QUALITY OF STREAMS IN THE UPPER SKAGIT RIVER WATERSHED USING THE REFERENCE CONDITION APPROACH
Final Report
February 4, 2010
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Quality of streams in the upper Skagit River watershed
QUALITY OF STREAMS IN THE UPPER SKAGIT RIVER WATERSHED USING THE REFERENCE CONDITION APPROACH
Final Report
Submitted to
Skagit Environmental Endowment Commission North Vancouver, B.C.
Prepared by
C.J. Perrin and S. Bennett Limnotek Research and Development Inc., Vancouver, BC
February 4, 2010
Limnotek February 2010 Quality of streams in the upper Skagit River watershed
Citation: Perrin, C.J. and S. Bennett. 2010. Quality of streams in the upper Skagit River watershed using the reference condition approach. Report prepared by Limnotek Research and Development Inc. for Skagit Environmental Endowment Commission. 73p.
Cover photo: Skagit River upstream of Ross Lake, 2007. Photo by Chris Perrin.
© 2010 Skagit Environmental Endowment Commission.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without prior permission from the Skagit Environmental Endowment Commission.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed
EXECUTIVE SUMMARY
The reference condition approach (RCA) was used to describe baseline water quality in streams of the 1,054 km2 Skagit River watershed north of the Canada/USA border. Reference condition describes a suite of biological and habitat attributes found at sites having little or no exposure to stressors caused by land use and other human activities. The premise behind the RCA is to sample a large number of sites in reference condition and use relationships between biological and environmental descriptors to build a predictive model that allows comparison of a test site with a reference condition. The Skagit model was called the Beast Assessment of Skagit Streams (BASS) in which “Beast” is an acronym for “Benthic Assessment of Sediment”, a type of RCA model that has been developed in Canada as a national bioassessment protocol. The bioassessment procedures are explained on the website called CABIN
(Hhttp://cabin.cciw.ca/application/welcome.asp?Lang=en-caH ). The project provides the Skagit Environmental Endowment Commission (SEEC) with an overview of present water quality in the Canadian portion of the Skagit watershed and a framework for monitoring potential development and natural changes to water quality in future years.
A total of 49 reference sites were sampled throughout the Skagit watershed in late August of 2007 and 2008, resulting in a sample density of 22 km2/sample, which is among the most dense sample layouts in regional scale RCA programs worldwide. Ten additional sites were sampled downstream of the Sunshine Valley Resort, the Giant Copper mineral exploration site on Silverdaisy Mountain, near highway drainage, or near unstable roads and range lands where cattle grazing was occurring. These “test” sites were potentially disturbed and were excluded from reference condition modeling. Assemblages of benthic invertebrates were sampled because they are good indicators of water quality. Accompanying environmental data was measured in the field or derived from GIS data layers. It included geomorphic variables, forest and riparian cover, physical habitat attributes, land use variables, water temperature, and concentrations of nutrients, basic chemical analytes, and metals in water. The benthic invertebrates were sampled using kick net procedures and family level enumerations were used in the RCA modeling.
Clustering and ordination techniques (Step 1 in RCA modeling) revealed three biological sample groups defined by differences in abundance of mayflies, stoneflies, chironomids, and oligochaetes. Discriminant function analysis (which defined the actual RCA model) showed the three sample groups could be distinguished by elevation of the sampling site and percent wetland in the land area upstream of the sampling site. Model testing showed high accuracy, precision and sensitivity that were within the range of tests reported for other published models.
Most of the Skagit streams are pristine and are characterized by diverse and abundant invertebrate communities dominated by larval forms of aquatic insects that indicate high water quality. These streams are clear and cool, they have low alkalinity, moderate dissolved solids, no metals contamination, and low macronutrient concentrations. The molar N:P showed biological productivity in some of these streams is potentially limited by phosphorus while in others, nitrogen limitation is more common. BASS showed a few point source disturbances or deviation from reference condition. Clearing of riparian vegetation downstream of the Sunshine Valley resort development was linked to biological enrichment of the Sumallo River. Silver Daisy Creek near its confluence with the Skagit River had unusually low numbers of mayflies. The effect was
Limnotek February 2010 Quality of streams in the upper Skagit River watershed linked to metals contamination likely coming from a nearby and abandoned mine adit from which metals drainage was observed. High Cd concentration of 0.05 μg/L in Silver Daisy Creek was above the water quality guideline of 0.017 μg/L, potentially producing toxicity. No effect from the Giant Copper property located at the top of Silver Daisy Mountain was found from sampling multiple sites draining the property from several aspects. Cattle grazing and sand accumulation possibly from road cuts at high elevations on range lands were linked to low overall invertebrate abundance. The only sampling site found to be extremely divergent from reference was a small tributary of the Skaist River near Highway 3. It was embedded with fines and had anomalous concentrations of Mo and SO4 that show possible upstream land disturbance exposing mineralization. Norwegian Creek which drains the north slope of Silver Daisy Mountain was unique in having a divergent invertebrate assemblage that was linked to extreme nitrogen deficiency.
BASS will be available for routine site testing after a few administrative steps are followed to have the model uploaded to the CABIN website. All raw data has already been uploaded to the site. As a result, CABIN is a data warehouse for the project. Recommendations are related to upload of the final model to CABIN and ongoing use of BASS:
1. Once the final version of this report is accepted by SEEC, authorization must be given to the CABIN representative of Environment Canada to review the report and proceed with uploading of the model to the CABIN website. This step is required by Environment Canada as custodians of the CABIN website. Once the model is uploaded, it is available for site testing by authorized users.
2. It is recommended that SEEC authorize selected members or others to complete CABIN training and that those people be involved in implementing site testing as needed. Once the training is complete, each person will be given a username and passcode for access to CABIN. Various levels of training are available. Information can be found at
Hhttp://www.unb.ca/research/institutes/cri/opportunities/courses/cabin-
rcba/index.htmlH.
3. It is recommended that BASS be used as a decision support tool for monitoring water quality in the Skagit watershed. Existing or new land disturbance and associated water protection measures can be followed using BASS to monitor surface water quality. It can provide criteria to show if stream protection measures are working and it can provide evidence to show if improvements are needed. BASS can also be used to monitor natural changes to water quality in the Skagit watershed. For example, a subset of reference sites may be selected for sampling annually or every set number of years. A rough guide might be to sample 10 sites per year. Results from this testing can show whether stream quality is sequentially changing over time, potentially contributing to interpretations of effects of climate change or other regional environmental change that may affect water quality (e.g. fire or insect infestation). In these ways, bioassessment using BASS is a screening and learning tool that can feed into decisions to protect water quality.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed
ACKNOWLEDGEMENTS
This project was completed under contract to the Skagit Environmental Endowment Commission (SEEC). Peter Caverhill (SEEC Commissioner) was the contract manager. Commission members including, Duane Jesson (BC Ministry of Environment), Scott Powell (SEEC U.S. Secretary), Ed Connor (Seattle City Light) and Dr. Ken Ashley (Science Advisor) are thanked for numerous discussions and input throughout the project, particularly in early stages of study design. Pete Caverhill is thanked for ensuring all Commission members were kept up to date with project activities. Other Commission members including Ken Farquharson, Peter Kennedy, and Tony Kilduff, are thanked for their input. Particular thanks goes to SEEC Canadian Secretary, Chris Tunnoch and Betsy Terpsma for time and effort in managing the contract. Staff of Triton Environmental Consultants, mainly Ryan Liebe, Scott McQuarrie, and Dave Warburton are thanked for their support and cooperation with the integration of their fish sampling activities with the present water quality sampling. The authors were assisted in the field by Kiyo Masuda, Marc Laynes, and Scott Cope. K. Masuda organized all sampling equipment and logistics. Helicopter support was provided by Valley Helicopters, Hope, B.C. Danusia Dolecki completed all benthic invertebrate enumerations. Water lab services were provided by the Pacific Environmental Science Centre and the Fisheries and Oceans specialty low level nutrients lab located at Cultus Lake. GIS support was provided by Scott McQuarrie of Triton Environmental Consultants. Stephanie Strachan of Environment Canada is thanked for support of the project and for providing funding to pay part of the water lab fees in 2007. Leon Gabor (BC Ministry of Environment) and Ashley Rawhouser (North Cascades National Park) are thanked for review of the draft report.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed
TABLE OF CONTENTS
Page
EXECUTIVE SUMMARY ...... III ACKNOWLEDGEMENTS ...... V TABLE OF CONTENTS ...... VI LIST OF FIGURES ...... VIII LIST OF TABLES ...... IX 1 INTRODUCTION ...... 1 2 LAYOUT AND DESIGN ...... 4 3 METHODS ...... 6 3.1 Study Area ...... 6 3.2 Sample Site Selection ...... 8 3.3 Field Logistics ...... 11 3.4 Field Sampling and Analysis in Laboratories ...... 11 3.5 Data Compilation ...... 16 3.6 Selection of Habitat Variables for Modeling ...... 22 3.7 Model Development and Testing ...... 24 4 RESULTS ...... 29 4.1 The BASS Model ...... 29 4.2 Model testing ...... 32 4.3 Other water quality variables ...... 39 5 DISCUSSION ...... 41 5.1 Predictor variables for BASS ...... 41 5.2 Model Performance ...... 43 5.3 Water quality in Skagit streams ...... 45 6 RECOMMENDATIONS ...... 47 7 LIST OF REFERENCES ...... 51 8 APPENDIX A: FIELD SHEET FOR RCA ASSESSMENTS ...... 58 9 APPENDIX B: ASSESSMENTS OF SAMPLES FOR TEST OF MODEL PRECISION 66 9.1 Introduction ...... 66 9.2 Sample S2 (duplicate reference sample) ...... 66 9.3 Sample S29 (duplicate reference sample) ...... 66 9.4 Sample 42 (duplicate reference sample) ...... 67
Limnotek February 2010 Quality of streams in the upper Skagit River watershed
9.5 Sample 42 (triplicate reference sample) ...... 67 10 APPENDIX C: ASSESSMENTS OF SAMPLES FOR TEST OF MODEL SENSITIVITY ...... 68 10.1 Introduction ...... 68 10.2 Sample S1 from 2007 (Sumallo River downstream of Sunshine Valley) ...... 68 10.3 Sample S1 from 2008 (Sumallo River downstream of Sunshine Valley) ...... 68 10.4 S24 from 2007 (Silver Daisy Creek) ...... 69 10.5 S24 from 2008 (Silver Daisy Creek) ...... 69 10.6 S41 (Captain Grant Creek) ...... 70 10.7 S43 (Noname tributary of Skaist River) ...... 70 10.8 S45 (Norwegian Creek) ...... 71 10.9 S46 (Upper Smitheram Creek) ...... 71 10.10 S49 (Skagit River upstream of Smitheram Creek) ...... 72 10.11 S64 (Hilton Creek) ...... 72 11 APPENDIX D: ASSESSMENTS OF NON-REFERENCE SAMPLES NOT USED FOR MODEL TESTING ...... 73 11.1 S33 (Lower Sumallo River) ...... 73 11.2 S47 (Lower Smitheram Creek) ...... 73
Limnotek February 2010 Quality of streams in the upper Skagit River watershed
LIST OF FIGURES
Page
Figure 1. The Skagit River watershed upstream of Ross Lake showing park areas, mine and recreational development sites, road and trail access, sampling sites, and watercourses...... 3 Figure 2. Ordination plots in each of 3 dimensions (top, middle, bottom) for a hypothetical test site (red) and reference sites (blue) belonging to a given reference sample group...... 28 Figure 3. Cluster dendrogram of square-root transformed abundance data from all reference sites...... 31 Figure 4. NMDS ordination of reference samples labelled by sample group...... 32 Figure 5. Image of a mine adit from which seepage was found entering Silver Daisy Creek upstream of the S24 sampling site...... 45 Figure 6. Schematic illustration of where the BASS fits in environmental decision- making in the Skagit watershed...... 49
Limnotek February 2010 Quality of streams in the upper Skagit River watershed
LIST OF TABLES
Page
Table 1. List of reference site densities known among regions where RCA models are in use...... 6 Table 2. List of sampling site numbers as they appear in Figure 1 with associated local names...... 9 Table 3. Methods for measurement of habitat variables in the field and associated laboratory procedures...... 13 Table 4. List of data sources, method of calculation, and description of habitat variables extracted from landscape databases...... 17 Table 5. Candidate natural gradient predictor variables used in the RCA modeling...... 23 Table 6. Average values of invertebrate metrics compared between samples in groups defined by clustering and NMDS...... 30 Table 7. Statistics of the discriminant model for Skagit streams...... 30 Table 8. Summary of assessments for replicate reference samples collected for the test of model precision...... 33 Table 9. Summary of assessment at test sites to examine model sensitivity. Test output is shown in Appendix C...... 34 Table 10. Differences in invertebrate assemblages between test and corresponding reference sites...... 37 Table 11. Habitat differences between reference and non-reference sites...... 39 Table 12. Mean (±SE) values of stressor gradient variables among reference sample groups...... 40
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 1
1 0BINTRODUCTION
The Skagit Environmental Endowment Commission (SEEC) is mandated by treaty between Canada and the USA to support initiatives to maintain environmental quality in the Skagit watershed upstream of the Ross Dam. An estimated 60% of the watershed in Canada consists of protected lands of Manning Provincial Park, Skagit Valley Provincial Park, and the Cascade Recreational Area that are used for outdoor recreation, research, and education. The remaining 40% is not protected and can be developed, which in the past included logging, mining, highways, water extraction, and human settlement. Some of this unprotected land is within Park boundaries (Figure 1). The watershed is highly valued for recreation, wilderness, fish populations that support a renowned recreational fishery, and high biodiversity. The SEEC maintains several goals, including protection and restoration of the natural history through land and water management and research to improve understanding and management of the biological integrity of the whole watershed.
Among possible developments, Sunshine Valley (Rota Development) that is located adjacent to Hwy 3 on the Sumallo River, an upper tributary of the Skagit River (Figure 1), is considering possible expansion. The population of 165 residents and 213 dwelling units (2001 census cited by Ferguson, 2005) may increase under a vision to develop additional villages with condominiums, chalets and cluster cabins, a retirement complex, campsites and a commercial area (Ferguson 2005). With this development, demand for water and requirements for waste treatment and disposal and storm water management may introduce environmental stresses on the Skagit River. In addition, the village owners have made application to extract 1,090 m3 of spring water per day from the Sumallo River to produce bottled water. This water withdrawal may affect bull trout which are known to rely on spring water for spawning and rearing (SEEC letter to Land and Water BC dated July 30, 2003).
Mine development is being considered on claims known as the Giant Copper property on Silverdaisy Mountain (Figure 1). It is drained to the west by Silverdaisy Creek and to the north by Norwegian Creek, Smitheram Creek, and other small tributaries in the Skagit headwaters. Imperial Metals Corporation retains valid title of the claims through 2011 and has spent over $1 million annually for the past 10 years to develop the property. The company reports reserves of 3.7 million tonnes grading 1.08% copper, 0.47g/t gold, 19.18 g/t silver and 0.010% molybdenum based on 22,000 metres of drilling and 6 kilometres of underground workings that date to the 1930’s
(Hhttp://www.imperialmetals.com/s/GiantCopper.aspH ). Drilling results from 2006 indicated higher grades than were found earlier, suggesting the reserves may be greater than originally estimated. If mine development proceeds, protection of downstream water quality will be required as part of permitting. Even with environmental protection
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 2 measures in place, the mine development may increase risk of change to water quality in the Skagit River.
In view of the environmental values in the Skagit River system that may be affected by land development, the SEEC is interested in establishing water quality monitoring within the Skagit drainage in Canada. An objective of the SEEC is to measure baseline water quality that can be used to assess change as a result of the mineral exploration and potential extraction, increased human settlement, other developments, and even climate change that may occur in the future in the Skagit watershed. This report outlines results of sampling to describe baseline water quality of Skagit watershed streams north of the Canada/USA border in 2007 and 2008.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 3
Figure 1. The Skagit River watershed upstream of Ross Lake showing park areas, mine and recreational development sites, road and trail access, sampling sites, and watercourses.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 4
2 1BLAYOUT AND DESIGN
Sample sites were situated throughout the Canadian portion of the Skagit watershed. Streams draining the two known land developments (Sunshine Valley and Giant Copper) were included. No definitive plans have been made for either a mine or village development, making it difficult to lay out a sampling design that will remain valid over the long term. Without a spatial footprint, there is risk that sampling sites laid out in what may seem to be good reference locations today may become disturbed after development proceeds, thus limiting the use of monitoring data in future assessments in which designs require reference sites to remain undisturbed in future years. From this viewpoint it made sense to lay out many sampling sites over the whole upper Skagit watershed that can be used not only for assessing possible impacts from the two known developments, but also address other stresses that may occur in the future. Most importantly, data from the whole watershed will establish reference conditions for presently undisturbed and highly valued stream ecosystems throughout the drainage. This approach is being used by the SEEC for fish population monitoring (Triton 2008). A combination of fish and water quality sampling over a network of similar sites will be very powerful for assessing future stresses, wherever they may occur.
Water chemistry is commonly used to indicate water quality impairment (e.g. anomalous dissolved Cu concentrations indicates that copper contamination is present).
Comparison of chemical concentrations to environmental quality guidelines (Hhttp://ceqg-
rcqe.ccme.ca/H ) can indicate that some analyte is or is not potentially toxic. Actual impact by some stressor, however, can only be determined by monitoring of biological communities. Benthic invertebrates, for example, are used in over 90% of running water quality assessment programs in the United States (Diamond et al. 1996) and are the basis of national and continental water quality assessment programs in the UK (Wright et al. 2000), Australia (Parsons and Norris 1996), Europe (European Commission 2000, Wallin et al. 2003), and Canada (Bailey et al. 2004, Sylvestre et al. 2005, Reynoldson et al. 1997, Reynoldson et al. 2001).
Benthic invertebrates are good indicators of water quality (Rosenberg and Resh 1993) and ecosystem health (Reice and Wohlenberg 1993, Boulton 1999, Norris and Thoms 1999, Norris and Hawkins 2000). Because of continuous exposure to water flow, they provide an integrated record of physical and chemical environmental quality. They are ubiquitous, largely sedentary, and there are large numbers of species that can provide an integrated measure of response to stress. These characteristics allow effective spatial and temporal analyses of disturbance among streams, within reaches of streams, and between streams over wide geographic areas (Bailey et al. 2004). Invertebrates are a major food supply for fish in British Columbia streams, and thus provide an indication of food available to fish populations through time and space. Benthic invertebrates respond rapidly to change in environmental conditions. They, along with epilithic algae, are often the first organisms of an aquatic community to respond to environmental stress and they
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 5 are usually the first to recover from it. The result is that monitoring of benthic invertebrates can provide a clear indication of change in the quality of water they inhabit.
Testing an effect or degree of disturbance on biota in surface waters can involve multiple lines of evidence from univariate statistical tests. A common layout involves sampling at reference and potentially impacted sites before and after start-up of a disturbance or discharge, facilitating a before-after/control-impact (BACI) design (Stewart-Oaten et al. 1986). It can be used to test for effects of a known point or non- point source discharge on metrics using analysis of variance (ANOVA). There are several variations of the BACI design ranging from single control and treatment sites from which replicates can be samples collected through time, to stronger layouts involving multiple control sites that are analysed by asymmetric analysis of variance (Underwood 1994). All these designs involve univariate analytical tests on one metric that summarizes counts or biomass of important taxa or all taxa combined. Metrics might be the sum of mayfly, stonefly, and caddisfly counts; abundance of chironomids; etc. Each metric must be run in a separate ANOVA. Output from many analyses might be combined to form several lines of evidence to determine the effect of specific disturbances and to contribute to analyses examining cause – effect pathways that are needed to support water management decisions. Most of these approaches require long time periods before a definitive description of water quality and cause – effect pathways may be found although the time required for field testing can be shortened and additional control can be applied to the tests by running experiments (e.g. Perrin and Richardson 1997). There must be enough foresight of the putative impact to collect the ‘before’ data at both the test and suitable control sites. In this regard, the stressor or impact must be known before it actually occurs. This requirement eliminates BACI designs from assessments of “accidents” or surprising events. The designs can be expensive and impractical to complete on a large regional scale. In addition, basic assumptions of the statistical analyses may be violated, perhaps due to insufficient funding to collect enough samples, difficult logistics that prevent repeated sampling of true replicates, or other factors that constrain an ideal layout (Bailey et al 2004). These approaches are most powerful at the site specific level and are best suited to definitive experimentation rather than providing evidence of water quality condition at a regional or watershed level.
An alternative is the reference condition approach (RCA) to monitoring (Bailey et al. 2004). Reference condition describes a suite of biological and habitat attributes found at sites having little or no exposure to stressors caused by land use and other human activities. The premise behind the RCA is to sample a large number of sites in reference condition and use relationships between biological and environmental descriptors to build a predictive model that allows comparison of a test site with an appropriate reference condition. The test site is determined to be in reference condition if the biological assemblage (composition and abundance) is similar to that of the reference sites. If the test site falls outside the range of natural variability found at reference sites, the null hypothesis that the test site is the same as the reference group
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 6 is rejected (Bailey et al. 2004). Thereby, a pool of many sites, as opposed to repeat samples of the same site that can be the basis of a BACI design, serve as replicates (Reynoldson et al. 1997).
Sampling of streams in the upper Skagit River watershed was designed for development of an RCA model. The distribution of sampling sites was similar to that assigned to fish sampling in 2007 (Triton 2007), allowing the two different data sets to be compared and contrasted or even integrated in later analyses. The sampling was completed at 30 reference sites in 2007 and 29 sites in 2008, including replicate sampling at some sites for use in testing model performance. Ten sites were strategically placed near land development at Sunshine Valley, Giant Copper, along Highway 3, and near unstable roads and range lands where cattle grazing was occurring for testing model sensitivity. A total of 49 undisturbed sites were used to build the reference condition model. The areal sample density was 22 km2/sample, which is equal to that in the UK where the areal density is best among RCA models worldwide. Low areal density means high number of sites per unit area, which is desirable for RCA modeling (Table 1).
Table 1. List of reference site densities known among regions where RCA models are in use. Data from regions other than Skagit were provided by S. Pappas (Pers. Comm, Env. Canada, Vancouver, B.C.)
Model or Country Survey area Number of reference Reference sample (km2) samples used in model density (km2/sample) development Fraser Basin 230,000 219 1050 Georgia Basin 57,037 55 1037 Skagit 1054 49 22 England and Wales 22 Australia 1502 USA 727
3 2BMETHODS
3.1 11BStudy Area
The upper Skagit River drains an area of 1,054 km2 in the Cascade Range of the Coast and Mountains Ecoprovince (Demarchi et al. 1990) in southwestern British Columbia. This area is 13% of the entire 8,029 km2 watershed of the Skagit River that empties into Puget Sound at Mt. Vernon in Washington State
(Hhttp://www.pacificwatersheds.net/ontheground/skagit.htmH ). The spatial limit of the present study area includes the complete Skagit watershed north of the Canada/US
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 7 border. The river originates as many small tributaries among steep and rugged alpine peaks at an elevation up to 2,561 m and flows in a curved northwest to southwest to southerly pattern, emptying into Ross Lake Reservoir at the Canada/US border at an elevation of 479 m (Figure 1). Major tributaries include the Sumallo and Klesilkwa Rivers from the west, and the Skaist River and Snass Creek from the north. Smaller tributaries emptying into lower reaches of the mainstem include Nepopekum Creek, Shawatum Creek, St. Alice Creek, and McNaught Creek, among many others.
Five biogeoclimatic zones are found in the upper Skagit watershed (Demarchi et al. 1990, Green and Klinka 1994). This large range in zonation is mainly due to the large elevation range coupled with climatic effects of warm and moist conditions affecting the drainage from the west and a cooler and drier continental influence from the east. The zones as defined by Green and Klinka (1994) include:
• Alpine tundra at highest elevations above treeline,
• Interior Douglas Fir (wet warm subzone called IDFww) along the lower mainstem,
• Engelman Spruce Subalpine Fir (moist warm subzone called ESSFmw) in northeastern drainages,
• Mountain Hemlock (moist maritime subzone called MHmm2) at intermediate mountain elevations mainly in the Sumallo drainage, and
• Coastal Western Hemlock (moist submaritime subzone called CWHms1) along valley bottoms.
Along valley bottoms (CWHms1 and IDFww), main tree species are western hemlock and Douglas fir with common understory riparian shrubs including huckleberry, falsebox, prince’s pine, and Oregon grape. On the steeper slopes and moderate elevations of ESSFmw and MHmm2, main tree species are western hemlock, mountain hemlock (only in MHmm2), amabilis fir, and subalpine fir, with Engelmann spruce and lodgepole pine only occurring within the ESSFmw zone. Dominant shrubs in these zones include rhododendron, and huckleberry with blueberry and false azalea being more common along streams of the MHmm2 zone. Alpine tundra is limited to highest peaks and meadows where rock outcrops dominate with scattered pockets of mountain heather.
Several fish species are common in the Skagit watershed and are highly valued. They include rainbow trout, cutthroat trout, Dolly Varden, bull trout, and brook trout. Rainbow and bull trout are of international significance because they migrate between the Ross Lake Reservoir in the United States and the Upper Skagit River in British Columbia. A sport fishery for rainbow trout and char is very active in the Skagit mainstem between Hwy 3 and Ross Lake Reservoir (Triton 2008). Dolly Varden is mostly caught in small tributaries to the mainstem while adult bull trout are mainly found
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 8 in the mainstem. Bull trout rearing occurs in large tributaries such as Nepopekum Creek, the Klesilkwa River and the Sumallo River (McPhail and Taylor 1995). Westslope cutthroat trout were incidentally found in occasional sampling surveys dating from 1983 (Triton 2008). A survey of fish species composition at 70 sites throughout the watershed was completed at the same time as the present study as outlined by Triton (2008).
3.2 12BSample Site Selection
To ensure water quality and fish sampling sites were closely matched, an initial layout of sites was the same as that defined by Triton (2007) using the BC Ministry of Environment Field Data System tool (FDIS) developed by Miers (2001). The tool stratified stream reaches based on basin type, gradient, channel pattern, and stream order where reach was defined as a length of stream having the same gradient, channel pattern, and order. FDIS randomly selected a proportion of reaches from each stratum based on assigned sample size (B.C. Fisheries, 2001). This process resulted in the selection of 112 reaches. Decisions were then made by Triton (2007) to omit reaches considered inaccessible to fish due to the presence of passage barriers, high gradient (>20%), high elevation (>1,500 m) unless lake headed, catchment area too small to support a fish population, and high clustering of reaches by the FDIS. Further filtering removed sites that were considered inaccessible by truck, trail or helicopter based on initial field reconnaissance by helicopter. This process resulted in the removal of 60 reaches. The remaining 52 were augmented with 18 reaches where there was uncertainty about fish distribution or they were underrepresented in the original listing by FDIS. This process resulted in the selection of 70 reaches distributed throughout the watershed.
A subset of 30 reaches was selected from the 70 fish sampling reaches for the RCA sampling in 2007. The selection was based on a ground reconnaissance that was conducted one month prior to sampling. We omitted sites that were not accessible and we added sites to cover reaches upstream and downstream of Sunshine Valley. We added sites downstream of the Giant Copper Property, and ensured the distribution of sites covered all biogeoclimatic zones. A few reaches selected using FDIS were dry or were minor depressions in the landscape where no actual stream existed. These reaches were replaced with nearby accessible streams where water was flowing and were suitable for completing all RCA sampling activities. This same procedure was used at the time of sampling if a reach was dry or not accessible. An actual sampling site was located at the downstream end of an assigned reach.
The 29 sites sampled in 2008 were selected the same way, focussing on filling in reaches that were underrepresented in 2007. They mainly included sites at medium to high elevations. Although initial site selection using FDIS omitted fishless high elevation sites, some of these streams were added in 2008 because future water quality testing
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 9 could include small watercourses at high elevations regardless of whether they support fish.
The resulting distribution of the 59 sites sampled in both years is shown in Figure 1 and the sites are listed in Table 2. Ten are potentially affected by landscape disturbance and were not used in model building. They were downstream of the Sunshine Valley development (S1 and further downstream at S33), the Giant Copper property (S24, S45, S46, S47). Others could be affected by drainage from Hwy 3 (S49) or cattle using range lands (S41) or from sediment transported from high elevation road cuts (S64). One site (S43) had extensive silt deposits in the substrate, which indicated upstream disturbance. These potentially disturbed sites were excluded from modeling and were called “test” sites. Some were used in testing model performance and were used to provide insight into potential importance of disturbances already present in the watershed (See section 5.3). The 49 sites not affected by disturbances, called “reference sites”, were used in model building.
Several reference sites were placed in the vicinity of what appears to be extensive road networks in Figure 1 (e.g. site 21, 19, 27, 62, 35). Roads in these areas were found to be highly stable with little or no evidence of failure and no sediment transport that could influence benthic communities. No sediment or accumulation of fines was found in streams so they were left as reference sites. In contrast, sand accumulation in the stream bottom of Site 64 showed evidence of disturbance from upstream roads making us assign it as a test site.
Table 2. List of sampling site numbers as they appear in Figure 1 with associated local names. Site assignment (test or reference) is shown and potential disturbance affecting test sites is identified.
Site number Site name Type Potential disturbance at test (blank is sites reference) S1 Sumallo R. downstream of Test Sunshine Valley development Sunshine Valley bridge (riparian disturbance, nutrient loading) S2 Sumallo R. upstream of Sunshine Valley S3 Skaist River above Turnbull S4 Upper Turnbull S5 Snass Cr. S6 Ferguson Cr. S7 Passage Cr. S8 Upper Nepopakum Cr. S9 Upper Twentysix Mile Cr. S10 Skagit R. ¾ km upstream of campsite S11 28 Mile Cr. S12 Silvertipped Cr.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 10
Site number Site name Type Potential disturbance at test (blank is sites reference) S13 Skagit R. upstream of campsite S14 Skagit R. upstream of suspension bridge S15 Lower Nepopakum Cr. S16 Skagit R. upstream of Nepopakum Cr. S17 Skagit R. near Shawatum Cr. S18 Klesilkwa Cr. above Maselpanic S19 Lower Maselpanic Cr. S20 Klesilkwa Cr. upstream of Skagit confluence S21 Maselpanic Cr. S22 Upper Klesilkwa Cr. S23 McNaught Cr. S24 Silver Daisy Cr. Test Metals drainage from upstream mine adit and Giant Copper property S25 Skagit R. upstream of Sumallo confluence S26 Lower Sumallo R. S27 Upper Sumallo R. S28 Skagit R. above Dryner Cr. S29 Dryner Cr. upstream of Skagit confluence S30 Triton site 25 (unnamed Cr.) S33 Mid-Lower Sumallo Test Sunshine Valley development mainstem (riparian disturbance, nutrient loading) S35 Upper Ferguson Creek S36 Upper Laforgue Creek S37 East Snass Creek S38 Middle Snass Creek S39 Upper Snass Creek S40 Upper Skaist River S41 Captain Grant Creek (trib of Test Free range cattle grazing upper Skaist R) (erosion, nutrient loading) S42 Skaist River at Hwy crossing S43 Noname trib1 of Skaist Test Silt in sediments, cause near Hwy unknown S44 Upper Grainger Creek S45 Norwegian Creek Test Metals drainage from Giant Copper property S46 Upper Smitheram Creek Test Metals drainage from Giant Copper property S47 Lower Smitheram Creek Test Metals drainage from Giant
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Site number Site name Type Potential disturbance at test (blank is sites reference) Copper property S49 Skagit River upstream of Test Drainage from Hwy 3 (fines Smitheram transport, embeddedness, turbidity) S50 Lower Twentysix Mile Creek S52 Nepopakum Cr. North arm S53 Upper Nepopakum Cr. east S54 Upper Nepopakum Cr. South S55 Lightening Creek downstream lake #2 S56 Lightening Creek downstream lake #1 S57 Frosty Creek S58 St Alice Creek S59 Upper McNaught Creek S60 Upper Maselpanic Cr. S62 Tearse Creek S64 Hilton Creek (replaced S31) Test Free range cattle grazing and upstream roads (erosion, nutrient loading) S65 Unnamed tributary to Sumallo R.
3.3 13BField Logistics
Fieldwork was performed during August 14 - 23, 2007 and August 12 - 19, 2008. Late summer is standard for RCA sampling because it is the easiest time to collect samples, benthic invertebrates are abundant, low stream flow makes stream access by wading relatively simple, and leaf litter that can hinder sample processing in the lab has not yet accumulated in the stream. At the beginning of sampling, a crew of 3-4 individuals reviewed all sampling procedures at one or more of the sampling sites with guidance from the crew leader who had most experience with RCA sampling protocols. The community of Hope served as the staging point for field activities. Most sites were accessed by truck and trail using logging and mining roads. More remote sites mainly at high elevations were accessed by helicopter.
3.4 14BField Sampling and Analysis in Laboratories
A standard field data sheet (Appendix A) was filled out at each site. Entries were consistent with those required for the Canadian Aquatic Biomonitoring Network database
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(CABIN, Hhttp://cabin.cciw.ca/application/welcome.asp?Lang=en-caH ), which is where Skagit data will ultimately reside for use in site testing. The CABIN protocols follow those reported by Reynoldson et al. (2003).
Upon arriving at a potential site, a visual assessment was completed to determine if adequate flow of 0.2-0.8 m/s and depths of 10 to 30 cm were present. An assessment reach was defined as 6 times the bankfull width, which was enough to satisfy all measurements and observations noted on the field form. All sampling was completed at wadeable sites. If continuous riffle habitat was not available, the stream was still assessed, with the benthic invertebrate sample collected from multiple riffles, moving in an upstream direction.
If a site was suitable, tasks were assigned and the crew members began sample collections and measurements. Water samples were collected before anyone entered the stream to avoid contamination. One crew member then collected the benthic invertebrate sample while another timed the collection. All crew members worked together to record notes and observations and complete the habitat measurements. The average sampling time at each site was 1-2 hours. Including travel times between sites, an average of 4 sites were completed in an 8-10 hour field day by the single field crew.
Each invertebrate sample was collected using a 400 μm mesh kick-net, according to the timed procedure reported by Reynoldson et al (2003). The method was modified to include sampling only from riffle sections. Other habitat types (pools, glides, etc.) were not sampled. The kick-net operator moved upstream in a zig-zag pattern, kicking substrate and collecting sample for 3 minutes. The 3 minute timer was stopped anytime the sampler did not have the net in the water (e.g. moving to an upstream riffle or climbing over a log). After kick netting was completed, the sample was dispensed into a plastic tray, large debris was cleaned and removed, and excess water was strained off through a 400 μm mesh sieve. Contents of the sieve and sample in the tray were placed in labelled 500 mL plastic jars and preserved in 10% formalin.
A set count approach was used to enumerate the invertebrates in the laboratory. The sample was washed through a 1 mm and a 400 μm mesh sieve to yield a macrobenthos fraction (>1 mm) and a microbenthos fraction (<1 mm and >400 um). In this process, all animals were picked from twigs, grasses, clumps of algae, and other debris and were returned to the top sieve. Microbenthos was split into 16 subsamples using a large plankton splitter. The subsamples were enumerated until 200 animals were counted. If the target of 200 animals was reached part way through the sorting of a subsample, that subsample was sorted in its entirety. The subsample count was multiplied by the subsample fraction to determine the total animal count in the microbenthos fraction. For example, assume that 2 of 16 subsamples had to be counted to achieve the minimum count of 200 animals. In those 2 subsamples, 50 individuals of species “x” were counted. The count of species “x” in the whole microbenthos fraction is 400 animals (50 individuals x (16 possible subsamples/2 subsamples that were
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 13 enumerated)). The same approach applied to macrobenthos. That fraction was placed in a 400 μm sieve and the sieve was placed in a shallow tray containing a 5 cm depth of water. Contents of the sieve were swirled in a circular motion to evenly distribute particles. The sieve was gently lifted out of the water, taking care to keep it level. The surface of the sieve mesh was partitioned into 4 equal parts, each part being a subsample. Animals were enumerated from successive subsamples until 200 animals were counted. If the target of 200 animals was reached part way through the sorting of a subsample, that subsample was sorted in its entirety. In many cases all 4 subsamples of the macrobenthos fraction had to be enumerated to achieve the required minimum count of 200 animals. The subsample count of the macrobenthos fraction was multiplied by the fraction of subsamples sorted to determine the count of animals in the macrobenthos fraction. The sum of total microbenthos and total macrobenthos was the sample count. Using this approach, a minimum of 400 animals were counted in the subsampling process (200 in each of the macrobenthos and microbenthos fractions). The animals were identified to genus or lowest reliable taxonomic level using keys in Edmundson (1959), Merritt and Cummins (1996), and Pennak (1978).
One in ten samples was sorted twice to test efficiency of the first sort. A target for acceptable sorting was that 90% of the sample must be enumerated on the first sort. If efficiency was <90%, all samples in the group to which the test applied were re-sorted. All tests showed >90% efficiency on the first sort.
Methods used to measure habitat variables in the field and complete related sample collections and analysis in laboratories are listed in Table 3.
Table 3. Methods for measurement of habitat variables in the field and associated laboratory procedures.
Variable Method or Standard Used
Documentation based on personal observation; choices include: Current and recent storm, rain, showers, overcast, and sunny. weather
Site is noted as being reference or test based on knowledge and observations of stressors in the watershed. If there was any question Reference/test site of site status it was assigned as a test site. Site allocations are listed allocation in Table 2.
A hand-held global positioning system was used to record latitude and longitude (or UTM coordinates). A site description was noted and site Location diagram completed. Elevation is not accurate on GPS receivers so it was determined using GIS following field sampling.
Taken looking upstream, downstream, across, and at the substrate Photographs (substrate photo included a 50cm quadrat for scale).
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Variable Method or Standard Used
Water temperature, pH, TDS, conductivity, Measured using a YSI model 6920 Sonde calibrated on the day of turbidity, dissolved measurement. oxygen
All samples for analysis of dissolved fractions were filtered in the field through 0.45 μm membrane filters. An 80 mL bottle was filled for analysis of TP. An 80 mL bottle was filled for TDP analysis. A 125 mL brown glass bottle was filled for SRP analysis. A 250 mL poly bottle was filled for NO3-N and NH4-N analysis. The samples were delivered Nutrient concentrations to the Fisheries and Oceans lab at Cultus Lake for analysis within 24 (NO3-N, NH4-N, soluble hours of collection. Samples for the TP and TDP analysis were reactive P (SRP), total digested and analysed using Menzel and Corwin’s (1965) potassium dissolved P (TDP), total persulfate method. SRP was analysed using the molybdenum blue P (TP)) method (Murphy and Riley 1962). NH4-N and NO3-N were analysed using a Technicon autoanalyzer (Stainton et al. 1977). The sum of NH4-N and NO3-N was called dissolved inorganic N (DIN). QAQC procedures included tests of field duplicates and field blanks.
A 1 L poly bottle was submitted for analysis of all analytes. Water Concentration of total N samples were analyzed at the Pacific Environmental Science Centre (TN) and general (North Vancouver, B.C.). All analyses were run using standard analytes (alkalinity, true methods reported in APHA (2007). TN samples were UV digested, colour, sulphate, total followed by colourimetric analysis of NO3-N using automated methods suspended solids (TSS), in a Technicon Autoanalyzer. Hardness was determined by calculation hardness) following analysis of metals concentrations.
Sample water was dispensed to 250 mL acid washed bottle, with HNO3 preservative added in the field. Samples were sent to the Pacific Environment Science Centre in North Vancouver by courier to Concentration of achieve the recommended 72hr holding time. Analysis of dissolved dissolved metals metals was done using ICPMS scan, and all parameters were analyzed according to methods in APHA (2007).
Measured using a clinometer and reported in %. Stream gradient
A visual estimate of the % of the stream reach that is comprised of pool, a combination of glide and run, riffle, and cascades. The Habitat composition estimate was made by each of three members of the field crew and average values were recorded.
Measurements of the wetted and bankful width were taken from 3 well spaced locations within the survey reach. The average value was used as a representative measurement. Measurements were Stream Widths completed using a survey tape for widths <3 m and a rangefinder if the stream width was >3 m.
Measured using a meter stick at approximately 1 m intervals across a Water depth representative width profile.
Water velocity Measured using a Swoffer Instruments (Seattle WA) velocity sensor at
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Variable Method or Standard Used
approximately 1 m intervals across a representative width profile with the sensor placed at a depth that was 60% of total depth.
Calculated using velocity and depth measurements made at 5-8 equidistant points across the stream width following methods in RIC Water flow (1998).
A visual estimate of the percent of the streambed that is covered by Macrophyte Coverage aquatic macrophytes (Reynoldson et al 2003).
A visual ranking (1=none to 4=abundant) of the wetted surface area that contained each of the following cover types: woody debris, Specific cover boulders, undercut banks, deep pools, and overhanging vegetation. A ranking was applied to each cover type.
Identification of dominant riparian vegetation where 1=barren, 2=grass/herb, 3=shrub, 4=deciduous tree, 5=conifer, 6=mixed forest, Dominant riparian class 7=mixed shrub and forest.
Ranking of structural stage of forest where 1=non-vegetated or initial stage following disturbance with <5% cover, 2=shrub/herb with <10% tree cover, 3=pole sapling stage with trees overtopping shrub layer, Forest stage 15-20 years old, 4=young forest (30-80 years old) with canopy differentiating into distinct layers, 5=mature forest with well developed understory.
Ranking of forest canopy closure where 1=0%, 2=1-25%, 3=26-50%, Forest canopy closure 4=51-75%, and 5=76-100%.
A visual estimate of the percent of the stream reach that is covered with various particle sizes (sand, gravel, pebble, cobble, boulder and bedrock), according to the Wentworth Scale (Wentworth 1922). The Substrate composition estimate was made by each of three members of the field crew and average values were recorded.
An estimate of how embedded the cobbles were in the surrounding fines (measured in the riffle habitat) where 1=not embedded, 2=25% Embeddedness embedded, 3= 50% embedded, 4=75% embedded, 5=completely embedded.
A Wolman Pebble Count (Wolman 1954), where the intermediate diameter of 100 randomly-selected particles within the stream reach was measured using a gravelometer (Wildco, Buffalo NY). From this data, dominant particle size (highest value of percent composition among 100 randomly selected particles throughout the survey area) Pebble Count and subdominant particle size (second highest value of percent composition among 100 randomly selected particles throughout the survey area) was calculated.
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Variable Method or Standard Used
Identification of stream colour type where: 1=glacial turbidity present, Stream colour 2=clear, 3=tannin and lignin stained, 4=other.
Visual observations to describe presence of scour, sediment wedges, extensive riffles and limited pools, unvegetated and mid-channel bars, multiple channels, eroding banks, isolated sidechannels, recently Disturbance indicators formed large woody debris (LWD) jams and LWD parallel to banks. Modified from RIC (2001).
Presence documented on field sheet (Appendix A). Odours/Oils
Visual estimate of percent of riparian trees that are pine. Riparian mountain pine Visual estimate of riparian pine trees that are “red”. beetle Infestation Visual estimate of riparian pine trees that are “grey”.
Ranking of local watershed erosion where 1=heavy, 2=moderate, Erosion 3=light, and 4=none.
3.5 15BData Compilation
Family level enumerations of invertebrate samples and habitat measurements from the field (Table 3) were compiled into spreadsheet files ready for modelling and analysis. Metadata accompanying each observation included local site name, a sample number, date of collection, and a unique sample identifier. This identifier was required as a single code for use in uniquely identifying a sample in statistical software. Each biological observation was matched to an accompanying and complete compilation of habitat data. Habitat variables measured in the field were combined with data describing landscape attributes that were accessed from existing databases. Landscape data sources, method of extraction from the databases, and description of each variable is provided in Table 4.
The biological and habitat data were uploaded to the CABIN website for archiving and for use in running the model and site testing. Uploading was done according to procedures outlined by Sylvestre et al. (2005) following receipt of a project username and password from Environment Canada (S. Pappas, Environment Canada, Vancouver, B.C. Pers. Comm.).
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Table 4. List of data sources, method of calculation, and description of habitat variables extracted from landscape databases.
Variable Description Data source Method
Area Area of watershed Province of BC, Watershed Atlas polygons upstream of sample site Integrated Land were manually altered for (m2). Management each site based on Bureau, Land and interpretation of provincial Resource Data TRIM contours. Warehouse (LRDW), WSA_WS_SVW polygon layer Tot_strlgth Cumulative stream length Province of BC, Streams were intersected upstream of sampling site Ministry of with the watershed (m). Sustainable polygons. Streams were Resource then dissolved based on
Management, HBase watershed. Mapping and Geomatics Services
BranchH, Terrain Resource Inventory Mapping (TRIM) twtrl polyline layer.
Drndnsty Drainage density of all Not applicable By calculation: (stream rivers and streams. length divided by watershed area upstream of sampling site (m-1)). Strmorder Stream order Province of BC, Sample sites were spatially 1:20,000 Corporate joined using E.S.R.I.’s Watershed Base, ArcMap 9.2 spatial join tool Stream Network to the nearest 1:20,000 polyline layer. Corporate Watershed Base (CWB) Stream Network stream. A manual visual check was completed to ensure the correct stream was joined to each site for those sites near two or more streams. SiteElev Elevation of the sampling Province of BC, TRIM digital elevation site above sea level (m). Ministry of model point data was Sustainable converted to a raster image Resource of elevation values using
Management, HBase E.S.R.I. ArcGIS 9.2 Spatial Mapping and Analyst extension. Geomatics Services Statistics were then derived
BranchH, Terrain for each watershed Resource Inventory polygon using Spatial Mapping (TRIM) Analysts Zonal Statistics digital elevation tool. model point layer. MaxElev Highest elevation above Same as SiteElev Same as SiteElev
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Variable Description Data source Method
sea level of land in the watershed upstream of the sampling site Relief MaxElev minus SiteElev Not applicable By calculation (m). Pct_agriculture Percent of Area having Province of BC, Present Land Use land based agricultural Integrated Land polygons were intersected activities undifferentiated Management with the watershed as to crop (i.e. land is Bureau, Land and polygons then dissolved used as the producing Resource Data based on the watershed medium). Warehouse and PLU Label fields. Land (LRDW), use area was divided by BTM_PLU_V1 area upstream of the polygon layer sampling site (variable called Area) and multiplied by 100 to determine percent.
Pct_alpine Percent of Area in alpine Same as Same as Pct_agriculture (above treeline). Pct_agriculture Pct_ice Percent of Area having Same as Same as Pct_agriculture year round glaciers and Pct_agriculture snow. Pct_oldgrowth Percent of Area Province of BC, VRI polygons were queried comprised of old growth Integrated Land for age class 6 or higher forest (>140 yrs and Management (>100 years old) then greater than 6 meters in Bureau, Land and intersected with the height). Resource Data watershed polygons. An Warehouse OLD_GROWTH field was (LRDW), added. All polygons with VEG_L2_PLY age class 6 or higher were polygon layer given a value of OLD_GROWTH=yes and those with age class < 6 a value of OLD_GROWTH=no. Polygons were then dissolved based on the watershed and OLD_GROWTH fields. Area was divided by total watershed area and multiplied by 100 to determine percent.
Pct_range Percent of Area Same as Same as Pct_agriculture containing unimproved Pct_agriculture pasture and grasslands based on cover rather than use. Cover includes drought tolerant grasses,
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Variable Description Data source Method
sedges, scattered shrubs to 6 metres in height and less than 35% forest cover. Pct_yforest Percent of Area Same as Same as Pct_agriculture comprised of forest less Pct_agriculture than 140 years old and greater than 6 metres in height. Areas defined as Recently Logged and Selectively Logged land uses are excluded from this class. Pct_ava Percent of Area that is Same as Same as Pct_agriculture below the tree line and is Pct_agriculture devoid of forest growth due primarily to snow avalanches. Usually herb or shrub covered. Pct_wtlnd Percent of Area that is Same as Same as Pct_agriculture wetland including Pct_agriculture swamps, marshes, bogs or fens. This class excludes lands with evidence or knowledge of haying or grazing in drier years. Pct_barren Percent of Area that is Same as Same as Pct_agriculture comprised of rock Pct_agriculture barrens, badlands, sand and gravel flats, dunes and beaches where unvegetated surfaces predominate, except where these condition occur in ALPINE areas. Pct_ Burn Percent of Area that is Same as Same as Pct_agriculture virtually devoid of trees Pct_agriculture due to wildfire within the past 20 years. Forest less than or equal to 15% cover.
Pct_logged Percent of Area having Same as Same as Pct_agriculture timber harvesting within Pct_agriculture the past 20 years, or older if tree cover is less than 40% and under 6 metres in height.
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Variable Description Data source Method
Rd_den Road density Province of BC, TRIM based roads were 1:20,000 Terrain classed into 3 categories: Resource Inventory paved (divided 1 lane, Mapping (TRIM) , divided 2 lanes, undivided 4 Road layer. Lanes), gravel (1 land and 2 lanes), and block (road unimproved, cut roadway, embankment or fill roadway, trial road, trail or skid trail). Each road class layer was separately intersected with both the 2007 and 2008 watershed polygons. Roads were then dissolved based on watershed and road lengths were calculated. Each road class table was then joined by watershed ID to the watershed polygon table to generate a final table with Site, Road Length and Watershed Area. Road density was road length divided by watershed area upstream of the sampling site multiplied by a unit conversion factor of 108.
Pct_park Percent of Area occurring Province of BC, Park and Protected Area within Provincial park Integrated Land polygons were intersected boundaries. Management with the watershed Bureau, Land and polygons. A PARK field Resource Data was added. All polygons Warehouse within a park or protected (LRDW), area were given a value of PKS_PTD_AR PARK=yes and those polygon layer outside parks or protected areas were given a value of PARK=no. Polygons were then dissolved based on the watershed and PARK fields. Area was divided by total watershed area and multiplied by 100 to determine percent.
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Variable Description Data source Method
Pct_urban Percent of Area having Same as Same as Pct_agriculture compact settlements Pct_agriculture including built up areas of cities, towns and villages as well as isolated units away from settlements such as manufacturing plants, rail yards and military camps. In most cases residential use will predominate in these areas.
Pct_sedimentary Percent of Area underlain Province of BC, Rock class polygons were by sedimentary rock Integrated Land intersected with the Management watershed polygons. Bureau, Land and Polygons were then Resource Data dissolved based on the Warehouse watershed and (LRDW), ROCK_CLASS fields. Area NM10_geology_utm was divided by total polygon layer. watershed area and Description: NM10 multiplied by 100 to Digital Geology - determine percent. Digital regional compilations of the geology of B.C. at 1:250,000-scale. Possible classifications =intrusive, volcanic, sedimentary, metamorphic, or ultramafic Pct_intrusive Percent of Area underlain Same as Same as Pct_sedimentary by intrusive rock Pct_sedimentary Pct_volcanic Percent of Area underlain Same as Same as Pct_sedimentary by volcanic rock Pct_sedimentary Pct_metamorphic Percent of Area underlain Same as Same as Pct_sedimentary by metamorphic rock Pct_sedimentary Pct_ultramafic Percent of Area underlain Same as Same as Pct_sedimentary by ultramafic rock Pct_sedimentary EcoZone/EcoRegion EcoZone/EcoRegion Environment Sample sites were spatially Canada / Research joined using E.S.R.I.’s Branch, Agriculture ArcMap 9.2 spatial join tool and Agri-Food to the EcoZone and Canada,Canadian EcoRegion data. A manual Soil Information visual check was System, EcoZones completed to ensure and EcoRegions correct results polygon layers (required by CABIN).
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3.6 16BSelection of Habitat Variables for Modeling
Two groups of variables describing habitat attributes were compiled. One was from the field (mainly physical and chemical variables listed in Table 3) and the other was from landscape databases (watershed and geomorphic variables listed in Table 4). Variables that do not vary with anthropogenic disturbance (Reynoldson et al. 2001, Sloane and Norris 2003) were called natural gradient variables. They described geomorphological and physical attributes and forest cover conditions listed in Table 5. Some groups of well known variables were not included in this list. Nutrients were not included because anomalous discharges can modify growth of periphyton (Stockner and Shortreed 1978, Perrin et al. 1987, Bothwell 1989) and cause change in whole system production (Johnston et al. 1990, Deegan and Peterson 1992). Concentration of metals were not included because they can cause toxicity in stream biota (Campbell and Stokes 1985, Hickey and Clements 1998) while treatment of mine water discharge with lime (e.g. major cations) can reduce this toxicity (Perrin et al. 1992). Even basic electrochemical analytes including total dissolved solids/conductivity, alkalinity, pH, and dissolved oxygen were not included because they can be modified by anthropogenic disturbance or water treatment. Stressor gradient variables were those that are related to human activity. A suite of these variables was measured at each site but they were not used in developing the reference condition model. These data were collected for examining potential cause of site impairment at test sites if divergence from reference condition was detected (see Section 4.2). The stressor gradient variables (defined in Tables 3 and 4) included:
• Percent logged, • Road density (block, gravel, paved), • Dominant riparian class (considered sensitive to riparian disturbance and clearing), • Water chemistry variables (pH, TDS, conductivity, dissolved oxygen concentration, turbidity, total suspended solids, nutrient concentrations, dissolved metals concentrations, sulphate concentration, alkalinity), • Embeddedness, • Erosion, • Percent urban, • Percent agriculture, • Percent range,
Using a list of all natural gradient variables that were measured, redundant variables were identified and omitted from a final list that was used in development of the RCA model (Table 5).
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Table 5. Candidate natural gradient predictor variables used in the RCA modeling.
Candidate natural X Method of Rationale for not being selected for modeling gradient variables indicates measurement (coding described selected (field, GIS, in Tables 3 and 4) for calculation modeling from GIS) Easting X Field Northing X Field Area X GIS Tot_strlgth GIS Correlated with Drndnsty Drndnsty X GIS Strmorder GIS Redundant with Drndnsty and SiteElev SiteElev X GIS Maxelev GIS Correlated with Relief Relief X Calculation Pct_alpine X GIS Pct_ice X GIS Pct_ava X GIS Pct_wtlnd X GIS Pct_barren X GIS Pct_ Burn X GIS Pct_oldgrowth GIS Correlated with Forstage Pct_yforest GIS Correlated with Forstage Pct_park GIS Too ambiguous so not a useful predictor variable Pct_sedimentary X GIS Pct_intrusive X GIS Pct_volcanic X GIS Pct_metamorphic X GIS Pct_ultramafic X GIS WT X Field Gradient Field Replaced with Relief that was considered more accurate than field based clinometer measurements Pool X Field Glide_run X Field Riffle X Field Cascade X Field Wwave Field Channel attributes better defined using bankfull width Bwave X Field Dave Field Channel attributes better defined using bankfull width Vave Field Channel attributes better defined using bankfull width Maccov X Field Oveg X Field Forstage X Field Canclosure Field Redundant with Forstage Domsub X Field Subdomsub X Field Colour Lab No variation found in colour data so no value as a predictor variable
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3.7 17BModel Development and Testing
A reference condition model called BASS (BEAST Assessment of Skagit Streams) was built following the protocols used in development of RCA models for the Fraser Basin (Rosenberg et al. 1999, Reynoldson et al. 2001), Georgia Basin (Sylvestre et al. 2005), and Skeena Region (Perrin et al. 2007) which followed from original application of RCA methods in the Great Lakes (Reynoldson et al. 1995). BEAST refers to “Benthic assessment of sediment”. It is an approach for modeling reference conditions and has been incorporated into CABIN as a national analytical framework. Routine testing of sites can be done on the CABIN website
(Hhttp://cabin.cciw.ca/Main/cabin_about.aspH ). A BEAST model relevant to the watershed of interest runs behind the CABIN interface, allowing users to complete site tests without having to know the inner statistical routines. It has application beyond sediment assessment but the acronym is still used.
Invertebrate samples from the 49 reference sites were first classified into groups in which assemblages among samples in a group were more similar to each other than they were to assemblages in other groups. Classification was done at the family level based on findings by Reynoldson et al. (2001), Bailey et al. (2001), Arscott et al. (2006), and Chessman et al. (2007) that family assemblage data are equally sensitive to lower taxonomic levels for evaluating site condition in resource management applications. The Bray-Curtis similarity coefficient (Clarke and Gorley 2006) was used to prepare a matrix of similarities of assemblages among all samples. Recent practice to adjust Bray Curtis to avoid collapse of ordinations that include samples having zero counts (Clarke et al. 2006) was not necessary because no samples were devoid of animals. From a similarity matrix, a dendogram was plotted using the group average linkage in the hierarchical, agglomerative clustering algorithm in Primer v6 (Clarke and Gorley 2006). The dendogram was examined for clear groupings of samples by moving a slice line down the image. The line was fixed at a point where dissimilar sample groups could be formed with fewest outliers (samples not aligning with any group and could not form a group among themselves). Our objective was to have more than 10 samples in any one group but fewer samples were acceptable if it meant fewer outliers. At least 10 samples are desirable in any group for eventual site testing (Reynoldson et al. 2001). If sample groups were not clearly discernable, the counts were square-root transformed, which has the effect of down-weighting the influence of abundant taxa and increasing the weighting of less abundant taxa on the Bray Curtis distance measure. This weighting can improve the resolution of sample groups on a dendrogram. If the square root transform was not effective, a fourth root transform was tried or even a log transform. This sequence of square root to fourth root to log is a gradient of increasingly severe inverse weighting of abundant and rare taxa. Its only purpose was to assist in resolving sample groups. Once the groups were defined, a group label (1,2,3…n) was assigned to each sample.
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The assignment of a sample to a group was further aided by interpretation of a non-metric multi-dimensional scaling analysis (NMDS) run on the same similarity matrix that was used for the cluster analysis (Clarke and Gorley 2006). NMDS is a visualization procedure for fitting a set of points in space such that the distances between points correspond as closely as possible to dissimilarities between them. Output is displayed on two-dimensional or three-dimensional images called ordinations. An ordination has no scaling units and the space between points on the image reflects the dissimilarity between them. A ‘stress value’ is calculated for each 2D ordination, measuring distortion of the multidimensional data on the 2D plot. Stress increases with decreasing dimensionality of the ordination and indicates if the plot is a usable summary of sample relationships. Interpretation of sample groups was done on the 3D ordinations when the 2D ordination had a stress value >0.2.
The composition of invertebrates in sample groups was summarized by counts of major taxa and values of family richness (number of families), total abundance, and Simpson’s Diversity calculated as:
2 D −=− ∑ pi )(11 Equation 1
Where: D)1( =− Simpson’s index of diversity and
pi = Proportion of individuals of species i in the community
Simpson’s Index is a measure of community heterogeneity and tends to weight the common taxa more than the rare taxa (Krebs 1999). This measurement provided a suitable contrast to taxonomic richness that weights all taxa evenly. The SIMPER (SIMilaritiesPERcentages) routine in Primer v6 (Clarke and Gorley 2006) was used to identify taxa contributing most to similarities within and dissimilarities between sample groups. This procedure compared the percentage composition each taxon made to the different assemblages.
Stepwise discriminant function analysis (DFA) run in Systat (2004) was used to define functions that best assigned a sample to one of the biological groups. By iteration, the combination of variables that produced the lowest error rate based on the jackknife cross-validation procedure in Systat and had tolerance values greater than 0.5, was selected for the final model. Tolerance is a measure of correlation between predictor variables. We wanted to minimize this correlation to avoid redundancy of predictor variables. Where the tolerance value for one or more predictor variables was <0.5, the variables were reviewed for possible redundancy (and thus high correlation). Where possible high correlation was found between any two variables, the one having the lowest F-to-remove value was removed and the DFA was re-run. In the jackknife test, one observation from a reference group is held-back, and the remaining observations
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 26 are used to build the discriminant functions and predict group memberships. Jackknife classification success is measured as the percentage of held-back observations that are correctly classified. An acceptable model achieved >60% correct classification among all sample groups. DFA output provided a number of discriminant functions, also known as canonical variables. The number of canonical variables was one less than the number of sample groups. The function that individually explained most of the total dispersion was accepted as the final model.
BASS was tested for accuracy, precision, and sensitivity. Reference sample classification success rate determined in the DFA was the measure of accuracy, defined as the ability of the model to correctly assign a reference sample to its correct group. Duplicate or triplicate invertebrate samples were collected from several reference sites for a test of precision defined as the proportion of all reference replicates that were found to be in reference condition. Each replicate sample was assigned to its correct sample group (i.e. the group to which replicate 1 was assigned in the sample classification stage of model building). Type I error was defined as 1-precision (probability of mistakenly failing a sample). Model sensitivity was the percent of test sites known to be disturbed to some degree that were correctly assessed as having assemblages that were different from reference. Each test sample was assigned to its predicted sample group. The predicted sample group, as shown in output from the DFA, was the group having a centroid closest to an unclassified test sample in multidimensional space. These distances are called Mahalanobis distances, named after the Indian statistician Prasanta Mahalanobis (Systat 2004). Type II error was 1-sensitivity (probability of mistakenly passing a sample). For tests of precision and sensitivity, the similarities between assemblages of the test sample and all reference samples in the group to which the test sample was assigned were calculated and displayed as a 3D ordination using NMDS. No transformations were applied. Because each test sample will skew the ordination space differently, each sample was tested separately. Three Gaussian bivariate probability ellipses (90%, 99%, and 99.9%) were laid on each plot of ordination coordinates using the ellipse option in the plot command of Systat v11 (Systat 2004) (see example in Figure 2). In any one of the 3D plots (axis 1 versus axis 2, axis 1 versus axis 3, axis 2 versus axis 3) for a given site and its reference group, if a test site coordinate was inside of the first ellipse (90%), called Band 1, it was considered to be in reference condition. If the coordinate was between the first (90%) and second (99%) ellipses (Band 2), the site was considered slightly divergent from reference. If the site was between the second (99%) and third ellipses (99.9%) (Band 3) it was considered divergent from reference. If it was outside of the third ellipse (Band 4), it was considered extremely divergent from reference. This visual assessment from ordination plots was accompanied by calculation of the average Bray Curtis dissimilarity, expressed in percent, between the test sample and all reference samples in the group to which the test sample was being compared.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 27
Sites assigned for the test of sensitivity were considered disturbed to some degree based on observed upstream land use (listed in Table 2). They included S1 (Sumallo River immediately downstream of Sunshine Valley), S24 (Silver Daisy Creek), S41 (Captain Grant Creek), S43 (tributary to Skaist River), S45 (Norwegian Creek), S46 (Upper Smitheram Creek), S49 (Skagit River near Hwy 3), and S64 (Hilton Creek). The remaining two test sites in Table 2 were not included because we perceived no disturbance during field observations or we were uncertain that the sites were disturbed. They included S33 (lower Sumallo River) that was far from potential effects of Sunshine Valley and S47 (Smitheram Creek) that was far from mineral exploration sites on Silverdaisy Mountain.
The test sites that were found to be disturbed to some degree were further examined to determine association between habitat attributes and divergence of the invertebrate assemblages between test and corresponding reference sites. The SIMPER (SIMilaritiesPERcentages) routine in Primer v6 (Clarke and Gorley 2006) was used to identify taxa contributing most to dissimilarities between the test and reference samples. This procedure compared the percentage composition each taxon made to the average dissimilarity between test and reference samples. The resulting taxa individually contributing more than 5% of between sample dissimilarity were considered biological indicators. LINKTREE (Clarke and Gorley 2006) was used to show what stressor variable(s) and what value of that variable (or variables) was unique at the test site compared to all reference sites in the sample group to which the test sample was being compared. Stressor variables were limited to those most likely to contribute to stress at a given site based on knowledge of site conditions. A test sample always split off from the reference samples to form an independent high level branch of the tree where the tree is a network of sample groups split at various levels by amount of biotic dissimilarity. Anomalous values of the stressor variable(s) that were found at the test site showed environmental conditions coinciding with the biotic dissimilarity. Those conditions provided insight into factors contributing to site disturbance and provided insight into the ability of the model to detect biotic divergence among different conditions.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 28
3
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Figure 2. Ordination plots in each of 3 dimensions (top, middle, bottom) for a hypothetical test site (red) and reference sites (blue) belonging to a given reference sample group. The ellipses correspond to 90% (inner ellipse), 99% (middle ellipse), and 99.9% (outer ellipse) probabilities. In this case the test site is considered divergent because it lies outside of the 99% ellipse in at least one plot.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 29
4 3BRESULTS
4.1 18BThe BASS Model
Clustering based on square-root transformed invertebrate family counts provided best resolution to form sample groups. The cluster dendrogram and accompanying ordination from NMDS revealed three sample groups (called Groups 1, 2, and 3) separating at a similarity level of 57% (Figures 3 and 4). Assemblages in 5 samples were not similar to other groups and were considered outliers (Sites 4, 12, 38, 53, 56). Group 2 had 7 samples, which was less than our intended 10 sample minimum, but to avoid losing these samples as outliers, they were retained as a unique group because they showed >62% similarities among themselves, which was acceptable (Figure 3).
All sample groups had 21 to 23 families per sample, high heterogeneity (Simpsons Index of 0.77 to 0.84) and abundances ranging from 2391 to 8418 individuals/sample (Table 6). Group 1 samples had lowest abundance mainly due to lower numbers of stoneflies (Plecoptera), mayflies (Ephemeroptera), and chironomids than in the other groups. Group 1 had highest numbers of oligochaetes. Group 2 had the highest total abundance among all groups due to high numbers of stoneflies, chironomids, dipterans other than chironomids, and particularly the mayflies. Lowest average numbers of oligochaetes were found in Group 2. Abundance in Group 3 was intermediate, determined again by the stoneflies, mayflies, chironomids, oligochaetes, and to a lesser extent the caddisflies. Abundance of the Planariidae (flatworms) and Arachnida (all were water mites (Acarina)) was relatively high in Group 3 samples.
Iterations of the DFA resulted in a model having two predictor variables; site elevation and percent wetland (Table 7). The tolerance values were 1 indicating no correlation between the predictors. Group 2 samples were from lowest elevations in drainages having an average of 1.4% wetland by area upstream of the sampling site. Group 3 samples were from highest elevations where no wetland was present. Group 1 samples were from forested intermediate elevations where wetland was present but represented ≤0.3% of drainage areas. There was large overlap of the elevation range between sample groups, indicating a gradient of change in invertebrate assemblages from small streams in the alpine to larger streams at lower elevations including the Skagit mainstem. There was also large overlap in extent of wetland between Groups 1 and 2.
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Table 6. Average values of invertebrate metrics compared between samples in groups defined by clustering and NMDS. Taxa contributing to >90% of dissimilarities of assemblages between the sample groups are shown. Families within higher taxa that cumulatively contributed to these differences are shown in brackets.
Invertebrate Metric or Taxa Group Mean metric value or number/sample Group 1 (n=20) Group 2 (n=7) Group 3 (n=16) Family Richness 21.3 20.7 22.9 Total Abundance 2391 8418 4860 Simpson's Diversity 0.79 0.77 0.84
Oligochaeta (Enchytraeidae and Lumbriculidae) 353 123 250 Plecoptera (Peltoperlidae, Nemouridae, Taeniopterygidae, Capniidae, Perlidae, Chloroperlidae, Perlodidae and Leuctridae) 259 1146 1058
Ephemeroptera (Ephemerellidae, Baetidae, Heptageniidae, Leptophlebiidae and Ameletidae) 1370 5071 2473
Chironomidae 178 1264 525 Planariidae 20 17 151 Trichoptera (Rhyacophilidae, Uenoidae, Hydropsychidae, Limnephilidae, Glossosomatidae and Philopotamidae) 101 163 243
Diptera other than Chironomidae (Simuliidae, Empididae, Tipulidae, Psychodidae and Ceratopogonidae) 92 539 54 Arachnida (Lebertiidae, Torrenticolidae, Protziidae and Hydrozetidae) 14 83 101
Table 7. Statistics of the discriminant model for Skagit streams.
Predictor Variable F value Tolerance Mean and range Group 1 Group 2 Group 3 (n=20) (n=7) (n=16) Site elevation (m) 6.18 1 844 690 1173 (488 – 1368) (506 – 1241) (780 – 1656) Percent wetland 7.63 1 0.3 1.4 0 (0-2.1) (0 – 3.3) (0 – 0)
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20 Sample group 1 2 3 Outlier 40
60 Similarity
80
100 SKGT052 SKGT059 SKGT035 SKGT030 SKGT028 SKGT037 SKGT005 SKGT040 SKGT044 SKGT036 SKGT042 SKGT062 SKGT065 SKGT056 SKGT012 SKGT038 SKGT004 SKGT053 SKGT039 SKGT002 SKGT057 SKGT023 SKGT007 SKGT029 SKGT011 SKGT027 SKGT058 SKGT003 SKGT010 SKGT013 SKGT014 SKGT019 SKGT020 SKGT015 SKGT021 SKGT022 SKGT008 SKGT050 SKGT006 SKGT025 SKGT016 SKGT017 SKGT026 SKGT018 SKGT055 SKGT054 SKGT009 SKGT060 Samples
Figure 3. Cluster dendrogram of square-root transformed abundance data from all reference sites. Three sample groups were defined plus 5 outliers.
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2D Stress: 0.16 Sample group 1 2 3 Outlier
Figure 4. NMDS ordination of reference samples labelled by sample group.
4.2 19BModel testing
Using the jackknife cross validation test, 55%, 71%, and 81% of reference samples from Groups 1, 2, and 3 respectively were classified correctly, resulting in an overall classification success rate or accuracy of 67%. This result means that 67% of samples from all reference sites were classified to correct groups based on combinations of percent wetland and elevation. Other natural gradient variables listed in Table 5 were relatively unimportant in contributing to the sample classification. The result also means there is a 33% chance that a reference sample can be misclassified to a wrong sample group. Implications of this error are discussed in Section 5.
Three of four duplicate test samples were found to be in the same condition as their respective replicate 1 samples, indicating 75% precision (Table 8). The one exception differed from its replicate 1 sample by only one Band width.
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Table 8. Summary of assessments for replicate reference samples collected for the test of model precision. No data transformations were applied for model testing. Sample group was the one to which the reference sample belonged that was used in modeling (Figure 3). Test output is shown in Appendix B.
Reference Site name Sample Condition of reference sample Group replicate sample
S2 Sumallo R. upstream of 1 Slightly divergent* Sunshine Valley S29 Dryner Cr. Upstream of Skagit 1 Reference** confluence S42 Skaist R. at highway crossing 3 Reference** (replicate 2) S42 Skaist R. at highway crossing 3 Reference** (replicate 3) Precision (percent of replicate tests that were in reference 75% condition)
*Reference sample was collected in 2007 and the duplicate was collected in 2008. **Reference and duplicate samples were collected in 2008.
Model sensitivity differed by type of disturbance (Table 9). The Sunshine Valley development showed minimal (2008 sample) or no (2007 sample) disturbance. Silver Daisy Creek and Norwegian Creek that is downstream of Giant Copper were divergent from reference but Upper Smitheram Creek that drains Giant Copper to the northwest was not divergent. Captain Grant Creek and Hilton Creek were slightly divergent from reference condition. One test sample collected from the Skagit River adjacent to Highway 3 was found to be in reference condition. An extremely divergent site was found on a small tributary to the Skaist River. Sensitivity of the model to disturbance among all tested sites was 70%.
The two test sites that were not used in evaluating model sensitivity (S33 (Lower Sumallo River) and S47 (lower Smitheram Creek)) were both found to be in reference condition using test procedures that were the same as for the other test sites (Appendix D).
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Table 9. Summary of assessment at test sites to examine model sensitivity. Test output is shown in Appendix C.
Site Site name Predicted Assessment by Average Bray number sample ordination Curtis group dissimilarity from reference (%) Sumallo R. downstream of S1 (2007) 1 Reference n/a Sunshine Valley bridge
Sumallo R. downstream of Slightly S1 (2008) 1 56 Sunshine Valley bridge divergent Silver Daisy Creek S24 downstream of Giant 1 Divergent 67 (2007) Copper Silver Daisy Creek S24 downstream of Giant 1 Divergent 68 (2008) Copper Captain Grant Creek S41 Slightly (tributary of upper Skaist 3 58 (2008) divergent River) S43 Noname tributary of Skaist Extremely 1 77 (2008) River near Hwy 3 divergent S45 Norwegian Creek 1 Divergent 72 (2008) S46 Upper Smitheram Creek 3 Reference n/a (2008) S49 Skagit River upstream of 1 Reference n/a (2008) Smitheram S64 Slightly Hilton Creek 3 63 (2008) divergent Sensitivity (percent of test samples found to be divergent from reference condition) 70%
Abundance of invertebrate indicator taxa was different between the divergent test sites and corresponding reference sites (Table 10) and habitat variables that were associated with variation in biological assemblages also differed among sites (Table 11). Some details are listed as follows.
S1 (downstream of Sunshine Valley) in 2008 (Slightly divergent from reference)
Overall abundance (5722/sample) was higher than at reference sites (2391/sample) largely due to high numbers of Enchytraeidae, Chironomidae, and to a lesser extent two mayfly families (Heptageniidae, and Ephemerellidae). These higher numbers of several taxa corresponded with prevalence of shrub and open canopy in the riparian zone compared to a closed canopy of conifer or mixed conifer and deciduous
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 35 forest at reference sites (Table 11). Density of gravel roads was also at the high end of the range found among reference sites.
S24 (Silver Daisy Creek) in 2007 and 2008 (Divergent from reference)
Assemblages at S24 differed between the two years. In 2007 total abundance and richness was higher than at reference sites mainly due to the Enchytraeidae. Mayfly abundance was lower than at reference sites but Nemouridae (stonefly) abundance was greater. In 2008, overall abundance was lower and richness was greater at S24 than at reference sites. Low numbers were mainly due to the mayflies and chironomids. In both years, metals (Cd, Pb, and Zn) concentrations were higher than at reference sites (Table 11). Cd concentration (0.5 μg/L), was greater than the guideline for protection of aquatic life of 0.017 μg/L (CCME 2009) making it potentially toxic. Pb (0.14 μg/L in 2007 and 0.21 μg/L in 2008), and Zn (2.2 μg/L in 2007 and 3.7 μg/L in 2008) concentrations were less than the guideline concentrations of 1 μg/L for Pb and 30 μg/L for Zn. Pb and Zn individually may not have been acutely toxic but synergistic effects of combined metals or chronic sublethal effects may be important at this site. Further investigation by experimentation or toxicity tests would be required to examine degree of metals toxicity that appears important at this site.
Captain Grant Creek (S41, Slightly divergent from reference)
Total invertebrate abundance at S41 was lower than at reference sites but richness was slightly higher. The lower abundance was due to lower numbers of mayflies (Heptageniidae, Ephemereliidae, and Baetidae), chironomids, and the
Enchytraeidae. The difference in assemblage coincided with NH4-N concentration that was at the high end of the range at reference sites and a very low N:P ratio (Table 11). The molar ratio of bio-available N:bio-available P in water can indicate the relative supply of N and P for algae in streams. Bio-available N is approximated as dissolved inorganic N or the sum of NH4-N and NO3-N concentration. Bio-available P is approximated as SRP concentration. Rhee (1978) has shown that, for a given species of algae, there is a sharp transition between P-limited and N-limited growth. The particular N:P ratio at which the transition between N and P-limitation occurs is species dependent, varying from as low as 7:1 for some diatoms (Rhee and Gotham 1980) to as high as 45:1 for some blue-greens (Healey 1985). It is commonly regarded that below a molar N:P ratio of 20, the growth of most algal species will be limited by N whereas P- deficient growth is prevalent at molar N:P ratios greater than 50 (Guildford and Hecky, 2000). Because an optimum N:P ratio (above which P limitation occurs and below which N limitation occurs) can vary widely among freshwater algae, the range between 20 and 50 can be regarded as a transition range in a community where some species will be P- limited and others will be N-limited. These criteria show that N limitation was potentially
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 36 present at S41. The N:P was at the low end of the range among reference sites indicating it was not completely unique with respect to potential N deficiency but it was a factor potentially defining variation in the invertebrate assemblage found at the site.
S43 (no-name tributary of the Skaist River, Extremely divergent from reference)
S43 had less than half the total abundance of invertebrates in reference samples mainly due to low numbers of mayflies (Heptageniidae, Ephemereliidae, and Baetidae) and Enchytraeidae. The test sample had relatively high numbers of Planariidae and Empididae and overall high family richness. These differences made S43 extremely divergent from reference in association with higher turbidity and relatively high concentrations of SO4 and Mo than at reference sites (Table 11). Unknown land disturbance possibly exposing mineralization may be related to the turbidity and chemical anomalies.
S45 (Norwegian Creek, Divergent from reference)
S45 had lower invertebrate abundance than reference again due mainly to the mayflies and the Enchytraeidae. The difference was linked to very low dissolved inorganic N concentrations that contributed to an extremely low N:P ratio (Table 11). The low ratio potentially shows extreme N deficiency of algal growth supporting the benthic community.
S64 (Hilton Creek, Slightly divergent from reference)
Total invertebrate abundance at S64 was lower than at reference sites but richness was slightly higher, similar to findings at S41. The lower abundance was due to low numbers of Heptageniidae, Ephemereliidae, chironomids, Peltoperlidae, Enchytraeidae and complete absence of Baetidae at the test site. These differences in assemblage were associated with high density of gravel roads close to the sampling site and high percent of range lands on which cattle were observed at the time of sampling. Both of these land uses may have contributed to higher turbidity than was found at reference sites although the turbidity value was low (1.2 NTU) and would not be expected to greatly affect the benthos. Dissolved oxygen concentrations were at the low end of the range found at reference sites. In combination, diversion of these habitat variables from reference show some land disturbance influencing water quality.
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Table 10. Differences in invertebrate assemblages between test and corresponding reference sites.
Test site Metric or indicator taxa* (percent Count (number/sample) contribution to dissimilarity of invertebrate assemblages between test and reference samples) Test Reference (average among samples in reference group ± SE) S1 (2008) Total abundance 5722 2391 ± 213 Sumallo River downstream of Richness 17 21 ± 0.8 Sunshine Valley Enchytraeidae (32%) 1758 348 ± 109 bridge Chironomidae (29%) 1480 178 ± 41 Heptageniidae (9.3%) 934 567 ± 60 Baetidae (7.6%) 456 579 ± 111 Ephemerellidae (5.2%) 432 209 ± 30
S24 (2007) Total abundance 2547 2391 ± 213 Silver Daisy Creek Richness 26 21 ± 0.8 downstream of Enchytraeidae (35%) 1364 348 ± 109 Giant Copper Baetidae (15%) 38 579 ± 111 Heptageniidae (13%) 111 567 ± 60 Nemouridae (5.9%) 253 67 ± 11 Ephemerellidae (5.3%) 28 209 ± 30 Planariidae (5.3%) 186 20 ± 5.7
S24 (2008) Total abundance 1235 2391 ± 213 Silver Daisy Creek Richness 27 21 ± 0.8 downstream of Heptageniidae (19%) 82 567 ± 60 Giant Copper Baetidae (18%) 95 579 ± 111 Enchytraeidae (11%) 214 348 ± 109 Planariidae (8%) 202 20 ± 5.7 Ephemerellidae (7.2%) 26 209 ± 30 Chironomidae (6.9%) 20 178 ± 41
S41 (2008) Total abundance 2875 4860 ± 667 Captain Grant Creek (tributary of Richness 27 23 ± 1 upper Skaist Heptageniidae (17%) 80 862 ± 136 River) Ephemerellidae (13%) 164 969 ± 383 Baetidae (10%) 122 558 ± 122 Planariidae (6.4%) 402 151 ± 35 Chironomidae (5.4%) 292 525 ± 107 Enchytraeidae (5.1%) 4 230 ± 105
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Test site Metric or indicator taxa* (percent Count (number/sample) contribution to dissimilarity of invertebrate assemblages between test and reference samples) Test Reference (average among samples in reference group ± SE)
S43 (2008) Total abundance 1004 2391 ± 213 Noname tributary of Skaist River Richness 32 21 ± 0.8 near Hwy 3 Heptageniidae (20%) 49 567 ± 60 Baetidae (20%) 27 579 ± 111 Enchytraeidae (11%) 27 348 ± 109 Planariidae (7.9%) 208 20 ± 5.7 Ephemerellidae (7.7%) 2 209 ± 30 Empididae (6.8%) 178 14 ± 3.3
S45 (2008) Total abundance 830 2391 ± 213 Norwegian Creek Richness 26 21 ± 0.8 Baetidae (23%) 20 579 ± 111 Heptageniidae (21%) 74 567 ± 60 Enchytraeidae (13%) 0 348 ± 109 Ephemerellidae (7.9%) 19 209 ± 30
S64 (2008) Total abundance 1883 4860 ± 667 Hilton Creek Richness 25 23 ± 1 Heptageniidae (15%) 214 862 ± 136 Ephemerellidae (15%) 113 969 ± 383 Baetidae (14%) 0 558 ± 122 Chironomidae (6.1%) 252 525 ± 107 Peltoperlidae (6.1%) 40 309 ± 71 Enchytraeidae (5.6%) 48 230 ± 105 *Those taxa individually contributing to more than 5% of dissimilarity between assemblages of the test site and reference sites.
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Table 11. Habitat differences between reference and non-reference sites.
Test sample Reference Most important habitat Test site Range at sample variables contributing value reference sites group to test site divergence as shown by linkage tree analysis
S1 (2008) Sumallo 1 Gravel road density* 39.5 0 - 49.2 River downstream of Dominant riparian class shrub Conifer or mixed Sunshine Valley bridge forest
S24 (2007) 1 Cd concentration 0.05 μg/L <0.01 μg/L Silver Daisy Creek Pb concentration 0.14 μg/L <0.01 μg/L downstream of Giant Zn concentration 2.2 μg/L 0.3 – 1.8 μg/L Copper
S24 (2008) 1 Cd concentration 0.05 μg/L <0.01 μg/L Silver Daisy Creek Pb concentration 0.21μg/L <0.01 μg/L downstream of Giant Zn concentration 3.7μg/L 0.3 – 1.8 μg/L Copper
S41 (2008) 3 Molar N:P 8.1 6.3 - 277 Captain Grant Creek NH4-N concentration 4.1 μg/L 1.3 – 5.5 μg/L
S43 (2008) 1 Turbidity 3.9 NTU 0.1 - 0.9 NTU Noname tributary of SO4 concentration 21.4 mg/L 0.6 – 16.7 mg/L Skaist River near Hwy 3 Mo concentration 1.23 μg/L 0.33 – 1.18 μg/L
S45 (2008) 1 Dissolved inorganic N 3.1 μg/L 4.1 – 174 μg/L Norwegian Creek (DIN) concentration Molar N:P 2.1 2.7 – 282
S64 (2008) 3 Percent range 74% 0 – 8.8% Hilton Creek Turbidity 1.2 NTU 0.2 – 0.9 NTU Gravel road density* 133 0 – 11.4 Dissolved oxygen 8.3 mg/L 8.6 – 11.7 mg/L concentration *Road density is road length divided by watershed area multiplied by unit conversion of 108.
4.3 20BOther water quality variables
For descriptive purposes unrelated to BASS, average values of general chemical analytes and other stressor gradient variables among reference streams are listed in Table 12. The streams were pristine and clear as indicated by turbidity ≤0.5 NTU. The pH was slightly alkaline and alkalinity, otherwise known as acid neutralizing capacity, was 46-50 mg CaCO3/L with values slightly higher in Group 2 than in Groups 1 and 3. Total dissolved solids concentrations were moderate. Average ammonium concentrations of 2.5 – 3.9 μg/L were exceeded by NO3-N concentrations of 26-43 μg/L, indicating that nitrification was favoured among streams. Soluble reactive P
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(orthophosphate ion and soluble acid labile P compounds) concentration was 1.8 – 2.1 μg/L and TP (SRP plus large molecular weight dissolved P and particulate P) concentration was 3.8-4.8 μg/L with highest concentrations occurring among Group 3 samples. The average molar N:P ratio was 85 and 71 in Groups 1 and 2 respectively, indicating phosphorus deficiency (see explanation of N:P ratios in Section 4.2). The average value of 38 in Group 3 streams showed more nitrogen limitation of algal growth. Dissolved metals were either not detectable by ICPMS or in extremely low
concentrations, well below Canadian water quality guidelines (Hhttp://ceqg-
rcqe.ccme.ca/H). Instantaneous temperatures were typical of cool streams with cover from coniferous or deciduous riparian vegetation in the cascades in late summer. The streams had some embeddedness and local erosion was absent or light. These pristine conditions were largely due to little agriculture or residential development in the area. Free range cattle grazing was present but limited to <1% of drainage areas. Some logging has occurred in up to 5% of reference drainages based on the most recent GIS data layer. Roads were present in all sample groups with most at higher elevations being “block” roads that are largely old and decommissioned and in many cases grown over (e.g. Maselpanik Creek watershed). Gravel roads were rare at the high elevations of Group 3 sites but more numerous at the lower elevations of Groups 1 and 2 sites. Paved road only influenced sites that were downstream of drainage from Hwy 3 and park access roads. Only one of these sites was present in sample Group 3, three were in Group 1 and 4 were in Group 2.
Table 12. Mean (±SE) values of stressor gradient variables among reference sample groups.
Variable (μg/L unless Sample group otherwise indicated) 1 2 3
NO3-N 43±9 41±8 26±9
NH4-N 2.5±0.3 3.9±0.8 2.6±0.3 DIN 45±9 45±7 28±9 TN (mg/L) 0.06±0.01 0.07±0.01 0.04±0.01 SRP 1.9±0.3 1.8±0.3 2.1±0.2 TP 3.8±0.4 3.9±0.5 4.8±0.9 Molar N:P (relative value) 85±18 71±16 38±17 Alkalinity 46±5 50±4 46±7 pH (scale of 0-14) 7.9±0.06 7.9±0.06 7.8±0.1 Dissolved Cd <0.01 <0.01 <0.01 Dissolved Cu 0.4±0.03 0.5±0.07 0.3±0.03 Dissolved Pb <0.01 <0.01 <0.01 Dissolved Zn 0.6±0.09 0.6±0.06 1.0±0.14 Temperature (ºC) 9.3±0.6 11.3±0.8 8.8±0.7
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Variable (μg/L unless Sample group otherwise indicated) 1 2 3 TDS 69±8 76±7 70±11 Turbidity (NTU) 0.5±0.05 0.5±0.09 0.4±0.04 Embeddedness (%) 25 – 50 25 25 Erosion (categorical) Light to none Light to none Light to none Dominant riparian class Deciduous or Conifer or mixed forest Mixed forest (categorical) coniferous forest Percent agriculture (%) 0.02 ± 0.01 0.08 ± 0.05 0 Percent range (%) 0.3 ± 0.2 0.2 ± 0.12 0.7 ± 0.6 Percent logged (%) 5.1 ± 1.8 5.4 ± 1.9 1.5 ± 1.3 Percent urban (%) 0.01 ± 0.006 0.05 ± 0.03 0 Block road density 22.0 ± 6.0 41.6 ± 13.5 14.1 ± 7.4 Gravel road density 13.3 ± 3.7 13.4 ± 5.6 1.2 ± 0.8 Paved road density 1.7 ± 1.0 7.1 ± 2.7 4.2 ± 4.2
5 4BDISCUSSION
5.1 21BPredictor variables for BASS
We selected candidate predictor variables that might explain dissimilarities among the biological assemblages. Position in the watershed (easting, northing, and elevation) was potentially important because it may define stream size and flow that can potentially influence invertebrate community structure. Channel morphology defined by drainage density, relief, and bankfull width was selected because it can be a central driver of biological assemblages over a stream continuum (Vannote et al. 1980). Proportions of different habitat types (pool, glide-run, riffle, cascade) were selected because invertebrates can be differentially adapted to variation in hydraulic conditions across habitats (Halwas et al. 2005, Rempel et al. 2000). Percent alpine, ice, wetland, and barren land can affect water supply for streams, particularly in late summer and potentially influence biological communities according to variation in water depths and velocity. Percent of areas burned was included because of its potential importance in modifying macroinvertebrate richness and density (Minshall et al. 2001). Weathering of geological materials (percent sedimentary, intrusive, volcanic, metamorphic, and ultramafic rock) can determine nutrient concentrations that contribute to biological production in streams. Temperature was selected because of its importance in overall biological production (Jacobsen et al. 1997, Huryn and Wallace 2000). Macrophyte cover was selected because its foliage provides surfaces on which biofilms can develop (Diehl and Kornijow 1997, Suren and Winterbourn 1992), thereby supplying food for benthic inverterbrates. Forest stage can be important in moderating water temperature (Mellina et al. 2002) and providing litter as food for benthos, particularly the aquatic
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 42 insects (Richardson 1992). Dominant and subdominant particle size was selected because it can modify the composition of invertebrate communities (Williams and Mundie 1978). All these variables had potential importance in discriminating sample groups. We chose not to include broad landscape classifications. Biogeoclimatic zone, for example, was not included because we did not see linear order in its coding, which is needed for discriminant analysis. In addition, biogeoclimatic zone is a classification defined by climate, geology, and vegetation, which were already included in the list of variables. Finally, landscape classifications can be imprecise predictors and they can increase the likelihood of mistakenly passing a site when divergence is present (Type II error, Hawkins et al. 2000).
Elevation and percent wetland were best predictors of the biological classification in BASS. They were not statistically correlated, which is favourable for model performance. Together the two variables may indicate a common process determining the biological assemblages. The assemblages formed a biological gradient over the range of elevations and amounts of wetland that were present upstream of sampling sites. Wetlands are water reservoirs that can maintain flow in streams during dry periods and they are sites of biogeochemical cycling, high biological productivity and diversity (Stevenson and Hauer 2002). We hypothesize that stream water quality and biological assemblages in those streams are sensitive to summertime flows that may be determined by extent of wetland supplying water during the relatively dry summer period. One assemblage occurring at highest elevations was characteristic of small streams that have low summertime flows due to the absence of upstream wetland. This assemblage grades into others over declining elevations that are increasingly influenced by sustained flow from wetland. Even small areas of wetland occupying <2% of drainage areas upstream of sampling sites exerted change on biological assemblages. This finding shows that not only is wetland important in generally defining quality of streams in the Skagit watershed but even the smallest of wetlands relative to watershed size are of value.
One might argue that with the large elevation range over which samples were collected, temperature should be important in defining biological assemblages and be an important predictor in the model. Instantaneous water temperature did vary from 4ºC at highest elevations (e.g. Site S52, Figure 1) to 18.5ºC in outflow from the Lightning Lakes (Site S56, Figure 1), but the variation with elevation was not consistent enough to make it an important predictor. In fact water temperature was one of the first variables to be removed in the forward stepping DFA, indicating it was of no value in discriminating between the sample groups. This finding does not mean that temperature is not important in defining the biological assemblages. It only shows that instantaneous temperature measured at each site in summer was not a good predictor. We might anticipate mean annual temperature would be a better discriminator since it integrates over life spans of the aquatic invertebrates. This measurement would require the
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 43 placement of temperature loggers at sampling sites over a year before the biological sampling, which was beyond the scope of the present study.
5.2 22BModel Performance
The 67% accuracy of BASS was at the high end of the range reported among other BEAST models. Reynoldson et al. (2001), Sylvestre et al. (2005), and Mazor et al. (2006) reported classification success or accuracy of 56%, 53%, and 48% respectively. For several RIVPACS type models that use the same procedure as in BEAST modeling to define sample groups, Ostermiller and Hawkins (2004) reported classification success rates of only 15 – 39%. These low rates are largely related to difficulty in defining discrete sample groups in cluster analysis because biological assemblages often form gradients across a continuum of streams in a survey area. This distinction has been noted by many researchers and the Skagit data are no different. Efforts to avoid a classification approach by selecting reference sites close to a test site (Linke et al. 2005) or matching reference sites to test sites using environmental criteria (Chessman et al. 2008) have produced only modest improvements in performance over classification methods. Improvements are certainly not enough to warrant change to considerable software development in several countries for use in routine site testing and analysis (e.g. AUSRIVAS, Coysh et al. 2000 or CABIN, Sylvestre et al. 2005). For practical purposes, results from classification and gradient analysis appear no different.
There are some difficulties in the use of classification success as a measure of accuracy for comparison among studies. Accuracy can decline with increasing sample size because the probability of finding discrete sample groups declines as the number of samples increases (Reynoldson et al. 2001). Accuracy can be affected by decisions on where to separate sample groups in cluster analysis and ordination. Any consolidation of sample groups due to unclear separation over a biological gradient will invariably increase classification success because there are fewer chances of the model placing a sample in the wrong group. The ultimate consolidation of samples is a null model (one sample group) where there is no classification error and thus 100% accuracy. Conversely, by accepting a relatively large number of sample groups, the model classification error can increase. Effectively, this would also make the model more sensitive to detecting habitat disturbance because biological variability within groups will be lower than if there are fewer groups. Because researchers differ in their approach to defining sample groups, comparison of model accuracy based on classification success rates may not be the best way of comparing model performance, particularly where biological gradients are evident.
A preferred comparative measure may be precision, that we defined as the proportion of replicate reference samples not used in model building that were correctly assessed as in reference condition. We did not use replicate divergent samples in this
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 44 testing because we could not be certain that many were truly disturbed. We found 75% precision among the few samples that were available. For the S2 sample from 2008 and the first sample from 2007, we assumed no change in site condition between years. It is possible that site condition changed between the two years, in which case the 2008 sample should not have been used in the test of precision and actual precision would have been 100%. The estimated precision of 75% could go up or down with more samples added but as a start, this level of precision is acceptable. It is close to the average value of 77% for other BEAST models and 82% for RIVPACS models that was calculated by Mazor et al. (2006) in their comparison of model performances. The value of 1 – precision, which for BASS was 0.25, is a measure of Type 1 error (mistakingly failing a site). For managing water quality, this error can be advantageous because it is preferable to mistakingly fail a site than it is to pass a site that truly is divergent (Type II error). In the former case, site condition is likely to be investigated further, either to confirm the initial finding or examine potential cause of divergence. If further assessment shows the site is actually in reference condition, due diligence has been completed. In the latter case, there is risk that no further action would be taken until another round of site assessment is done, in which case disturbance may go undetected and potentially lead to further degradation if left unchecked.
Type II error has a value of 1 - sensitivity, which for BASS was 0.3. An assumption in tests of sensitivity is that sites must actually be divergent from reference condition. This information was not known unequivocally. Disturbance of S24 (Silver Daisy Creek) was apparent by the presence of an abandoned mine adit from which seepage containing iron oxide and potentially other metals drained into Silver Daisy Creek upstream of the sampling site (Figure 5). Effects from mineral exploration at Giant Copper on Silver Daisy Creek at S24 were not obvious from observation in the field. Given the lack of metals or associated land disturbance affecting Smitheram Creek and Norwegian Creek that also drained Giant Copper, there is no evidence that the exploration activity was affecting surface streams at the time of sampling in 2007 and 2008. We certainly could not assume Giant Copper was contributing to divergence of biological assemblages at S24 for purposes of testing model sensitivity. It was more likely related to drainage from the old adit. Similarly, we did not know unequivocally that resort development at Sunshine Valley was causing change in water quality and biological assemblages in the Sumallo River. There was, however, high likelihood that riparian disturbance downstream of the development could contribute to divergence of assemblages from reference condition based on knowledge of the importance of riparian communities for stream functioning (Naiman et al. 2005). The same argument applies to the small streams where free range cattle were observed to be grazing in adjacent meadow (Captain Grant Creek and Hilton Creek). These streams had relatively high
NH4-N concentrations or turbidity that was intuitively linked to cattle grazing or road cuts and was different from reference conditions. These chemical differences indicated some change to water quality although the values were low and by themselves did not indicate
water quality impairment (Hhttp://ceqg-rcqe.ccme.ca/H ). We did not know before
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 45 assessment using the model that the changes might cause biological divergence from reference condition. The same situation was true for S43 where heavy silt accumulation in sediments was found. We perceived Norwegian Creek would be divergent from reference because headwaters drained Giant Copper that may introduce metals. In fact, this stream was divergent because of very low DIN concentrations that may produce extreme nitrogen deficiency. Due to these uncertainties about actual site disturbance, the test of sensitivity can be considered conservative. The 70% value is probably at the low end of actual model sensitivity because we included test samples that were perceived as divergent but in fact were not.
Figure 5. Image of a mine adit from which seepage was found entering Silver Daisy Creek upstream of the S24 sampling site.
5.3 23BWater quality in Skagit streams
BASS detected a range of water quality conditions among streams in the Skagit watershed. The reference stream sites are prevalent throughout the watershed. They are pristine due to little or no upstream land disturbance in recent years and are characterized by diverse and abundant invertebrate communities dominated by larval forms of aquatic insects that indicate high water quality (Rosenberg and Resh 1993). These streams are clear and cool, they have low alkalinity, moderate dissolved solids, no metals contamination, and low macronutrient concentrations. The molar N:P showed algal growth in some of these streams is potentially limited by phosphorus while in others nitrogen limitation is more common.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 46
Test site analysis using BASS showed a few point source disturbances or deviation of habitat conditions in the watershed. The test site at S1 (Sumallo River) showed no effect of the Sunshine Valley resort development in 2007 and biological enrichment in 2008. The effect was linked to a more open riparian community that potentially allowed greater irradiance to reach the stream, possibly increasing rates of periphyton production that is a major food source for the aquatic invertebrates. In contrast, at S24 (Silver Daisy Creek), low numbers of mayflies were key in showing divergence of the site from reference conditions. The effect was linked to metals contamination likely coming from a nearby and abandoned mine adit from which metals drainage was observed. The relatively high Cd concentration of 0.05 μg/L was above the
water quality guideline of 0.017 μg/L (Hhttp://ceqg-rcqe.ccme.ca/H ), potentially producing toxicity, particularly among the mayflies that are known to be sensitive to metals (e.g. Hickey and Clements 1998). Any association of the effect on water quality from mineral exploration activity on the Giant Copper property located at the top of Silver Daisy Mountain was ruled out because other sites draining the property showed no divergence from reference due to metals contamination (Norwegian Creek, both the upstream and downstream sites on Smitheram Creek). Cattle grazing on range lands and limited road cuts at high elevations in the watershed were linked to low overall invertebrate abundance mainly associated with ammonium and turbidity. Based on observations of cattle trails with support from the GIS analysis, the extent of cattle grazing in the watershed is small, occurring on far less than 1% of drainage areas. The only sampling site found to be extremely divergent from reference was S43, a small tributary of the Skaist River near Highway 3. It was embedded with fines and relatively high turbidity, and anomalous Mo and SO4 concentration was linked to low invertebrate abundance, mainly due to low numbers of mayflies. No land use activity was observed upstream of the site that could explain the effect but we did not conduct a detailed survey to examine potential cause. Further review of site conditions is something that can be done as a follow-up to our initial screening. Divergence of Norwegian Creek from reference condition was unique in being linked to very low inorganic nitrogen concentrations, not to contaminants or other disturbance. It was a steep, heavily forested site where the NO3-N concentration was only 1.4 μg/L and the NH4-N concentration was only 1.7 μg/L. These very low concentrations combined to produce the lowest DIN concentrations in the whole study area, and they show nitrogen demand in that drainage was extreme, possibly associated with very tight nitrogen cycling in forest soils. The SRP concentration in Norwegian Creek was 3.2 μg/L making the molar N:P only 2.1, which indicates extreme nitrogen deficiency for algal growth in the stream. That deficiency may have been enough to modify the invertebrate assemblage from the typical reference condition.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 47
6 5BRECOMMENDATIONS
We stated in Section 3.5 that all biological and habitat data were uploaded to the
CABIN website (Hhttp://cabin.cciw.ca/Main/cabin_about.aspH ) for archiving and later use in site testing. All raw data from the project now resides on that website and with a valid user ID and passcode can be accessed at any time. The website is managed by Environment Canada with the local authority being Stephanie Strachan
([email protected] ). Before BASS can be uploaded to the CABIN website, Stephanie must complete a review of this report and have it archived in Environment Canada files as a technical reference for the model. That process will happen as soon as SEEC approves the final report. Users authorized by SEEC must then complete an on-line course to familiarize themselves with site testing procedures using CABIN protocols. Once these tasks are complete, site testing can be done. Again, an advantage of using the CABIN website is that site testing can be done quickly and easily with little or no statistical knowledge. The model runs in the background and output is provided as requested by the user according to test needs.
It is important to review how BASS can be used as a decision support tool for monitoring water quality in the Skagit watershed. If mining proceeds at Giant Copper, decisions will be needed on what waste treatment and disposal techniques should be used to best protect downstream water quality. A pilot water treatment system could be assessed using BASS with results being used to select a final process. BASS will also be ideal for routine stream quality monitoring in all areas of the project site. If site impairment is detected, the results will provide technical criteria to recommend improvements to wastewater treatment and disposal. Similarly, BASS can be used to monitor contamination of Silver Daisy Creek or monitor water quality if expansion of the Sunshine Valley development proceeds. It can be used to confirm impairment at S43 and if action is taken to repair site disturbance, testing using BASS can be used to confirm effectiveness of remedial measures. If new disturbance occurs on unprotected land in the watershed, BASS can be used to monitor surface water protection measures. It can provide criteria for improvements should evidence show that development is contributing to divergence of water quality from reference conditions. In this way, bioassessment using BASS is a screening and learning tool that can feed into decisions to protect water quality.
BASS can also be used to monitor natural changes to water quality in the Skagit watershed. With climate change driving environmental initiatives and technological innovation there is need for a tool to monitor regional change in water quality. Monitoring of water quality might also be needed if a regional change to the landscape occurs such as fire or insect infestation. BASS can fill that role. For example, a subset of reference sites may be selected for sampling annually or every set number of years. Position of coordinates from those samples in ordination space can be used to see if overall condition of streams is migrating away or staying within the reference cloud of coordinates from samples that were collected in 2007 and 2008. If test sample migration
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 48 is not detected, the data will show no regional change in water quality. Those samples can then be used to improve estimates of model precision.
Figure 6 conceptually shows a decision support system using BASS. It starts by identifying sites that require monitoring. At any given site, an invertebrate sample is collected along with measurement or calculation of natural and stressor gradient variables, including the two that are required to run BASS (elevation and percent wetland). This measurement step requires some work with GIS to derive the percent wetland values. The stressor gradient variables are measured to provide data for examining factors potentially contributing to any divergence. If no divergence is found in the ordination test, the sample passes and can be considered to be in reference condition. Although conclusions from a single sample may be perceived as weak, sample replication either at a single point in time or over time does not change conclusions gained from a single test in most cases (Hose et al. 2004). Regardless, a passing grade from several samples either collected through time or at different places under the same condition will instil confidence that the result is correct. If a sample fails the test, potential cause of divergence from reference condition will need to be examined. Results will provide technical criteria to support management actions to improve site quality. Linktree analysis as we applied in this study is one way to link biological divergence to water condition but other statistical approaches can be used such as conventional detrended correspondence analysis, or principle components analysis. These analyses can be run using the existing reference data and that collected at the test site as we did for the site testing. In some cases, site specific experimentation may be needed although in most cases this may not be necessary. With this evidence as a guide, a follow-up site survey can be completed to identify sources of disturbance that can lead to decisions on what level of remedial measures should be implemented. Once remediation is complete, the site can again be tested using BASS to confirm if actions were or were not successful, again following the steps in Figure 6. Used in this way, BASS can be an effective environmental screening tool that can be used to quickly and inexpensively assess many sites with scientific rigour throughout the Skagit watershed.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 49
Valued Ecosystem Components require monitoring in Skagit watershed (fish, water quality, landscape attributes)
Sites divergent Site Screening Sites found in using BASS Reference Condition
Site specific testing to define cause of divergence
Action taken to improve site quality
Figure 6. Schematic illustration of where the BASS fits in environmental decision-making in the Skagit watershed.
Three main recommendations follow from this discussion:
1. Once the final version of this report is accepted by SEEC, authorization must be given to Stephanie Strachan, the CABIN representative within Environment Canada, to review the report and proceed with uploading of the model to the CABIN website. This step is required by Environment Canada as custodians of the CABIN website. Once the model is uploaded, it is available for site testing by authorized users.
2. It is recommended that SEEC authorize selected members or others to complete CABIN training and that those people be involved in implementing site testing as needed. Once the training is complete, each person will be given a username and passcode for access to CABIN. Various levels of training are available. Information can be found at
Hhttp://www.unb.ca/research/institutes/cri/opportunities/courses/cabin-rcba/index.htmlH.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 50
3. It is recommended that BASS be used as a decision support tool for monitoring water quality in the Skagit watershed. Existing or new land disturbance and associated water protection measures can be followed using BASS to monitor surface water quality. It can provide criteria to show if stream protection measures are working and it can provide evidence to show if improvements are needed. BASS can also be used to monitor natural changes to water quality in the Skagit watershed. For example, a subset of reference sites may be selected for sampling annually or every set number of years. A rough guide might be to sample 10 sites per year. Results from this testing can show whether stream quality is sequentially changing over time, potentially contributing to interpretations of effects of climate change or other regional environmental change that may affect water quality (e.g. fire or insect infestation). In these ways, bioassessment using BASS is a screening and learning tool that can feed into decisions to protect water quality.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 51
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Reynoldson, T.B., R.C. Bailey, K.E. Day, and R.H. Norris.1995. Biological guidelines for freshwater sediment based on BEnthic Assessment of SedimenT (the BEAST) using a multivariate approach for predicting biological state. Australian Journal of Ecology 20: 198-219.
Reynoldson, T.B., C. Logan, T. Pascoe and S.P. Thompson. 2003. CABIN (Canadian Aquatic Biomonitoring Network) Invertebrate Biomonitoring Field and Laboratory Manual. National Water Research Institute, Environment Canada.
Reynoldson, T.B., R.H. Norris, V.H. Resh, K.E. Day, and D.M. Rosenberg. 1997. The reference condition approach: a comparison of multimetric and multivariate approaches to assess water quality impairment using benthic macroinvertebrates. Journal of the North American Benthological Society 16: 833-852.
Reynoldson, T.B., D.M. Rosenberg and V.H. Resh. 2001. Comparison of models predicting invertebrate assemblages for biomonitoring in the Fraser River catchment, British Columbia. Canadian Journal of Fisheries and Aquatic Sciences 58:1395-1410.
Rhee, G.-Y. 1978. Effects of N:P atomic ratios and nitrate limitation on algal growth, cell composition, and nitrate uptake. Limnology and Oceanography 23:10-25.
Rhee, G.-Y. and I.J. Gotham. 1980. Optimum N:P ratios and coexistence of planktonic algae. Journal of Phycology 16:486-489.
Richardson, J.S. 1992. Food, microhabitat, or both? Macroinvertebrate use of leaf accumulations in a montane stream. Freshwater Biology. 27: 169-176.
Rosenberg, D.M. and V.H. Resh. (eds). 1993. Freshwater biomonitoring and benthic macroinvertebrates. Chapman and Hall. New York.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 56
Rosenberg, D.M., T.B. Reynoldson and V.H. Resh. 1999. Establishing reference conditions for benthic invertebrate monitoring in the Fraser River catchment, British Columbia, Canada. Environment Canada’s Fraser River Action Plan
Report DOE FRAP 1998-32. Hhttp://www.rem.sfu.ca/FRAP/9832.pdfH.
Sloane, P.I.W. and R.H. Norris. 2003. Relationship of AUSRIVAS-based Macroinvertebrate Predictive Model Outputs to a Metal Pollution Gradient. Journal of the North American Benthological Society 22:457-471.
Stainton, M.P., M.J. Chapel, and F.A. Armstrong. 1977. The chemical analysis of freshwater. 2nd. ed. Fish. Environ. Can. Misc. Spec. Publ. 25.
Stewart-Oaten, A. W.M. Murdoch, and K.R. Parker. 1986. Environmental impact assessment: “Pseudoreplication” in time? Ecology 67: 929-940.
Stockner, J. and K.R.S. Shortreed. 1978. Enhancement of autotrophic production by nutrient addition in a coastal rainforest stream on Vancouver Island. Journal of the Fisheries Research Board of Canada 35: 28-34.
Suren, A.M. and M.J. Winterbourn. 1992. The influence of periphyton, detritus and shelter on invertebrate colonization of aquatic bryophytes. Freshwater Biology. 27: 327-339.
Stevenson, R.J. and F.R. Hauer. 2002. Integrating hydrogeomorphic and Index of Biotic Integrity approaches for environmental assessment of wetlands. Journal of the North American Benthological Society 21: 502-513.
Sylvestre, S., M. Fluegel, and T. Tuominen. 2005. Benthic invertebrate assessment of streams in the Georgia Basin using the reference condition approach: expansion of the Fraser River invertebrate monitoring program 1998-2002. Environment Canada report EC/GB/04/81.
SYSTAT 11 Software, Inc. 2004. SYSTAT 11 Statistics 1. Richmond, CA.
Triton, 2008. Reconnaissance (1:20,000) fish and fish habitat inventory of the Canadian Skagit River watershed. Report prepared by Triton Environmental Consultants, Prince George, BC for BC Ministry of Environment Surrey, BC. 69pp plus appendices.
Triton. 2007. Reconnaissance (1:20,000) fish and fish habitat inventory of the upper Skagit River watershed: Phase 1 to 3 pre-field planning report. Report prepared by Triton Environmental Consultants, Prince George, BC for BC Ministry of Environment Surrey, BC. 30pp plus appendices.
Underwood, A.J. 1994. On beyond BACI: sampling designs that might reliably detect environmental disturbances. Ecological Applications 4: 3-15.
Vannote, R.L., G. W. Minshall, K.W. Cummins, J.R. Sedell, and C.E. Cushing. 1980. The river continuum concept. Canadian Journal of Fisheries and Aquatic Science 37: 130-137.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 57
Wallin, M., T. Wiederholm, and R.K. Johnson. 2003. Guidance on establishing reference conditions and ecological status class boundaries for inland surface waters. Final Report to the European Commission from CIS Working Group 2.3 - REFCOND.
Wentworth, C.K. 1922 A scale of grade and class terms for clastic sediments. Journal of Geology 30:377-392.
Williams, D.D. and J.H. Mundie. 1978. Substrate size selection by stream invertebrates and the influence of sand. Limnology and Oceanography 23: 1030-1033.
Wolman. M. 1954. A method of sampling coarse bed material. American Geophysical Union Transactions 35:951-956.
Wright, J.F., D.W. Sutcliffe and M.T. Furse. 2000. Assessing the biological quality of freshwaters: RIVPACS and other techniques. Freshwater Biological Association, Ambleside, UK.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 58
8 7BAPPENDIX A: FIELD SHEET FOR RCA ASSESSMENTS
Stream Name EMS #
Date Time Field Crew
UA. Weather Conditions
Now: storm (heavy rain) Past 24 hours: storm (heavy rain) rain (steady rain) rain (steady rain) showers (intermittent) showers (intermittent) overcast overcast clear/ sunny clear/ sunny Has there been a heavy rain in the past 7 days? Y N
UB. General Site Information U Potential Reference Site? Y N
GPS Unit # GPS Datum Elevation (m)
Latitude (decimal Longitude (decimal Waypoint Name degrees) degrees)
Site Description
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Sample Site Diagram (draw a diagram of the site and indicate areas sampled; include a scale)
Map Scale: 0 Å ------Æ ____m
Photos: Field Sheet Upstream Downstream Across Substrate (use grid)
UC. Water Quality
Field Measurements: Air Temp Water Temp pH Spec. D.O. (ºC) (ºC) Conductance (mg/L) (µS)
Water Samples: General Ions (1L) Nutrients (250mL) TOC (250mL) Metals (Do not rinse)
Preserved (H2SO4) Preserved (HNO3)
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UD. Benthic Invertebrates
Sample riffle zones using 400µm Kicknet for 3 minutes Sample Number 1 2 (QA/QC) Operator Name Typical Sampling Depth (cm) Number of sample jars filled Notes:
UE. Stream Channel Characteristics
Gradient: ______(report % using clinometer)
Habitat Units Present (estimate % of visible channel area occupied by each; consider 6x bankfull width)
___ Pools __ Glides/Runs ____Riffles ___ Cascades/Rapids ___ Other
Stream Widths (measure wetted width and bankfull width at 3 different locations within the 6x bankfull; Middle measurement should be from within kicked area) Stream Location Wetted Width (m) Bankfull Width (m) Downstream Middle Upstream
Stream Profile at Upstream End (from somewhere in the kicked area, measure depth and velocity at 5-8 equidistant points across the stream)
Current meter used for velocity measurements: ______
Wetted
Width (m) Tape reading
(m)
Depth (cm) 0 0
Velocity 0 0 (m/s)
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UF. Macrophytes and Periphyton
Macrophyte Coverage (circle the one which describes the amount of stream bed covered by macrophytes; include moss, but add comments)
0% 1-25% 26-50% 51-75% 76-100%
Periphyton Coverage (circle the one which best describes the presence of periphyton in running water)
1. Rocks not slippery at all, no colour 2. Rocks slightly slippery, light yellow-brown in colour 3. Rocks have noticeable slippery feel, slippery to walk on, may be some patches of green/brown algae 4. Rocks are very slippery, can rub algae off with finger, and may be numerous large clumps of algae, dark brown colour 5. Rocks mostly obscured by algal mat, extensive algal mass (may be in long strands) brown or black colour
UG. Cover
Overall Percent Cover (observe the entire stream reach and visually estimate % coverage of the wetted surface area, within 1 m of the water surface; consider the cover types listed below) ______%
Cover Types and Amount (indicate abundance of each cover type by ticking the appropriate category) Cover Type None Trace Moderate Abundant Woody debris Boulders Undercut banks Deep pools Overhanging Vegetation
UH. Riparian Vegetation
Dominant Riparian Class (indicate which class describes the dominant vegetative cover in the riparian) Unvegetated (bare soil present) Deciduous Forest Grass/Herb Coniferous Forest Shrub (may include grasses and herbs beneath) Mixed Forest
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Structural Stage (indicate the structural stage of the dominant vegetation) Non-vegetated or initial stage following disturbance, with less than 5% cover Shrub / herb stage, less than 10% tree cover Pole-sapling stage, with trees overtopping the shrub layer, usually less than 15-20 years old Young forest (30- 80 years) - forest canopy is differentiating into distinct layers Mature forest - well developed understory
Canopy Closure (circle the proportion of the surface area of the stream covered by the projecting riparian canopy; hint - stand in the middle of the stream and look up!)
0% 1-25% 26-50% 51-75% 76-100%
UI. Substrate Characteristics
% Composition (estimate % composition of each substrate type within the reach – Wentworth Scale)
____sand ____gravel ___pebble ____cobble ____boulder _____bedrock (< 2mm) (2-4mm) (4mm – 6cm) (6cm – 26cm) (>26cm)
Embeddedness (circle the one which describes how embedded the cobbles are in the riffle zone)
Not Embedded ¼ ½ ¾ Completely Embedded
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Wolman Pebble Count (zig-zag along the entire stream reach, stopping every 2 steps to select and measure pebble diameter – record the intermediate diameter to the nearest 0.1cm)
Pebble Diameter Pebble Diameter Pebble Diameter Pebble Diameter # (cm) # (cm) # (cm) # (cm) 1 26 51 76 2 27 52 77 3 28 53 78 4 29 54 79 5 30 55 80 6 31 56 81 7 32 57 82 8 33 58 83 9 34 59 84 10 35 60 85 11 36 61 86 12 37 62 87 13 38 63 88 14 39 64 89 15 40 65 90 16 41 66 91 17 42 67 92 18 43 68 93 19 44 69 94 20 45 70 95 21 46 71 96 22 47 72 97 23 48 73 98 24 49 74 99 25 50 75 100
UStream Type:
Glacial Clear Stained Other______
UDisturbance Indicators: Indicate the presence of the following disturbance indicators at the site:
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Bed Characteristics Extensive areas of scour Extensive areas of (unvegetated) bar Large extensive sediment wedges Elevated mid-channel bars Extensive riffle zones Limited pool frequency and extent
Channel Pattern Multiple channels (braiding)
Banks Eroding banks Isolated sidechannels or backchannels
Large Woody Debris Most LWD parallel to banks Recently formed LWD jams
USubstrate Characteristics:
Odours and Oils (indicate the presence of the following in the substrate)
Odours: None Sewage Petroleum Anaerobic (H2S) Chemical Other____
Oils: Absent Slight Moderate Profuse
ULand Use
Surrounding Land Use (consider what is visible from the sample site, or known/suspected to occurring upstream; tick all that apply)
None (Forest) Range Agriculture (incl. fields) Residential None (Park) Mining Commercial / Industrial Recreation
Notes:
Local Watershed Erosion (visible at sample site) Heavy Moderate Comments: Light None
Local Watershed NPS Pollution
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Obvious sources Some potential sources Comments: No evidence
UMountain Pine Beetle:
Mountain Pine Beetle Infestation - Forest Health (estimate riparian by considering 30m from stream and estimate watershed based on observations en route to site)
RIPARIAN WATERSHED
% Trees that are Pine
% Pine Trees that are “Red”
% Pine Trees that are “Grey”
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 66
9 8BAPPENDIX B: ASSESSMENTS OF SAMPLES FOR TEST OF MODEL PRECISION
9.1 24BIntroduction
The following ordination images show results of testing duplicate samples from reference sites. Each replicate sample was assigned to its correct sample group (i.e. the group to which replicate 1 from each site was assigned in the sample classification stage of model building). No transformations were applied. Other methods are explained in Section 3.7.
9.2 25BSample S2 (duplicate reference sample)
3 3 3
1 1 1
3
2
3
s
s
s
i
i
i
x
x
x
A
A
A -1 -1 -1
-3 -3 -3 -3 -1 1 3 -3 -1 1 3 -3 -1 1 3 Axis 1 Axis 1 Axis 2
Test sample (red dot) is between the first (90%) and second (99%) ellipses (Band 2) in at least one dimension. The sample is slightly divergent from reference.
9.3 26BSample S29 (duplicate reference sample)
3 3 3
1 1 1
3
3
2
s
i
s
s
i
i
x
x
x
A
A
A -1 -1 -1
-3 -3 -3 -3 -1 1 3 -3 -1 1 3 -3 -1 1 3 Axis 1 Axis 1 Axis 2
Test sample (red dot) is inside the first ellipse in all plots. The sample is in reference condition.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 67
9.4 27BSample 42 (duplicate reference sample)
3 3 3
1 1 1
2
3
3
s
s
i
s
i
i
x
x
x
A
A
A -1 -1 -1
-3 -3 -3 -3 -1 1 3 -3 -1 1 3 -3 -1 1 3 Axis 1 Axis 1 Axis 2
Test sample (red dot) is inside the first ellipse in all plots. The sample is in reference condition.
9.5 28BSample 42 (triplicate reference sample)
3 3 3
1 1 1
2
3
3
s
s
i
i
s
i
x
x
x
A
A
A -1 -1 -1
-3 -3 -3 -3 -1 1 3 -3 -1 1 3 -3 -1 1 3 Axis 1 Axis 1 Axis 2
Test sample (red dot) is inside the first ellipse in all plots. The sample is in reference condition.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 68
10 9BAPPENDIX C: ASSESSMENTS OF SAMPLES FOR TEST OF MODEL SENSITIVITY
10.1 29BIntroduction
The following ordination images show results of testing samples from non- reference sites. Each sample was assigned to the sample group to which it was predicted based on DFA. No transformations were applied. Other methods are explained in Section 3.7.
10.2 30BSample S1 from 2007 (Sumallo River downstream of Sunshine Valley)
3 3 3
1 1 1
3
2
s
3
i
s
i
x
s
x
i
A
x
A
A -1 -1 -1
-3 -3 -3 -3 -1 1 3 -3 -1 1 3 -3 -1 1 3 Axis 1 Axis 1 Axis 2
Test sample (red dot) is inside the first ellipse in all plots. The sample is in reference condition.
10.3 31BSample S1 from 2008 (Sumallo River downstream of Sunshine Valley)
3 3 3
1 1 1
2
3
3
s
i s
s
i
i
x
x
x
A
A
A -1 -1 -1
-3 -3 -3 -3 -1 1 3 -3 -1 1 3 -3 -1 1 3 Axis 1 Axis 1 Axis 2
Test sample (red dot) is between the first (90%) and second (99%) ellipses (Band 2) in at least one dimension. The sample is slightly divergent from reference.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 69
10.4 32BS24 from 2007 (Silver Daisy Creek)
3 3 3
1 1 1
2
3
3
s
s
i
i
s
i
x
x
x
A
A
A -1 -1 -1
-3 -3 -3 -3 -1 1 3 -3 -1 1 3 -3 -1 1 3 Axis 1 Axis 1 Axis 2
Test sample (red dot) is between the second (99%) and third (99.9%) ellipses (Band 3) in at least one dimension. The sample is divergent from reference.
10.5 33BS24 from 2008 (Silver Daisy Creek)
3 3 3
1 1 1
2
3
3
s
s
i
i
s
i
x
x
x
A
A
A -1 -1 -1
-3 -3 -3 -3 -1 1 3 -3 -1 1 3 -3 -1 1 3 Axis 1 Axis 1 Axis 2
Test sample (red dot) is between the second (99%) and third (99.9%) ellipses (Band 3) in at least one dimension. The sample is divergent from reference.
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10.6 34BS41 (Captain Grant Creek)
3 3 3
1 1 1
2
3
s
3
s
i
i
x
s
x
i
A
x
A
A -1 -1 -1
-3 -3 -3 -3 -1 1 3 -3 -1 1 3 -3 -1 1 3 Axis 1 Axis 1 Axis 2
Test sample (red dot) is between the first (90%) and second (99%) ellipses (Band 2) in at least one dimension. The sample is slightly divergent from reference.
10.7 35BS43 (Noname tributary of Skaist River)
3 3 3
1 1 1
2
3
3
s
s
i
i
s
i
x
x
x
A
A
A -1 -1 -1
-3 -3 -3 -3 -1 1 3 -3 -1 1 3 -3 -1 1 3 Axis 1 Axis 1 Axis 2
Test sample (red dot) is outside of the third (99.9%) ellipse (Band 4) in at least one dimension. The sample is extremely divergent from reference.
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10.8 36BS45 (Norwegian Creek)
3 3 3
1 1 1
3
2
3
s
s
i
i
s
i
x
x
x
A
A
A -1 -1 -1
-3 -3 -3 -3 -1 1 3 -3 -1 1 3 -3 -1 1 3 Axis 1 Axis 1 Axis 2
Test sample (red dot) is between the second (99%) and third (99.9%) ellipses (Band 3) in at least one dimension. The sample is divergent from reference.
10.9 37BS46 (Upper Smitheram Creek)
3 3 3
1 1 1
2
3
3
s
i
s
s
i
i
x
x
x
A
A
A -1 -1 -1
3
-3 -3 -3 - -3 -1 1 3 -3 -1 1 3 -3 -1 1 3 Axis 1 Axis 1 Axis 2
Test sample (red dot) is inside the first ellipse in all plots. The sample is in reference condition.
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10.10 38BS49 (Skagit River upstream of Smitheram Creek)
3 3 3
1 1 1
2
3 3
s
i
s s
x
i i
x x
A
A A -1 -1 -1
-3 -3 -3 -3 -1 1 3 -3 -1 1 3 -3 -1 1 3 Axis 1 Axis 1 Axis 2
Test sample (red dot) is inside the first ellipse in all plots. The sample is in reference condition.
10.11 39BS64 (Hilton Creek)
3 3 3
1 1 1
3
2
3
s
s
s
i
i
i
x
x
x
A
A
A -1 -1 -1
-3 -3 -3 -3 -1 1 3 -3 -1 1 3 -3 -1 1 3 Axis 1 Axis 1 Axis 2
Test sample (red dot) is between the first (90%) and second (99%) ellipses (Band 2) in at least one dimension. The sample is slightly divergent from reference.
Limnotek February 2010 Quality of streams in the upper Skagit River watershed 73
11 10BAPPENDIX D: ASSESSMENTS OF NON-REFERENCE SAMPLES NOT USED FOR MODEL TESTING
11.1 40BS33 (Lower Sumallo River)
3 3 3
1 1 1
2
3
3
s
i
s
s
i
i
x
x
x
A
A
A -1 -1 -1
-3 -3 -3 -3 -1 1 3 -3 -1 1 3 -3 -1 1 3 Axis 1 Axis 1 Axis 2
Test sample (red dot) is inside the first ellipse in all plots. The sample is in reference condition.
11.2 41BS47 (Lower Smitheram Creek)
3 3 3
1 1 1
2
3
3
s
i
s
s
i
i
x
x
x
A
A
A -1 -1 -1
-3 -3 -3 -3 -1 1 3 -3 -1 1 3 -3 -1 1 3 Axis 1 Axis 1 Axis 2
Test sample (red dot) is inside the first ellipse in all plots. The sample is in reference condition.
Limnotek February 2010