EmmaLeigh Kaleb Given. Submitted in Partial Fulfillment of the Requirements. For the degree of. Master of Science. In the. Biological Sciences

Transcription

1 Evaluating long-term effects of destructive flooding on in-stream riparian characteristics and macroinvertebrate abundance in low order headwater streams By EmmaLeigh Kaleb Given Submitted in Partial Fulfillment of the Requirements For the degree of Master of Science In the Biological Sciences Program Youngstown State University May, 2014

2 Evaluating long-term effects of destructive flooding on in-stream riparian characteristics and macroinvertebrate abundance in low order headwater streams EmmaLeigh Kaleb Given I hereby release this thesis to the public. I understand this thesis will be made available from the OhioLINK ETD Center and the Maag Library Circulation Desk for public access. I also authorize the University or other individuals to make copies of this thesis as needed for scholarly research. Signature: EmmaLeigh K. Given, Student Date Approvals: Dr. Thomas P. Diggins, Thesis Advisor Date Dr. Ian J. Renne, Committee Member Date Dr. Carl G. Johnston, Committee Member Date Dr. Salvatore A. Sanders, Associate Dean of Graduate Studies Date

3 Abstract. In August of 2009 a flashflood scoured an assemblage of fourteen 1 st 3 rd - order headwater streams surrounding Zoar Valley Canyon in western New York State USA. Macroinvertebrate composition, watershed variables, and habitat features of these streams were quantitated in 2006 and reported in the peer-reviewed literature. The objective of this study was to determine long-term disturbance effects within these impacted streams, particularly as relation to meta-community assembly. Biotic and environmental assessments from 2011, 2012, and 2013 mirrored those from 2006, with biota collected from riffle/cobble segments by Surber net, and environmental/habitat variables quantified by a widely used Qualitative Habitat Evaluation Index (QHEI) that assesses in-stream and riparian characteristics. Instream environmental variables such as substrate diversity and in-stream cover initially declined in quality and converged leading to homogenization of stream patches. Dissimilarity among stream communities for both biotic and environmental characteristic from year to year (assessed by Non-metric Multidimensional Scaling ordination) revealed that streams were differentially impacted and also suggested changes in meta-community composition in response to the disturbance. In 2006, partial correlations suggested a niche-based species sorting of organisms, whereas by 2011, this structuring was lost, suggesting a switch to either equivalence based neutral theory or homogenous patch dynamics. By 2013, although QHEI numbers were reaching pre-flood values, an environmental/biotic partial correlation (as seen in 2006) had yet to re-emerge, suggesting that macroinvertebrate communities were still facing the effects of this disturbance.

4 Acknowledgment First and foremost I would like to thank my thesis advisor, Dr. Thomas Diggins, for the countless hours of guidance and support throughout my graduate career at Youngstown State University. I would also like to thank my committee members Dr. Ian Renne and Dr. Carl Johnston for their input into this research. I would also like to thank my mother, Dr. Karen Larwin, not only for mining through data analysis with me, but also for acting as my number one confidant and supporter through many difficult hurdles as a graduate student. Additionally, I would like to thank Arika McGraw and Danielle Mutchler for helping with data collection. Without them, research trips to Zoar Valley would have taken much longer and wouldn t have been as much fun. Finally, my thanks go out to Tiffanie Baumiller, whom without I would still be currently identifying macroinvertebrates.

5 TABLE OF CONTENTS Evaluating long-term effects of destructive flooding on in-stream riparian characteristics and macroinvertebrate abundance in low order headwater streams EmmaLeigh Kaleb Given ABSTRACT ACKNOWLEDGEMENTS TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES CHAPTER iii iv v vii viii PAGE I. INTRODUCTION 1 Disturbance 2 Flooding 2 Meta-Community Theory _4 Zoar Valley 8 II. METHODS 12 Study Design _ 12 Site Characterization _ 12 Habitat Characterization _12 Benthic Community Sampling 24 Statistical Analysis 26 III. RESULTS 28 Characterization of Streams _28 Macroinvertebrate Diversity and Abundance _48 Macroinvertebrate Community Composition _48 Macroinvertebrate Community Composition Over Time 78 Associations among Environmental Distance and Biotic Dissimilarity Matrices 79 IV. DISCUSSION 99 Changes in Physical and Environmental Conditions _ 99 Change in Total Macroinvertebrate Community Composition _ 101 Year- to- Year Changes in Ordination Space 104

6 Changes in Meta-community Composition _105 Future work and Broader Impacts 108 REFERENCES 110 APPENDIX A Previous Studies Assessing Flood Response of Macroinvertebrate Communities. 115 APPENDIX B Qualitative Habitat Evaluation Index and Use Assessment Field Sheet. 117 APPENDIX C-1 Average occurrence of benthic macroinvertebrate taxa for 2006 reported as number of individuals per stream. _ 119 APPENDIX C-2 Average occurrence of benthic macroinvertebrate taxa for 2011 reported as number of individuals per stream. _ 135 APPENDIX C-3 Average occurrence of benthic macroinvertebrate taxa for 2006 reported as number of individuals per stream. _ 151 APPENDIX C-4 Average occurrence of benthic macroinvertebrate taxa for 2006 reported as number of individuals per stream. _ 167

7 LIST OF TABLES Table 1. Watershed and Habitat Characterization of Study Streams in 2006 before the Flood Event _ 30 Table 2. Watershed and Habitat Characterization of Study Streams in 2010/2011 _ 32 Table 3. Watershed and Habitat Characterization of Study Streams in Table 4. Watershed and Habitat Characterization of Study Streams for 2013/2014 _36 Table 5. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=14) of Macroinvertebrates Found within Study Streams in 2006 Before The Flood Event 51 Table 6. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=7) of Macroinvertebrates Found within Study Streams in 2011 Before The Flood Event _56 Table 7. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=14) of Macroinvertebrates Found within Study Streams in 2012 Before The Flood Event _ 59 Table 8. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=16) of Macroinvertebrates Found within Study Streams in 2013 Before The Flood Event _ 63 Table 9. Partial Correlations of Distance Matrices from

8 LIST OF FIGURES Figure 1. The Four Meta-community Theories Explained in Three Axes _6 Figure 2. Regional Location of Zoar Valley, New York _ 9 Figure 3. The Three Geographic Subunits of Zoar Valley Canyon 14 Figure 4. A Topographical Map of the Southwest Study Area 16 Figure 5. A Topographical Map of the Southeast Study Area 18 Figure 6. Photographs of Typical Feeder Brook Streams in the Study Area before the Flood Event 20 Figure 7. Photographs of Typical Feeder Brook Streams in the Study Area after the Flood Event 22 Figure 8. NMDS Ordination of Environmental Variables from Figure 9. NMDS Ordination of Environmental Variables from 2010/2011 _ 40 Figure 10. NMDS Ordination of Environmental Variables from Figure 11. NMDS Ordination of Environmental Variables from 2013/2014 _44 Figure 12. Graph of QHEI Scores Separated by Order and by Year _46 Figure Ordination Plot Including Macroinvertebrate Loadings _ 68 Figure Ordination Plot Including Macroinvertebrate Loadings _ 70 Figure Ordination Plot Including Macroinvertebrate Loadings _ 72 Figure Ordination Plot Including Macroinvertebrate Loadings _ 74 Figure Ordination Plot without Outlying Stream #14, Including Macroinvertebrate Loadings 76 Figure 18. Ordination plot of all streams from Figure 19. Ordination Plots for (Including all Streams) Scaled down to Show Distribution more Clearly (First-order Streams #2 and #8 are Enlarged) _82 Figure 20. Ordination Plots for (Including all Streams) Scaled down to Show Distribution more Clearly (Second-order Streams #3 and #4 are Enlarged) 90

9 Figure 21. Ordination Plots for (including all Streams) Scaled down to Show Distribution more Clearly (Third-order Streams #1 and #7 are Enlarged) 94

10 Chapter 1 Introduction You cannot step twice into the same river Greek Philosopher Heraclitus Although the Greek Philosopher Heraclitus intended to portray changes in everyday life nearly 2,500 years ago, this sentiment reflects the nature of stream systems. Daily shifts in spatial and temporal characteristics of waterways lead to a constant variation in the abiotic and biotic community of flowing waters. One common source of this variation is disturbance (Resh, et al. 1988). Disturbances, whether natural or anthropogenic, are important to study because of their influence in shaping community structure and their function during some point of every stream s life (Wallace, 1990). Fortunately, research shows a shift from controlling and manipulating aquatic systems (e.g., flooding prevention) to safeguarding biotic diversity and environmental health (Myers & White, 1993). One method of assessing stream quality is to examine the diversity of aquatic communities, which serve as indicators of overall stream well-being. For example, aquatic community diversity in low-order headwater streams is particularly high within both individual systems and regional assemblages as attested to by Diggins and Newman s (2009) 2006 report confirming 137 taxa from 23 small feeder streams of the Cattaraugus Creek of Zoar Valley, New York. In 2009, flash flooding scoured and denuded these same streams of their habitat and biota. This disturbance event formed the basis for this study, which will examine 16 of these first- to third- order feeder streams, to assess how large-scale flooding

11 events affect in-stream riparian characteristics and macroinvertebrate assemblages in low-order streams. Disturbance A disturbance is defined as an event that changes ecological resources (living space and food), modifies habitats, and kills or harms populations. Disturbances are categorized by the effect on stream biota and by the intensity of the application of disturbing forces (Lake, 2000). There are a variety of types of disturbances, including both anthropogenic (e.g., dam construction, organic pollution, toxic chemicals, and pesticides) and natural (e.g., droughts and flooding). The size of the stream must be considered in addition to the severity of disturbance when estimating the effect of a disturbance event on stream recovery. For instance, high-gradient streams reach bank-full levels faster and do not have floodplain buffering as do low gradient high order streams. Streams are classified by size through stream order, a classification system that progresses as stream tributary complexity increases. The smallest stream is classified as a first-order and has no tributaries; a second-order stream is then comprised of two first-order streams joining and forming a larger stream. Tenth- to twelfth- order rivers are considered to be the world s largest (Wallace, 1990). Flooding Floods are one of the most common natural disturbances in lotic systems (Lake, 2000), and may effectively reorganize aquatic communities (Hickey & Salas, 1995). Current changes in climate are leading to more intense but less frequent rain events (Bertrand, et al., 2013), which cause severe fluctuations in flow regime, changes in sedimentation, and scouring of stream channels (Anderson & Ferrington, 2013). Also,

12 destructive flooding alters community assemblages by acting as a purge of habitats and macroinvertebrates (Gray & Fisher, 1980; Hickey & Salas, 1995). Macroinvertebrates are important to study not only because they act as good biological indicators of overall stream health, but also because they link aquatic and terrestrial ecosystems. For example, their larval stages mature into terrestrial flying adults that serve as food for birds, bats, and other large insects. The restructuring of stream resources (habitat, food availability, etc.) results in the restructuring of community composition of aquatic organisms, such as macroinvertebrates (Argerich, et al., 2004). Severe flood events not only destroy patches composed of habitats, resources, and biota, but also create patches, some with very distinct communities that may be maintained long after all other flood effects have diminished (Lake, 2000). Studies of stream macroinvertebrate communities have typically been conducted in systems without major disturbance events, giving only an assessment of long-term dynamic equilibrium conditions (e.g., Diggins & Newman, 2009). Conversely, few studies have been done to examine flooding effects on metacommunity structure in perennial mesic streams with consistently wet conditions (Hickey & Salas, 1995). Recent research has been conducted with desert (arid) streams (e.g. Clinton, et al.,1996; Gray & Fisher, 1981; Lytle, 2000; Sponseller, et al., 2010; Stanley, et al., 1994) where floods are perhaps predictable events due to seasonal changes in water flow (Resh et al., 1988). Macroinvertebrates in harsh desert streams have been shown to have both a higher resistance (capability of tolerating a disturbance) and resilience (capability of

13 recolonization after disturbance) often allowing their meta-community composition to remain unaltered (Boulton & Lake, 1992; Fritz & Dodds, 2004). Unpredictable flooding events are much more damaging than seasonal floods which merely change habitat structure, rather than destroying it (Lake, 2000). If the disturbing force does not significantly alter the system being studied (i.e., it was predictable) then it cannot be called a disturbance; rather, it would be called a condition (Hendricks, et al., 1995). Therefore, it is reasonable to suggest that in mesic streams, where stable perennial flow is expected and flash flood events are unpredictable, such events may denude the aquatic environment to a greater degree than predictable high flows. This may ultimately cause significant alteration of abiotic stream processes and subsequent meta-community composition of macroinvertebrates. Meta-Community Theory A meta-community is defined as a set of local communities which are connected by the dispersal of multiple potentially interacting species (Gilpin & Hanski, 1991; Wilson, 1992). Headwater streams are warranted to this area of study based on their ubiquity and their diverse taxa. However, there have been few studies of stream benthos for both experimental and observation tests of meta-community theory according to (Logue, et al., 2011). There are four conceptual frameworks within this theory, each explaining species and habitat characteristics along with dispersal rates (Liebold, et al., 2004). Species sorting framework (SS). This framework postulates high patch heterogeneity and low dispersal among communities. This is a niche-based sorting with spatial niche separation (Logue et al., 2011; Liebold et al., 2004).

14 Mass effects framework (ME). According to mass effects, the immigration and emigration of species are high among the heterogeneous communities, and thus, underlying niche-sorting dynamics are overtaken. If a species cannot survive a niche in one community, it may emigrate into a different community where it may function better ecologically. Without dispersal data, SS typically cannot be distinguished from ME (Logue et al., 2011; Liebold et al., 2004). Patch dynamics framework (PD). In this framework, patches are environmentally homogeneous with variable dispersal rates. These patches are subjected to both stochastic and deterministic extinctions that are altered by interspecific interactions and countered by the variable mobility of species (Logue et al., 2011; Liebold et al., 2004). Neutral theory framework (NT). In this framework, all species have similar competitiveness, mobility, and fitness. Species composition is not driven by competition or mobility, but by loss due to extinction, emigration, and gains from immigration and speciation (Logue et al., 2011; Liebold et al., 2004). Figure 1 outlines the difference between these four paradigms along three axes of heterogeneity, dispersal, and equivalence.

15 Figure 1. The Four Meta-community Theories Explained in Three Axes

16 Adapted from

17 Zoar Valley The Zoar Valley Canyon in western New York State (N 42 26, w ) is the most intact of tributary to the American shoreline of Lake Erie fifth- to sixth-order river corridor within a 1185-hectare state owned multiple use/conservation area. The vertical canyon walls, 70 to 130 meters in height, are split by the Cattaraugus Creek into the south branch and the main branch, which acts as the boundary between Erie and Cattaraugus County, as seen in Figure 2 (Kershner, 1994).

18 Figure 2. Regional Location of Zoar Valley, New York.

19

20 On August 9, 2009, one of the largest floods in history struck Zoar Valley and its surroundings. As a result, stream gauge height and discharge of the main creek were the second highest recorded since a United States Geological Survey (USGS) gauge was installed in The effects of this storm were greatest along first- to thirdorder feeder streams located on the southern reaches of Zoar Valley, where most of the precipitation fell (nearly 20 cm in less than two hours). These streams, previously studied in 2006 by Diggins and Newman (2009), were physically and ecologically devastated. Most physical structure was removed from the streambed, including the associated biota. The primary goal of this research is to quantify long-term, post-flood changes in the physical and biotic environment within 16 of the wooded headwater streams studied in There are several discovery driven sub-objectives, including gaining insight into how systems respond to dramatic alteration. This study will quantify biotic and abiotic changes over time, and will apply this knowledge to understand how anthropogenic changes are influencing lotic systems. In addition, this project will explore the frameworks of the meta-community theory discussed above. Diggins and Newman (2009) found a pre-disturbance environmental niche-based structuring to these streams, with little indication of spatial autocorrelation, likely precluding neutral theory and/or patch dynamics frameworks.

21 Chapter 2 Method Study Design This study quantified long-term, post-flood changes in the physical and biotic factors within 16 of the first- to third- order streams studied in Biotic (Surber Sampler) and Environmental (Qualitative Habitat Evaluation Index [QHEI], Ohio EPA, 2002; An, et al., 2002) data collection mirrored those from Diggins and Newman (2009) and were collected from summer 2011 through fall In 2006, watershed delineation established stream order and watershed land cover types for each stream, including mature and logged forest, and former farmland. Several in-stream characteristics prone to change were recorded without reference to previous data, while large-scale riparian-corridor attributes beyond reach of the flood remained consistent year to year. To compare the distribution and abundance of macroinvertebrate taxa of 16 streams, a multivariate ordination analysis was implemented. Site Characterization Two different geographic subunits were defined in the study area, southeast and southwest (Figure 3), delineated with reference to extent of public land, entrance access, and parking availability. The north geographic subunit, although considered in Diggins and Newman (2009), was not included in this study as the flash-flooding was generally more intense in the southern portion above the Zoar Valley canyon. The vast majority of land to the north of Zoar Valley slopes to the north, as a result those creeks did not catch the great amount of precipitation as the streams to the south of

22 the canyon. Seven streams were included in the southwest geographical region, all of which flow into the South Branch of Cattaraugus Creek (Figure 4). Nine streams are included in the southeast geographical region, all of which flow into the Main Branch of Cattaraugus Creek (Figure 5). Figure 6 shows photographs of various feeder-brook streams before flooding, and Figure 7 shows photographs after flooding within the study area.

23 Figure 3. The Three Geographic Subunits of Zoar Valley Canyon

24 The three geographic subunits of the area above the Zoar Valley canyon are outlined in a box with the name labeled accordingly. An asterisk indicates the parking area for each study area. The enclosed area labeled north was not included in this study, but was included in Diggins and Newman (2009).

25 Figure 4. A Topographical Map of the Southwest Study Area

26 Dotted red lines indicate watersheds while solid blue lines indicate streams.

27 Figure 5. A Topographical Map of the Southeast Study Area

28 Dotted red lines indicate watersheds while solid blue lines indicate streams.

29 Figure 6. Photographs of Typical Feeder Brook Streams in the Study Area before the Flood Event

30 10

31 Figure 7. Photographs of Typical Feeder Brook Streams in the Study Area after the Flood Event

32

33 Habitat Characterization In 2006, in order to quantify stream parameters, watershed delineation was completed using USGS: 1:24,000 quadrangle topographic maps and extensive ground-truthing of each stream. The parameters include: 1. Quality of forest surrounding each stream watershed (i.e. mature forest, logged, but not farmed, logged and farmed), 2. Percent canopy cover around sampling sites: measured using an optical densitometer, 3. Percent mature forest: evaluated using topographical maps, historical aerial photographs, and onsite verification; and 4. Stream order was verified by foot ground-truthing of each stream. Watershed areas of the 16 first-third order streams are quite variable, ranging from 4.92 ha ha (Table 1). Only four streams within this study had a watershed area less than 10ha, while only one stream had a watershed area above 100ha (third order stream #7). These watershed variables were not altered by the 2009 flood and thus remain consistent for this study. A Global Positioning System (GPS) was used to identify sampling sites at each stream. All watershed and/or forested areas on maps were measured by planimetry in 2006, which is an instrument used to measure the area of a plane figure. QHEI (Ohio EPA, 2002; An et al., 2002) was used each year to assess stream habitat conditions. In-stream characteristics that can vary temporally were evaluated without regard to previous characterization. Assessed parameters included: substrate type, in-stream cover, sinuosity, channel morphology, riparian zone and bank erosion, pool/glide and riffle/run quality, and stream gradient. The QHEI method is a quasisubjective index, however, the same evaluator (T.P. Diggins) was present when

34 assessing QHEI variables through all years of data collection. The QHEI score sheet is included in Appendix A (Ohio EPA, 2002). QHEI scores indicate stream health and are determined using the above parameters. Ranging from 100-0, scores are categorized for headwater streams using four levels (Ohio EPA, 2006), including (a) excellent (score: >70), (b) good (score: 55-69), (c) fair (score: 43-54), (d) poor (score: 30-42), and (e) very poor habitat quality (score: <30). Benthic Community Sampling Three quantitative samples and one qualitative sample were taken from riffles of each stream in summer and fall each year of the study from 2010 through Quantitative samples were collected using a 30cm x 30cm Surber Sampler with a 500-µm net, agitating the substrate for two minutes. Collected substrate was rinsed and filtered (800-µm net) and preserved in 70% ethanol. The single qualitative sample was obtained by scraping debris from rocks into a water reservoir for ten minutes, with samples filtered and preserved using 70% ethanol. Macroinvertebrates were sorted from samples under illuminated 3x magnification. Identification of all taxa was completed to family, with Chironomids identified to genus, using broad-field scopes at 7x-40x magnification according to Merrit & Cummins, 1996; Peckarsky, et al., 1990; Pennak, 1953; Simpson & Bode, 1980; and Wiederholm, Invertebrate collections were stored in 70% ethanol for future use as voucher specimens. Chironomids were slide-mounted in Canadian balsam, per Diggins and Stewart (1998), identifying to genus and species where possible. Head capsules were

35 removed from larvae and digested in warm 10% Potassium Hydroxide (KOH) for two to five minutes, then slide-mounted in Canadian balsam and identified under phasecontrast scopes at x. Chironomid slides were stored for future use as voucher specimens. Statistical Analysis Ordinations of benthic community composition were generated using Non- Metric Multidimensional Scaling (NMDS) (SPSS 13.0, alscal algorithm) to maintain continuity with Diggins and Newman (2009). NMDS ordinations are robust in the face of non-monotonic data distributions, which means that the data neither increasing nor decreasing consistently along a gradient, in fact, sometimes nonmonotonic data are unimodal. NMDS are preferred to Principal Components Analysis (PCA) when analyzing species distributions for this reason. NMDS ordinations are used to reveal levels of dissimilarity between variables by condensing data into dimension scores plotted on two axes derived from species compositions. Five separate ordinations were run; four to show the patterns of all taxa of macroinvertebrates for each year sampled (2006, 2011, 2012, and 2013), and one including all four years of data on one plot. This was done to assess how the streams progress over time in ordination space from before to after the flooding event. The distance in ordination space corresponds to the dissimilarity or similarity of the streams particular characteristics. For instance, for an environmental NMDS ordination, if two streams were close in ordination space they would have similar environmental characteristics, while two streams far apart in ordination space would have dissimilar environmental characteristics.

36 After generating the ordination plots, u-par Spearman s rank correlation coefficients, which are equivalent to statistical loadings generated by eigenvector analysis such as PCA, were then calculated. This was done to determine how NMDS dimension scores compare to original species abundances, to assess the influence of the abundances on the ordination axes. To evaluate the biotic distance between pairs of streams, Euclidian distance matrices were generated for all taxa. To assess the relative contribution of dispersal (patch dynamics), equivalence (neutral theory), and/or environmental (niche-based) factors, Partial Correlations were used to compare Euclidian distance matrices based on geographic distance, benthic community composition, and environmental characteristics (comprised from percent mature forest, percent canopy coverage, QHEI score, and log10 of watershed area). Watershed area was subjected to log10 in order to normalize the large data distribution. Both benthic community composition and environmental characteristics change over time while geographic distance remains unchanged.

37 Chapter 3 Results Characterization of Streams In 2006, all streams were perennial; however, streams 5 & 6 had reduced flow during the summer months, but were still samplable. After the flooding event, streams 5 & 6 consistently had too low of a flow to obtain a Surber sample, and thus were removed from this study. QHEI scores were very variable from year to year. In 2006, scores ranged from (Table 1), with the lowest score belonging to stream two (a first-order), and the highest score belonging to stream one (a third-order). The three streams with the highest QHEI scores were all third-order: streams #1, #13, and #7, which had scores of 82, 80, and 78 respectively (Table 1). Between 2006 and 2012 (Table 2) all QHEI scores declined. The largest change from before the flood event was seen in stream #9, which decreased 22 points, primarily due to decreased in-stream cover. The least impacted stream was #15, which only decreased 1 QHEI point, although it had already low score of 55 before the flood event (Table 2). Between 2011 and 2012 (Table 3) ten streams showed an increase in QHEI score. The greatest increase was in stream #10, which increased 11 points, primarily due to an increase in substrate and in-stream cover. In contrast, stream #15 had the largest decrease of 11 points, primarily due to a simplification in channel morphology. Still, all but one of the 2012 QHEI scores remained lower than the 2006 scores. Only stream #10 showed an increase from 2006 to 2012 in four points from

38 59 to 63 points, notably due to increased substrate heterogeneity. Stream #3 has remained the most impacted in 2012 with a QHEI score of 56 that is 21 points lower from the value of 77 in 2006 (Table 3). QHEI scores from 2013/2014 (Table 4), indicate that almost all streams showed an increase from Streams #13 and #14 declined in their QHEI scores by three and four points, respectively. Compared to 2006, almost all streams had lower QHEI scores. Four streams had QHEI scores in the excellent ranking, and no streams were classified as poor quality (Figure 12). Canopy cover above the streams and percent mature forest showed no change as a result of the flooding event in This is as expected because these are variables which measure forest characteristics and not stream characteristics. An NMDS ordination of stream environmental variables revealed that in 2006, streams were arranged in a cluster of second- and third- orders with nearly all first-orders diverging outwards (Figure 8). In 2011, all clustering by stream order was lost as some of all three stream orders were evident in the center of the plot (Figure 9). In 2012 (Figure 10), third-order streams clustered while second- and first-orders diverged outwards. In the NMDS ordination of stream environmental variables for 2013 (Figure 11) a similar pattern was observed. The third-orders clustered even tighter while the second- and first-orders diverged outwards. However, it can be noted that the second-order streams are slightly more clustered in 2013 than they were in 2012.

39 Table 1. Watershed and Habitat Characterization of Study Streams in 2006 before the Flood Event Stream Order Latitude Longitude Area (ha) Canopy cover % Mature forest

40 Table 1 Cont. Watershed and Habitat Characterization of Study Streams for 2006 Stream QHEI Components Instream Cover /20 Channel Morphology /20 Riparian zone /10 Poolcurrent /12 QHEI Score Substrate / Summary scores given for five major QHEI components. Scores do not total to entire QHEI because some miscellaneous components are not listed here, as they do not significantly contribute to the overall QHEI score.

41 Table 2. Watershed and Habitat Characterization of Study Streams in 2010/2011 after The Flood Event Stream Order Latitude Longitude Area (ha) Canopy cover % Mature forest

42 Table 2 Cont. Watershed and Habitat Characterization of Study Streams for 2010/2011 Stream QHEI Components Channel Morphology /20 Poolcurrent /20 Substrate / 20 Instream Cover /20 Riparian zone / Summary scores given for five major QHEI components. Scores do not total to entire QHEI because some miscellaneous components are not listed here, as they do not significantly contribute to the overall QHEI score.

43 Table 3. Watershed and Habitat Characterization of Study Streams in 2012 Stream Order Latitude Longitude Area (ha) Canopy cover % Mature forest

44 Table 3 Cont. Watershed and Habitat Characterization of Study Streams for 2012 Stream QHEI Score 2012 QHEI change From 2010 QHEI change From 2006 QHEI Components Substrate /20 Instream Cover /20 Channel Morphology /20 Riparian zone / Poolcurrent /12 Summary scores given for five major QHEI components. Scores do not total to entire QHEI because some miscellaneous components are not listed here, as they do not significantly contribute to the overall QHEI score.

45 Table 4. Watershed and Habitat Characterization of Study Streams for 2013/2014 Stream Order Latitude Longitude Area (ha) Canopy cover % Mature forest

46 Table 4 Cont. Watershed and Habitat Characterization of Study Streams for 2013/2014 Summary scores given for five major QHEI components. Scores do not total to entire QHEI because some miscellaneous components are not listed here, as they do not significantly contribute to the overall QHEI score.

47 Figure 8. NMDS Ordination of Environmental Variables from 2006

48 3 rd order 2 nd order 1 st order

49 Figure 9. NMDS Ordination of Environmental Variables from 2010/2011

50 3 rd order 2 nd order 1 st order

51 Figure 10. NMDS Ordination of Environmental Variables from 2012

52

53 Figure 11. NMDS Ordination of Environmental Variables from 2013/2014

54 rd order 2 nd order 1 st order

55 Figure 12. Graph of QHEI Scores Separated by Order and by Year

56 x

57 Macroinvertebrate Diversity and Abundance From , 88 taxa were collected, represented in 45 families, which were primarily dominated by juvenile insects. In the order Diptera (true flies), 12 families were present, and from the family Chironomidae (midges) 33 genera were identified. Over the four sampling years, the most abundant taxa were Hydropsychidae (order Trichoptera/ Caddisflys), Chloroperlidae (order Plecoptera/ stoneflies), Parametriocnemus (Chironomidae), Leptophlebiidae (order Ephemeroptera/ mayflys), Tipulidae (Craneflies/ order: Diptera), Perlodidae (Plecoptera), and Nemouridae (Plecoptera). In 2006, 37 taxa comprising 27 families and 10 chironomid genera were identified with an average of 502 individuals. In 2011 after the flooding event, 44 taxa composing 28 families, and 16 chironomid genera were identified with an average of 301 individuals collected. In 2012, 57 taxa comprising 43 families, and 14 chironomid genera were identified with an average of 203 individuals collected. In 2013, 61 taxa, 42 families, and 19 chironomid genera were identified with an average of 400 individuals collected per year. The most abundant taxa (i.e. upper quartile in terms of abundance in all streams and all replicate) can be found for 2006, 2011, 2012, and 2013 in Tables 5-8. Averages for all streams, for all years of data can be found in Appendix B. Macroinvertebrate Community Composition NMDS ordinations for 2006, 2011, and 2012, are presented in Figures For 2013, the ordinations are presented in Figures 16 and 17 presenting data with and without stream #14 which was an outlier. However, the same NMDS ordination is presented, the axes are simply rescaled for better resolution. Spearman rank

58 correlation coefficients vectors (comparable to loadings in Principle Components Analysis vectors) were then added to all NMDS ordinations to assess the influence of specific taxa to the generation of the NMDS axes. The 2006 NMDS ordination plot of family abundance and chironomid genera (Figure 13) revealed individual clusters of second- and third-order streams, whereas the diverse first-order streams diverged outwards. Spearman correlation of taxa abundances with dimension scores revealed four orders (Plecoptera, Trichoptera, Diptera, and Oligochaeta) (Figure 13) that were significantly correlated (p <0.05) with either Dimension 1 or 2 or both. All vectors loaded negatively on the y-axis. Interestingly, these taxa were thus negatively associated with most streams, which plotted above the x-axis. The 2011 NMDS ordination plot (Figure 14) showed no clustering by stream order. Many of the first-, second-, and third-order streams intermingle in the center of the plot. Spearman correlations of taxa abundances revealed only five taxa, from three orders, that were significantly correlated (p <0.05) with the ordination dimensions. Four of the five taxa load toward the bottom of the ordination plot, again away from the majority of the streams. Organisms of the family Perlodidae (Plecoptera) load almost parallel to the x-axis towards a single outlying third-order stream. The NMDS ordination plot of 2012 (Figure 15) demonstrated a distinct divergence of third-order streams whereas first- and second-orders somewhat clustered towards the center top left quadrant. Spearman correlation of taxa abundances suggested that six taxa from three orders were significantly correlated (p <0.05) with the ordination axes of this plot. The loading vectors for these taxa were

59 associated with the third-order streams, which diverged to the bottom and right portion of the plot. For 2013, two ordination plots are presented for macroinvertebrate abundances. The first (Figure 16) includes outlying stream #14, whereas in the second (Figure 17) the axes were scaled down for better resolution, although the outlying stream is no longer visible. In 2013 (Figure 16), first-order streams diverged across the ordination plot whereas second- and third-order streams cluster towards the center. Spearman correlation of taxa abundance revealed that 12 taxa from four orders were significantly correlated with dimensions from this 2013 ordination plot. The vector for Limnephilidae (Trichoptera) diverged from all other abundant taxa towards the first-order streams in the upper right quadrant. All vectors loaded away from the second- and third-order streams clustered below the x-axis again suggesting these taxa were negatively associated with these streams.

60 Table 5. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=14) of Macroinvertebrates Found within Study Streams in 2006 Before The Flood Event Order Trichoptera Plecoptera Dipetera Family Hydropsychidae Chloroperlidae Chironomidae Subfamily Orthocladiinae Tribe Parametriocnemus Parametriocnemus Stream Genus lundbecki (Joh.)

61 Table 5 Cont. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=14) of Macroinvertebrates Found within Study Streams in 2006 Before The Flood Event Order Diptera Ephemeroptera Diptera Family Tipulidae Leptoplebiidae Ceratopogonidae Subfamily Tribe Stream Genus

62 Table 5 Cont. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=14) of Macroinvertebrates Found within Study Streams in 2006 Before The Flood Event Order Oligocheatae Plecoptera Trichoptera Family Tubificidae Perlodidae Polycentropidae Subfamily Tribe Stream Genus

63 Table 5 Cont. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=14) of Macroinvertebrates Found within Study Streams in 2006 Before The Flood Event Order Ephemeroptera Plecoptera Ephemeroptera Family Heptageniidea Nemouridae Ephemerellidae Subfamily Tribe Stream Genus

64 Table 5 Cont. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=14) of Macroinvertebrates Found within Study Streams in 2006 Before The Flood Event Order Plecoptera Ephemeroptera Family Peltoperlidae Ephemeridae Subfamily Tribe Stream Genus

65 Table 6. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=7) of Macroinvertebrates Found within Study Streams in 2011 Before The Flood Event Order Plecoptera Trichoptera Family Lectridae/Chloroperlidae Hydropsychidae Subfamily Tribe Stream Genus

66 Table 6 Cont. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=7) of Macroinvertebrates Found within Study Streams in 2011 Before The Flood Event Order Dipetera Plecoptera Diptera Family Chironomidae Nemouridae Tipulidae Subfamily Orthocladiinae Tribe Parametriocnemus Parametriocnemus lundbecki Stream Genus (Joh.)

67 Table 6 Cont. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=7) of Macroinvertebrates Found within Study Streams in 2011 Before The Flood Event Order Plecoptera Ephemeroptera Family Perlodidae Leptoplebiidae Subfamily Tribe Stream Genus

68 Table 7. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=14) of Macroinvertebrates Found within Study Streams in 2012 Before The Flood Event Order Trichoptera Plecoptera Orthocladiinae Family Hydropsychidae Leuctridae Subfamily Tribe Stream Genus

69 Table 7 Cont. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=14) of Macroinvertebrates Found within Study Streams in 2012 Before The Flood Event Order Dipetera Plecoptera Ephemeroptera Family Chironomidae Chloroperlidae Leptoplebiidae Subfamily Orthocladiinae Tribe Parametriocnemus Parametriocnemus Stream Genus lundbecki (Joh.)

70 Table 7 Cont. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=14) of Macroinvertebrates Found within Study Streams in 2012 Before The Flood Event Order Diptera Oligocheatae Plecoptera Diptera Family Tipulidae Tubificidae Perlodidae Simuliidae Subfamily Tribe Stream Genus

71 Table 7 Cont. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=14) of Macroinvertebrates Found within Study Streams in 2012 Before The Flood Event Order Ephemeroptera Plecoptera Ephemeroptera Diptera Family Heptageniidea Perlidae Baetidae Chironomidae Subfamily Chironomidae Tribe Tanytarsus Stream Genus Tanytarsus guerlus

72 Table 8. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=16) of Macroinvertebrates Found within Study Streams in 2013 Before The Flood Event

73 Table 8 Cont. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=16) of Macroinvertebrates Found within Study Streams in 2013 Before The Flood Event Order Stream Genus Plecoptera Dipetera Plecoptera Family Leuctridae Chironomidae Perlodidae Subfamily Orthocladiinae Tribe Parametriocnemus (Joh.) Parametriocnemus lundbecki

74 Table 8 Cont. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=16) of Macroinvertebrates Found within Study Streams in 2013 Before The Flood Event

75 Table 8 Cont. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=16) of Macroinvertebrates Found within Study Streams in 2013 Before The Flood Event Order Dipetera Dipetera Ephemeroptera Family Chironomidae Chironomidae Heptageniidea Subfamily Orthocladiinae Orthocladiinae Tribe Tanytarsini Parametriocnemus Stream Genus Tanytarsus guerlus Georthocladius

76 Table 8 Cont. Average Occurrences (Organisms Per Replicate Surber Sample) of the Upper Quartile (n=16) of Macroinvertebrates Found within Study Streams in 2013 Before The Flood Event

77 Figure Ordination Plot Including Macroinvertebrate Loadings

78 Pearson Correlation with NMDS st Order - 2 nd Order - 3 rd Order Order Plecoptera Trichoptera Diptera Oligochaeta Pearson Correlation with NMDS 1 Plecoptera Chloroperlidae Perlodidae Trichoptera 3. Hydropsychidae 4. Polycentropodidae Diptera 5. Total Chironimidae 6. Orthocladiinae 7. Tipulidae 8. Ceratopogonidae Oligochaeta 9. Tubificidae

79 Figure Ordination Plot Including Macroinvertebrate Loadings

80 Pearson Correlation with NMDS st Order - 2 nd Order - 3 rd Order Pearson Correlation with NMDS 1 Order Plecoptera Trichoptera Diptera Plecoptera Perlodidae Chloroperlidae Nemouridae Trichoptera 4. Hydropsychidae Diptera 5. Total Chironimidae

81 Figure Ordination Plot Including Macroinvertebrate Loadings

82 Pearson Correlation with NMDS st Order - 2 nd Order - 3 rd Order Order Ephemeroptera Plecoptera Trichoptera Pearson Correlation with NMDS 1 Ephemeroptera Leptophlebiidae Heptageniidae Baetidae Plecoptera 4. Leuctridae 5. Chloroperlidae Trichoptera 6. Hydropsychidae

83 Figure Ordination Plot Including Macroinvertebrate Loadings

84 Pearson Correlation with NMDS 2-1 st Order - 2 nd Order - 3 rd Order Pearson Correlation with NMDS 1 Order Ephemeroptera Plecoptera Trichoptera Diptera Ephemeroptera Ephemerellidae Heptageniidae Leptophlebiidae Plecoptera 4. Chloroperlidae 5. Perlodidae 6. Nemouridae Trichoptera 7. Hydropsychidae 8. Limnephilidae Diptera 9. Parametriocnemus 10. Simuliidae 11. Ceratopogonidae 12. Tipulidae

85 Figure Ordination Plot without Outlying Stream #14, Including Macroinvertebrate Loadings

86 Pearson Correlation with NMDS st Order - 2 nd Order - 3 rd Order Pearson Correlation with NMDS 1 Order Ephemeroptera Plecoptera Trichoptera Diptera Ephemeroptera Ephemerellidae Heptageniidae Leptophlebiidae Plecoptera 4. Chloroperlidae 5. Perlodidae 6. Nemouridae Trichoptera 7. Hydropsychidae 8. Limnephilidae Diptera 9. Parametriocnemus 10. Simuliidae 11. Ceratopogonidae 12. Tipulidae

87 Macroinvertebrate Community Composition Over Time An NMDS ordination of macroinvertebrate abundances from 2006, 2011, 2012 and 2013 combined was generated (Figure 18). Individual streams were highlighted to reveal their changes in ordination space between 2006 and 2013 (Figures 19-21). For the figures with individual streams highlighted, the axes were scaled down to allow better resolution, although outlying streams are no longer visible. Three possibilities exist for year-to-year movement in ordination space of streams from before to after the flood event: No change from 2006 to 2013: non-significant year-to-year movement of the stream in ordination space. A directional change from 2006 to 2013: change to a different state with no return to original state by A directional change to a different state from 2006 to 2013 but return to original (before flood) state by First-order streams are presented in Figure 19. Two first-order streams demonstrated no directional change (streams #2 and #15), one suggested an alternate state after the flooding event (stream #8), and three suggested directional change after the flooding with a return to the original state (streams #10, #12, and #14). Second-order streams are presented in Figure 20. One stream demonstrated no change (stream #4), two suggested an alternate state after the flooding event (streams #3 and #11), and one suggested a directional change after the flooding with a return to the original state (stream #9).

88 Third-order streams are presented in Figure 21. All three third-order streams suggested a directional change after the flooding with a return to the original state (streams #1, #7, and #13). Associations among Environmental Distance and Biotic Dissimilarity Matrices Partial correlations (Table 9) among biotic, environmental, and spatial distances revealed a significant relationship (p= <0.001, r=0.596) in 2006 between environmental and biotic community composition when identified to genus level. In 2011, no significant relationships were shown. In 2012, a weak to moderate significant relationship emerged for both partial and environmental (p=0.022, r=0.242) and spatial and biotic (p=0.022, r= 0.241) variables, both with family level identification. In 2013, however, a significant relationship was shown only between spatial and biotic (p=0.010, r=0.271).

89 Figure 18. Ordination plot of all streams from

90 4 3 NMDS Dimension NMDS Dimension 1

91 Figure 19. Ordination Plots for (Including all Streams) Scaled down to show distribution more Clearly (First-order Streams #2 and #8 are Enlarged)

92

93 Figure 19 Cont. Ordination Plots for (Including all Streams) Scaled Down to Show Distribution more Clearly (First-order Streams #10 and #12 are Enlarged)

94

95 Figure 19 Cont. Ordination Plots for (Including all Streams) Scaled down to Show Distribution more Clearly (First-order Streams #14 and #15 are Enlarged)

96

97 Figure 19 Cont. Ordination Plots for (Including all Streams) Scaled Down to Show Distribution more Clearly (First-order Stream #16 is Enlarged)

98

99 Figure 20. Ordination Plots for (Including all Streams) Scaled down to Show Distribution more Clearly (Second-order Streams #3 and #4 are Enlarged)

100

101 Figure 20 Cont. Ordination Plots for (Including all Streams) Scaled down to Show Distribution more Clearly (Second-order streams #9 and #11 are Enlarged)

102