EFFECTS OF FINE SEDIMENT DEPOSITION ON - … Logan... · EFFECTS OF FINE SEDIMENT DEPOSITION ON BENTHIC INVERTEBRATE COMMUNITIES By Olivia D. Logan BSc University of New Brunswick

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EFFECTS OF FINE SEDIMENT DEPOSITION ON BENTHIC INVERTEBRATE COMMUNITIES

By

Olivia D. Logan

BSc University of New Brunswick (Saint John) 2004

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

Masters of Science

In the Graduate Academic Unit of Biology

Supervisor: R.A. Curry, Ph.D., Canadian Rivers Institute, UNB (Fredericton) Joseph Culp, Ph.D., Canadian Rivers Institute, UNB (Fredericton) Examining Board: Les Cwynar, Ph.D., Department of Biology, UNB Fredericton,

Chair Donald Baird, Ph.D., Department of Biology, UNB Glenn Benoy, Ph.D., Environment Canada and Agriculture and Agri-Food Canada

This thesis is accepted by the Dean of Graduate Studies

THE UNIVERSITY OF NEW BRUNSWICK

November, 2007

© Olivia D. Logan, 2007

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DEDICATION

To my parents who have always been there for me.

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ABSTRACT

This study examines the effects of fine sediment deposition on benthic

invertebrates. Despite numerous studies identifying deposited sediment as a factor

altering benthic invertebrate communities, it is not fully understood why some taxa are

more tolerant or better adapted to increases in sediment deposition. As a result, there is

no universal metric that can be applied to stream ecosystems to determine severity of

sediment impacts. Therefore determinations of metrics that can be specifically used to

identify sediment as the factor affecting the benthic invertebrate community is warranted.

Benthic invertebrate biomonitoring metrics and species traits composition along a

sediment deposition gradient were examined. Additionally the sensitivity of select

benthic invertebrate taxa to sediment deposition was examined. Several metrics were

determined to be useful in identifying the effects of sediment deposition as they were

significantly correlated and responded consistently to increasing fine sediment deposition

across seasons. The metrics % burrower and % Chironominae of chironomidae were

positively associated with increasing fine sediment deposition. While the metrics: %

EPT, EPT abundance and % Orthocladiinae of Chironomidae were negatively associated.

Additionally several species traits were associated with increased sediment deposition.

The species traits: plastron respiration, elongate body form, depositional rheophily and

predation were positively associated. While the traits: univoltine, abdominal gill

placement, flattened body form, gills present, gill respiration, slow- seasonal

development, mulitvoltine, plate- like gills, strong swimmer, streamlined body and

erosional rheophily were negatively associated. Therefore, species traits patterns and

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certain biomonitoring metrics may lead to diagnostic bioassessments of deposited

sediment impacts in lotic environments.

PREFACE

The following thesis has been prepared in article format. Chapter 1 is a

general introduction to the thesis and Chapter 5 contains the general discussion

and conclusions. Three articles are also included these are:

Chapter 2: Reducing benthic invertebrate sample processing time and costs using

coarse sieve subsampling. The authors on this paper will be O.D. Logan; R.A. Curry

and J.M. Culp.

Chapter 3: Determination of benthic invertebrate biomonitoring metrics responsive

to fine sediment deposition. The authors on this paper will be O.D. Logan; R.A. Curry

and J.M. Culp.

and,

Chapter 4: Using species traits as a diagnostic bioassessment tool The authors on

this paper will be O.D. Logan; R.A. Curry and J.M. Culp.

The above authors are the main contributors to the creation, execution and

composition of the articles herein and are thus represented.

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ACKNOWLEDGEMENTS

Foremost, I would like to thank Drs. R. A. Curry and J. M. Culp for all their help

in the development and execution of this project and for providing me with this

opportunity. Thanks to my examining committee, Dr. Baird and Dr. Benoy for reading

and reviewing my thesis. Thanks to M. Gautreau, M. Finley, K. Heard, S. Clark and N.

Chisti for their help in the field and laboratory.

I would also like to thank my fellow lab mates for their help and advice. Also Eric

thank you to Eric Luiker and Dave Hyrn for all their help in logistical planning and

keeping the lab running. Last but not least, thanks to Jacob Sanford, who supported and

encouraged me through this whole process.

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TABLE OF CONTENTS

DEDICATION........................................................................................ ii

ABSTRACT............................................................................................ iii

PREFACE............................................................................................... iv

ACKNOWLEDGEMENTS.................................................................... v

TABLE OF CONTENTS........................................................................ vi

LIST OF TABLES.................................................................................. xi

LIST OF FIGURES ................................................................................ viii

CHAPTER 1: INTRODUCTION........................................................... 1-10

1.0 INTRODUCTION.............................................................................. 1

1.1 IDENTIFICATION OF PROBLEM .................................................. 2

1.2 AIMS AND OBJECTIVES ................................................................ 4

1.3 SIGNIFICANCE OF STUDY ............................................................ 6

1.4 LITERATURE CITED....................................................................... 7

1.5 TABLES ............................................................................................. 10

CHAPTER 2: REDUCING BENTHIC INVERTEBRATE SAMPLE

PROCESSING TIME AND COSTS USING COARSE SIEVE

SUBSAMPLING. ..........................................................................................11- 44

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2.0 ABSTRACT....................................................................................... 11

2.1 INTRODUCTION .............................................................................. 12

2.2 MATERIALS AND METHODS ....................................................... 14

2.2.1 Sample Collection ................................................................................ 14

2.2.2 Sample Processing ............................................................................... 15

2.2.3 Statistical Analysis ............................................................................... 15

2.3 RESULTS ........................................................................................... 17

2.3.1 Community Composition ....................................................................... 17

2.3.2 Abundance Measures ............................................................................ 18

2.3.3 Metrics.............................................................................................. 18

2.3.4 Percent Capture of Taxa......................................................................... 19

2.4 DISCUSSION..................................................................................... 20

2.4.1 Community Composition ....................................................................... 20

2.4.2 Metrics.............................................................................................. 21

2.4.3 Conclusions ............................................................................................ 23

2.5 ACKNOWLEDGEMENTS................................................................ 24


2.7 TABLES ............................................................................................. 27

2.8 FIGURES............................................................................................ 34

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CHAPTER 3: DETERMINATION OF BENTHIC INVERTEBRATE

BIOMONITORING METRICS RESPONSIVE TO FINE SEDIMENT

DEPOSITION. ............................................................................................ 44-96

3.0 ABSTRACT....................................................................................... 44

3.1 INTRODUCTION .............................................................................. 45

3.2 MATERIALS AND METHODS ....................................................... 47

3.2.1 Site Selection and Location .................................................................... 47 3.2.2 Site Descriptions ................................................................................. 48

3.2.3 Field Sampling ................................................................................... 48

3.2.4 Algal Biomass (Chlorophyll a) ............................................................... 49

3.2.5 Benthic Macroinvertebrates ................................................................... 50

3.2.6 Total Suspended Solids ......................................................................... 50

3.2.7 Sediment Measures .............................................................................. 51

3.2.8 Biomonitoring metrics .......................................................................... 52

3.2.9 Statistical Analysis .............................................................................. 52

3.3 RESULTS ........................................................................................... 53

3.3.1 Sediment Measures and Environmental Variables ........................................... 53

3.3.2. Biomonitoring Metrics ............................................................................. 54

3.3.3 Biomonitoring metrics and environmental variables ..................................... 56

3.3.4 Overall metric responses to deposited fine sediment ..................................... 57

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3.4 DISCUSSION..................................................................................... 57

3.5 CONCLUSIONS ................................................................................ 63

3.6 ACKNOWLEDGEMENTS................................................................ 64


3.8 TABLES ............................................................................................. 68

3.9 FIGURES............................................................................................ 87

CHAPTER 4: USING SPECIES TRAITS AS A DIAGNOSTIC

BIOASSESSMENT TOOL. ............................................................ 96- 146

4.0 ABSTRACT....................................................................................... 96

4.1 INTRODUCTION .............................................................................. 97

4.2 MATERIALS AND METHODS ..................................................... 100

4.2.1 Site Selection and Location .................................................................. 100 4.2.2 Site Descriptions ............................................................................... 101

4.2.3 Field Sampling ................................................................................. 101

4.2.4 Benthic Invertebrate Traits ................................................................... 102

4.2.5 Statistical Analysis .................................................................................. 103

4.2.6 Benthic Invertebrate Tolerance to Deposited Sediment..................................... 104

4.3 RESULTS ......................................................................................... 105

3.3.1 Benthic Invertebrate Relative Abundance Data ......................................... 105

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3.3.2. Sediment Tolerances ......................................................................... 107

3.3.3 Species Traits Relative Abundance Data .................................................. 108

4.4 DISCUSSION................................................................................... 111

4.5 CONCLUSIONS .............................................................................. 116

4.6 ACKNOWLEDGEMENTS.............................................................. 118

4.7 LITERATURE CITED..................................................................... 118

4.8 TABLES ........................................................................................... 124

4.9 FIGURES.......................................................................................... 138

CHAPTER 5: DISCUSSION.......................................................... 146- 149

5.1 IMPLICATIONS OF RESULTS..................................................... 147

5.2 FUTURE RESEARCH..................................................................... 147


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LIST OF TABLES

CHAPTER 1: INTRODUCTION...................................................................... 10 Table 1.1: Objectives and hypotheses by chapter................................................10 CHAPTER 2: REDUCING BENTHIC INVERTEBRATE SAMPLE PROCESSING TIME AND COSTS USING COARSE SIEVE SUBSAMPLING ....................... 27-33

Table 2.1: Stream sites and GPS coordinates sampled from the central portion of Prince Edward Island, Canada, 25- 27 July 2005 ................................................27 Table 2.2: Results of the analysis of similarity (ANOSIM) tests performed on the coarse/total replicates for individual sites............................................................28 Table 2.3: Results of 1-way ANOVA comparing coarse and total family richness values within individual sites...............................................................................29 Table 2.4: Results of 1-way Kruskal-Wallis nonparametric test comparing coarse and total EPT richness values within individual sites..........................................30 Table 2.5: Results of 1-way ANOVA comparing coarse and total % chironomidae values within individual sites...............................................................................31 Table 2.6: Results of 1-way Kruskal-Wallis nonparametric test comparing coarse and total evenness values within individual sites ................................................32 Table 2.7: Results of 1-way ANOVA comparing coarse and total % EPT values within individual sites ..........................................................................................33

CHAPTER 3: DETERMINATION OF BENTHIC INVERTEBRATE BIOMONITORING METRICS RESPONSIVE TO FINE SEDIMENT DEPOSITION ............................................................................................................................... 68- 85

Table 3.1: Study sites sampled on Prince Edward Island, including their watershed and geographical location ....................................................................................68 Table 3.2: sizes used during substrate particle size distribution analysis ............69 Table 3.3: Biomonitoring metrics tested for a response to sediment deposition .70 Table 3.4: Spearman rank correlation coefficients and associated p- values for deposited sediment and substrate measures for July 2005...................................71

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Table 3.5: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and environmental variables for July 2005 ..........72 Table 3.6: Spearman rank correlation coefficients and associated p- values for deposited sediment and substrate measures for October 2005 ...........................73 Table 3.7: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and environmental variables for October 2005 ....74 Table 3.8: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and % habit/ feeding group metrics for July 2005..............................................................................................................................75 Table 3.9: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and biomonitoring metrics for July 2005. ............76 Table 3.10: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and % habit/ feeding group metrics for October 2005...............................................................................................................................78 Table 3.11: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and biomonitoring metrics for October 2005.......79 Table 3.12: Spearman rank correlation coefficients and associated p- values for habit/ feeding group metrics responsive to deposited sediment and environmental variables for July 2005.........................................................................................81 Table 3.13: Spearman rank correlation coefficients and associated p- values for metrics responsive to deposited sediment and environmental variables for July 2005......................................................................................................................83 Table 3.14: Spearman rank correlation coefficients and associated p- values for metrics responsive to deposited sediment and environmental variables for October 2005......................................................................................................................85

CHAPTER 4: USING SPECIES TRAITS TO DETERMINE THE EFFECTS OF FINE SEDIMENT DEPOSITION............................................................125- 36

Table 4.1: Biological traits and categories examined along a fine sediment deposition gradient ...........................................................................................125 Table 4.2: Spearman rank correlation coefficients and associated p- values for PCA axis site scores of average benthic invertebrate relative abundance data and environmental variables for July 2005..............................................................126

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Table 4.3: Spearman rank correlation coefficients and associated p- values for PCA axis site scores of average benthic invertebrate relative abundance data and environmental variables for October 2005. .....................................................128 Table 4.4: Spearman rank correlation coefficients and associated p- values for PCA axis site scores of average species trait relative abundance data and environmental variables for July 2005......................................................................................130 Table 4.5: Spearman rank correlation coefficients and associated p- values for PCA axis site scores of average species trait relative abundance data and environmental variables for October 2005................................................................................132 Table 4.6: Species tolerances for July 2005......................................................134 Table 4.7: Species tolerances for October 2005 ...............................................136

LIST OF FIGURES

CHAPTER 2: REDUCING BENTHIC INVERTEBRATE SAMPLE PROCESSING TIME AND COSTS USING COARSE SIEVE SUBSAMPLING ....................... 34-43

Figure 2.1: Location of watersheds sampled for benthic invertebrates, 25- 27 July 2005......................................................................................................................34

Figure 2.2: Ordination of benthic invertebrate assemblages within sites from the coarse and total samples.......................................................................................35

Figure 2.3: Abundance (mean ± 1SE) of benthic invertebrate taxa from total and coarse fraction samples ........................................................................................36

Figure 2.4: Relative abundance (mean ± 1SE) of benthic invertebrate taxa from total and coarse fraction samples .........................................................................37 Figure 2.5: Family Richness (mean ± 1SE) of total and coarse samples at all 13 sites..............................................................................................................................38 Figure 2.6: EPT Richness (mean ± 1SE) of total and coarse samples at all 13 sites. Sites values are the average of all three replicates...............................................39 Figure 2.7: Percent EPT and percent chironomidae (mean ± 1SE) of benthic invertebrate taxa from total and coarse fraction samples.....................................40 Figure 2.8: Shannon Diversity and Evenness (mean ± 1SE) of benthic invertebrate data from coarse fraction and total samples.........................................................41

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Figure 2.9: Mean percent capture (number in coarse/ total number of individuals) of different taxa by the coarse sieve (2mm).............................................................42 Figure 2.10: Mean percent capture (number in coarse/ total number of individuals) of Chironomids by site in the coarse sieve (2mm) ..............................................43

CHAPTER 3: DETERMINATION OF BENTHIC INVERTEBRATE BIOMONITORING METRICS RESPONSIVE TO FINE SEDIMENT DEPOSITION............................................................................................................................... 87- 95

Figure 3.1: Percentage of different land-use types within the six watersheds sampled in the summer (25- 27 July) and fall (19-21 October) of 2005 on Prince Edward Island, Canada ........................................................................................87 Figure 3.2: A.) 1m2 Quadrat used to designate area from which samples were to be collected. B.) Placement of algal substrate within the quadrat ............................88 Figure 3.3: A.) Artificial algal substrate used for Chlorophyll- a analysis. B.) Algal substrate deployed in river ...................................................................................89 Figure 3.4: Site- Season biplot diagram summarizing the effect of season on benthic invertebrate community composition ..................................................................90 Figure 3.5: Relationships between % habit/ functional feeding group metrics and % deposited fine sediment that had significant correlations in July 2005 ...............91 Figure 3.6: Relationships between biomonitoring metrics and % deposited fine sediment that had significant correlations in July 2005.......................................92 Figure 3.7: Relationship between chironomidae metrics and % deposited fine sediment that had significant correlations in July 2005.......................................93 Figure 3.8: Relationships between metrics and % deposited fine sediment that had significant correlations in October 2005..............................................................92 Figure 3.9: Relationships between metrics and % deposited fine sediment that had significant correlations in October 2005..............................................................95

CHAPTER 4: USING SPECIES TRAITS TO DETERMINE THE EFFECTS OF FINE SEDIMENT DEPOSITION ............................................................... 138- 145

Figure 4.1: RDA ordination of average species relative abundance and environmental variables significantly correlated with PCA axes 1 and 2, July 2005..............................................................................................................................138

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Figure 4.2: RDA ordination of average species relative abundance and environmental variables significantly correlated with PCA axes 1 and 2, October 2005......................................................................................................................139 Figure 4.3: RDA ordination of benthic invertebrate taxa tolerances to deposited sediment (axis 1), July 2005 ................................................................................140 Figure 4.4: RDA ordination of benthic invertebrate taxa tolerances to deposited sediment (axis 1), October 2005 ..........................................................................141 Figure 4.5: RDA ordination of species traits and environmental variables correlated with PCA axes 1 and 2, July 2005 .......................................................................142

Figure 4.6: RDA ordination of species traits and environmental variables correlated with PCA axes 1 and 2, October 2005.................................................................143 Figure 4.7: RDA ordination of average trait relative abundances and sediment variables July 2005 ..............................................................................................144 Figure 4.8: RDA ordination of average trait relative abundances and sediment variables, October 2005 .......................................................................................145 CURRICULUM VITAE APPENDIX A

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1 GENERAL INTRODUCTION 1.1 Background

In 1998 the US Environmental Protection Agency (EPA) identified deposited

sediment as the number one source of stream impairment and habitat degradation

nationwide (Zweig 2000). Sediment impacting rivers and streams can be suspended or

deposited (Waters 1995). Suspended sediment is defined as the particles that are carried

in the water column, while deposited sediment refers to the particles covering the

streambed (Waters 1995; Wilcock 2004). Sediment is any organic/ inorganic particulate

matter that can be transported and deposited in aquatic environments. Suspended

sediments impair respiration, feeding and visual foraging of aquatic biota (Waters 1995;

Wood and Armitage 1997). Sediment deposition degrades benthic habitat used by algae,

benthic invertebrates and fish (Waters 1995). The accumulation of excess fine sediment

on streambeds decreases substrate particle size, resulting in a change from a

heterogeneous substrate with complex habitats to a more homogenous sand/silt substrate

(Minshall 1984). Fine sediment can: clog interstitial spaces (Waters 1995, Zanetell &

Peckarsky 1996), reduce substrate stability (Nuttall 1972; Cobb et al. 1992; Richards et

al. 1997; Jowett 2003), and increase substrate embeddedness (Brusven & Prather 1974;

Sylte & Fischenich 2002). For benthic invertebrates, sedimentation means the loss of

habitat variability, quality and quantity.

Anthropogenic disturbances at the landscape scale result in the greatest

degradation of riverine habitat (Allan 2004). Anthropogenic activities that increase

sediment loads to rivers and streams include: agriculture, forestry operations, mining,

road construction, dam reservoirs and urban runoff (Waters 1995; Wood & Armitage

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1997; Zweig 2000; Parkhill & Gulliver 2002). Of these agriculture has been identified as

the greatest contributor of sediment to streams and rivers (Waters, 1995; Zweig, 2000).

Agricultural practices increase the amount of fine sediment entering aquatic

environments from channel (originate from within the stream and its tributaries) and

nonchannel (originate from within the catchment) sources (Wood & Armitage 1997). The

removal of riparian vegetation to increase crop production area and increase livestock

access decrease bank stability causing erosion and increased fine sediment inputs.

Floodplain lands are highly productive and cultivation of row-crop increases the amount

of soil exposed to erosion and thus fine sediments in surface run-off (Waters 1995).

However it is difficult to isolate the source since sediment can be entering from multiple

sources (e.g. roads, construction etc.). This thesis will focus on the effects of sediment

deposition on benthic invertebrate community structure, biological trait composition and

methods of quantifying or measuring the impact in small agricultural streams.

1.2 Identification of Problem

Deposited sediment affects benthic invertebrates: substrate preference (Brusven &

Prather 1974), behavior such as drift (McClelland & Brusven 1980; Culp et al. 1986;

Fairchild et al. 1987), community composition (Cordone & Kelley 1961; Lenat et al.

1981; Kreutzweiser et al. 2004), feeding efficiency (Lemly 1982; Waters 1995) and

respiration (Lemly 1982). Sediment can reduce the critical habitat of certain benthic

invertebrate taxa causing a decrease in density and community diversity (Nuttall 1972;

Lenat et al. 1981; Minshall 1984; Henley et al. 2000; Roy et al. 2003). Certain sensitive

taxa, mainly in the orders Ephemeroptera, Plecoptera and Trichoptera (EPT), decrease in

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abundance as fine sediment deposition increases (Waters, 1995), while other taxa, such as

chironomids and oligochaetes can increase in abundance and richness (Gray and Ward

1982).

Despite numerous studies identifying deposited sediment as a factor altering

abundance and species composition of benthic invertebrate communities, it is not fully

understood why some taxa are more tolerant or better adapted to increases in sediment

deposition. As a result, there is no universal metric that can be applied to stream

ecosystems to determine severity of sediment impacts. Many metrics are used to

generally identify a problem (e.g. EPT richness, diversity indices, Hilsenhoff index),

however these metrics are too general to specifically identify sediment as the factor

affecting the benthic invertebrate community. Relyea et al. (2000) and Zweig and Rabeni

(2001) examined the sensitivity of individual taxa to deposited fine sediment, which were

subsequently used in the development of a deposited sediment index for ecoregions of

Missouri, Idaho, Oregon and Washington, therefore specific to regional species. Several

studies have attempted to empirically determine metrics sensitive or responsive to fine

sediment deposition (Angradi 1999; Zweig & Rabeni 2001; Kaller et al. 2001).

Unfortunately, existing studies focused on fine sediment addition and a short range of

sedimentation conditions. For example, Angradi (1999) examined experimentally added

fine sediment between 0 and 30% surface cover.

Identifying benthic invertebrate community response patterns to fine sediment

deposition, which would allow for identification of the source of impairment across

communities differing in taxonomic composition, is highly desirable for biomonitoring

purposes (Poff et al. 2006; Vieira et al. 2006). Biological traits, which are the functional

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attributes , e.g., morphological, physiological, behavioural and ecological characteristics,

of a species may provide a more generalized metric for use in any stream ecosystem

(Vieira et al. 2006). Statzner et al. (2004) observed that “habitat characteristics are filters

for the biological traits of organisms” or they are forces that act on the trait composition

of communities such as fine sediment deposition. In areas where environmental

conditions are constant, the community trait composition should be similarly constant

because the suite of biological traits an individual can draw from remains stable across

different taxonomic groups (Southwood 1977, 1988). For that reason biological traits can

be used for assessing effects of sediment deposition over many geographic areas without

refinement across communities with different taxonomic compositions. Differences in

morphology, feeding mechanism and mode of respiration along with other biological

traits may also help account for the varying degrees of sediment tolerance observed in

benthic invertebrate taxa.

1.3 Aims and Objectives

My thesis examines communities of benthic invertebrates in sediment impacted

streams to determine if biological traits, individually or in combination can explain

differences in community structure. By sampling multiple streams from the same

geographic area to take into account intra and inter-stream variability, I hope to be able to

extend my results beyond a single stream. My ultimate goal is to begin the process of

establishing a trait-based metric for assessing the impact of deposited sediment on a

stream’s benthic ecosystem.

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The primary objectives of my study are to determine the sensitivity of select

benthic invertebrate taxa from small streams on Prince Edward Island (PEI), Canada to

deposited fine sediment (< 2mm) to be used in a deposited sediment index and the

development of a list of benthic invertebrate biological traits (e.g. life history,

morphological, behavioural, or functional) indicative of fine sediment deposition (Table

1.1). My hypothesis is that certain taxa are more tolerant, because they possess life

history, physical, behavioural, or functional traits more adapted to fine sediment habitats.

My hypothesis is that biological traits in invertebrate communities are related to levels of

fine sediment deposition, and therefore some traits will become identifiable as percent

fine sediment deposition increases in a stream. A secondary objective was to determine

biomonitoring metrics that respond to changes in deposited sediment and therefore can be

used to assess the impacts of deposited fine sediment on benthic invertebrate

communities (Table 1.1). My hypothesis is that several metrics will respond positively or

negatively to increased sediment deposition in streams.

1.4 Significance of study

This study will add to the knowledge of the relationship between individual

benthic invertebrates/communities and fine sediment deposition in agricultural areas. A

deposited sediment index for PEI would be of benefit to bioassessment approaches,

because it is a high sediment risk area and may be indicative of Canadian patterns of

distribution and abundance in areas of sedimentation. Identification of biomonitoring

metrics responsive to sedimentation will allow for more expedited source of impairment

identification and remediation. Identification of a suite of biological traits that are

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responsive or indicative of fine sediment deposition will improve our ability to monitor/

diagnose problems in aquatic environments. Assessment of biological traits will also

allow us to become more predictive, by give a-priori predictions of trait compositions, so

the impacts can be identified sooner and reduced.

1.5 LITERATURE CITED

Allan, J.D. 2004. Landscapes and riverscapes: The influences of land use on stream ecosystems. Annual Review of Ecological Systems 35: 257- 284. Angradi, T.R. 1999. Fine sediment and macroinvertebrate assemblages in Appalachian

streams: a field experiment with biomonitoring applications. Journal of the North American Benthological Society 18(1): 49- 66.

Brusven, M.A., and K.V. Prather. 1974. Influence of stream sediment on distribution of Macrobenthos . Journal of the Entomological Society of British Columbia 71:

25- 32. Cobb, D.G., T.D. Galloway and J.F. Flannagan. 1992. Effects of discharge and substrate stability on density and species composition of stream insects. Canadian Journal of Fisheries and Aquatic Sciences. 49: 1788- 1795. Cordone, A.J., and D.W. Kelley. 1960. The influences of inorganic sediment on the aquatic life of streams. California Department of Fish and Game. Culp, J.M., F.J. Wrona and R.W. Davies. 1986. Response of stream benthos and drift to fine sediment deposition versus transport. Canadian Journal of Zoology. 64 : 1345- 1351. Fairchild, J.F., T. Boyle, W.R. English and C. Rabeni. 1987. Effects of sediment and contaminated sediment on structural and functional components of experimental stream ecosystems. Water, Air, and Soil Pollution. 36: 271- 293. Gray, L.J., and J.V. Ward. 1982. Effects of sediment releases from a reservoir on stream macroinvertebrates. Hydrobiologia. 96: 177- 184. Henley, W.F., M.A. Patterson, R.J. Neves and A.D. Lemly. 2000. Effects of

sedimentation and turbidity on lotic food webs: a concise review for natural resource managers. Reviews in Fisheries Science. 8(2): 125- 139.

Jowett, I.G. 2003. Hydraulic constraints on habitat suitability for benthic invertebrates

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in gravel- bed rivers. River Research and Applications. 19: 495- 507. Kaller, M.D., K.J. Hartman, and T.R. Angradi. 2001. Experimental determination of benthic macroinvertebrate metric sensitivity to fine sediment in Appalachian streams. Proc Annu Conf SEAFWA 55: 105-115. Kreutzweiser, D.P., S.S. Capell and K.P. Good. 2004. Effects of fine sediment inputs

from a logging road on stream insect communities: a large- scale experimental approach in a Canadian headwater stream. Aquatic Ecology. 00: 1- 12.

Lemly, A.D. 1982. Modification of benthic insect communities in polluted streams: combined effects of sedimentation and nutrient enrichment. Hydrobiologia. 87: 229- 245. Lenat, D.R., D.L. Penrose and K.W. Eagleson. 1981. Variable effects of sediment addition on stream benthos. Hydrobiologia. 79: 187- 194. McClelland, W.T., and M.A. Brusven. 1980. Effects of sedimentation on the behavior and distribution of riffle insects in a laboratory stream. Aquatic Insects.

2(3): 161- 169. Minshall, G.W. 1984. Aquatic insect- substratum relationships. Pages 358- 400 in Resh, V.H., and Rosenburg, D.M. The ecology of Aquatic Insects. Praeger Publishers, One Madison Ave. New York, NY. Nuttall, P.M. 1972. The effects of sand deposition upon the macroinvertebrate fauna of the River Camel, Cornwall. Freshwater Biology 2: 181- 186. Parkhill, K.L., and J.S. Gulliver. 2002. Effect of inorganic sediment on whole- stream productivity. Hydrobiologia 472: 5- 17. Poff, N.L., J.D. Olden, N.K.M. Vieira, D.S. Finn, M.P. Simmons and B.C. Kondratieff. 2006. Functional trait niches of North American lotic insects: trait- based ecological application in light of phylogenetic relationships. Journal of the North American Benthological Society 25(4): 730- 755. Relyea, C.D., G.W. Minshall and R.J. Danehy. 2000. Stream insects as bioindicators of fine sediment. Watershed Management 2000 Conference, Water Environment Federation. Richards, C., R.J. Haro, L.B. Johnson and G.E. Host. 1997. Catchment and reach- scale properties as indicators of macroinvetebrate species traits. Freshwater Biology. 37: 219- 230. Roy, A.H., A.D. Rosemond, D.S. Leigh, M.J. Paul and J.B. Wallace. 2003. Habitat- specific responses of stream insects to land cover disturbance: biological

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consequences and monitoring implications. Journal of the North American Benthological Society 22(2): 292- 307. Southwood, T.R.E. 1977. Habitat, templet for ecological strategies. Oikos 46: 337-365. Southwood, T.R.E. 1988. Tactics, strategies and templets. Oikos 52: 3-18. Statzner, B., S. Doledec and B. Hugueny. 2004. Biological trait composition of European stream invertebrate communities: assessing the effects of various trait filter types. Ecography. 27: 470- 488. Sylte, T., and C. Fischenich. 2002. Techniques for measuring substrate embeddedness. ERDC TN- RMRRP- SR- 36, September 2002. Vieira, N.K.M., N.L. Poff, D.M. Carisle, S.R. Moulton, M.L. Koski, and B.C. Kondratieff. 2006. A database of lotic invertebrate traits for North America. U.S. Geological Survey Data Series 187, http://pubs.water.usgs.gov/ds187. Waters, T.F. 1995. Sediment in streams: sources, biological effects and control. Monograph 7. American Fisheries Society, Bethesda, Maryland. Wood, P.J., and P.D. Armitage. 1997. Biological effects of fine sediment in the lotic environment. Environmental Management. 21(2): 203- 217. Wood, P.J., and P.D. Armitage. 1999. Sediment deposition in a small lowland stream- management implications. Regulated Rivers: Research & Management. Zanetell, B.A, and B.L. Peckarsky. 1996. Stoneflies as ecological engineers- hungry predators reduce fine sediment in stream beds. Freshwater Biology. 36: 569- 577. Zweig, L.D. 2000. Effects of deposited sediment on stream benthic macroinvertebrate communities. MSc Thesis, University of Missouri- Columbia. Zweig, L.D. and C.F. Rabeni. 2001. Biomonitoring for deposited sediment using benthic

invertebrates: a test on 4 Missouri streams. Journal of the North American Benthological Society 20(4): 643- 657.

9

1.6 TABLES

Table 1.1 Chapter Objectives and Hypotheses

Chapter Objectives Hypotheses 2

Reducing benthic invertebrate sample processing time and

costs using coarse sieve subsampling.

• Determine if removal of the fine fraction changes BMI community trends.

• Determine how much

additional information the fine fraction adds to BMI community analyses.

• Trends observed in BMI community structure/ composition will not significantly change.

• The abundance of

select taxa will increase with the addition of the fine fraction, but no overall significant effects will be observed.

3

Determination of benthic invertebrate

biomonitoring metrics responsive to fine

sediment deposition.

• Determine if BMI community composition is determined by the level of deposited sediment in PEI streams.

• Determine which

biomonitoring metrics are sensitive or respond to fine sediment deposition.

• As % fine sediment cover increases, BMI taxa intolerant of fine sediment will disappear and tolerant taxa will increase in abundance.

• Metrics incorporating tolerant taxa will respond to increased sedimentation positively. Metrics using intolerant taxa will respond negatively to increased sediment deposition.

10

4

Using species traits as a diagnostic

bioassessment tool.

• Determine which physical, behavioural, or functional traits are most sensitive to fine sediment deposition.

• Develop a list of

sediment tolerant traits.

• Determine sediment tolerance of select BMI taxa.

• Biological traits benefical in areas of increased sediment will be positively associated with increased sediment.

• Biological traits

will become identifiable as % fine sediment cover increases. Trait composition will shift and the frequency of maladaptive traits will decrease as sediment deposition increases.

• BMI taxa with

tolerant traits will be more tolerant of deposited sediment.

11

2.0 ABSTRACT

This study examines the implications of subsampling benthic invertebrate samples

using only the coarse fraction of a sample. Benthic invertebrate samples were collected

from 13 sites along a deposited sediment gradient, from streams located in PEI, Canada.

Samples were separated into a coarse fraction (> 2 mm) and a fine fraction (between

2mm and 250 µm) in the laboratory and then sorted and identified to the lowest practical

taxonomic level. Community composition, abundance of different taxa and various

biomonitoring metrics were compared between the coarse fraction and total sample to

determine whether coarse fraction subsampling gives a true representation of the original

sample. Nonmetric multidimensional scaling (nMDS) ordination analysis revealed the

relationship among benthic invertebrate communities at the 13 sampling sites was similar

regardless of whether the analysis included the total sample or only the coarse fraction.

Individual ANOSIM tests reinforced the nMDS ordination results and indicate

community structure of the coarse and total samples were similar. Abundance in the total

samples was significantly higher for chironomids and ephemeropterans and, in addition

the relative abundance of chironomids was significantly higher for total samples. As well

taxa richness and EPT richness were underestimated by use of only the coarse fraction.

The metrics, % Chironomidae and evenness were significantly different between the

overall coarse and total samples, but not at the individual site level. Shannon Diversity

and % EPT were not significantly different for the coarse and total samples. These results

indicate coarse fraction subsampling of a benthic invertebrate sample is a simple and

cost-effective subsampling method that gives an adequate representation of the

composition of the original sample.

12

2.1 INTRODUCTION

Obtaining representative benthic invertebrate samples is an important

consideration for both ecological and biomonitoring studies that require the collection of

multiple replicate samples from numerous sites (Barbour and Gerritsen 1996; Rosenberg

and Resh 2001). Processing this large amount of material is often a limiting factor in

benthic research. Given normal constraints on time, effort and financial resources, it can

be difficult to detect impacts to benthic invertebrates, because sample processing

considerations can lead to compromises in sample design that reduce sample size and

statistical power (Barbour and Gerritsen 1996; King and Richardson 2002; Lorenz et al.

2004). The processing, sorting and identification of benthic invertebrate samples in the

laboratory is very costly, labor intensive and time consuming and many benthic sampling

protocols require total sample processing (Courtemanch 1996; Cao et al. 1998).

Subsampling of the original sample is a widely used method for overcoming these

constraints by decreasing processing time of benthic invertebrate samples, thereby

enabling the researcher to increase sample size and potentially statistical power (Vinson

and Hawkins 1996; Walsh 1997; Carter and Resh 2001; King and Richardson 2002).

Benthic invertebrate samples are a subset of the Riverine population that can be

used to make inferences about the community. Subsamples are subsets of the original

samples, which involves the division of benthic invertebrate samples into portions to

reduce the number of individuals to be counted and the volume of material to be

processed (Ciborowski 1991). In the scientific literature, a large number of studies have

explored optimizing subsampling methods and their effects (e.g. Barbour and Gerritsen

1996; Courtemanch 1996; Growns et al. 1997; Larsen and Herlihy 1998; Carter and Resh

13

2001; King and Richardson 2002; Lorenz et al. 2004). Carter and Resh (2001) found the

most commonly used method of subsampling by US state agencies is the fixed count

method, where a fixed number of organisms are removed from the sample (usually 100).

Alternative subsampling methods involve sorting a fixed proportion of the sample using a

gridded tray or frame (e.g. Carter and Resh 2001), subsampling by weight (Sebastien et

al. 1988), sorting for a fixed period of time (e.g. Growns et al. 1997), or volumetric

subsampling (Imhoff cone suspension subsampling; Wrona et al. 1982).

In general, subsampling offers a more economical method (in terms of both

finances and time) for processing of benthic invertebrate samples. However, the

processing of subsamples can still be relatively time consuming because the finer

fractions (< 2 mm) contain small instars that are more difficult to sort and identify. Often,

the inclusion of the subsampled fine fraction results in the processing and counting of

many small organisms that can only be identified to higher taxonomic levels (family or

order). In many subsampling techniques (such as the Imhoff Cone; Wrona et al. 1982),

sieves are used to reduce the amount of matter for sorting and to divide the sample into a

“coarse” fraction and a “fine” fraction (Wrona et al. 1982; Ciborowski 1991; Morin et al.

2004). Indeed, Morin et al. (2004) found that unbiased benthic invertebrate community

descriptions can be obtained by using coarser sieves (> 1 mm). Thus, limiting benthic

analysis to the coarse portion of a sample can reduce effort required for sorting and

identification, ultimately leading to savings in terms of the time and cost of sample

processing.

In this study we hypothesized that the coarse fraction (> 2 mm) of a benthic

invertebrate sample was sufficient for biomonitoring assessments, because a large

14

amount and diversity of benthic invertebrates are retained. The main objective was to

determine if removal of the subsampled fine fraction (< 2 mm) from the sample analysis

significantly changed the interpretation of observed trends in community composition. In

addition, we determined the effect of the inclusion of the fine fraction (in the total

sample) on the performance of various biomonitoring metrics. Finally, the percentage of

different taxonomic groups captured by the coarse sieve was investigated in order to

identify any taxonomic-specific biases related to size-fraction subsampling as this type of

bias could confound data interpretation.

2.2 MATERIALS AND METHODS 2.2.1 Sample Collection

Benthic invertebrate samples were collected on 25- 27 July 2005 from six 2nd and

3rd order streams located in the central portion of Prince Edward Island, Canada (Figure

2.1). Sample sites were selected from small streams within agricultural areas to obtain a

gradient of fine deposited sediment conditions, but which also had a narrow range of

other environmental variables (e.g. temperature, dissolved oxygen, pH) that could affect

benthic community composition. A total of 13 sites were sampled from the 6 streams

(Table 2.1).

Benthic invertebrates were sampled from riffle/ run areas using a U-net

(Scrimgeour et al. 1993; 30 cm opening, 400 µm mesh, 0.055 m2). The U-net was placed

on the stream substrate and the bed material within the area was disturbed to a depth of 8-

10 cm by hand for 3 min. A second U-net was then taken from within the same riffle and

combined with the contents of the first to produce a single sample. A total of 3 replicate

15

samples (area of 0.11 m2 each) were taken in this way at each site. Once collected the

samples were poured onto a 250 µm mesh sieve, rinsed, placed into containers, preserved

with 10 % formalin and returned to the laboratory for identification.

2.2.2 Sample Processing

Benthic invertebrate samples were transferred into 70 % ethanol in the laboratory.

For sorting purposes the samples were separated into a coarse fraction (> 2 mm) and a

fine fraction (> 250 µm) by passing the material through sieves. Coarse material was

sorted, counted, and identified to the lowest practical taxonomic level. In contrast the fine

fraction was subsampled using an Imhoff cone (Wrona et al. 1982). At least 3 sub-

samples containing 200 individuals were sorted, counted, and identified to the lowest

practical taxonomic level. Precision between subsamples and precision of the cone were

evaluated (> 20% error whole sample sorted), done by checking whether number of

invertebrates in each subsample was similar. Elutriation was used to separate the organic

matter from the fine sediment in the samples before subsampling. The remaining

sediment was then checked for invertebrates. The organic fine fraction was then

subsampled and sorted.

2.2.3 Statistical Analyses

Ordination analysis was used to determine if trends in benthic invertebrate

communities among the sites differed between data sets which included either the total

sample (i.e., coarse and fine fractions combined) or only the coarse fraction. The two-

dimensional ordination was performed in the PRIMER software package using nonmetric

multidimensional scaling (nMDS) of log (x +1) transformed Bray- Curtis similarity

16

matrices (PRIMER v.5; Plymouth Marine Laboratory, Plymouth, UK). One-way analysis

of similarity (ANOSIM; PRIMER, v5) based on log (x + 1) transformed Bray-Curtis

similarity coefficients was also performed to determine if benthic invertebrate community

composition differed between the total sample and coarse fraction. ANOSIM tests were

performed on individual sites and by using all-site averages. The ANOSIM routine

generates a global R value which lies between - 1 and + 1, a value of zero indicating no

difference among a set of samples. We interpreted R- values of > 0.75 as well separated

(different); R > 0.5 as overlapping, but still different and R < 0.25 as barely separable

(Clarke and Gorley 2001). A similarity percentages (SIMPER) routine was used to

identify species accounting for observed assemblage differences between the coarse and

total samples (PRIMER v.5; Plymouth Marine Laboratory, Plymouth, UK).

Analysis of variance (ANOVA) was performed on abundance, relative abundance

and metric data using the software packages, SPSS (SPSS 13.0; Chicago, IL, USA) and

MINITAB (MINITAB Release 14; State College, PA, USA). Relative abundance was

calculated by dividing the number of taxa found by the total number of individuals in the

sample. Two-way ANOVAs were performed on mean abundance and mean relative

abundance of benthic invertebrate site data. Various metrics were calculated for the

coarse and total sample data including, EPT Richness, Family Richness, Shannon

Diversity, Evenness, % EPT and % Chironomidae. One-way ANOVAs were performed

on calculated metric values for coarse and total sample site data and overall coarse and

total sample data. Assumptions of analysis of variance, including normality (Ryan Joiner

test or Kolmogorov-Smirnov) and homogeneity of variance (Levene’s Test) were tested

(MINITAB 14, SPSS). When these assumptions were not met, data were transformed and

17

the residuals checked. When transformation was not sufficient to meet the assumptions of

parametric testing, the nonparametric one-way Kruskal-Wallis test was used in lieu of a

one- way ANOVA. We selected α = 0.05 for all statistical tests, with values reported as

mean ± one standard error.

2.3 RESULTS

2.3.1 Community Composition

Ordination (nMDS) analysis revealed that the relationship among benthic

invertebrate communities at the 13 sampling sites was similar regardless of whether the

analysis included the total sample or only the coarse fraction (Figure 2.2). In most cases

the total and coarse fraction samples were grouped together. Site trends along the

sediment gradient remained similar between the two sample types. Individual ANOSIM

results reinforced the nMDS ordination results and indicate community structure of

coarse and total samples was similar. Furthermore, invertebrate composition at the

individual site level was not changed by the inclusion of the fine fraction to the

community data set as most ANOSIM tests gave an R value of < 0.25 (Table 2.2).The

sole exception to this trend occurred at the North Brook Mid site (R- value > 0.75) (Table

2.2). Additionally, community composition was not significantly different between

overall average coarse and total samples (ANOSIM: Global R value 0.062; p = 0.106).

The SIMPER routine revealed taxa contributing most to the slight difference between the

coarse and total samples were Elmidae larvae, Baetidae, Ephemerellidae, Heptageniidae,

Chironomidae and Pelecypoda.

18

2.3.2 Abundance Measures

The abundance of Chironomids was significantly greater (H = 35.73; p = <0.01)

in the total sample (237 ± 76 individuals/ 0.11 m2) compared to the coarse fraction (90 ±

39 individuals/ 0.11 m2; Figure 2.3). Similarly the abundance of Ephemeropterans was

significantly greater (F = 18.09; p = <0.01) in the total sample (245 ± 97 individuals/ 0.11

m2) in comparison with the coarse fraction (163 ± 69 individuals/ 0.11 m2; Figure 2.3).

Examination of the abundance of Ephemeroptera families revealed Baetidae was the

major contributor to the mayfly difference between the total and coarse samples. The

relative abundance of Chironomids was also significantly greater (F = 31.97; p = <0.01)

in the total sample (0.30 ± 0.06 individuals/ 0.11 m2) than the coarse sample (0.19 ± 0.05

individuals/ 0.11 m2; Figure 2.4).

2.3.3 Metrics

Overall family richness was significantly different (H = 17.86; p = <0.01)

between coarse and total samples, with family richness in the total samples (19.6 ± 0.9

taxa/ 0.11 m2) being higher than values for coarse samples (16.3 ± 1.2 taxa/ 0.11 m2;

Figure 2.5). Despite this overall trend, the within site comparison of coarse and total

samples found that only four of the thirteen sites had significantly different coarse and

total family richness values (Table 2.3). Similarly, EPT richness was significantly greater

(F = 19.69; p = <0.01; Figure 2.6) in the overall total (9.5 ± 0.7 taxa/ 0.11 m2) versus

coarse (7.9 ± 0.7 taxa/ 0.11 m2) samples. However, only two of the sites (BKV Lo and

Wil Up) were significantly different for EPT richness when values for coarse versus total

data sets were compared within sites (Table 2.4). Percent Chironomidae and evenness

19

were also significantly different for the coarse and total samples (F = 25.74; p = <0.01;

Figure 2.7 and F = 9.175; p = 0.003; Figure 2.8). Percent Chironomidae was higher in the

total samples (0.29 ± 0.05 % taxa/0.11 m2 versus 0.19 ± 0.05 % taxa/ 0.11 m2; Figure

2.7). Despite overall coarse and total metric values being significant, within site analysis

showed significant differences between coarse and total sample values for only four of

the thirteen sites for percent Chironomidae (Table 2.5) and only one of the thirteen sites

for evenness (i.e., NBR Lo; H= 3.97; p= 0.046; Table 2.6).

The metrics percent EPT and Shannon Diversity were not significantly different

(F = 1.30; p = 0.258; Figure 2.7 and F = 0.345; p = 0.559; Figure 2.8), between coarse

and total samples. Within site comparison of Shannon Diversity values for coarse and

total samples was significant for one site (i.e., NBR Mid; F = 12.49; p = 0.024), while no

significant differences within sites were noted for percent EPT (Table 2.7).

2.3.4 Percent Capture of Taxa

Greater than 60 % of the total number of individuals in the different taxa groups

were retained in the coarse sieve (> 2 mm) with the exception of Chironomidae. Less

than 40 % of the Chironomids were retained in the coarse sieve (Figure 2.9). Examination

of individual sites revealed percent capture of Chironomids was highest at NBR Low (62

%) and SWB Low (66 %), while being lowest at BKV Up (11 %) (Figure 2.10).

20

2.4 DISCUSSION

2.4.1 Community Composition

A major assumption of subsampling is that abundance and composition of

subsamples provides an unbiased, adequate representation of the benthic invertebrate

community in the original total sample. Our results show that subsampling using the

coarse fraction of a benthic invertebrate sample did not significantly change estimates of

the invertebrate composition. Even though information from the fine fraction was lost,

the sites were adequately represented as seen in the ANOSIM tests (Table 2.2). Thus, the

exclusion of the fine fraction did not have a significant effect on the overall sample

invertebrate composition, even though total taxa richness was significantly different

between the coarse and total samples (Figures 2.5 and 2.6). This indicates that the taxa

being lost were less abundant and arguably less important in the analysis. Further

examination of the data revealed many of the new taxa being acquired in the fine fraction

were relatively rare and therefore possibly excludable from the analysis. Previous studies

have found the inclusion of rare taxa contributes very little new information in terms of

community composition (Barbour and Gerritsen 1996). An alternative explanation of

why these differences in taxa richness did not affect community composition may be

attributable to nMDS being robust to the inclusion of rare taxa in the fine fraction, such

that their inclusion would have little effect on the multivariate patterns (Walsh 1997).

These results indicate the coarse fraction of a benthic invertebrate sample provides

sufficient information to interpret differences along a stressor gradient.

2.4.2 Metrics

21

Subsampling using the coarse fraction of a benthic invertebrate sample had an

effect on several metrics (taxa richness, EPT richness, % Chironomidae, Evenness), but

little effect on others (% EPT, Shannon Diversity). Few studies have examined the effect

of benthic subsampling on a suite of metrics (e.g. Doberstein et al. 2000). Our results

demonstrate use of the coarse fraction alone can result in an underestimation of taxa

richness and EPT richness (Figures 2.5 and 2.6). This is consistent with the findings of

previous subsampling studies, which indicate taxa richness increases as the total number

of individuals sorted increases (Courtemanch 1996; Vinson and Hawkins 1996; Larsen

and Herlihy 1998). As the total number of individuals counted increases, taxa of lower

proportions in the sample are more likely to be encountered (Courtemanch 1996). In fact,

the accumulation of taxa is a function of sampling effort with an initial rapid increase in

new taxa followed by a drop off in the number of new taxa encountered (Larsen and

Herlihy 1998). Vinson and Hawkins (1996) found taxa richness increased rapidly up to

200 organisms after which new taxa were encountered at a much slower rate.

Additionally, Growns et al. (1997) found both family and EPT richness to be sample-

size-dependent (number of individuals counted) with the highest values being obtained

when the whole sample was processed. The question then is whether the information

gained from processing whole samples to estimate taxa richness is worth the increased

sample processing effort in terms of time and cost? The problem as Vinson and Hawkins

(1996) stated is “we can never completely census a taxonomic assemblage or entire

community; we have to rely instead on estimates that describe some portion of the real

taxa richness of an assemblage.” Although use of the coarse fraction as a subsampling

method will underestimate taxa richness due to not being retained in the coarse sieve and

22

low probability of encounter, we conclude the cost-saving benefit outweighs the potential

information loss, because even whole samples are estimates of the assemblage and will

therefore underestimate taxa richness. Vinson and Hawkins (1996) found that although

subsampling by a fixed count underestimated taxa richness, statistical tests for differences

in richness between sites was as sensitive as analyses based on whole samples. Therefore

coarse sieve subsampling should be able to distinguish and allow for comparison of

relative differences in taxa richness along a disturbance gradient.

Metrics based on counts of Chironomids are also affected by the removal of the

fine fraction from analyses. The abundance and relative abundance of Chironomids was

significantly greater in the total samples when compared to coarse fraction values. The

coarse sieve fails to retain a large amount of the smaller Chironomids, leading to the

lowest mean capture efficiency (< 40 %) of all taxa. Because the coarse sieve

underestimates Chironomid abundance, any metric based on their relative abundance

(e.g. % Chironomidae) will also be underestimated if a large amount of small

chironomids are present in the sample. The metrics of Evenness and Shannon diversity

were only significantly different between the coarse and total samples at one site. Many

coarse and total metric values were significant overall, but not at the individual site level.

Previous studies have found that metric values calculated from fixed count subsampling

are able to distinguish sites based on ecological impairment and are stable when

compared to larger samples (Growns et al. 1997). In summary, results produced from

coarse fraction subsampling are a good representation of the original sample.

23

2.4.3 Conclusions

In conclusion, coarse fraction subsampling is a simple and cost-effective method,

which gives an adequate representation of the benthic invertebrate community. The

additional information gained from use of the coarse fraction as well as the fine fraction

of a benthic invertebrate sample produced only marginally different results. Thus, for

environmental gradient analyses such as that described here, processing the entire sample

would appear to be unnecessary and certainly not worth the increase in processing costs.

Subsampling using a coarse sieve can potentially cut sample sorting time by half. As well

coarse sieves retain a large percentage of the total number of individuals in a sample,

including both large and smaller invertebrates. Morin et al. (2004) found the proportion

of the biomass retained in coarse sieves is very high and they have a tendency to retain a

large amount of the smaller organisms as well. We found > 60 % of the total number of

individuals of the major taxonomic groups (with the exception of Chironomidae) were

captured in the coarse sieve. Benthic invertebrates not retained in the coarse sieve were

smaller and earlier instars that are often difficult to identify and lead to greatly increased

processing time and effort. Family and EPT richness were underestimated by this

subsampling method, however, many of the taxa being added from the fine fraction

appear to be rare and their inclusion contributes very little information to community

analyses.

In benthic research there are often trade-offs between sample processing time,

effort and the number of samples collected. Our results suggest that, coarse sieve

subsampling is an acceptable trade-off for reducing sample processing time and cost to

24

allow for more sites to be sampled. We have shown that subsampling using the coarse

fraction does not affect sample community composition. Thus, the collection of more

benthic invertebrate samples to increase statistical power and sample size to monitor the

invertebrate community is a better option than whole sample processing.

Coarse sieve subsampling is appropriate in bioassessment and ecological studies,

since the ability to discriminate sites along a stressor gradient is not compromised.

Although taxa and EPT richness are underestimated, coarse sieve subsampling is robust

enough to test for differences along a stressor gradient using relative richness differences

(Vinson and Hawkins 1996). In addition coarse sieves capture large, rare taxa that have

been deemed important for bioassessment and ecological information (Courtemanch

1996; Vinson and Hawkins 1996; Cao et al. 1998). However, coarse sieve subsampling

does have limitations and therefore may not be suitable for all studies. For example,

studies that require both small and large benthic invertebrates, such as life history and

size distribution studies would not be suitable for coarse sieve subsampling. These

studies often require all instars of the taxa of interest including the smaller, earlier instars

usually found in the fine fraction. We conclude the use of the coarse fraction of a benthic

invertebrate sample is a simple and cost-effective subsampling method viable for certain

studies. Coarse fraction subsampling reduces sample processing time allowing more sites

to be sampled and more replicates to be collected to better assess the benthic community.

2.5 ACKNOWLEDGEMENTS

I would like to acknowledge the help and support of the lab. I am particularly

indebted to Megan Finley for her help with the sorting of numerous benthic invertebrate

25

samples. I would also like to thank Allen Curry, Joseph Culp and Kristie Heard for their

guidance and help.


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Walsh, C.J. 1997. A multivariate method for determining optimal subsample size in the

analysis of macroinvertebrate samples. Marine and Freshwater Research 48: 241-248.

Wrona, F.J., J.M. Culp, and R.W. Davies. 1982. Macroinvertebrate subsampling: A

simplified apparatus and approach. Canadian Journal of Fisheries and Aquatic Sciences 39: 1051-1054.

27

2.7 TABLES

Table 2.1: Stream sites and GPS coordinates sampled from the central portion of Prince Edward Island, Canada, 25- 27 July 2005. Site Watershed Latitude Longitude North Brook Lower Dunk N 46º 20’ 48.6” W 63º 37’ 54.8”

North Brook Middle Dunk N 46º 21’ 32.8” W 63º 36’ 53.7”

North Brook Upper Dunk N 46º 21’ 51.2” W 63º 36’ 50.0”

Southwest Brook Lower Dunk N 46º 20’ 28.6” W 63º 38’ 13.0”

Southwest Brook Middle Dunk N 46º 20’ 05.2” W 63º 37’ 48.6”

Southwest Brook Upper Dunk N 46º 18’ 46.3” W 63º 35’ 55.5”

Dunk River Lower Dunk N 46º 21’ 18.6” W 63º 33’ 57.2”

Dunk River Upper Dunk N 46º 21’ 05.6” W 63º 29’ 20.4”

Wilmot River Upper Wilmot N 46º 23’ 32.3” W 63º 33’ 16.2”

Wilmot River Lower Wilmot N 46º 24’ 29.3” W 63º 35’ 46.1”

East Branch Lower Tryon N 46º 17’ 50.8” W 63º 31’ 39.0”

East Branch Upper Tryon N 46º 15’ 45.4” W 63º 31’ 45.2”

Brookvale Lower West N 46º 17’ 02.4” W 63º 24’ 29.4”

Brookvale Upper West N 46º 19’ 58.8” W 63º 25’ 19.5”

28

Table 2.2: Results of the analysis of similarity (ANOSIM) tests performed on the coarse/total replicates for individual sites. North Brook Mid was the only site with the coarse and total samples well separated (different) (R- value >0.75).

Site R- value Significance level NBR Up 0.074 40%

NBR Mid 0.815 10%

NBR Lo -0.185 80%

SWB Up -0.296 70%

SWB Mid -0.037 70%

SWB Lo 0.037 60%

Dunk Up -0.111 70%

Dunk Lo -0.259 80%

Wil Up 0.148 40%

BKV Up 0.037 50%

BKV Lo 0.185 30%

EasBr Up 0.037 60%

EasBr Lo -0.074 70%

29

Table 2.3: Results of 1-way ANOVA comparing coarse and total family richness values within individual sites. Stars denote significant difference ( p <0.05). Site SS df F p BKV Lo Between groups 16.67 1 6.25 0.067

Within groups 10.67 4 Total 27.33 5

BKV Up Between groups 32.67 1 3.56 0.132 Within groups 36.67 4 Total 69.33 5

Dunk Lo Between groups 6.0 1 0.327 0.598 Within groups 73.33 4 Total 79.33 5

Dunk Up Between groups 13.5 1 40.5 0.003 * Within groups 1.33 4 Total 14.83 5

EasBr Lo Between groups 6.0 1 0.947 0.386 Within groups 25.33 4 Total 31.33 5

EasBr Up Between groups 16.67 1 3.23 0.147 Within groups 20.67 4 Total 37.33 5

NBR Lo Between groups 24.0 1 3.79 0.123 Within groups 25.33 4 Total 49.33 5

NBR Mid Between groups 28.17 1 16.9 0.015 * Within groups 6.67 4 Total 34.83 5

NBR Up Between groups 32.67 1 3.5 0.135 Within groups 37.33 4 Total 70.0 5

SWB Lo Between groups 13.50 1 2.31 0.203 Within groups 23.33 4 Total 36.83 5

SWB Mid Between groups 42.67 1 8.0 0.047 * Within groups 21.33 4 Total 64.0 5

SWB Up Between groups 2.67 1 0.348 0.587 Within groups 30.67 4 Total 33.33 5

Wil Up Between groups 13.5 1 7.36 0.053 * Within groups 7.33 4 Total 20.83 5

30

Table 2.4: Results of 1-way Kruskal-Wallis nonparametric test comparing coarse and total EPT richness values within individual sites. Stars denote significant difference ( p <0.05). Site BKV Lo Chi-Square 4.09 df 1 p 0.043 * BKV Up Chi-Square 2.63 df 1 p 0.105 Dunk Lo Chi-Square 0.429 df 1 p 0.513 Dunk Up Chi-Square 2.63 df 1 p 0.105 EasBr Lo Chi-Square 1.82 df 1 p 0.178 EasBr Up Chi-Square 1.82 df 1 p 0.178 NBR Lo Chi-Square 0.784 df 1 p 0.376 NBR Mid Chi-Square 0.833 df 1 p 0.361 NBR Up Chi-Square 2.72 df 1 p 0.09 SWB Lo Chi-Square 2.72 df 1 p 0.09 SWB Mid Chi-Square 2.33 df 1 p 0.127 SWB Up Chi-Square 0.22 df 1 p 0.637 Wil Up Chi-Square 3.97 df 1 p 0.046 *

31

Table 2.5: Results of 1-way ANOVA comparing coarse and total % chironomidae values within individual sites. Stars denote significant difference ( p <0.05). Site SS df F p BKV Lo Between groups 0.012 1 1.39 0.303 Within groups 0.033 4 Total 0.045 5 BKV Up Between groups 0.101 1 8.36 0.044* Within groups 0.048 4 Total 0.149 5 Dunk Lo Between groups 0.015 1 3.37 0.140 Within groups 0.017 4 Total 0.032 5 Dunk Up Between groups 0.103 1 30.83 0.005* Within groups 0.013 4 Total 0.116 5 EasBr Lo Between groups 0.014 1 0.963 0.382 Within groups 0.060 4 Total 0.074 5 EasBr Up Between groups 0.013 1 3.48 0.136 Within groups 0.015 4 Total 0.028 5 NBR Lo Between groups 0.001 1 0.072 0.801 Within groups 0.061 4 Total 0.063 5 NBR Mid Between groups 0.023 1 8.70 0.042* Within groups 0.011 4 Total 0.034 5 NBR Up Between groups 0.004 1 0.175 0.697 Within groups 0.101 4 Total 0.106 5 SWB Lo Between groups 0.000 1 0.002 0.963 Within groups 0.038 4 Total 0.038 5 SWB Mid Between groups 0.006 1 2.61 0.181 Within groups 0.101 4 Total 0.016 5 SWB Up Between groups 0.007 1 2.09 0.222 Within groups 0.014 4 Total 0.021 5 Wil Up Between groups 0.057 1 49.47 0.002* Within groups 0.005 4 Total 0.062 5

32

Table 2.6: Results of 1-way Kruskal-Wallis nonparametric test comparing coarse and total evenness values within individual sites. Stars denote significant difference ( p <0.05). Site BKV Lo Chi-Square 3.86 df 1 p 0.05 BKV Up Chi-Square 0.429 df 1 p 0.513 Dunk Lo Chi-Square 3.86 df 1 p 0.05 Dunk Up Chi-Square 3.86 df 1 p 0.05 EasBr Lo Chi-Square 1.19 df 1 p 0.275 EasBr Up Chi-Square 0.048 df 1 p 0.827 NBR Lo Chi-Square 3.97 df 1 p 0.046 * NBR Mid Chi-Square 3.86 df 1 p 0.05 NBR Up Chi-Square 0.429 df 1 p 0.513 SWB Lo Chi-Square 1.19 df 1 p 0.275 SWB Mid Chi-Square 1.19 df 1 p 0.275 SWB Up Chi-Square 1.19 df 1 p 0.275 Wil Up Chi-Square 0.429 df 1 p 0.513

33

Table 2.7: Results of 1-way ANOVA comparing coarse and total % EPT values within individual sites. Stars denote significant difference ( p <0.05). Site SS df F p BKV Lo Between groups 0.013 1 4.65 0.09 Within groups 0.011 4 Total 0.023 5 BKV Up Between groups 0.006 1 0.08 0.791 Within groups 0.305 4 Total 0.311 5 Dunk Lo Between groups 0.004 1 0.89 0.40 Within groups 0.016 4 Total 0.020 5 Dunk Up Between groups 0.009 1 0.67 0.46 Within groups 0.051 4 Total 0.06 5 EasBr Lo Between groups 0.005 1 3.03 0.157 Within groups 0.007 4 Total 0.012 5 EasBr Up Between groups 0.011 1 0.485 0.524 Within groups 0.09 4 Total 0.101 5 NBR Lo Between groups 0.002 1 0.098 0.770 Within groups 0.064 4 Total 0.066 5 NBR Mid Between groups 0.014 1 4.49 0.101 Within groups 0.013 4 Total 0.027 5 NBR Up Between groups 0.003 1 0.161 0.709 Within groups 0.081 4 Total 0.084 5 SWB Lo Between groups 0.004 1 1.90 0.240 Within groups 0.009 4 Total 0.013 5 SWB Mid Between groups 0.004 1 0.679 0.456 Within groups 0.023 4 Total 0.027 5 SWB Lo Between groups 0.002 1 0.119 0.747 Within groups 0.062 4 Total 0.064 5 Wil Up Between groups 0.016 1 1.71 0.261 Within groups 0.037 4 Total 0.053 5

34

2.8 FIGURES

Figure 2.1: Location of watersheds sampled for benthic invertebrates, 25- 27 July 2005. A total of 13 sites were sampled from within the 6 streams.

35

Figure 2.2: Ordination of benthic invertebrate assemblages within sites from the coarse and total samples. The nonmetric multidimensional scaling (MDS) is based on log transformed abundances and Bray-Curtis similarities (stress = 0.2).

36

TaxaEph

emero

ptera

Plecop

tera

Tricho

ptera

Diptera

Coleop

tera

Chiron

omida

e

Oligoc

haeta

Num

ber o

f Ind

ivid

uals

0

50

100

150

200

250

300

350

Coarse Total {{* *

Figure 2.3: Abundance (mean ± 1SE) of benthic invertebrate taxa from total and coarse fraction samples. Stars denote significant differences in abundance (p<0.01) in comparison of the coarse and total sample fractions.

37

TaxaEphemeroptera

Plecoptera

Trichoptera

Diptera

Coleoptera

Chironomidae

Oligochaeta

Rel

ativ

e A

bund

ance

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

Coarse Total

{*

Figure 2.4: Relative abundance (mean ± 1SE) of benthic invertebrate taxa from total and coarse fraction samples. Stars denote significant differences in relative abundance (p <0.01) in comparison of the coarse and total sample fractions.

38

Site

NBR Up

NBR MidNBR Lo

SWB Up

SWB MidSWB Lo

Dunk UpDunk Lo

Wil Up

EasBr Up

EasBr LoBKV Up

BKV Lo

Num

ber o

f fam

ilies

0

5

10

15

20

25

30

Coarse Total

{

{

{ **

*

Figure 2.5: Family Richness (mean ± 1SE) of total and coarse samples at all 13 sites. Family richness was significantly greater in the total samples overall and for each site. Stars denote significant differences (p <0.05) between coarse and total sample family richness values.

39

Site

NBR Up

NBR Mid

NBR Lo

SWB Up

SWB Mid

SWB Lo

Dunk Up

Dunk LoWil U

p

EasBr U

p

EasBr L

o

BKV Up

BKV Lo

Num

ber o

f EP

T fa

mili

es

0

2

4

6

8

10

12

14

16Coarse Total

{ *{*

Figure 2.6: EPT Richness (mean ± 1SE) of total and coarse samples at all 13 sites. Sites values are the average of all three replicates. Stars denote significant differences in EPT richness (p<0.05) in comparison of the coarse and total sample fractions.

40

Metric

% EPT % Chironomidae

# In

divi

dual

s in

Tax

a/ T

otal

# In

divi

dual

s

0

10

20

30

40

50

Coarse Total

*

Figure 2.7: Percent EPT and percent chironomidae (mean ± 1SE) of benthic invertebrate taxa from total and coarse fraction samples. Star denote significant difference (p<0.01) in comparison of the coarse and total sample.

41

Taxa

Evenness Shannon Diversity

Valu

e

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

Coarse Total

{ *

Figure 2.8: Shannon Diversity and Evenness (mean ± 1SE) of benthic invertebrate data from coarse fraction and total samples. Star denotes significant difference (p<0.05) in comparison of the coarse and total sample.

42

TaxaEphemeroptera

Plecoptera

TrichopteraDiptera

Coleoptera

Chironomidae

Oligochaeta

Coa

rse/

Tota

l

0.0

0.2

0.4

0.6

0.8

1.0

Figure 2.9: Mean percent capture (number in coarse/ total number of individuals) of different taxa by the coarse sieve (2mm). Percent capture values are the average for all sites and replicates.

43

Site

Dunk UpDunk Lo

NBR Up

NBR MidNBR Lo

SWB Up

SWB MidSWB Lo

Wil Up

EasBr UpEasBrLo

BKV UpBKV Lo

Coa

rse/

Tota

l Abu

ndan

ce

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Figure 2.10: Mean percent capture (number in coarse/ total number of individuals) of Chironomids by site in the coarse sieve (2mm). Capture values (mean ± 1SE) are the average of sites replicates.

44

3.0 ABSTRACT

This study examines the response of several commonly used biomonitoring

metrics to a gradient of deposited fine (< 2 mm) sediment conditions to determine metrics

useful for identifying and quantifying the effects of increased fine sediment deposition.

Thirteen sites within six agricultural streams on Prince Edward Island, Canada were

sampled during the summer and fall of 2005. Fine sediment deposition was quantified by

three methods: a visual estimate of percent cover of deposited fine sediment, calculated

% embeddedness and % fines < 2 mm from a substrate sample.

Several metrics were significantly correlated and responded consistently to

increasing fine sediment deposition across seasons. The metrics % burrower and %

Chironominae of Chironomidae responded positively to increasing fine sediment

deposition. While the metrics % EPT, EPT abundance and % Orthocladiinae of

Chironomidae responded negatively to increasing fine sediment deposition. Indicating

several commonly used metrics can help to detect the impact of fine sediment deposition.

45

3.1 INTRODUCTION

The amount and rate at which fine sediment enters aquatic environments has been

greatly increased by anthropogenic activities such as agriculture, forestry, mining, road

construction and urbanization (Waters 1995). Increased sediment rates have overloaded

many rivers and streams, exceeding their ability to flush sediment (Relyea et al. 2000),

resulting in sedimentation and impairment of benthic habitat. In fact, the US

Environmental Protection Agency has identified sediment as the number one source of

substrate impairment to streams nationwide (Zweig 2000; Zweig and Rabeni 2001).

Deposited fine sediment reduces benthic substrate quality and quantity by: increasing

substrate embeddedness (Brusven and Prather 1974; Sylte and Fischenich 2002), altering

substrate particle size distribution (Waters 1995), clogging interstitial spaces (Zanetell

and Peckarsky 1996), as well as decreasing streambed stability (Cobb et al. 1992; Roy et

al. 2003) and heterogeneity (Minshall 1984). Habitat alterations have major impacts on

riverine biotic communities, however we lack specific methods that quantify the extent of

impairment due to increased deposited sediment in streams.

Biomonitoring often involves the use of benthic invertebrates to determine the

quality or extent of impairment of aquatic environments. Benthic invertebrates are

popular choices because they are ubiquitous, semi- sedentary, long lived and speciose

(Rosenberg and Resh 1993). Benthic invertebrates live in close contact with the substrate

and substrate quality is one of the primary factors regulating their abundance and

distribution. This makes benthic invertebrates ideal candidates for evaluating the impact

of increased sediment deposition in streams (Minshall 1984; Zweig and Rabeni 2001).

46

Despite numerous studies investigating the relationship between deposited sediment and

benthic invertebrates, few response patterns are known.

Excessive deposited fine sediment affects benthic invertebrate behaviors such as:

drift (McClelland & Brusven 1980; Culp et al. 1986; Fairchild et al. 1987), feeding

(Lemly 1982; Waters 1995), substrate preference (Brusven & Prather 1974), and

respiration (Lemly 1982). At high levels of sediment deposition, decreases in density,

overall abundance and community diversity have been observed (Nuttall 1972; Lenat et

al. 1981; Minshall 1984; Henley et al. 2000; Roy et al. 2003). Changes in community

composition have also been reported (Cordone & Kelley 1961; Lenat et al. 1981;

Kreutzweiser et al. 2004). Certain sensitive taxa, mainly in the orders Ephemeroptera,

Plecoptera and Trichoptera, decrease in abundance as fine sediment deposition increases

(Waters, 1995). In contrast, other taxa, such as chironomids and oligochaetes can increase

in abundance and richness (Gray and Ward 1982). Few studies have examined the

sensitivity of benthic invertebrate taxa and biomonitoring metrics along a gradient of

deposited sediment conditions. I know of only four other studies which attempted to

empirically determine metric sensitivity or responsiveness to fine sediment deposition

(Angradi 1999; Relyea et al. 2000; Kaller et al. 2001; Zweig and Rabeni 2001). Two of

these studies focused on fine sediment addition and a small range of sedimentation

conditions, so the results are not broadly applicable (Angradi 1999; Kaller et al. 2001).

Thus, the responsiveness of biomonitoring metrics under natural conditions along a

deposited sediment gradient warrants further examination.

Rivers draining agricultural lands such as those found on Prince Edward Island

(PEI, Canada), are ideal to investigate these relationships as sediments are continually

47

being added and a range of deposited sediment conditions exist. Many PEI streams are

impacted by high sediment loads, caused by agricultural activities (Resource Inventory

and Modeling Section, Department of Agriculture and Forestry 2003). Each year, as

much as 14 tons of topsoil can be lost from an acre of farmland on PEI (InfoPEI 2006).

Extensive agriculture, sandy soil and the fact that most streams are groundwater fed

makes PEI aquatic environments especially vulnerable to increased sediment deposition

through erosion and run-off (Caissie and Arseneau 2002; Curry et al. 2006).

The objective of the present study was to test for relationships between

biomonitoring metrics and changes in fine (< 2 mm) sediment deposition in small

agricultural streams and to identify a set of metrics to be used in monitoring aquatic

environments for sediment impacts. I hypothesized certain metrics would respond

positively to increases in deposited fine sediment, while others would have a negative

response. More specifically, metrics incorporating taxa known to be sensitive (e.g. EPT

taxa) will respond negatively, while metrics using tolerant taxa (e.g. chironomids) will

respond positively to increases in fine sediment deposition.

3.2 M ATERIALS AND METHODS 3.2.1 Site Selection and Location Study sites were selected within six, 2nd and 3rd order streams located in the

central portion of Prince Edward Island, Canada (Figure 2.1). The island covers a land

area of approximately 5660 km2 (Cairns 2002) with predominantly sandstone and

siltstone bedrock geology (Purcell 2003). The overall topography varies, with the central

portion being more elevated than the eastern and western sections of the island (DeGrace

48

1989; Purcell 2003). On PEI, 39% of the land is devoted to agriculture, 45% is forested

and 8% is urban land use (Resource Inventory and Modeling Section, Department of

Agriculture and Forestry 2003).

3.2.2 Site Descriptions

A total of 13 sites within six streams (Table 3.1) were sampled during the summer

(25-27 July) and fall (19-21 October) of 2005. Sample sites were selected from small

streams within agricultural areas (Figure 3.1) to obtain a gradient of fine deposited

sediment conditions (10-90 %), but which also had a narrow range of other

environmental variables (e.g. temperature, pH). The construction of a beaver dam

necessitated the removal of the Southwest Brook Upper site from the fall sampling.

Similarly, North Brook Middle samples were taken further upstream during the fall due

to the construction of a beaver dam. Data from the Southwest Brook Upper site for the

summer sampling was removed from the analyses as the total suspended solids levels

were 4 times that of the other streams, resulting in spurious correlations.

3.2.3 Field Sampling

Three replicates were taken of all measurements in riffle/ run habitats at each site.

First a 1L water sample was collected for total suspended solids (TSS) analysis in the

laboratory. A 1 m2 quadrat was then placed around an artificial algal substrate deployed

prior to sampling to designate the area from which all measurements for that replicate

were to be taken (Figure 3.2). A YSI ® 556 MPS multimeter was used to measure water

temperature, pH, dissolved oxygen, and conductivity. Flow was measured using a Marsh-

Mc Birney ®, model 2000 portable flowmeter. These measurements were taken to ensure

similar and typical conditions at all sites. Additionally measurements of percent fine

49

sediment cover and substrate embeddedness along with benthic macroinvertebrate,

Chlorophyll a and streambed substrate samples were collected from within the 1 m2

quadrat (Figure 3.2). Channel measurements, such as bankfull and wetted width as well

as slope of the sample site were also collected.

3.2.4 Algal Biomass (Chlorophyll a)

Preliminary sampling in October 2004 revealed that suitable rocks for chlorophyll

a sampling were not present at all sites. Chlorophyll a measurements were used to

estimate algal biomass. This necessitated the deployment of artificial algal substrates

prior to the two sampling periods in 2005. Each artificial substrate consisted of 3 small

ceramic tiles (48.25 mm x 48.25 mm) affixed to a cement brick with silicon and plastic

cable ties (Figure 3.3). Twenty days prior to sampling in the summer and fall, 3 replicate

bricks were deployed at each site. The artificial substrates were placed in riffle/ run

habitats, and dug into the substrate a few cm to ensure they remained submerged.

Algal samples for chlorophyll a analysis were obtained by scraping all algae from

the tiles on a brick with a scalpel into a scintillation vial containing water. Samples were

then frozen until analysis. In the laboratory samples were blended and filtered with a pre-

ashed glass-microfiber filter. Chlorophyll a was extracted by placing the filters in a 80 °C

bath of 90 % ethanol for 5 min. Chlorophyll a fluorescence was measured using a Turner

Designs® (Sunnyvale, CA, USA) 10- AU Fluorometer equipped with a red sensitive

photomultiplier tube, a round 680 -nm emission filter, and a reference filter (Culp et al.

2003).

50

3.2.5 Benthic Macroinvertebrates

Benthic invertebrates were collected from within the 1 m2 quadrat using a U-net

(Scrimgeour et al. 1993; 30 cm opening, 400 µm mesh, 0.055 m2; Figure 3.2). The U-net

was placed on the stream substrate and the bed material within the area was disturbed to a

depth of 8-10 cm by hand for 3 min. A second U-net was then taken from within the

same quadrat and combined with the contents of the first to produce a single sample. A

total of 3 replicate composite samples (area of 0.11 m2 each) were taken in this way at

each site. Once collected the samples were poured onto a 250 µm mesh sieve, rinsed,

placed into containers, preserved with 10 % formalin and returned to the laboratory for

identification.

Benthic invertebrate samples were transferred into 70 % ethanol in the laboratory.

For sorting purposes the samples were separated into a coarse fraction (>2 mm) and a

fine fraction (>250 um) by passing the material through stacked sieves. All coarse

material was sorted, counted, and identified to the lowest practical taxonomic level.

3.2.6 Total Suspended Solids

A 1L water sample was collected at each site and kept cool until analyzed back in

the laboratory for total suspended solids (TSS). Sample bottles were shaken for 1 min by

hand to re-suspend settled sediment particles in the laboratory. Sample volume was

measured using a graduated cylinder. The contents of the graduated cylinder where then

filtered onto a pre-weighed and ashed glass-microfiber filter. All filters were then

weighed and recorded. The filters were placed in a drying oven for 8 hours and then

51

ashed for 4 hours. Filters were reweighed and TSS per unit volume (mg/L) was

calculated.

3.2.7 Sediment Measures

For the purposes of this study, fine sediment was classified as particles < 2 mm,

which encompasses the particle categories of clay, silt and sand (Waters 1995). Fine

sediment deposition was quantified two ways and a substrate sample was collected at

each replicate per site.

Percent deposited fine sediment

The percentage of the streambed surface covered by fine sediment within a 1 m2

quadrat was visually estimated in 10 % increments prior to subsequent sampling (Zweig

2000; Zweig & Rabeni 2001).

Substrate embeddedness

From within the 1 m2 quadrat 10 rocks with a diameter > 8 mm were removed

from the streambed. The total height of the rock based on its orientation to the streambed

was measured with a ruler and recorded. The height to the silt line on the rock was then

measured and recorded. The percent embeddedness of each rock was calculated and a

sample average determined (Zweig 2000; Sylte & Fischenich 2003).

Substrate particle size distribution

Substrate samples were collected from within the quadrat using a shovel similar to

the methods of Grost et al. 1991 and Zweig 2000 (Figure 3.3). The method involved

pushing the shovel into the substrate at roughly a 40º angle and then lifting it from the

water. A U-net (400 µm mesh) placed downstream of the sample area caught the

52

resuspended fine sediment. Two shovels of substrate and fine sediment caught in the U-

net were placed in sample bags for analysis in the laboratory. Substrate samples were air

dried in the laboratory for 24 hours and organic material (leaves and woody debris) was

removed. Samples were further dried in an oven at 120 ºC for 12- 24 hours (depending on

the amount of water). The samples were then shaken through a series of coarse (25, 19,

12.5, 9.5, 6.3, 4.75 and 4 mm) and fine sieves (2, 1, 0.5, 0.25, 0.125, 0.038 mm and silt)

(10 min each; Table 3.2). Each size fraction was weighed and recorded. Percent < 2mm,

% < 4 mm, % gravel (2- 16 mm) and % pebble (16- 64 mm) were also calculated for each

replicate sample.

3.2.8 Biomonitoring metrics

A variety of commonly used biomonitoring metrics were calculated from the

benthic invertebrate data (Table 3.3). Information on benthic invertebrate habit and

functional feeding group for metric calculations was obtained from Merritt & Cummins

(1996) and Poff et al. (2006).

3.2.9 Statistical Analysis

Due to seasonal variance in the benthic invertebrate composition of the samples,

analyses were performed separately on the two sampling periods (summer n = 39 and fall

n = 36) (Figure 3.4). The relationships between calculated biomonitoring metrics,

deposited sediment metrics and environmental variables were examined using correlation

analysis (SPSS 13.0; Chicago, IL, USA). Data normality (Kolmogorov-Smirnov test) and

variance homogeneity (Levene’s Test) was tested prior to analysis (SPSS 13.0; Chicago,

IL, USA). Since many of the sediment measures and metrics did not meet these

53

assumptions even after transformation, the nonparametric Spearman’s Rank Correlation

test was used in lieu of the Pearson Correlation test. Scatterplots were constructed for

biomonitoring metrics found to be correlated with deposited sediment measures to

determine their sensitivity to increasing fine sediment deposition using all data points (no

averaging of replicates) to obtain a better visual representation of the relationships.

Spearman’s rank correlation coefficients (Sr) are reported along with their p-values.

3.3 RESULTS

Relationships between biomonitoring metrics, sediment measures and

environmental variables were evaluated for both summer 2005 and fall 2005. The

relationship between biomonitoring metrics and sediment was examined using only

percent surface cover of deposited fine sediment.

3.3.1 Sediment Measures and Environmental Variables

Summer 2005

In the summer (2005) sampling, percent deposited fine sediment was highly

correlated with calculated percent embeddedness (Table 3.4). Percent deposited fine

sediment was also highly correlated with smaller substrate particle classes (from shovel

cores) such as % < 2 mm and % < 4 mm (Table 3.4). Neither percent embeddedness nor

TSS was significantly correlated with sediment sample particle size classes. In the

summer the deposited sediment gradient ranged from 10 % (e.g. North Brook Low) to 90

% (e.g. Brookvale Up) surface cover of fine sediment (< 2 mm).

54

Percent deposited fine sediment was negatively correlated with percent slope of

the sampling sites in the summer sampling (Sr = -0.485, p = 0.003; Table 3.5). However,

upon examination of the scatter plots it was found the correlation was spurious due

partially to one site having a slightly higher slope. Percent embeddedness was not

significantly correlated with any of the environmental variables measured (Table 3.5)

TSS was correlated with many of the water chemistry variables such as water

temperature, pH, dissolved oxygen and flow (Table 3.5).

Fall 2005

As in the summer, deposited fine sediment and percent embeddedness were

highly correlated in the fall. Both percent deposited fine sediment and percent

embeddedness were significantly correlated with substrate particle size classes (e.g. % <

2mm, % < 4mm and % pebble; Table 3.6). TSS was not significantly correlated with any

of the other sediment measures (Table 3.6). In the fall the deposited sediment gradient

ranged from 10 % (e.g. Dunk Low) to 100 % (e.g. North Brook Mid) surface cover of

fine sediment (< 2 mm).

Unlike in the summer, percent deposited fine sediment and percent embeddedness

were negatively correlated with flow in the fall (Sr = -0.523, p= 0.001 and Sr = -0.472, p=

0.004; Table 3.7). Spearman’s correlations showed TSS was negatively correlated with

percent slope and dissolved oxygen (Sr = -0.608, p= <0.001 and Sr = -0.614, p= <0.001).

The correlation between percent slope and TSS was the result of one site (Brookvale Up)

having a higher slope than the other sites sampled.

3.3.2. Biomonitoring Metrics

55

Several biomonitoring metrics responded to various sediment measures (Tables

3.8 - 3.14). However, since percent deposited sediment and percent embeddedness were

highly correlated and TSS was not correlated with any other sediment measures, relations

between percent deposited fine sediment (< 2 mm) and the biomonitoring metrics will

only be examined. Additionally percent deposited fine sediment was significantly

correlated with only one environmental variables in each season, while TSS was

correlated with the majority of environmental variables (Tables 3.5 and 3.7).

Summer 2005

In the summer the habit/ feeding group metrics: % burrower, % sprawler, %

swimmer, % gatherer, % filterer and % predator were significantly correlated with

deposited fine sediment (Table 3.8). However, upon examination of the scatterplots, the

correlation between percent filterer and percent deposited fine sediment was found to be

spurious as no relationship was apparent. Percent burrower, % sprawler and % predator

were all positively correlated with percent deposited fine sediment (Sr = 0.507; p = 0.002;

Sr = 0.347; p = 0.038 and Sr = 0.339; p = 0.043 respectively; Figure 3.5). Percent

swimmer and % gatherer were both strongly and negatively correlated with percent

deposited fine sediment (Sr = -0.436; p = 0.008 and Sr = - 0.513; p = 0.001 respectively;

Figures 3.5 and 3.6).

The following metrics were also significantly correlated with deposited fine

sediment: evenness, EPT abundance, % EPT, % Orthocladiinae of Chironomidae and %

Chironominae of Chironomidae (Table 3.9). Evenness and % Chironominae of

Chironomidae were positively correlated with percent deposited sediment (Sr = 0.483; p

= 0.003 and Sr = 0.453; p = 0.006 respectively; Figures 3.6 and 3.7). The metrics EPT

56

abundance, % EPT and % Orthocladiinae of Chironomidae were strongly and negatively

correlated with percent deposited fine sediment (Sr = - 0.711; p = < 0.001; Sr = - 0.612; p

= < 0.001 and Sr = - 0.431; p = 0.009 respectively; Figures 3.6 and 3.7).

Fall 2005

In the fall, % burrower was the only habit/functional feeding group metric

significantly correlated with percent deposited fine sediment (Sr = 0.334; p = 0.046;

Table 3.10). Increased surface cover of deposited fine sediment resulted in an increase in

the percentage of burrowers (Figure 3.8). Additionally the metrics: EPT abundance, %

EPT, % Orthocladiinae of Chironomidae, % Chironominae of Chironomidae and

Trichoptera richness were significantly correlated with deposited fine sediment (Table

3.11). Percent Chironominae had a strong positive correlation with percent deposited

fine sediment (Sr = 0.669; p = < 0.001; Figure 3.9). EPT abundance, % EPT, %

Orthocladiinae and Trichoptera richness were negatively correlated with percent

deposited fine sediment (Figures 3.8 and 3.9).

3.3.3 Biomonitoring metrics and environmental variables

Metrics significantly correlated with percent deposited fine sediment were

examined for correlations with environmental variables.

Summer 2005

In the summer, % burrower, % sprawler, % swimmer, % predator and evenness

were significantly correlated with water pH (Table 3.12 and 3.13). Percent swimmer, %

predator and evenness were also significantly correlated with conductivity and flow

57

(Table 3.12). Percent EPT, EPT abundance, % Orthocladiinae and % Chironominae were

significantly correlated with local slope (%) of the study sites (Table 3.13).

Fall 2005

In the fall, % burrower, EPT abundance, % EPT and Trichoptera richness were

significantly correlated with flow (Table 3.14). As in the summer, several metrics were

significantly correlated with local slope (%) (Table 3.14). Significant correlations also

existed between various other biomonitoring metrics and environmental variables (e.g.

EPT abundance and pH; Sr = 0.378; p = 0.023).

3.3.4 Overall metric responses to deposited fine sediment

Several biomonitoring metrics were consistently significantly correlated with

percent surface cover of deposited fine sediment. The metrics percent burrower and %

Chironominae had a significant positive correlation with deposited fine sediment in both

the summer and fall (Figures 3.5, 3.7, 3.8 and 3.9). Percent EPT, EPT abundance and %

Orthocladiinae of chironomidae were highly negatively correlated with deposited fine

sediment in both seasons (Figures 3.6, 3.7 and 3.8).

3.4 DISCUSSION

In biomonitoring, response patterns of benthic invertebrate metrics are often used

to identify and quantify stream ecosystem impairment (Rosenberg and Resh 1996).

Therefore, understanding how specific metrics respond to individual stressors is of great

value. This study examined the response of several commonly used biomonitoring

metrics to a gradient of deposited fine (< 2 mm) sediment conditions to determine metrics

58

useful for identifying and quantifying the effects of increased fine sediment deposition.

Several metrics were significantly correlated and responded consistently to increasing

fine sediment deposition across seasons. The metrics % burrower and % Chironominae of

Chironomidae responded positively to increasing fine sediment deposition. While the

metrics: % EPT, EPT abundance and % Orthocladiinae of chironomidae responded

negatively to increasing fine sediment deposition.

Sediment Measures

For this study fine sediment deposition was quantified by three methods: a visual

estimate of percent cover of deposited fine sediment, calculated % embeddedness and %

fines < 2 mm from a substrate sample (shovel method). However, only the percent fine

sediment deposition was used to investigate the response of the biomonitoring metrics to

increasing fine sediment. This measure was used because it was highly correlated with %

embeddedness and small particle size classes. Percent fine sediment deposition was also

correlated with only a few environmental variables. Additionally, it is relatively easy and

the least time consuming method for quantifying deposited fine sediment.

Metric Responses

The percentage of burrowers was positively correlated with percent cover of

deposited fine sediment. This result was expected since as fine sediment is deposited the

interstitial spaces are filled reducing the habitat available to non-burrowing benthic

invertebrates (McClelland and Brusven 1980; Waters 1995; Wood and Armitage 1997).

Previous studies have found sensitive taxa, such as the EPT taxa decrease in abundance

as fine sediment deposition increases (Waters, 1995), while other taxa, such as burrowing

chironomids, oligochaetes and bivalves increase in abundance and richness (Gray and

59

Ward 1982). Rabeni et al. (2005) found significant increases in burrower and climber

densities as fine sediment increased, while density decreases were seen for clingers and

sprawlers. In my study additional functional feeding and habit group metrics were

correlated with percent deposited fine sediment. However, these correlations were only

apparent in the summer and not in the fall. The lack of correlations in the fall may be due

to the fact that sampling occurred when water levels had receded following a large rain

event, adding large quantities of fine sediment to the sample sites. All taxa groups were

probably drastically reduced and the community may not have had sufficient time to

recover when sampling occurred.

Percent Orthocladiinae of chironomidae decreased as percent deposited fine

sediment increased, while % Chironominae of chironomidae increased (Figures 3.8, 3.9

and 3.10). This is contrary to the findings of Lenat et al. (1981) and Angradi (1999),

which found % Orthocladiinae to increase and % Chironominae to decrease as percent

fine sediment increased. Chironomids are generally viewed as pollution-tolerant taxa,

however at the species level large variation in tolerance is often observed (Brinkhurst

1993). The difference in metric responses between the two studies may be due in part to

the two subfamilies being composed of species with different pollution or sediment

tolerances. Rosenberg and Wiens (1978) found three chironomid taxa to be unaffected by

fine sediment addition, while four taxa were affected. Additionally chironomid species

present in these studies may belong to different functional feeding groups, which can

affect their distribution and abundance. Chironominae are known to prefer depositional

areas only, while Orthocladiinae use both depositional and erosional areas (Merritt and

Cummins 1996). Therefore Chironominae is the dominant chironomid in areas of fine

60

sediment, while Orthocladiinae dominate in areas composed of cobble and gravel (Pinder

1986). The metrics chironomid richness and abundance were not significantly correlated

with percent deposited sediment, which coincides with the results of Zweig and Rabeni

(2001). While Angradi (1999) found chironomidae abundance decreased as deposited

fine sediment increased, his study only examined a short range of sediment levels (0 –

30%) and correlations were only weak. The difference in metric response between

Angradi’s (1999) study and mine may be because a larger natural gradient of sediment

conditions was used and therefore real impacts were observed in my study. Angradi’s

(1999) results may be incorrect since only a short gradient of artificially deposited

sediment conditions were examined. The fact that chironomid abundance and richness

were not significantly correlated with deposited sediment lends evidence to the fact that

the tolerance of individual species varies greatly (Zweig and Rabeni 2001). The

abundance of non-sediment tolerant species may have decreased, while the abundance of

more sediment tolerant species increased to compensate. Further studies are needed to

determine the true response patterns of chironomid metrics.

The metric EPT abundance was significantly, negatively correlated with percent

deposited fine sediment. Zweig and Rabeni (2001) found EPT density to consistently

decrease as surface cover of deposited fine sediment increased. Additionally the metric %

EPT had a significant negative correlation with percent deposited fine sediment in our

study. The decrease in % EPT and abundance of EPT taxa is probably the result of a

reduction in the heterogeneous substrate preferred by these taxa (Lenat 1981; Minshall

1984; Waters 1995). Despite the significant negative correlation with EPT abundance

deposited fine sediment was not significantly correlated with EPT richness in either

61

season. My results do not concur with previous studies that found EPT richness decreases

as deposited fine sediment increases (Angradi 1999; Zweig and Rabeni 2000; Kaller et al.

2001 and Kaller et al. 2004). The lack of correlation between EPT richness and deposited

fine sediment is puzzling, as generally the taxa in these three orders are sensitive to

pollution (Lenat 1988). However, the EPT taxa present in our samples may have

ecological or physiological traits that allow for higher sediment tolerances than the taxa

sampled in previous studies. It is highly likely that EPT taxa intolerant to sediment

declined, however this was compensated for by an increase in sediment tolerant taxa

resulting in a lack of correlation between EPT richness and deposited fine sediment.

Relyea et al. (2000) found individual taxa of the EPT orders to have varying sediment

tolerances and the metric EPT richness not to be responsive to fine sediment as well. My

results indicate the metrics % EPT and EPT abundance are good indicator metrics of

increased fine sediment; however EPT richness does not show promise as the tolerance of

the taxa in these orders to sediment deposition varies.

Increased fine sediment deposition reduces critical habitat for many benthic

invertebrates, often lowering species diversity (Gray and Ward 1982). However our data

did not show a significant association between species diversity, measured as Shannon

Diversity and increased fine sediment deposition. Results of the present study agree with

Zweig and Rabeni (2001) and are contrary to the results of other studies which have

found a reduction in diversity associated with increased fine sediment deposition (Nuttall

1972; Lenat et al. 1981; Henley et al. 2000; Roy et al., 2003). In the present study

evenness increased significantly in the summer, but richness, and therefore Shannon

diversity remained unchanged. Taxa richness has been shown to decrease in response to

62

deposited fine sediment (Nuttall 1972; Lemly 1982; Zweig and Rabeni 2001) in several

studies while no response has been observed in others (Angradi 1999). My results

suggest the metrics: Shannon Diversity, evenness and taxa richness are not effective at

quantifying the impact fine sediment deposition has on the benthic invertebrate

community. The lack of correlation may be the result of a shift in the community to more

sediment tolerant benthic invertebrates, therefore additional metrics or indices which take

into account the relative tolerance of the taxa present may be required.

Several response patterns of the metrics evaluated in this study differed from

those of previous work, which may be due to the fact that a larger natural deposited

sediment gradient was used in the present study instead of a smaller range of human

manipulated conditions. Sampling along a natural deposited sediment gradient increased

the range of sediment conditions, strengthened the observed relationships and allowed the

observation of real impacts. The significant correlations in my study have shown that

several commonly used biomonitoring metrics can be used in monitoring for the effects

of increased fine sediment deposition. However care must be taken using these metrics

when monitoring for sediment since the metrics were often correlated with one or more

other environmental variables. Therefore there may be problems in distinguishing

between the effects of fine sediment and other perturbations. New metrics or indices need

to be developed specially targeted at monitoring for the effects of fine sediment

deposition, since many of the commonly used metrics incorporate taxa that are known to

be sensitive to various pollutants, such as EPT taxa. Metrics taking into account the

relative tolerance and ecological/ physiological adaptations of individual taxa are

required to better detect and quantify the impact of fine sediment deposition. However

63

the metrics responsive to deposited fine sediment in this study can be used for detecting

the effects along a natural gradient.

3.5 CONCLUSIONS

In conclusion, my study has shown that several commonly used biomonitoring

metrics are responsive along a gradient of sediment conditions. Five biomonitoring

metrics were significantly correlated with percent deposited fine sediment. These metrics

generally showed a linear relationship with increasing percent deposited sediment

however, no sediment threshold was evident. The responses of the metrics were generally

consistent with results of previous studies, with the exception of the metrics, %

Chironominae and % Orthocladiinae. Percent Chironominae responded positively, while

% Orthocladiinae responded negatively to increasing fine sediment, which is contrary to

the results of Angradi (1999). Previous studies have shown reductions in EPT richness,

taxa richness and diversity as fine sediment deposition increased. However, percent fine

sediment deposition was not significantly correlated with these metrics in my study. The

lack of correlation may be the result of a shift in the community to more sediment

tolerant benthic invertebrates. Taxa intolerant to fine sediment probably decreased in

abundance or were replaced by taxa more tolerant to deposited fine sediment, resulting in

no change in the metric values even though a shift in the community had occurred.

However, it is also probable these metrics are more responsive to additional habitat or

environmental variables. Further testing spatially and temporally of the different metrics

along natural sediment gradients is necessary, since variation in metric responses has

been shown between studies.


64


indebted to Wendy Monk for her help with the statistical analysis. I would also like to

thank Allen Curry, Joseph Culp and Kristie Heard for their guidance and help. I would

also like to thank Mark Gautreau, Kristie Heard, Megan Finley, Natsha Chisti and Sean

Clarke for their help in the field. I would also like to thank Jacob Sanford for his help

sieving sediment samples.


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sedimentation and turbidity on lotic food webs: a concise review for natural resource managers. Reviews in Fisheries Science. 8(2): 125- 139.

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Lenat, D.R., D.L. Penrose, and K.W. Eagleson. 1981. Variable effects of sediment addition on stream benthos. Hydrobiologia 79: 187- 194. Lenat, D.R. 1988. Water quality assessment of streams using a qualitative collection method for benthic macroinvertebrates. Journal of the North American Benthological Society. 7: 222-233. McClelland, W.T., and M.A. Brusven. 1980. Effects of sedimentation on the behavior and distribution of riffle insects in a laboratory stream. Aquatic Insects.

2(3): 161- 169. Merritt, and Cummins. 1996. An introduction to the aquatic insects of North America (3rd Edition). Kendall/Hunt Publishing Company. Dubuque, Iowa. Minshall, G.W. 1984. Aquatic insect- substratum relationships. Pages 358- 400 in Resh, V.H., and Rosenburg, D.M. The ecology of Aquatic Insects. Praeger Publishers, One Madison Ave. New York, NY. Nuttall, P.M. 1972. The effects of sand deposition upon the macroinvertebrate fauna of the River Camel, Cornwall. Freshwater Biology. 2: 181- 186. Pinder, L.C.V. 1986. Biology of freshwater Chironomidae. Annual Review of Entomology. 31: 1- 23. Poff, N.L., J.D. Olden, N.K.M. Vieira, D.S. Finn, M.P. Simmons and B.C. Kondratieff. 2006. Functional trait niches of North American lotic insects: trait- based ecological applications in light of phylogenetic relationships. Journal of the North American Benthological Society 25: 730- 755. Purcell, L. 2003 The river runs through it: Evaluation of the effects of agricultural land use practices on macroinvertebrates in Prince Edward Island streams using both new and standard methods. MSc Thesis, University of Prince Edward Island. Rabeni, C.F., K.E. Doisy and L.D. Zweig. 2005. Stream invertebrate community functional responses to deposited sediment. Aquatic Sciences 67: 395- 402. Relyea, C.D., G.W. Minshall and R.J. Danehy. 2000. Stream insects as bioindicators of fine sediment. Watershed Management 2000 Conference, Water Environment Federation. Resource Inventory and Modeling Section, Department of Agriculture and Forestry.

October 2003. 2000/02 Prince Edward Island Corporate land use inventory, land use and land cover summary. 12 pgs.

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Territories, Canada. Acta Universitatis Carolinae- Biologica 1978: 181-192. Rosenberg, D.M. and V.H. Resh. 1996. Introduction to freshwater biomonitoring and

Benthic macroinvertebrates. Pages 1-10 in D.M. Rosenburg and V.H. Resh (editors). Freshwater biomonitoring and benthic macroinvertebrates. Chapman and Hall, New York, New York.

Roy, A.H; A.D. Rosemond, D.S. Leigh, M.J. Paul and J.B. Wallace. 2003. Habitat- specific responses of stream insects to land cover disturbance: biological consequences and monitoring implications. Journal of the North American Benthological Society 22(2): 292- 307. Scrimgeour, G.J., J.M. Culp and N.E. Glozier. 1993. A new technique for sampling lotic invertebrates. Hydrobiologia 254: 65-71 Sylte, T., and C. Fischenich. 2002. Techniques for measuring substrate embeddedness. ERDC TN- RMRRP- SR- 36, September 2002. Waters, T.F. 1995. Sediment in streams: sources, biological effects and control. Monograph 7. American Fisheries Society, Bethesda, Maryland. Wood, P.J, and Armitage, P.D. 1997. Biological effects of fine sediment in the lotic environment. Environmental Management. 21(2): 203- 217. Zanetell, B.A., and B.L. Peckarsky. 1996. Stoneflies as ecological engineers- hungry predators reduce fine sediment in stream beds. Freshwater Biology. 36: 569- 577. Zweig, L.D. 2000. Effects of deposited sediment on stream benthic macroinvertebrate communities. MSc Thesis, University of Missouri- Columbia. Zweig, L.D. and C.F. Rabeni. 2001. Biomonitoring for deposited sediment using benthic invertebrates: a test on 4 Missouri streams. Journal of the North American Benthological

Society 20(4): 643- 657.

68

3.7 TABLES

Table 3.1: Study sites sampled on Prince Edward Island, including their watershed and geographical location Site Watershed Latitude (N) Longitude (W)

North Brook Lower Dunk 46º 20’ 48.6” 63º 37’ 54.8”

North Brook Middle Dunk 46º 21’ 32.8” 63º 36’ 53.7”

North Brook Upper Dunk 46º 21’ 51.2” 63º 36’ 50.0”

Southwest Brook Lower Dunk 46º 20’ 28.6” 63º 38’ 13.0”

Southwest Brook Middle Dunk 46º 20’ 05.2” 63º 37’ 48.6”

Southwest Brook Upper Dunk 46º 18’ 46.3” 63º 35’ 55.5”

Dunk River Lower Dunk 46º 21’ 18.6” 63º 33’ 57.2”

Dunk River Upper Dunk 46º 21’ 05.6” 63º 29’ 20.4”

Wilmot River Upper Wilmot 46º 23’ 32.3” 63º 33’ 16.2”

Wilmot River Lower Wilmot 46º 24’ 29.3” 63º 35’ 46.1”

East Branch Lower Tryon 46º 17’ 50.8” 63º 31’ 39.0”

East Branch Upper Tryon 46º 15’ 45.4” 63º 31’ 45.2”

Brookvale Lower West 46º 17’ 02.4” 63º 24’ 29.4”

Brookvale Upper West 46º 19’ 58.8” 63º 25’ 19.5”

69

Table 3.2: Sieves sizes used during substrate particle size distribution analysis.

Coarse Sieves Fine Sieves

25 mm 2 mm

19 mm 1 mm

12.5 mm 0.5 mm

9.5 mm 0.25 mm

6.3 mm 0.125 mm

4.75 mm 0.038 mm

4 mm Silt

70

Table 3.3: Biomonitoring metrics tested for responsiveness to sediment deposition (Rosenberg and Resh 1993; Merrit and Cummins 1995). Metric

Chironomid Abundance

% Orthocladiinae

EPT Abundance

% Chironominae

Taxa Richness

% Burrower

EPT Richness

% Clinger

Chironomidae Richness

% Sprawler

Ephemeroptera Richness

% Climber

Plecoptera Richness

% Swimmer

Trichoptera Richness

% Filterer

Shannon Diversity

% Scraper

Evenness

% Gatherer

% EPT

% Shredder

% Chironomidae

% Predator

% Dominant Taxa

71

Table 3.4: Spearman rank correlation coefficients and associated p- values for deposited sediment and substrate measures for July 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * significant at the 0.05 level).

% Deposited

Sediment

%

Embeddedness

Total Suspended

Solids

% < 2mm

Correlation Coefficient

0.639 **

0.115

- 0.105

p- value

< 0.001

0.503

0.544

% < 4mm


0.641 **

0.119

- 0.125

p- value

< 0.001

0.489

0.469

% Gravel


- 0.152

0.026

- 0.111

p- value

0.375

0.88

0.52

% Pebble


- 0.542 **

- 0.067

-0.111

p- value

< 0.001

0.697

0.519

% Deposited


1.00

0.335 *

0.109

Sediment p- value

< 0.001

0.046

0.528

%


0.335 *

1.00

0.229

Embeddedness p- value

0.046

< 0.001

0.18

72

Table 3.5: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and environmental variables for July 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).

% Deposited Sediment

% Embeddedness

Total Suspended

Solids

Water

Correlation Coefficient - 0.143 - 0.06 0.755 **

Temperature p- value

0.405 0.729 < 0.001

pH

Correlation Coefficient -0.12 0.107 0.611 **

p- value

0.485 0.535 < 0.001

Dissolved

Correlation Coefficient - 0.136 - 0.05 - 0.581 **

Oxygen p- value

0.431 0.774 < 0.001

Conductivity

Correlation Coefficient - 0.227 - 0.13 - 0.084

p- value

0.183 0.449 0.627

Flow

Correlation Coefficient - 0.279 0.013 0.597 **

p- value

0.099 0.941 < 0.001

% Slope

Correlation Coefficient - 0.485 ** - 0.275 - 0.413 *

p- value

0.003 0.104 0.012

Chlorophyll- a


p- value

0.141 0.928 0.003

73

Table 3.6: Spearman rank correlation coefficients and associated p- values for deposited sediment and substrate measures for October 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).

% Deposited

Sediment

%

Embeddedness

Total Suspended

Solids

% < 2mm


0.393 *

0.464 **

0.192

p- value

0.018

0.004

0.262

% < 4mm


0.398 *

0.475 **

0.196

p- value

0.016

0.003

0.252

% Gravel


0.289

0.055

- 0.035

p- value

0.087

0.749

0.840

% Pebble


- 0.481 **

- 0.499 **

- 0.173

p- value

0.003

0.002

0.314

% Deposited


1.00

0.670 **

0.211

Sediment p- value

0.000

< 0.001

0.216

%


0.670 **

1.00

0.043


< 0.001

0.000

0.805

74

Table 3.7: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and environmental variables for October 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).


% Embeddedness

Total Suspended

Solids

Water

Correlation Coefficient 0.077 - 0.077 0.231


0.655

0.969 0.175

pH

Correlation Coefficient - 0.159 0.033 0.014

p- value

0.353 0.847 0.934

Dissolved

Correlation Coefficient - 0.035 0.033 - 0.614 **

Oxygen p- value

0.852 0.849 < 0.001

Conductivity

Correlation Coefficient - 0.209 0.108 0.084

p- value

0.22 0.532 0.627

Flow

Correlation Coefficient - 0.523 ** - 0.472 ** - 0.149

p- value

0.001 0.004 0.386

% Slope


p- value

0.121 0.196 < 0.001

Chlorophyll- a

Correlation Coefficient - 0.012 0.283 - 0.106

p- value

0.944 0.099 0.543

75

Table 3.8: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and % habit/ feeding group metrics for July 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at 0.05 level).


% Embeddedness

Total Suspended

Solids %

Correlation Coefficient 0.507 ** 0.404 ** - 0.235

Burrower p- value

0.002 0.014 0.168

%

Correlation Coefficient 0.194 0.003 0.24

Climber p- value

0.256 0.984 0.158

%

Correlation Coefficient 0.347 * - 0.073 - 0.094

Sprawler p- value

0.038 0.672 0.587

%


Clinger p- value

0.45 0.159 0.002

%

Correlation Coefficient - 0.436 ** - 0.214 0.087

Swimmer p- value

0.008 0.211 0.615

%

Correlation Coefficient - 0.513 ** - 0.219 - 0.031

Gatherer p- value

0.001 0.2 0.858

%

Correlation Coefficient 0.577 ** 0.550 ** 0.416 *

Filterer p- value

< 0.001 0.001 0.012

%


Scraper p- value

0.755 0.97 0.001

%

Correlation Coefficient 0.339 ** - 0.022 - 0.234

Predator p- value

0.043 0.899 0.17

%

Correlation Coefficient 0.196 - 0.199 - 0.255

Shredder p- value

0.251 0.244 0.134

76

Table 3.9: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and biomonitoring metrics for July 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at 0.05 level).


% Embeddedness

Total Suspended

Solids Taxa

Correlation Coefficient - 0.247 - 0.048 0.370 *

Richness p- value

0.146 0.78 0.027

Evenness

Correlation Coefficient 0.483 ** 0.049 - 0.059

p- value

0.003 0.778 0.731

Shannon

Correlation Coefficient 0.24

- 0.014

0.146

Diversity p- value

0.142 0.933 0.394

Chironomid


- 0.182

0.131

Richness p- value

0.821 0.288 0.446

Chironomid


Abundance p- value

0.089 0.724 0.118

EPT

Correlation Coefficient - 0.123 0.036 0.440 **

Richness p- value

0.473 0.837 0.007

EPT


Abundance p- value

< 0.001 0.161 0.76

% EPT


Taxa p- value

< 0.001 0.082 0.11

%

Correlation Coefficient - 0.431 ** 0.092 - 0.084

Orthocladiinae p- value

0.009 0.594 0.627

%

Correlation Coefficient 0.453 ** - 0.015 0.007

Chironominae p- value

0.006 0.931 0.967

77


% Embeddedness

Total Suspended

Solids % Dominant


Taxa p- value

0.061 0.884 0.343

Ephemeroptera


Richness p- value

0.151 0.365 0.001

Plecoptera


Richness p- value

0.229 0.068 0.753

Trichoptera


Richness p- value

0.945 0.433 0.341

78

Table 3.10: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and % habit/ feeding group metrics for October 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at 0.05 level).


% Embeddedness

Total Suspended

Solids %

Correlation Coefficient 0.334 ** 0.138 0.081

Burrower p- value

0.046 0.424 0.637

%


Climber p- value

0.198 0.857 0.228

%

Correlation Coefficient 0.126 0.211 - 0.329 *

Sprawler p- value

0.465 0.218 0.05

%

Correlation Coefficient - 0.111 - 0.281 0.216

Clinger p- value

0.518 0.097 0.206

%


Swimmer p- value

0.234 0.649 0.254

%


Gatherer p- value

0.091 0.454 0.74

%


Filterer p- value

0.409 0.557 0.394

%

Correlation Coefficient - 0.166 - 0.451 ** 0.16

Scraper p- value

0.334 0.006 0.351

%


Predator p- value

0.775 0.075 0.791

%

Correlation Coefficient - 0.174 0.033 - 0.412 *

Shredder p- value

0.31 0.847 0.013

79

Table 3.11 : Spearman rank correlation coefficients and associated p- values for deposited sediment measures and biomonitoring metrics for October 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at 0.05 level).


% Embeddedness

Total Suspended

Solids Taxa


Richness p- value

0.85 0.215 0.839

Evenness


p- value

0.258 0.698 0.975

Shannon


Diversity p- value

0.262 0.351 0.847

Chironomid


Richness p- value

0.434 0.575 0.221

Chironomid


Abundance p- value

0.088 0.285 0.429

EPT

Correlation Coefficient - 0.286 - 0.409 * - 0.221

Richness p- value

0.091 0.013 0.196

EPT


Abundance p- value

< 0.001 0.006 0.699

% EPT


Taxa p- value

< 0.001 < 0.001 0.18

%

Correlation Coefficient - 0.770 ** - 0.560 ** - 0.575 **

Orthocladiinae p- value

< 0.001 < 0.001 < 0.001

%

Correlation Coefficient 0.669 ** 0.562 ** 0.448 **

Chironominae p- value

< 0.001 < 0.001 0.006

80


% Embeddedness

Total Suspended

Solids % Dominant


Taxa p- value

0.103 0.778 0.802

Ephemeroptera


Richness p- value

0.33 0.142 0.555

Plecoptera

Correlation Coefficient - 0.054 - 0.359 * - 0.053

Richness p- value

0.752 0.032 0.758

Trichoptera

Correlation Coefficient - 0.342 * - 0.364 * - 0.236

Richness p- value

0.041 0.029 0.165

81

Table 3.12 : Spearman rank correlation coefficients and associated p- values for habit/ feeding group metrics responsive to deposited sediment and environmental variables for July 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).

% Burrower

% Sprawler

% Swimmer

% Predator

Water

Correlation Coefficient - 0.276

- 0.242

0.079

- 0.224


0.099 0.156 0.646 0.189

pH

Correlation Coefficient - 0.416 *

- 0.489 **

0.614 **

- 0.526 **

p- value

0.027 0.002 < 0.001 0.001

Dissolved


- 0.077

0.088

0.03

Oxygen p- value

0.53 0.657 0.609 0.86

Conductivity


- 0.101

0.753 **

- 0.473 **

p- value

0.054 0.56 < 0.001 0.004

Flow


- 0.198

0.539 **

- 0.418 *

p- value

0.07 0.248 0.001 0.011

% Slope


0.191

- 0.005

- 0.02

p- value

0.064 0.264 0.979 0.907

82

Chlorophyll- a


- 0.192

- 0.106

- 0.08

p- value

0.068 0.262 0.539 0.642

83

Table 3.13 : Spearman rank correlation coefficients and associated p- values for metrics responsive to deposited sediment and environmental variables for July 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).

Evenness

EPT

abundance

%

EPT

%

Orthocladiinae

%

Chironominae

Water


- 0.214

0.195

-0.068

-0.141

- 0.092


0.21

0.255

0.695

0.413

0.594

pH


- 0.482 **

0.171

0.047

0.223

-0.105

p- value

0.003

0.317

0.785

0.192

0.542

Dissolved


0.045

-0.154

0.115

0.386 *

- 0.102

Oxygen p- value

0.793

0.37

0.505

0.02

0.555

Conductivity


- 0.382 *

0.337 *

0.286

0.057

0.063

p- value

0.021

0.044

0.091

0.741

0.714

Flow


- 0.343 *

0.308

0.045

-0.048

0.021

p- value

0.04

0.068

0.793

0.781

0.905

84

% Slope


- 0.168

0.482 **

0.528 **

0.353 *

- 0.526 **

p- value

0.327

0.003

0.001

0.035

0.001

Chlorophyll- a


0.166

-0.037

-0.001

- 0.144

0.087

p- value

0.334

0.829

0.995

0.401

0.614

85

Table 3.14 : Spearman rank correlation coefficients and associated p- values for metrics responsive to deposited sediment and environmental variables for October 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).

%

Burrower

EPT

abundance

%

EPT

%

Orthocladiinae

%

Chironominae

Trichoptera

Richness

Water


- 0.408 *

0.006

- 0.206

- 0.268

- 0.101

0.024


0.013

0.971

0.228

0.114

0.56

0.891

pH


- 0.291

0.378 *

0.091

- 0.006

- 0.127

- 0.126

p- value

0.085

0.023

0.596

0.975

0.46

0.465

Dissolved


- 0.054

0.114

0.07

0.267

- 0.437 **

0.089

Oxygen p- value

0.756

0.508

0.684

0.115

0.008

0.607

Conductivity


- 0.106

0.485 **

0.340 *

0.052

0.14

- 0.29

p- value

0.54

0.003

0.042

0.762

0.415

0.087

Flow


- 0.475 **

0.501 **

0.586 *

0.289

- 0.188

0.340 *

p- value

0.003

0.002

< 0.001

0.087

0.273

0.042

86

% Slope


- 0.212

0.082 0.365 *

0.430 **

- 0.326

0.616 **

p- value

0.214

0.636

0.029

0.009

0.053

< 0.001

Chlorophyll- a


0.390 *

0.028

- 0.082

0.101

0.131

- 0.038

p- value

0.02

0.874

0.64

0.564

0.453

0.83

87

3.9 FIGURES

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Dunk River North Brook SouthwestBrook

TryonRiver(EastBranch)

West River(Brookvale)

Wilmot River

Watershed

Perc

ent % Agriculture

% Forestry% Other

Figure 3.1: Percentage of different land-use types within the six watersheds sampled in the summer (25- 27 July) and fall (19-21 October) of 2005 on Prince Edward Island, Canada.

88

A.)

B.)

Figure 3.2: A.) 1m2 Quadrat used to designate area from which samples were to be collected. B.) Placement of algal substrate within the quadrat.

89

A.)

B.)

Figure 3.3: A.) Artificial algal substrate used for Chlorophyll- a analysis. B.) Algal substrate deployed in river, dug 1cm down in substrate.

90

Figure 3.4: Site- Season biplot diagram summarizing the effect of season on benthic invertebrate community composition. Sites separated into two distinct clumps based on season. Further analyses were performed on benthic invertebrate data for the fall and summer separately.

91

% Burrower


0 10 20 30 40 50 60 70 80 90 100

Burr

ower

abu

ndan

ce/ T

otal

abu

ndan

ce

0

10

20

30

40

50

60

70

80

Sr = 0.507 p = 0.002

% Sprawler


0 10 20 30 40 50 60 70 80 90 100

Spr

awle

r abu

ndan

ce/ T

otal

abu

ndan

ce

0

5

10

15

20

25

30

35

Sr = 0.347 p = 0.038

% Swimmer


0 10 20 30 40 50 60 70 80 90 100

Sw

imm

er a

bund

ance

/ Tot

al a

bund

ance

0

20

40

60

80

Sr = - 0.436 p = 0.008

% Predator


0 10 20 30 40 50 60 70 80 90 100

Pre

dato

r abu

ndan

ce/ T

otal

abu

ndan

ce

0

5

10

15

20

25

30

35

40

Sr = 0.339 p = 0.043

Figure 3.5: Relationships between % habit/ functional feeding group metrics and % deposited fine sediment that had significant correlations in July 2005. Spearman rank correlation coefficients and their associated p-values are shown.

92

% Gatherer


0 10 20 30 40 50 60 70 80 90 100

Gat

here

r abu

ndan

ce/ T

otal

abu

ndan

ce

0

10

20

30

40

50

60

70

80

90

100

Sr = - 0.513 p = 0.001

Evenness


0 10 20 30 40 50 60 70 80 90 100

Even

ness

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Sr = 0.483 p = 0.003

EPT Abundance


0 10 20 30 40 50 60 70 80 90 100

No.

of E

PT

Indi

vidu

als

0

100

200

300

400

500

600

700

800

900

1000

Sr = -0.711p = < 0.001

% EPT


0 10 20 30 40 50 60 70 80 90 100

EPT

abun

danc

e/ T

otal

abu

ndan

ce

0

20

40

60

80

100

Sr = -0.612p = < 0.001

Figure 3.6: Relationships between biomonitoring metrics and % deposited fine sediment that had significant correlations in July 2005. Spearman rank correlation coefficients and their associated p-values are shown

93

% Orthocladiinae


0 10 20 30 40 50 60 70 80 90 100

Orth

ocla

d ab

unda

nce/

Chi

rono

mid

abu

ndan

ce

0

20

40

60

80

100

120

Sr = -0.431 p = 0.009

% Chironominae


0 10 20 30 40 50 60 70 80 90 100

Chi

rono

min

ae a

bund

ance

/ Chi

rono

mid

abu

ndan

ce

0

20

40

60

80

100

120

Sr = 0.453 p = 0.006

Figure 3.7: Relationship between chironomidae metrics and % deposited fine sediment that had significant correlations in July 2005. Percent Orthocladiinae decreased, while % Chironominae increased. Spearman rank correlation coefficients and p-values are shown.

94

% Burrower


0 10 20 30 40 50 60 70 80 90 100

Burr

ower

abu

ndan

ce/ t

otal

abu

ndan

ce

0

10

20

30

40

50

60

70

80

Sr = 0.334 p = 0.046

EPT Abundance


0 10 20 30 40 50 60 70 80 90 100

No.

EPT

indi

vidu

als

-200

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

Sr = -0.613p = < 0.001

% EPT


0 10 20 30 40 50 60 70 80 90 100

EPT

abun

danc

e/ T

otal

abu

ndan

ce

10

20

30

40

50

60

70

80

90

100

Sr = -0.678 p = <0.001

% Orthocladiinae


0 10 20 30 40 50 60 70 80 90 100

Orth

ocla

d ab

unda

nce/

tota

l abu

ndan

ce

-40

-20

0

20

40

60

80

100

120

Sr = -0.770p = < 0.001

Figure 3.8: Relationships between metrics and % deposited fine sediment that had significant correlations in October 2005. Spearman rank correlation coefficients (Sr) and their associated p-values are shown.

95

% Chironominae


0 10 20 30 40 50 60 70 80 90 100

Chi

rono

min

ae a

bund

ance

/ tot

al a

bund

ance

-20

0

20

40

60

80

100

120

Sr = 0.669p = < 0.001

Trichoptera Richness


0 10 20 30 40 50 60 70 80 90 100

No.

Tric

hopt

era

taxa

0

1

2

3

4

5

6

7

8

Sr = -0.342 p = 0.041

Figure 3.9: Relationships between metrics and % deposited fine sediment that had significant correlations in October 2005. Spearman rank correlation coefficients and their associated p-values are shown.

96

4.0 ABSTRACT

This study examines the relationships between levels of deposited sediment and

benthic invertebrate species traits, to determine traits indicative of deposited sediment in

agricultural areas. The tolerance of individual benthic invertebrates to deposited sediment

is also examined. Thirteen sites within six agricultural streams on Prince Edward Island,

Canada were sampled during the summer and fall of 2005. Benthic invertebrate, sediment

and water chemistry samples were collected along a gradient of deposited sediment

conditions. Fine sediment deposition was quantified by three methods: a visual estimate

of percent cover of deposited fine sediment, calculated % embeddedness and % fines < 2

mm from a substrate sample. Benthic invertebrate taxa were assigned as tolerant,

moderately tolerant or sensitive to sediment based on their association with the deposited

sediment gradient. EPT taxa were represented in all sediment tolerance categories in both

seasons. Several traits were positively and negatively associated with increased deposited

sediment levels. Overall, several species traits were associated with deposited sediment

and helped to explain the tolerance of certain benthic invertebrate taxa. Species traits

positively associated with deposited sediment were plastron respiration, elongate body

form, depositional rheophily and predation. In contrast species traits negatively

associated were free-moving/ sessile, univoltine, abdominal gill placement, flattened

body form, gills present, gill respiration, slow- seasonal development, mulitvoltine, plate-

like gills, strong swimmer, streamlined body and erosional rheophily. Therefore, species

traits patterns may lead to diagnostic bioassessments of deposited sediment impacts in

agricultural lotic environments.

97

4.1 INTRODUCTION

Studies assessing the effects of anthropogenic stressors, such as increased

sedimentation from agricultural practices on aquatic benthic invertebrate communities,

have focused on biotic indices, various metrics, community composition and diversity

measures (Reviewed by: Waters 1995). These biomonitoring methods are useful in

detecting an impact, however very little information on the type or source of impairment

can be gleaned from these assessment methods. For biomonitoring and remediation

purposes it is necessary to be able to quantify ecologically significant impacts and

discriminate the source of impairment through diagnosis of response patterns.

Biotic indices have been developed for specific pollutants such as Hilsenhoff’s

Biotic Index for Stream Organic Pollution, and therefore allow for source of impairment

identification (Hilsenhoff 1987). Many of these indices have been developed using

species from distinct ecoregions, for example, Relyea et al. (2000) and Zweig and Rabeni

(2001) examined the sensitivity of individual taxa to deposited fine sediment in order to

develop a deposited sediment index. Since these studies were carried out in distinct

ecoregions, namely Missouri, Idaho, Oregon and Washington, refinement through the

application to species pools in other geographic regions is required to determine the

broad applicability of these metrics. Ideally, ecologically-relevant biomonitoring

approaches should be applicable across many ecoregions without the constraint of species

distribution limitations. These approaches should also be able to separate the effects of

different anthropogenic activities (Dolédec et al. 1999) as this will improve our ability to

link a causal diagnosis to the observed patterns of effect.

98

Biological traits, which are the functional attributes (e.g., morphological,

physiological, behavioural and ecological characteristics) of a species provide a more

generalized metric for use in stream ecosystems, referred to as species traits hereon

(Vieira et al. 2006), as they are expected to be responsive to environmental gradients

(Poff et al. 2006). Species traits approaches are based on the habitat template model of

Southwood (1977, 1988), which states that habitat selects for characteristic life history

traits through natural selection. Therefore, there should be a correlation between species

traits and habitat characteristics over an evolutionary time period (Richards et al. 1997).

The habitat template model was adapted to lotic environments by Townsend and Hildrew

(1994), who proposed that riverine spatial and temporal habitat templates select for

adaptive species traits. Additionally, Statzner et al. (2004) observed that “habitat

characteristics are filters for the biological traits of organisms”, in other words they are

forces that act on the trait composition of communities. Many studies have corroborated

this model showing that benthic invertebrate trait composition can be explained by

environmental or habitat characteristics (e.g. Scarsbrook and Townsend 1993; Richards et

al. 1997; Statzner et al. 1997; Townsend et al. 1997; Finn and Poff 2005; Heino 2005). In

addition, Dolédec et al. (1999) demonstrated the general framework and potential for

species traits to be used as a biomonitoring tool. Various studies have given support to

this approach showing that benthic invertebrate trait assemblages change in predictable

ways along gradients of hydrologic (Richards et al. 1997; Townsend et al. 1997) and

anthropogenic (Charvet et al. 1998) disturbance. Furthermore, European studies have

used species traits to define benthic invertebrate assemblages and to determine expected

99

reference conditions (Charvet et al. 1998; Doledec et al. 1999; Usseglio-Polatera et al.

2000 a,b; Charvet et al. 2000; Statzner et al. 2001a; Gayraud et al. 2003).

A number of studies have investigated the response of commonly used

biomonitoring metrics to increasing sediment deposition to determine a method for

detecting and quantifying sediment impacts to the benthic invertebrate community

(Angradi 1999; Relyea et al. 2000; Kaller et al. 2001; Zweig and Rabeni 2001). Several

metrics respond to increases in fine sediment deposition, however, variation in metric

responses has been shown between studies. In addition, many of the commonly used

metrics incorporate taxa (EPT taxa) that are known to be sensitive to various pollutants.

Thus, distinguishing between the effects of fine sediment and other perturbations may be

difficult. Since taxa display morphological and physiological adaptations for their

preferred environment through the traits they possess, species traits have the potential to

elucidate the major processes structuring the benthic invertebrate community or the

source of impairment (Southwood 1988, Townsend 1989; Townsend and Hildrew 1994;

Poff 1997; Archaimbault et al. 2005). Trait patterns can be indicators of the source of

impairment because anthropogenic disturbances select for highly adaptative species and,

as a result, only species possessing adaptative traits are likely to remain (Vieira et al.

2006). For that reason species traits should provide valuable diagnostic biomonitoring

information when assessing the effects of sediment deposition. Furthermore, this species

traits approach may be applicable over many geographic areas without refinement across

communities differing in taxonomic composition (Statzner et al. 2004).

Our primary objective was to determine whether species traits can be used to

monitor for the effects of an anthropogenic stressor (sediment). This was accomplished

100

by examining the biological trait composition of benthic invertebrate communities along

a deposited fine sediment gradient to determine traits indicative of deposited sediment. A

secondary objective was to refine a previously developed sediment biotic index for

streams of Prince Edward Island,Canada, using tolerance values of common taxa (Zweig

2000; Zweig & Rabeni 2001). The trait assemblage of sediment-tolerant taxa and traits

indicative of fine sediment were compared to determine which ones were possessed by

tolerant taxa and therefore are may infer tolerance. Our main hypothesis was that traits

associated with resistance to sedimentation disturbance (e.g., small body size, fast

development, > 1 generation per year, etc.) as well as certain respiration and body form

traits would be associated with higher levels of deposited fine sediment.

4.2 Materials and Methods 4.2.1 Site Selection and Location Study sites were selected from six, 2nd and 3rd order streams located in the central

portion of Prince Edward Island, Canada (Figure 2.1). Prince Edward Island (PEI) is

situated north of Nova Scotia and east of New Brunswick in the Gulf of Saint Lawrence.

The island covers a land area of approximately 5660 km2 (Cairns 2002) with

predominantly sandstone and siltstone bedrock geology (Purcell 2003). The overall

topography varies, with the central portion being more elevated than the eastern and

western sections of the island (DeGrace 1989; Purcell 2003). On PEI, 39 % of the land is

devoted to agriculture, 45 % is forested and 8 % is urban land use (Resource Inventory

and Modeling Section, Department of Agriculture and Forestry 2003).

4.2.2 Site Descriptions

101

A total of 13 sites from 6 streams (Table 3.1) were sampled during the summer

(25- 27 July) and fall (19-21 October) of 2005. Sample sites were selected from small

streams within agricultural areas to obtain a gradient of deposited fine sediment

conditions, but which also had a narrow range of other environmental variables (e.g.

temperature, dissolved oxygen, pH). The construction of a beaver dam necessitated the

removal of the Southwest Brook Upper site from the analyses. Similarly, North Brook

Middle samples were taken further upstream during the fall due to beaver dam

construction.

4.2.3 Field Sampling

For the purposes of this study, fine sediment is classified as particles < 2 mm,

which encompasses the particle categories of clay, silt and sand (as in Waters 1995).

Three replicates were taken of all measurements in riffle/ run habitats at each site. First a

1L water sample was collected for total suspended solids (TSS) analysis in the laboratory.

A 1 m2 quadrat was then placed around an artificial algal substrate (chlorophyll-a)

deployed prior to sampling to designate the area from which all measurements for that

replicate were to be taken (Figure 3.1). Water temperature, pH, dissolved oxygen, and

conductivity were measured with a YSI 556 MPS® multimeter. Flow was measured

using a Marsh- Mc Birney®, model 2000 portable flowmeter. Benthic

macroinvertebrates (BMI) were sampled from within the 1 m2 quadrat using a U- net and

preserved until analysis in the laboratory. Fine sediment deposition was quantified

through a visual estimate of percent deposited fine sediment and calculated percent

embeddedness. As well chlorophyll a and streambed substrate samples were collected

from within the 1 m2 quadrat (Figure 3.1). Cross- sections taken from all sites on a single

102

river along with the slopes were used to calculate the tractive force at bankfull water

depth and the present depth to help determine bed stability.

Tractive force was calculated as following Cobb et al, 1992:

τ (kg/m2) = depth (m) · reach gradient (%) · 1000 (kg/m3 specific weight of

water).

For more detailed information on sample collection and laboratory processing, see

the Materials and Methods section in Chapter 3.

4.2.4 Benthic Invertebrate Traits

Benthic invertebrate data identified to the genus level was used to determine the

biological trait composition along a deposited sediment gradient. We used a binary

approach, where benthic invertebrate taxa are assigned to one category for each trait

(mutually exclusive) as opposed to the “fuzzy coding” approach, where each taxa is given

an affinity score for each category of the trait (Doledec et al. 2006, Poff et al. 2007).

Ephemeroptera, Plecoptera, Trichoptera, Coleoptera and Diptera taxa were assigned to

one category for each of the 15 traits (Table 4.1). When trait information was not

available at the genus level, information from higher taxonomic levels was used. The

selected traits represented life history, resilience/ resistance, biological and physiological

characteristics of the organisms. Information on benthic invertebrate traits was obtained

through a review of the literature and previous studies (Merritt and Cummins 1996; Poff

et al. 2007; Vieira et al. 2006; U.S. Geological Survey (USGS) website). Benthic

invertebrate trait assignments are provided in Appendix A.

4.2.5 Statistical Analysis

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A multivariate approach was used to investigate the relationships between,

benthic invertebrate data, species traits and deposited fine sediment. Analyses were

performed separately on the summer and fall data as community composition differed

between the two seasons (Figure 3.5). Site averages of all replicates were taken for

benthic invertebrate, species traits and environmental variable data prior to analysis.

Multivariate analyses (DCA, PCA, RDA) were performed using the software package

CANOCO (ter Braak & Smilauer, 1998).

Detrended correspondence analysis (DCA) of benthic invertebrate and species

traits, with detrending by segments and nonlinear rescaling was used to determine the

gradient length of the datasets. Gradient lengths were used to select the appropriate model

(ordination procedure) for the unconstrained and constrained ordinations. DCA of

invertebrate and species traits abundances gave gradient lengths < 2 standard deviations

for axes 1 and 2, indicating that linear response models would be appropriate for the

datasets (Leps and Smilauer 2003). Accordingly, Principal Component Analysis (PCA)

and Redundancy Analysis (RDA) were used in the ordination of the invertebrate and

species traits data (CANOCO 1998). In PCA and RDA, invertebrate and species traits

abundances were log transformed, while proportional data (relative abundances) were

square root transformed.

Principal Components Analysis (PCA) was performed on the datasets to choose

the environmental variables which were most strongly associated with each of the four

principal axes. Sample axis scores (4) from the PCA were correlated with the

environmental variables to determine which variables would be used in the constrained

ordination (RDA). Data normality (Kolmogorov-Smirnov test) and variance homogeneity

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(Levene’s Test) was tested prior to correlation analysis (SPSS 13.0; Chicago, IL, USA).

Since many of the environmental variables did not meet these assumptions, the

nonparametric Spearman’s Rank Correlation test was used in lieu of the Pearson

Correlation test. Environmental variables significantly correlated with PCA axis 1 and 2

were selected for use in the constrained ordination (RDA). However, selected

environmental variables that were highly correlated or redundant with another selected

variable were removed from the analysis. Manual selection of environmental variables,

with significance of the environmental variables tested with 499 Monte Carlo

permutations, was used in the RDAs.

4.2.6 Benthic Invertebrate Tolerance to Deposited Sediment

Benthic invertebrates were assigned tolerance values based on their sensitivity to

deposited sediment. Each taxa was assigned a number that represented their sediment

tolerance: 1 = sensitive, 2 = moderately tolerant, 3 = tolerant. Tolerance values were

determined for taxa present in > 2 streams (Zweig and Rabeni 2001). Redundancy

analysis was used to determine individual taxa tolerance to deposited fine sediment. The

RDA was constrained by using only one environmental variable (% deposited sediment)

so that it would be represented as axis 1. Thus, species scores for axis 1 could be used to

infer tolerance values. Scatter-plots were constructed for axis 1 species scores for July

and October 2005 data. Groupings were observed for species scores of < -0.2 and > 0.2.

It was determined that taxa with species scores of < -0.2 were sediment tolerant as these

were associated with increasing axis 1 scores or increased sediment levels. Taxa with

species scores of > 0.2 were determined to be sediment sensitive as they were associated

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with lower deposited sediment levels. Taxa with species scores between -0.2 and 0.2

were assigned as moderately tolerant to deposited sediment.

4.3 RESULTS 4.3.1 Benthic Invertebrate Relative Abundance Data July The 1st and 2nd PCA axes of average invertebrate relative abundance data and

sites for July 2005 explained 50.7 % of the variance in the data set; eigenvalues were

0.302 for the 1st and 0.205 for the 2nd PC axes (eigenvalues for the 3rd and 4th PC axes

were 0.146 and 0.102, respectively). The first PCA axis is correlated with the

environmental variables: water temperature (SR -0.692; p-value 0.013), pH (SR - 0.678; p-

value 0.015), flow (SR - 0.664; p-value 0.018) and total suspended solids (SR – 0.923; p-

value <0.001; Table 4.). The second PCA axis is interpreted as representing a sediment

gradient as % deposited fine sediment and % embeddedness were highly correlated with

this axis (SR -0.666; p-value 0.018 and SR -0.671; p-value 0.017 respectively; Table 4.2).

The environmental variables water temperature, flow, % deposited sediment and %

embeddedness were chosen for use in the RDA (constrained ordination), as they were not

inter-correlated. The additional environmental variables correlated with PCA axes 1 and

2, namely pH and TSS, were also correlated with water temperature and flow and,

therefore were redundant.

Variance explained by the 1st and 2nd RDA axes was 45.4 %, eigenvalues for the 4

axes were 0.278, 0.176, 0.081 and 0.038 respectively. The 1st and 2nd RDA axes

explained 79.3 % of the variance in the species- environment relation. The Monte Carlo

Permutation Test under a reduced model was significant with a p-value of 0.0080 and an

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F- ratio of 2.88. The most important environmental gradients structuring the invertebrate

community in the summer were % deposited fine sediment and flow (Figure 4.1). Several

taxa were associated positively with the deposited sediment measurements, while several

other taxa were negatively associated (Figure 4.1). The taxa Orthocladiinae, Baetis, Dixa,

Ostracoda and copepoda (numbers: 56, 2, 60, 84 and 85) had the greatest positive

affinity for the deposited sediment gradient. Whereas the taxa Tabanus, Dicranota,

Pseudolimnophila, Sialis and Tanytarsini (numbers: 66, 69, 72, 76 and 59) had a negative

affinity for the deposited sediment gradient, therefore preferring areas of greater %

deposited fine sediment. Several taxa also had a strong affinity with the flow gradient.

The taxa Isoperla, Apatania, Pyralida and Leptophlebia (numbers: 26, 27, 77 and 13) had

the most affinity for faster flows, while the taxa, Sweltsa, Epeorus, Attennella (numbers:

18, 9 and 3) had a greater affinity for slower flow velocities (Figure 4.1).

October

The variance explained by the 1st and 2nd PCA axes of invertebrate relative

abundance and site data for October 2005 was 51.3%, with eigenvalues of 0.30, 0.213,

0.132 and 0.114 for the 1st, 2nd, 3rd and 4th axes respectively. In October the first PCA

axis was highly correlated with the environmental variables: water temperature (SR -

0.692; p-value 0.013), pH (SR - 0.678; p-value 0.015), flow (SR - 0.664; p-value <0.018)

and total suspended solids (SR – 0.923; p-value <0.001; Table 4.3). The second axis is

represented by a sediment gradient, as it was correlated with % deposited sediment (SR

0.722; p-value 0.008), % embeddedness (SR 0.594; p-value 0.042), % <2mm (SR 0.614;

p-value 0.034) and % <4mm (SR 0.622; p-value 0.031; Table 4.3). Environmental

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variables chosen for use in the constrained ordination (RDA) were: water temperature,

flow, % deposited sediment and % embeddedness, as they were not inter-correlated. The

environmental variables pH and TSS although correlated with PCA axis 1 were not

chosen as they were also correlated with additional variables and were therefore

redundant.

The 1st and 2nd RDA axes accounted for 33.8% of the variance in the species

data, while they accounted for 72.3 % of the variance in the species-environment relation.

Eigenvalues for the 4 RDA axes were 0.239, 0.098, 0.091 and 0.038. The Monte Carlo

permutation test under a reduced model was significant with a p-value of 0.0040 and an

F- ratio of 2.71. In the fall the strongest environmental gradients were flow, water

temperature and % embeddedness, unlike in the summer the % deposited sediment

gradient was not as important. The taxa Tabanus, Pseudiolimnophila, hydracarina,

Curculionidae and Isogenoides (numbers: 66, 72, 86, 47 and 25; Figure 4.2) had the

greatest positive affinity for the deposited sediment gradient.Taxa negatively associated

with the deposited sediment gradient were Leuctra, Parapsyche and Cinygmula (numbers

20, 34 and 8).

4.3.2 Sediment Tolerances

In July and October 2005, 56 and 44 taxa were assigned tolerance values for

deposited sediment (Figures 4.3, 4.4). In July, 16 taxa were classified as sediment

tolerant, 20 as moderately tolerant and 16 sensitive, while in October 12 were classified

tolerant, 18 moderately tolerant and 14 sensitive (Table 4.6, 4.7). In both seasons EPT

taxa were represented in all 3 tolerance groups. The EPT taxa present and number of taxa

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in each tolerance group varied between seasons (Figures 4.3, 4.4 and Tables 4.6, 4.7). In

some cases the tolerance value assigned to a taxon varied between the 2 sampling

periods. For example, Baetis, was classified as sensitive following analysis of the July

samples, but was determined to be moderately tolerant in October (Figures 4.3 and 4.4).

However for some taxa their sediment tolerance classification remained constant between

the 2 seasons, such as the taxa Orthocladiinae and Chironominae which were classified as

sensitive and tolerant respectively in both seasons (Figures 4.3 and 4.4).

4.3.3 Species Traits Relative Abundance Data

July

The 1st and 2nd PCA axes of average trait relative abundance data and sites for

July 2005 explained 68.3 % of the variance in the data set; eigenvalues were 0.353 for

axis 1, 0.329 for axis 2, 0.135 for axis 3 and 0.074 for axis 4. The first PCA axis was

significantly correlated with conductivity (SR 0.832; p-value 0.001; Table 4.4) and

slightly correlated with % deposited sediment. The second axes was correlated with water

temperature (SR 0.669; p-value 0.017), pH (SR 0.872; p-value <0.001), flow (SR 0.764; p-

value 0.004) and TSS (SR 0.718; p-value 0.009; Table 4.4). The variables chosen for use

in the RDA were conductivity, % deposited sediment, water temperature and flow.

For the RDA axes 1 and 2 explained 56.8% of the variance in the trait data, while

they explained 88.6% of the variance in the trait- environment relation. Eigenvalues for

the 4 axes were 0.297, 0.271, 0.059 and 0.014 respectively. The Monte Carlo Permutation

test under a reduced model was significant with a p-value of 0.022 and an F- ratio of

3.04. The most important environmental gradient structuring the invertebrate trait

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composition in the summer was conductivity, however, % deposited sediment was

important (Figure 4.1). Several species traits were associated with the deposited sediment

gradient, such as elongate body form (Body 6), plastron respiration (Resp 3) and filter

feeding (Feed 2; Figure 4.5). A second RDA was run with only the deposited sediment

variables, to determine species traits associated with deposited sediment.

The most important sediment gradient was % deposited sediment. Several traits

were associated with higher levels of deposited sediment, such as depositional only

rheophily (Rheo 1), heavy armouring (case, Arm 3), sprawling habit (Habi 3), predators

(Feed 4) and plastron respiration (Resp 3). Traits associated with lower deposited

sediment levels were erosional only rheophily (Rheo 3), abdominal gill placement (Place

2), strong swimmers (Swim 3), slightly flattened body form (Form 2), collector gathering

(Feed 1). Several traits were also associated with TSS and % embeddedness. These

included tegument respiration (Resp 1), no swimming ability (Swim 1), scrapers (Feed 3)

and sceloritized armouring (Arm 2).

October

The variance explained by the 1st and 2nd PCA axis of average trait relative

abundance and site data for October 2005 is 57.0 %, eigenvalues were 0.416 for the 1st

and 0.154 for the 2nd PC axes (eigenvalues for the 3rd and 4th PC axes were 0.149 and

0.101, respectively). The first PCA axis was significantly correlated with water

temperature (SR 0.678; p-value 0.015), dissolved oxygen (SR -0.650; p-value 0.022) and

TSS (SR 0.811; p-value 0.001), while the 2nd PCA axis was not significantly correlated

with the environmental variables. The second PCA axis was only slightly correlated with

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% deposited sediment (SR -0.511; p-value 0.089) however it was used in the RDA along

with water temperature.

Axes one and two in the RDA explained 32.9% of the variance in the species trait

data and 100% of the variance in the species trait-environment relation. The eigenvalues

for the 4 axes were 0.243, 0.087, 0.258 and 0132 respectively. Water temperature and %

deposited sediment were equally important in structuring the invertebrate trait

composition, as the gradient lengths in the RDA were equal (Figure 4.6). A second RDA

was run with only the deposited sediment variables, to determine species traits associated

with deposited sediment.

The most important sediment gradient was % deposited sediment. Several traits

were associated with higher levels of deposited sediment, such as depositional only

rheophily (Rheo 1), plastron respiration (Resp 3), predator (Feed 4), large body size (Size

3), burrowing (Habi 1) and an elongate body (Body 6, Figure 4.8). Traits negatively

associated with increased deposited sediment were shredders (Feed 5), sedentary/ free-

moving (Mobi 3), Univoltine (Volt 2), gill respiration (Resp 2), abdominal gill placement

(Place 2), erosional rheophily (Rheo 3), flattened body form (Body 1) and slow- seasonal

development (Devel 2; Figure 4.8). Several traits were positively associated with

increased TSS levels, such as tegument respiration (Resp 1), collector gather (Feed 1), no

gills (Gill 2) and semi-voltine (Volt 1; Figure 4.8). The traits, collector filter (Feed 2),

weak swimming ability (Swim 2) and medium size (Size 2) were negatively associated

with increased TSS.

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4.4 DISCUSSION Ecologically-relevant biomonitoring approaches need to be diagnostic and

predictive, being able to determine the source of impairment as well as allow for a priori

predictions of community responses. Species traits have the potential to determine the

stressor type impacting the benthic community through the response patterns, since taxa

are adapted to their preferred environment through the traits they possess. Various studies

have shown that species traits have relationships with environmental variables. However

before species traits can be used as a biomonitoring tool, relationships between species

traits and different environmental gradients require further examination (Richards et al.

1997). The objective of the current study was to determine species traits positively and

negatively associated with deposited sediment, as well as the sediment tolerance of select

invertebrate taxa.

Taxa Sediment Tolerances

Deposited sediment was shown to be one of the important environmental

gradients structuring the benthic invertebrate community. Our hypothesis that benthic

invertebrate taxa would exhibit different tolerances to deposited sediment, with some taxa

being tolerant while others are intolerant was supported. EPT taxa, which are generally

viewed as pollution intolerant showed a range of sediment tolerances, with taxa from

each Order being present in all tolerance groups (tolerant, moderately and intolerant).

Two Ephemeroptera (Leptophlebia and Attenella), one Plecoptera (Sweltsa) and

five Trichoptera (Glossosoma, Rhyacophila, Onocosmoecus, Parapsyche and

Psychoglypha) taxa were classified as sediment tolerant. While four Ephemeroptera

(Paraleptophlebia, Cinygmula, Drunella and Epeorus) and three Plecoptera (Leuctra,

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Isoperla and Diura) taxa were classified as sediment intolerant. Generally our

classifications of these taxa agreed with the results of previous studies, however there

were exceptions. Relyea et al. (2000) found Glossosoma to be sediment sensitive or

moderately intolerant, which is contrary to our results. The fact that Glossosoma is free-

moving and use its tegument for respiration in lieu of gills may help to account for its

sediment tolerance in this study. Several taxa were also found to change tolerance with

season. Epeorus was classified as sediment sensitive in the summer and moderately

tolerant in the fall in our study. The results from the summer agree with those of Relyea

et al. (2000) and McClelland and Brusven (1980) which found Epeorus to be sediment

sensitive. Baetis was also found to change tolerance with the season, as it was classified

as sediment sensitive in the summer and moderately tolerant in the fall. Previous studies

have found Baetis spp. to be sediment tolerant, increasing in abundance as sediment

deposition increases (Lenat et al. 1981; Gray and Ward 1982, Relyea et al. 2000). The

change of sediment tolerance with season might be the result of different instars being

present. Many organisms have ontogenetic shifts in their traits (e.g. change feeding mode

and habit) therefore their tolerance to sediment may also change.

Five Chironomid taxa were assigned tolerance values. Chironominae was

classified as sediment tolerant, which is consistent with the results of Zweig 2000.

Prodiamesinae and Tanypodinae were determined to be moderately tolerant to deposited

sediment, while Tanytarsini and Orthocladiinae were classified as sensitive. The

difference in sediment tolerance of the different Chironomid taxa is consistent with the

results of previous studies. Rosenberg and Wiens (1978) found three chironomid taxa to

be unaffected by fine sediment addition, while four taxa were affected.

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Species Traits

Benthic invertebrate tolerance to different stressors is determined by the traits

individuals posses. Therefore, benthic invertebrate and species traits response patterns

may help to suggest the stressor type impacting a site, which could allow biomonitoring

approaches to become more diagnostic and aid in remediation. Determination of sediment

tolerant traits may also help to improve interpretations of taxa tolerances. Our ordination

analysis revealed a gradient of species traits responses to deposited sediment. Several

traits exhibited negative and positive relationships with increased sediment deposition

allowing determination of traits indicative of high and low deposited sediment areas. It

was expected species traits associated with resilience/ resistance would be positively

associated with increased deposited sediment, as the substrates are often unstable and

more susceptible to disturbance. Therefore, long-lived; large bodied species were not

expected to be present in higher sediment levels (Richards et al. 1997). However, traits

beneficial in areas of higher disturbance and deposited sediment, such as small body size,

habitat generalists and clingers were expected to be associated with deposited sediment

(Townsend et al. 1997).

Species traits positively correlated with increased sediment deposition in both the

summer and fall were plastron respiration, elongate body form, depositional rheophily

and predation. In the summer heavy armouring, sprawling and large body size were also

positively correlated with increased sediment deposition. The predominance of plastron

respiration was not unexpected as other respiration techniques, such as tegument and gills

are affected by the deposition of fine sediment. Sedimentation can cover the gills of taxa

interfering with respiration (Waters 1995). Predators are generally mobile and, therefore,

114

have the ability to move out of sedimentation areas when the conditions become

extremely unfavorable, which may account for their correlation with increased sediment

deposition. Additionally, sprawlers generally inhabit floating leaves and fine sediments,

which make their positive correlation with increased sedimentation anticipated. They also

have modifications for maintaining their respiratory surfaces free of fine sediment

(Merritt and Cummins 1996). The correlation between large body size and increased

sediment deposition was not expected as generally in high disturbance areas smaller

bodied organisms with faster lifecycles are favoured. However larger invertebrates are

able to move within areas of deposited sediment allowing taxa with this trait to be able to

withstand increased sediment (Lamoroux et al. 2004). Benthic invertebrates whose

rheophily preference is depositional prefer areas of slow flow such as pools and margins

where fine sediment is deposited (Merritt and Cummins 1996). Therefore, the strong

correlation between high levels of deposited sediment and depositional rheophily can be

explained because of the strong negative correlation of erosional rheophily and deposited

sediment.

Species traits negatively associated with increased sediment deposition varied

between the two sampling periods, which may be a result of seasonal changes in

community composition and therefore the traits expressed. Different taxa may possess

different traits that allow them to be tolerant or intolerant of deposited sediment, therefore

resulting in different traits being sensitive. The traits free-moving/ sessile, univoltine,

abdominal gill placement, flattened body form, gills present, gill respiration, slow-

seasonal development, mulitvoltine, plate- like gills, strong swimmer, streamlined body

and erosional rheophily were negatively associated with increased sediment deposition

115

(Figures 4.7 and 4.8). The correlation between these traits and low levels of deposited

sediment was not unexpected as many of these traits are adaptations to areas of higher

substrate stability and flow or erosional areas where fine sediment is unlikely to be

deposited. The one exception however is multivoltinism, which refers to an organism

which produces more than two generations per year. This trait would be expected to be

more associated with high disturbance, such as deposited sediment areas where substrate

stability is lower. Whereas Doledec et al. (2006) found the univoltine trait to decrease

across a landuse gradient. However in the EPT orders voltinism is a less labile trait,

because some groups have little variation among genera, e.g. all Trichopterans are

univoltine (Poff et al. 2007). Lamoroux et al. (2004) and Doledec et al. (2006) found

small invertebrates with streamlined bodies to be associated with relatively coarse

substrates which agrees with our assignment of streamlined body forms as a sediment

sensitive trait.

Our results suggest benthic invertebrate taxa need not possess all tolerance traits

to be tolerant of deposited sediment. Therefore there may be degrees of sediment

tolerance with taxa that possess more of the tolerance traits being more tolerant to higher

levels of sediment deposition than others. Benthic invertebrate taxa classified as sediment

tolerant were found to possess one or more of the traits determined to be associated with

increased sediment deposition. The majority of sediment tolerant taxa also possessed

some traits that were thought to infer sediment tolerance, but were not found to be

positively associated with increased sediment (e.g. the habit burrower and fast- seasonal

development). The majority of sediment tolerant taxa were either predators or collector

gathers, which agrees with the results of Zweig (2000) who also determined these modes

116

of feeding to be unaffected by sediment deposition. Several of the tolerant taxa were also

shredders, which consume coarse particulate organic matter normally found in slow-

flowing areas where sediment is deposited. A large majority of the tolerant taxa (e.g.

Sweltsa and Glossosoma) also lacked gills and relayed on other respirations techniques,

which is consistent with the results from the trait analysis that showed gill respiration to

be sensitive to increased sediment deposition. However several of the tolerant taxa rely

on gill respiration, but they also posses other sediment tolerant traits such as burrowing

and sprawling.

The lack of response of some traits may be due to that fact that percent deposited

sediment was not the most important gradient, therefore traits may be responding to

additional environmental gradients. Furthermore traits expected to be associated with

increased sediment (e.g. burrowers) may be linked with other traits that are responding to

another gradient or they may be constrained by low evolutionary lability (Poff et al.

2006). As well a number of habitat trait filters may be acting upon the benthic

invertebrate community and therefore the trait composition hierarchically at multiple

scales (Poff 1997, Lamouroux et al. 2004).

4.5 CONCLUSIONS

In conclusion, our findings show that environmental gradients act as filters on benthic

invertebrates trait compositions as predicted by the habitat template model, which states

that habitat is the filter that acts on the trait composition. The positive and negative

correlations between certain species traits and increased sediment deposition gives

support to the use of species traits in the monitoring and assessment of sediment in lotic

117

environments. Our results have shown several species traits are correlated with deposited

sediment, however, a full understanding of the environmental variables responsible for

the presence/ absence and distribution of traits is necessary before trait monitoring tools

can be fully useful (Richards et al. 1997). Future work should focus on additional traits

and the response of traits to additional habitat characteristics. Once fully understood

species trait approaches may allow for a priori predictions of benthic invertebrate

responses and trait composition (Doledec et al. 2006). In contrast to taxonomic

composition, species traits approaches can be compared across geographic regions, as

well as determine most sensitive life history characteristics (Doledec et al. 2006). Our

results have also shown species traits can give an understanding of the presence and

absence of species in particular environments and may help to diagnose impacts by

observing response patterns. Benthic invertebrate tolerance to deposited sediment is not

dependent on a taxa possessing all sediment tolerant traits. Tolerant taxa possess a

combination of sediment sensitive and tolerant traits, therefore the degree of tolerance

may be dependent on the number of sediment tolerant traits possessed. Further work is

needed on linking benthic invertebrate tolerance and the species traits possessed.

Although a full understanding of trait responses to various human impact gradients and

reference conditions is required, our results demonstrate benthic invertebrate species

traits patterns show great promise as a more ecologically-relevant biomonitoring tool.

Therefore use of species traits patterns may be useful in the diagnosis of impacts during

the biomonitoring of lotic environments.

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indebted to Wendy Monk and Nellie Horgan for their help with the multivariate statistical

analysis. I would also like to thank Allen Curry, Joseph Culp and Kristie Heard for their

guidance and help. I also greatly appreciate Kristie Heard’s help in identifying the

numerous benthic invertebrate samples.


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Richards, C., Haro, R.J., Johnson, L.B, and Host, G.E. 1997. Catchment and reach- scale properties as indicators of macroinvetebrate species traits. Freshwater Biology. 37: 219- 230. Rosenberg, D.M. and A.P. Wiens. 1980. Responses of Chironomidae (Diptera) to short-term experimental sediment additions in the Harris River, Northwest Territories, Canada. Acta Universitatis Carolinae- Biologica 1978: 181-192. Roy, A.H; A.D. Rosemond, D.S. Leigh, M.J. Paul and J.B. Wallace. 2003. Habitat-

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predators reduce fine sediment in stream beds. Freshwater Biology. 36: 569- 577. Zweig, L.D. 2000. Effects of deposited sediment on stream benthic macroinvertebrate communities. MSc Thesis, University of Missouri- Columbia. Zweig, L.D. and Rabeni, C.F. 2001. Biomonitoring for deposited sediment using benthic invertebrates: a test on 4 Missouri streams. Journal of the North American Benthological Society. 20(4): 643- 657. Species Trait Database References: Adler, P.H., D.J. Giberson and L.A. Purcell. 2005. Insular black flies Diptera: Simuliidae of North America: tests of colonization hypotheses. Journal of Biogeography. 32: 211- 220. Alba- Tercedor, J.and A. Sanchez- Ortega. 1991. Overview of the strategies of Ephemeroptera and Plecoptera. Proceedings of the VI th International

Ephemeroptera conference (24- 28 July 1989) and X th International symposium on Plecoptera (27- 30 July 1989), Granada, Spain. The Sandhill Crane Press, Inc. Gainesville, Florida, U.S.A.

Burks, B.D. 1953. The mayflies, or Ephemeroptera of Illinois. Bulletin of the Illinois Natural History Survey. 26(1): 216. Dobrin, M. and D.J. Giberson. 2003. Life history and production of mayflies, stoneflies,

and caddisflies (Ephemeroptera, Plecoptera, and Trichoptera) in a spring-fed stream in Prince Edward Island, Canada: evidence for population asynchrony in spring habitats? Canadian Journal of Zoology. 81: 1083- 1095.

Edmunds, G.F., S.L. Jensen and L. Berner. 1976. The mayflies of North and Central America. University of Minnesota. McAlpine, J.F., B.V. Peterson, G.E. Shewell, H.J. Teskey, J.R. Vockeroth and

D.M. Wood. 1981. Manual of Nearctic Diptera. Volume 1. Research Branch Agriculture Canada. Monograph No. 27. 674 pgs.

Merritt, R.W. and K.W. Cummins. 1996. An introduction to the aquatic insects of North America (3rd Edition). Kendall/Hunt Publishing Company. Dubuque, Iowa. Poff, N.L., J.D. Olden, N.K.M. Vieira, D.S. Finn, M.P. Simmons and B.C. Kondratieff. 2006. Functional trait niches of North American lotic insects: trait- based ecological application in light of phylogenetic relationships. Journal of the North American Benthological Society. 25(4): 730- 755. Vieira, N.K.M., N.L. Poff, D.M. Carlisle, S.R. Moulton, M.L. Koski and B.C. Kondratieff. 2006. A database of lotic invertebrate traits for North America.

123

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http://pubs.water.usgus.gov/ds187�

124

4.7 TABLES

Table 4.1: Biological traits and categories examined along a fine sediment deposition gradient (Merritt and Cummins 1996; Usseglio- Polaters et al. 2000; Gayraud et al. 2003; Poff et al. 2007). Functional Feeding Group Collector gather Feed 1 Collector filterer Feed 2 Scraper Feed 3 Predator Feed 4 Shredder Feed 5 Habit (mode of existence) Burrower Habi 1 Climber Habi 2 Sprawler Habi 3 Clinger Habi 4 Swimmer Habi 5 Skater Habi 6 Mobility Free moving Mobi 1 Sedentary Mobi 2 Both Mobi 3 Rheophily Depositional Rheo 1 Erosional/ Depositional Rheo 2 Erosional Rheo 3 Voltinism (Lifecycle) Semivoltine Volt 1 Univoltine Volt 2 Bi- Multivoltine Volt 3 Swimming Ability None Swim 1 Weak Swim 2 Strong Swim 3 Body Size Small (< 9mm) Size 1 Medium (9- 16mm) Size 2 Large (> 16mm) Size 3 Body Form Flattened Body 1 Slightly Flattened Body 2 Streamlined Body 3 Cylindrical Body 4

125

Sub-cylindrical Body 5 Elongate Body 6 Armouring None Arm 1 Some Arm 2 Good Arm 3 Respiration Technique Tegument Resp 1 Gills Resp 2 Plastron Resp 3 Gills Present Gill 1 Absent Gill 2 Gill Placement Lateral Plac 1 Abdominal Plac 2 Thorax Plac 3 Sub-mental Plac 4 Anal Pac 5 None Plac 6 Gill Form Lanceolate Form 1 Plate-like Form 2 Filament (single, branch) Form 3 Operculate (semi, full) Form 4 Tufts Form 5 None Form 6 Armouring None Arm 1 Sceloritized (some) Arm 2 Case (good) Arm 3 Development Fast Seasonal Devel 1 Slow Seasonal Devel 2 Non-Seasonal Devel 3

126

Table 4.2 : Spearman rank correlation coefficients and associated p- values for PCA axis site scores of average benthic invertebrate relative abundance data and environmental variables for July 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).

Axis 1 Axis 2 Axis 3 Axis 4 Water


- 0.692*

- 0.168

- 0.469

0.210


0.013 0.602 0.124 0.513

pH


- 0.678*

- 0.510

- 0.119

- 0.210

p- value

0.015 0.09 0.713 0.513

Dissolved


0.566

- 0.161

0.636*

- 0.154

Oxygen p- value

0.055 0.618 0.026 0.633

Conductivity


0.210

- 0.406

- 0.021

- 0.364

p- value

0.513 0.191 0.948 0.245

Flow


- 0.664*

- 0.476

0.035

- 0.021

p- value

0.018 0.118 0.914 0.948

% Deposited


- 0.189

0.722**

- 0.088

- 0.536

Sediment p- value

0.556 0.008 0.787 0.073

%


- 0.455

0.594*

0.343

- 0.210


0.138 0.042 0.276 0.513

% Slope


0.357

- 0.133

0.105

0.580*

p- value

0.255 0.681 0.746 0.048

Total


- 0.923**

0.014

- 0.224

0.077

Suspended Solids

p- value

0.000 0.966 0.484 0.812

Chlorophyll- a


0.420

- 0.070

0.343

0.049

p- value 0.175 0.829 0.276 0.880

% < 2mm


0.021

0.49

- 0.455

- 0.762 **

127

p- value

0.948

0.106

0.138

0.004

% < 4mm


- 0.028

0.503

- 0.517

- 0.713 **

p- value

0.931

0.095

0.085

0.009

Bankfull Tractive


0.14

- 0.252

0.168

0.671 *

Force p- value

0.665

0.43

0.602

0.017

Tractive Force


- 0.119

0.049

0.329

0.329

p- value

0.713

0.88

0.297

0.297

128

Table 4.3: Spearman rank correlation coefficients and associated p- values for PCA axis site scores of average benthic invertebrate relative abundance data and environmental variables for October 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).



- 0.692 *

- 0.18

- 0.469

0.21


0.013 0.602 0.124 0.513

pH


- 0.678 *

- 0.51

- 0.119

- 0.21

p- value

0.015 0.09 0.713 0.513

Dissolved


0.566

- 0.161

0.636 *

- 0.154

Oxygen p- value

0.055 0.618 0.026 0.633

Conductivity


0.21

- 0.406

- 0.021

- 0.364

p- value

0.513 0.191 0.948 0.245

Flow


- 0.664 *

- 0.476

0.035

- 0.021

p- value

0.018 0.118 0.914 0.948

% Deposited


- 0.189

0.722**

- 0.088

- 0.536

Sediment p- value

0.556 0.008 0.787 0.073

%


- 0.455

0.594*

0.343

- 0.21


0.138 0.042 0.276 0.513

% Slope


0.357

- 0.133

0.105

0.580*

p- value

0.255 0.681 0.746 0.048

Total


- 0.923**

0.014

- 0.224

0.077

Suspended Solids

p- value

0.00 0.966 0.484 0.812

Chlorophyll- a


0.42

- 0.07

0.343

0.049

p- value

0.175 0.829 0.276 0.88

129

% < 2mm


- 0.287 0.643 * 0.007 - 0.427

p- value

0.366

0.024

0.983

0.167

% < 4mm


- 0.287

0.643 *

0.007

- 0.427

p- value

0.366

0.024

0.983

0.167

Bankfull Tractive


0.14

- 0.252

0.168

0.671 *

Force p- value

0.665

0.43

0.602

0.017

Tractive Force


- 0.119

0.049

0.329

0.329

p- value

0.713

0.88

0.297

0.297

130

Table 4.4: Spearman rank correlation coefficients and associated p- values for PCA axis site scores of average species trait relative abundance data and environmental variables for July 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).



- 0.287

0.669*

0.224

- 0.182


0.366 0.017 0.484 0.572

pH


0.287

0.872**

0.175

- 0.056

p- value

0.366 0.00 0.587 0.863

Dissolved


0.462

- 0.459

- 0.126

0.042

Oxygen p- value

0.131 0.134 0.697 0.897

Conductivity


0.832**

0.312

0.056

0.441

p- value

0.001 0.324 0.863 0.152

Flow


0.259

0.764**

0.084

- 0.014

p- value

0.417 0.004 0.795 0.966

% Deposited


- 0.385

- 0.044

- 0.242

0.504

Sediment p- value

0.216 0.892 0.449 0.094

%


- 0.427

- 0.109

- 0.161

- 0.028


0.167 0.737 0.618 0.931

% Slope


0.035

- 0.406

0.559

- 0.238

p- value

0.914 0.19 0.059 0.457

Total


- 0.322

0.718**

0.217

0.091

Suspended Solids

p- value

0.308 0.009 0.499 0.779

Chlorophyll- a


0.098

- 0.396

- 0.357

- 0.266

p- value

0.762 0.203 0.255 0.404

131

% < 2mm


0.021 0.49 - 0.455 - 0.762 **

p- value

0.948

0.106

0.138

0.004

% < 4mm


- 0.028

0.506

- 0.517

- 0.713 **

p- value

0.931

0.095

0.085

0.009

Bankfull Tractive


0.14

- 0.252

0.168

0.671 *

Force

p- value

0.665

0.43

0.602

0.017

Tractive Force


- 0.119

0.049

0.329

0.329

p- value

0.713

0.88

0.297

0.297

132

Table 4.5: Spearman rank correlation coefficients and associated p- values for PCA axis site scores of average species trait relative abundance data and environmental variables for October 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).



0.678*

0.448

- 0.308

0.51


0.015 0.145 0.331 0.09

pH


0.329

0.497

- 0.741**

0.154

p- value

0.297 0.101 0.006 0.633

Dissolved


- 0.650*

- 0.224

0.196

- 0.643*

Oxygen p- value

0.022 0.484 0.542 0.024

Conductivity


- 0.322

- 0.168

- 0.42

- 0.126

p- value

0.308 0.602 0.175 0.697

Flow


0.385

0.538

- 0.42

0.091

p- value

0.217 0.071 0.175 0.779

% Deposited


0.284

- 0.511

0.102

- 0.165

Sediment p- value

0.372 0.089 0.753 0.609

%


0.483

- 0.098

0.371

- 0.455


0.112 0.762 0.236 0.138

% Slope


- 0.294

0.07

0.063

0.322

p- value

0.354 0.829 0.846 0.308

Total


0.811**

0.336

- 0.203

0.28

Suspended Solids

p- value

0.001 0.286 0.527 0.379

Chlorophyll- a


- 0.238

0.084

0.308

- 0.28

p- value

0.457 0.795 0.331 0.379

133

% < 2mm


0.538

- 0.259

0.294

- 0.189

p- value

0.071

0.417

0.354

0.557

% < 4mm


0.538

- 0.259

0.294

- 0.189

p- value

0.071

0.417

0.354

0.557

Bankfull Tractive


- 0.154

0.28

0.014

0.357

Force

p- value

0.633

0.379

0.966

0.255

Tractive Force


0.049

0.308

- 0.007

0.231

p- value

0.88

0.331

0.983

0.471

134

Table 4.6: Species tolerances for July 2005. (Tolerant = 3, moderately tolerant = 2, sensitive = 1).

Taxa Species Score Tolerance Value Pelecypoda -0.7208 3

Dicranota -0.7012 3 Pseudolimnophila -0.519 3

Chironominae -0.5174 3 Limnophila -0.4957 3 Oligochaeta -0.4839 3

Prodiamesinae -0.4584 3 Onocosmoecus -0.4548 3

Rhyacophila -0.4522 3 Parapsyche -0.3817 3

Sialis -0.3613 3 Tabanus -0.3015 3

Psychoglypha -0.2929 3 Nematoda -0.248 3 Sweltsa -0.2337 3 Attenella -0.2248 3 Nemoura -0.17 2

Neophylax -0.1335 2 Tanytarsini -0.1275 2

Lepidostoma -0.1242 2 Isogenoides -0.0739 2

Curculionidae adult -0.0511 2 Leuctra -0.0482 2

Paraleuctra -0.0243 2 Tanypodinae -0.0149 2 Amphinemura 0.0012 2 Epeorus (Iron) 0.0022 2

Dannella 0.0189 2 Serratella 0.0196 2

Elmidae larva 0.0251 2 Elmidae adult 0.0413 2 Ephemerella 0.0542 2 Heptagenia 0.0615 2

Mallochohelea 0.0798 2 Simulium 0.1063 2

Tipula 0.1457 2 Hesperophylax 0.2186 1

Chelifera 0.2384 1 Apatania 0.2551 1

Prosimulium 0.2723 1

135

Diura 0.2819 1 Micrasema 0.3157 1 Atopsyche 0.3157 1

Paraleptophlebia 0.3332 1 Cinygmula 0.3674 1

Isoperla 0.4442 1 Drunella 0.4528 1

Limnophora 0.4944 1 Hydroptila 0.5263 1 Antocha 0.5286 1 Baetis 0.598 1

Orthocladinae 0.7889 1

136

Table 4.7: Species tolerances for October 2005. (Tolerant = 3, moderately tolerant = 2, sensitive = 1).

Taxa Species Score Tolerance Value Dicranota -0.4911 3

Oligochaeta -0.4781 3 Chironominae -0.4691 3

Simulium -0.4189 3 Pseudolimnophila -0.3771 3

Glossosoma -0.3117 3 Elmidae adult -0.2906 3

Hirudinea -0.2719 3 Sweltsa -0.2651 3

Leptophlebia -0.2495 3 Nematoda -0.2485 3

Pelecypoda -0.2078 3 Rhyacophila -0.1399 2

Baetis -0.1223 2 Prodiamesinae -0.0882 2

Tipula -0.0644 2 Brachycentrus -0.0639 2 Lepidostoma -0.0182 2 Parapsyche 0.02 2 Molophilus 0.0321 2 Limnophora 0.0978 2

Antocha 0.1196 2 Nemoura 0.1312 2

Neophylax 0.1373 2 Heptagenia 0.1427 2

Tanypodinae 0.1446 2 Paracapnia 0.1531 2 Ephemerella 0.1672 2

Elmidae larvae 0.181 2 Serratella 0.1883 2

Mallochohelea 0.205 1 Tanytarsini 0.2058 1 Hydroptila 0.2106 1 Chelifera 0.2159 1

Epeorus (Iron) 0.2389 1 Apatania 0.2738 1

Orthocladinae 0.3222 1 Leuctra 0.3295 1 Isoperla 0.3297 1

Platyhelminth 0.3297 1

137

Cinygmula 0.3752 1 Paraleptophlebia 0.3927 1

Hydropsyche 0.4379 1 Micrasema 0.5402 1

138

4.8 FIGURES

1 Acerpenna 23 Nemoura 49 Elmidae larve 71 Molophilus 2 Baetis 24 Diura 50 Elmidae adult 72 Pseudolimnophila 3 Attenella 25 Isogenoi 53 Atherix 73 Tipula 4 Dannella 26 Isoperla 54 Mallocho 74 Corixida 5 Drunella 27 Apatania 55 Chironom 75 Gerridae 6 Ephemerella 29 Micrasema 56 Orthocladiinae 76 Sialis 7 Serratella 30 Glossosoma 57 Prodiame 77 Pyralida 8 Cinygmula 31 Atopsyche 58 Tanypodi 78 Oligochaeta 9 Epeorus 34 Parapsyche 59 Tanytars 79 Hirudine

10 Heptageniidae 35 Hydroptila 60 Dixa 80 Platyhelminthes 11 Leucrocu 37 Lepidost 61 Chelifera 81 Nematoda 12 Leptophl 38 Hesperop 62 Limnophora 82 Pelecypoda 13 Paralept 39 Onocosmo 64 Simulium 83 Gastropoda 16 Paracapnia 41 Psyhogl 65 Prosimulium 84 Ostracoda 18 Sweltsa 45 Rhyacophilidae 66 Tabanus 85 Copepoda 20 Leuctra 46 Neophylax 67 Antocha 86 Hydracar 21 Paraleuctra 47 Curculio 69 Dicranota 88 Collembolla 22 Amphinemura 48 Agabus 70 Limnophila Figure 4.1: RDA ordination of average species relative abundance and environmental variables significantly correlated with PCA axes 1 and 2, July 2005.

Axis 1

139

2 Baetis 25 Isogenoides 49 Elmidae larvae 69 Dicranota 3 Attenella 26 Isoperla 50 Elmidae adult 70 Limnophila 6 Ephemerella 27 Apatania 52 hydrophilidae 71 Molophilus 7 Serratella 28 Brachycentrus 53 Atherix 72 Pseudolimnophila 8 Cinygmula 29 Micrasema 54 Mallochohelea 73 Tipula 9 Epeorus 30 Glossosoma 55 Chironominae 78 Oligochaeta

10 Heptagenia 33 Hydropsyche 56 Orthocladiinae 79 Hirudinea 12 Leptophlebia 34 Parapsyche 57 Prodiamesinae 80 Platyhelminthes 13 Paraleptophlebia 35 Hydroptila 58 Tanypodinae 81 Nematoda 14 Capnura 37 Lepidostoma 59 Tanytarsini 82 Pelecypoda 15 Isocapnia 42 pycnopsyche 61 Chelifera 83 Gastropoda 16 Paracapna 43 Chimarra 62 Limnophora 84 Ostracoda 18 Sweltsa 44 Oligostomis 63 Pericoma 85 Copepoda 20 Leuctra 45 Rhyacophila 64 Simlium 86 Hydracar 22 Amphinemura 46 Neophylax 66 Tabanus 87 Isopoda 23 Nemoura 47 Curculionidae 67 Antocha 89 Collembola

Figure 4.2: RDA ordination of average species relative abundance and environmental variables significantly correlated with PCA axes 1 and 2, October 2005.

Axis 1

140

Figure 4.3: RDA ordination of benthic invertebrate taxa tolerances to deposited sediment (axis 1), July 2005. Taxa within the red and green boxes are classified as tolerant (value = 3) or sensitive (value = 1), while taxa within the middle are considered to be moderately tolerant (value = 2).

Tolerant Sensitive

Axis 1

141

Figure 4.4: RDA ordination of benthic invertebrate taxa tolerances to deposited sediment (axis 1), October 2005. Taxa within the red and green boxes are classified as tolerant (value = 3) or sensitive (value = 1), while taxa within the middle are considered to be moderately tolerant (value = 2).

Tolerant Sensitive

Axis1

142

Figure 4.5: RDA ordination of species traits and environmental variables correlated with PCA axes 1 and 2, July 2005.

143

Figure 4.6: RDA ordination of species traits and environmental variables correlated with PCA axes 1 and 2, October 2005.

Axis 1

144

Figure 4.7: RDA ordination of average trait relative abundances and sediment variables July 2005.

145

Figure 4.8: RDA ordination of average trait relative abundances and sediment variables, October 2005.

146

5.0 DISCUSSION Several commonly used biomonitoring metrics were responsive along a gradient

of deposited sediment conditions. The metrics % burrower and % Chironominae of

Chironomidae responded positively to increasing fine sediment deposition. While the

metrics: % EPT, EPT abundance and % Orthocladiinae of Chironomidae responded

negatively to increasing fine sediment deposition. Additional metrics were significantly

correlated with deposited sediment however these results were not consistent, i.e., only

occurring in one of the seasons sampled. Diversity measures (Shannon Diversity, Taxa

richness and EPT richness) were not affected by increased fine sediment deposition,

indicating these metrics are not appropriate for testing the effects of a sediment stressor.

The results suggest sediment deposition causes a change in the benthic invertebrate

community under the conditions during this study, which can not be detected by current

metrics (e.g. Shannon Diversity, Species richness). Taxa intolerant to fine sediment may

have decreased in abundance or were replaced by taxa more tolerant to deposited fine

sediment, resulting in no change in the metric values even though a shift in the

community had occurred.

Species traits that are modifications or adaptations for living with fine sediment

were positively associated with increased sediment deposition. Tolerance traits included

plastron respiration, depositional preference, free moving, heavy armour and collector

gather. These traits allow benthic invertebrates to survive in fine sediment conditions or

escape (e.g. free moving). Tolerant benthic invertebrates were found to possess a

combination of tolerant and non-tolerant species traits, suggesting tolerance is not

dependant on the possession of all adaptive traits.

147

5.1 IMPLICATIONS OF RESULTS

Understanding how metrics respond to individual stressors is important for

biomonitoring purposes. The consistent responsiveness of several metrics over the two

seasons suggests they may be used for the detection of fine sediment deposition effects in

biomonitoring programs. The response patterns of metrics can be used to identify and

quantify the source of impairment if the response patterns of metrics to individual

stressors are known. The lack of correlations between certain metrics, previously found to

respond to deposited sediment, in my study shows a combination of metrics and indices

may be need to be incorporated into biomonitoring programs to detect impacts. Metrics

incorporating sediment tolerant and intolerant taxa, e.g. % EPT and % Orthocladiinae, as

well as deposited sediment biotic indices would be useful in biomonitoring.

Species traits offer a more ecologically relevant biomonitoring approach than

community composition and diversity measures. The results of this study have shown

that species traits are responsive to sediment stressors and may be used as an alternative

biomonitoring metric to determine the extent and source of the impact. Species traits can

help to explain the presences/ absences of benthic invertebrate taxa in particular

environments, as well as identify the source of impairment. For example, sediment

tolerant benthic invertebrates possessed one or more of the traits determined to be

associated with increased sediment deposition.

5.2 FUTURE RESEARCH

148

Further research is needed to understand how changes in the benthic invertebrate

community composition and taxa abundances by sediment deposition affect riverine

ecosystems. Since benthic invertebrates transfer energy to higher trophic levels the

effects of sediment deposition may not be limited to these taxa (Heino 2005). The

implications of these observed effects on benthic invertebrates at the reach and watershed

scale require examination. Additional testing spatially and temporally of the

biomonitoring metrics and species traits along natural deposited sediment gradients may

be necessary as I have demonstrated variation between studies and seasons.

Future traits studies should focus on both biological and ecological species traits

as both are important in discriminating different anthropogenic impacts on benthic

invertebrates (Charvet et al. 2000). Individual taxa sediment tolerance in terms of species

traits requires further examination, as it is not known whether the degree of tolerance is

based on the number of sediment tolerant traits a taxa possesses. Determination of which

species traits correlated with sediment allow the greatest species tolerance will be

beneficial in understanding and determining benthic invertebrate responses to sediment

and potentially other stressors.


Charvet, S., B. Statzner, P. Usseglio- Polatera and B. Dumonts. 2000. Traits of benthic macroinvertebrates in semi- natural French streams: an initial application to biomonitoring in Europe. Freshwater Biology 43: 277- 296. Heino, J. 2005. Functional biodiversity of macroinvertebrate assemblages along major

ecological gradients of boreal headwater streams. Freshwater Biology 50: 1578 1587

CURRICULUM VITAE

Olivia Dawn Logan

BSc, University of New Brunswick (Saint John), 2004

MSc Thesis Publications: Logan, O.D., Curry, R.A. and J.M. Culp. In prep. Reducing benthic invertebrate sample processing time and costs using coarse sieve subsampling. Logan, O.D., Curry, R.A. and J.M. Culp. In prep. Determination of benthic invertebrate biomonitoring metrics responsive to fine sediment deposition. Logan, O.D., Curry, R.A. and J.M. Culp. In prep. Using species traits as a diagnostic bioassessment tool. MSc Conference Presentations: Logan, O., Curry, R.A., Culp, J. and K. Heard. 2006. Effects of sediment deposition on

benthic invertebrate community structure and biological trait composition. In Proceedings of the Joint Assembly of the NABS. June 5-9, 2006. Anchorage, AK.

150

APPENDIX A

151

Genus Trophic Habit Rheo Volt Respir Gills Gill

Form Gill

Placement Body Form Mobility

Acerpenna 1 4 3 3 2 1 2 2 3 1 Baetis 1 4 3 3 2 1 2 2 3 1 Attenella 1 4 2 2 2 1 2 2 1 1 Dannella 1 4 2 2 2 1 4 2 1 1 Drunella 3 4 2 2 2 1 2 2 1 1 Ephemerella 1 4 2 2 2 1 4 2 1 1 Serratella 1 4 2 2 2 1 2 2 1 1 Cinygmula 3 4 3 2 2 1 2 1 1 1 Epeorus (Iron) 1 4 3 2 2 1 2 1 1 3 Heptagenia 3 4 3 2 2 1 2 1 1 1 Leucrocuta 3 4 2 2 2 1 2 1 1 1 Leptophlebia 1 5 3 2 2 1 1 2 2 1 Paraleptophlebia 1 5 3 2 2 1 1 2 2 1 Capnura 5 3 2 2 1 2 6 6 4 1 Isocapnia 5 3 3 1 1 2 6 6 4 1 Paracapnia 5 3 2 2 1 2 6 6 4 1 Sasquaperla 0 4 3 0 1 2 6 6 4 1 Sweltsa 4 4 3 1 1 2 6 6 4 1 Utaperla 5 4 2 2 1 2 6 6 4 1 Leuctra 5 1 2 2 1 2 6 6 4 1 Paraleuctra 5 3 2 2 1 2 6 6 6 1 Amphinemura 5 3 2 2 2 1 5 3 5 3 Nemoura 5 3 3 2 1 2 6 6 5 3 Diura 3 4 3 2 1 2 6 6 1 1 Isogenoides 4 4 2 2 2 1 3 4 4 1 Isoperla 4 4 3 2 1 2 6 6 4 1 Apatania 3 4 3 2 2 1 3 2 4 3 Brachycentrus 2 4 3 2 2 1 3 2 4 3 Micrasema 5 4 3 2 2 1 3 2 4 3 Glossosoma 3 4 3 3 1 2 6 6 4 1 Atopsyche 4 4 3 0 2 2 3 2 4 3

152

Homoplectra 2 4 3 2 2 1 3 2 4 3 Hydropsyche 2 4 3 2 2 1 3 2 4 3 Parapsyche 5 4 3 2 2 1 3 2 4 3 Hydroptila 3 4 2 2 2 1 3 2 1 3 Theliopsyche 5 2 3 2 2 1 3 2 4 3 Lepidostoma 5 2 2 2 2 1 3 2 4 3 Hesperophylax 5 3 2 2 2 1 3 2 4 3 Onocosmoecus 5 3 1 2 2 1 3 2 4 3 Pseudostenophylax 5 3 2 2 2 1 3 2 4 3 Psychoglypha 5 3 2 2 2 1 3 2 4 3 pycnopsyche 5 3 1 2 2 1 3 2 4 3 Chimarra 2 4 3 2 1 2 6 6 4 3 Oligostomis 4 4 2 0 2 1 3 2 4 0 Rhyacophila 4 4 3 2 2 1 0 0 4 1 Neophylax 3 4 3 2 2 1 3 2 4 3 Curculionidae adult 5 4 0 0 0 2 6 6 0 0 Agabus 4 5 2 1 3 2 6 6 1 1 Elmidae larva 1 4 3 1 1 2 6 6 4 1 Elmidae adult 1 4 3 1 1 2 6 6 4 1 Psephenidae 3 4 3 1 2 1 0 0 1 3 hydrophilidae 4 2 1 1 0 2 6 6 0 0 Atherix 4 3 2 2 1 1 0 0 6 1 Mallochohelea 4 1 1 2 1 2 6 6 6 1 Chironominae 1 1 1 3 2 1 3 0 4 1 Orthocladinae 1 1 2 2 2 1 3 0 4 1 Prodiamesinae 1 1 3 2 2 1 3 0 4 1 Tanypodinae 4 3 2 2 2 1 3 0 4 1 Tanytarsini 2 1 2 0 0 0 0 0 4 0 Dixa 1 5 2 2 0 0 0 0 4 0 Chelifera 4 3 1 2 1 2 6 6 4 1 Limnophora 4 1 3 0 3 2 6 6 0 0 Pericoma 1 1 1 2 2 2 6 6 4 1 Simulium 2 4 3 3 1 2 6 6 4 3

153

Prosimulium 2 4 3 2 1 2 6 6 4 3 Tabanus 4 1 2 2 3 2 6 6 0 0 Antocha 1 4 3 3 2 1 3 5 6 0 Arctoconopa 5 1 2 0 0 0 0 0 6 0 Dicranota 4 3 2 2 3 2 6 6 6 0 Limnophila 4 1 1 0 3 2 6 6 6 0 Molophilus 0 1 1 0 3 2 6 6 6 0 Pseudolimnophila 0 1 1 0 3 2 6 6 6 0 Tipula 5 1 2 1 0 0 0 0 0 0 Sialis 4 1 2 2 1 2 6 6 6 1 Pyralidae 5 1 1 2 0 2 6 6 0 0

Genus Swimming

Ability Armouring Development Max Body Size Acerpenna 3 1 1 1 Baetis 3 1 1 1 Attenella 2 1 2 1 Dannella 2 1 0 1 Drunella 2 1 2 2 Ephemerella 2 1 2 1 Serratella 2 1 2 1 Cinygmula 2 1 1 2 Epeorus (Iron) 2 1 1 2 Heptagenia 3 1 1 2 Leucrocuta 2 1 1 1 Leptophlebia 3 1 2 2 Paraleptophlebia 2 1 1 2 Capnura 2 1 1 1 Isocapnia 2 1 1 1

154

Paracapnia 2 1 2 1 Sasquaperla 0 1 0 0 Sweltsa 2 1 2 2 Utaperla 2 1 2 1 Leuctra 2 1 1 1 Paraleuctra 2 1 1 1 Amphinemura 1 1 1 1 Nemoura 1 1 2 1 Diura 2 2 2 0 Isogenoides 2 2 2 3 Isoperla 2 2 2 2 Apatania 1 3 2 2 Brachycentrus 1 3 2 2 Micrasema 1 3 2 1 Glossosoma 1 3 2 1 Atopsyche 1 2 0 0 Homoplectra 1 3 2 2 Hydropsyche 1 3 2 0 Parapsyche 1 3 2 2 Hydroptila 1 3 2 1 Theliopsyche 1 3 0 1 Lepidostoma 1 3 0 2 Hesperophylax 1 3 2 3 Onocosmoecus 1 3 2 3 Pseudostenophylax 1 3 2 3 Psychoglypha 1 3 2 3 pycnopsyche 1 3 0 3 Chimarra 1 3 2 0 Oligostomis 1 3 0 3 Rhyacophila 1 0 2 2 Neophylax 1 3 2 3 Curculionidae adult 0 0 0 1 Agabus 3 2 2 1

155

Elmidae larva 1 2 3 1 Elmidae adult 1 2 3 1 Psephenidae 1 2 2 1 hydrophilidae 0 0 0 1 Atherix 1 2 2 0 Mallochohelea 3 2 1 2 Chironominae 1 1 1 0 Orthocladinae 1 1 1 0 Prodiamesinae 1 1 1 2 Tanypodinae 3 1 1 0 Tanytarsini 1 0 0 0 Dixa 0 0 0 0 Chelifera 1 1 2 1 Limnophora 0 0 0 0 Pericoma 1 2 1 0 Simulium 1 1 1 1 Prosimulium 1 1 1 2 Tabanus 0 0 0 3 Antocha 0 1 2 0 Arctoconopa 0 1 2 0 Dicranota 0 1 2 0 Limnophila 0 1 2 0 Molophilus 0 1 2 0 Pseudolimnophila 0 1 2 0 Tipula 0 1 2 0 Sialis 1 2 2 3 Pyralidae 0 0 0 0

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EFFECTS OF FINE SEDIMENT DEPOSITION ON - … Logan... · EFFECTS OF FINE SEDIMENT DEPOSITION ON BENTHIC INVERTEBRATE COMMUNITIES By Olivia D. Logan BSc University of New Brunswick