for short periods of time to climb over branches, or walk around impenetrable thickets or beaver dams. In the headwaters, some reaches were too shallow to obtain reliable measurements. These conditions were encountered often enough that it was impractical to record the time of each instance that a logger was out of the water. Therefore, the recorded data required a significant amount of processing to ensure that measurements reflected actual water temperature. Furthermore, surveys were conducted in different months, and thus are not directly comparable, but provide general information about longitudinal trends.

Processing the survey data began by matching temperature measurements with GPS coordinates using the recorded time. Temperature and conductivity were graphed to identify and remove data recorded when a logger was out of the water (temperature increase and conductivity decrease). Since surveys occurred over many hours, temperatures were corrected for diel temperature variations. Discretely placed loggers closest to the downstream end of the stream were used to track temperature increases every half hour. That temperature increase was divided by 180 (the number of 10 second temperature intervals in 30 minutes) and added to the recorded temperature successively such that recorded temperatures accounted for the cumulative increase in temperature during the survey. Corrected temperature surveys were mapped using ArcGIS (ArcMap 10.3).

4.2.3.2 Discrete logger deployment To capture annual temperature trends in study area streams, temperature loggers (Onset HOBO UA-001-08, UA-002-64, and U24-001) were deployed to 27 sites over 2 years (May 2015 to October 2016; Figure 4-2). Loggers were anchored by rebar driven into the substrate and were housed in light gray PVC pipe to avoid effects from solar radiation and shield them from damage caused by shifting substrate. The loggers recorded data every 30 minutes from May to November, and every 60 minutes from November to April.

Occasionally, water levels dropped below the depth of the temperature loggers. Logger data were examined for records that indicated the logger was out of water (e.g., spikes in temperature or light levels, or decrease in conductivity to near 0 for the combination temperature/conductivity loggers). These records were removed and remaining data were added to a Microsoft Access database for storage and summary. Over the course of the

14 research program, various loggers were lost due to human interference or high water events, creating gaps in the temperature profiles and these were dealt with on a case-by-case basis.

4.2.3.3 Quantification of groundwater inflow Hydrograph separation techniques divide streamflow into surface runoff and baseflow components and can be used to quantify groundwater discharge assuming that baseflow represents the contribution of groundwater to streamflow (Kalbus et al. 2006). A Kennebecasis River gauging station at Apohaqui (station code 01AP004) has recorded discharge from 1961 to 2013 (more recent data are not publicly available; Environment Canada 2016b). The USGS software, Groundwater Toolbox (version 1.1.1; USGS 2015), was used to run six different hydrograph separation models: BaseFlow Index (BFI; Standard and Modified; Barlow et al. 2015), HYSEP (Fixed Interval, Sliding Interval, and Local Minimum; Sloto and Crouse 1996), and PART (Rutledge 1998).

For the BFI program, the analysis was run by water year (October 1 to September 30) using default settings (duration of surface runoff = 5 days; turning point test factor = 0.90, daily recession index = 0.979), and a drainage area of 1100 km2 (converted to mi2). The other programs did not require input parameters other than drainage area. The output was summarized as mean annual and monthly runoff and baseflow, and BFI for the period 1961 to 2013.

Hydrograph separation by the above methods is subject to a variety of assumptions summarized by Barlow et al. (2015 and references therein). Stream discharge is assumed to be composed of surface runoff from precipitation and groundwater discharge from a single aquifer. Groundwater recharge is diffuse, evenly distributed throughout the basin, and occurs continuously, implying that the basin is composed of a single hydrogeologic unit and receives evenly distributed precipitation. Groundwater is assumed to discharge to the receiving stream except for water lost due to riparian evapotranspiration. This implies that groundwater must not be not leaving the system via well water withdrawals or regional groundwater flow systems. Further, the stream discharge must not be regulated or influenced by dams, reservoirs, diversions, or other water inputs.

Mean monthly runoff, baseflow, and BFI (proportion of total discharge contributed by baseflow) were calculated for each hydrograph separation method to identify the range of possible groundwater contributions to stream discharge upstream of the Apohaqui gauging station.

4.3 Results 4.3.1 Airborne IR surveys: Thermal anomalies and groundwater inflow In the five rivers surveyed, 268 thermal anomalies were identified from the thermal images. Of these, 219 were likely groundwater driven (Table 4-3), and 49 were likely surface-water driven (Table 4-4) (more details in Appendix A). The Pollet River had the highest density of groundwater-driven thermal anomalies (2.8 anomalies/km) followed by Trout Creek (1.0 anomalies/km), while densities were lower in the Kennebecasis River (0.4 anomalies/km), Smiths Creek (0.4 anomalies/km), and South Branch (0.3 anomalies/km).

15

Table 4-3. Groundwater driven thermal anomalies in southeast New Brunswick rivers, August 2015.

Cold Cold side Hyporheic River Lateral seep Springbrook alcove channel upwelling Total No. Area No. Area No. Area No. Area No. Area No. Area Kennebecasis 18 677 7 3,256 1 30 2 112 - - 28 4,076 South Branch 3 817 - - - - 2 1,632 - - 5 2,449 Smiths Creek 3 32 6 1,593 1 55 3 3,309 1 7 14 4,995 Trout Creek 9 1,639 4 2,831 3 167 2 2,235 4 58 22 6,930 Pollett 87 8,690 20 8,671 21 1,798 10 13,022 12 738 150 32,920 Total 120 11,855 37 16,351 26 2,050 19 20,310 17 803 219 51,369 Note: Area in m2

Table 4-4. Surface water driven thermal anomalies in southeast New Brunswick rivers, August 2015.

Tributary confluence Wall-base River plume channel Total No. Area No. Area No. Area Kennebecasis 10 9,746 1 109 11 9,855 South Branch 1 145 - - 1 145 Smiths Creek 12 5,074 - - 12 5,074 Trout Creek 1 263 - - 1 263 Pollett 24 6,563 - - 24 6,563 Total 48 21,791 1 109 49 21,900 Note: Area in m2

Lateral seeps were the most common, accounting for 45% of all anomalies but only 16% of anomaly area, followed by tributary confluence plumes (18% of anomalies covering 30% of anomaly area), springbrooks (14% of anomalies covering 22% of anomaly area), cold alcoves (10% of anomalies covering 3% of anomaly area), cold side channels (7% of anomalies covering 28% of anomaly area), hyporheic upwellings (6% of anomalies covering 1% of anomaly area), and wall-base channels (<1% of anomalies covering <1% of anomaly area).

Thermal anomalies and river temperature were mapped and are presented in Figure 4-3 to Figure 4-7. Thermal anomaly temperatures ranged from 8.9°C to 23.3°C. South Branch had fewer thermal anomalies compared to other rivers, but also had the coldest mean temperature (14.2°C) in areas where thermal anomalies were found. In contrast, the Pollett River had the most thermal anomalies and had the highest mean temperature (21.4°C) in areas where thermal anomalies were found. This indicates that river temperature affects the detectability of thermal anomalies, and by extension, the detectability of groundwater inflow. On average, thermal anomalies were 3.1°C cooler than ambient river water temperature, though temperature differences of almost 10°C were recorded.

16

Figure 4-3. Kennebecasis River, thermal anomaly temperature and location estimated from Airborne IR survey, August 2015.

17

Figure 4-4. South Branch, thermal anomaly temperature and location estimated from Airborne IR survey, August 2015.

18

Figure 4-5. Smiths Creek, thermal anomaly temperature and location estimated from Airborne IR survey, August 2015.

19

Figure 4-6. Trout Creek, thermal anomaly temperature and location estimated from Airborne IR survey, August 2015.

20

Figure 4-7. Pollet River, thermal anomaly temperature and location estimated from Airborne IR survey, August 2015.

21 4.3.2 Longitudinal stream temperature profiles (Thermal IR Surveys) Mean August water temperature in the Kennebecasis River was 18.6°C upstream of Calamingo Brook, though fluctuations between 17.5°C and 19.7°C occurred, likely because of the small water volumes in the headwaters (Figure 4-3). Calamingo Brook, a cold water tributary, and the area of groundwater inflow below Calamingo Brook decreased water temperature in the Kennebecasis River to a low of 15.8°C near Portage Vale. Lower water temperatures gradually increased with downstream distance to a high of 21.5°C near Sussex.

South Branch had the coolest mean August water temperature (14.2°C) among the study rivers. The highest temperature (16.2°C) was actually recorded in the headwaters, with mid-reach and lower-reach water temperatures of approximately 14.5°C (Figure 4-4). Some mid-reach areas were as cool as 12.7°C and had a strong groundwater influence. At its confluence with the Kennebecasis River, the South Branch water temperature was over 4°C cooler than the Kennebecasis River (Figure 4-3 amd Figure 4-4).

August water temperature in the Smiths Creek gradually increased from the headwaters where temperatures were as low as 16.3°C to its lower reaches where the temperatures reached 20.4°C (Figure 4-5). Trout Creek had a similar pattern during August with mean water temperatures increasing from a low of 13.6°C in the headwaters to 18.6°C in its lower reaches (Figure 4-6)

The Pollett River temperature profile had a stepped appearance due to a general trend of increasing temperature with downstream distance, punctuated with temperature decreases in areas of groundwater inflow (Figure 4-7). The coldest water temperatures (18.3°C) were recorded in the headwaters and increased to 23.3°C before an area of groundwater and cold tributary inflow decreased the water temperature to 20.0°C near Elgin. Water temperature increased for 6 km downstream of Elgin to 23.5°C before dropping back to 20.0°C for the next 4 km. A relatively steady water temperature increase occurred in the lower reaches where the water reached 24.7°C.

4.3.3 In-stream surveys 4.3.3.1 Longitudinal stream temperature profiles (Logger Surveys) Early June water temperature in the Kennebecasis River was 8.6°C just upstream of Calamingo Brook (Figure 4-8). The water temperature began dropping at the confluence with Calamingo Brook and reached a low of 7.0°C near the groundwater inflows near Portage Vale. Downstream of Portage Vale, water temperature gradually increased to 9.4°C near Crockets Corner which marked the end of the first day of the survey. On the second day of the survey, water temperature near Crockets Corner had increased to 10°C overnight. Water temperature was relatively stable downstream of Crockets corner, ranging from 9.9°C to 10.4°C.

Mid-June water temperature in Calamingo Brook was near 11°C throughout most of the stream (Figure 4-9). In the lower reach, a small temperature increase to approximately 12°C was recorded before groundwater inflows near the confluence of the Kennebecasis caused the water temperature to drop as low as 9.0°C.

Mid-June water temperature in Negro Brook was 11.5°C at the upstream end (Figure 4-10). In the middle reach, temperatures gradually increased to 12.7°C before decreasing to 11.3°C

22

Figure 4-8. Kennebecasis River, longitudinal stream temperature profile, measured via an in-stream survey, June 2015.

23

Figure 4-9. Calamingo Brook, longitudinal stream temperature profile, measured via an in-stream survey, June 2015.

24

Figure 4-10. Negro Brook (tributary of the South Branch River), longitudinal stream temperature profile, measured via an in-stream survey, June 2015.

25

Figure 4-11. South Branch, longitudinal stream temperature profile, measured via an in-stream survey, June 2015.

26

Figure 4-12. Stone Brook, longitudinal stream temperature profile, measured via an in-stream survey, July 2015.

27

Figure 4-13. McLeod Brook, longitudinal stream temperature profile, measured via an in-stream survey, July 2015.

28 near its confluence with South Branch. South Branch water temperature was 12.3°C upstream of Negro Brook, and dipped to 11.0°C at its confluence of with Negro Brook before rebounding to 12.6°C a few hundred meters downstream (Figure 4-11). At this point, water temperature began to gradually decrease downstream toward Springdale where a cold water inflow measured 6.9°C and ambient water temperature was 10.4°C. Downstream of Springdale, water temperature gradually rose to 11.8°C at the confluence of the Kennebecasis.

Early July water temperature in the headwaters of Stone Brook were 10.7°C, and increased gradually to a maximum of 13.8°C in the middle reach (Figure 4-12). From the middle reach, water temperature decreased to 12.5°C at its confluence with the Kennebecasis River.

Mid-July water temperatures at the upstream end of the McLeod Brook survey was 8.7°C (Figure 4-13). A few hundred metres downstream, the temperature dropped to a low of 6.5°C before gradually increasing with downstream distance to a high of 9.6°C at its confluence with the Kennebecasis River.

4.3.3.2 Groundwater contribution to streamflow 4.3.3.2.1 Hydrograph Separation Overall, hydrograph separation indicated that the Kennebecasis River at Apohaqui was dominated by groundwater. Peaks in the contribution of baseflow to total discharge (BFI) occurred during winter (February) when precipitation fell as snow and during the drier summer period (August, Figure 4-14). A smaller peak occured during May (Figure 4-14), though this may be an artifact of snowmelt. Though the monthly trend was consistent among the six separation methods, there was considerable variation in the estimate of runoff, baseflow, and BFI obtained among methods (Figure 4-14). PART gave the highest estimate of baseflow with monthly BFIs ranging from 0.60 (March) to 0.80 (February), with an August BFI of 0.77 (i.e., 77% of stream discharge in August was baseflow/groundwater). The HYSEP methods yielded similar results: HYSEPFixed 0.57 (March) to 0.72 (February) with an August BFI of 0.72, HYSEPSlide 0.57 (March) to 0.73 (May) with an August BFI of 0.72, HYSEPLocMin 0.51 (March) to 0.72 (August). Of the different separation methods, the BFI methods gave the lowest baseflow and BFI estimates: BFIStandard 0.43 (March) to 0.68 (August), and BFIModified 0.43 (March) to 0.69 (August).

While there were considerable difference among BFI estimates, the summer (August) BFI estimates converged to a fairly narrow range: 0.68 to 0.77. The Apohaqui gauging station is located almost 22 km downstream of the upper Kennebecasis watershed and thus integrates water (groundwater or runoff) contributions outside the area of interest. However, the results of the hydrograph separation using data from the Apohaqui gauging station are useful as a rough check on groundwater contributions in the region.

29

Figure 4-14. Mean monthly runoff, baseflow, and baseflow index (BFI) for the Kennebecasis River at Apohaqui using six hydrograph separation techniques.

30 4.3.3.3 Annual temperature trends A reliable water temperature record was obtained for the 27 sites where loggers were deployed (Figure 4-2). Data gaps occurred when loggers were washed away or vandalized (removed), logged files were corrupt, or loggers were not fully submerged for a portion of the survey period (May 2015 to October 2016). The summer (June 1 to August 31) maximum, mean, and minimum temperatures are presented for 2015 and 2016 in Figure 4-15. Minimum, mean, and maximum water temperatures during fall 2015 (September to November), winter 2015-2016 (December to February), and spring 2016 (March to May) are presented in Figure 4-16.

During the summer months of June, July, and August, water temperatures reached their maximum. This is a critical time for cold-water species such as Atlantic Salmon that prefer water temperatures between 8 and 19°C. Though Atlantic Salmon can tolerate water temperatures from 19 to 23°C, they may suffer decreased growth (Elliott and Elliott 2010). Water temperatures greater than 23°C represent a critical threshold where sub-lethal effects (growth, feeding) and lethal effects may occur (Elliott and Elliott 2010). Sub-lethal effects may also occur at cold temperatures (0 to 7°C).

Summer water temperature trends in surveyed streams were similar between 2015 and 2016, though temperatures were modestly higher (0.5 - 2.0°C) during 2016 (Figure 4-15). Water temperatures at headwater sites tended to be cooler than downstream sites. Similarly, tributary sites tended to be cooler than mainstem sites. However, the low discharge characteristic of headwater sites allowed for greater temperature variation (e.g., greater maximum temperatures), especially for sites with limited shading (e.g., KB1). The influence of significant groundwater inflow regions in the middle reaches (e.g., KB4) also complicated the trend of increasing temperature with downstream distance.

Mean summer water temperatures on the mainstem Kennebecasis were similar (12.4 - 15.6°C) from the headwaters (KB1) to the valley bottom (KB5; Figure 4-15). After this point, a gradual warming was observed from KB5 to KB6 and KB7 (14.4-19.3°C) due to decreased riparian cover (shading) in this agricultural land-use dominated area. Site KB4 was located downstream of numerous groundwater inflows and a cold, groundwater-fed tributary (Calamingo Brook). KB4 had the lowest mean summer water temperature among Kennebecasis mainstem sites (Figure 4-15).

In the Kennebecasis tributaries, Calamingo Brook (CB1) had the lowest summer mean water temperature observed in both 2015 (9.8°C) and 2016 (9.1°C; Figure 4-15). Based on observations of springs and intermittent surface discharge in 2015, summer discharge in Calamingo Brook is belived to be almost entirely derived from groundwater and this accounts for the low observed water temperatures. South Branch was the next coolest tributary with mean summer temperatures ranging from 10.6°C at the headwater site SB1 in 2015 to 15.0°C at the downstream site SB3A in 2015 (Figure 4-15). The cooler mean temperature observed at SB3A in 2016 (11.5°C) demonstrates the inter-year variability in groundwater discharge, as this site is located downstream of a known groundwater inflow.

At other upper Kennebecasis tributary headwater sites, mean summer water temperatures ranged from 11.8°C (Stone Brook site ST1) to 16.3°C (Trout Creek site TC1; Figure 4-15). At the downstream tributary sites, mean summer water temperatures ranged from 12.3°C at the McLeod Brook site MB2 to 20.3°C at the Trout Creek site TC3.

31 Mean summer water temperatures in the Pollett watershed were greater than those observed in the upper Kennebecasis watershed (Figure 4-15). At mainstem sites, mean summer water temperatures increased modestly from the headwater site PL1 (16.8°C) to PL6 (18.1°C). A marked increase in water temperature occurred downstream of PL6 and a mean of 22.4°C was recorded at the PL7 in 2015. Pollett River tributary sites were cooler compared to mainstem sites with mean summer water temperatures ranging from 11.3°C at PL5 to 16.3°C at PL4 (Figure 4-15).

Mean fall 2015 and spring 2016 water temperatures had similar trends with cooler temperatures recorded at headwater sites compared to downstream sites, though fall mean temperatures were modestly higher (~1°C) than spring temperatures (Figure 4-16). Mean fall 2015 water temperatures in the Kennebecasis Watershed ranged from 4.6°C at Trout Creek site TC2 to 11.2°C at Trout Creek site TC3, while temperatures in the Pollett Watershed ranged from 8.6°C at the tributary site PL5 to 10.9°C at the mainstem site PL7. Mean spring 2016 water temperatures ranged from 3.59°C at the Stone Brook site ST1 to 9.4°C at the Kennebecasis River site KB2. In the Pollett Watershed, mean spring water temperatures ranged from 6.1°C at the tributary sites PL4 and PL5 to 14.1°C at the mainstem site PL7 (Figure 4-16).

Temperature variation among sites during winter 2015-2016 was low compared to other seasons. Calamingo Brook (CB1) was the warmest site with a mean winter water temperature of 4.2°C followed by the South Branch site SB1 (3.0°C; Figure 4-16). At other sites in the Kennebecasis and Pollet watersheds, mean winter water temperature ranged from 0.8°C at the Kennebecasis River site KB1 to 2.5°C at the Stone Brook site ST1 (Figure 4-16). The warmer winter temperatures recorded at Calamingo Brook (CB1) and possibly South Branch sites SB1 and SB3 are likely due to groundwater inflows moderating temperature differences between summer and winter seasons.

32

Figure 4-15. Summer water temperatures at sites in the Upper Kennebecasis and Pollett River watersheds, 2015-2016.

33

Figure 4-16. Fall 2015, Winter 2015-2016, and Spring 2016 water temperatures at sites in the Upper Kennebecasis and Pollett River watersheds.

34 4.4 Conclusions Longitudinal surveys and logger data indicated thermal differences in stream reaches throughout the Kenebecasis and Pollett River catchments. The Pollett River catchment experienced the the highest summer temperatures noted across the study area. Headwaters in the Kenebecasis River catchment were generally cooler in the summer, warming towards the lower reaches of the mainstem where less shade was available from riparian vegetation. There was evidence of temperature moderation by groundwater in the Kenebecasis River catchment, where cool summer temperatures and warm winter temperatures were noted in Calamingo Brook and South Branch. The characterization of the thermal regime in these systems is important for identifying ideal habitats for temperature-sensitive fish.

Airborne IR surveys were used to obtain a comprehensive snapshot of river water temperatures along the river’s length and allow for the identification of thermal anomalies, which may be related to groundwater inflows. The airborne IR surveys indicated that most thermal anomalies (suggestive of groundwater inflow) were located at the stream margins. These areas of the stream potentially represent important habitat for organisms that are temperature-sensitive (e.g., Brook Trout). Where temperature surveys focus on the thalweg (such as the in-stream surveys conducted in this study), these anomalies may be missed, affecting the characterization of the thermal habitat in these systems. However, airborne IR surveys require fairly expensive charter flights, specialized equipment, and specialized expertise to complete the survey and analyze the results. This is outside the resources and ability of most, if not all, watershed groups. Areas with significant groundwater inflow or cold tributary inflow were detected by the in-stream longitudinal temperature surveys, though smaller anomalies might be missed. In addition, the in-stream surveys were conducted from June to mid-July while water temperatures and flow conditions were not optimal for detecting thermal anomalies, and adjustment to this schedule might improve the chances of identifying these areas.

The in-stream longitudinal temperature surveys were time consuming and required a significant amount of data processing post survey. However, little specialized expertise or equipment is required to perform the surveys, data processing, and analysis. Given the right stream conditions and study goals, the in-stream temperature profile surveys may be an appropriate tool for watershed management groups to identify the largest thermal anomalies (i.e., potential thermal refuges for cold-water species) in a river system. For example, identifying the presence of a large thermal anomaly would be valuable for developing a watershed management plan or planning a stock enhancement program.

Airborne IR surveys, in-stream longitudinal surveys, and in-stream logger surveys provided valuable information about the natural variability of water temperature and the presence of groundwater inflows in the study area. These data can be used to describe trends observed in biological communities (see Section 5). Reported baseline temperature and groundwater inflow data can be used to inform management and mitigation plans relating to the cumulative effects of potential future development in the study area. Finally, the results obtained using these methods can be used to inform future monitoring programs in this region and others.

35 5 Project B: Baseline characterization of water quality and biological community structure 5.1 Introduction Monitoring of freshwater resources can provide managers with a valuable tool with which to assess potential impacts to a system and predict which areas are most sensitive to perturbation through the use of water quality and biotic endpoints. In the absence of disturbance, water quality and biotic monitoring provides the baseline information about what is “normal” or “natural” for a region, which enhances our ability to detect any future changes when they do occur (Stoddard et al. 2006). However, variability in local conditions, including geology and climate patterns, can affect water chemistry and habitat characteristics of an area, as well as the types of biotic communities that might be expected in an undisturbed system (Stoddard et al. 2006, Herlihy et al. 2008). Moreover, in areas where some development has already occurred, it may be unrealistic to expect a return to pristine conditions, thus necessitating an assessment of the current state of the environment and reasonable expectations for critical effects in monitoring and management (Stoddard et al. 2006, Hawkins et al. 2010).

Determining how freshwater quality varies across natural gradients and in relation to pre- existing perturbations is particularly important when planning land-use activities that are spatially predetermined (e.g., resource extraction) and potentially with limited impact zones.. For example, mining and energy resource development will be focused on areas with specific surface deposits and underlying bedrock formations (Dyni 2003, Rivard et al. 2014). Geology can play a large role in driving the water chemistry of freshwater systems (Johnson et al. 1997, Dow et al. 2006), which may in turn affect the composition of biotic communities (Cannan and Armitage 1999, Leland and Porter 2000, Esselman et al. 2006, Kratzer et al. 2006, Neff and Jackson 2011). That the exploitation of resources can be limited spatially and the same geologic constraints can create differences in water quality and biotic community structure suggests that monitoring designs must recognize the natural variability that may exist across regions, e.g., across surface and bedrock formations.

Evaluation of water quality through the combination of chemical monitoring and biomonitoring provides an ideal means to assess short- and long-term changes within a system. Water chemistry samples alone provide a snapshot of immediate water quality in an area, but with continued monitoring, may allow for the detection of seasonal trends and long-term trends over many years. Biomonitoring (evaluating biotic communities within the system) provides an integrated view of long-term patterns in water chemistry and habitat conditions through the response of communities such as benthic macroinvertebrates and fish (Bonada et al. 2006). Furthermore, information about taxonomic preferences and tolerances allows us to determine the source of impact when community shifts are evident, and may better allow for the detection and prediction of impacts than water quality alone (Hering et al. 2006). Different groups of organisms (algae, benthic macroinvertebrates, fish) differ in their response to perturbation, and each group has benefits and disadvantages to its use in biomonitoring, suggesting that the combined assessment of multiple groups may provide the best characterization of baseline conditions and biotic integrity in a system (Bonada et al. 2006, Hering et al. 2006).

36 Benthic macroinvertebrates are well-established as a useful biomonitoring tool because they are widespread, species-rich, reflect local conditions due to their largely sedentary nature, vary in lifespan from short- to long-lived, are easy to sample and have well-documented tolerances to perturbation (Bonada et al. 2006). Benthic macroinvertebrates respond well to a number of changes in the stream system and surrounding area, including water chemistry changes, in- stream habitat shifts, landuse changes, and general degradation (Hering et al. 2006). In addition, national monitoring protocols for benthic macroinvertebrates are well-established (i.e., Canadian Aquatic Biomonitoring Program, CABIN; Environment Canada 2012) and allow for assessment of samples in relation to reference condition models that have been developed (e.g., the Atlantic Reference Model; Armanini et al. 2013).

Fish communities are also commonly used for biomonitoring because they are widespread, easy to sample, can be identified to species in the field, are long-lived, and have well- documented tolerances to perturbation, although they are mobile and may not reflect local conditions as well as benthic macroinvertebrates (Hering et al. 2006). The response of fish to stressors differs from that of benthic macroinvertebrates, potentially providing a different view of ecosystem shifts. Fish respond well to eutrophication pollution and habitat changes, particularly temperature shifts, but they may show less of a response to changes in landuse and general degradation due to their ability to move away from disturbances (Hering et al. 2006). Evaluation of fish as a biomonitoring tool may also focus on particular species and evaluate the health of those species across a region based on condition, relative gonad size and relative liver size (Gray and Munkittrick 2005, Galloway and Munkittrick 2006, Munkittrick et al. 2010, Brasfield et al. 2013). Slimy Sculpin (Cottus cognatus) are a small-bodied fish with a cool-water preference (11-16°C; Lyons 1990) that have been used as an indicator species of fish health in prior studies because they reflect the conditions of the local site due to their small home range, and they may reflect changes in environmental conditions more quickly than larger fish because of their short lifespan (Munkittrick et al. 2000, Gray 2003).

The purpose of this study was to characterize baseline conditions in water quality, physical habitat, benthic macroinvertebrate communities, fish communities, and fish health within the study region. Assessment focused on establishing current conditions in abiotic and biotic ecosystem components in stream reaches underlain by Early Carboniferous bedrock (with high potential for shale gas production), Late Carboniferous bedrock, and older classes of sedimentary and non-sedimentary bedrock. Stream stations were characterized with respect to the spatial and temporal variability in chemical parameters across the region and water quality was assessed by comparing current baselines with established guidelines. Biodiversity of benthic macroinvertebrate and fish communities was characterized and drivers of change in these communities were identified. Finally, biotic integrity across sites was assessed through the use of a reference condition model for benthic macroinvertebrates and through the evaluation of fish health for Slimy Sculpin. In addition to assessing current levels of ecosystem health within the region, these data serve as a baseline to inform future monitoring and to allow the assessment of future shifts within these systems.

5.2 Methods 5.2.1 Study design An extensive spatial survey of biotic and abiotic ecosystem components was conducted to characterize community structure and environmental drivers in the Kenebecasis and Pollet River watersheds. The sample design was developed using existing GIS data for topography,

37 geology, landuse, and groundwater inputs (Chapter 4), complemented by the regulatory and local knowledge, including existing habitat maps (e.g., Somers and Curry 2009, 2011). Site selection followed a stratified random sampling design to ensure coverage was sufficient to maximize spatial interpretations and transferability across the region (Green 1979). 28 stream sampling stations were selected throughout the study area to represent the range of natural conditions, with 20 sites located in the Upper Kennebecasis watershed and eight sites in the Pollett River watershed (Figure 5-1). Sample stations were located on the mainstem and on tributaries to ensure a range of system sizes was included in the survey (catchment area for study sites ranged from 7.25 km2 to 308.7 km2; Table 5-1; Figure 5-1). Stations were primarily located on Early and Late Carboniferous bedrock, with additional stations sampled in areas with older bedrock age classes (Devonian-Carboniferous and Neoproterozoic; Table 5-1; Figure 5-2). By sampling over a variety of catchment sizes across a natural habitat gradient, the study was designed to establish baseline conditions within the area by maximizing the range of conditions described.

Stream sampling for water quality, benthic macroinvertebrates, and fish primarily took place in late summer 2015, with supplementary data (benthic algae and fish health) collected in spring/summer 2016. Not all parameters were collected at every station, but 26 of the 28 stations included both biotic and abiotic sampling (Table 5-1). The abiotic environment was characterized at 26 stations (Table 5-1) by analyzing water quality and by measuring physical habitat descriptors. Biological community structure was characterized for the study sites by sampling benthic macroinvertebrates (at 26 stations) and conducting fish surveys (at 27 stations; Table 5-1). Supplementary benthic algae samples were collected for analysis of chlorophyll a in summer 2016 at 20 stations. A special study on fish health was conducted through lethal sampling of the sentinel species Slimy Sculpin (Cottus cognatus) at 15 stations (Table 5-1). Contemporary water quality data collected in 2014 and 2015 by the New Brunswick Department of Environment and Local Government (NBDELG) and the Kennebecasis Watershed Restoration Committee (KWRC) were available for an additional 15 stations in the area (Table 5-2; Figure 5-2), and were used for a more extensive spatial analysis of environmental drivers in the region.

Historical data were available for a number of stations, allowing for a temporal analysis of trends and quantification of changes that have occurred in the abiotic environment and biotic communities of this region in recent years. Water quality data (collected by the NBDELG and KWRC) were available for a large number of sites from 1999 or later, with up to 16 years of data at a site (Table 5-2). Analysis of these data for long-term trends focused on the stations that were sampled in 2014 or 2015 in order to evaluate historical trends in the context of recent data. Fish survey data were available for three sites from 1996-1998 and 2008-2009 for comparison with recent data (KB3, SB2, and ST1); however, sampling methods for fish differed between historical and contemporary data, limiting the extent of the data comparison. Historical data for the region were used to put the contemporary analysis into context and estimate the extent to which the ecosystem has changed in recent history.

38 Table 5-1 Stations sampled as part of the 2015-2016 CRI monitoring program, including station codes, stream names, coordinates (latitude and longitude in decimal degrees), upstream catchment area (area, km2), dominant bedrock geology age in a 1 km upstream buffer, and a record of water quality, physical habitat, chlorophyll a (chl a), benthic macroinvertebrate (BMI), and fish data from each station (X = sample collected).

Station Stream Name Latitude Longitude Area km2 Bedrock Geology Age Chemistry Habitat Chl a BMI Fish Sculpin CB1 Calamingo Brook 45.82396 -65.22416 18.33 Late Carboniferous X X X X X X KB1 Kennebecasis R. 45.72493 -65.20672 16.08 Older Classes X X X X X X KB2 Kennebecasis R. 45.79116 -65.17107 52.47 Early Carboniferous X X X X X X KB3 Kennebecasis R. 45.82706 -65.21902 77.73 Late Carboniferous X X X X X X KB4 Kennebecasis R. 45.83904 -65.24611 108.39 Late Carboniferous X X X X X X KB5 Kennebecasis R. 45.81035 -65.29384 119.82 Early Carboniferous X X X X X X KB6 Kennebecasis R. 45.77914 -65.37976 220.07 Early Carboniferous X X X X X KB7 Kennebecasis R. 45.76548 -65.42001 292.73 Early Carboniferous X X X X KB8 Kennebecasis R. 45.74291 -65.44499 308.70 Early Carboniferous X X X MB1 McLeod Brook 45.72736 -65.36009 7.25 Early Carboniferous X X X X X X MB2 McLeod Brook 45.77112 -65.38745 24.17 Early Carboniferous X X X X X X MP1 Millpond Brook 45.73120 -65.47202 13.39 Early Carboniferous X X X X X PL1 Pollett River 45.70245 -65.09772 72.37 Older Classes X X X X PL2 Pollett River 45.75614 -65.07883 122.99 Older Classes X X X X X PL3 Pollett River 45.79697 -65.10211 148.30 Older Classes X X X X X PL4 Lee Brook 45.80344 -65.11241 22.36 Early Carboniferous X X X X X PL4A Pollett River 45.81056 -65.10694 181.71 Early Carboniferous X PL5 Colpitts Brook 45.86169 -65.07990 12.61 Late Carboniferous X X X X PL6 Pollett River 45.93900 -65.07997 247.8 Late Carboniferous X X X X X PL7 Pollett River 45.97603 -65.08706 303.92 Late Carboniferous X SB1 Negro Brook 45.73813 -65.29926 8.14 Early Carboniferous X X X X X X SB2 South Branch 45.75476 -65.29966 46.37 Late Carboniferous X X X X X X SB3A South Branch 45.77139 -65.32639 55.61 Late Carboniferous X X X X X X ST1 Stone Brook 45.80803 -65.36037 19.68 Late Carboniferous X X X X X ST2 Stone Brook 45.78099 -65.38686 27.84 Early Carboniferous X X X X TC1 Cedar Camp 45.68205 -65.36880 36.43 Older Classes X X X X X X TC2 Parlee Brook 45.68751 -65.41667 38.97 Early Carboniferous X X X X X X TC3 Trout Creek 45.70886 -65.47225 150.16 Early Carboniferous X X X X X X

39

Figure 5-1 Stations where CRI baseline characterization surveys were conducted in 2015/2016, including collection of chemical/physical data, chlorophyll a, benthic macroinvertebrates, fish community, and sculpin data. Symbols indicate which suite of parameters was collected at each station (see Table 5-1 for full details).

40

Figure 5-2 Stations where CRI baseline characterization surveys were conducted in 2015/2016 for the collection of chemical/physical data, chlorophyll a, benthic macroinvertebrates, fish community, and sculpin data, with underlying bedrock geology age indicated. Stations were classified based on the dominant bedrock geology age in a 1km upstream buffer.

41 Table 5-2 Details about additional water quality stations in the assessment, including stations monitored by the New Brunswick Department of Environment and Local Government (NBDELG) and the Kennebecasis Watershed Restoration Committee (NWRC), with stream names, coordinates (latitude and longitude in decimal degrees), and the years of water quality data that were used for assessment. All listed stations were included in the analysis of broad-scale water quality (using average August- September data from 2015 or 2014, whichever was available, to be comparable with CRI data collection), but only those stations with multiple years of data indicated in the table were included in the assessment of long-term trends.

Monitoring Years of Data Used for Station Stream Name Lat Long Program Assessment KR-5Points Kennebecasis River NBDELG 45.80745 -65.30034 2003-2015 1999-2001, 2010, KV-06 Kennebecasis River KWRC 45.74429 -65.47079 2013-2014 1999-2001, 2010-2011, KV-07 Smiths Creek KWRC 45.74797 -65.51255 2013-2014 1999-2001, 2010, KV-10 Wards Creek KWRC 45.72206 -65.50368 2013-2014 KV1-002 Walkerville KWRC 45.64972 -65.49890 2014 1999-2001, 2010-2011, KV-11 Trout Creek KWRC 45.73292 -65.52152 2013-2014 1999-2001, 2010-2011, KV-14 Millstream River KWRC 45.70372 -65.59946 2013-2014 KV-18 Kennebecasis River NBDELG 45.57928 -65.75717 1999-2015 Little South Branch KV2-001 KWRC 45.77477 -65.34893 2014 Kennebecasis River KV-28 Almshouse Brook KWRC 45.63188 -65.70840 2000-2001, 2013-2014 KV3-002 King Brook KWRC 45.79988 -65.45917 2014 KV-36 Sheck Brook KWRC 45.88349 -65.53026 2014 KV4-003 Bell Brook KWRC 45.71553 -65.60750 2014 KV6-004 Ossekeag Creek KWRC 45.53098 -65.81937 2014 SB- South Branch NBDELG 45.77659 -65.37104 2003-2015 Penobsquis Kennebecasis River

5.2.2 Water quality and physical habitat 5.2.2.1 Sample collection and processing 5.2.2.1.1 Water quality Water grab samples were collected at 24 of the CRI water quality stations by CRI field crew between September 1st and September 4th, 2015 (n=1/site). The Kennebecasis Watershed Restoration Committee collected samples at the remaining two water quality sites, KB3 and KB5, on August 17th and September 3rd, 2015 respectively, as part of their ongoing water monitoring program. Water samples were collected following provincial guidelines and were kept cool until delivery within 30 hours to the NBDELG’s analytical lab in Marysville, NB. The samples were analyzed for a standard suite of metals, nutrients, and ions (see Table 5-3 for the list of parameters considered for this assessment) following standardized analytical procedures.

42

Figure 5-3 Stations where water quality samples were collected by CRI, NBDELG, and/or KWRC in 2014 or 2015. Stations with historical records that were sufficient for inclusion in the temporal trend analysis include the three NBDELG stations (KR-5Points, SB-Penobsquis, and KV-18) as well as KV-06, KV-07, KV-10, KV-11, KV-14, and KV-28 (see Table 5-2 for details of years of sampling).

43

Figure 5-4 Stations where water quality samples were collected by CRI, NBDELG, and/or KWRC in 2014 or 2015, with bedrock geology age indicated. Stations were classified by the dominant geological age class in a 1 km upstream catchment buffer..

44

Data from additional water quality stations that were sampled by NBDELG and KWRC from 1999 to 2015 were included to expand the spatial and temporal scope of the baseline water quality assessment. The NBDELG provincial surface water monitoring network includes three stations within the target study area: Kennebecasis River above Five Points Bridge, Route 1 (KR-5Points; monitored 2003 to present); South Branch Kennebecasis River above Penobsquis Bridge (SB-Penobsquis; monitored 2003 to present); and Kennebecasis River above Bloomfield Bridge (KV-18; monitored 1999 to present; Table 5-2). Water sample collection for this set of samples followed provincial guidelines, and samples were analyzed at the NBDELG’s analytical lab in Marysville, NB. Samples were analyzed for a similar suite of metals, nutrients, and ions (see Table 5-3 for the list of parameters considered for this assessment) following standardized analytical procedures. Water quality samples were collected at these three locations a minimum of four times per year (although fewer data were available for some years in the historical record). Data were most consistently available for all three sites for May, July, September, and November of each monitoring year.

The KWRC monitored water quality across a broader spatial area from 1999 to 2015, and 12 stations from this monitoring program were chosen for inclusion in the broad-scale water quality assessment. As with the CRI and NBDELG samples, KWRC water samples were collected following provincial guidelines and were processed at the NBDELG’s analytical lab in Marysville, NB. The samples were analyzed for a standard suite of metals, nutrients, and ions (see Table 5-3 for the list of parameters considered for this assessment) following standardized analytical procedures. Similar to the NBDELG station, water quality samples were collected several times per year; however, stations were not monitored annually over the entire time period. Stations were assessed to select records of at least 4 years’ length, with two years prior to 2005 and two years after 2010. Seven of the KWRC monitoring stations included a sufficient number of years of data for inclusion in the analysis of long-term trends (Table 5-2).

Limits of quantitation (LOQs) differed for a small number of parameters during the extended period of record. In particular, the LOQ for suspended solids (SS) changed from 15 mg/L to 10 mg/L in 2005, and this parameter was excluded from statistical analysis to avoid the use of mixed-level LOQs. In addition, LOQs for several metals (cadmium, chromium, copper, and iron) differed for a selection of samples collected in 1999 and 2003 (n = 21 in 1999 and n= 7 in 2003), indicating that samples were diluted. These samples were excluded from statistical analysis to avoid the use of mixed-level LOQs. For all data (2015 CRI samples and 1999-2015 NBDELG and KWRC samples), parameters measured below the LOQ were adjusted to half of the LOQ value and parameters were log-transformed where appropriate for statistical analysis.

5.2.2.1.2 Physical habitat Physical habitat descriptions were recorded at the 26 CRI water quality stations (Table 5-1) following the protocol of the Canadian Aquatic Biomonitoring Network (CABIN; Environment Canada 2012; see Table 5-4 for a list of variables). At each site, measurements were taken to describe the slope and the mean and maximum channel depth, width, and stream velocity. The coverage and composition of in-stream and riparian vegetation were quantified and the physical habitat was described with respect to the extent of riffles, runs, and pools within the stream reach. In-stream substrate composition was estimated by conducting a pebble count; field crew selected 100 particles from the streambed at random, and measured the length of the b-axis of each particle. Substrate measurements were grouped by grain size (using the Wentworth scale; see Environment Canada 2012) to quantify the percent composition of different size classes

45 Table 5-3 Chemistry parameters, organized by test group, measured across the CRI, NBDELG, and KWRC water quality sample stations, including abbreviated parameter names, units of measurement, and limits of quantitation (LOQs). Included are parameters measured across the full range of stations. All parameters, with the exception of pH, were log10-transformed prior to use in analysis.

Limit of Test Group Parameter Abbreviation Units Quantitation (LOQ) Calcium Ca mg/L 0.1 Chloride Cl mg/L 0.05 Conductivity Cond μS/cm -- Fluoride F mg/L 0.1 Ions Potassium K mg/L 0.05 Magnesium Mg mg/L 0.1 Sodium Na mg/L 0.1 Sulphate SO4 mg/L 0.05 Ammonia, Total NH3T mg/L 0.01 Nitrate as N NO3 mg/L 0.05 Nutrients Nitrite as N NO2 mg/L 0.05 Nitrogen, Total TN mg/L 0.3 Phosphorous, Total TP mg/L 0.002 Alkalinity, Total Alk mg/L -- Colour, True CLR HU 5 Physicals Hardness, Total HARD mg/L 0.67 pH pH -- -- Total Organic TOC mg/L 1 Carbon Aluminum Al mg/L 0.001 Antimony Sb μg/L 1 Arsenic As μg/L 1 Cadmium Cd μg/L 0.1 Chromium Cr mg/L 0.0005 Metals Copper Cu mg/L 0.0005 Iron Fe mg/L 0.01 Lead Pb μg/L 1 Manganese Mn mg/L 0.005 Nickel Ni mg/L 0.005 Zinc Zn mg/L 0.005

46 Table 5-4 Physical habitat descriptors, organized by group, estimated for the CRI study sites by CRI field crew, including abbreviated parameter names, units of measurement, and data transformation. Included are the variables that were considered for the analysis.

Group Variable Abbreviation Units Transformation

Depth, avg AvgDepth cm log10

Depth, max MaxDepth cm log10

Depth, bankfull Depth_Bank cm log10 Channel Width, wetted Width_Wet m log10 Measurements Width, bankfull Width_Bank m log10 Slope Slope % arcsin√

Velocity, avg Vel_Avg m/s log10

Velocity, max Vel_Max m/s log10 Periphyton cover %Peri -- arcsin√ Macrophyte cover %Macrophy -- arcsin√ Canopy cover %Canopy -- arcsin√ Coniferous dominant ConifDom -- -- Deciduous dominant DecidDom -- -- Vegetation Grass/Fern dominant GrassDom -- -- Shrubs dominant ShrubDom -- -- P/A Coniferous Conif -- -- P/A Deciduous Decid -- -- P/A Grasses Ferns Grass -- -- P/A Shrubs Shrub -- -- Bedrock, percent %Bedrock % arcsin√ Boulder, percent %Boulder % arcsin√ Cobble, percent %Cobble % arcsin√ Gravel, percent %Gravel % arcsin√ Pebble, percent %Pebble % arcsin√ Sand, percent %Sand % arcsin√ Substrate Silt/Clay, percent %SiltClay % arcsin√

D50 D50 cm log10

Dg Dg cm log10

Interstitial size class Interstit -- log10 st 1 Dominant class 1stDom -- log10 nd 2 Dominant class 2ndDom -- log10 Embeddedness, avg Embed % arcsin√

47 Table 5-5 Physical habitat descriptors, organized by group, estimated in ArcGIS for the CRI study sites, including abbreviated parameter names, units of measurement, data transformation, and data source. Included are the variables that were considered for the analysis. Group Variable Abbreviation Units Transformation Source

Catchment 2 Catchment area Area km log10 DEM Descriptors

Forest percent Forest % arcsin√ Agriculture percent Agr % arcsin√ Land Cover Industry percent Indus % arcsin√ and Infrastructure percent Infra % arcsin√ GeoNB Landuse Settlement percent Set % arcsin√ Barren land percent Barren % arcsin√ Recreational percent Rec % arcsin√

Morainal/Colluvial SurfG_AMCB % arcsin√ Surficial Blanket/veneer (MV2) SurfG_MV2 % arcsin√ Geology Blanket/veneer (MV3) SurfG_MV3 % arcsin√ GeoNB Provincial Colluvial with SurfG_CB/MB3 % arcsin√ blanket/veneer Intrusive Intru % arcsin√ Bedrock Sedimentary Sedi % arcsin√ Geology Volcanic Vol % arcsin√ Geogratis National Sedimentary and SediVolc % arcsin√ Volcanic

(e.g., sand, gravel, cobble). Measurements were also used to calculate median particle diameter (D50) for each site, representing the particle size at the cumulative 50% point of a particle size distribution, and the geometric mean particle diameter (Dg).

Additional habitat descriptors were obtained by using ArcGIS (Version 10.3, ESRI, St. Paul, MN, USA), a Geographic Information System (GIS), to summarize geospatial data for the catchment upstream of sample sites. A 30-m resolution Digital Elevation Model (DEM) was used for catchment delineation in GIS (ref for DEM). Catchments were delineated by using the ESRI Watershed Delineation toolbox (Noman, 2007; http://arcscripts.esri.com/details.asp?dbid=15148). Tools from the Spatial Analyst toolbox and extraction tools from the Geospatial Modelling Environment program (Baier and Neuwirth 2007, Beyer 2010, R Development Core Team 2015) were used to extract geospatial variables, which included national and provincial bedrock and surficial geology (see Figure 5-5 for national geology layers), and land cover/landuse measurements from a provincial layer (see Table 5-5 for details). Geospatial variables and field habitat descriptors were log10(x) or arcsin(√x) transformed as appropriate.

5.2.2.2 Data analysis 5.2.2.2.1 Station classifications Stations were grouped by the age of underlying bedrock geology for the characterization of water quality and biotic communities and for statistical analysis of station differences to examine the relationship of geology with water chemistry and community structure of streams. Focus was

48 a

b

Figure 5-5 (a) Surficial geology and (b) bedrock geology underlying the 41 water quality stations sampled by CRI, NBDELG, and KWRC in 2014 or 2015. Geology layers are national layers obtained from Geogratis, with surficial and national bedrock class names from Geological Survey of Canada.

49 placed on geology within an extended reach upstream of the stations by intersecting the NB provincial bedrock substratum layer (Service New Brunswick-GeoNB) with a 1 km buffer (using only the upstream catchment) for each station. The area of each geological age class within the buffers was calculated in ArcMap (Version 10.3) to estimate relative proportions and determine the dominant age class. Catchment buffers were generally dominated by a single geological age class that made up >70% of the buffer area, with the exception of two stations that had dominant age classes at 57% (SB2) and 62% (TC2) of the buffer area. Dominant geological age classes for the 26 CRI stations included Early Carboniferous (14 stations), Late Carboniferous (9 stations), Devonian-Carboniferous (2 stations), and Middle Neoproterozoic (3 stations). For the purpose of analysis, Devonian-Carboniferous and Middle Neoproterozoic were combined into one class to represent stations with underlying geology older than Early Carboniferous (termed Older Classes; Table 5-1). To account for differences in system size, stations were also classified into groups based on catchment size for some statistical analyses, with groups defined as small catchment (area up to 25 km2; 9 stations), medium catchment (area of 25-80 km2; 8 stations), or large catchment (area of 100-310 km2; 9 stations). Each geological classification included systems with small, medium, and large catchment sizes.

5.2.2.2.2 Chemical and physical habitat characterization Two-way orthogonal ANOVAs were used to assess differences in water chemistry variables among geological age classes and catchment size classes. Water chemistry data included a suite of major ions, nutrients, physicals, and metals, and correlations among water chemistry parameters were assessed prior to analysis to identify redundancy among parameters and reduce the set of water chemistry variables for analysis. Conductivity (μS/cm) was highly correlated with ions, alkalinity, and hardness (|r| > 0.75 in all cases) and was retained for analysis. Nitrate (mg/L), total nitrogen (TN; mg/L), total phosphorous (TP; mg/L), and total organic carbon (TOC; mg/L) were not highly correlated with other parameters (|r| < 0.6 for most parameters) and were retained in the analysis as a measure of nutrients. Finally, turbidity was included because it was highly correlated with metals (because total metals were measured), but not with other parameters selected for analysis. For each of the chosen water chemistry parameters, differences in means among the geological classes and catchment size classes were assessed by testing the Model 1 ANOVA model: Y = Geology + Size + Geology * Size, where Y was the chosen water chemistry parameter, Geology tested for mean differences due to the geological age class of the station, Size tested for mean differences due to the catchment size class of the station, and Geology * Size tested for an interaction between the two orthogonal factors. Because there were missing values due to unequal replication of size classes in each geological class (water chemistry was missing two medium Early Carboniferous stations, one large Late Carboniferous station, and one small Older Classes station), the ANOVAs were adjusted for unequal replication. Average values were calculated for each of the geology-size combinations that were missing values, and those averages were included as additional dummy samples in the analysis of the ANOVA model to create a proportional, though still unbalanced model (5 Early Carboniferous samples, 3 Late Carboniferous, and 2 Older Classes samples of each catchment size class). To remove the effect of the dummy samples, the total degrees of freedom (DF) and the error DF calculated in the ANOVA were adjusted to remove the added samples, and all F-ratios were recalculated using the adjusted error Mean Square (Zar 1999). If the interaction term was not significant (at α = 0.05), differences among levels of any significant terms in the model were analyzed using a Tukey test. Tukey tests compared the original group means without the addition of the dummy sample values, and were adjusted for unequal sample sizes among groups, to account for the differences in replication for each geological age group and catchment size class (Zar 1999). Analyses were conducted in Systat 12 with α = 0.05.

50

Multivariate data analysis was used to further characterize the abiotic environment of the CRI stream sites. Principal Components Analysis (PCA) was run separately for water quality data and habitat data (26 stations) to evaluate similarities among stations with respect to abiotic descriptors. Prior to performing the PCAs, data were assessed to remove highly redundant parameters and water quality parameters with extremely low variability (defined as a water quality parameter that was only measured above the LOQ for less than two sites). The resulting subsets of water quality and habitat variables were analyzed in a PCA of the correlation matrix (using post-transformed variable scores), to standardize the data and control for the range of measurement scales in the abiotic variables. PCA biplots were examined to evaluate associations of sample sites in multidimensional space, reflecting similarity in water quality or habitat conditions.

Water quality data from the expanded provincial and regional monitoring networks (NBDELG and KWRC stations) were used in combination with the data from the 2015 CRI stream sites to provide a more extensive view of the range of conditions across the region. NBDELG and KWRC water quality samples were collected in mid-August and late September at most sites. Values for these dates were averaged to provide data comparable with the CRI samples that were collected in early September. Stations included the assessment of broad-scale patterns were restricted to those sampled in either 2014 or 2015 to ensure a similar time period of measurement among all samples. A PCA of the correlation matrix was used to assess the similarity among the 41 selected stations based on water quality. The PCA biplot was examined to visually assess associations among sites with respect to water quality.

5.2.2.2.3 Water quality assessment Water quality parameters for the CRI sample sites and the expanded provincial and regional water quality sites (NBDELG and KWRC) were compared with the Canadian Council of Minister’s (CCME) Water Quality Guidelines for the Protection of Aquatic Life (http://st- ts.ccme.ca/en/index.html) to identify any exceedances in values. Written guidelines for short- term or long-term exposure exist for a subset of the parameters listed in Table 5-3, including a selection of ions and nutrients. Assessment of water quality therefore focused only on the parameters for which guidelines were found. The primary focus of this assessment was on recent water quality, and only data from 2014-2015 (n = 41 sites) were selected for comparison with CCME guidelines (with the exception of aluminum, which was assessed throughout the period of record (1999 to 2015) due to differing levels of exceedances).

5.2.2.2.4 Water chemistry trends The three stations that were monitored regularly as part of the NBDELG’s provincial surface water monitoring network (KR-5Points, KV-18, and SB-Penobsquis) had water chemistry records with sufficient data to allow individual assessment of long-term trends. All three stations had more than 10 years of monitoring data available for the months of May, July, September, and November. The van Belle and Hughes test for homogeneity of trend was used to determine whether similar trends were evident over time in all four sampling months (van Belle and Hughes 1984, Yu et al. 1993), with the test calculated separately for each chemical parameter and for each station. This analysis tested two null hypotheses: (1) that there was no difference in long-term trend in the chosen parameter among the four sampling months (homogeneity among trends), and (2) that the long-term trend did not reflect a significant monotonic (unidirectional) increase or decrease over time. Both tests were based on a χ2 statistic and were tested at α = 0.05. The first hypothesis was of primary interest in this analysis, as it indicated

51 whether data could be combined across sampling months for an overall assessment of long- term trends.

If trends were found to be homogeneous among the four sampling months, seasonal Kendall analysis (Yu et al. 1993) was used as a more formal test for long-term trends. The analysis tested the null hypothesis of no significant long-term monotonic (unidirectional) trends in the data for a chemical parameter at a station, given seasonal sampling in each year. Sen’s slope was calculated (with 95% confidence intervals) as a measure of the magnitude and direction of trend (Yu et al. 1993). All trend tests were conducted separately for each chemical parameter and for each station to evaluate which parameters showed significant monotonic increases or decreases over the period of record (2003 to 2015 for KR-5Points and SB-Penobsquis, and 1999 to 2015 for KV-18).

Additional water quality stations sampled by the KWRC generally had 4-6 years of data, with monitoring occurring primarily between June and October of each year. The number of years of data collected at these stations was too small for reliable detection of individual station trends; however, it was possible to use these data in combination with the longer records of the three NBDELG stations to evaluate regional trends. Analysis focused on data from July and September, when there was the greatest overlap between KWRC and NBDELG monitoring activities. For the three NBDELG stations and the 5-6 KWRC stations with four or more years of data in July and September, the van Belle and Hughes test for homogeneity of trends was used to assess (1) whether long-term trends were homogeneous among all monitoring stations for the chemical parameter of interest, and (2) whether the long-term trend across stations showed a significant monotonic (unidirectional) increase or decrease. Chemical parameters were included in this analysis if they were found to show a significant increasing or decreasing trend in July or September at one of the NBDELG stations, or if there was evidence of an increase or decrease over time (although not a significant trend) at all three GNB DELG stations. All multivariate analyses of water quality were conducted in Canoco 4.5 and univariate analyses were conducted in Systat 12.

5.2.3 Biological community 5.2.3.1 Sample collection and processing 5.2.3.1.1 Benthic chlorophyll a Benthic algal samples were collected for analysis of chlorophyll a content to estimate bulk algal biomass at study sites. Benthic algae were sampled at 20 stations in 2016 to provide supplementary information about primary production in these systems. At 15 of the stations, sampling took place in June, July, and August 2016 to represent changes in algal biomass throughout the summer; at the remaining 5 stations (CB1, KB6, MB2, MP1, SB1), samples were collected in June 2016. At each station, 5 rocks were selected at random from within the stream. A known area on each rock was scraped completely with a scalpel to remove all biofilm, and the scraped sample was put into a scintillation vial with 90% ethanol.

In the lab, chlorophyll a concentrations were estimated for samples through hot ethanol extraction. Unfiltered samples were placed in a hot water bath (80°C) for seven minutes, and fluorescence was measured to obtain a chlorophyll a estimate (in mg/m2, standardized by the rock area that was scraped in the field). Chlorophyll a concentrations were averaged for the five replicate rock samples to obtain an overall average for each station at each time period.

5.2.3.1.2 Benthic macroinvertebrates

52 Benthic macroinvertebrate samples were collected at 26 stations (Table 5-1) in late August and early September 2015 following the standard protocol of the national Canadian Aquatic Biomonitoring Network (CABIN; Environment Canada 2012). Sample collection followed these standardized procedures to ensure comparability of data with other samples from Atlantic Canada and with other data on a national scale. Use of this protocol also allows data to be used in the Atlantic Reference Condition model (Armanini et al. 2013), which assesses stream health based on benthic community composition. Benthic samples were collected at each station using a 3-minute travelling-kick method, in which the operator held a 400-μm-mesh kick-net downstream while travelling through the stream channel in a zig-zag fashion and disturbing the substrate for a period of 3 minutes (Environment Canada 2012). Samples were preserved in the field with 10% formalin. Samples were transferred to 90% ethanol in the lab after one week to preserve sample integrity.

Macroinvertebrate samples were randomly subsampled in the laboratory using the marchant box method (Environment Canada 2010) until a minimum of 300 organisms was counted. Chironomidae taxa were sent to EcoAnalysts, Inc. to be identified to species by SFS-certified taxonomists. Because of high abundances of Chironomidae in the samples, taxonomists at EcoAnalysts, Inc. sub-sampled the Chironomidae by pouring the sample into a marked dish and selecting cells for identification at random (similar to the marchant box method, but on a smaller scale) until at least 150 individuals were identified. Remaining chironomids in the sample were enumerated and a subsampling correction factor was applied to unidentified chironomids. The remainder of the macroinvertebrate samples (non-chironomid portion) was sent to an independent SFS-certified taxonomist for identification, and organisms were identified to the genus level where possible. Whole-sample abundances were estimated by adjusting counts based on the percentage of the sample that was processed to reach 300 organisms. Samples contained large numbers of taxa that were too small (early instars) to identify to the level of genus. To avoid mixed-level taxonomy within an order (which may lead to misrepresentation of trends), data were rolled up to the level of subfamily for Chironomidae and the level of family or higher for all other organisms for multivariate analysis (not for calculation of biotic metrics). Bowman and Bailey (1997) demonstrated that analysis of benthic macroinvertebrate communities at the level of genus did not improve the ability to detect patterns in the data.

5.2.3.1.3 Fish Fish community surveys were conducted at 27 stations, including two stations that were not sampled for benthic macroinvertebrates or water chemistry (Table 5-1). Sampling took place in August 2015, and was conducted by using backpack electrofishing techniques (Murphy and Willis 1996). The lowest site on the Kennebecasis River (KB8) was excluded from fish sampling as it was too deep to safely conduct a backpack electrofishing survey. A backpack electrofishing unit (Smith-Root LR-24) was used to fish an extent of the stream site containing at least one riffle and one pool or run in a single upstream pass. The effort was standardized by maintaining power within a range of 65-75W (voltage varied depending on conductivity of the water). All fish were identified to species-level, and length (to the nearest 1 mm) and weight (to the nearest 0.01 g) were measured in the field. Sampled habitat areas were measured and electrofishing time was recorded in seconds to generate estimates of catch per unit effort (/area and /time).

In the spring of 2016, 50-60 Slimy Sculpin at least 50 mm in length were collected at 15 sites (Table 5-1) using a backpack electrofishing unit (Smith-Root LR-24). Collections were carried out over a two-week period between April 19 and 29, 2016. Slimy Sculpin spawn in early to mid- May, largely dependant on water temperatures. Lethal sampling was conducted pre-spawning in order to reduce variability and maximize reproductive endpoint information for both mature

53 females and males (Barrett and Munkittrick 2010). The goal of the electrofishing effort was to collect at least 20 females and 20 males in order to have sufficient statistical power to detect differences among stations (Munkittrick et al. 2010).

The length (± mm) and weight (± 0.01g) of each sacrificed fish was measured, and the liver and gonads were removed and weighed (± 0.01g). Once 20 males and 20 females had been sampled, the remaining fish were returned to the stream channel where they had been collected. The weight of the viscera (± 0.01g) was also recorded in order to calculate a carcass weight (wet weight – [liver weight + gonad weight + viscera]) to control for differences in liver, gonad and stomach size among fish. Condition factor was calculated using the following equation: (K) = 100,000 (carcass weight (g)/(total length (mm)3) (after Ricker 1975). The liversomatic index (LSI) and gonadosomatic index (GSI) were calculated as the weight of the liver and gonad, respectively, divided by the carcass weight multiplied by 100 (Heidinger and Crawford 1977, Delahunty and de Vlaming 1980).

Historical data were available for three stations (KB3, SB2, and ST1) from 1996-1998 and 2008- 2009, in addition to the contemporary data collected in 2015. However, sampling methods for historical data differed from contemporary protocols. Data for 1996 and 1997 were collected with a backpack electrofisher by using the depletion method, in which a stream reach is blocked off with nets and repeated passes with the electrofisher are made through the stream reach until a reduction in catch of at least 50% is noted. CPUE for these data was estimated per unit area, and the more intensive sampling of the designated area may have led to higher fish abundance as well as the collection of additional species and sizes of fish than may have been captured through the single-pass method. Data in 1998 were collected from fish counting fences, with captured fish marked and released for attempted recapture. Fish counting fences rely on movement of fish into the trap area to estimate counts, and provide a more opportunistic sampling method than the intensive electrofishing methods described. In 2008 and 2009, the electrofishing depletion method was used again within the study sites, but a number of reaches was selected to represent specific habitat types, with replicates of pools, runs, and riffles sampled on different occasions. Fish length and wet weight measurements and species identification data were available for the three stations in each year of sampling.

5.2.3.2 Data Analysis 5.2.3.2.1 Algal biomass Chlorophyll a samples were collected in 2016, and thus did not represent algal biomass at the time of sampling of water quality, benthic macroinvertebrate, and fish data. Because of this difference in sampling periods and because of the reduced number of sampling stations for chlorophyll a, algal biomass was not included in analyses as a direct driver of benthic macroinvertebrate or fish community structure. However, the range of chlorophyll a values was compared across stations in order to characterize algal biomass in 2016. Trophic status of the stations was classified based on the TP levels recorded in 2015, in order to visually assess whether chlorophyll a levels were consistent with patterns of trophy from the previous sampling season.

5.2.3.2.2 Benthic macroinvertebrate community characterization Benthic macroinvertebrate data were summarized as community metrics to evaluate biodiversity at the study sites. Benthic metrics included total abundance at a station, taxonomic richness (i.e., number of families of insects and non-insects), and taxonomic diversity (estimated by Simpson’s 1-D Index, a measure of both richness and numerical dominance of taxa within the

54 samples). Additional metrics were used to gauge the relative importance of different taxonomic groups, including the relative abundance of Chironomidae (midges; generally considered to be tolerant to perturbation), and the relative abundance of Ephemeroptera, Plecoptera, and Trichoptera (EPT, or mayflies, stoneflies, and caddisflies; generally considered to be sensitive to perturbation).

Stations were classified by bedrock geology age and catchment size as described in section 5.2.2.2.1, resulting in at total of 13 Early Carboniferous stations, 8 Late Carboniferous stations, and 5 Older Class stations (Table 5-1). Two-way orthogonal ANOVAs were used to evaluate whether metrics differed based on bedrock geology age and system size by testing the Model 1 ANOVA model: Y = Geology + Size + Geology * Size, where Y was the chosen BMI metric, Geology tested for mean differences due to the geological age class of the station, Size tested for mean differences due to the catchment size class of the station, and Geology * Size tested for an interaction between the two orthogonal factors. Because there were missing values due to unequal replication of size classes in each geological class (BMI was missing two medium Early Carboniferous stations, one large Late Carboniferous station, and one small Older Classes station), the ANOVAs were adjusted for unequal replication. As described in section 5.2.2.2.2, group averages were used as dummy variables in the ANOVA model to create a proportional (though unbalanced) design, and the ANOVA and Tukey post-hoc tests were adjusted for unequal sample size.

Multivariate analysis was used to further characterize benthic macroinvertebrate community composition among the study sites. Macroinvertebrate data were analyzed at the level of family or higher for all taxa, with the exception of Chironomidae, which were at the level of subfamily. Relative abundance data were calculated to focus the analysis on compositional differences among sites, and relative abundance data were log10(x+1)+0.1 transformed in order to downweight the effects of rare taxa. Detrended Correspondence Analysis estimated the length of the first gradient in the data to determine whether community data were best described by a linear or unimodal model. The first DCA axis length was less than 2.5 SDs, which indicated that a linear model (i.e., PCA) provided the best fit to the data. A PCA of benthic macroinvertebrate relative abundance data was run on the correlation matrix by post-transforming species scores. The PCA biplot was assessed to evaluate similarities and differences among stations with respect to benthic community composition, with stations coded based on bedrock geology age.

Associations between benthic macroinvertebrate community data and environmental variables were used to identify potential drivers of composition among stations. Redundancy Analysis (RDA) was run separately using water quality variables or physical habitat/GIS variables to evaluate the association of community data with the chemical and physical drivers in the stream systems. Subsets of variables were selected for inclusion in the assessments by testing Pearson correlations among drivers and removing highly redundant variables. The final subset of environmental variables for the water quality RDA included aluminum, conductivity, chromium, nitrate, total phosphorus, total nitrogen, total organic carbon, and turbidity (all variables log-transformed). The final subset of environmental variables for the physical habitat/GIS RDA included catchment area, canopy coverage, periphyton coverage, forest percent, agriculture percent, presence of coniferous vegetation, deciduous vegetation dominant, % boulder, D50, relative area of intrusive bedrock, and relative area of MV2 and MV3 surficial geology (sum of two blanket/veneer categories). For each RDA, the variance explained by each axis was compared with the total variance in the benthic community (unconstrained variance) to assess the strength of the chosen variables to explain patterns in community similarities among stations. Biplots were used to visually assess biotic-abiotic associations among stations.

55

To further assess the potential response of benthic community composition to environmental drivers, variables that were found to be strongly associated with the biotic community in the RDAs were used as predictors in least-squares multiple linear regressions of benthic metric data. Regressions tested the effects of combinations of chemical and physical drivers on benthic macroinvertebrate abundance, richness, evenness, and the percent Chironomidae and percent EPT to determine whether environmental drivers could be used as predictors of benthic diversity. Maximum summer water temperature in 2015 (estimated from temperature loggers) was included as a potential environmental driver if it appeared to have a relationship with a benthic metric; however, inclusion of this variable required the exclusion of stations KB8 and PL3, for which there were no temperature logger data for summer 2015. The corrected Akaike’s Information Criterion (AICc) was used to measure the relative quality of the statistical models, with the lowest AICc value indicating the best model. Models with AICc values that differed from the best model by 0-2 were considered to have a substantial level of support, while models with AIC values that differed from the best model by 4-7 had considerably less support and models with AIC <10 higher than the best model had essentially no support (Burnham and Anderson 2004). Comparison of AICc values and R2 among statistical models was used to select the model that best described the relationship between the diversity metrics and the environmental drivers. Multivariate analyses of benthic macroinvertebrates were conducted in Canoco 4.5 and univariate analyses were conducted in Systat.

5.2.3.2.3 Reference Condition Approach assessment of stream health Identification of communities that are characteristic of reference or undisturbed conditions within a region is dependent upon local conditions such as ecoregion, geology, and climate (Stoddard et al. 2006, Armanini et al. 2013). The reference condition approach (RCA) was developed as a way to compare study sites with communities representative of what might be expected at an undisturbed station, given local conditions (Stoddard et al. 2006, Armanini et al. 2013). The Atlantic Reference Model (ARM) was developed using data collected throughout Atlantic Canada through the CABIN program, and was designed to select reference conditions for comparison with test sites after classification of stations using bedrock geology and temperature range data (Armanini et al. 2013). Test sites are compared with reference sites in a River Invertebrate Prediction and Classification System approach to obtain estimates of observed over expected (O/E) for a number of biological metrics. O/E values for metrics for each station are compared with an acceptable range to determine whether the station is classified as normal, divergent, or highly divergent.

Benthic macroinvertebrate community data, along with the percent coverage of bedrock geology types (from the national bedrock layer) and the average temperature range within the catchment (from a national long-term average temperature layer) were input into the program GenGIS 2.5.0 to run RCA analysis using the ARM. O/E values for metrics were compared with reference ranges and plotted to assess stream health based on benthic macroinvertebrate community structure.

5.2.3.2.4 Fish community characterization Fish community data were summarized by metrics that described abundance and diversity of species to characterize biodiversity at the study sites. Community metrics included catch per unit effort (CPUE; a measure of abundance standardized by sampling effort), species richness, and species diversity (estimated by Simpson’s 1-D Index, a measure of both richness and dominance of species within the samples). Additional metrics were used to gauge differences in

56 the importance of particular species among stations, and included the relative abundance of Brook Trout and the relative abundance of Slimy Sculpin (both dominant species).

Stations were classified by bedrock geology age and catchment size as described in section 5.2.2.2.1, resulting in at total of 13 Early Carboniferous stations, 9 Late Carboniferous stations, and 5 Older Class stations (Table 5-1). Two-way orthogonal ANOVAs were used to evaluate whether metrics differed based on bedrock geology age and system size by testing the Model 1 ANOVA model: Y = Geology + Size + Geology * Size, where Y was the chosen fish metric, Geology tested for mean differences due to the geological age class of the station, Size tested for mean differences due to the catchment size class of the station, and Geology * Size tested for an interaction between the two orthogonal factors. Because there were missing values due to unequal replication of size classes in each geological class (fish was missing two medium Early Carboniferous stations, and one small Older Classes station), the ANOVAs were adjusted for unequal replication. As described in section 5.2.2.2.2, group averages were used as dummy variables in the ANOVA model to create a proportional (though unbalanced) design, and the ANOVA and Tukey post-hoc tests were adjusted for unequal sample size.

Multivariate analysis further characterized the community composition of fish among the study sites. The relative abundance of each fish species was calculated to focus the analysis on compositional differences among sites. Prior to analysis, relative abundance data were log10(x+1) transformed in order to downweight the effects of rare and extremely dominant taxa. Detrended Correspondence Analysis estimated the length of the first gradient in the data to determine whether community data were best described by a linear or unimodal model. The first DCA axis length was between 2.5 and 3.5 SDs, which indicated that either a linear or unimodal model would be appropriate for the data. Principal Components Analysis (PCA; a linear test) was chosen in order to focus the analysis on correlations among communities rather than on species optima. A PCA of fish relative abundance data was run on the correlation matrix by post-transforming species scores. The PCA biplot was assessed to evaluate similarities and differences among stations with respect to fish community composition, with stations coded based on underlying bedrock geology age.

Associations between fish community data and environmental variables were used to identify potential drivers of composition among stations. Redundancy Analysis (RDA) was run separately using water quality variables and physical habitat/GIS variables to evaluate the association of community data with the chemical and physical drivers in the stream systems. Subsets of variables were selected for inclusion in the assessments by testing Pearson correlations among drivers and removing highly redundant variables. The final subset of environmental variables for the water quality RDA included aluminum, conductivity, chromium, nitrate, total phosphorus, total nitrogen, total organic carbon, and turbidity (all variables log- transformed). The final subset of environmental variables for the physical habitat/GIS RDA included catchment area, canopy coverage, periphyton coverage, forest percent, agriculture percent, presence of coniferous vegetation, D50, relative area of intrusive bedrock, and relative area of MV2 and MV3 surficial geology (sum of two blanket/veneer categories). For each RDA, the variance explained by each axis was compared with the total variance in the fish community (unconstrained variance) to assess the strength of the chosen variables to explain patterns in community similarities among stations. Biplots were used to visually assess biotic-abiotic associations among stations.

To further assess the potential response of fish communities to environmental drivers, variables that were found to be strongly associated with the fish community in the RDAs were used as predictors in least-squares multiple linear regressions of fish metric data. Regressions tested

57 the effects of combinations of chemical and physical drivers on fish CPUE, species richness, evenness, and the percent Brook Trout and percent Slimy Sculpin to determine whether environmental drivers could be used as predictors of fish diversity. Maximum summer water temperature in 2015 (estimated from temperature loggers) was included as a potential environmental driver if it appeared to have a relationship with a fish metric; however, inclusion of this variable required the exclusion of station PL3, for which there were no temperature logger data for summer 2015. Comparison of AICc values and R2 among statistical models was used to choose the best description of the relationship between the diversity metrics and the environmental drivers. Multivariate analyses of fish communities were conducted in Canoco 4.5 and univariate analyses were conducted in Systat 12.

The distinct differences in fish sampling methods between the 1996-1997, 1998, 2008-2009, and 2015 sampling periods precluded direct comparison of abundances, CPUE, evenness, or other metrics dependent on fish counts. Analysis of historical and contemporary data instead focused on taxonomic composition, comparing the presence/absence of all fish species among sampling years. Species richness was also assessed over time by using the van Belle and Hughes test for homogeneity of trends to determine whether changes in fish species richness from 1996 to 2015 were homogeneous among the three stations and whether they displayed a significant positive or negative trend in richness over time. However, it should be noted that the comparability of species composition among the sampling methods could be questionable due to the variability in effort expended to collect fish using the different methods of single-pass electrofishing, depletion electrofishing, and fish counting fences, and any differences in species composition over time among the three sites should not be taken to represent absolute shifts in communities, but rather as indications of where future monitoring should be focused.

5.2.3.2.5 Sculpin health Analysis of Slimy Sculpin health focused on the relationship between organ size (gonad weight and liver weight) and body weight and the condition of the fish, described by the relationship between fish weight and length. Relationships were assessed for sculpin to characterize fish health among the stations, and biotic data were related to environmental variables in order to evaluate drivers of fish health. Data were cleaned to remove outliers in gonad weight, carcass weight or liver weight where values were clearly a result of recording error. Fish data were log10- transformed prior to statistical analysis to improve conformity with the assumptions of normality and homoscedasticity for parametric analysis.

ANCOVA was used to compare the liver and gonad weights among males and females with the model log10y= log10CarcassWeight + Sex + CarcassWeight*Sex, where y was either gonad weight or liver weight. The interaction term CarcassWeight*Sex tested whether the slope of the relationship between y and carcass weight was the same for both males and females (at α = 0.05). Condition of Slimy Sculpin was assessed by comparing carcass weight to length and sex in the ANCOVA model log10CarcassWeight= log10Length+ Sex + log10Length*Sex. If the interaction term was found to be significant, this indicated that the relationship between carcass weight and length (a measure of the condition of the fish) differed among males and females, and a separate set of ANCOVAs was then performed for males and females to compare the differences in organ weight among stations, with carcass weight as the covariate. The same models were used, with the Sex term replaced by Station. Tukey’s post hoc tests were conducted to identify significant differences in the group means. Differences in gonadasomatic and liversomatic indices across stations were visually assessed in relation to the coefficient of variation of winter temperature (a standardized measure, calculated as the standard deviation divided by the mean, converted to a percentage), to assess potential response to thermal

58 variation within the system. All statistical tests were run at α = 0.05. Analyses of fish health were run in R version 3.2.3 (R Development Core Team 2015).

5.2.3.2.6 Community concordance Procrustes Analysis was used as a test of community concordance to determine whether similar gradients were evident when stations were assessed on the basis of the fish or benthic macroinvertebrate communities. To test community concordance, the spatial arrangement of stations in multivariate space is compared between ordinations using each set of data (fish and benthic macroinvertebrates in this example). Procrustes Analysis compares two ordinations (one target ordination, one rotational ordination) by translating and rotating the rotational ordination to match the target ordination as closely as possible. Residual vectors describe deviations of sample points from their location in the target ordination to their location in the 2 rotational ordination, and the sum of squared residuals (m12 ) provides an estimate of the fit of 2 the two ordinations, with a high m12 statistic being indicative of strong differences between the two ordinations.

Community concordance for the CRI stations was assessed by using Procrustes Analysis to compare PCA ordinations of stations based on benthic macroinvertebrate relative abundances and fish relative abundances. The 25 stations with both fish and benthic macroinvertebrate data were included in this analysis (Table 5-1). For the analysis, the fish PCA was the target ordination and the benthic macroinvertebrate PCA was the rotational ordination. A 2 randomization test was used as part of the analysis to test the significance of m12 by comparing 9999 random configurations of the sample points with the target ordination; a significant p-value (at α = 0.05) was an indication that the target ordination and rotational ordination were more similar than could be obtained by chance. Procrustes analysis was run in R version 3.2.3 (R Development Core Team 2015) using the vegan package (Oksanen et al. 2015).

5.3 Results and Discussion 5.3.1 Water Quality and Physical Habitat 5.3.1.1 Chemical and physical habitat characterization The clearest difference in water chemistry among geological age classes was related to ion levels. Conductivity levels differed significantly among the three geological age classes (F2,17 = 13.0; p < 0.001), with Early Carboniferous stations displaying higher conductivity (median = 167.91 mg/L) than the other two age classes (mean = 65.90 and 41.62 mg/L for Late Carboniferous and Older Classes, respectively; Figure 5-6; Table 5-6). Examination of related parameters indicated that there was a general pattern for conductivity, alkalinity, and major ions of highest average values in Early Carboniferous stations, lower average values in Late Carboniferous stations, and lowest average values in Older Class stations, although for a small number of ions the difference between geological classes appeared minimal (e.g., magnesium and potassium). Among the major ions, calcium, chloride, and sulphate showed the strongest differences between the three geological age classes, following the pattern of highest concentrations in Early Carboniferous stations and lowest concentrations in Older Class stations (Table 5-6).

Differences in water chemistry among geological age classes were weaker or less consistent for other water chemistry parameters. Turbidity levels were significantly different among the

59 Table 5-6 Mean (± standard error) conductivity (μS/cm), alkalinity (mg/L), and concentrations of major ions (mg/L) for stream stations grouped by geological age class determined by the dominant age class within a 1 km upstream catchment buffer (Early Carboniferous, Late Carboniferous, or Older Classes, which includes Neoproterozoic and Devonian-Carboniferous). Values below LOQ were included as half the LOQ.

Water chemistry Early Carboniferous Late Carboniferous Older Classes parameter (n = 13) (n = 8) (n = 5) Conductivity (μS/cm) 167.91 ± 41.67 65.90 ± 6.42 41.62 ± 7.35 Alkalinity (mg/L) 42.35 ± 4.89 22.69 ± 2.55 15.98 ± 3.90 Calcium (mg/L) 23.56 ± 8.16 6.97 ± 1.05 4.54 ± 1.44 Chloride (mg/L) 8.38 ± 2.15 2.34 ± 0.32 1.63 ± 0.13 Magnesium (mg/L) 1.76 ± 0.15 1.36 ± 0.14 0.96 ± 0.26 Potassium (mg/L) 0.90 ± 0.12 0.51 ± 0.03 0.25 ± 0.02 Sodium (mg/L) 7.83 ± 1.52 3.48 ± 0.36 2.31 ± 0.14 Sulphate (mg/L) 28.96 ± 16.98 6.06 ± 0.83 2.12 ± 0.27

Figure 5-6 Water chemistry data summarized for each of the three geological age classes (Early Carboniferous, Late Carboniferous, or Older Classes, which include Neoproterozoic and Devonian- Carboniferous), including (A) conductivity (μS/cm), (B) total organic carbon (mg/L), (C) nitrate (mg/L), and (D) total phosphorous (mg/L). Lower-case letters on each plot indicate significant differences (at α = 0.05) among geological age classes, as determined by Tukey tests adjusted for unequal sample sizes. In each box plot, the median (central horizontal line) is in a box bounded by the 25th and 75th percentiles, with whiskers indicating 10th and 90th percentiles and points for statistical outliers.

60 geological age classes (F2,17 = 5.7; p = 0.012) and appeared to show higher levels in Early Carboniferous stations (not shown), but when the Tukey test was adjusted for unequal sample sizes, there was insufficient power to detect a difference among geological age classes. Similarly, total phosphorous appeared higher in both Early and Late Carboniferous stations than in Older Class stations (Figure 5-6), but the results of the ANOVA were non-significant (p > 0.05 for all terms). There were significant differences among geological age classes for both nitrate (F2,17 = 6.6; p = 0.008) and total organic carbon (F2,17 = 5.8; p = 0.012), but these other nutrients did not display a similar pattern of lowest levels in Older Class stations. Nitrate levels were significantly lower in Late Carboniferous than in Early Carboniferous stations, but did not differ significantly from the other two classes in Older Class stations (Figure 5-6). In contrast, total organic carbon was significantly higher in Older Class stations than in Late Carboniferous stations (not significantly different from Early Carboniferous; Figure 5-6), which was contrary to the patterns in other water chemistry variables.

The PCA based on water quality data revealed a distinct separation of Early Carboniferous and Older Class stations that was associated with a strong gradient in ions, nutrients, and metals (Figure 5-7). The three geologic age classes were distributed along the first axis gradient that explained 54.4% of the variance among stations, with a selection of Early Carboniferous

Figure 5-7 Principal Components Analysis (PCA) biplot of the 26 CRI water quality stations sampled in 2015, with station symbols indicating the geological age of the bedrock within a 1 km upstream buffer. Water quality variable abbreviations are listed in Table 5-3.

61 stations on the positive end of the gradient, Older Class stations on the negative end of the gradient, and an overlap of Late Carboniferous stations and the remaining Early Carboniferous stations in the middle (Figure 5-7). Consistent with the results of the ANOVA, Early Carboniferous stations were positively associated with ions, alkalinity, hardness, and conductivity, whereas Older Class stations were negatively associated with these parameters. The spatial distribution of stations in relation to water chemistry parameters was consistent with identified patterns of decreasing ionic concentrations from Early Carboniferous stations through Older Class stations.

The second axis of the PCA formed a gradient of stations within each of the surficial geology classifications, and explained an additional 19% of the variance in the data (Figure 5-7). This axis reflected the strength of the negative correlation of some stations with nutrients and suspended particles, which did not appear to differ based on bedrock geology age. Stations such as TC1, TC2, TC3, and KB4 (representative of all three geology age classes) were negatively correlated with parameters such as TN, TOC, TP, colour, and turbidity and associated total metals (Figure 5-7). In contrast, the Older Class stations on the Pollett River (PL1, PL2, and PL3) were positively associated with these parameters along the second axis while still reflecting the negative association with ions along the first axis.

Figure 5-8 Principal Components Analysis (PCA) biplot of the 41 water quality stations sampled by CRI, NBDELG, and KWRC in 2014 and 2015, with station symbols indicating the geological age of the bedrock within a 1 km upstream buffer. Water quality variable abbreviations are listed inTable 5-3.

62 A similar geology-driven gradient in chemistry was evident when additional water quality stations were included in the broader assessment of water chemistry patterns (Figure 5-8). With the expanded dataset, which mostly included Early Carboniferous stations, there was again strong evidence of a gradient in stations from Early Carboniferous to Older Class stations. The gradient along the first axis, which accounted for 54.1% of the variance among stations, again indicated a stronger positive correlation between stations underlain by Early Carboniferous bedrock levels of ions in the water (Figure 5-8), reflecting higher levels of these parameters in Early Carboniferous stations than in Older Class stations (Table 5-7). There were two Older Class stations that did not plot out as expected based on underlying bedrock. Whereas the upstream 1 km catchment buffer for station KV4-003 was dominated by older classes of bedrock, water chemistry values (including particularly high conductivity levels) reflected the dominant geology type further upstream in the catchment (Figure 5-8). In contrast, the upstream catchment of station KV6-004 was dominated by older classes of bedrock, but the station was located on the edge of Early Carboniferous bedrock and may have more strongly reflected local conditions.

Table 5-7 summarizes water chemistry parameter values for all stations included in the broad- scale water quality assessment, and for the bedrock geology age classes (excluding stations KB4-003 and KB6-004, which appeared to be outliers). With the inclusion of additional Early Carboniferous stations and one Late Carboniferous station from the broad-scale monitoring, the patterns in Table 5-6 remained evident. The majority of ions, alkalinity, hardness, and conductivity were higher in Early Carboniferous stations than in Older Class stations. This was particularly evident for conductivity, calcium, chloride, sodium, and sulphate.

Geology has been shown to play a significant role in the determination of groundwater and surface water chemistry (Johnson et al. 1997, Thornton and Dise 1998), affecting pH, ionic composition, and acid neutralizing capacity (Clair et al. 1995, Reimann et al. 2009). Within the study area, Early Carboniferous bedrock stations were characterized by higher solute levels than stations on Late Carboniferous or older age classes of geology, particularly with respect to calcium, chloride, sodium, and sulphate. The processes by which the solute levels of freshwaters are affected by geology are complex, and depend on various aspects of the form and composition of geological minerals, and the degree to which surface water and groundwater make contact with those minerals (Hem 1985, Drever 1997). High levels of calcium, sodium, and sulphate have been found in groundwater of the Carboniferous basin of New Brunswick (Stapinsky et al. 2002), suggesting that elevated levels of these ions are characteristic of freshwaters in this geological area. The increased solute levels that were found in Early Carboniferous compared with Late Carboniferous stations could reflect differences in mineral levels among the geological formations found in each age class, or could be due to differing levels of connectivity of surface waters to groundwater among these streams, as discussed in section 4. Where surrounding landuse is dominated by activities such as industry and agriculture, both of which have the potential to alter surface water chemistry, geology may play a smaller role in controlling water quality (e.g., Johnson et al. 1997). In particular, increases in agriculture may contribute significantly to nutrient levels in a system (e.g., nitrate) and shift the importance of geological influences (Reimann et al. 2009). The strong association of water chemistry with surficial geology classifications suggests that geology plays a more dominant role in determining water quality in the stream systems of the Kennebecasis region than does landuse or anthropogenic impacts. Moreover, the relationship between chemistry and geology highlighted the importance of considering geology and other large-scale drivers when selecting stations for inclusion in a monitoring plan and when planning future development and resource

63 Table 5-7 Summary table of average (± SE) chemistry values for the 41 water quality stations sampled by CRI, NBDELG, and KWRC in 2014 and 2015, with average (± SE) for stations within Early Carboniferous (n=25), Late Carboniferous (n=9), and Older Classes of bedrock (n=5). Two Older Class stations were omitted from geology-specific summary statistics because they appeared to be influenced by Early Carboniferous bedrock in the local vicinity. Values below LOQ were included as half the LOQ and results are reported only for parameters that were measured above the LOQ for at least one station.

Early Late Parameter Units All stations Older Classes Carboniferous Carboniferous Al mg/L 0.049 ± 0.011 0.060 ± 0.017 0.019 ± 0.002 0.032 ± 0.005 Alk mg/L 36.60 ± 3.21 42.72 ± 3.32 22.76 ± 2.25 15.98 ± 3.90 As ug/L 0.75 ± 0.12 0.89 ± 0.19

exploration. Assessment of potential impacts must compare stations with similar geological influences to ensure that this is not introduced as a confounding factor in analyses.

Assessment of physical habitat and GIS data supported the importance of geology in the study area, with both surficial geology and bedrock geology playing a dominant role in distinguishing among stations. However, the PCA provided additional information about the physical habitat differences that characterized streams in the study area, including strong differences in stream size, substrate size, and surrounding landuse. The first axis of the PCA, which explained 30.8% of the variance among stations, was dominated by a geology gradient that ranged from intrusive bedrock and morainal/colluvial surficial materials at the positive end of the gradient (associated

64

Figure 5-9 Principal Components Analysis (PCA) biplot of habitat variables collected on site and estimated through GIS for the 26 water quality stations sampled by CRI in 2015, with station symbols indicating the dominant bedrock geology age in a 1 km upstream catchment buffer. Habitat variable abbreviations are listed in Table 5-4 and GIS variable abbreviations are listed in Table 5-5.

with stations PL1, PL2, and PL3) to sedimentary bedrock and blanket/veneer surficial material at the negative end of the gradient (associated with stations MP1, MB2, ST1, and ST2; Figure 5-9). Stations that were positively correlated with intrusive bedrock and morainal/colluvial materials were also positively associated with mean substrate size, the percent cobble and boulder at a station, and the size of first and second dominant substrate types, which overall indicated larger substrate size in these systems (Figure 5-9). In contrast, stations that were positively correlated with sedimentary bedrock and blanket/veneer substrates were positively associated with the percent silt/clay and gravel in the substrate (Figure 5-9).

The second axis, which explained 18.2% of the variance in habitat data, characterized stations on the basis of system size (catchment area, stream depth, and stream width, which contributed to both axes), slope, land cover, and additional measures of sediment size and geology (Figure 5-9). In particular, stations MB1 and SB1 were negatively correlated with system size and positively correlated with slope, dominance of coniferous vegetation, % canopy coverage, percent bedrock, and the relative area of colluvial surface material with blanket/veneer. Other dominant patterns associated with the second axis included a positive correlation of KB6, KB7,

65 Table 5-8 The number of water quality samples (collected at CRI, NBDELG, and KWRC stations) that exceed the CCME Water Quality Guidelines for the Protection of Aquatic Life (Freshwater) in 2014 and 2015.

Number of Parameter Abbreviation Guideline Exceedances 2014 2015 Total Dependent on pH and NH3T 0 0 Ammonia temperature Arsenic As 5 μg/L 0 0 1.0 μg/L (short-term) Cadmium Cd 0 0 0.09 μg/L (long-term) 0.640 mg/L (short-term) Chloride Cl 0 0 0.120 mg/L (short-term) 2 μg/L at hardness < 82 mg/L; Copper Cu 0 0 higher depending on hardness Fluoride F 0.120 mg/L 0 1 1 μg/L at hardness ≤ 60 mg/L; Lead Pb 0 0 higher depending on hardness 25 μg/L at hardness ≤ 60 mg/L; Nickel Ni 0 0 higher depending on hardness 550 mg/L (short-term) Nitrate NO3 0 0 13 mg/L (long-term) Nitrite NO2 60 μg/L 0 0 pH pH Acceptable range 6.5-9.0 0 0 Zinc Zn 30 μg/L 0 0

and KB8 with shrubs, industry, and the % pebbles, and a negative correlation of these stations with coniferous vegetation and the size of interstitial and dominant substrate (Figure 5-9).

Although the primary separation of stations was based on geology, the first axis also reflected a gradient in water temperature across the stations. The maximum summer temperature in 2015 (see section 4) was correlated with the relative area of sedimentary bedrock (r = -0.66) and the relative area of intrusive bedrock (r = 0.55). Maximum summer temperatures for PL1, PL2, and PL6, which had high relative areas of intrusive bedrock in upstream catchments, were warmer than other sites, and ranged from 26°C to 27.5°C. In contrast, stations MB2, MP1, ST1, and ST2, which were dominated by sedimentary bedrock, had maximum summer temperatures ranging from 15.75°C to 22.5°C.

Several physical factors may have contributed to these differences in thermal regime. Johnson (2004) found evidence of an influence of geology type on temperature in small streams, with systems underlain by bedrock experiencing higher maximum and minimum temperatures (greater temperature extremes) than systems underlain by alluvial materials. In the current study stations along the Pollett River at PL1, PL2, PL3, and PL6, which have been shown to have warm temperatures, are underlain by bedrock, in contrast to the cooler streams at stations

66 MB2, MP1, ST1, and ST2, which are underlain by morainal/colluvial materials. Temperatures in these stream stations may have also been affected by riparian vegetation and the presence of shading. Johnson (2004) found evidence that shading affected maximum temperatures in small stream systems. The temperature of stations on the Pollett River were negatively correlated with the percent forest, and were positively correlated with stream width and catchment area, which indicated that these were large systems that may have had little shading of the stream channel by any surrounding riparian vegetation, potentially contributing to increased temperatures relative to other systems.

5.3.1.1 Water quality assessment There was only one exceedance of water quality guidelines for measured parameters (Table 5-8). Water chemistry data were generally within recommended short-term and long-term exposure guidelines as outlined in the CCME Water Quality Guidelines for the Protection of Aquatic Life (Freshwater). The single exceedance was for the fluoride guideline in 2015 (Table 5-8); the value at MP1 was 0.153 mg/L, in excess of the guideline of 0.120 mg/L. Examination of historical water quality monitoring data (prior to 2014; not shown) also provided little evidence of exceedances for the parameters listed in Table 5-8, and confirmed that stream and river water quality in the study area has generally remained within acceptable limits.

Table 5-9 Results of the seasonal Kendall test to evaluate water quality trends over time (incorporating data from May, July, September, and November in each year) for three NBDELG water quality stations with the longest records. All parameters showed homogeneous long-term patterns among the four sampling months in preliminary testing. Colours and directional arrows indicate the direction and significance of any long-term monotonic trends, with dark colours and arrows for significant trends and light colours and arrows for non-significant trends (see legend for details).

Station Parameter KR-5Points KV-18 SB-Penobsquis Aluminum ↔ ↑ ↔ Legend Alkalinity ↓ ↔ ↓ ↓ Significant decrease Arsenic ↔ ↔ ↔ ↓ Non-significant decrease Calcium ↓ ↓ ↓ ↑ Significant increase Chloride ↓ ↓ ↓ ↑ Non-significant increase Colour ↑ ↑ ↔ ↔ No trend Conductivity ↓ ↓ ↓ (EV) Extreme value affecting Chromium ↓ ↔ ↓ Trend Copper ↔ ↔ ↔ Iron ↔ ↔ ↓ Hardness ↓ ↓ ↓ Potassium ↔ ↓ ↓ Magnesium ↓ ↓ ↓ Manganese ↔ ↔ ↓ Sodium ↓ ↓ ↓ Nitrate ↔ ↔ ↑ (EV) pH ↔ ↔ ↔ Sulphate ↓ ↓ ↓ Ammonia ↑ (EV) ↓ ↑ (EV) Nitrite/Nitrate ↑ ↔ ↔ TP ↓ ↔ ↔ TOC ↔ ↔ ↓

67 5.3.1.1 Water chemistry trends Initial assessment of long-term water chemistry data for the three NBDELG stations with the van Belle and Hughes test for homogeneity of trends indicated that temporal patterns were homogeneous across May, July, September, and November (results not shown). Thus, individual months did not display different trends over time, and all months were used in seasonal Kendall analysis to evaluate long-term trends for each water chemistry parameter.

Long-term water quality trends (incorporating seasonal sampling data) were relatively consistent among the three NBDELG stations for most parameters with respect to the direction of trend; however, the magnitude and significance of trends differed across stations (Table 5-9). KV-18, which had the longest period of record (1999 to 2015) had the largest number of significant trends among the parameters sampled (nine out of 22 parameters tested), but SB-Penobsquis was also found to display a large number of significant trends (seven parameters; Table 5-9). Although trend results were generally not significant at KR-5Points, there was some evidence of weaker (non-significant) trends at this station that were consistent in direction with trends at KV-18 and SB-Penobsquis. Non-significant trends were generally due to differences in the temporal patterns displayed for each sampling month, since the seasonal trend test incorporates all values rather than averaging them. Although trends were found to be homogeneous across the four sampling months in the initial assessment, the magnitude of trend differed and was often most pronounced in July and September samples.

The temporal analysis revealed decreasing trends for most ions (significant for calcium, chloride, sodium, sulphate) and for alkalinity, conductivity (see Figure 5-10), and hardness, each of which reflects ionic composition (Table 5-9). In contrast, there was evidence of significant increasing trends for aluminum, colour (a proxy for dissolved organic carbon, DOC), and nitrite/nitrate (Table 5-9). The three stations with long-term water chemistry data were all classified as Early Carboniferous stations, which were found to have the highest conductivity and ion concentrations compared with stations on other bedrock geology age classes. The noted temporal declines in ions and conductivity suggest that the distinction among geology age classes with respect to surface water chemistry may have been even stronger 10 or more years ago (conductivity showed a higher annual peak in KV-18 and SB-Penobsquis prior to 2007; Figure 5-10), although this conclusion is speculative due to the lack of long-term water chemistry data for stations in other bedrock geology age classes.

Detailed evaluation of patterns in water quality parameters indicated that monotonic trends were detected in spite of seasonal fluctuations in a number of parameters. Conductivity at the three NBDELG stations displayed wide fluctuations throughout the year, but long-term patterns indicated an overall decline in conductivity levels (Figure 5-10). Where seasonal changes contain a stronger signal than long-term changes, trends may be masked by noise in the data; however, KV-18 and SB-Penobsquis showed clear declines in conductivity from 1999 to 2015 and from 2001 to 2015, respectively (Figure 5-10). The decline in KR-5Points (non-significant; Table 5-9) was minimal compared to the other two long-term stations, but overall levels of conductivity were also lower at this station across all years. The station was downstream of the confluence with a tributary dominated by Late Carboniferous bedrock in its headwaters, and this may have contributed to more moderate ion levels at this station.

Parameters that displayed significant trends at one of the three NBDELG stations or non- significant but consistent trends across all three stations were included in the broad-scale assessment of trends. Most of the additional stations included in this assessment had insufficient temporal data to have confidence in individual assessments of long-term trends

68

Figure 5-10 Temporal trends in conductivity (uS/cm) with data plotted for annual samples collected in May, July, September, and November from NBDELG stations (a) KV-18 and (b) KR-5Points and SB- Penobsquis.

69 Table 5-10 Results of the homogeneity of trend test to compare water quality patterns across all long- term trend stations (see Table 5-2 for site details) for parameters that showed evidence of trends in July or September in the three NBDELG water quality stations. All parameters showed homogeneous patterns across the stations, but colours and directional arrows indicate the direction and significance of any long- term monotonic trends, with dark colours and arrows for significant trends and light colours and arrows for non-significant trends (see legend for details).

Month Parameter July September Aluminum ↔ ↔ Legend Alkalinity ↓ ↔ ↓ Significant decrease Calcium ↓ ↔ ↓ Non-significant decrease Chloride ↓ ↔ ↑ Significant increase Colour ↑ ↑ ↑ Non-significant increase Conductivity ↓ ↔ ↔ No trend Hardness ↓ ↔ Potassium ↓ ↔ Magnesium ↓ ↔ Nitrite/Nitrate ↔ ↔ Sodium ↓ ↔ Sulphate ↓ ↓ Ammonia ↔ ↔

(e.g., only 4-5 years of data for July and September). However, it was possible to assess the patterns in these additional stations in relation to the three NBDELG stations and determine whether there were homogeneous and significant trends across a broader region. Homogeneity of long-term trends was assessed separately for July and September to evaluate any differences in trends between months. This assessment can provide information about parameters that should be included in continued monitoring, so that more detailed evaluations of long-term trends may take place in the future at a broader range of stations.

Long-term patterns were generally homogeneous across the expanded set of stations, but trends were stronger in July than in September. Broad-scale assessment of July trends revealed significant declines in a number of ions (calcium, chloride, potassium, magnesium, sodium, and sulphate), as well as alkalinity, conductivity, and hardness (Table 5-10). There was no evidence of trends in these parameters in September, with the exception of sulphate, which showed a significant declining trend (Table 5-10). Colour was the only parameter with increasing trends that were still evident across the expanded set of stations (with significant increasing trends in July and September). The lack of trends in aluminum, nitrite/nitrate, and ammonia was a reflection in part of the addition of stations with low values for these parameters that were often below the LOQ.

Overall, the evaluation of trends on a broader scale confirmed the patterns in the three key monitoring stations, which suggested that a shift has occurred in the ionic composition of these systems over time. Furthermore, trends appeared to be strongest in July, which indicates that the magnitude of seasonal fluctuations (i.e., the strength of the increase in ions that occurs in the summer months) may be decreasing. This is apparent in the plots of long-term trends in conductivity levels, as the lowest portion of the seasonal fluctuation (generally over winter) appears to have changed minimally over time, whereas the highest levels in the seasonal fluctuation of conductivity (generally summer) appear to have shown the strongest decline over time (Figure 5-10). This pattern should continue to be monitored across this region to detect

70 whether there continues to be a trend of declining ions in the summer, particularly given the importance of these ions for differentiating among stations in different age classes of bedrock geology.

5.3.2 Biological community 5.3.2.1 Benthic chlorophyll a Benthic chlorophyll a levels were variable among stations and among sampling dates (Figure 5-11), with generally higher levels of chlorophyll a in August and September. The increase in chlorophyll a during the growing season may have reflected seasonal shifts in nutrients and flows. Chlorophyll a was generally low for stations CB1, MB1, PL2, PL4, and SB1 (Figure 5-11); however, samples were only taken at CB1 and SB1 in July and levels may have increased later in the season. The highest average chlorophyll a levels were found in KB2 (September), KB6 (July, with no sampling in August or September), and TC3 (September). TP levels, although estimated from samples collected in September of the previous year, were indicative of general differences in July chlorophyll a samples among some stations. For example, relative differences in July chlorophyll a among KB1, KB2, KB3, and KB4 were similar to patterns in TP levels in those stations in September of the previous year (Figure 5-11). Similarly, differences in July chlorophyll a samples between PL6, SB2, SB3A, and TC1 were reflective of the September TP values for these stations (Figure 5-11). However, KB6 displayed a high average level of chlorophyll a in July 2016 whereas September 2015 TP levels did not indicate nutrient enrichment. Similarly, TP levels were elevated in MB2 and MP1 in September 2015, but chlorophyll a levels for both stations in the following July were low to moderate (Figure 5-11).

Figure 5-11 Chlorophyll a results (mg/m2) for samples taken in July, August, and September 2016, with TP results (line plot) from the same stations in 2015. Samples were collected from 20 CRI stations in July 2016, and from 15 stations in August and September 2016.

71 These relationships were certainly affected by the long time lag between sampling events; however, as a measure of algal biomass, benthic chlorophyll a represents a more time- integrated measure that is less affected by short-term pulses, and it may provide a more accurate view of nutrient status in a system than chemical analysis of one-time grab samples. The assessment of chlorophyll a results from 2016 provides guidance on the inclusion of these samples in regular monitoring to evaluate primary productivity in these systems. Chlorophyll a levels in these streams appear to be related to TP levels, but concurrent sampling would be required in order to assess the direct relationship between these two variables (and to determine whether chlorophyll a can be used as a proxy measure that integrates short-term changes in nutrient levels). Moreover, assessment of the samples identified stations with high levels of chlorophyll a that should be monitored to assess trophic status of these systems.

5.3.2.2 Benthic macroinvertebrate community characterization 5.3.2.2.1 Community composition Benthic macroinvertebrate communities in the study area were taxonomically rich, with a total of 110 genera identified in 44 families of insects and 13 families/classes of non-insects collected across the 26 CRI stations. (Table 5-11). Taxonomic richness at the genera level was on average (± SE) 35.2 ± 1.1 across the 26 stations. The most diverse groups were Diptera (true flies), Ephemeroptera (mayflies), Plecoptera (stoneflies), and Trichoptera (caddisflies), though Trichoptera were the most diverse of all groups, with 23 genera in 16 families found across the study area. Although tolerances and preferences differ across and within families, the orders Ephemeroptera, Plecoptera, and Trichoptera (EPT) are generally considered to be sensitive to perturbation within stream systems due to generalized low tolerances to pollution, and low relative abundance or richness of these groups is taken as an indicator of impaired water quality (Barbour et al. 2001), and thus the high richness and abundance of these orders may be indicative of good water quality in these systems.

Although EPT were found to be taxonomically rich, other insect orders were low in diversity, including Coleoptera (beetles), and Odonata (dragonflies and damselflies; Table 5-11). In their study of the Nashwaak River, Renous River, and Little Southwest Mirimichi River in New Brunswick, Curry et al. (2012b) found an average of 16.4 ± 4.1 Trichoptera genera per study site and 8.4 ± 4.5 Odonata genera per site. Samples from streams in the Kennebecasis area in the current study included only one genera of Odonata in the family Gomphidae, well below the average for other New Brunswick streams. The sampling techniques employed by Curry et al. (2012b) were more intensive and focused on maximizing richness of these orders, which may have contributed in part to the discrepancy; regardless, the results suggest that taxonomic richness of Odonata in the Kennebecasis region should be expected to be higher than that observed.

Abundances of benthic macroinvertebrates were generally high, but differed among stations by an order of magnitude, ranging from 4200 to 66440 individuals per sample (Table 5-12). Samples tended to be fairly diverse, with Simpson’s Index (1-D) ranging from 0.738 to 0.928 (a value of 1 indicates a perfectly even sample, with equal numbers of all taxa; Table 5-12). Taxonomic richness (with mites rolled up to order level) was on average (± SD) 25.6 ± 2.8 across the 26 stations (Table 5-12). Curry et al. (2012a) found that riffle habitats within the Nashwaak catchment of New Brunswick had an average taxonomic richness (± SD) of 32 ± 5.1, which indicates lower taxonomic richness of the benthic macroinvertebrate communities sampled in streams in the Kennebecasis region.

72

Table 5-11 Classifications of benthic macroinvertebrates collected in the 26 CRI study sites in 2015, indicating the number of stations in which each family (subfamily for Chironomidae) was found, and the taxonomic abbreviation used in analysis.

Number of Number of Group Class/Order Family/Subfamily Abbreviation stations Group Class/Order Family/Subfamily Abbreviation stations Beetles Coleoptera Elmidae C_Elm 24 Apataniidae T_Apa 4 Athericidae D_Ath 10 Brachycentridae T_Bra 22 Ceratopogonidae D_Cerat 18 Goeridae T_Goer 5 Chironomidae Chironominae D_C_Chin 26 Glossosomatidae T_Glos 19 Chironomidae Diamesinae D_C_Dia 7 Helicopsychidae T_Hel 4 Chironomidae Orthocladiinae D_C_Orth 26 Hydropsychidae T_Hpsy 24 Chironomidae Tanypodinae D_C_Tany 23 Hydroptilidae T_Hpti 13 True Flies Diptera Empididae D_Emp 15 Lepidostomatidae T_Lepi 23 Caddisflies Trichoptera Ephydridae D_Eph 1 Leptoceridae T_Lept 5 Muscidae D_Musc 1 Limnephilidae T_Limn 1 Sciomyzidae D_Scio 2 Odontoceridae T_Odont 5 Simuliidae D_Simu 24 Philopotamidae T_Phil 19 Tabanidae D_Tab 1 Polycentropodidae T_Poly 3 Tipulidae D_Tipu 24 Psychomiidae T_Psy 2 Baetidae E_Bae 26 Rhyacophilidae T_Rhya 26 Baetiscidae E_Baesc 2 Uenoidae T_Ueno 3 Ephemerellidae E_Ephel 26 Trombidiformes (6 Mites Acari Acari 26 Mayflies Ephemeroptera Heptageniidae E_Hept 21 families combined) Isonychiidae E_Iso 3 Gammaridae and Amphipods Amphipoda Amph 2 Leptohyphidae E_Lepthy 2 unknown Leptophlebiidae E_Lepto 25 Clams Bivalvia Pisidiidae Bivalv 5 Dobsonflies Megaloptera Corydalidae M_Cory 4 Snails Gastropoda Gastropoda_unk Gast 1 Dragonflies Odonata Gomphidae O_Gomp 3 Leeches Hirudinea Glossiphoniidae Hiru 5 Capniidae P_Cap 9 Oligochaeta Enchytraeidae Oli_Enchy 5 Segmented Chloroperlidae P_Chl 24 Lumbriculidae Oli_Lumb 15 Worms Leuctridae P_Leu 14 Naididae Oli_Naid 19 Nemouridae P_Nem 1 Stoneflies Plecoptera Perlidae P_Perl 20 Perlodidae P_Perlo 23 Pteronarcyidae P_Ptero 1 Taeniopterygidae P_Taen 3

73

Table 5-12 Diversity metrics calculated for benthic macroinvertebrates sampled at each CRI station in 2015, including macroinvertebrate abundance, richness (calculated at subfamily level for Chironomidae and family level for remaining taxa), diversity (Simpson’s 1-D Index, which ranges from 0 to 1), and the percent of the sample composed of Chironomidae (midges) and Ephemeroptera, Plecoptera, and Trichoptera (EPT; mayflies, stoneflies, and caddisflies).

Station Abundance Richness Diversity % Chironomidae % EPT

CB1 38300 27 0.738 48.1 39.2 KB1 5170 24 0.900 15.5 69.8 KB2 34160 24 0.884 33.1 55.2 KB3 26200 29 0.876 44.0 37.5 KB4 66440 27 0.825 30.6 57.6 KB5 31300 27 0.902 19.5 66.8 KB6 16380 25 0.842 39.7 44.9 KB7 30200 29 0.873 26.0 48.5 KB8 27080 29 0.786 50.1 28.1 MB1 4200 27 0.910 23.8 46.2 MB2 26240 26 0.829 56.3 27.5 MP1 18900 32 0.928 20.1 42.5 PL1 9340 22 0.871 30.8 65.3 PL2 4200 24 0.910 14.8 77.6 PL3 15520 24 0.905 14.3 79.3 PL4 17480 30 0.909 32.0 55.9 PL5 14100 22 0.805 46.0 41.3 PL6 12160 25 0.880 7.6 86 SB1 20820 23 0.818 47.0 45.1 SB2 15140 24 0.869 29.1 56.1 SB3A 12560 21 0.768 16.7 69.4 ST1 21480 25 0.878 28.4 59.3 ST2 13520 27 0.914 30.8 48.5 TC1 8440 23 0.803 17.8 75.6 TC2 63700 27 0.836 31.3 58 TC3 21620 22 0.848 38.3 49.4

74 Samples were numerically dominated by Ephemeroptera, Plecoptera, Trichoptera, and Chironomidae (Table 5-12). These taxa are well-established as indicators for use in bioassessment of streams, with abundance of EPT used as an indication of good water quality, and Chironomidae (which include a number of general that are tolerant of perturbation) used as an indicator of potential impairment (Barbour et al. 2001). The relative proportions of EPT and Chironomidae differed among stations, and these groups made up similar proportions of the assemblage in some stations; however, 20 of the 26 stations had higher proportions of EPT than Chironomidae (Table 5-12).

Similar to water chemistry, benthic macroinvertebrate metrics differed in response to geological age class but not to catchment size (p > 0.05 for size and size x geology terms in all ANOVA models). Abundance of benthic macroinvertebrates differed among geological age classes (F2,17 = 8.1; p = 0.003), and was significantly higher at Early and Late Carboniferous stations (mean = 25046 and 25797, respectively) than at Older Class stations (mean = 8534; Figure 5-12). Genus richness also differed among geological age classes (F2,17 = 4.1; p = 0.034), but richness was significantly higher in Early Carboniferous stations (mean = 37.9) than in either Late Carboniferous or Older Class stations (mean = 33.9 and 30.2, respectively; Figure 5-12). In

Figure 5-12 Benthic macroinvertebrate community metric values summarized for each of the three geological age classes (with geology classified by geological age as Early Carboniferous, Late Carboniferous, or Older Classes, which include Neoproterozoic and Devonian- Carboniferous), including (A) abundance, (B) genus richness, (C) percent Chironomidae, and (D) percent EPT. Lower-case letters on each plot indicate significant differences (at α = 0.05) among geological age classes, as determined by Tukey tests adjusted for unequal sample sizes. In each box plot, the median (central horizontal line) is in a box bounded by the 25th and 75th percentiles, with whiskers indicating 10th and 90th percentiles and points for statistical outliers.

75 contrast, Simpson’s Index did not differ significantly among geological age classes (p > 0.05; results not shown), indicating similar diversity among the stations grouped by geology. Despite similarities in diversity among geological age classes, there was evidence of a shift in the relative proportions of major taxonomic groups, with increased dominance of EPT and lower abundance of Chironomidae in Older Class stations relative to Early or Late Carboniferous stations. The percent Chironomidae declined from a mean of 36.5% in Early Carboniferous stations and 31.3% in Late Carboniferous stations to a mean of 18.6% in Older Class stations (Figure 5-12). Although the percent Chironomidae was significantly different among geological age classes (F2,17 = 3.7; p = 0.045), there was insufficient power to detect differences among classes in post-hoc analysis when results were adjusted for unequal sample sizes. There was stronger evidence of the compositional difference when the percent EPT was examined, as the percent EPT differed among geological age classes (F2,17 = 9.1; p = 0.002) and was significantly higher in Older Class stations (mean = 73.5%) than in Early or Late Carboniferous stations (mean = 47.3% and 55.8%, respectively; Figure 5-12).

Multivariate assessment of benthic macroinvertebrate community structure indicated a similar arrangement of stations as for the results of the water quality PCA, with a general clustering of stations underlain by Early Carboniferous, Late Carboniferous, and Older Classes of bedrock

Figure 5-13 Principal Components Analysis (PCA) biplot based on relative abundances of benthic macroinvertebrate taxa collected at the 26 stations sampled by CRI in 2015, with station symbols indicating dominant bedrock geology age in a 1 km upstream catchment buffer. Due to the high diversity among stations, taxonomic associations have been summarized by highlighting particular taxa and their approximate location on the ordination. Taxonomic abbreviations can be found in Table 5-11.

76 (Figure 5-13). The dominant gradient of the benthic macroinvertebrate PCA (PC1: 32% of the community variance) appeared to describe the separation of Older Class stations from Early and Late Carboniferous stations, whereas the distinction between samples underlain by Early and Late Carboniferous stations drove the second axis (15.4% of community variance; Figure 5-13). The primary clustering of samples based on underlying geology indicated a strong influence of water chemistry on BMI assemblage composition. The position of taxa along the first gradient may have reflected water chemistry preferences and tolerances of taxa (with higher levels of ions in Early Carboniferous and Late Carboniferous stations). The second axis also appeared to reflect geological influences, with separation of Early and Late Carboniferous stations (Figure 5-13). The position of taxa along this second gradient may have reflected water chemistry preferences, as there were differences (though not significant) evident between these geology age classes.

Plecoptera families were distributed along the length of the first axis, and this may have partially represented a gradient in thermal preference. Stations on the negative end of the first axis tended to have cooler water temperatures, and were associated with Plecoptera taxa such as Leuctridae, Nemouridae, and Chloroperlidae (Figure 5-13), which prefer small cool-water streams (McCafferty 1998). In contrast, stations PL1, PL2, PL3, and PL6 were warm, with higher maximum summer temperatures than other systems (including PL4 and PL5, which were spatially separated from PL1-3 and PL6 in the ordination), and they were positively correlated with Pteronarcyidae, Perlidae, and Perlodidae (Figure 5-13), which inhabit larger systems and are more tolerant of warm temperatures (McCafferty 1998).

5.3.2.2.2 Environmental drivers of benthic macroinvertebrate communities Water chemistry was strongly related to macroinvertebrate community composition, with the first axis of the RDA explaining 24.5% of the unconstrained variance in the benthic community, and the second axis explaining an additional 12.5% of the unconstrained (community) variance (Figure 5-14). The RDA biplot reflected a similar spatial arrangement of sites (and arrangement of taxa; not shown) as was found in the PCAs of both water chemistry and benthic community data, with a strong separation of stations underlain by Early Carboniferous and Older Classes of bedrock, and a weaker separation of Late Carboniferous bedrock stations. The first axis primarily reflected the separation of sites due to conductivity, although a positive association of TP and turbidity contributed to this separation. The Older Class stations that were negatively associated with these variables were positively associated with TOC along the first axis (Figure 5-14).

Nutrients (NO3, TN, TOC) and total aluminum were most strongly correlated with the second RDA axis and contributed to a separation of Late Carboniferous stations from other bedrock geology age classes. The data included in the RDA were from samples collected in early September, and aluminum and nitrate levels tended to be low at this point in the year (results not shown). In winter/spring, when levels of these parameters were higher, there may be a shift in the importance of these drivers linked to high flows in early fall and early spring. Aluminum poses a large threat to macroinvertebrates in acidic systems due to increasing solubility of aluminum as pH lowers (Driscoll et al. 1987, Havens 1993), and can damage gills and filter- feeding apparatuses (e.g., Havens 1993, Havas and Rosseland 1995), but there is less information about the potential impacts of organically-bound aluminum in neutral systems. Simuliidae, which have a mouth brush for filter feeding, and which are susceptible to aluminum

77

Figure 5-14 Redundancy Analysis (RDA) biplot of benthic macroinvertebrate relative abundance constrained by water quality data for 26 CRI study sites sampled in 2015, with station symbols indicating dominant bedrock geology age in a 1 km upstream catchment buffer. Water quality abbreviations can be found in Table 5-3. impacts in acidified systems, were negatively correlated with aluminum, nitrate, TN, and TOC in the Kennebecasis area streams, which may have indicated a similar sensitivity to levels of these parameters in neutral systems. However, additional monitoring may be necessary to determine any coherent response to these water quality parameters. Although benthic community sampling in these systems during winter/spring may be prohibitive due to accessibility issues, repeated sampling in the summer/fall may reveal the extent to which communities are resilient to seasonal shifts in these parameters.

Taxonomic associations (which remained similar between the PCA and RDA) reflected some known preferences of benthic macroinvertebrates. In particular, non-insects were most strongly associated with high conductivity, high turbidity, and high TP at the glacial veneer sites. In the case of Bivalvia and Gastropoda, these associations are not surprising, as both are highly dependent on uptake of calcium from the water to maintain shell growth (Havas and Rosseland 1995).

Redundancy Analysis of the benthic macroinvertebrate community constrained by habitat and GIS variables explained more unconstrained community variance (Axis 1: 28% and Axis 2: 12.6%); however, this was likely due to the fact that more variables were included in the RDA (11 variables in the habitat/GIS RDA compared with seven variables in the water quality RDA).

78 Although the ordination showed evidence of a distinction among geology types, the spatial arrangement of stations in Figure 5-15 differed more from that seen in the PCA of the benthic macroinvertebrate community, particularly with respect to the spread of Older Class stations, indicating that habitat/GIS variables were less directly associated with the underlying gradients in the community data than was water quality.

There were several strong gradients in habitat/GIS variables along which the stations differed. The first axis described a gradient in bedrock geology, land cover, catchment area, and substrate size (Figure 5-15). Several stations were positively associated with intrusive bedrock, catchment area, % boulders, and dominance of deciduous trees along this gradient (e.g., PL1, PL2, PL3, and PL6). However, a large number of stations differed along a gradient that was intermediate to the first and second axis and was characterized by a negative relationship between substrate size (median particle size, D50) and the relative abundance of blanket and veneer surficial material (Figure 5-15). Among the stations that were positively associated with substrate size, there was a gradient in periphyton coverage along the Late Carboniferous stations varied (Figure 5-15). Several taxa were positively associated with catchment area, and thus larger stream systems, including Gomphidae, Perlidae, and Perlodidae, which was consistent with preferences indicated in the literature (McCafferty 1998). These taxa were also positively associated with the % boulder at a station, as was the sprawler Heptageniidae. Food resources also played a role in determining taxonomic composition. For example, Baetidae, Simuliidae, and associated taxa were positively associated with D50 but were more strongly

Figure 5-15 Redundancy Analysis (RDA) biplot of benthic macroinvertebrate relative abundance constrained by habitat and GIS data for 26 CRI study sites sampled in 2015, with station symbols indicating dominant bedrock geology age in a 1 km upstream catchment buffer. Habitat abbreviations can be found in Table 5-4 and GIS variable abbreviations can be found in Table 5-5.

79 Table 5-13 Results of the least-squares linear regression analysis of benthic macroinvertebate metrics and environmental drivers (water quality and physical/habitat drivers) for 26 CRI study sites, showing the models that had the lowest AICc value and explained the greatest amount of variation in the response metric. Reported statistics include the standardized regression coefficient for each driver, the p value for each driver, the residual mean square for model (RMS; a measure of error around the estimate), the p value for each model, and the R2 (or adjusted R2 for multiple regression models).

Standardized (Adjusted) Benthic Metric Drivers p RMS Model p Coefficient R2

log10 TOC -0.548 0.004 log10 Catchment log10 Abundance 0.425 0.025 0.065 0.010 0.317 area 0.342 0.059 log10 Conductivity

log10 Richness log10 Conductivity 0.707 < 0.001 0.001 < 0.001 0.500 logit Percent Max Summer -0.600 0.002 0.027 0.002 0.360 Chironomidae Temp

logit Percent log10 D50 0.464 0.046 0.009 < 0.001 0.442 EPT log10 Conductivity -0.283 0.211

correlated with periphyton coverage and other variables related to food availability, including canopy coverage, periphyton coverage, the presence of coniferous vegetation, and the percent forest. The associations of taxa with this gradient in riparian and in-stream vegetation reflected feeding preferences such as scraping, filtering, and shredding.

Linear regression analysis was used to further explore associations between dominant drivers in the multivariate analysis and benthic diversity metrics. Conductivity was the strongest overall predictor of benthic macroinvertebrate diversity (Table 5-13), and was included in the final model for three of the four final benthic metrics (Simpson’s 1-D index was not found to be significantly related to any chosen variables, and was thus excluded from the model selection process; this may have been a reflection of the similarity in index values among stations). Conductivity was positively related to both abundance and richness, and negatively related to the percent EPT. The relationship between conductivity and richness was the strongest of the benthic metric relationships, and had the highest variance explained (R2 = 0.500; Table 5-13). Although the error around the model was low (RMS = 0.001), there was evidence of some variability in the relationship between taxonomic richness and conductivity when it was plotted (Figure 5-16). However, there was generally a pattern of increasing richness with increasing conductivity among the stations, which may have been a result of increased prevalence of non- insects in Early Carboniferous stations (as evident in Figure 5-13).

The D50 (a summary measure equating median sediment size) was selected in the final model for percent EPT in addition to conductivity, reflecting the substrate size preference of many families in this taxonomic group. EPT was positively related to D50 (preferring larger sediment size; Figure 5-17a), which is in line with the habits of EPT taxa, many of which are crawlers or sprawlers and rely on larger sediment particles (McCafferty 1998). In contrast, EPT was negatively related to conductivity (Figure 5-17b), reflecting decreased dominance of these taxa in Early Carboniferous stations. This model was the second strongest of those tested, with an R2 of 0.442 (Table 5-13) The relationship of EPT with both variables appeared to be strong, but

80

Figure 5-16 Relationship between log10 taxonomic richness of benthic macroinvertebrates (at subfamily for Chironomidae, family for other taxa) and log10 conductivity at the 26 stations sampled by CRI in 2015. A linear regression line is fit to the data.

it is worth noting that EPT was significantly related to conductivity alone, similar to richness, which further speaks to the merit of this parameter as a descriptor of benthic diversity in these stream systems.

Abundance was related to multiple parameters, including the size of the catchment (and thus system size), the conductivity in the system, and TOC concentrations. However, the model was the most variable, and had the highest error around the estimates (RMS = 0.065). This indicated that it did not provide as good of a fit to the data as the other models, and suggested that benthic macroinvertebrate abundance may be difficult to predict within these systems. The model chosen for percent Chironomidae was notably the only model to include a temperature metric. The percent Chironomidae was strongly negatively associated with the maximum summer temperature, indicating a preference of chironomids for cooler streams. Stations PL2, PL3, and PL6, which were among the warmest stations, had notably low percent Chironomidae (less than 15% in each station), which may have contributed significantly to this relationship. Chironomidae are noted for their tolerance of cold-water streams, particularly glacial systems (Milner et al. 2001), and temperature preferences of Chironomidae in lakes are well-established for use in thermal reconstruction in paleolimnology (Eggermont and Heiri 2012), but the mechanism driving the strong relationship between chironomids and temperature is largely unknown. The RDA of habitat and GIS variables indicated that Orthocladiinae in particular were negatively correlated with warm-water stations, but further analysis of specific thermal preferences among chironomid subfamilies or genera may provide a useful tool to monitor response to climate warming in these systems.

81

Figure 5-17 Relationship of the log10-transformed percent Ephemeroptera, Plecoptera, and Trichoptera (EPT) with (a) log10 conductivity and (b) log10 D50 (average particle size) at the 26 stations sampled by CRI in 2015. A linear regression line is fit to each to visualize direct relationships in the data the data.

5.3.2.2.3 Reference Condition Approach to assess stream health The Reference Condition Approach provided a different picture of stream health depending on whether benthic macroinvertebrate stations were assessed on the basis of taxonomic richness or taxonomic diversity (Simpson’s 1-D diversity index). O/E values for richness were below the normal range for reference condition in over half of the stations (Figure 5-18 and Figure 5-19). CB1 provided the strongest evidence for deviations from reference condition, with the lowest O/E at 0.69. CB1 was largely groundwater-fed (see section 4), and this may have affected

82

Figure 5-18 Observed/Expected (O/E) richness of benthic macroinvertebrate data from 26 CRI stations that were assessed using the Atlantic Reference Model. The upper dashed line indicates the breakpoint between normal samples (> 0.95) and divergent samples (0.47-0.95). The lower limit of the divergent class and upper limit of the highly divergent class is indicated by the lower dashed line. Samples that fall within the divergent range are coloured in orange.

Figure 5-19 Map of CRI benthic macroinvertebrate sampling locations indicating samples that were within normal range for reference sites with respect to taxonomic richness (blue circles) and samples that were divergent from reference condition (red squares).

83 community composition at this site enough to cause the significant deviation from reference condition. However, O/E values for the remaining divergent stations were generally close to the threshold value of 0.95, with eight stations above 0.90. The lower richness estimates may in part have reflected absences of taxa such as Odonata at a number of sites, as Curry et al. (2012b) found odonates to be diverse in other stream systems in New Brunswick.

Assessment of reference condition using diversity (Simpson’s 1-D Index; Figure 5-20) resulted in a higher proportion of samples being classified as within normal reference condition range. The samples that were found to be divergent included CB1, KB8, SB3A, and TC3, although all O/E values were close to the threshold of 0.96. These results indicated that samples for most stations were within the normal range with respect to distribution of abundance among taxa (Figure 5-18). Stations CB1, KB8, and TC3 were found to be divergent for both richness and evenness, which indicated that these stations may be impaired with respect to their benthic community. In the case of CB1, this may reflect different communities characteristic of spring- fed systems, as they may not be properly represented in the Atlantic Reference Model. However, repeated sampling of all three of these stations would be beneficial to monitor stream health with respect to benthic community composition.

Figure 5-20 Observed/Expected (O/E) evenness (Simpson’s Index) of benthic macroinvertebrate data from 26 CRI stations that were assessed using the Atlantic Reference Model. The upper dashed line indicates the breakpoint between normal samples (> 0.96) and divergent samples (0.48-0.96). The lower limit of the divergent class and upper limit of the highly divergent class is indicated by the lower dashed line. Samples that fall within the divergent range are coloured in orange.

84

Figure 5-21 Map of CRI benthic macroinvertebrate sampling locations indicating samples that were within normal range for reference sites with respect to taxonomic evenness calculated as Simpson’s Index (blue circles) and samples that were divergent from reference condition (red squares).

Table 5-14 Summary of fish species collected in 27 CRI stations sampled in 2015, with taxonomic codes used in figures, the number of stations at which each species was collected, and the CPUE (/100 s) averaged across stations at which each taxon was found.

Number of Average CPUE Species Code stations /100s American Eel EEL 13 0.56 Atlantic Salmon ATS 2 0.04 Blacknose Dace BND 17 2.54 Blacknose Shiner BNS 1 0.06 Brook Trout BKT 22 2.05 Burbot BUR 3 0.07 Common Shiner CSH 4 0.95 Golden Shiner GSH 1 0.05 Lake Chub LKC 7 0.12 Ninespine Stickleback 9SB 1 0.11 Sea Lamprey SLP 10 0.20 Slimy Sculpin SLS 21 11.39 Threespine Stickleback 3SB 13 0.46 White Sucker WHS 5 0.50

85 5.3.2.3 Fish community characterization 5.3.2.3.1 Community composition Fish community monitoring in 27 CRI stations resulted in the collection of 14 different species, with Brook Trout and Slimy Sculpin captured at the most stations (22 and 21 stations, respectively; Table 5-14). Other species including Atlantic Salmon, Blacknose Shiner, and Golden Shiner were more rare and only collected at 1-2 stations (Table 5-14). Average catch per unit effort (CPUE; standardized per 100 seconds) was highest for Slimy Sculpin (CPUE = 11.39), Blacknose Dace (CPUE = 2.54), and Brook Trout (CPUE = 2.05; Table 5-14), and was much lower for the remaining species.

Table 5-15 Biological metrics summarizing fish communities collected at 27 CRI stations in 2015, including the total number of fish collected, the catch per unit effort (CPUE; standardardized per 100 s), species richness, Simpson's Index (1-D), percent Brook Trout and percent Slimy Sculpin.

Total CPUE Species Simpson’s Percent Percent Station Count /100s richness Index (1-D) Brook Trout Slimy Sculpin CB1 186 13.46 4 0.59 28.0 55.4 KB1 83 5.11 3 0.41 26.5 72.3 KB2 564 36.53 5 0.08 3.0 95.7 KB3 394 31.55 3 0.05 2.5 97.2 KB4 606 27.85 5 0.18 9.1 90.1 KB5 210 11.36 7 0.35 17.6 78.6 KB6 57 2.78 9 0.51 3.5 68.4 KB7 37 1.39 9 0.83 5.4 5.4 MB1 36 8.59 2 0.50 47.2 52.8 MB2 159 7.76 4 0.51 18.9 66.0 MP1 32 3.99 4 0.62 56.3 18.8 PL1 66 2.19 2 0.06 3.0 0.0 PL2 41 7.12 1 0.00 0.0 0.0 PL3 22 4.00 3 0.37 0.0 0.0 PL4 124 11.41 9 0.76 24.2 22.6 PL4A 49 7.67 4 0.41 0.0 35.3 PL5 255 19.84 3 0.57 54.5 0.0 PL6 34 8.11 4 0.71 0.0 0.0 PL7 71 16.40 3 0.31 0.0 0.0 SB1 148 12.91 2 0.47 37.2 62.8 SB2 327 18.17 2 0.10 5.5 94.5 SB3A 441 24.30 2 0.07 3.6 96.4 ST1 174 9.70 8 0.62 27.0 53.4 ST2 43 2.97 6 0.68 44.2 32.6 TC1 453 16.17 7 0.30 3.1 83.0

86 Total CPUE at a station was variable, ranging from 1.39 to 36.53 (Table 5-15). Richness was low across many stations, and ranged from one species per station (at PL2) to a maximum of nine species at a station (at KB6, KB7, and PL4). Simpson’s 1-D Index (a measure of diversitys that ranges from 0 to 1, with 1 representing a perfectly even distribution of species) indicated low diversity and moderately to highly uneven communities, with 14 stations having an index value below 0.50 (Table 5-15). The percent Slimy Sculpin at a station provided clear evidence of

Figure 5-22 Fish community metric values summarized for each of the three catchment size classes (small, medium, and large), including (A) percent Brook Trout, (B) percent Slimy Sculpin, and (C) diversity measured by Simpson’s (1-D) Index. Lower-case letters on each plot indicate significant differences (at α = 0.05) among geological age classes, as determined by Tukey tests adjusted for unequal sample sizes. In each box plot, the median (central horizontal line) is in a box bounded by the 25th and 75th percentiles, with whiskers indicating 10th and 90th percentiles and points for statistical outliers.

87 the uneven distribution of species within some communities, as this single species made up 90% to 97.5% of the assemblage at five stations, and over 50% of the assemblage at 14 of the 27 stations (Table 5-15). Although fish assemblages are generally much less diverse than benthic macroinvertebrate communities because they are a less speciose group, Simpson’s Index combined with the species-specific CPUE and percent composition of Slimy Sculpin indicated that fish communities at many stations lacked diversity and were overly dominated by Slimy Sculpin.

In contrast to what was found for water chemistry and benthic macroinvertebrates, catchment size appeared to be strongly related to some measures of fish community structure, although geological age class remained important for two metrics (and there was no evidence of an interaction between the two factors; p > 0.05 for interaction terms in all ANOVA models). The strongest effect of catchment size was evident for the percent Brook Trout at a station (F2,17 = 20.8; p < 0.001), as there was a significantly higher percent Brook Trout in small catchments

Figure 5-23 Fish community metric values summarized for each of the three geological age classes (with geology classified by geological age as Early Carboniferous, Late Carboniferous, or Older Classes, which include Neoproterozoic and Devonian-Carboniferous), including (A) species richness, and (B) catch per unit effort (CPUE) per 100s. Lower-case letters on each plot indicate significant differences (at α = 0.05) among geological age classes, as determined by Tukey tests adjusted for unequal sample sizes. In each box plot, the median (central horizontal line) is in a box bounded by the 25th and 75th percentiles, with whiskers indicating 10th and 90th percentiles and points for statistical outliers.

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(mean = 35.5%) than in either medium or large catchments (mean = 8.4% and 3.7%, resepectively; Figure 5-22A). The other dominant species, Slimy Sculpin, was also associated with catchment size (F2,17 = 5.0; p = 0.019), but the percent Slimy Sculpin was significantly higher in medium-sized catchments (mean = 74.5%) than in large catchments (mean = 32.9%; Figure 5-22B). The percent Slimy Sculpin was also lower in small catchments (mean = 48.8%), but these stations did not differ significantly from small or large catchments. Simpson’s (1-D) Index differed among catchment sizes (F2,17 = 6.2; p = 0.009), and reflected the dominance of Slimy Sculpin with significantly lower diversity in medium-sized catchments than in small catchments, where Brook Trout and Slimy Sculpin were present in more even abundances (Figure 5-22C).

Geological age class was more associated with general measures of abundance and taxonomic composition, as it had a significant effect on both species richness (F2,17 = 4.7; p = 0.023) and CPUE (F2,17 = 3.6; p = 0.047), whereas catchment size was not significant for either model. Species richness was significantly higher for Early Carboniferous stations (mean = 5.5) than for Older Class stations (mean = 3; Figure 5-23A), similar to the patterns that were noted for benthic macroinvertebrates. Differences in CPUE among geological age classes were not significant in post-hoc analysis when results were adjusted for unequal sample sizes, but appeared to show slightly higher abundance in Late Carboniferous stations than Older Classes (Figure 5-23B).

Figure 5-24 Principal Components Analysis (PCA) biplot based on fish relative abundance for species collected at the 27 stations sampled by CRI in 2015, with station symbols indicating underlying surficial geology. Taxonomic abbreviations can be found in Table 5-14. Stations PL7 (glacial blanket) and PL4A (undifferentiated bedrock) are stations where only fish data were collected, but are included in this analysis to increase geospatial coverage of the assessment.

89 The PCA based on fish relative abundance data explained a great deal of variation in the community data and appeared to separate the stations into three distinct groups, but the groups were not associated with bedrock geology age (Figure 5-24). The first axis, which explained 72.5% of the variance, separated the warmer stations (including PL1, PL2, PL3, PL6, and PL7) from the remaining (cooler) stations (Figure 5-24). Warmer stations were associated with Blacknose Dace, American Eel, White Sucker, and Common Shiner. At the opposite end of the first-axis gradient, cooler stations were associated with Slimy Sculpin (Figure 5-24), which is as expected based on recognized thermal preferences of this species (Lyons 1990, Coker et al. 2001). Brook Trout was also strongly negatively associated with the warmer stations, but it was also positively associated with the second axis gradient (Figure 5-24).

The second axis, which explained 13.1% of the variance, partially appeared to separate sites based on whether they were dominated by Brook Trout (stations on the positive end of the gradient) or Slimy Sculpin (stations on the negative end of the gradient; Figure 5-24). Similar to the first axis gradient, it did not appear to separate out bedrock geology age classes, and thus appeared to be related to other differences among stations.

5.3.2.3.2 Environmental drivers of fish communities Redundancy Analysis of fish relative abundance data constrained to water quality data indicated a stronger separation of stations based on bedrock geology age due to the strong association between bedrock and water chemistry. Along the first axis, Older Class stations were separated from Early and Late Carboniferous stations, while the second axis differentiated between the latter two classes (Figure 5-25). The first axis of the RDA, which explained 39.7% of the unconstrained community variance, primarily represented a gradient in TOC and aluminum, with high levels of these parameters at stations PL1, PL2, and PL3, and low levels at CB1, SB1, and KB5 (Figure 5-20). High levels of TOC and aluminum were positively correlated with Blacknose Dace and negatively correlated with Slimy Sculpin and Atlantic Salmon relative abundances. Aluminum has the been shown to be particularly detrimental to fish species in acidic waters or at a mixing zone between acidic and circum-neutral waters, as aluminum precipitates onto gills of fish and can interfere with ionic regulation (Havas and Rosseland 1995). The inorganic form of aluminum that persists in acidic systems may be more toxic to fish than the organic form that binds to organic matter and that was likely dominant in the study systems (Driscoll et al. 1987), but sensitive fish species may still be limited by aluminum levels in neutral waters such as those in the Kennebecasis region.

Brook trout have been shown to be sensitive to aluminum levels in acidified systems (Schofield and Trojnar 1980), but relative abundance of this species was uncorrelated with aluminum, and was instead positively correlated with conductivity, which drove the separation of Older Class stations from Early Carboniferous stations along a gradient that was intermediate to the first and second axes (Figure 5-25). Along the second axis, which explained 8.4% of unconstrained community variance, there was a gradient in turbidity, TP, and conductivity that further separated some Early Carboniferous stations from Late Carboniferous stations (Figure 5-25). Brook Trout and Threespine Stickleback relative abundances were strongly correlated with conductivity, whereas Sea Lamprey was more strongly correlated with turbidity (Figure 5-25). Conductivity has been found to correlate with abundance (Scarnecchia and Bergersen 1987) and size (as outlined in Copp 2003) of some fish species including trout, with high conductivity stations equated to more productive systems. The positive association of Brook Trout relative abundance with conductivity in these streams appears to support this idea. However, many of the stations with high low levels of conductivity were also stations with warmer temperatures, which represents an additional barrier for several species that prefer cold waters

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Figure 5-25 Redundancy Analysis (RDA) biplot of fish relative abundance data constrained to water chemistry variables collected at the 25 CRI stations with both fish and water quality data collection in 2015, with station symbols indicating dominant bedrock geology age in a 1 km upstream catchment buffer. Taxonomic abbreviations can be found in Table 5-14 and water quality abbreviations can be found in Table 5-3.

(including Brook Trout and Atlantic salmon with preferred temperatures of 16°C, and Slimy Sculpin with preferred temperatures of 10-13°C; Coker et al. 2001).

Assessment of the importance of habitat/GIS drivers to fish community composition provided a better fit to the fish community relative abundance data than was obtained with water quality data alone. The first RDA axis, which explained 52.7% of the unconstrained community variance, was most strongly associated with the relative area of intrusive bedrock, with some contribution of catchment area (Figure 5-26). As previously established, bedrock geology was strongly correlated with summer maximum water temperatures, and therefore the gradient of stations along the first axis was also indicative of a temperature gradient. In the ordination plot, stations PL1, PL2, and PL3 were most positively correlated with the relative area of intrusive bedrock, and these were among the warmest stations, with maximum summer temperatures ranging from 26°C to 27.5°C. Blacknose Dace and American Eel were the species that were most strongly associated with a high area of intrusive bedrock and warm temperatures, and these species have preferred temperatures of 24.6°C and 19°C, respectively (Coker et al. 2001). In contrast, Slimy Sculpin has a preferred temperature of 10-13°C (Coker et al. 2001) and it shared the strongest negative association with this bedrock/temperature gradient, as was predicted when the PCA was examined.

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Figure 5-26 Redundancy Analysis (RDA) biplot of fish relative abundance data constrained to habitat variables estimated from the field and from GIS at the 25 CRI stations with both fish and habitat data collection in 2015, with station symbols indicating dominant bedrock geology age in a 1 km upstream catchment buffer. Taxonomic abbreviations can be found in Table 5-14 and habitat variable abbreviations can be found inTable 5-4 and Table 5-5.

A number of the stations that were highly correlated with Slimy Sculpin relative abundance were positively correlated with D50 and catchment area along the second axis, which explained 13% of the unconstrained community variance. Other important parameters along the second axis included surficial geology (area of blanket/veneer), and the canopy coverage and percent forest. Brook Trout relative abundance was negatively correlated with intrusive bedrock/temperature, catchment area, and D50, and was positively correlated with riparian vegetation descriptors (canopy coverage and percent forest). The preferred temperature for Brook Trout is 16°C (Coker et al. 2001), and trout in these systems have been shown to be associated with riparian vegetation, specifically the percent overhanging vegetation (Somers and Curry 2011). The negative relationship with D50 may have reflected the preference of Brook Trout to spawn in areas of small substrate size (e.g., sand, gravel, and other small particles; Curry and Noakes 1995).

Several strong relationships were evident when regression analysis was used to evaluate the response of fish biotic metrics to environmental drivers that were dominant in the RDAs. In particular, there was an extremely strong relationship between the percent Brook Trout and the wetted width of the sample area that explained 82.6% of the variance in the percent composition of brook trout (Table 5-16). The relationship exhibited some variability at the smallest widths, but there was a strong negative response of percent brook trout to wetted width across all systems (Figure 5-27a). The prevalence of brook trout also responded to substrate size, with a negative relationship with D50. Although this relationship was weaker, with higher variability around the

92 Table 5-16 Results of the least-squares linear regression analysis of fish metrics and environmental drivers (water quality, physical/habitat drivers, and temperature) for the CRI study sites, showing the models that had the lowest AICc value and explained the greatest amount of variation in the response metric. Reported statistics include the standardized regression coefficient for each driver, the p value for each driver, the residual mean square for model (RMS; a measure of error around the estimate), the p value for each model, and the R2 (or adjusted R2 for multiple regression models). Where the AICc and R2 indicated that models provided similar fits to the data, both models are included.

Standardized (Adjusted) Fish Metric Drivers p RMS Model p Coefficient R2

log10 CPUE log10 Al -0.803 < 0.001 0.055 < 0.001 0.630

log10 Conductivity 5.697 < 0.001 log10 Richness 0.038 0.001 0.436 2 (log10 Conductivity) -5.334 < 0.001

log10 Simpson’s log10 Conductivity 0.472 0.017 0.217 0.017 0.223 1-D Diversity

logit Percent Avg Summer Temp -0.505 0.009 0.190 0.009 0.255 Slimy Sculpin

log10 Wetted width -0.909 < 0.001 0.013 < 0.001 0.826 logit Percent log10 Wetted width -0.835 < 0.001 Brook Trout 0.012 < 0.001 0.835 log10 D50 -0.168 0.083

regression line (Figure 5-27b), the AICc indicated that there was nearly equal support for a model containing just wetted width and one containing both wetted width and D50, and there was a slight increase in variance explained (R2 = 0.835; Table 5-16).

The model describing CPUE at a station was the second strongest model in the fish data. CPUE was negatively related to aluminum levels in a model that explained 63% of the variance in the standardized abundance measure. The slope of the relationship was driven in part by one extreme aluminum value (Figure 5-28), but the plot of the data indicated that without that extreme point, the slope of the relationship would be even more steep, indicating a stronger negative response of CPUE to total aluminum levels. The response of fish abundance to aluminum levels is important because of the lack of information about aluminum impacts in neutral systems. Although aluminum may have significant negative impacts on fish health in acidified systems, even causing fish kills (Havas and Rosseland 1995), there is little research on the impacts in higher pH systems such as those in the Kennebecasis area. Continued research into the specific response of fish to aluminum in these system, particularly to dissolved aluminum levels, may provide information about potential long-term impacts of periodic exposure to fish health.

Temperature played a more direct role in driving the Slimy Sculpin populations in the streams, with a negative relationship between percent Slimy Sculpin and average summer temperature providing the best fit for that species. This relationship was expected based on the low temperature preference of this species (Coker et al. 2001). However, the relationship was highly variable (RMS = 0.190) and explained relatively little of the variance in percent composition of Slimy Sculpin (R2 = 0.255), which indicated that another parameter not included in the potential models played a stronger role in driving these populations. Similarly, diversity was best

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Figure 5-27 Relationship between the log10 percent Brook Trout at a station and (a) log10 wetted width (m) and (b) log10 D50 (a measure of substrate size) for the 25 CRI stations with both fish sampling and habitat description in 2015. A linear regression line is fit to the data.

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Figure 5-28 Relationship between log10 CPUE and log10 aluminum (mg/L) at 25 CRI stations with both fish and water quality sample collection in 2015. A linear regression line is fit to the data.

Figure 5-29 Relationship between log10 fish species richness and log10 conductivity (μS/cm) at the 25 CRI stations with fish and water quality sampling in 2015. A second-order polynomial regression line is fit to 2 the data, fitting the model log10Richness = log10Conductivity + (log10Conductivity) .

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described by a model that included conductivity, but there was a great deal of variation in the model (RMS = 0.217) and the variance explained was low (R2 = 0.223).

Conductivity was also related to species richness among the stations, but this relationship was unique in that it displayed a unimodal pattern, with increasing richness in response to conductivity up to a threshold, after which richness began to decline (Figure 5-29). The polynomial regression fit to the data explained 43.6% of the variance in species richness (Table 5-16). Species richness appeared to increase until conductivity reached a value of approximately 160 mg/L (roughly log conductivity value of 2.2 in Figure 5-29), after which it began to decline. This threshold may indicate a point at which conductivity levels no longer contribute to system productivity and become a barrier to some fish due to ionic regulation requirements.

5.3.2.3.3 Temporal trends Brook trout and Slimy Sculpin were the fish species found most consistently over the period of 1996 to 2015 in the three stations with historical fish data. In KB3 and SB2, both species were found in all six sampling years, whereas in ST1 there was only one year when Slimy Sculpin was not found (2008; Table 5-17). There were species present in the earliest records that were not found in more recent records (e.g., Atlantic Salmon, Creek Chub, Burbot in some stations; Table 5-17); however, it is difficult to confirm these apparent trends as sampling methods and effort differed over the period of record, and an inability to capture these species in later years may have been a result of different sampling methods. Similarly, there were new species found in ST1 in 2015 (Ninespine Stickleback and Blacknose Shiner), but the change in sampling method may have resulted in a more effective technique for collecting these taxa.

Results of the van Belle and Hughes test for homogeneity of trends indicated that all stations displayed a homogeneous trend of declining richness over time. However, these results should be interpreted with caution, as they may be highly dependent on the change in sampling method

Table 5-17 Presence of fish species at three stream stations sampled in 1996-1998, 2008-2009, and 2015. An X indicates that the species was collected during sampling events in the indicated year.

Portage Vale (KB3) Stone Brook (ST1) Fish Species Fish Species 1996 1997 1998 2008 2009 2015 1996 1997 1998 2008 2009 2015 Threespine Stickleback X X X X Threespine Stickleback X X X X X X Atlantic Salmon X X X X Ninespine Stickleback X Brook Trout X X X X X X Atlantic Salmon X X X Blacknose Dace X X X X X Brown Bullhead X X X X Burbot X Brook Trout X X X X X X American Eel X X X Blacknose Dace Sea Lamprey X X X X Blacknose Shiner X X Slimy Sculpin X X X X X X Creek Chub X X White Sucker X X Common Shiner X X X Golden Shiner X X X Lake Chub X X South Branch (SB2) Longnose Sucker X 1996 1997 1998 2008 2009 2015 Pearl Dace X Atlantic Salmon X X X Pumpkinseed Sunfish X X Brook Trout X X X X X X Sea Lamprey X X X X X American Eel X Slimy Sculpin X X X X X Slimy Sculpin X X X X X X White Sucker X X X

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over time (which may have changed the effectiveness with which different species were collected). Nevertheless, the overall picture of presence/absence data over time is one of shifting composition, and future sampling should endeavour to employ consistent sampling methods in order to provide data that are directly comparable.

5.3.2.3.4 Sculpin health Across all sites sampled in 2016, male and female Slimy Sculpin showed significant differences in relative gonad size, relative liver size and condition factor. In general, females had higher relative liver size than males at comparable carcass weights (Figure 5-30a), with a range of 2.5%-8.5% of carcass weight compared to 0.6-3.9% for males. Regression slopes did not differ for males and females (full model interaction term p = 0.686), which indicated that differences in liver size between males and females were consistent across all sizes of fish. Females also had larger gonads than males at comparable carcass weights (Figure 5-30b), accounting for between 12.1%-43.2% of their carcass weight, while males had a much smaller range of 0.8- 3.6%. Slopes of the relationship between gonad size and fish size were also found to be similar between males and females (full model interaction term p = 0.465), indicating that the larger gonad size in females was consistent across all sizes of fish. In contrast, males and females showed differences in body weight at different lengths (p < 0.001 for the model interaction term; Figure 5-31), with female carcass weight becoming lower than that of males at longer fish lengths. Females generally had lower condition factors than males, with females ranging from 0.72-1.19 and males ranging from 0.90-1.5, but the difference between the two depended on fish length. Due to differences in the average weights of gonads and livers and the significant interaction term in the condition model, female and male sculpin data were analyzed separately.

Significant differences in female gonad size were evident among many stations when controlling for carcass weight, whereas male gonad size varied little across stations (Figure 5-32). Differences in the gonadosomatic index among stations (for females) appeared to be related to differences in the coefficient of variation (CV) of winter temperature, which provides a measure of the range of water temperatures during the winter months (Figure 5-32). In contrast, the gonadosomatic index did not appear to differ across the range of winter temperature CV for males. Male sculpin are likely less sensitive to differences in winter stream temperature as gonad development occurs primarily in the fall season between September and December, after which male gonad size is fairly stable through the winter period (Barrett and Munkittrick 2010).

Significant differences in the relationship between liver weight and size (visually approximated by the liversomatic index) were evident among stations for both female and male Slimy Sculpin (Figure 5-33). The relationship between liver weight and size for male fish had a different slope at three stations (CB1, KB1 and ST1), which indicated that fish at these stations had smaller or larger than expected livers for their size. Differences among stations were not as clearly associated with winter temperature CV as they were for gonad size, but there was still some evidence of increased liver size at moderate levels of variation in winter temperatures (Figure 5-33).

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Figure 5-30 (a) The log10 liver weight and (b) log10 gonad weight of Slimy Sculpin as a function of log10 carcass weight, with different regression lines fit to males and females.

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Figure 5-31 The relationship between log10 carcass weight and log10 length among male and female Slimy Sculpin, with separate regression lines fit to males and females.

Figure 5-32 Boxplots of differences in gonadosomatic index for female (solid fill) and male (no fill) Slimy Sculpin among stations ordered from highest to lowest winter water temperature coefficient of variation (see section 4). Letters indicate significant differences among stations (Tukey’s post hoc test).

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Figure 5-33 Boxplots of differences in liversomatic index for female (solid fill) and male (no fill) Slimy Sculpin among stations ordered from highest to lowest winter water temperature coefficient of variation (see section 4). Letters indicate significant differences among stations (Tukey’s post hoc test).

Female Slimy Sculpin tended to display differences in organ weight across stations in a more consistent manner than males, suggesting they may be more appropriate for examining between-station differences in relation to environmental parameters. Slimy Sculpin appear to be sensitive to chemical and physical differences between sites, particularly temperature, as noted in the fish community assessment and in the association of winter temperature variation with differences among stations. This sensitivity, combined with the dominance of Slimy Sculpin at a number of stream sites, suggests that they would be an ideal monitoring species for the Upper Kennebecasis watershed. One potential limitation in the use of this species is that Slimy Sculpin are not present throughout the entire study area. For example, Slimy Sculpin were not found in the mainstem of the Pollett River and were only present in low numbers in the stations lower on the main stem. However, this is likely due to the stream temperatures consistently reaching temperatures outside of the thermal range of Slimy Sculpin. By understanding the current patterns in Slimy Sculpin health across sites in the Kennebecasis watershed, shifts in fish health could be used as an indication of perturbation within this system.

5.3.2.4 Community concordance Community concordance was examined to evaluate the similarity of the station characterizations provided by benthic macroinvertebrates and fish. The PCA ordinations of each group appeared to display a similar overall pattern, with primary separation of stations due to drivers related to water quality, bedrock geology, and temperature, and secondary separation due to drivers related to surficial geology, and some common clusters of stations were evident in both PCAs. However, the use of Procrustes analysis allowed for a closer examination of whether the associations among stations were the same when either benthic macroinvertebrates or fish was examined.

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Figure 5-34 Residuals from Procrustes Analysis of fish (target) and benthic macroinvertabrate (rotational) PCA ordinations, indicating the residual distance between a site’s location in the target ordination and its location in the rotational ordination after translation and rotation. The solid and dotted horizontal lines represent type 7 quartiles of the residuals.

Procrustes analysis indicated that the fit of the benthic macroinvertebrate ordination (rotational ordination) to the fish ordination (target ordination) was more similar than could be obtained by 2 chance (m12 = 0.79; p = 0.023 after 999 permutations). The largest residual difference was found for station KB7 (residual vector length = 0.503), but four other stations had moderately long residual vectors that were outside the quartile range (e.g., CB1, PL3, PL6, and ST1; Figure 5-34), indicating that the similarity of these stations to other stations differed depending on whether fish or benthic macroinvertebrate data were considered. However, residual vectors were short for most stations, indicating significant community concordance that was evident as a strong overall similarity between the fish ordination and the benthic macroinvertebrate ordination.

The detection of significant community concordance has two potential implications: (1) that environmental drivers act on benthic macroinvertebrates and fish in a similar manner, resulting in similar groupings of sites regardless of the organism examined, and (2) that there is redundancy in the evaluation of both benthic macroinvertebrate community structure and fish community structure within the same system, as they both lead to the same conclusion (Jackson and Harvey 1993). In the case of the Kennebecasis and Pollett River study area, the evaluation of both benthic macroinvertebrate communities and fish communities, although providing similar information about the systems, cannot be considered fully redundant as these groups of organisms appeared to respond most strongly to different environmental drivers. Though both groups of organisms were related to geology at the largest scale, benthic macroinvertebrates appeared to be more directly driven by differences in water chemistry, whereas fish appeared to respond most strongly to physical habitat variables and temperature. Although this resulted in similar spatial associations among stations, the patterns shown by fish and benthic macroinvertebrates represent responses to different environmental drivers, which suggests that each community could be expected to respond differently to future perturbation. For example, benthic macroinvertebrates may be more sensitive to water quality than fish, and may respond more quickly to any shifts in water chemistry. Similarly, fish may be more sensitive to temperature range than benthic macroinvertebrates, and may respond more quickly to shifts

101 in thermal maximums. Continued monitoring of the similarity of systems based on these two groups of organisms may allow managers to determine whether future changes are in response to shifts in water quality (benthic macroinvertebrates), temperature (fish) or a more catastrophic impact (both benthic macroinvertebrates and fish). 5.4 Conclusions Potential for shale gas production is linked to geological composition and age (Macauley et al. 1985), and geology is known to influence water chemistry and biotic communities of freshwater systems (Snelder et al. 2004, Dow et al. 2006). In this study, we found evidence of differences in water chemistry and community structure among stream sites that were classified into different geological age classes. Early Carboniferous stations, which had the highest potential for shale gas production, were found to have the highest ions levels, highest abundance and genus richness of invertebrates, and highest species richness of fish, with the strongest differences noted between Early Carboniferous and older classes of bedrock.

Geology plays a significant role in the determination of groundwater and surface water chemistry (Johnson et al. 1997, Thornton and Dise 1998), affecting pH, ionic composition, and acid neutralizing capacity (Clair et al. 1995, Reimann et al. 2009). Within the study area, Early Carboniferous bedrock stations were characterized by higher solute levels than stations on Late Carboniferous or older age classes of geology, particularly with respect to calcium, chloride, sodium, and sulphate. The differences in water chemistry among bedrock geology age classes were strongly associated with benthic macroinvertebrate composition, suggesting that geology plays a significant role in determining assemblage structure through its control of water chemistry. A strong association between benthic macroinvertebrates and water chemistry is well-documented (e.g., Sandin and Johnson 2004, Lento et al. 2008), including evidence of variability in composition in response to conductivity levels (Kratzer et al. 2006), and temporal shifts in composition following trends in ion levels (Lento et al. 2012). In contrast, fish were less affected by water chemistry and appeared to be more strongly associated with physical habitat descriptors such as temperature and stream size. Brook Trout and Slimy Sculpin are both noted to be temperature-sensitive, preferring smaller streams with cooler temperatures, and the dominance of these species in some streams drove not only differences in the compositional metrics for these two species, but also the measure of diversity that incorporated evenness. Biotic interactions within these systems may have contributed to the weaker response of fish to geology type, with brook trout and slimy sculpin out-competing other fish species in small stream systems (Jackson et al. 2001). However, It is possible that geology and associated changes to water chemistry generally exert less control over fish composition than other physical habitat parameters. Despite the apparent differences in the importance of various environmental drivers for determining the composition of fish and benthic invertebrate communities in these watersheds, mapping of site similarity based on community structure produced similar results whether fish or benthic macroinvertebrates were used, suggesting that both groups can be utilized in concert to monitor changes to a range of habitat conditions and perturbations.

Biotic assessments in areas of potential resource development must consider environmental factors such as geology in the development of study design and site selection. Biotic metrics such as richness and abundance differed by geology type for both benthic invertebrates and fish, and compositional metrics for benthic invertebrates also differed across geology age classes. Such differences in composition might suggest a measure of impairment in test sites, even though differences are due to the natural gradient in geology and water chemistry. For example, samples were numerically dominated by Ephemeroptera, Plecoptera, Trichoptera, and

102 Chironomidae, but the abundance of each group differed across geology types, with the highest proportion of EPT in Older Class stations and the highest proportion of Chironomidae in Carboniferous (Early and Late) stations. Although tolerances and preferences differ across and within families, the orders Ephemeroptera, Plecoptera, and Trichoptera are generally considered to be sensitive to perturbation within stream systems due to generalized low tolerances to pollution, and low abundance or richness of these groups is taken as an indicator of impaired water quality (Barbour et al. 2001). In contrast, the family Chironomidae includes a number of genera that are tolerant of perturbation, and thus is used as a general indicator of potential impairment (Barbour et al. 2001). Applying these general principles to the assessment of the study sites without information about environmental variability might suggest that some stations were of lower water quality than others, when in fact the difference in relative proportions of EPT and Chironomidae appears to have been driven by natural differences in geology. Assessment with fish provides an additional challenge, as fish composition appeared to be more strongly associated with system size than geology type for some compositional measures (particularly the proportion of brook trout and slimy sculpin). However, there was a significant difference in species richness of fish among geology age classes, which indicated the importance of this geological classification for some measures of fish diversity.

The results of this section highlight the importance of selecting appropriate reference sites for bioassessment of stations in areas of resource development to ensure that response to perturbation can be differentiated from natural variability due to environmental gradients in geology or other resource-dependent environmental variables. The results further indicate that rivers and streams in areas of Carboniferous bedrock, with the potential for resource extraction, have higher conductivity and ion levels and higher taxonomic richness of benthic invertebrates and fish than in areas with older classes of sedimentary and non-sedimentary bedrock. Streams in such areas can be expected to have naturally different water chemistry and composition of benthic invertebrates, though fish composition may be more linked to system size. Assessment of water quality in areas such as the Kenebecasis and Pollett River watersheds with high shale gas potential must therefore consider the natural differences in water quality and community structure that occur in freshwaters as a result of geology type.

103 6 Project C: Sediment and Water Geochemistry 6.1 Introduction In New Brunswick as well as in other western countries, the rise of shale gas development has triggered an intense public debate regarding the potential environmental and human health effects from hydraulic fracturing. Vengosh et al. (2014) identified four potential risks that shale gas operations pose to water resources: 1. the contamination of shallow aquifers with fugitive hydrocarbon gases (i.e., stray gas contamination), or their salinization of through leaking natural gas wells and subsurface flow; 2. the contamination of surface water and shallow groundwater from spills, leaks, and/or the disposal of inadequately treated shale gas wastewater; 3. the accumulation of toxic and radioactive elements in soil or stream sediments near exploitation, disposal or spill sites; 4. the over extraction of water resources for high-volume hydraulic fracturing that could induce water shortages or conflicts with other water users, particularly in water-scarce areas. As a result, this project component was designed to address the third potential issue in the only area where exploitation of shale gas occurred in New Brunswick, the Sussex area. Heavy metals, naturally occurring radionuclides in shale rocks and man-made radionuclide tracers injected as part of the fracking fluids may be released to the aquatic ecosystem, especially with flowback (Vengosh et al. 2014, Torres et al. 2016). Heavy metals, the radionuclide decay products and their isotopic signatures (stable isotope ratio) can be used as potential tracers of contamination in shale gas development areas.

6.2 Methods Sampling was conducted in the autumn of 2015. A total of 26 surface streambed sediments and 26 water samples above the corresponding sediment were collected from the same 8 rivers and tributaries characterized for physical and biological baselines in Project B (Figure 5-1). Characterization of a suite of 34 elements was realized following standardized sampling methods of the US Geological Service (Shelton and Capel 1994) and others (Levitan et al. 2014). Metals (Table 6-1), long-lived radionuclides and short-lived radionuclide decay products (specified in Table 6-2), were systematically measured in water and sediment. Concentrations were compared to the the Canadian Council of Ministers of the Environment guidelines for Al, Ag, As, Cd, Cu, Fe, Ni, Pb and Zn in water and for As, Cd, Cr, Cu, Pb and Zn in sediment (Canadian Council of Ministers of the Environment 1999).

For short lived radionuclide sometimes used in the fracking fluid as mapping tracers by the industry (Whitten et al.), specific isotopes of their decay products were analysed and isotopic ratios calculated when possible. Any deviation to the natural isotopic ratio of the decay product may highlight an anthropogenic impact.

104 Table 6-1. Abbreviations and their elements measured in overlaying water and sediments in the Kennebecasis and Pollett Rivers baseline characterization project.

Abbreviation Element Abbreviation Element Abbreviation Element Li lithium Mn manganese Te tellurium Be beryllium Fe iron Cs cesium Na sodium Co cobalt Ba barium Mg magnesium Ni nickel La lanthanum Al aluminum Cu copper Ce cerium K potassium Zn zinc Eu europium Ca calcium As arsenic 192Pt platinum Sc scandium Rb rubidium Pb lead Ti titanium Sr strontium Bi bismuth V vanadium Ag silver Th thorium Cr chromium Cd cadmium U uranium Sb antimony

Table 6-2. Long lived radionuclides and short lived radionuclide decay products measured (shaded) in this project.

Radionuclide Half-life Decay product

235U 4.5 × 109 years 231Th

238U 7.04 × 108 years 234Th

232Th 1.41 × 1010 years 228Ra Long lived

209Bi 1.9 × 1019 years 205Tl

124Sb 60.2 days 124Te

133Xe 5.2 days 133Cs

46Sc 83.8 days 46Ti Short lived

192Ir 73.8 days 192Pt

For sediments, conservative elements were also measured as a means to normalize the pollutant concentrations to eliminate the potential effect of sediment granulometry.

105 6.2.1 River water sample collection, treatment and preparation All water samples were collected manually in acid-washed 250-mL high-density polyethylene sampling bottles (Nalgene bottles, Thermo Scientific) that were rinsed with ambient water before collection of the samples. A portion of that sample (approximately 40-45 mL) was filtered on site into a pre-washed 50 mL polypropylene conical tube (Corning Falcon tube, BD) using 0.45-μm sterile capsule filters (cellulose acetate membrane, VWR International) and 10-mL polypropylene luer slip syringe (Discardit II syringe, BD). Both unfiltered and filtered samples were preserved immediately upon collection with concentrated nitric acid (Trace metal grade HNO3, 1% v/v) and kept at 4°C until further analysis. One field sample (KB-5) was realised in triplicate. Reproductibilities of the measured elements (once detectable) were always better than 12% for both unfiltered and filtered samples

6.2.2 Sediment sample collection, treatment and preparation Superficial sediments (top 2 cm) were collected manually using a plastic scoop, packed and sealed in polyethylene bags and immediately transferred to the laboratory where they were stored at 4°C until further analysis. In the laboratory, sediments were mixed thoroughly and approximately 100 g of sub-sample was dried at 60°C in acid cleaned petri-dishes for at least 48 hours to constant weight, and then homogenized using a porcelain mortar. For determining the relationship between grain size and metal contents, the sediment samples were dry-divided into six fractions: a combined silt and clay fraction (< 0.063 mm), a very fine sand fraction (0.063- 0.125 mm), a fine sand fraction (0.125-0.250 mm), a medium sand fraction (0.250-0.500 mm), a coarse sand fraction (0.500-2.000 mm), and finally a gravel fraction (> 2.000 mm). Size fractionation was performed using a mechanical shaker (Fisher-Wheeler Sieve Shaker, Fisher Scientific) for a period of 40 minutes paired with a series of stainless steel sieves (Fieldmaster Soil Sampling Sieve Set, Science First). After separation, the total mass of all six grain-size fractions generated was recorded in order to estimate the granulometric composition for each sampling site. Each sediment size fraction was mineralized as described below using concentrated acid and analysed by ICP-MS. Results from each individual fraction were integrated using a weigthed arithmetic mean to calculate whole sediment elemental concentration.

For metal content determination, total digestion of sediments was performed using a hotplate open-vessel procedure involving the use of concentrated hydrofluoric, nitric and hydrochloric acids (trace metal grade HF, HNO3 and HCl). Our procedure, adapted from the USEPA method 3052 (US Environmental Protection Agency 1996), is briefly described as follows: an aliquot of 0.2 g of sieved sample was weighted into a 50-mL PTFE beaker equipped with a cover (Delta Scientific) and digested in 7.5 mL of concentrated HF overnight at a temperature ramping from 65 to 85°C. After evaporation to near dryness, the remaining material was dissolved in an acid mixture of HNO3: HCl (15 mL, 2:1 v/v) and heated at constant temperature of ~ 95°C for at least 5-6 hours or until the formation of brown NOx fumes has ceased. On completion of the digestion, samples were transferred quantitatively to graduated polypropylene digestion vessels (Environmental Express, Delta Scientific) and made up to a final volume of 50 mL with Milli-Q water. Before analyses on the ICP/MS, digests were finally diluted by a factor of 5 to 10 using Milli-Q water, so that the sample matrix did not exceed 3-6% v/v acid. Two sediment samples (KB1 and MP1) were analysed in triplicate. Reproductibilities of the measured elements (once detectable) were always better than 15% for all granulometric fractions.

106 6.2.3 Instrumentation Metal contents in the sediment digests or water samples were determined using the Thermo Scientific iCAP-Q ICP MS (Bremen, Germany) interfaced with the ASX-520 autosampler from CETAC Technologies (Omaha, USA). Polyatomic interferences were minimized using the collision cell configuration of the kinetic energy discrimination (KED) mode of the instrument with He as a collision gas. Internal standard solution was mixed online with the samples prior to introduction into the ICP/MS during the whole sequence for instrumental drift correction purposes. The solution was composed of indium (In) (5 μg L-1 in 2 % v/v HNO3) for the purpose of sediment analysis and of a mixture of yttrium (Y) (15 μg L-1) and In, terbium (Tb), and -1 holmium (Ho) (5 μg L in 2 % v/v HNO3) for water samples. Instrument parameters (e.g., argon (Ar) gas flow, torch settings and extraction lens voltage) were tuned daily for maximum ion intensity and signal stability, while assuring minimum oxides and double-charged interferences. Operating conditions, instrumentation, reagents, QA/QC, and standard recoveries are summarized in Appendix B. Limit of detection (LOD) and limit of quantification (LOQ) were defined repectively as the lowest concentration detectable and the lowest concentration quantifiable within 95% confidence. Following analytical standard procedures, 10 field blanks were analyzed: LOD was calculated as the mean blank value plus 3 time the standard deviation of the blanks while LOQ corresponded to the mean blank value plus 10 times the standard deviation of the blanks. For sediment, a fictive mass of 0.2 g was used to calculate the corresponding LOD and LOQ.

6.3 Results and Discussion 6.3.1 Water samples Analytical results as well as limit of detection (LOD) and limit of quantification (LOQ) are compiled in Table 6-3 and Table 6-4 for total and dissolved concentrations, respectively. Total concentrations refer to unfiltered samples while dissolved concentrations correspond to the filtered ones.

With the exception of two samples for total iron (MP-1 and ST-02), all other water samples were in compliance with Canadian Water Guidelines for the Protection of Aquatic Life (Canadian Council of Ministers of the Environment 1999) revealing no major pollution of the selected rivers potentially impacted by Shale gas activities. MP-1 and ST-02 present an iron concentration slightly above the CCME recommendation value of 300 μg/L. However, at these two particular sites, most of the iron is bound to particles (determined as the difference between dissolved and total concentration), which limits its impact and bioavailability for aquatic life. On top of the metals (Al, Ag, As, Cd, Cu, Fe, Ni, Pb, U and Zn) for which guidelines have been determined by the CCME, long life radionuclide (209Bi, 232Th, 235U and 238U) and short lived radionuclide’s daughter element (46Ti, 124Te, 133Cs, 192Pt) have been measured as potential indicators of shale gas exploitation impact on water quality. Low or non detectable concentrations were monitored for long lived radionuclide and decay products of occasionaly injected short lived radionuclides in the fracking fluid suggesting a limited, if not inexistent, impact of the shale gas industry on the water quality for theses parameters in fall 2015. Once the sampling locations were discriminated between 3 areas (McCully gas field; Frederick Brook prospected shale area, and a background area), no statistical differences were observed for all measured elements in water (as total or dissolved concentration) between the 3 groups of data (p >0.05).

The dataset could be therefore compiled and the statistical mean value (such as the median ±2 median absolute deviation) for each element proposed as a first attempt to determine a regional

107 Table 6-3. Total concentrations of Li, Na, Mg, Al, K, Ca, Sc, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Ag, Cd, Sb, Te, Cs, Ba, Pt, Pb, Bi, Th and U measured by ICP-MS in water at the different sampling locations on the Kennebecasis and Pollett Rivers. Limit Of Detection (LOD) and Limit Of Quantification (LOQ) also indicated for each element (list of abbreviations in Table 6-1).Values exceeding CCME guidelines for aqatic life are highlighted in green.

Li Na Mg Al K Ca Sc 46Ti 47Ti Cr Mn Fe Co Ni Cu Zn As ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L LDD 0,01 9 4 0,00 15,3 321 0,00 0,000 0,000 0,00 0,00 0,06 0,00 0,01 0,03 0,05 0,00 LDQ 0,05 30 14 0,02 44,3 1084 0,01 0,001 0,000 0,00 0,01 0,19 0,00 0,03 0,11 0,21 0,02 TC-1 0,29 2453 861 8,73 273,1 9313 0,01

108 Table 6-3. Cont’d Sr Ag Cd 121Sb 124Te 125Te 128Te Cs Ba 192Pt 195Pt 206Pb 207Pb 208Pb Bi Th 235U 238U ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L LDD 0,004 0,001 0,000 0,001 0,001 0,000 0,000 0,000 0,074 0,000 0,000 0,000 0,000 0,001 0,001 0,000 0,000 0,000 LDQ 0,012 0,002 0,000 0,004 0,004 0,000 0,001 0,001 0,231 0,000 0,001 0,000 0,000 0,001 0,003 0,001 0,000 0,000 TC-1 43,26

109 Table 6-4. Dissolved concentrations of various elements measured by ICP-MS in water after filtration at the different sampling locations on the Kennebecasis and Pollett Rivers. Limit Of Detection (LOD) and Limit Of Quantification (LOQ) also indicated for each element (list of abbreviations in Table 6-1).

Li Na Mg Al K Ca Sc 46Ti 47Ti Cr Mn Fe Co Ni Cu Zn As ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L LDD 0,01 68 14 0,71 74,9 122 0,00 0,000 0,000 0,01 0,05 0,56 0,01 0,05 0,10 0,42 0,01 LDQ 0,02 83 42 0,82 155,5 317 0,01 0,000 0,000 0,02 0,05 0,60 0,02 0,08 0,17 0,44 0,02 TC-1-filtre 0,30 2707 927 5,83 249,0 9212 0,01

110 Table 6-4 cont. Sr Ag Cd 121Sb 124Te 125Te 128Te Cs Ba 192Pt 195Pt 206Pb 207Pb 208Pb Bi Th 235U 238U ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L ug/L LDD 0,05 0,00 0,00 0,285 0,00 0,00 0,00 0,00 0,52 0,00 0,00 0,00 0,00 0,00 0,00 0,000 0,000 0,000 LDQ 0,05 0,00 0,00 0,408 0,00 0,00 0,00 0,00 1,79 0,00 0,00 0,00 0,00 0,00 0,00 0,000 0,000 0,000 TC-1-filtre 43,27

111

reference for further analyses in the future. Any major deviation for an element from its defined baseline may reflect pollution. Distinction between total and dissolved concentration presents the advantage to discard a potential abnormal contribution from particles to the measured concentration. In order to reinforce these baseline values, temporal trends of these elements should be determined (see chapter 5). Once the temporal variabilities will be integrated, the baseline values of theses potential impact indicators will be more solid.

6.3.2 Sediment samples Most of the sampled sediment had sandy or slightly silty sand/slightly clayey sand characteristics (Figure 6-1). Such differences in granulometry may affect the metal/pollutant content as most of trace elements present a higher affinity for fine particles (i.e. silt and clay). For each sampling site, sediments were therefore dried, homogenized, ground and sieved. All granulometric fractions were digested and analysed for metals. Metal concentrations are reported in Table 6-6 for the whole sediment as well as its fine fraction (< 63 μm: silt and clay fraction).

0 a : sand 100 b : gravelly sand c : sandy gravel d : gravel a e : sligthtly silty sand / slightly clayey sand e f : gravelly and silty sand / gravelly and clayey sand b 50 50 g : sandy and silty gravel / sandy and clayey gravel f h : silty gravel/ clayey gravel k i i : sandy and gravelly silt / sandy and gravelly clay c j g j : limon gravelo-sableux / argile gravelo-sableuse k : sandy silt / sandy and clayey silt / m l h d sandy and silty clay / sandy clay 100 0 l : gravelly silt/ gravelly and clayey silt / 100 50 0 gravelly and silty clay / gravelly clay % silt and clay m : silt / clayey sily / silty clay / clay

Figure 6-1. Sampled sediment classification according to their granulometry characteristics (ISO 14688- 2).

Enrichment factors, defined as the elemental concentration in a pecular granulometric fraction divided by the elemental concentration in the whole sediment, were calculated for all monitored elements and results for Ni, Cs and U are presented in Figure 6-2. With the exception of Na, K and to a lesser extent Ag, concentrations reported for all elements were systematically higher in the fine sediment fraction (i.e., silt and clay) than in the whole sediment. Sand and coarse particles for which trace elements have a lower affinity contribute to diluting the signal of a potential impact by the shale gas activity. Using a conservative element (Li, Be, Al, Ti, Mg or Ca) to normalize the pollutant concentration or focusing on the fine particles is therefore a necessary step to determine the real impact of anthropogenic activities on aquatic ecosystem (Farrah et al. 1980, Narayana and Rajashekara 2010, Prajith et al. 2016).

112 Ni Cs U

240% 240% 240%

160% 160% 160%

80% 80% 80% Enrichment factor Enrichment

0% 0% 0% <63µm <125µm <250µm <500µm <2000µm >2000µm <63µm <125µm <250µm <500µm <2000µm >2000µm <63µm <125µm <250µm <500µm <2000µm >2000µm Figure 6-2 Average enrichment factor for Ni, Cs and U monitored in the sampled sediment from the Kennebecasis and Pollett Rivers. Error bars represent the enrichment factor standard deviation measured for the 26 samples.

Excepted for Cr in 8 sediment samples, all other collected samples were in compliance with CCME guidelines for sediment revealing no major contamination of the selected rivers. Even for the 8 samples where Cr concentration exceed the CCME probable effect level (PEL) of 90 µg/g, contamination remained debatable. CCME guidelines are based on mild digestion (aqua regia, nitric acid or hydrochloric acid) which extract potentially biological available metals and not residual metals (i.e. those metals held within the lattice framework of the sediment). The stronger digestion method used in this project involved hydrofluoric acid and removed both the bioavailable and residual fractions of metals in sediment. For instance, we have observed in our lab that less than 60% of Cr were extracted from PACS-2 sediment reference material using a mild aqua regia digestion compared to the more complete HF one. In regard of CCME guidelines for the protection of aquatic life, the measured metallic concentration are certainly overestimated. In most of the sediment samples from the Kenebecasis and Pollet Rivers, total concentrations which include bioavailable and residual fractions were lower than the CCME guidelines. For the few samples (especially for KB1 and KB2) where Cr exceed CCME guideline, a mild acidic extraction should be realized to statuate over their CCME compliance.

In the geochemical context of this project, a complete digestion of the sample was realized to highlight any abnormalities in the sediment metallic compostion potentially related to shale gas activities.

Once the sampling locations were discriminated between 3 areas (McCully gas field; Frederick Brook prospected shale area, and a background area), U concentration in the fines particles were slightly more elevated in rivers adjacent to the McCully gas field. In fact, small but significant differences were observed for U concentrations and U concentrations normalized by a conservative element between the 3 groups of data (t student test, p < 0.05). During the fracking process, U and others radionuclides such as radium may be released in the environment and trapped into the sediment (Tsai and Rice 2010, Vengosh et al. 2014, Torres et al. 2016).

To further interprate our dataset for U, a distinction between sampling sites located upstream and downstream producing gas well was realised for each river (Figure 6-3). For the Kenebecasis River, Stone brook and McLeod brook, U concentrations in sediment fine particles were systematically higher in sites downstream existing active gas wells. However, it would be a hasty conclusion to point the shale gas industry responsible for this apparent enrichment: the number of sampled sites downstream gas well remained extremely limited (3 in Kenebecasis

113 Table 6-5. Concentration of Li, Be, Na, Mg, Al, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Zn, As, Rb, Sr, Ag, Cd, Sb, Cs, Ba, La, Ce, Eu, Pt, Pb, Bi, Th and U measured by ICP-MS in sediment at the different sampling locations on the Kennebecasis and Pollett Rivers. Limit Of Detection (LOD) and Limit Of Quantification (LOQ) also indicated for each element (list of abbreviations in Table 6-1).

7Li 9Be 23Na 25Mg 27Al 39K 42Ca 44Ca 45Sc 47Ti 51V 52Cr 55Mn 57Fe 59Co 60Ni 63Cu ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g LOD 0,025 0,021 13 4 41 8 91 75 0,003 1,63 0,44 0,2 0,24 16 0,011 0,4 1,1 LOQ 0,084 0,070 44 12 137 25 302 251 0,011 5,45 1,46 0,5 0,81 52 0,038 1,4 3,6 <63 µm 50,1 1,5 9127 6408 47263 14196 11449 11579 10,1 5273 91,9 59,0 380 27983 10,4 26,9 28,3 MP1-1 total 30,0 1,1 11699 4068 42566 14871 8005 8241 6,6 3075 51,6 26,7 254 15634 5,5 12,5 13,6 <63 µm 51,5 1,6 9429 6796 54664 14146 11989 11914 10,9 5156 89,8 56,9 379 27248 10,6 25,4 25,9 MP1-2 total 29,0 1,1 11767 4130 44749 15509 8027 8164 6,9 3095 50,1 27,2 281 15452 5,5 12,2 12,2 <63 µm 49,0 1,5 8970 6816 56526 14422 11302 11381 11,7 5271 93,8 55,0 375 27506 10,2 25,1 23,7 MP1-3 total 27,8 1,0 11283 4035 43906 15857 7677 7881 6,7 3025 50,3 25,9 253 15235 5,4 12,3 12,2 <63 µm 17,2 1,5 19523 20353 61730 12481 17354 17062 14,1 4272 105,3 211,1 2118 46386 32,1 100,4 60,1 KB1-1 total 11,5 0,9 16053 15077 45840 13656 17467 17291 11,2 2759 76,6 163,6 804 33077 14,9 65,2 17,0 <63 µm 15,7 1,3 18286 18904 57470 11563 15930 15739 13,4 4156 103,1 184,0 1809 44036 29,3 85,6 35,2 KB1-2 total 9,5 0,9 17778 11545 49611 14021 14864 14856 9,7 2822 73,5 79,8 702 27269 13,1 43,5 12,0 <63 µm 17,9 1,5 21331 22107 67885 13222 18338 18024 15,1 4502 107,8 207,3 2223 51820 35,5 105,7 63,2 KB1-3 total 12,1 1,2 21406 14773 54149 14267 19437 19304 11,6 3660 85,8 202,6 918 36692 17,2 63,4 19,1 <63 µm 82,8 2,1 6910 19079 73307 22961 6775 6873 15,9 4491 126,3 80,8 689 36760 12,4 40,3 37,1 TC1 total 51,1 1,4 11209 14318 63706 18529 9819 10083 12,9 4302 110,9 56,5 791 33246 11,1 29,3 21,2 <63 µm 59,2 2,2 12538 12276 71377 22297 6562 6880 13,7 4807 94,4 61,6 1279 40269 13,1 34,9 39,7 TC2 total 25,7 1,1 14469 5664 47398 18951 5661 5933 7,2 2702 47,5 25,1 573 21881 6,1 13,2 11,8 <63 µm 44,4 1,5 13346 11005 61479 18169 9843 9977 13,0 5141 99,1 61,3 954 38379 12,0 32,8 45,6 TC3 total 22,4 1,0 14171 6036 47547 16206 8808 9103 8,2 3584 74,2 26,6 525 27264 6,9 13,8 13,8 <63 µm 27,1 1,7 17349 20138 73617 15827 17258 17174 15,3 4582 117,0 188,4 1937 49896 24,3 83,2 38,4 KB2 total 17,1 1,1 16557 11361 56745 16110 14308 14321 11,4 3755 93,4 78,1 743 34969 13,2 36,1 20,9 <63 µm 29,8 1,6 11069 10805 59985 13529 13514 13428 13,1 4588 97,5 145,7 1457 39643 17,4 49,0 33,7 KB3 total 16,0 0,9 9134 5782 40241 13560 7835 7963 6,7 2613 53,4 45,0 461 21917 7,3 19,7 11,8 <63 µm 30,2 1,3 8327 7860 52864 12401 9141 9181 11,1 4887 83,3 96,7 663 32508 12,0 36,2 29,9 KB4 total 18,5 0,9 8454 5232 41259 12546 7637 7722 7,4 3186 58,3 50,6 446 25769 7,9 21,1 16,8 <63 µm 32,1 1,4 12162 8986 61281 14402 12480 12667 12,7 4836 100,5 101,8 922 39769 14,1 42,8 36,7 KB5 total 14,8 0,9 11834 4940 42391 15581 9047 9194 6,6 2452 55,5 34,6 412 21467 6,7 16,4 11,1 <63 µm 34,2 1,6 10034 8038 57711 14073 9907 10149 12,2 5204 93,9 102,0 443 37860 12,4 36,2 33,3 KB6 total 14,7 0,7 8688 3721 35778 13470 5689 5836 5,5 2251 38,3 33,8 210 14237 4,9 13,0 9,1 <63 µm 30,1 1,2 10918 7052 52810 13588 10498 10625 12,1 5485 85,7 96,0 927 32546 11,8 32,1 31,6 KB7 total 12,1 0,6 8361 2949 31244 13119 4506 4640 4,4 1864 31,3 25,0 261 11457 4,1 10,4 6,6 <63 µm 35,9 1,4 9534 7470 55663 13166 9407 9591 12,2 5183 87,9 82,2 664 34501 11,6 30,2 24,3 KB8 total 15,4 0,7 8540 3509 34439 12750 5459 5587 5,3 2161 37,3 26,1 257 13563 4,7 11,0 8,4 <63 µm 39,3 1,5 3791 4327 45132 10867 6151 6115 8,9 4473 61,3 44,7 1635 27601 12,9 26,3 23,6 ST1 total 20,4 0,9 5106 2852 31122 10585 4989 5011 5,0 2195 34,8 21,8 1056 15166 6,9 13,3 11,4 <63 µm 27,6 1,2 11601 6217 50941 14512 10553 10744 9,4 4083 77,0 45,4 4592 29884 12,4 28,9 31,2 ST2 total 12,9 0,8 11191 3409 35430 13791 5148 5280 4,4 1693 32,9 14,9 1066 13226 4,9 9,5 7,2 <63 µm 54,7 1,3 13657 7244 54085 14323 8391 8676 11,1 4664 80,0 47,0 1341 29302 9,7 21,4 22,2 MB01 total 34,2 1,0 18842 5464 53129 18109 7995 8290 7,7 3115 59,5 26,8 729 23174 7,2 14,0 12,3 <63 µm 52,8 1,5 10583 7024 52039 12641 11873 12041 11,2 4204 86,0 49,0 1150 31974 10,7 24,4 30,6 MB02 total 35,7 1,1 13865 5916 47443 14461 12200 12333 8,5 3407 69,3 33,5 806 25476 8,4 16,5 19,2 <63 µm 35,7 2,2 11080 7908 64819 14691 10457 10744 13,4 4232 91,4 46,8 1512 33245 12,4 25,4 32,2 SB1 total 19,0 1,3 13123 6156 50977 17278 8914 9173 7,9 2834 61,2 20,3 556 22537 7,1 11,6 14,3 <63 µm 24,5 1,5 13796 11643 65817 14700 15325 15320 12,6 4194 97,6 82,5 1711 39276 16,1 43,8 33,4 SB2 total 14,6 1,0 17161 7960 53131 14519 10465 10536 8,0 2765 61,2 24,6 674 24773 8,9 19,1 12,0 <63 µm 35,7 1,7 2329 4663 81659 15419 3766 3876 16,9 5633 121,0 113,9 426 44857 11,5 37,6 27,2 SB3A total 22,7 1,2 5336 4044 53825 12777 4397 4524 10,3 3573 86,7 63,8 331 34514 8,2 23,3 14,9 <63 µm 44,4 1,7 5607 5432 54151 12173 8315 8195 11,3 3907 69,0 73,7 1494 31306 11,2 36,1 23,8 CB02 total 25,3 1,0 6097 3690 38027 12317 6301 6253 6,7 2437 42,8 41,1 800 17440 6,8 20,3 10,8 <63 µm 14,8 1,2 23135 15683 70395 13687 28526 28510 15,6 4702 133,4 71,1 976 49213 14,8 35,0 41,4 PL1 total 9,5 1,1 23525 10984 65114 14014 31312 31169 12,4 4158 121,7 44,6 832 40417 13,0 23,9 15,9 <63 µm 21,9 1,6 21380 11498 64629 17409 17774 17883 12,5 4220 100,7 55,6 891 34871 13,9 30,3 42,2 PL2 total 12,2 1,2 23069 7751 56540 16586 16249 16365 8,2 2572 70,8 30,3 510 24278 8,0 14,9 12,8 <63 µm 23,7 1,6 18657 11907 65156 16865 19861 19758 13,9 4827 113,1 57,2 1021 38879 15,3 33,5 42,8 PL3 total 13,9 1,0 18780 8071 53763 16608 15930 16012 8,9 2942 75,1 28,9 519 25059 8,5 15,0 13,5 <63 µm 36,3 1,7 12457 11171 62093 18638 10208 10328 12,0 5162 92,6 54,6 1362 37902 14,2 33,9 53,8 PL4 total 16,3 0,9 11514 5884 41618 15737 7150 7387 6,4 2647 50,0 47,4 488 18976 6,7 13,7 14,3 <63 µm 42,3 1,6 7272 6172 56456 13674 5851 5888 10,6 4982 72,2 51,1 584 23853 10,1 28,2 18,6 PL5 total 23,0 0,9 5396 3106 33956 11760 3182 3202 5,4 2395 36,9 23,1 296 12538 5,3 13,5 8,2 <63 µm 20,8 1,2 12965 7521 53761 14921 11065 11137 9,8 3803 71,6 37,2 630 24863 8,4 17,9 13,8 PL6 total 15,6 0,9 15615 7047 50938 15526 10740 10930 7,7 2650 59,5 35,4 495 21109 7,3 14,3 10,9

114 Table 6-5. Cont’d. 66Zn 75As 85Rb 88Sr 107Ag 111Cd 121Sb 133Cs 135Ba 139La 140Ce 153Eu 192Pt 206Pb 209Bi 232Th 238U ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g ug/g LOD 4,9 0,020 0,030 0,29 0,002 0,004 0,004 0,001 0,201 0,010 0,023 0,001 0,13 0,03 0,001 0,003 0,002 LOQ 16,3 0,065 0,098 0,98 0,005 0,013 0,013 0,005 0,671 0,034 0,077 0,002 0,43 0,11 0,003 0,010 0,008 <63 µm 101,7 14,9 67,6 109,0 0,1 0,3 1,0 4,2 400,5 31,0 69,4 1,3 0,5 22,4 0,2 6,5 4,1 MP1-1 total 53,9 7,3 59,2 99,8 0,1 0,1 0,7 2,5 349,3 17,0 35,9 0,7 0,2 13,2 0,1 4,0 1,9 <63 µm 98,4 15,0 65,3 110,9 0,1 3,1 0,9 4,1 392,8 29,9 66,0 1,3 0,6 22,0 0,2 6,5 4,0 MP1-2 total 51,6 7,4 58,5 99,3 0,1 0,6 0,7 2,4 351,5 15,9 33,9 0,7 0,2 12,4 0,1 3,8 1,8 <63 µm 105,8 15,1 66,8 109,2 0,1 0,3 0,9 4,3 393,9 30,1 67,1 1,2 0,5 21,0 0,2 6,6 3,9 MP1-3 total 51,5 7,0 58,6 98,0 0,1 0,1 0,6 2,4 350,6 16,3 33,9 0,7 0,2 12,3 0,1 3,7 1,7 <63 µm 206,9 4,4 49,9 137,4 0,1 1,1 0,3 1,4 350,3 18,9 46,9 1,1 0,4 44,0 0,1 5,8 1,3 KB1-1 total 72,7 4,7 41,2 140,9 0,1 0,4 0,2 0,8 338,2 27,2 33,4 0,9 0,2 18,9 0,1 3,2 1,0 <63 µm 136,8 3,9 45,2 122,9 0,1 0,9 0,2 1,2 318,7 17,0 41,7 1,0 0,3 35,9 0,1 4,9 1,2 KB1-2 total 63,2 2,2 46,4 142,3 0,1 0,3 0,2 0,9 374,0 13,4 28,1 0,8 0,2 14,7 0,1 3,3 0,8 <63 µm 212,4 4,4 52,1 142,8 0,1 1,1 0,3 1,5 359,5 20,0 52,0 1,2 0,4 45,4 0,1 6,1 1,3 KB1-3 total 80,4 3,4 42,8 160,8 0,1 0,4 0,2 0,8 356,4 14,9 33,2 0,9 0,3 19,0 0,1 3,9 1,1 <63 µm 117,3 10,7 112,4 78,2 0,1 0,4 0,8 7,6 424,2 26,2 56,0 1,1 0,2 18,0 0,2 6,6 2,1 TC1 total 77,1 6,6 79,5 123,3 0,1 0,2 0,5 4,7 381,2 19,8 41,5 0,9 0,2 11,8 0,1 5,1 1,4 <63 µm 141,4 15,0 95,4 105,1 0,1 0,5 0,9 5,1 514,8 29,6 69,9 1,3 0,4 27,0 0,2 8,1 2,4 TC2 total 62,9 5,5 63,1 83,8 0,1 0,2 0,5 2,3 424,3 13,6 29,6 0,7 0,3 14,3 0,1 4,3 1,2 <63 µm 117,9 12,3 73,3 125,2 0,1 0,3 0,9 3,5 433,6 29,1 65,2 1,3 0,5 21,1 0,2 7,6 2,4 TC3 total 56,8 6,9 54,4 113,0 0,1 0,1 0,6 1,8 371,5 17,4 36,1 0,8 1,9 13,5 0,1 4,1 1,2 <63 µm 148,5 9,3 64,3 158,2 0,1 1,1 0,4 2,4 455,7 21,6 52,6 1,2 0,4 25,1 0,1 5,8 1,8 KB2 total 74,1 5,8 49,2 143,6 0,1 0,3 0,3 1,3 376,3 17,7 31,7 0,9 0,3 13,5 0,1 3,5 1,0 <63 µm 109,9 9,8 60,2 116,4 0,1 0,8 0,4 2,6 375,8 25,8 60,6 1,3 0,4 21,3 0,1 5,3 1,9 KB3 total 46,9 4,7 48,9 85,3 0,1 0,2 0,3 1,4 284,7 15,3 35,7 0,7 0,2 10,8 0,1 3,2 1,0 <63 µm 87,8 6,7 60,6 98,8 0,1 0,4 0,4 2,8 339,9 25,3 57,8 1,0 0,4 19,0 0,1 6,1 2,0 KB4 total 52,1 14,3 51,3 87,0 0,1 0,2 0,5 1,8 286,2 18,7 40,4 0,8 0,2 13,7 0,1 4,1 1,3 <63 µm 107,4 9,1 63,4 136,2 0,1 0,4 0,5 2,4 397,8 29,5 70,1 1,4 0,4 18,9 0,1 7,3 2,4 KB5 total 43,1 3,8 51,4 107,5 0,1 0,1 0,3 1,2 361,8 14,6 32,3 0,7 0,2 9,9 0,1 3,8 1,1 <63 µm 108,6 7,6 61,4 127,3 0,1 0,4 0,5 2,8 429,7 30,7 71,5 1,3 0,5 20,8 0,1 8,0 3,4 KB6 total 38,2 3,0 47,5 80,0 0,1 0,1 0,3 1,3 278,5 16,3 34,9 0,7 0,2 9,0 0,0 3,4 1,1 <63 µm 97,2 6,7 56,2 129,2 0,1 0,3 0,5 2,3 450,2 31,4 71,0 1,3 0,9 18,6 0,1 8,3 3,8 KB7 total 31,7 2,8 45,8 69,7 0,1 0,1 0,3 1,1 282,7 14,6 29,6 0,6 0,2 8,3 0,0 3,1 1,0 <63 µm 89,1 7,4 58,5 123,8 0,1 0,4 0,5 3,0 491,2 28,3 63,1 1,3 0,6 18,9 0,1 7,4 4,0 KB8 total 33,2 2,7 45,5 78,2 0,1 0,1 0,3 1,3 292,7 14,5 30,7 0,6 0,2 8,9 0,1 3,5 1,4 <63 µm 95,5 7,3 59,5 59,6 0,1 0,5 0,5 3,3 392,3 28,3 67,5 1,2 0,5 16,8 0,1 7,0 2,5 ST1 total 49,9 3,8 45,1 53,2 0,1 0,2 0,3 1,7 299,9 16,0 36,8 0,7 0,2 9,5 0,1 3,6 1,3 <63 µm 103,6 7,9 55,5 119,9 0,1 1,9 0,5 2,1 652,5 21,9 53,1 1,1 0,3 15,0 0,1 6,5 3,6 ST2 total 34,2 2,7 45,3 73,2 0,1 0,1 0,3 1,2 361,6 10,4 22,9 0,5 0,1 7,4 0,0 3,2 1,0 <63 µm 83,1 8,5 61,8 116,1 0,1 0,4 0,5 2,9 602,2 27,0 57,6 1,3 0,5 19,2 0,1 6,8 2,4 MB01 total 59,2 5,9 59,6 121,5 0,1 0,2 0,4 1,9 525,5 15,0 30,6 0,8 0,3 14,1 0,1 4,8 1,5 <63 µm 140,2 7,8 56,6 147,0 0,1 0,5 0,5 3,3 566,3 30,7 61,9 1,6 0,4 21,2 0,2 6,2 4,6 MB02 total 100,0 5,7 54,1 157,8 0,1 0,4 0,4 2,4 506,7 23,0 45,1 1,2 0,3 16,4 0,1 4,8 3,2 <63 µm 121,2 9,3 69,1 169,0 0,1 0,4 0,5 3,3 518,9 32,3 83,9 2,0 0,4 22,5 0,2 8,1 3,6 SB1 total 55,4 5,4 58,4 140,9 0,1 0,1 0,3 1,7 437,4 25,2 41,3 0,8 0,3 11,6 0,1 4,6 1,5 <63 µm 98,6 6,8 62,7 183,7 0,1 0,4 0,4 2,6 467,6 25,4 58,1 1,2 0,4 17,7 0,2 7,5 2,1 SB2 total 51,0 3,7 48,9 134,6 0,1 0,1 0,3 1,3 371,8 13,9 30,5 0,8 0,2 9,7 0,1 4,3 1,1 <63 µm 57,0 9,9 80,3 61,7 0,1 0,2 1,3 5,8 274,4 31,3 70,4 1,2 0,5 18,6 0,3 10,4 3,1 SB3A total 38,8 8,3 55,8 67,2 0,1 0,1 1,0 3,1 249,6 20,2 43,4 0,8 0,3 14,9 0,2 6,9 2,0 <63 µm 94,1 9,0 70,8 65,6 0,1 0,7 0,4 3,9 332,4 29,9 79,4 1,5 0,3 20,7 0,1 7,3 2,0 CB02 total 52,5 4,6 54,1 61,9 0,1 0,4 0,3 2,1 268,2 20,4 50,9 1,0 0,2 12,4 0,1 4,5 1,2 <63 µm 95,7 3,7 48,9 274,9 0,1 0,3 0,2 1,4 409,6 21,2 43,1 1,1 0,3 16,0 0,1 6,1 1,9 PL1 total 58,4 2,9 40,9 341,4 0,0 0,2 0,2 0,7 438,9 18,6 37,1 1,1 0,2 13,0 0,1 2,8 1,2 <63 µm 101,3 5,5 66,7 196,4 0,1 0,3 0,4 2,3 468,2 20,9 44,9 1,0 0,4 16,1 0,1 7,5 2,0 PL2 total 53,1 2,6 50,9 202,4 0,1 0,1 0,3 1,2 436,1 13,5 26,5 0,7 0,1 10,7 0,1 3,4 1,1 <63 µm 110,0 7,1 67,4 202,6 0,1 0,3 0,5 2,9 464,3 24,6 53,1 1,2 0,5 20,1 0,2 8,3 2,4 PL3 total 50,4 3,3 55,5 189,5 0,1 0,1 0,3 1,5 432,0 15,4 31,3 0,7 0,2 11,0 0,1 4,0 1,2 <63 µm 129,9 15,4 76,0 133,6 0,1 0,5 0,6 3,4 496,7 29,7 66,2 1,3 0,5 32,7 0,2 8,3 2,7 PL4 total 46,4 5,8 52,7 116,7 0,1 0,2 0,3 1,6 372,5 15,6 32,3 0,7 0,2 108,1 0,1 3,8 1,1 <63 µm 114,4 7,7 76,1 74,1 0,2 1,2 0,5 3,8 375,5 28,5 61,3 1,2 0,5 19,4 0,1 6,7 2,4 PL5 total 54,9 4,0 52,1 47,5 0,1 0,6 0,3 1,9 262,8 16,8 34,5 0,7 0,2 10,4 0,1 4,3 1,2 <63 µm 60,8 6,1 60,2 144,0 0,1 0,2 0,4 2,2 368,7 23,1 51,4 1,1 0,3 13,4 0,1 4,6 1,5 PL6 total 49,5 3,6 54,7 150,3 0,1 0,1 0,3 1,5 391,5 21,1 39,0 0,7 0,2 10,9 0,1 3,5 1,0

River, 1 in stone brook and 1 in McLeod brook) and the range of U natural background for these rivers undetermined. A larger survey focusing on the Kenebecasis River should be conducted multiplying sampling sites upstream and downstream existing gas well locations. A proper value for U background concentration in sediment from the Kenebecasis River could be determined and a threshold defined to statiscally identify anomalous values (Reimann et al. 2005). A plot showing the measured concentration vs. distance downstream, starting upstream of the gas field and continuing downstream could be therefore establish to assess any effect of shale gas exploitation on U in sediment. As Ra has been previously identified as an indicator of sediment contamination from shale gas activities (Warner et al. 2013, Vengosh et al. 2014, Torres et al.

115 2016), its concentration should be measured in the same samples. Alternatively, if a depositional zone is clearly identified in the Kenebecasis River adjacent to McCully gas field area, a sediment core may be sampled, sliced, dated by radiometric method (i.e., 210Pb) and U and Ra concentration profiles with depth measured. If the enrichment of Ra and U in the fine particles of the dated sediment core start at the same period as shale gas exploitation have started in the area, contamination and impact of this industry would be demonstrated.

a b 4.8

3.2

[U] in µg/g 1.6 reference site site downstream gas well

0.0 Pollet River Trout Creek Stone Brook Sampling site Kenebecasis South Branch South McLeod Brook Oil and gas well Calamingo Brook Calamingo locations

Figure 6-3 a) Gas well locations and sediment sampling sites map from the Canadian Rivers Institute’ online data portal developed as part of this program and b) U concentration in sediment fine particles for each river system. Reference sites were defined as upstream locations from existing and active gas well.

a 390 b 1.13

370 1.11 U Ti 47 235 Ti/ U/ 46 238 350 1.09 reference site reference site site downstream gas well site downstream gas well

330 1.07 Pollet River Pollet River Trout Creek Trout Creek Stone Brook Stone Brook Kenebecasis Kenebecasis South Branch South Branch South McLeod Brook McLeod Brook Calamingo Brook Calamingo Brook Calamingo

Figure 6-4 Isotopic signatures for a) U (238U/235U) and b) Ti (46Ti/47Ti) in sediment from the selected rivers. Reference sites were defined as upstream locations from existing and active gas well.

116

Isotopic signatures were determined for U (238U/235U) and Ti (46Ti/47Ti). U isotopic ratio varied largely among and within the selected rivers (figure 6-4). The larger range of 238U/235U ratio (from 357 to 383) was observed in sediments from the Pollet River. However, for the Kenebecasis River and Stone Brook, an enrichment of 238U may be highlighted between the sites located upstream and downstream producing gas well. Due to the limited number of sampling sites located downstream gas well, statistical tests were not run to confirm such difference. Yet, this isotopic approach appears to be promising to investigate potential environmental impact of shale gas on sediment and should be included in the larger monitoring program focusing on the Kenebecasis River proposed aboved. In comparison, variation of Ti isotopic signatures among and within the sampled rivers was limited. No enrichment of 46Ti, the product decay of 46Sc, a tracer potentially added to the fracking fluid was observed among the sediment samples located upstream and downstream producing gas well. A more precise instrument devoted to isotopic signature such as a multi-collector ICP-MS may be needed to pick up difference in Ti ratios. 6.4 Conclusions The accumulation of toxic and radioactive elements in soil and or stream sediments near exploitation, disposal or spill sites for shale gas operations was identified as one of the four potential risks that shale gas operations pose to water resources (Vengosh et al. 2014). This section addressed the risk of accumulation of toxic and reactive elements in soil and stream sediments as part of the shale gas extraction process in the Sussex area, New Brunswick. Water samples collected complied with CCME guidelines for the protection of aquatic life or were not bioavailable. Water samples showed no major pollution from shale gas activities in the sampled rivers. Long life radionuclide and short-lived radionuclide’s daughter elements measured as potential indicators of shale gas exploitation in water suggested a limited impact of the shale gas industry on the water quality for these parameters in fall 2015. However, once sampling locations were compared no statistical differences were observed. The combined dataset provides baseline values and any major deviation for an element from its defined baseline may reflect pollution. The distinction between total and dissolved concentration presents the advantage to discard a potential abnormal contribution from particles to the measured concentration. In order to reinforce these baseline values, temporal trends of these elements should be determined. Once the temporal variabilities are integrated, the baseline values of these potential impact indicators will be more solid.

Sediment samples were in compliance with CCME guidelines for sediment or were demonstrated not likely bioavailable, revealing no major contamination of the selected rivers. For metals contamination potentially related to shale gas exploitation that isotopic approach appears to be promising in investigating potential environmental impacts on sediment and should be included in the larger monitoring program. The instruments and methodologies used to detect metals from hydraulic fracturing may influence detection and interpretation of results. We suggest the use of a complete digestion of samples to highlight any abnormalities in the sediment metallic composition potentially related to shale gas activities as well as the use of precise instruments devoted to isotopic signature such as a multi-collector ICP-MS.

117 7 Project D: An assessment of a stream- based methane monitoring method 7.1 Introduction Monitoring of dissolved methane in streams (herein referred to as stream methane) has recently been proposed (Heilweil et al. 2013, Heilweil et al. 2015) as a means to estimate dissolved methane concentrations in groundwater discharge and as an approach to detect changes in groundwater methane concentrations related to shale gas extraction (Osborn et al. 2011). In geographic regions where groundwater discharge contributes significantly to stream flow (gaining streams), increases in stream methane concentrations near hydraulic fracturing or shale gas extraction sites could be indicative of stray gas migration along vertical faults in the ground, or improperly constructed well casings (Darrah et al. 2014). An important advantage of this proposed method is that gaining streams may provide an integrated measure of groundwater quality over a relatively large catchment area (Heilweil et al. 2013). The monitoring of stream methane to detect changes in groundwater methane concentrations has not been widely applied, and it requires knowledge of baseline stream methane concentrations and a better understanding of methane losses from streams due to, for example, degassing.

The objectives of this study were twofold. First, given that there have been no previous investigations of dissolved methane in streams in southern New Brunswick, regional baseline concentrations for selected streams were to be established. Second, an in-stream tracer test and data on physical characteristics were to be used to assess methane losses from a reach of a small stream in the study area. The research was intended to provide a better basis for deciding whether the monitoring of stream methane is a viable approach for assessing stray gas migration in areas of natural gas development.

In order to establish regional baseline stream methane concentrations, twenty-four streams in and around the Town of Sussex, NB, were selected to be part of a reconnaissance survey. Based on the results from the reconnaissance survey, four streams were selected for further study. Synoptic surveys consisting of longitudinal methane measurements and water temperature profiles were conducted. One of these four streams, Parsons Brook, was subsequently selected as the location for a tracer test to investigate methane losses. The overall approach used in this study is similar to approaches employed by Heilweil et al. (2013), and Heilweil et al. (2014) in studies in northeastern Pennsylvania and Utah.

7.2 Methods 7.2.1 Reconnaissance Dissolved Methane Survey A reconnaissance survey was conducted to address the study’s first objective, determining baseline concentrations for streams in southern New Brunswick. Consistent with the other project components, the locations for the reconnaissance survey were within the Kennebecasis and Pollett River watersheds. Twenty-four streams, (listed in Appendix C), were initially selected to be part of the survey. These streams were selected because they collectively covered a large portion of the two watersheds, and were expected to be of an appropriate size for subsequent field investigations. Consideration was also given to accessibility and to the presence of a relatively long (>1 km) stretch of stream free of tributaries or other flow changes. Care was also

118

Figure 7-1. Reconnaissance survey sample locations within New Brunswick (insert). Green triangles indicate the reconnaissance survey stream sampling locations, while the shaded pink area is the approximate extent of the McCully Gas Field.

taken to avoid marshes and wetlands because of the potential for biogenic methane production in such areas.

The survey was conducted over two days on July 6 and 7, 2015 during seasonally low-flow conditions. Each site was visited in turn and visually assessed to ensure the stream was of an appropriate size and had not been modified by human activity. During the survey six streams (Kennebecasis River near its source, Dove Hollow Brook, Duncan Brook, Gibson Brook, McGregor Brook, and Pinnacle Brook) were eliminated from the study because they were too small or inaccessible. The final number of streams sampled was therefore reduced to 18 as shown in Figure 7-1.

At each site, a stream methane grab sample was collected in duplicate in 40 mL glass vials, closed with plastic caps. Vials were submerged, and a small pump was used to purge several volumes of water through the vial in order to remove any bubbles clinging to the vial. An injection of 0.67 mL of 6N hydrochloric acid preservative was then added to each vial before capping the vial underwater. Care was taken to ensure that no headspace was present in the bottles.

119 Within seven days of collection, stream samples were analyzed at RPC Laboratories in Fredericton, NB, for dissolved methane, ethane, and propane. The analyses were conducted using a Bruker Gas Chromatograph coupled to a Flame Ionization Detector (GC-FID), according to AQS90. A reporting limit of 1 ppb (0.001 mg/L) was attained.

At each sampling location water quality probes (Orion pH meter, and/or YSI63 and YSI85, YSI Inc.) were used to record six general water quality parameters: pH, dissolved oxygen, temperature, conductivity, specific conductance, and salinity salinity (based on measured conductivity and temperature). A pair of acoustic Doppler velocimeters (Flowtracker, Sontek) was also used to obtain discharge measurements for some of the streams.

7.2.2 Synoptic Surveys Once baseline methane concentrations had been established, three streams with some of the highest methane concentrations were selected for further study. It was hypothesized that since they contained relatively high dissolved methane concentrations, these streams may have had larger groundwater inflows and/or lower methane losses. The three streams were Parsons Brook, Shannon Brook, and McLeod Brook (Figure 7-1).

These streams were examined in more detail as part of a series of synoptic surveys conducted in July 2015 (Parsons first round, McLeod), October 2015 (Shannon), and May 2016 (Parsons second round). Temperature, dissolved methane concentration, general water quality parameters, and discharge were examined along selected reaches of the respective streams in order to produce profiles, which were based on the distance upstream from the point where methane was originally measured in the stream. These distances were converted to chainages (i.e. distance along the stream) based on the most downstream point.

7.2.3 Temperature profiling Given the times of the year during which temperature profiling was conducted, groundwater inflow was expected to be cooler than surface water and would create a drop in water temperatures downstream of any significant inflow location(s). A water level and temperature logger (Levelogger, model 3001, Solinst; stated temperature accuracy of ± 0.05˚C) was mounted to a hockey stick wich was submerged near the middle of the channel while wading upstream along selected reaches of the streams. The logger was programmed to record a reading every five seconds and a handheld GPS receiver, also set to record every five seconds, was carried along with the temperature logger. The result was a temperature profile of the streams, consisting of thousands of points, each paired with a set of GPS coordinates. Given the five second frequency of the readings, it is unlikely that the temperature logger had time to equilibrate with the surrounding water (e.g., 95% response time of approximately 5 min.; Stonestrom and Blasch 2003); however, given the significant distances surveyed (e.g. 2 to 5 km) it is expected that the large-scale spatial trends in water temperature would be adequately resolved.

120 7.2.4 Methane sampling

For the synoptic surveys, stream methane sampling points were selected to approximately correspond to the peaks and troughs of the stream temperature profile in an attempt to determine whether dissolved methane was related to groundwater inflow. Distances between sampling locations thus varied, ranging from 20 to 150 metres. Figure 7-2, Figure 7-3, and Figure 7-4 show the locations of methane sampling points and other features of interest for Parsons, Shannon and McLeod Brooks. In Parsons Brook, a second methane run was performed in 2016 in order to add more points to the methane profile in preparation for the tracer test (described below).

Figure 7-2. Map of locations of interest, Parsons Brook (see Figure 7-1 for location). Water in the brook flows from south to north. The temperature profiling was conducted on July 15, 2015; methane sampling was conducted in July, 2015, and in May, 2016.

121

Figure 7-3. Map of locations of interest, McLeod Brook (see Figure 7-1 for location). Water in the brook flows from south to north. The temperature profiling was conducted on July 16-17, 2015; methane sampling was conducted in August, 2015.

122

Figure 7-4. Map of locations of interest, Shannon Brook. (see Figure 7-1 for location). Water in the brook flows from east to west. The temperature profiling was conducted in July, 2015; methane sampling was conducted in October, 2015.

Stream water samples for dissolved methane, ethane and propane were collected and analyzed as described previously. General water quality parameters (temperature, dissolved oxygen, pH, conductivity, specific conductance, and salinity (based on measured conductivity and temperature)) were also recorded at each methane sampling location.

7.2.5 Discharge Stream discharge was determined at 17 locations along Parsons Brook (Figure 7-2) in order to confirm the presence of gaining reaches. This entailed measurements of water velocity, water depth, and cross-sectional area using acoustic Doppler velocimeters (FlowTracker, SonTek), a standard top-setting wading rod, and velocity-area calculations. At each location two velocimeters were used, one which measures velocity along both horizontal axes, and one with an additional sensor to measure vertical velocity. Both devices provided similar accuracy and uncertainty.

Discharge measurements locations were roughly matched to the methane sampling locations, but occasionally were shifted by a few metres in order to have a better stream gauging location.

123 Discharge was determined on June 20 and 21, 2016; no precipitation occurred during those dates.

7.2.6 In-stream Tracer Test In order to estimate dissolved methane losses from a typical small stream in the study area, a tracer test consisting of an injection of dissolved bromide and methane was undertaken. Bromide served as a conservative tracer, as opposed to methane, which can be lost by evasion to the atmosphere or consumed by bacteria.

In order to perform the dissolved gas injection, a methane tank (7.2 cu.m. of gas at STP, Commercial grade methane) was attached to approximately 100 metres of gas-permeable silicone tubing(13 mm OD, 1.6 mm wall thickness). The loosely coiled tubing was submerged in the stream, with the outflow end of the tube being unsubmerged but partially restricted by a valve. The methane thus diffused through the submerged tubing into the stream. Plastic sheeting was also used to cover the stream surface over the area of the coiled tubing. A constant flow of methane was maintained through the use of a flow regulator, and a second regulator was used to maintain a pressure of 83 kPa within the tubing for the duration of the injection. The equipment setup for the methane injection can be seen in Figure 7-5.

Figure 7-5. Methane injection equipment including silicone tubing (left) and methane gas source and controls (right), September 2016.

Prior to conducting the tracer test a trial methane injection was conducted in a small stream (O’Leary Brook) near Fredericton, NB. This brook was about 20 cm deep, and 120 cm wide downstream of the injection location; using the methane injection system described above, a

124 maximum dissolved methane concentration of 0.286 mg/L was measured 25 m downstream of the injection location after four hours of methane injection. The methane injection equipment setup used in the Sussex-area stream was identical to that used for the O’Leary Brook trial tracer test.

Parsons Brook was chosen for the tracer test because of the relatively high variability in water temperature measured in the upstream section of the stream, and because of easy access. Within the selected reach (see Figure 7-2 for the location of the reach and tracer injection), Parsons Brook is between 100 and 150 cm wide, with a depth ranging from 10 to 20 cm. A discharge of approximately 8 L/s was measured in June 2016. Figure 7-6 shows the stream conditions immediately downstream of the tracer injection location during the tracer test in September 2016.

Figure 7-6. Parsons Brook below the tracer injection location, September 2016.

During the test a small pump was used to inject a potassium bromide solution (350 g KBr/L) at a rate of 5 mL/minute. Both methane and KBr were injected for a period of 24 hours. Water samples and general water quality parameters were collected approximately every hour just upstream of the injection site for the duration of the injection. An ion selective electrode (ISE) sensitive to bromide (ISE) (27504-02, Cole Parmer) was periodically used to obtain bromide measurements downstream of the injection site, allowing the tracer plume to be tracked as it made its way downstream. Stream water samples for bromide analyses were also collected every hour during the tracer test at two locations (374 m downstream of injection, and 773 m downstream of injection) using auto samplers (Model 6712, Teledyne Isco). At the end of the 24-hour injection period stream water samples were collected for methane and bromide

125 analyses at 11 locations, with the furthest location being 423 m downstream from the injection location.

Samples for methane analyses were sent to RPC Laboratories, Fredericton, while the bromide samples were analyzed at UNB, using a Metrohm 761 ion chromatograph (IC), providing a detection limit of 0.0129 mg/L (Lamb 2016).

7.3 Results and Discussion 7.3.1 Reconnaissance Survey Table 7-1 summarizes the methane concentrations for the 18 streams sampled in the Kennebecasis and Pollett River watersheds (locations shown in Figure 7-1), and shows that 10 of the 18 had dissolved methane concentrations above the detection limit of 0.001 mg/L. During sampling, a spring was found just next to McLeod Brook site 2 and a sample was also taken at that location, as shown in Table 7-1.

Table 7-1. Stream methane concentrations from the reconnaissance survey, July 2015,. McLeod Brook Spring denotes a spring found during sampling which was also sampled

Stream name Methane Stream name Methane (mg/L) (mg/L) Parlee Brook <0.001 Mapleton 0.001 Brook Parsons Brook 0.011 Calamingo <0.001 Brook Wards Creek 0.002 Higgins Brook <0.001 Shaffer Brook 0.001 Montgomery <0.001 Brook Shannon Brook 0.003 Webster <0.001 Brook Miller Brook <0.001 Dobson Brook 0.018 McLeod Brook 1 0.004 McCarthy <0.001 Brook McLeod Brook 2 0.003 Hawkes 0.004 Brook McLeod Brook 0.002 South Branch <0.001 Spring Negro Brook 0.001

None of the stream water samples contained dissolved propane or ethane, which are more often associated with thermogenic natural gas, above the detection limit of 1 ppb (0.001 mg/L). The other water quality results obtained during the reconnaissance survey are presented in Table C-1 in Appendix C. At the time of sample collection, water temperature ranged from a low of 6.7 oC at McLeod Brook Spring to 20.8 oC at Wards Creek, while pH ranged from 6.25 at Calamingo Brook to 8.39 at Wards Creek. The largest stream discharge was measured at Wards Creek, with a discharge of 0.443 m3/s, but most discharge values were between 0.139 m3/s, measured at South Branch, and 0.031 m3/s, measured at Mapleton Brook.

126 7.3.2 Synoptic Survey The synoptic surveys were conducted at four stream sites, which were selected based on the results of the reconnaissance survey.

7.3.2.1 Temperature profiles Figure 7-7 shows the longitudinal water temperature profiles collected in McLeod Brook, Parsons Brook, and Shannon Brook. In general McLeod Brook exhibited the coolest water temperatures (data collected on July 16 and 17 2015), with the smaller and shallower Parsons Brook having the warmer temperatures (data collected on July 15, 2015).

While Shannon Brook exhibited gradual warming in the downstream direction, likely indicating very little groundwater inflow, Parsons Brook showed significant decreases in temperature between approximately 1700 m and 500 m (Figure 7-7), which is suggestive of groundwater inflow. From 3000 m to about 500 m, McLeod Brook tended to follow the same gradual warming trend as Shannon Brook. However, McLeod Brook is much larger (discharge of 0.332 m3/s for McLeod, compared to 0.052 m3/s for Shannon) and it did exhibit a significant temperature decline of about 5°C around 3400 m (Figure 7-7).

25

Parsons 20 McLeod Shannon C) o 15

10 Temperature ( Temperature

5

0 0 1000 2000 3000 4000 5000 Chainage (m)

Figure 7-7. Temperature profiles for Parsons Brook, McLeod Brook, and Shannon Brook (July 2015). The downstream flow direction is to the left of the plot. Maps showing the locations of the temperature profiles are shown in Figure 7-2, Figure 7-3, and Figure 7-4.

7.3.2.2 Dissolved methane Parsons Brook was sampled twice, in July 2015 and again in May 2016. As Figure 7-8 demonstrates, methane concentrations in Parsons Brook follow a similar pattern in both years, even overlapping for a significant portion of the sampled reach. In addition, an inverse relationship can be observed between methane concentrations and temperature, with the former increasing as the latter falls, going from upstream to downstream.

127

21 0.014

20.5 0.012 Temperature 20 Methane - 2015 0.010 19.5 Methane - 2016 0.008 19 0.006 18.5 Temperature (C) Temperature 0.004 18 Methane Concentration (mg/L) Concentration Methane 17.5 0.002

17 0.000 0 500 1000 1500 2000 Chainage (m)

Figure 7-8: Methane concentrations (July 2015, June 2016) and temperature profile (July 2015) for Parsons Brook. The downstream flow direction the left of the plot. The locations of the methane sampling sites are shown in Figure 7-2.

Table C-2 in Appendix C shows the data collected from the first methane sampling event in Parsons Brook, while Table C-3 provides a full breakdown of the results from the second event.

On August 18, 2015, sampling was performed in McLeod Brook. McLeod Brook had smaller longitudinal variations in water temperature than Parsons Brook (see Figure 7-7), and so fewer methane samples were taken. Figure 7-9 shows that methane concentrations in McLeod Brook were very low. Only one of eight samples was above the detection limit for methane (0.001 mg/L), while no samples contained detectable ethane or propane. Table C-4 presents the methane sampling results as part of Appendix C.

A methane sampling event was also performed in Shannon Brook in October 2015. Figure 7-10 presents the results from Shannon Brook. Methane concentrations in Shannon Brook were also very low, with four of six samples at or below the detection limit. Table C-5 in Appendix C contains the full data results.

128 14 0.006 Temperature Methane - 2015 12 0.005

10

C) 0.004 o 8 0.003 6

Temperature ( Temperature 0.002 4 Methane Concentration (mg/L) Concentration Methane 2 0.001

0 0.000 0 1000 2000 3000 4000 5000 6000 Chainage (m)

Figure 7-9: Methane concentrations (August 2015), and temperature profile (July 2015) for McLeod Brook. The downstream flow direction is toward lower chainage. The locations of the methane sampling sites are shown in Figure 7-3.

14 0.0045

0.004 12 0.0035 10 0.003 C) o 8 0.0025 Temperature 6 0.002 Methane - 2015 Temperature ( Temperature 0.0015 4

0.001 (mg/L) Concentration Methane 2 0.0005

0 0 0.00 500.00 1000.00 1500.00 2000.00 2500.00 3000.00 Chainage (m)

Figure 7-10: Methane concentrations (October 2015) and temperature profile (July 2015) for Shannon Brook. The downstream flow direction is to the left of the plot. The locations of the methane sampling sites are shown in Figure 7-4.

129 7.3.2.3 Discharge Discharge in Parsons Brook was measured at a total of seventeen cross sections in June 20 and 21, 2016. Figure 7-2 shows the locations of these cross sections, while Figure 7-11 shows a profile of discharge, as related to distance upstream. In general, the discharge is increasing in the downstream direction, which is expected and supports the hypothesis that Parsons Brook is a gaining stream within this reach. In Figure 7-11, the 2D and 3D series correspond to the two different velocity meters used, while the error bars represent the associated uncertainty for each. In this reach Parsons Brook has one tributary, of unknown (i.e. unmeasured) discharge located at approximately 440 m, which likely accounts for the step increase in discharge around 400 metres. All other variations in the discharge are believed to be related to groundwater discharge. The full results can be consulted in Table C-6, in Appendix C. 0.016

0.014

3D 0.012 2D

0.01 /s) 3

0.008

Discharge (m 0.006

0.004

0.002

0 0 500 1000 1500 2000 2500 Chainage

Figure 7-11: Parsons Brook discharge, June 2016. The downstream flow direction is to the left of the plot. The locations of the stream discharge measurements are shown in Figure 7-2.

7.3.2.4 In-stream tracer test Figure 7-12 shows the longitudinal profile of bromide in Parsons Brook after 24 hours of injection, as well as the baseline (pre-test) bromide concentrations. Although the bromide concentrations are clearly above the baseline up to 400 m downstream of the injection location, steady-state concentrations (as estimated by the ISE and autosampler bromide results, presented next) were likely only achieved within 50 m of the injection site. A full presentation of the bromide data is available in Table B-ss in Appendix B.

130

35

30

25

20

15 Concentration (mg/L) Concentration

10

5

0 -200 0 200 400 600 800 1000 1200 1400 1600 Distance downstream of injection site (m)

Figure 7-12: Parson Brook bromide concentrations, pre- and 24 hours post-tracer test, September 6-7, 2016. The downstream flow direction is to the right of the plot.

0.014

0.012

0.01

0.008

0.006 Conc. (mg/L) Conc. 0.004

0.002

0 -5 0 5 10 15 20 25 Time (h)

Figure 7-13: Temporal variability of methane concentration 25 m upstream of injection site on Parsons Brook (September 6-7, 2016)

131 Measurements were made with the bromide selective electrode periodically throughout the test, and are available in Table C-7, in Appendix C. The bromide samples collected by the two autosamplers did not exhibit significant increases compared to the baseline concentrations. It is apparent that the bromine tracer did not reach the autosampler located 773 m downstream of the injection, while it may have just reached the autosampler at 374 m near the end of the 24-hr test. Both autosampler data sets are given in Table C-8, in Appendix C.

The hourly methane samples collected 25 m upstream of the injection site provide an indication of the natural temporal variability of the stream methane concentrations, and are shown in Figure 7-13. The methane concentrations for this single point in the brook varied from 0.006 mg/L to 0.012 mg/L, demonstrating that methane concentrations were not temporally constant. The average methane concentration over 24 h in the water entering the tracer test reach was .0097 mg/L.

General water quality parameters were also measured periodically at the same upstream location as the methane. Table 7-2 presents the average values obtained, while a full description of the time series results is given as part of Table C-9, in Appendix C.

Table 7-2: Average water quality parameters, for a 24-hr period, at a location 25 m upstream of the tracer injection location, Parsons Brook.

Parameter Value pH 6.93 Dissolved Oxygen (%) 77.5 Dissolved Oxygen (mg/L) 7.22 Conductivity (µS/cm) 311.9 Specific Conductance (µS/cm) 353.2 Salinity (ppt) 0.2 Temperature (°C) 18.9

Figure 7-14 shows the baseline methane concentrations and concentrations after 24 hours of injection. Compared to bromide, the methane concentrations did not increase significantly as a result of the injection, even for sampling locations within 100 m of the injection site. Only the station located at 25 m downstream from the injection site recorded an increase in methane potentially related to the injection. The increase in methane concentration observed at the two most downstream locations (373 and 423 m downstream) cannot be explained by the tracer test, and is believed to represent natural temporal variability. The full results, available in Table C-10 in Appendix C, show that methane concentrations vary by up to a factor of four.

132 0.06

0.05 Baseline Methane Concentration 0.04 Post Injection Methane 0.03 Concentration

0.02 Concentration (mg/L) Concentration

0.01

0 -100 0 100 200 300 400 500 600 Distance downstream of injection site (m)

Figure 7-14: Methane concentrations, Parsons Brook September 6-7 2016.

Several stream discharge measurements were also taken during the tracer test, indicating very low discharge with high uncertainties. Table 7-3 summarizes these results.

Table 7-3: Discharge results, Parsons Brook, September 7 2016.

Distance Mean Absolute downstream Point Discharge Uncertainty of injection (m3/sec) (m3/sec) site (m) Parsons 37 0.0001 0.0001 Brook 2150 Parsons Brook Station 27 0.0001 0.0001 1 Parsons Brook Station 34 0.0005 0.0001 1 Shifted

Table 7-4 compares the stream methane concentrations determined as part of the reconnaissance survey to those reported by Heilweil et al. (2014). For calculations of the mean and median for the data collected during this study, nondetect concentrations were taken as half the detection limit, or 0.0005 mg/L. In the Heilweil et al. (2014) investigation, a series of sixteen streams in Pennsylvania were sampled in an area, roughly the same size as the one in this study, that is also underlain by gas-rich geological formations. The Pennsylvania study area is densely populated with gas wells (approximately 0.26 gas wells/km2 on average), as opposed to the current New Brunswick area which is still largely undeveloped. The streams sampled in

133 the Pennsylvania study were first-order streams, with water temperatures ranging from 15°C to 25°C; the discharges were not recorded.

Table 7-4: Comparison of stream methane concentrations in New Brunswick 2015 (18 streams and one near stream spring) and in Pennsylvania (16 streams; Heilweil et al. 2014).

Methane (mg/L) Pennsylvania New Brunswick Concentration Range <0.001 - 0.0685 <0.001 - 0.018 Mean Concentration 0.0063 0.0028 Median Concentration 0.001 0.001

The most significant difference between the two data sets is the maximum concentration. In Pennsylvania, a maximum concentration of 0.0685 mg/L was measured, while in the Sussex area, the maximum concentration was only 0.018 mg/L, or just over 6 times the mean. While the mean concentration in Pennsylvania was slightly higher, the median value was identical to the detection limit in both data sets. Based on a t-test for sample sizes less than 30, with a level of significance of 90%, there was no difference in the mean concentrations of the two data sets (p=0.1848).

It is important to note that the Pennsylvanian stream with the highest concentration (nearly six times as high as the next greatest concentration) was sampled below a swampy section of stream, which may have been a source of biogenic methane (Heilweil et al. 2014). The same sampling site was also located in an area with high gas-well density, which is a property that Osborn et al. (2011) have suggested may contribute to increases in stream thermogenic methane concentrations. The concentrations of propane and ethane, which were below the detection limit in all the New Brunswick streams, were not reported in the Pennsylvania study.

The results of the more detailed synoptic surveys of three streams indicated that Parsons Brook, unlike Shannon and McLeod brooks, exhibited general cooling in the downstream direction over a distance of approximately 1.5 km, combined with significant (up to approximately 0.01 mg/L) dissolved methane concentrations. The measured discharge over the same segment of Parsons Brook approximately doubled (0.005 to 0.009 m3/sec), indicating a gaining reach (groundwater inflow). These factors suggested that Parsons Brook should have been suitbale for an in-stream tracer test to investigate dissolved methane transport and losses.

Based on the trial injection test performed at O’Leary Brook, dissolved methane concentrations near the tracer injection were expected to reach at least 0.25 mg/L, and persist downstream in Parsons Brook for several hundred metres. However, the methane in Parsons Brook did not persist within the stream, as even the sampling station located 70 m downstream of the injection site showed no appreciable increase in methane concentration. after 24 hours of injection. The possible reasons for this include the very low stream discharge at the time of the test, very high rates of loss (i.e. evasion) from the stream, or lower than expected concentrations at the injection location. The last possibilitiy is considered unlikely as the equipment set up was the same as used during previous trials.

Heilweil et al. (2014) provide empirical equations (originally from Raymond et al. 2012) for estimation of the methane gas transfer velocity: = ( ) . × . × 5037 (1) where: 0 89 0 54 600 K600 is gas transfer𝐾𝐾 velocity𝑉𝑉𝑉𝑉 in freshwater𝐷𝐷 for a gas having a Schmidt number of 600 (m/d)

134 V is stream velocity (m/s) S is stream slope (unitless) D is stream depth (m) = 1898 114.28( ) + 3.29( ) 0.0391( ) (2) where: 2 3 𝐶𝐶𝐶𝐶4 SCCH4 is the Schmidt𝑆𝑆𝑆𝑆 number for− methane𝑇𝑇 𝑇𝑇 − 𝑇𝑇 T is temperature (°C) = (600/ ) (3) where: 𝑛𝑛 n is the Schmidt number𝐾𝐾𝐶𝐶𝐶𝐶4 exponent,𝐾𝐾600 𝑆𝑆which𝑆𝑆𝐶𝐶𝐶𝐶4 ranges from 0.5 to 1 (0.5 used to be consistent with (Heilweil et al. 2014)).

Using these equations, the gas transfer velocity(KCH4) for methane in Parsons Brook is calculated to be 0.36 m/d. Table 7-5 presents a comparison of several key characteristics of streams from Utah (UT) and Pennsylvania (PA), drawn from Heilweil et al. (2014), as well as the computed gas transfer velocities. It is important to note that the discharge in Parsons Brook during the tracer test was the lowest observed in this study, and considerably lower than expected. Raymond et al. (2012) note that for smaller streams, most methods of calculating K600, and therefore KCH4, tend to have higher uncertainties, and correlate less well with observed values.

Table 7-5: Gas transfer velocities and key stream characteristics for several streams in which gas evasion studies have been conducted Stream K600 KCH4 Stream Velocity, Depth, Discharge, (m/d) (m/d) Slope, S V (m/s) D (m) Q (m3/s) (unitless) Parsons Brook 0.38 0.36 0.025 0.0053 0.056 0.00027* Sugar Run, PA 16.5 14.8 0.043 0.15 0.12 0.053 Nine-Mile Creek, 5.8 -- 0.007 0.26 0.12 0.12 UT * Note: This is the average discharge value for Parsons Brook determined on September 7, 2016. This value is much lower than the discharges measured during June 2016, and is included as it represents the discharge on the day that the other parameter values were collected.

The gas transfer coefficient for Parsons Brook is very low, owing in large part to the very low stream velocity during the tracer test. A low gas transfer velocity means methane should persist longer within the stream; however, combined with the low stream velocity, the minimal evasion rate was apparently sufficient to significantly reduce methane concentrations before reaching the downstream sampling locations. The very shallow water depths and relatively high aeration of the flow during the test may also have contributed to higher evasion than predicted by the empirical equations.

7.4 Limitations and recommendations for future work The origin of the stream methane detected within this study could not be established. An analysis of the isotopic signature of methane (stable isopes of C and H) would normally be used to assess whether the methane is of thermogenic or biogenic origin (New York State Water Research Institute 2011). However, in order to perform such analyses a minimum dissolved methane concentration of approximately 0.1 mg/L is typically required (Loomer et al. 2016). The highest concentration of stream methane encountered was only 0.018 mg/L. Further work,

135 including seeking laboratories that may be able to deal with lower dissolved concentrations, would be required to obtain isotopic data that may assist with determining the origin of the stream methane.

The tracer test was only conducted in one section of Parsons Brook and the stream conditions during the test were some of the driest observed during this study. The resulting very low flow conditions may have impacted the ability to monitor methane evasion. As part of future studies, tracer tests should be considered in some of the larger streams in the area, or during periods of higher discharge. By performing tracer tests in a series of streams of varying sizes, a better determination could be made of the rates of methane loss. Future work should also focus on transport modeling of the bromide and methane concentration data from the tracer test to establish if the empirical estimates of gas transfer velocities presented here are consistent with model-calibrated values.

7.5 Conclusions The reconnaissance survey results show that the dissolved methane concentrations in streams within the Kennebecasis and Pollett River watersheds are relatively low. Eight of 19 samples were below the detection limit (0.001 mg/L) for methane, and the highest observed concentration (0.018 mg/L) was low compared to stream methane results from recent studies in Pennsylanania with active shale gas extraction. Dissolved ethane and propane, which are more frequently associated with thermogenic natural gas, were not detected in any of the streams sampled during the reconnaissance survey. Determination of the origin of the methane by analysis of stable isotopes was not possible as current analytical methods typically require that samples contain more than 0.1 mg/L of dissolved methane.

The synoptic surveys of three streams, and in particular Parsons Brook, provided a good overview of the spatial variability of both temperature and methane concentrations, as well as allowing for selection of a site to perform a tracer test. Parsons Brook displayed a temperature profile and stream discharge results indicative of a gaining stream over a reach of approximately 1.5 km. Increases in dissolved methane in Parsons Brook corresponded with decreases in water temperature, suggesting the methane may have been delivered to the stream by groundwater discharge.

The results of the methane tracer test in Parsons Brook were inconclusive. The very shallow water depth in the brook apparently caused dissolved methane to evade before reaching sampling locations within 70 m of the injection point. The spacing of sampling locations required to enable reliable determination of methane evasion for the conditions experienced during the tracer test is likely less than 25 m. In the context of monitoring stream methane to detect changes as a result of natural gas development, more effort would be required to locate streams that would be both large enough to retain methane for a significant distance, yet small enough to have a relatively large component of groundwater discharge. Should suitable gaining reaches of streams be located downgradient of shale gas development/production areas, stream methane monitoring should be considered in conjunction with more established groundwater monitoring methods such as sampling of monitoring wells.

136 8 Project E: ArcGIS online and Story Mapping

In order to manage the amount of data generated within this program, and have it publicly available, the Canadian Rivers Institute’ online data portal was developed (http://canadarivers- gis.maps.arcgis.com; ArcGIS Online). Most data will have some spatial and temporal context, and presenting that data in meaningful, understandable, and accessible ways is always a challenge as the majority of stakeholders do not read primary scientific literature or large reports like as this one.

ArcGIS Online is a secure cloud platform for creating maps, analytics, sharing and storing data in various formats. The platform can be shared within project groups, the organization as a whole, or with the public. ArcGIS Online is an extension of the ArcGIS system developed by ESRI (Environmental Systems Research Institute) and is easily integrated with purchasable software such as ArcGIS for Desktop, ArcGIS Server, etc.

The organizational benefits of ArcGIS Online give the administrator the ability to control a hierarchy of privacy settings and privileges so users and employees can access information as it pertains to their role. The privileges are categorized by user, publisher and administrator or by a custom role.

ArcGIS Online offers a variety of pre-made maps that exceed the general basemap, including population demographics, businesses, landscape, etc. It also allows you to configure your own maps with data created with ArcGIS Desktop software. Beyond basic mapping, it offers the ability to visualize your data in a variety of ways to grasp the viewer’s attention. ArcGIS Online has a variety of 'Apps' that are pre-configured to allow easy manipulation of information whether for navigation, collecting data, performing an analysis, or communication.

For the Surface Water Monitoring project particular apps were used to visually engage the public, share information and present data. The Story Map applications are especially applicable to display project content such as project studies, themes, information, data, maps and photographs. This project uses a Story Map Journal app specifically, that is more narrative based and contains a scrolling panel of information and images related to a large adjacent map of data (Figure 8-1).

137

Figure 8-1. Screen shot of the NBEI Surface Water Monitoring program Story Map introduction with descriptive text in the side panel and larger map. Full current web address is: http://arcg.is/1YnSOKx.

This style is particularly effective in guiding the user to explore the data layers and helps the user to understand the context of the available data. This app can incorporate embedded media (e.g., pictures, videos, websites) within its side panel making it more visually appealing than plain text. ‘Actions’ are also available in the panel, allowing an interactive aspect where users can click on words to zoom in to specific areas or content within the map. For example, in the panel describing landuse shown in Figure 8-2, when the user clicks on each site name, the map window zooms into the drainage basin of that site to shown the land-uses within (Figure 8-3).

Figure 8-2. Panel showing overall land-use within the upper Kennebecasis and Pollett River watersheds.

138

.

Figure 8-3. Landuses within the watershed of site MP1 are displayed when the "MP1 Watershed Landuse" text is clicked on by the user.

The Story map outlines the purpose of the project and displays data collected in the Kennebecasis and Pollett River watersheds. For this research program, the basemap and location remain consistent and the data and descriptions change as you scroll through the side panel. Data can be displayed visually in a variety of ways to highlight the spatial differences within the watershed. For example, differences in average temperature were displayed by using a colour scale with colder sites showing as blue and warmer sites showing as red (Figure 8-4A). Differences can also be shown by the size of the symbol. For example, site with higher conductivity were shown to have larger sized circles than sites with lower conductivity (Figure 8-4B).

A B

Figure 8-4. Differences in temperature displayed by colour scale within the study area (A), and differences in conductivity displayed by the size of the circle (B).

139 Graphs can also be used to display data about each site. For example, in the panel of the story map describing physical characteristics, the substrate data can be explored by clicking on each site to see a pie chart that displays the percent of substrate in each size class (cobble, pebble, gravel, boulder or bedrock) (Figure 8-5).

Figure 8-5. Substrate types displayed in pie charts when the user clicks on each site with results for site KB1 shown.

A bar that can be dragged across the screen is effective in showing the change in a parameter over time at the same location. For example, it was used in the Story Map to show stream methane concentrations sampled in Parson’s Brook in the summer of 2015 versus summer of 2016 (Figure 8-6).

140

Figure 8-6. Sliding bar used to show temporal differences in methane concentrations in Parson's Brook (2015-2016). 8.1 Next steps and future utility ArcGIS online offers a wide range of applications that could be used in future research and monitoring projects.

A large amount of time is spent on data conversion from paper copies to digital format. The ArcGIS online Collector for ArcGIS app can reduce the amount of time spent on that process. This app can be used by a smart phone or tablet to collect and update information in the field whether there is a data connection or not. Updating site-specific information to an app that can be easily integrated with other ArcGIS online apps and converted in to other data formats would benefit project timelines and overall cost.

141 9 Recommendations for Future Monitoring

The baseline conditions described within this report offer a snapshot of the chemical, physical, and biological characteristics of the Kennebecasis and Pollett Rivers and tributaries. Where long-term data were available, it was possible to describe current and historical trends to improve understanding of the ecological processes at work in these ecosystems. Long-term data, however, were lacking for most ecosystem components, limiting the extent of the characterization of natural variability in the study area. We recommended the development of a well-defined monitoring program with clear goals that build upon the results presented in this report to inform future water quality assessments and provide managers with the tools needed to detect impacts and predict ecosystem response to change.

Key to future monitoring is determining the best way to separate natural variability from human- induced change as a result of development or resource extraction. This report outlines some of the key variables that describe the natural variability in the Kenebecasis and Pollett Rivers, including bedrock geology age, stream size, and temperature, as well as the physical and biological responses that are currently most common in these systems. Future efforts need to continue to consistently monitor the key elements relationships to allow for a better understanding of ecosystem resilience and to identify biological and chemical limits that should result in an action response (e.g., regulation, pollution control, investigation-of-cause (IOC) activities). There are specific recommendations for monitoring each component of the chemical, physical, and biological characteristic of these stream systems, based on the findings in this report. 9.1 Water Temperature and Source 9.1.1 Temperature logging Water temperature appeared to be a significant driver of fish community structure, limiting distributions of fish species, such as Slimy Sculpin and Brook Trout, with defined temperature preferences. Temperature also played a role in determining benthic macroinvertebrate community structure, driving Chironomidae abundance and likely contributing to the distribution of stonefly families among stations. Temperature loggers in stream systems allow for continuous measurement of water temperatures providing data that can be used to summarize and compare temperature trends across stations and relate temperature metrics to biotic communities. Moreover, continuous measurement of temperature over several years can provide valuable information about changing thermal regimes for relatively little cost. Given the importance of water temperature to fish and macroinvertebrate species occurrence and abundance, and the potential impacts of climate change, there are many benefits to having a long-term temperature record that could be used to predict community shifts, particularly in Slimy Sculpin, Brook Trout, and other cold-water fish. Based on the results of the study the following recommendations are provided for temperature logging:

• Selection of stations for continued temperature logging should focus on maximizing the variability in thermal regimes across the study area. • Site selection should include replicate systems from across the full temperature gradient identified within this study, to ensure trends at a particular station can be confirmed by examination of temperature trends at a similar station in another part of the watershed. Identification of stations with similar thermal regimes can be accomplished through

142 assessment of data collected thus far, as well as through assessment of temperature profiling data.

9.1.2 Temperature profiling Airborne IR surveys can be used to obtain a comprehensive snapshot of the spatial temperature profile of a river, e.g., identification of thermal anomalies that may be related to groundwater inflows, and that represent variable habitats for fish, benthic macroinvertebrates, and plants/algae. These are point-in-time assessments and expensive ventures, thus with limitations for monitoring. However, if funds can be leveraged for initial assessment of thermal profiles to inform site selection for temperature loggers and biotic community assessment, the information collected may prove valuable for monitoring program design. Based on the results of the study the following recommendations are provided for temperature profiling:

• This study indicated that most thermal anomalies were located at the stream margins. Therefore it is recommended that temperature monitoring should be conducted across the width of the stream channel to capture thermal refugia. • The use of in-stream longitudinal temperature surveys, which are less cost-prohibitive than airborne IR surveys to detect areas with significant cold-water inputs, e.g., groundwater or cold tributary inflow, is recommended. . • The best time for detection of anomalies is mid- to late August, when surface water levels are low and have been low for a period of time (with no rainfall). 9.2 Water and Sediment Quality 9.2.1 Water quality characterization One of the most important outcomes of the assessment of water quality for the Kennebecasis and Pollett watersheds was the characterization of a natural gradient in water quality that was driven by the age of bedrock geology underlying the study sites. There were clear differences in water chemistry values for stations underlain by Early Carboniferous bedrock compared with older classes of bedrock, and weaker but notable differences between Early and Late Carboniferous bedrock stations. In particular, conductivity and ion concentrations were higher for Early Carboniferous stations than stations underlain by other ages of bedrock. This information is vital to the development of a water quality monitoring program, particularly when there are targeted objectives relating to the detection of impacts. For example, assessment of water quality at an Early Carboniferous station and comparison with results from Devonian or Neoproterozoic stations could falsely indicate impairment. Based on the results of the study the following recommendations are provided for water quality characterization.

• Water quality monitoring must take into account underlying bedrock geology age, as this was a large driver of chemical composition among stations. Baseline conditions and regular monitoring should be established for both reference and test sites in Early Carboniferous bedrock if resource development is to occur in this geological area. Monitoring of water quality in stations underlain by other ages of bedrock may provide context for any geology-specific changes to water quality, but assessments must recognize the natural variability that is expected between geology age classes. • Water samples should be analyzed for both total and dissolved metals portions to characterize their bioavailability. • It is recommended that development (industrial or otherwise) within the watershed require continued monitoring of reference stations/sites specific to the underlying

143 geology of the area of interest. This will ensure water quality at test locations is held to the appropriate standard.

9.2.2 Spatial and temporal water quality sampling Long-term water chemistry data are vital to the understanding of variation in surface water quality. Long-term trends can be used to put contemporary data into context and identify unusual results for a system, or to identify any declining or increasing trends in parameters that would not be evident with short-term monitoring data. With sufficient long-term records, even small shifts in chemical composition may be detected, potentially providing sufficient information to investigate areas of possible perturbation before significant impacts are evident. For example, though variability within stations and among sampling periods was evident, the assessment of temporal trends across all stations that had at least 4 years of historical data revealed evidence of a significant declining trend in several parameters including conductivity. Based on the results of the study the following recommendations are provided for spatial and temporal water quality sampling:

• Long-term monitoring of water quality should be expanded with a regular rotation of stations to maximize spatial coverage of temporal data. Selection of stations should build upon the historical data from NBDELG and KWRC, but must consider underlying bedrock geology age.

9.2.3 Sediment quality Assessment of sediment quality is an important component of a monitoring program in areas of potential resource exploration or extraction, as these samples may provide evidence of contamination within the area. The instruments and methodologies used to detect metals from hydraulic fracturing may influence detection and interpretation of results. Based on the results of the study the following recommendations are provided for sediment quality characterization:

• The age of sediments should be determined using sediment cores to measure the timing of U enrichment in the area of shale gas exploitation and characterize the natural background levels of U upstream and downstream of operating wells. • We suggest the use of a complete digestion of samples to highlight any abnormalities in the sediment metallic composition potentially related to shale gas activities, as well as the use of precise instruments devoted to isotopic signature such as a multi-collector ICP-MS.

9.3 Biotic communities 9.3.1 Chlorophyll a Analysis of chlorophyll a provides a cost-effective, simple, and fast way to assess primary productivity and trophic status within stream systems. Chlorophyll a is a measure of bulk algal biomass in the system, and it relates to nutrient levels in the water, integrating changes in nutrients over time. Collecting samples simply requires using a scalpel or toothbrush to scrape the surface material from a known surface area of a rock (using a standard template), putting the sample in ethanol, and reading chlorophyll levels from the sample in the laboratory. Samples could easily be collected at the same time as water quality or other biotic samples, and would provide potentially important supporting information regarding nutrient levels and food availability for consumers, in addition to providing a coarse measure of algal community

144 biomass. Based on the results of the study the following recommendations are provided for Chlorophyll a characterization.

• Regular monitoring of chlorophyll a should be incorporated as a low-cost option to assess algal biomass and trophic status of stream systems. Samples will be most effective for analysis if they are collected on a similar time frame with other biotic samples.

9.3.2 Benthic macroinvertebrates Benthic macroinvertebrates are an important component of stream food webs, and they were shown in this study to closely reflect water chemistry patterns in these systems, while also integrating effects of substrate composition and water temperature. The strong association of community structure and biological metrics with conductivity suggests that these measures could be used to detect shifts in water chemistry in these systems. However, the compositional differences among geology age classes with respect to sensitive and tolerant taxa highlighted the importance of comparing stations within a single bedrock geology age to prevent misrepresentation of water quality due to community composition. Benthic communities may also reflect any future shifts in thermal regime in these systems, as there was evidence of a difference in Plecoptera taxa among stations that appeared to be associated with a temperature gradient. Continued monitoring of these groups is necessary in order to establish natural variability among these communities and among taxa, particularly since a number of stations were identified as deviating from reference condition for the Atlantic region. These deviations for richness may have partially reflected under-representation of small streams in the CABIN Atlantic Reference Model. Thus continued monitoring of these communities has the additional benefit of supporting the determination of natural and un-impacted levels of taxonomic richness in these particular systems, as well as supporting the assessment of long-term shifts in community structure. Based on the results of the study the following recommendations are provided for benthic macroinvertebrate characterization:

• Benthic macroinvertebrate monitoring should continue on a regular basis (1-2 years) following CABIN protocols, and stations should be selected based on the age of underlying bedrock geology types. • Identification of macroinvertebrates to family level is a relatively simple task that does not require a lot of taxonomic expertise, and it is something that can be learned to allow samples to be processed in-house at a reduced cost. • Site selection for continued benthic macroinvertebrate community monitoring should ensure that a representative number of reference and test sites are selected in Early Carboniferous bedrock areas.

9.3.3 Fish Analysis of fish community structure revealed three clusters of stations that differed due to thermal regime and physical habitat descriptors. Species were strongly related to water temperature (Sculpin) and stream size (Brook Trout), and these factors should be considered in the selection of stations for continued monitoring of fish communities. Study design should consider a representative sample of stations on Early Carboniferous bedrock, with warm or cool to cold-water thermal regimes, and should include both small and large streams (determined by wetted width) to ensure maximum variation in fish communities is captured. Assessments of fish communities to detect possible impacts will rely on data for systems with similar habitat

145 characteristics to ensure comparison with appropriate reference communities. Based on the results of the study the following recommendations are provided for fish.

• Fish monitoring should continue on a regular basis (1-2 years) using the rapid assessment method of single-pass electrofishing, but should be completed on a longer time interval (e.g., 3-5 years) at the three stations with historical monitoring data using more intensive depletion electrofishing methods to allow for temporal assessment of fish community data collected in a similar manner. • Site selection for fish monitoring must include a range of stream sizes (small, medium, and large catchments) within the Early Carboniferous bedrock area, as both system size and geology were found to be important. • Slimy sculpin should continue to be monitored as a measure of fish health within this region as a fairly ubiquitous and abundant fish species that reflects local conditions.

9.4 Stream Methane An analysis of the isotopic signature of dissolved methane (stable isotopes of C and H) would normally be used to assess whether the methane is of thermogenic or biogenic origin. However, in order to perform such analyses a minimum dissolved methane concentration of approximately 0.1 mg/L is typically required (Loomer et al. 2016). The highest concentration of stream methane encountered during this investigation was only 0.018 mg/L and further work, including seeking laboratories that may be able to deal with lower dissolved concentrations, would be required to obtain isotopic data that may assist with determining the origin of the stream methane. The tracer test was only conducted in one section of Parsons Brook and the stream conditions during the test were some of the driest observed during this study. The resulting low- flow conditions may have impacted the ability to monitor methane evasion. Based on the results of the study the following recommendations are provided for stream methane detection.

• It is recommended that tracer tests be conducted in some of the larger streams in the area, or during periods of higher discharge. Future work should focus on transport modeling of bromide and methane concentration data from tracer tests to establish if the empirical estimates of gas transfer velocities presented here are consistent with model- calibrated values. • Additional in-stream dissolved methane tracer tests should be conducted in some of the larger streams in the area, or during periods of higher discharge, so that methane transport and losses are better quantified. • The isotopic signature of stream methane (stable isotopes of C and H) should be investigated to assess whether the observed low concentrations of methane are of thermogenic or biogenic origin.

9.5 Conclusions This report provides an assessment of ecosystem conditions across the upper Kennebecasis and Pollet River watersheds, with baseline data describing abiotic conditions and biotic stream communities. The studies that were conducted identified clear patterns of temperature and water quality throughout these watersheds that were linked to groundwater inputs and the age of underlying bedrock geology, resulting in natural gradients in biological communities. In addition to characterizing these gradients, the analyses identified a number of recommendations for continued monitoring of water quality, including focusing sampling in an area characterized

146 by a single age class of bedrock geology that is relevant to resource development and continuing routine monitoring to detect temporal trends within these systems. Assessment of water quality in areas with high shale gas potential must consider the natural differences in water quality and community structure that occur in freshwaters as a result of geology type. Moreover, this idea can be extended to other forms of resource exploration/exploitation that are restricted to particular habitats, as biotic and abiotic characteristics of surrounding freshwaters may not be directly comparable with systems outside those habitat types. Managers should consider geology and other important environmental drivers (e.g., system size, flow, temperature) when selecting stations for inclusion in a monitoring plan and when planning future development and resource exploration, to ensure accuracy in water quality assessments.

147 10 Acknowledgments Funding for this project was provided by the New Brunswick Energy Institute (NBEI) through a research grant to the Canadian Rivers Institute at the University of New Brunswick. Additional funding was leveraged to support the thermal infrared imaging component (Project A) from the Atlantic Salmon Conservation Foundation (ASCF), and support for the water quality grab sampling (Project B) from the NB Environmental Trust Fund.

We would also like to acknowledge the valued contributions and assistance from the following people and organization:

• Fieldwork assistance from Kirk Roach, Dennis Cooper, Hunter Francis, Maggie Folkins, Patrick Gushue, Ryan Power, Jenna Strang, Brianna Levenstein, Daniel Arluison, Lindsay May, Wesley Tibbet. • Ben Whalen (Project Manager; Kennebecasis Watershed Restoration Committees (KWRC) was a key partner throughout and provided many forms of in-kind support from data sharing to site selection to office space (Projects A-E). • The Fort Folly Habitat Recovery (FFHR) program partnered and helped with site selection within the Pollett River and shared fish data, in particular Tim Robinson, Wendy Epworth, Tanya Cooper, and Tom Johnson (Project B). • Dr. Wendy Monk offered her expertise with the CABIN Reference Condition Approach and Procrustes statistical analyses (Project B). • Theresa Schell for conducting all chlorophyll-a analyses. • Local landowners who allowed field crews to access the streams and rivers from their properties. • François Lagacé for sample sollection and fieldwork (Project C). • Delphine Foucher, and Hamza Ben Yaala for preparation and analysis of the water and sediment geochemistry (Project C) • André Boissonault for fieldwork support and information processing (Project D). • Diana Loomer for help with information processing (Project D). • Bronwyn Fleet-Pardy for assistance and trouble shooting with the ArcGIS online tools and CRI online data portal (Projects A-E).

Allison Ferguson (UNB) and François Lagacé (UdeMoncton) will use elements of Projects B and C, respectively, for partial fulfillment of their Master of Science (MSc) degrees, and Martin Boissonault (UNB) will use elements of Project D for partial fulfillment of his Master of Science in Engineering (MScE).

All peer-reviewed scientific publications generated from the data collected during this research project will acknowledge the support of the NBEI funding.

148 11 References

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155 Appendix A: Location, area, and classification of thermal anomalies detected in southeast New Brunswick rivers, August 2015. Thermal Area Stream Longitude Latitude Infrared Classification Notes (m2) image Kennebecasis -65.165487 45.806668 0509r1 17 Lateral seep Kennebecasis -65.531364 45.728665 2272 41 Confluence plume Small drain from car park Large trib inflow, big mixing plume extends Kennebecasis -65.522803 45.732505 2246 2,863 Confluence plume several images downstream Kennebecasis -65.52168 45.740888 2211 106 Confluence plume Small field drain Kennebecasis -65.507583 45.741418 2173 90 Confluence plume Tributary/drain Kennebecasis -65.496817 45.744475 2076 128 Springbrook Kennebecasis -65.468569 45.742153 1849 1,046 Confluence plume Kennebecasis -65.451646 45.740346 1815 367 Springbrook Kennebecasis -65.387878 45.780583 1416 14 Lateral seep Kennebecasis -65.387391 45.780837 1414 13 Lateral seep Kennebecasis -65.379297 45.77926 1337 26 Lateral seep Large tributary inflow, mixing plume dominates Kennebecasis -65.373197 45.777204 1327 2,099 Confluence plume temperature downstream Kennebecasis -65.373005 45.77825 1326 452 Springbrook Kennebecasis -65.349574 45.786494 1262 12 Lateral seep Kennebecasis -65.337728 45.793603 1219 44 Lateral seep Kennebecasis -65.334975 45.792659 1212 109 Wall-base channel Kennebecasis -65.332395 45.794392 1202 79 Cold side channel Small trib input, hidden by canopy but visible in Kennebecasis -65.320535 45.796664 1170 24 Confluence plume TIR Kennebecasis -65.224523 45.839159 876 101 Lateral seep Extensive seep Large cool trib inflow, mixing plume dominates Kennebecasis -65.223253 45.829513 846 3,043 Confluence plume main channel Kennebecasis -65.21534 45.827023 813 81 Lateral seep Kennebecasis -65.213131 45.827949 806 30 Cold alcove Small cool inflow, probably from tributary or field Kennebecasis -65.204576 45.826528 784 232 Confluence plume drain

156 Thermal Area Stream Longitude Latitude Infrared Classification Notes (m2) image Need to georeference IMG0724 to see it in Kennebecasis -65.179068 45.825931 724 34 Springbrook optical images Kennebecasis -65.177733 45.825017 720 4 Lateral seep Kennebecasis -65.175978 45.824416 719 259 Springbrook Kennebecasis -65.176081 45.824588 717 11 Lateral seep Kennebecasis -65.166631 45.807417 511 509 Springbrook Pool forming springbrook Kennebecasis -65.166755 45.807694 511 126 Lateral seep Seepage due to presence of pool / springbrook Kennebecasis -65.174097 45.78711 292 1,508 Springbrook Springbrook / braided channel Kennebecasis -65.189728 45.76576 240 7 Lateral seep Kennebecasis -65.189694 45.765861 240 5 Lateral seep Kennebecasis -65.19788 45.760682 150 202 Confluence plume Kennebecasis -65.205789 45.75704 139 33 Cold side channel Kennebecasis -65.209968 45.746033 113 31 Lateral seep Kennebecasis -65.211101 45.74463 109 76 Lateral seep Kennebecasis -65.210533 45.744821 109 39 Lateral seep Kennebecasis -65.210727 45.744674 109 32 Lateral seep Kennebecasis -65.211976 45.744394 107 38 Lateral seep 1808 - Pollett -65.059085 45.731928 6,714 Cold side channel 1823 1947 - Pollett -65.058186 45.707703 3,252 Cold side channel Extensive side channel / marshland system 1960 Cool springbrook. No temperature plume in Pollett -65.090012 45.899381 746 2,030 Springbrook main stem, but cooler upstream and looks to be accessible to fish Large elongated seep persisting over several Pollett -65.099825 45.850132 1102 1,462 Lateral seep hundred metres (1093 - 1102) Large cool tributary inflow, mixing plume Pollett -65.075384 45.698554 2011 1,222 Confluence plume extends to IMG2003 approx Pollett -65.055765 45.726983 1830 1,077 Lateral seep DIffuse seepage between bars Pollett -65.099755 45.8506 1096 1,014 Cold side channel Pollett -65.106539 45.806093 1332 801 Springbrook Pollett -65.063527 45.707337 1970 775 Cold side channel

157 Thermal Area Stream Longitude Latitude Infrared Classification Notes (m2) image Pollett -65.094723 45.862698 1051 664 Confluence plume Pollett -65.085886 45.701283 2089 587 Springbrook Pollett -65.062107 45.739521 1793 541 Springbrook Pollett -65.079911 45.699702 2073 519 Springbrook Pollett -65.107386 45.808417 1323 510 Confluence plume Pollett -65.055526 45.7285 1827 482 Springbrook Pollett -65.083351 45.912214 700 480 Confluence plume Pollett -65.106282 45.810603 1318 475 Confluence plume Pollett -65.110364 45.708539 2173 457 Confluence plume Pollett -65.055585 45.724815 1836 456 Lateral seep Pollett -65.071395 45.748013 1677 434 Springbrook Extensive springbrook network Pollett -65.105841 45.814633 1307 428 Confluence plume Pollett -65.059309 45.732862 1813 424 Springbrook Pollett -65.084485 45.922349 619 422 Cold side channel Pollett -65.083334 45.762359 1629 415 Springbrook Pollett -65.070081 45.701298 1995 390 Lateral seep Sveral small seeps in this zone Pollett -65.118833 45.706202 2249 358 Springbrook Not springbrook because only in one location. Pollett -65.064552 45.744574 1744 344 Cold side channel Not sure if accesible to fish Extremely large seep, mixing plume extends Pollett -65.0998 45.851308 1093 336 Lateral seep several hundred metres downstream (to approx image 1086) Pollett -65.065899 45.703445 1983 331 Cold alcove Pollett -65.058771 45.709803 1942 322 Springbrook Pollett -65.066112 45.704704 1981 313 Springbrook Very faint, small temperature difference with Pollett -65.085875 45.946983 474 312 Springbrook main stem Pollett -65.101277 45.798926 1387 292 Confluence plume Tributary inflow creating fairly large mixing Pollett -65.086451 45.701657 2091 287 Confluence plume plume Pollett -65.090765 45.982156 194 286 Springbrook Pollett -65.098825 45.854351 1082 275 Lateral seep Very large seep, deflected by log in channel

158 Thermal Area Stream Longitude Latitude Infrared Classification Notes (m2) image Pollett -65.056414 45.708491 1947 273 Springbrook Large trib inflow causing downstream mixing Pollett -65.098828 45.852543 1089 265 Confluence plume plume Cool backwater, possibly resulting from Pollett -65.100742 45.833089 1202 257 Springbrook springbrook that is obscured by trees, but could also be due to hyporheic flow Pollett -65.080156 45.938727 558 256 Confluence plume Tributary, barely cooler than main stem Pollett -65.099997 45.859566 1065 250 Lateral seep Extremely large seep Pollett -65.081723 45.761686 1632 247 Lateral seep Pollett -65.101073 45.857303 1071 213 Lateral seep Pollett -65.098779 45.85408 1083 211 Lateral seep Very large seep Pollett -65.092265 45.7826 1540 210 Confluence plume Pollett -65.055513 45.722851 1860 209 Lateral seep Very large seepage zone Pollett -65.081288 45.950967 435 199 Lateral seep Pollett -65.05604 45.727956 1828 184 Cold alcove Could also be due to hyporheic flow Pollett -65.105752 45.701862 2150 182 Confluence plume Pollett -65.065533 45.705678 1978 181 Lateral seep Cold alcove with seepage around other side of Pollett -65.097041 45.86161 1058 176 Cold alcove gravel bar Pollett -65.099001 45.854107 1083 172 Lateral seep Pollett -65.059872 45.732996 1813 172 Hyporheic upwelling Pollett -65.055017 45.726712 1831 171 Confluence plume Pollett -65.074784 45.699389 2008 170 Lateral seep Large seep Pollett -65.095093 45.784884 1478 157 Cold side channel Pollett -65.080011 45.950771 437 152 Lateral seep Pollett -65.061917 45.737435 1799 146 Cold side channel Pollett -65.05843 45.731477 1818 142 Cold alcove Pollett -65.104786 45.802545 1347 140 Hyporheic upwelling Downstream end of bar Pollett -65.055955 45.729917 1823 135 Confluence plume

159 Thermal Area Stream Longitude Latitude Infrared Classification Notes (m2) image Patch shape is incorrect due because image 1062 doesn't appear to be georeferenced Pollett -65.098786 45.860618 1062 129 Springbrook meaning that I can't properly draw the extent of the springbrook Pollett -65.080547 45.947706 463 128 Cold alcove Pollett -65.067744 45.745061 1735 128 Cold alcove Pollett -65.055828 45.728527 1827 128 Lateral seep Difficult to classify - cool pool forming in Pollett -65.055888 45.725895 1833 122 Hyporheic upwelling depression, presumably due to hyporheic upwelling in bar Pollett -65.093312 45.881807 884 121 Lateral seep Several seeps in zone along bank Pollett -65.083163 45.943444 539 117 Lateral seep Faint seep, coincides with zone of slack water Pollett -65.091795 45.89986 753 117 Cold alcove Pollett -65.062637 45.743544 1749 115 Cold side channel Pollett -65.105131 45.803499 1342 114 Lateral seep Pollett -65.065727 45.705303 1979 112 Lateral seep Very faint mixing plume, probably due to Pollett -65.084906 45.76346 1626 111 Confluence plume tributary that is barely visible in the TIR image Pollett -65.089126 45.771789 1590 110 Lateral seep Several small seepage zones from gravel bar Pollett -65.098245 45.860998 1060 104 Cold alcove Pollett -65.101061 45.833108 1202 101 Lateral seep 2 - 3 seeps in this zone Pollett -65.073449 45.70031 2003 99 Hyporheic upwelling Pollett -65.057181 45.718155 1881 96 Lateral seep Could also be cold alcove Pollett -65.058164 45.709657 1944 94 Lateral seep Pollett -65.105054 45.829479 1216 92 Confluence plume Pollett -65.089458 45.769566 1598 92 Confluence plume Pollett -65.085036 46.000391 55 86 Springbrook Springbrook driven by cool pool off right bank Pollett -65.055134 45.717804 1884 86 Confluence plume Pollett -65.059581 45.733913 1809 85 Lateral seep Pollett -65.100193 45.850763 1095 83 Lateral seep Pollett -65.097846 45.868079 988 83 Cold side channel Pollett -65.069294 45.701522 1992 77 Lateral seep

160 Thermal Area Stream Longitude Latitude Infrared Classification Notes (m2) image Pollett -65.065942 45.705014 1980 77 Hyporheic upwelling Pollett -65.111772 45.708355 2175 75 Cold alcove Pollett -65.1013 45.857263 1072 70 Lateral seep Large seep causing downstream mixing plume Pollett -65.107212 45.81086 1318 69 Springbrook Pollett -65.079082 45.756326 1649 68 Lateral seep Pollett -65.06328 45.739037 1794 63 Lateral seep Pollett -65.095498 45.701969 2124 63 Cold alcove Pollett -65.105522 45.702095 2149 62 Lateral seep Pollett -65.093168 45.885907 871 60 Cold alcove Pollett -65.11149 45.708298 2174 57 Lateral seep Pollett -65.08442 45.981654 241 55 Confluence plume Pollett -65.106573 45.804472 1337 50 Cold alcove Pollett -65.095388 45.881247 887 48 Lateral seep Pollett -65.119529 45.706175 2252 48 Lateral seep Pollett -65.081492 45.951018 435 47 Cold alcove Pollett -65.068299 45.745857 1732 46 Lateral seep Difficult to be certain of type; could be Pollett -65.092509 45.882814 880 44 Confluence plume confluence plume or lateral seep Pollett -65.053571 45.724327 1845 44 Lateral seep Pollett -65.061704 45.737454 1799 42 Lateral seep Pollett -65.080813 45.761265 1634 38 Cold alcove Pollett -65.106448 45.81037 1319 37 Cold alcove Pollett -65.085445 45.764006 1623 36 Springbrook Pollett -65.072636 45.701195 1999 35 Lateral seep Pollett -65.064689 45.744087 1745r1 35 Lateral seep Pollett -65.100174 45.851001 1095 35 Lateral seep Pollett -65.062121 45.74037 1787 34 Lateral seep Faint upwelling around gravel bar Pollett -65.057021 45.708625 1946r1 34 Hyporheic upwelling Pollett -65.100665 45.832703 1203 33 Lateral seep Pollett -65.059751 45.719849 1871 33 Lateral seep Pollett -65.095349 45.862603 1053 33 Lateral seep Pollett -65.098752 45.853271 1086 32 Cold alcove

161 Thermal Area Stream Longitude Latitude Infrared Classification Notes (m2) image Pollett -65.117949 45.706478 2246 30 Lateral seep Pollett -65.102439 45.795482 1402 30 Cold alcove Pollett -65.092403 45.883088 879 28 Lateral seep Pollett -65.060862 45.742325 1776 28 Lateral seep Pollett -65.095148 45.881326 887 27 Lateral seep Pollett -65.076671 45.75201 1662 26 Lateral seep Pollett -65.065457 45.744433 1743 26 Lateral seep Pollett -65.102823 45.83064 1211 25 Hyporheic upwelling Pollett -65.085738 45.907563 715 25 Hyporheic upwelling Pollett -65.106927 45.702517 2153 24 Lateral seep Pollett -65.094427 45.777246 1568 24 Lateral seep Pollett -65.095409 45.785871 1475 24 Lateral seep Pollett -65.106604 45.810337 1319 23 Lateral seep Pollett -65.057507 45.709429 1946 22 Hyporheic upwelling Pollett -65.062491 45.73969 1793 22 Cold alcove Pollett -65.096722 45.890623 857 22 Confluence plume Pollett -65.086778 45.767345 1608 20 Lateral seep Pollett -65.102841 45.830811 1211 20 Lateral seep Could be hyporheic flow Pollett -65.098745 45.853526 1086 20 Lateral seep Pollett -65.107481 45.807975 1324 19 Lateral seep Pollett -65.104208 45.82106 1285 19 Lateral seep Pollett -65.068538 45.70185 1992 18 Lateral seep Pollett -65.091124 45.773518 1582 18 Confluence plume Small cool inflow from culvert Pollett -65.10026 45.8597 1066 17 Lateral seep Pollett -65.093643 45.876021 964 17 Lateral seep Pollett -65.094338 45.78403 1482 16 Lateral seep Pollett -65.056025 45.729146 1825 16 Lateral seep Odd seep at upstream end of bar Pollett -65.086173 45.947048 474 16 Cold alcove Pollett -65.099058 45.792037 1417 15 Lateral seep Pollett -65.083958 45.943538 538 15 Lateral seep Extremely faint seep from gravel bar Pollett -65.059347 45.718616 1879 14 Lateral seep Pollett -65.105552 45.801909 1359 14 Lateral seep

162 Thermal Area Stream Longitude Latitude Infrared Classification Notes (m2) image Pollett -65.074256 45.749995 1670 13 Hyporheic upwelling Pollett -65.095781 45.702368 2125 13 Lateral seep Pollett -65.095705 45.785465 1476 13 Lateral seep Pollett -65.09853 45.866403 993 12 Lateral seep Pollett -65.094593 45.864128 1004r4 12 Lateral seep Pollett -65.086623 45.767504 1608 11 Lateral seep Pollett -65.119171 45.706272 2252 11 Lateral seep Pollett -65.098801 45.792555 1417 11 Lateral seep Pollett -65.094175 45.783249 1484r1 10 Lateral seep Pollett -65.106826 45.811281 1317 10 Lateral seep Pollett -65.095693 45.78743 1471 10 Lateral seep Pollett -65.062567 45.740058 1787 9 Lateral seep Pollett -65.077033 45.752241 1662 9 Lateral seep Pollett -65.105376 45.801873 1359 9 Cold alcove There appears to be a tributary channel in the TIR image but it is very difficult to be sure due to Pollett -65.095679 45.785133 1477 9 Confluence plume the tree cover. If no tributary exists, should be classed as lateral seep Downstream end of gravel bar. Could be lateral Pollett -65.107614 45.807077 1327 8 Cold alcove seep Odd cool patch on upstream end of island. Pollett -65.105494 45.827866 1257 8 Lateral seep Can't be hyporheic because at upstream rather than downstream end Pollett -65.093507 45.875621 965 7 Lateral seep Pollett -65.096207 45.869655 983 7 Lateral seep Pollett -65.071721 45.748602 1676 6 Lateral seep Pollett -65.104255 45.825499 1266 4 Hyporheic upwelling Pollett -65.100717 45.859274 1066 4 Lateral seep Pollett -65.118537 45.706518 2248 4 Hyporheic upwelling Pollett -65.093278 45.876806 961 3 Lateral seep Smith Creek -65.451634 45.821761 833 2,517 Cold side channel Large cool side channel Smith Creek -65.51237 45.742604 1738 716 Confluence plume Smith Creek is warmer than Kennebecasis

163 Thermal Area Stream Longitude Latitude Infrared Classification Notes (m2) image Smith Creek -65.485475 45.778735 1356 691 Cold side channel Smith Creek -65.385807 45.875297 225 641 Confluence plume Not a conventional springbrook but cool spring Smith Creek -65.484593 45.781968 1344 633 Springbrook accumulation in depression/pool, creating thermal refuge Hard to classify because imagery only gives Smith Creek -65.45234 45.812024 968 578 Confluence plume partial coverage, but assuming it is a cool tributary Smith Creek -65.393212 45.870193 290 509 Confluence plume Smith Creek -65.43767 45.833267 776 507 Confluence plume Only marginally cooler than main stem Smith Creek -65.513192 45.750097 1646 482 Confluence plume Barely cooler than main stem Smith Creek -65.445626 45.828955 806 470 Confluence plume Field drain Smith Creek -65.471626 45.790285 1227 386 Confluence plume Smith Creek -65.475418 45.793118 1206 310 Confluence plume Smith Creek -65.379627 45.877089 191 297 Confluence plume No plume, but cool water upstream in Smith Creek -65.40177 45.861287 537 289 Springbrook springbrook Smith Creek -65.471889 45.791259 1308r1 248 Springbrook Smith Creek -65.395628 45.867447 379 210 Springbrook Smith Creek -65.390113 45.870991 241 131 Springbrook Smith Creek -65.426466 45.842054 734 124 Confluence plume Difficult to distinguish from shadow, but appears Smith Creek -65.446384 45.827809 808 101 Cold side channel to be a cool side channel along gravel bar Smith Creek -65.460826 45.799898 1143 82 Springbrook Smith Creek -65.474901 45.788928 1315 55 Cold alcove Smith Creek -65.497747 45.767384 1452 54 Confluence plume Very small tributary inflow Smith Creek -65.394954 45.867586 378 15 Lateral seep Smith Creek -65.364189 45.880503 41 9 Lateral seep Smith Creek -65.386661 45.873172 232 8 Lateral seep Difficult to see if seeping from gravel island Smith Creek -65.461423 45.800101 1144 7 Hyporheic upwelling (hyporheic) or bankside (lateral seep) South Branch -65.273988 45.739339 115 1,046 Cold side channel

164 Thermal Area Stream Longitude Latitude Infrared Classification Notes (m2) image Extensive seepage zone, probably linked to South Branch -65.326455 45.771569 366 685 Lateral seep ponds on bank but no obvious surface flow South Branch -65.319604 45.766469 346 586 Cold side channel Creates mixing plume than persists over 2-3 South Branch -65.327697 45.772949 371 145 Confluence plume images South Branch -65.276905 45.743008 129 86 Lateral seep Seepage zone (multiple seeps) South Branch -65.325893 45.770983 364 46 Lateral seep Trout Creek -65.385341 45.683845 346 2,125 Cold side channel Large springbrook network (could be tributary Trout Creek -65.450051 45.698949 603 981 Springbrook but unable to see from image extent). No plume but cool water upstream accessible to fish Trout Creek -65.460159 45.703169 697 966 Springbrook Trout Creek -65.4823 45.717287 839 695 Lateral seep Extensive seepage zone No plume but cool water upstream accessible to Trout Creek -65.451281 45.698653 605 524 Springbrook fish Optical image georeferencing slightly off here so Trout Creek -65.479656 45.717332 828 361 Springbrook polygon location might not be completely correct Trout Creek -65.429788 45.69177 511 300 Lateral seep Extensive seep Trout Creek -65.393464 45.687554 391 268 Lateral seep Extensive seepage zone Trout Creek -65.499425 45.720898 935 263 Confluence plume Trout Creek -65.388414 45.685576 380 165 Lateral seep Trout Creek -65.458235 45.702651 692 109 Cold side channel Trout Creek -65.451208 45.700201 662 100 Cold alcove Trout Creek -65.516413 45.732757 1098 77 Lateral seep Trout Creek -65.368766 45.682034 273 62 Lateral seep Optical image georeferencing slightly off here so Trout Creek -65.479622 45.71745 828 38 Lateral seep polygon location might not be completely correct Trout Creek -65.473924 45.71108 745 34 Cold alcove Trout Creek -65.424545 45.692866 499 32 Cold alcove Could alternatively be lateral seep based on Trout Creek -65.451335 45.700195 662 28 Hyporheic upwelling position Trout Creek -65.372273 45.682218 279 23 Lateral seep

165 Thermal Area Stream Longitude Latitude Infrared Classification Notes (m2) image Could alternatively be lateral seep based on Trout Creek -65.451571 45.70005 662 15 Hyporheic upwelling position Trout Creek -65.467073 45.708073 720 11 Lateral seep Very small seepage zone at downstream end of Trout Creek -65.425368 45.692799 501 8 Hyporheic upwelling bar Trout Creek -65.516243 45.733799 1102 7 Hyporheic upwelling

166 Appendix B: Water and sediment geochemistry project: Technical information for laboratory analyses (Clarisse Lab; U de Moncton).

Table B-1. Instrument operating conditions of the iCAP-Q ICP/MS and the sample introduction systems.

Plasma Parameters Cool gas 14.00 L min-1 Auxiliary gas 0.80 L min-1 RF Power 1550 W Collision (He) gas flow - KED mode * 4.50 mL min-1

ASX-520 autosampler Nebulizer sample gas * 0.9 – 1.1 mL min-1 Sample uptake rate 0.35 mL min-1 Internal standard uptake rate 0.20 mL min-1 * optimized daily Reagents All acids (HNO3, HCl, and HF) used in this work for washing plastic ware, as well as for the standards or sample preparations were of trace metal grade obtained from Fisher Scientific. The 18.2-ΩM-grade water was provided by a Milli-Q water purification system from EMD-Millipore (Darmstadt, Germany). The gaseous argon and helium necessary for ICP/MS needs were purchased from Praxair, Canada and were of ultra high purity (UHT 5.0: > 99.998% and > 99%, respectively).

All standards used during quantitative analyses were prepared from serial dilutions of commercial single or multi-elemental stock solutions at 10 or 1000 mg L-1. Commercial stock solutions for all metals were obtained from either PlasmaCAL (SCP Science, Canada), Inorganic Ventures or isoSpec (Delta Scientific, Canada). External and internal standard solutions as well as calibration blanks were prepared fresh daily at appropriate mass fractions as the samples to be analyzed and were sample-matrix matched (i.e. 1 % v/v HNO3 for water samples analyses and 4 % v/v HNO3:HCl=2:1 for metals in sediment digests).

Quality control Method and field blanks, verification check standards, sample replicates, as well as certified reference materials were applied to improve quality assurance during laboratory analysis and as a general rule, accounted for almost 50 % of all determinations made during a measuring session.

External instrumental calibrations and water measurements analyses were validated using several certified reference materials : SLRS-6, river water (National Research Council of Canada, NRCC, Ottawa, Canada) ; ES-L-2, synthetic groundwater (EnviroMAT, SCP Science, Canada) ; EP-L-3, synthetic drinking water (EnviroMAT, SCP Science, Canada) ; and, NIST- 1640a, natural freshwater (National Institute of Standard and Technology, NIST, Gaithersburg, USA). Accuracy for metal contents in sediments was evaluated using the certified reference materials: PACS-2, contaminated marine sediment (National Research Council of Canada, NRCC, Ottawa, Canada) ; HISS-1, uncontaminated marine sediment (National Research Council of Canada, NRCC, Ottawa, Canada) ; IAEA-158, marine sediment (International Atomic Energy Agency, IAEA, Vienna, Austria) ; and, IAEA-314, stream sediment (International Atomic

167

Table B-2. Recoveries of all elements of interest in certified reference sediment material. Results given in the table are average % recovery of certified values ± 1 SD obtained from method digestion replicates. IAEA-314 IAEA-158 PACS-2 HISS-1 Element n = 13 n = 15 n = 4 n = 3 Li, lithium 93.3 ± 6.1a 94.7 ± 7.2 108.9 ± 12.1 Be, beryllium 108.0 ± 4.3 95.0 ± 45.0 Na, sodium 93.9 ± 5.3 97.0 ± 7.7 96.4 ± 12.0 Mg, magnesium 97.4 ± 6.6 a 93.8 ± 7.2 91.9 ± 9.6 Al, aluminium 88.6 ± 7.2 91.3 ± 7.5 90.2 ± 6.4 K, potassium 95.6 ± 6.8 93.5 ± 6.5 96.4 ± 8.8 Ca, calcium 103.0 ± 5.8 103.3 ± 9.2 97.8 ± 9.7 Sc, scandium 92.5 ± 7.6 a Ti, titanium 96.3 ± 6.3 a 98.8 ± 9.4 104.3 ± 11.4 V, vanadium 100.4 ± 5.8 102.1 ± 9.6 104.8 ± 9.6 Cr, chromium 94.7 ± 6.2 97.3 ± 9.9 Mn, manganese 98.1 ± 6.2 97.6 ± 6.9 103.1 ± 14.8 Fe, iron 96.9 ± 4.5 98.3 ± 9.0 91.8 ± 9.3 Co, cobalt 92.7 ± 5.9 100.8 ± 7.6 96.2 ± 16.1 a Ni, nickel 97.3 ± 6.6 102.5 ± 9.9 105.0 ± 17.9 Cu, copper 93.0 ± 7.9 103.6 ± 9.4 101.3 ± 19.4 Zn, zinc 102.1 ± 6.9 113.6 ± 11.7 117.5 ± 31.2 As, arsenic 90.9 ± 8.5 109.4 ± 21.6 Rb, rubidium 94.6 ± 4.7 Sr, strontium 96.7 ± 4.6 96.7 ± 7.9 102.9 ± 9.6 Ag, silver 111.6 ± 6.9 89.1 ± 9.5 146.8 ± 13.0 Cd, cadmium 100.6 ± 8.2 115.1 ± 13.6 110.8 ± 11.4 Sb, antimony 94.2 ± 9.0 107.5 ± 13.6 84.7 ± 6.1 a Cs, caesium 87.6 ± 4.0 Ba, barium 94.3 ± 5.0 a La, lanthanum 89.6 ± 6.6 Ce, cerium 88.8 ± 8.0 Eu, europium 86.3 ± 3.9 Th, thorium 86.0 ± 5.9 84.3 ± 6.6 a U, uranium 82.2 ± 5.3 87.9 ± 6.8 85.8 ± 1.6 a a no certified concentration available – information value only HISS-1: Uncontaminated marine sediment, National Research Council of Canada (Ottawa, Canada) PACS-2: Contaminated marine sediment, National Research Council of Canada (Ottawa, Canada) IAEA-158: Marine sediment, International Atomic Energy Agency (Vienna, Austria) IAEA-314: Stream sediment, International Atomic Energy Agency (Vienna, Austria)

Energy Agency, IAEA, Vienna, Austria). Reproducibility was assessed by measuring replicate of sediment digests for which the entire chemical processing was repeated. The detection (LOD) and quantification (LOQ) limits were calculated from at least 10 procedural blanks analyses throughout different analytical runs. For all reference materials, recoveries and uncertainties of all elements of interest were compiled in Table A-2 and A-3.

168 Table B-3. Recoveries of all elements of interest in certified reference water material. Results given in the table are average % recovery of certified values ± 1 SD obtained from instrumental replicate measurements. EP-L-3 ES-L-2 SRLS-6 NIST-1640a Spike solutions Element n = 23 n = 26 n = 12 n = 6 (1 μg/L) n = 3 Li, lithium 114.4 ± 5.9 93.2 ± 6.2b 102 ± 12 Be, beryllium 105.0 ± 4.2 112.2 ± 5.1 90.4 ± 5.9b n.d. Na, sodium 104.2 ± 4.9 100.8 ± 4.9 94.0 ± 3.8 95.1 ± 3.5b Mg, magnesium 104.6 ± 4.6 103.1 ± 4.1 97.3 ± 3.7 91.8 ± 2.6b Al, aluminium 105.4 ± 9.1 111.7 ± 5.0 91.6 ± 3.6 K, potassium 107.4 ± 5.2 98.7 ± 4.4 98.3 ± 5.4 90.3 ± 3.4b Ca, calcium 104.1 ± 9.6 91.0 ± 10.5 97.1 ± 4.1 89.0 ± 3.9b Sc, scandium 87 ± 8 Ti, titanium 95 ± 2 V, vanadium 106.2 ± 3.6 94.6 ± 3.3 n.d. Cr, chromium 107.5 ± 3.4 107.5 ± 3.2 88.6 ± 3.8 91.2 ± 1.9 95 ± 2 Mn, manganese 106.5 ± 3.1 108.7 ± 3.3 101.7 ± 3.4 91.8 ± 1.8 Fe, iron 104.5 ± 4.9 83.6 ± 3.5 96.3 ± 5.0 92.8 ± 1.6 Co, cobalt 106.5 ± 4.5 109.8 ± 4.6 98.3 ± 4.8 b 91.7 ± 1.7 95 ± 1 Ni, nickel 106.5 ± 4.1 107.2 ± 5.5 91.0 ± 7.6 91.0 ± 1.5 97 ± 2 Cu, copper 109.1 ± 3.9 95.2 ± 3.7 106.0 ± 3.8 90.4 ± 1.4 90 ± 3 Zn, zinc 104.6 ± 3.4 83.3 ± 3.7 106.4 ± 11.7 87.6 ± 1.8 94 ± 4 As, arsenic 104.6 ± 3.8 110.6 ± 3.6 108.5 ± 2.9 89.5 ± 1.3 96 ± 3 Sr, strontium 102.2 ± 3.9 102.3 ± 3.1 95.6 ± 3.3 92.9 ± 0.9 Ag, silver 89.5 ± 0.9 94 ± 9 Cd, cadmium 104.0 ± 3.4 107.4 ± 3.6 90.8 ± 1.2 99 ± 1 Sb, antimony 98.9 ± 3.9 91.0 ± 3.4 94.6 ± 1.9 91.3 ± 1.2 105 ± 8 Te, tellurium 97 ± 8 Cs, caesium 93 ± 2 Ba, barium 101.4 ± 3.2 106.5 ± 3.2 91.8 ± 3.0 92.2 ± 2.4 Pt, platinum 94 ± 2 Pb, lead 98.6 ± 6.8 90.2 ± 6.0a 84.0 ± 5.7 91.9 ± 3.0 Bi, bismuth 92 ± 7 Th, thorium 93 ± 6 U, uranium 111.1 ± 7.8a 80.0 ± 5.5 96.8 ± 2.9 93 ± 4 a not certified concentration – information value only b not certified concentration - reference values EP-L-3: Reconstituted drinking water – low levels, EnviroMAT, SCP Science (Ottawa, Canada) ES-L-2: Reconstituted groundwater – low levels, EnviroMAT, SCP Science (Ottawa, Canada) SRLS-6: River water, National Research Council of Canada (Ottawa, Canada) NIST-1640a: Natural freshwater, National Institute of Standard and Technology (Gaithersburg, USA)

169 Appendix C: Dissolved methane in surface waters, reconnaissance and tracer studies. Table C-1. Reconnaissance survey water quality parameters, July 6 2015.

pH DO Conductivity Specific Conductance Salinity Temperature Probe Unit Probe % mg/L Probe Probe Probe Probe °C YSI 63 7.84 YSI 85 109.0 11.10 YSI 63 57 YSI 63 71.6 YSI 63 0.0 YSI 63 14.5 Parlee Brook Orion 7.87 ------YSI 85 57.7 YSI 85 72.2 YSI 85 0.0 YSI 85 14.5 YSI 63 8.37 YSI 85 109.0 10.13 YSI 63 195.1 YSI 63 221.8 YSI 63 0.1 YSI 63 18.9 Parsons Brook Orion 8.19 ------YSI 85 195.4 YSI 85 221.6 YSI 85 0.1 YSI 85 18.9 YSI 63 8.39 YSI 85 117.3 10.70 YSI 63 96.6 YSI 63 105.3 YSI 63 0.1 YSI 63 20.8 Wards Creek Orion ------YSI 85 98.8 YSI 85 107.5 YSI 85 0.1 YSI 85 20.8 YSI 63 6.59 YSI 85 93.7 9.85 YSI 63 20.8 YSI 63 26.6 YSI 63 0.0 YSI 63 13.3 Shaffer Brook Orion ------YSI 85 22.0 YSI 85 28.4 YSI 85 0.0 YSI 85 13.3

Pinnacle Brook Stream too small and difficult to access

Kennebecasis River River too broad; no access point upstream

YSI 63 7.95 YSI 85 107.1 10.95 YSI 63 104.7 YSI 63 131.3 YSI 63 0.1 YSI 63 14.3 Shannon Brook Orion 7.86 ------YSI 85 105.2 YSI 85 132.3 YSI 85 0.1 YSI 85 14.3 YSI 63 6.7 YSI 85 96.4 11.51 YSI 63 32.8 YSI 63 48.6 YSI 63 0.0 YSI 63 7.9 Miller Brook Orion ------YSI 85 33.1 YSI 85 49.3 YSI 85 0.0 YSI 85 7.8 YSI 63 8.15 YSI 85 99.8 10.02 YSI 63 172.3 YSI 63 213.2 YSI 63 0.1 YSI 63 15.1 McLeod Brook 1 Orion ------YSI 85 172.8 YSI 85 213.1 YSI 85 0.1 YSI 85 15.1 YSI 63 7.91 YSI 85 91.2 9.09 YSI 63 158.6 YSI 63 193.8 YSI 63 0.1 YSI 63 15.5 McLeod Brook 2 Orion ------YSI 85 159.1 YSI 85 194.8 YSI 85 0.1 YSI 85 15.6 McLeod Brook YSI 63 7.22 YSI 85 81.5 10.01 YSI 63 144.2 YSI 63 223.2 YSI 63 0.1 YSI 63 6.7 Spring Orion ------YSI 85 144.5 YSI 85 222.1 YSI 85 0.1 YSI 85 6.7

170 pH DO Conductivity Specific Conductance Salinity Temperature Probe Unit Probe % mg/L Probe Probe Probe Probe °C

Dove Hollow Brook Stream inaccessible

YSI 63 7.37 YSI 85 93.0 9.26 YSI 63 33.3 YSI 63 40.8 YSI 63 0.0 YSI 63 15.6 Negro Brook Orion ------YSI 85 34.7 YSI 85 42.3 YSI 85 0.0 YSI 85 15.6 YSI 63 7.67 YSI 85 92.8 9.43 YSI 63 94.6 YSI 63 117.5 YSI 63 0.0 YSI 63 14.8 Mapleton Brook Orion ------YSI 85 95.1 YSI 85 119.1 YSI 85 0.0 YSI 85 14.5

Gibson Brook Stream inaccessible

Duncan Brook Stream too small

YSI 63 6.25 YSI 85 104.4 12.21 YSI 63 34.6 YSI 63 49.8 YSI 63 0.0 YSI 63 8.6 Calamingo Brook Orion ------YSI 85 35.1 YSI 85 51.4 YSI 85 0.0 YSI 85 8.5 YSI 63 7.32 YSI 85 90.5 9.75 YSI 63 46.9 YSI 63 62.7 YSI 63 0.0 YSI 63 11.9 Higgins Brook Orion ------YSI 85 49.2 YSI 85 65.2 YSI 85 0.0 YSI 85 11.9 YSI 63 7.39 YSI 85 96.2 10.60 YSI 63 38.9 YSI 63 53.2 YSI 63 0.0 YSI 63 11.1 Montgomery Brook Orion ------YSI 85 39.7 YSI 85 54.1 YSI 85 0.0 YSI 85 11.1 YSI 63 7.44 YSI 85 95.7 9.34 YSI 63 51.4 YSI 63 61.0 YSI 63 0.0 YSI 63 16.7 Webster Brook Orion ------YSI 85 52.7 YSI 85 63.0 YSI 85 0.0 YSI 85 16.5 YSI 63 7.44 YSI 85 96.1 9.61 YSI 63 216.4 YSI 63 265.5 YSI 63 0.1 YSI 63 15.4 Dobson Brook Orion ------YSI 85 213.6 YSI 85 261.2 YSI 85 0.1 YSI 85 15.4 YSI 63 8.04 YSI 85 108.1 10.08 YSI 63 94.7 YSI 63 107.4 YSI 63 0.1 YSI 63 18.8 McCarthy Brook Orion 7.93 ------YSI 85 95.7 YSI 85 108.6 YSI 85 0.1 YSI 85 18.8 YSI 63 7.64 YSI 85 105.6 10.57 YSI 63 63.4 YSI 63 77.9 YSI 63 0.0 YSI 63 15.3 Hawkes Brook Orion 7.61 ------YSI 85 64.4 YSI 85 79.0 YSI 85 0.0 YSI 85 15.3

McGregor Brook Stream too small

171 pH DO Conductivity Specific Conductance Salinity Temperature Probe Unit Probe % mg/L Probe Probe Probe Probe °C YSI 63 7.23 YSI 85 93.6 9.44 YSI 63 39.3 YSI 63 49.5 YSI 63 0.0 YSI 63 15.0 South Branch Orion ------YSI 85 40.0 YSI 85 49.7 YSI 85 0.0 YSI 85 14.8

172 Table C-2: Methane and water quality parameters, Parsons Brook July 2015.

Name Sample Date: Lat. Long. M2 Ethane Propane Temp pH Cond- Specific Salinity % DO ID: uctivity Cond- DO uctance mg/L mg/L mg/L °C µS/cm µS/cm ppt % mg/L Parsons PRS002 28/07/ N45 W65 0.001 <0.001 <0.001 16.8 8.15 224.7 267.2 0.1 98.2 9.53 Brook 2 072801 2015 41.231 29.216 Parsons PRS003 28/07/ N45 W65 0.001 <0.001 <0.001 16.7 8.11 224.9 267.3 0.1 96.8 9.37 Brook 3 072801 2015 41.227 29.221 Parsons PRS004 28/07/ N45 W65 0.005 <0.001 <0.001 16.7 8.05 225.9 268.9 0.1 93.5 9.13 Brook 4 072801 2015 41.186 29.315 Parsons PRS005 28/07/ N45 W65 0.002 <0.001 <0.001 16.8 8.15 223.3 265.1 0.1 95.4 9.25 Brook 5 072801 2015 41.113 29.381 Parsons PRS006 28/07/ N45 W65 <0.001 <0.001 <0.001 16.7 8.11 226.6 269.4 0.1 94.7 9.17 Brook 6 072801 2015 41.027 29.389 Parsons PRS007 28/07/ N45 W65 0.001 <0.001 <0.001 16.7 7.96 227.7 270.9 0.1 91.2 8.80 Brook 7 072801 2015 40.978 29.391 Parsons PRS008 28/07/ N45 W65 0.003 <0.001 <0.001 16.9 7.84 235.8 273.6 0.1 99.2 8.71 Brook 8 072801 2015 40.926 29.440 Parsons PRS009 28/07/ N45 W65 0.007 <0.001 <0.001 17.1 8.10 237.3 279.7 0.1 97.9 9.45 Brook 9 072801 2015 40.892 29.461 Parsons PRS010 28/07/ N45 W65 0.004 <0.001 <0.001 16.9 7.84 236.8 280.3 0.1 87.1 8.42 Brook 10 072801 2015 40.846 29.486 Parsons PRS010 28/07/ N45 W65 0.003 <0.001 <0.001 Brook 10 072801 2015 40.846 29.486 (Duplicate) (Dup) Parsons PRS011 28/07/ N45 W65 0.007 <0.001 <0.001 17.2 7.74 237.8 279.9 0.1 87.1 8.37 Brook 11 072801 2015 40.836 29.500 Parsons PRS012 28/07/ N45 W65 0.009 <0.001 <0.001 17.2 7.85 237.2 278.6 0.1 90.9 8.75 Brook 12 072801 2015 40.784 29.504 Parsons PRS013 28/07/ N45 W65 0.013 <0.001 <0.001 17.6 7.78 244.6 284.6 0.1 96.2 10.60 Brook 13 072801 2015 40.729 29.521 Parsons PRS014 28/07/ N45 W65 0.004 <0.001 <0.001 18.1 8.32 246.3 284.2 0.1 109. 10.39 Brook 14 072801 2015 40.690 29.489 9 Parsons PRS015 28/07/ N45 W65 0.005 <0.001 <0.001 17.8 7.94 237.6 275.9 0.1 97.4 9.29 Brook 15 072801 2015 40.498 29.364

173 Name Sample Date: Lat. Long. M2 Ethane Propane Temp pH Cond- Specific Salinity % DO ID: uctivity Cond- DO uctance Parsons PRS015 28/07/ N45 W65 <0.001 <0.001 <0.001 Brook 15 072802 2015 40.498 29.364 (Blank) Parsons PRS015 28/07/ N45 W65 <0.001 <0.001 <0.001 Brook 15 072802 2015 40.498 29.364 (Blank) (Duplicate)

174 Table C-3: Methane and water quality parameters, Parsons Brook May 2016.

Name Sample ID: Sampling Latitud Longit Methan Etha Propa TE pH Cond Specific Salini % DO Date: e ude e ne ne MP - Cond- ty DO uctivi uctance ty mg/L mg/L mg/L °C µS/c µS/cm ppt % mg/ m L Parsons PRS0020728 26/05/2016 N45 W65 0.012 <0.0 <0.001 14. 8.1 163. 205.7 0.1 102. 10. Brook 1 01 41.324 29.106 01 3 5 3 9 58 Parsons PRS0020728 26/05/2016 N45 W65 0.012 <0.0 <0.001 Brook 1 01 (Dup) 41.324 29.106 01 (Duplicate) Parsons PRS0020728 26/05/2016 N45 W65 0.001 <0.0 <0.001 14. 8.2 159. 199.0 0.1 103. 10. Brook 2 01 41.231 29.216 01 4 5 2 3 52 Parsons PRS0030526 26/05/2016 N45 W65 <0.001 <0.0 <0.001 Brook 3 02 41.227 29.221 01 (Blank) Parsons PRS0040728 26/05/2016 N45 W65 0.005 <0.0 <0.001 14. 8.2 197. 197.5 0.1 100. 10. Brook 4 01 41.186 29.315 01 5 7 5 5 24 Parsons PRS0050728 26/05/2016 N45 W65 0.002 <0.0 <0.001 14. 8.4 153. 191.8 0.1 104. 10. Brook 5 01 41.113 29.381 01 5 2 1 1 62 Parsons PRS0060728 26/05/2016 N45 W65 <0.001 <0.0 <0.001 14. 8.3 152. 191.8 0.1 100. 10. Brook 6 01 41.027 29.389 01 3 0 4 0 26 Parsons PRS0060728 26/05/2016 N45 W65 <0.001 <0.0 <0.001 Brook 6 02 41.027 29.389 01 (Blank) Parsons PRS0070728 26/05/2016 N45 W65 0.001 <0.0 <0.001 14. 8.3 150. 188.7 0.1 102. 10. Brook 7 01 40.978 29.391 01 4 0 3 5 44 Parsons PRS0080728 26/05/2016 N45 W65 0.002 <0.0 <0.001 14. 8.2 151. 190.2 0.1 99.6 10. Brook 8 01 40.926 29.440 01 4 3 6 17 Parsons PRS0090728 26/05/2016 N45 W65 0.005 <0.0 <0.001 14. 8.4 153. 191.8 0.1 105. 10. Brook 9 01 40.892 29.461 01 5 7 4 0 70 Parsons PRS0100728 26/05/2016 N45 W65 0.004 <0.0 <0.001 Brook 10 01 40.846 29.486 01 Parsons PRS0110728 26/05/2016 N45 W65 0.006 <0.0 <0.001 Brook 11 01 40.836 29.500 01 Parsons PRS0110728 26/05/2016 N45 W65 0.006 <0.0 <0.001 Brook 11 01 (Dup) 40.836 29.500 01

175 (Duplicate) Parsons PRS0120728 26/05/2016 N45 W65 0.006 <0.0 <0.001 Brook 12 01 40.784 29.504 01 Parsons PRS0130728 26/05/2016 N45 W65 0.010 <0.0 <0.001 14. 8.4 156. 195.6 0.1 100. 10. Brook 13 01 40.729 29.521 01 6 4 7 7 13 Parsons PRS0140728 26/05/2016 N45 W65 0.006 <0.0 <0.001 Brook 14 01 40.690 29.489 01 Parsons PRS0140728 26/05/2016 N45 W65 <0.001 <0.0 <0.001 Brook 14 02 40.690 29.489 01 (Blank) Parsons PRS0150728 26/05/2016 N45 W65 0.004 <0.0 <0.001 Brook 15 01 40.498 29.364 01 Parsons PRS0150728 26/05/2016 N45 W65 0.004 <0.0 <0.001 Brook 15 01 (Dup) 40.498 29.364 01 (Duplicate) Parsons PRS1750052 26/05/2016 N45 W65 0.003 <0.0 <0.001 14. 8.3 149. 193.0 0.1 104. 10. Brook 1750 601 40.856 29.467 01 4 9 3 65 Parsons PRS1900052 26/05/2016 N45 W65 0.003 <0.0 <0.001 Brook 1900 601 40.809 29.494 01 Parsons PRS1900052 26/05/2016 <0.001 <0.0 <0.001 Brook 1900 602 N45 W65 01 (Blank) 40.809 29.494 Parsons PRS1950052 26/05/2016 N45 W65 0.005 <0.0 <0.001 14. 8.3 149. 187.7 0.1 104. 10. Brook 1950 601 40.793 29.494 01 5 7 8 6 65 Parsons PRS2100052 26/05/2016 N45 W65 0.008 <0.0 <0.001 Brook 2100 601 40.753 29.514 01 Parsons PRS2150052 26/05/2016 N45 W65 0.007 <0.0 <0.001 Brook 2150 601 40.736 29.532 01 Parsons PRS2250052 26/05/2016 N45 W65 0.005 <0.0 <0.001 Brook 2250 601 40.704 29.512 01 Parsons PRS2350052 26/05/2016 N45 W65 0.005 <0.0 <0.001 Brook 2350 601 40.672 29.462 01

176 Table C-4: Methane and water quality parameters, McLeod Brook August 2015.

Name Sample ID: Sampling Latitu Longit Meth Etha Prop TEM p Condu Specific Sali % D Date: de ude ane ne ane P H ctivity Conductan nity D O ce O mg/L mg/L mg/L unit unit unit % mg degre /L es C McLeod Brook 3 MCLS12015 18/08/2015 N45 W65 0.00 < < 14.1 7. 182.9 231.2 N/A 97. 9.9 081701 45.35 23.52 1 0.00 0.00 7 0 5 8 5 1 1 9 McLeod Brook 5 MCLS32015 18/08/2015 N45 W65 < < < N/A N/ N/A N/A N/A N/ N/ 081701 44.61 22.71 0.00 0.00 0.00 A A A 0 9 1 1 1 McLeod Brook 5 MCLS32015 18/08/2015 N45 W65 < < < N/A N/ N/A N/A N/A N/ N/ (Duplicate) 081701 44.61 22.71 0.00 0.00 0.00 A A A 0 9 1 1 1 McLeod Brook 4 MCLS2B201 18/08/2015 N45 W65 < < < 11.5 7. 181.8 245.1 N/A 97. 10. 5081701 44.66 23.07 0.00 0.00 0.00 9 0 20 4 6 1 1 1 9 McLeod Brook MCLS2CSP 18/08/2015 N45 W65 < < < 6.6 8. 140.6 217.0 N/A 89. 10. Split Spring R201508170 44.63 23.03 0.00 0.00 0.00 0 4 95 1 1 8 1 1 1 3 McLeod Brook MCLS2CSP 18/08/2015 N45 W65 < < < 6.6 8. 140.6 217.0 N/A 89. 10. Split Spring R201508170 44.63 23.03 0.00 0.00 0.00 0 4 95 (Duplicate) 2 1 8 1 1 1 3 McLeod Brook 6 MCLS42015 18/08/2015 N45 W65 0.00 < < 14.1 7. 202.0 255.8 N/A 10 10. 081801 43.77 21.76 5 0.00 0.00 7 4.0 72 0 1 1 1 8 McLeod Spring 2 MCLSPR220 18/08/2015 N45 W65 < < < N/A N/ N/A N/A N/A N/ N/ 15081801 44.61 22.82 0.00 0.00 0.00 A A A 3 8 1 1 1 McLeod Spring 1 MCL002SPR 18/08/2015 N45 W65 0.00 < < 6.9 8. 228.3 349.1 N/A 82. 9.9 2015081801 43.68 21.61 1 0.00 0.00 1 0 8 6 7 1 1 0 McLeod Spring 3 MCLBS2015 18/08/2015 N45 W65 0.00 < < N/A N/ N/A N/A N/A N/ N/ 081801 43.70 21.65 1 0.00 0.00 A A A 3 4 1 1 McLeod Spring 3 MCLBS2015 18/08/2015 N45 W65 0.00 < < N/A N/ N/A N/A N/A N/ N/ (Duplicate) 081801 43.70 21.65 1 0.00 0.00 A A A

177 3 4 1 1 McLeod Spring 3 MCLBS2015 18/08/2015 N45 W65 < < < N/A N/ N/A N/A N/A N/ N/ (Duplicate 2) 081802 43.70 21.65 0.00 0.00 0.00 A A A 3 4 1 1 1

178 Table C-5: Methane and water quality parameters, Shannon Brook August & October 2015.

Name Sample ID: Samplin Latitud Longit Meth Eth Prop TEMP p Condu Specific Sali % DO g Date: e ude ane ane ane H ctivity Conductanc nity DO e mg/L mg/ mg/L µS/cm µS/cm unit % mg L degre /L es C Shannon Brook 1 SHA0021008 2015-10- N45 W65 <0.0 <0. <0.0 (Blank) 01 08 41.583 23.742 01 001 01 Shannon Brook 1 SHA0021008 2015-10- N45 W65 0.00 <0. <0.0 7.4 7. 93.1 140.1 0.1 96. 11. 02 08 41.583 23.742 4 001 01 65 4 59 Shannon Brook 2 SHA0031008 2015-10- N45 W65 0.00 <0. <0.0 7.6 7. 93.4 140.1 0.1 91. 10. 01 08 41.800 23.538 1 001 01 73 9 99 Shannon Brook 3 SHA0041008 2015-10- N45 W65 0.00 <0. <0.0 7.5 7. 86.8 130.7 0.1 91. 10. 01 08 41.875 23.309 2 001 01 74 4 98 Shannon Brook 4 SHA0051008 2015-10- N45 W65 0.00 <0. <0.0 7.6 7. 85.9 128.9 0.1 90. 10. 01 08 41.901 22.836 1 001 01 74 2 79 Shannon Brook 5 SHA0061008 2015-10- N45 W65 0.00 <0. <0.0 7.5 7. 96.3 144.2 0.1 90. 10. 01 08 42.056 22.447 1 001 01 82 7 86 Shannon Brook 6 SHA0071008 2015-10- N45 W65 <0.0 <0. <0.0 7.5 7. 103.7 155.9 0.1 88. 10. 01 08 42.102 22.000 01 001 01 74 5 62 Shannon Brook 6 SHA0071008 2015-10- N45 W65 <0.0 <0. <0.0 (Duplicate) 01 (Dup) 08 42.102 22.000 01 001 01 Shannon Double SHADSW20 2015-08- N45 W65 <0.0 <0. <0.0 6.6 6. 100.7 155.3 0.1 55. 6.7 Seep W 15081801 18 41.583 23.742 01 001 01 72 4 7 Shannon Double SHADSE201 2015-08- N45 W65 <0.0 <0. <0.0 6.5 6. 101.3 156.9 0.1 54. 6.6 Seep E 5081801 18 41.800 23.538 01 001 01 68 3 7 Shannon Double SHADSE201 2015-08- N45 W65 <0.0 <0. <0.0 Seep E (Blank) 5081802 18 41.800 23.538 01 001 01

179 Table C-6: Parsons Brook discharge, June 2016.

Name Sample ID: Sampling Date: Latitude Longitude Discharge (2D) Discharge (3D) Avg. Disch. m3/s m3/s m3/L Parsons Cross Section 1 PRSC1 20/06/2016 N45 40.493 W65 29.364 0.0053 0.0057 0.0055 Parsons Cross Section 2 PRSC2 20/06/2016 N45 40.682 W65 29.462 0.0052 0.0033 0.0043 Parsons Cross Section 3 PRSC3 20/06/2016 N45 40.702 W65 29.462 0.0051 0.0038 0.0045 Parsons Cross Section 4 PRSC4 20/06/2016 N45 40.721 W65 29.533 0.0072 0.0056 0.0064 Parsons Cross Section 5 PRSC5 20/06/2016 N45 40.753 W65 29.510 0.0055 0.0043 0.0049 Parsons Cross Section 6 PRSC6 20/06/2016 N45 40.776 W65 29.508 0.0049 0.0041 0.0045 Parsons Cross Section 7 PRSC7 20/06/2016 N45 40.791 W65 29.496 0.0062 0.0047 0.0055 Parsons Cross Section 8 PRSC8 20/06/2016 N45 40.816 W65 29.501 0.0051 0.0047 0.0049 Parsons Cross Section 9 PRSC9 20/06/2016 N45 40.840 W65 29.491 0.005 0.0046 0.0048 Parsons Cross Section 10 PRSC10 20/06/2016 N45 40.859 W65 29.464 0.0088 0.0078 0.0083 Parsons Cross Section 11 PRSC11 20/06/2016 N45 40.888 W65 29.461 0.007 0.0078 0.0074 Parsons Cross Section 12 PRSC12 20/06/2016 N45 40.933 W65 29.433 0.0074 0.007 0.0072 Parsons Cross Section 13 PRSC13 20/06/2016 N45 40.999 W65 29.403 0.007 0.0073 0.0072 Parsons Cross Section 14 PRSC14 20/06/2016 N45 41.029 W65 29.395 0.0091 0.0093 0.0092 Parsons Cross Section 15 PRSC15 20/06/2016 N45 41.124 W65 29.378 0.009 0.0081 0.0086 Parsons Cross Section 16 PRSC16 20/06/2016 N45 41.197 W65 29.303 0.008 0.0077 0.0079 Parsons Cross Section 17 PRSC17 20/06/2016 N45 41.223 W65 29.215 0.0123 0.0123 0.0123 Parsons Cross Section 18 PRSC18 20/06/2016 N45 41.340 W65 29.106 0.0126 0.0134 0.0130

180 Table C-7: ISE results, tracer test September 2016.

PRS23 PRS22 PRS21 PRSPZ Time PRSS1 PRSS2 PRSS5 PRS12 PRS11 PRS8 PRS7 PRS6 PRS5 PRS4 50 50 50 4 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Pre- -- mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L Injecti 157.7 167.7 161.5 167.5 169.3 170.5 172.7 170.8 174.8 177.5 178.3 181.1 175.9 on -- mV mV mV mV mV mV mV mV mV mV mV mV mV 0.0 ------mg/L 17:50 168.4 ------mV 0.0 ------mg/L 18:20 159.8 ------mV 7.7 ------mg/L 19:20 119.9 ------mV 0.0 ------mg/L 19:52 158.2 ------mV 40 ------mg/L 20:55 88.0 ------mV 2.0 ------mg/L 21:10 137.3 ------mV 30 ------mg/L 23:30 95.0 ------mV 20 ------0:30 mg/L -- -- 106.3 ------

181 mV 4.0 ------mg/L 1:00 142.4 ------mV 13 ------mg/L 2:45 109.1 ------mV 4.0 ------mg/L 2:54 130.0 ------mV 40 ------mg/L 5:50 89.6 ------mV 30 ------mg/L 6:10 91.1 ------mV 20 ------mg/L 6:25 107.5 ------mV 40 ------mg/L 8:00 83.8 ------mV 20 ------mg/L 8:10 97.1 ------mV 0.4 ------mg/L 8:15 142.9 ------mV 55 10:20 ------mg/L

182 75.5 ------mV 50 ------mg/L 10:35 77.2 ------mV 22 ------mg/L 10:45 93.7 ------mV 3.0 ------mg/L 10:53 130.6 ------mV 28 ------mg/L 11:05 91.9 ------mV 30 ------mg/L 13:45 96.6 ------mV 30 ------mg/L 13:55 94.1 ------mV 30 ------mg/L 14:00 95.6 ------mV 9.7 ------mg/L 14:05 122.6 ------mV 6.3 ------mg/L 14:20 132.3 ------mV

183 Table C-8: Bromide results, autosamplers September 2016.

Name Upstream/Downstream Sample Latitude Longitude Distance D/S of injection Bromide concentration sampler ID: (m) (mg/L) Bottom 1 Downstream Bot-1 N45 W65 773.50 0.15319855 40.913 29.451 Bottom 2 Downstream Bot-2 N45 W65 773.50 0.1411117 40.913 29.451 Bottom 3 Downstream Bot-3 N45 W65 773.50 0.224879 40.913 29.451 Bottom 4 Downstream Bot-4 N45 W65 773.50 0.231293 40.913 29.451 Bottom 5 Downstream Bot-5 N45 W65 773.50 0.266277 40.913 29.451 Bottom 6 Downstream Bot-6 N45 W65 773.50 0.246093 40.913 29.451 Bottom 7 Downstream Bot-7 N45 W65 773.50 0.2446855 40.913 29.451 Bottom 8 Downstream Bot-8 N45 W65 773.50 0.2474355 40.913 29.451 Bottom 9 Downstream Bot-9 N45 W65 773.50 0.163962 40.913 29.451 Bottom Downstream Bot-10 N45 W65 773.50 0.2569955 10 40.913 29.451 Bottom Downstream Bot-11 N45 W65 773.50 0.2746555 11 40.913 29.451 Bottom Downstream Bot-12 N45 W65 773.50 0.239695 12 40.913 29.451 Bottom Downstream Bot-13 N45 W65 773.50 0.2204385 13 40.913 29.451 Bottom Downstream Bot-14 N45 W65 773.50 0.3010215 14 40.913 29.451 Bottom Downstream Bot-15 N45 W65 773.50 0.240217 15 40.913 29.451 Bottom Downstream Bot-16 N45 W65 773.50 0.259522 16 40.913 29.451 Bottom Downstream Bot-17 N45 W65 773.50 0.304901 17 40.913 29.451 Bottom Downstream Bot-18 N45 W65 773.50 0.3337815

184 18 40.913 29.451 Bottom Downstream Bot-19 N45 W65 773.50 0.249558 19 40.913 29.451 Bottom Downstream Bot-20 N45 W65 773.50 0.1698474 20 40.913 29.451 Bottom Downstream Bot-21 N45 W65 773.50 0.1720445 21 40.913 29.451 Bottom Downstream Bot-22 N45 W65 773.50 0.1767025 22 40.913 29.451 Middle 1 Upstream Mid-1 N45 W65 373.90 0.3310195 40.736 29.532 Middle 2 Upstream Mid-2 N45 W65 373.90 0.311623 40.736 29.532 Middle 3 Upstream Mid-3 N45 W65 373.90 0.3454915 40.736 29.532 Middle 4 Upstream Mid-4 N45 W65 373.90 0.18741325 40.736 29.532 Middle 5 Upstream Mid-5 N45 W65 373.90 0.337891 40.736 29.532 Middle 6 Upstream Mid-6 N45 W65 373.90 0.304981 40.736 29.532 Middle 7 Upstream Mid-7 N45 W65 373.90 0.318864 40.736 29.532 Middle 8 Upstream Mid-8 N45 W65 373.90 1.151982 40.736 29.532 Middle 9 Upstream Mid-9 N45 W65 373.90 0.298649 40.736 29.532 Middle 10 Upstream Mid-10 N45 W65 373.90 0.06159435 40.736 29.532 Middle 11 Upstream Mid-11 N45 W65 373.90 0.3264385 40.736 29.532 Middle 12 Upstream Mid-12 N45 W65 373.90 0.2013232 40.736 29.532 Middle 13 Upstream Mid-13 N45 W65 373.90 0.4103325 40.736 29.532 Middle 14 Upstream Mid-14 N45 W65 373.90 0.4586565 40.736 29.532 Middle 15 Upstream Mid-15 N45 W65 373.90 0.530922 40.736 29.532

185 Middle 16 Upstream Mid-16 N45 W65 373.90 0.9384875 40.736 29.532 Middle 17 Upstream Mid-17 N45 W65 373.90 1.539925 40.736 29.532 Middle 18 Upstream Mid-18 N45 W65 373.90 2.1431 40.736 29.532 Middle 19 Upstream Mid-19 N45 W65 373.90 0.6732425 40.736 29.532 Middle 20 Upstream Mid-20 N45 W65 373.90 2.401765 40.736 29.532 Middle 21 Upstream Mid-21 N45 W65 373.90 2.731855 40.736 29.532 Middle 22 Upstream Mid-22 N45 W65 373.90 3.605785 40.736 29.532

186 Table C-9: Water quality parameters, Tracer test Sept 6-7 2016.

ALL pH DO Conductivity Specific Conductance Salinity Temperature MEASUREMENTS AT PRSS1 (25 m upstream of Probe Unit Probe % mg/L Probe µS/cm Probe µS/cm Probe ppt Probe °C injection site) YSI 63 7.87 YSI 85 116.2 10.79 YSI 63 296.8 YSI 63 335.3 YSI 63 0.2 YSI 63 19.1 13:20 ------YSI 85 299.1 YSI 85 338.2 YSI 85 0.2 YSI 85 19.1 YSI 63 7.44 YSI 85 86.4 7.76 YSI 63 311.0 YSI 63 344.0 YSI 63 0.2 YSI 63 20.3 17:30 ------YSI 85 316.6 YSI 85 349.1 YSI 85 0.2 YSI 85 20.1 YSI 63 7.4 YSI 85 82.8 7.54 YSI 63 316.0 YSI 63 349.3 YSI 63 0.2 YSI 63 20.1 18:30 ------YSI 85 317.0 YSI 85 351.0 YSI 85 0.2 YSI 85 19.9 YSI 63 7.26 YSI 85 78.0 7.13 YSI 63 314.4 YSI 63 348.9 YSI 63 0.2 YSI 63 19.9 19:30 ------YSI 85 316.6 YSI 85 352.4 YSI 85 0.2 YSI 85 19.7 YSI 63 7.15 YSI 85 73.4 6.76 YSI 63 315.7 YSI 63 353.3 YSI 63 0.2 YSI 63 19.5 20:30 ------YSI 85 312.6 YSI 85 350.9 YSI 85 0.2 YSI 85 19.3 YSI 63 7.17 YSI 85 72.4 6.80 YSI 63 313.2 YSI 63 353.8 YSI 63 0.2 YSI 63 19.1 21:30 ------YSI 85 311.3 YSI 85 351.7 YSI 85 0.2 YSI 85 18.9 YSI 63 7.13 YSI 85 72.3 6.75 YSI 63 311.0 YSI 63 353.0 YSI 63 0.2 YSI 63 18.9 22:30 ------YSI 85 308.7 YSI 85 352.0 YSI 85 0.2 YSI 85 18.6 YSI 63 7.14 YSI 85 72.3 6.74 YSI 63 310.5 YSI 63 352.6 YSI 63 0.2 YSI 63 18.8 23:30 ------YSI 85 306.3 YSI 85 350.9 YSI 85 0.2 YSI 85 18.5 YSI 63 7.1 YSI 85 71.6 6.68 YSI 63 311.4 YSI 63 352.8 YSI 63 0.2 YSI 63 18.9 0:30 ------YSI 85 309.6 YSI 85 353.6 YSI 85 0.2 YSI 85 18.5 YSI 63 7.04 YSI 85 67.6 6.33 YSI 63 312.0 YSI 63 353.9 YSI 63 0.2 YSI 63 18.9 1:30 ------YSI 85 311.3 YSI 85 355.1 YSI 85 0.2 YSI 85 18.5 YSI 63 6.99 YSI 85 67.9 6.36 YSI 63 310.7 YSI 63 352.2 YSI 63 0.2 YSI 63 18.8 2:30 ------YSI 85 309.6 YSI 85 354.2 YSI 85 0.2 YSI 85 18.4 YSI 63 7.0 YSI 85 69.5 6.53 YSI 63 309.4 YSI 63 351.3 YSI 63 0.2 YSI 63 18.8 3:30 ------YSI 85 309.4 YSI 85 354.3 YSI 85 0.2 YSI 85 18.4

187 YSI 63 6.98 YSI 85 70.2 6.63 YSI 63 307.5 YSI 63 350.4 YSI 63 0.2 YSI 63 18.7 4:30 ------YSI 85 307.3 YSI 85 353.8 YSI 85 0.2 YSI 85 18.1 YSI 63 6.83 YSI 85 70.2 6.62 YSI 63 306.1 YSI 63 349.2 YSI 63 0.2 YSI 63 18.6 5:30 ------YSI 85 306.7 YSI 85 353.8 YSI 85 0.2 YSI 85 18.0 YSI 63 5.77 YSI 85 57.8 5.53 YSI 63 345.2 YSI 63 397.8 YSI 63 0.2 YSI 63 18.0 5:45 ------YSI 85 345.5 YSI 85 404.0 YSI 85 0.2 YSI 85 17.4 YSI 63 4.68 YSI 85 68.8 6.73 YSI 63 327.1 YSI 63 386.7 YSI 63 0.2 YSI 63 17.0 6:15 ------YSI 85 321.6 YSI 85 384.0 YSI 85 0.2 YSI 85 16.5 YSI 63 6.67 YSI 85 71.0 6.88 YSI 63 302.8 YSI 63 354.7 YSI 63 0.2 YSI 63 17.4 6:30 ------YSI 85 302.5 YSI 85 358.8 YSI 85 0.2 YSI 85 16.8 YSI 63 6.85 YSI 85 72.2 6.86 YSI 63 304.9 YSI 63 349.0 YSI 63 0.2 YSI 63 18.4 6:35 ------YSI 85 304.8 YSI 85 353.5 YSI 85 0.2 YSI 85 17.8 YSI 63 7.01 YSI 85 74.0 7.05 YSI 63 303.3 YSI 63 347.1 YSI 63 0.2 YSI 63 18.4 7:30 ------YSI 85 303.0 YSI 85 352.6 YSI 85 0.2 YSI 85 17.6 YSI 63 6.92 YSI 85 78.2 7.43 YSI 63 302.7 YSI 63 346.3 YSI 63 0.2 YSI 63 18.5 8:30 ------YSI 85 302.6 YSI 85 351.4 YSI 85 0.2 YSI 85 17.7 YSI 63 6.73 YSI 85 81.4 7.67 YSI 63 302.5 YSI 63 348.7 YSI 63 0.2 YSI 63 19.1 9:40 ------YSI 85 302.8 YSI 85 341.6 YSI 85 0.2 YSI 85 18.1 YSI 63 6.89 YSI 85 95.0 8.78 YSI 63 306.1 YSI 63 337.7 YSI 63 0.2 YSI 63 20.2 11:25 ------YSI 85 306.6 YSI 85 345.3 YSI 85 0.2 YSI 85 19.1 YSI 63 7.12 YSI 85 102.2 9.08 YSI 63 319.3 YSI 63 337.9 YSI 63 0.2 YSI 63 22.2 13:10 ------YSI 85 320.6 YSI 85 345.3 YSI 85 0.2 YSI 85 21.2 YSI 63 7.15 YSI 85 88.5 7.79 YSI 63 328.5 YSI 63 341.2 YSI 63 0.2 YSI 63 22.8 15:30 ------YSI 85 330.0 YSI 85 350.9 YSI 85 0.2 YSI 85 21.9

188 Table C-10: Bromide concentrations, tracer test September 2016.

Name Baseline/Post Sample ID: Latitude Longitude Distance D/S of injection Bromide concentration injection (m) (mg/L) Parsons Brook S1 Baseline PRSS1090601 N45 W65 -26.6 0.6259415 40.580 29.358 Parsons Brook S2 Baseline PRSS2090601 N45 W65 25.33 0.291225 40.601 29.376 Parsons Brook S5 Baseline PRSS5090601 N45 W65 119.53 0.3992315 40.635 29.419 Parsons Brook Baseline PRS23500906 N45 W65 210.32 0.3031355 2350 01 40.672 29.462 Parsons Brook Baseline PRS22500906 N45 W65 305.33 0.3307435 2250 01 40.704 29.512 Parsons Brook Baseline PRS21500906 N45 W65 373.97 0.3930284 2150 01 40.736 29.532 Parsons Brook 12 Baseline PRS12090601 N45 W65 476.00 0.405174 40.784 29.504 Parsons Brook 11 Baseline PRS11090601 N45 W65 599.00 0.2647475 40.836 29.500 Parsons Brook 9 Baseline PRS9090601 N45 W65 734.00 0.2133065 40.892 29.461 Parsons Brook 8 Baseline PRS8090601 N45 W65 807.00 0.206959 40.926 29.440 Parsons Brook 7 Baseline PRS7090601 N45 W65 957.00 0.2072165 40.978 29.391 Parsons Brook 6 Baseline PRS6090601 N45 W65 1056.00 0.2258665 41.027 29.389 Parsons Brook 5 Baseline PRS5090601 N45 W65 1225.00 0.1466854 41.113 29.381 Parsons Brook 4 Baseline PRS4090601 N45 W65 1381.00 0.1870465 41.186 29.315 Parsons Brook S1 Post injection PRSS1090699 N45 W65 -26.6 0.678313 40.580 29.358 Parsons Brook S2 Post injection PRSS2090699 N45 W65 25.3 29.82395 40.601 29.376 Parsons Brook S6 Post injection PRSS6090699 N45 W65 65.72 24.578 40.617 29.377 Parsons Brook S7 Post injection PRSS7090699 N45 W65 69.43 19.15545

189 40.618 29.403 Parsons Brook S5 Post injection PRSS5090699 N45 W65 119.53 18.44945 40.635 29.419 Parsons Brook S8 Post injection PRSS8090699 N45 W65 142.37 16.41715 40.645 29.435 Parsons Brook Post injection PRS23500906 N45 W65 210.32 20.09785 2350 99 40.672 29.462 Parsons Brook 14 Post injection PRS14090699 N45 W65 260.00 14.9137 40.690 29.489 Parsons Brook Post injection PRS22500906 N45 W65 305.33 6.958345 2250 99 40.704 29.512 Parsons Brook Post injection PRS21500906 N45 W65 373.97 4.589455 2150 99 40.736 29.532 Parsons Brook S9 Post injection PRS9090699 N45 W65 423.23 1.60918 40.757 29.509

190