EFFECTS OF FINE SEDIMENT DEPOSITION ON BENTHIC INVERTEBRATE COMMUNITIES

EFFECTS OF FINE SEDIMENT DEPOSITION ON BENTHIC INVERTEBRATE COMMUNITIES
1
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
ii
DEDICATION
To my parents who have always been there for me.
iii
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
iv
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.
v
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.
vi
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
vii
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
2.6 LITERATURE CITED ....................................................................... 25
2.7 TABLES ............................................................................................. 27
2.8 FIGURES............................................................................................ 34
viii
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
3.2.2 Site Descriptions
3.2.3 Field Sampling
.................................................................... 47
................................................................................. 48
................................................................................... 48
3.2.4 Algal Biomass (Chlorophyll a)
3.2.5 Benthic Macroinvertebrates
3.2.6 Total Suspended Solids
3.2.7 Sediment Measures
................................................................... 50
......................................................................... 50
.............................................................................. 51
3.2.8 Biomonitoring metrics
3.2.9 Statistical Analysis
............................................................... 49
.......................................................................... 52
.............................................................................. 52
3.3 RESULTS ........................................................................................... 53
3.3.1 Sediment Measures and Environmental Variables
3.3.2. Biomonitoring Metrics
........................................... 53
............................................................................. 54
3.3.3 Biomonitoring metrics and environmental variables
..................................... 56
3.3.4 Overall metric responses to deposited fine sediment
..................................... 57
ix
3.4 DISCUSSION ..................................................................................... 57
3.5 CONCLUSIONS ................................................................................ 63
3.6 ACKNOWLEDGEMENTS................................................................ 64
3.7 LITERATURE CITED ....................................................................... 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
4.2.2 Site Descriptions
4.2.3 Field Sampling
.................................................................. 100
............................................................................... 101
................................................................................. 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
x
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
5.3 LITERATURE CITED ....................................................................... 148
xi
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
xii
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
xiii
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
xiv
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
xv
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
1
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
2
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
3
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
4
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.
5
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
6
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
7
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. Habitatspecific responses of stream insects to land cover disturbance: biological
8
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 streammanagement 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
2
Objectives
Determine if removal
of the fine fraction
changes BMI
community trends.
Hypotheses
• Trends observed in
BMI community
structure/
composition will
not significantly
change.
•
Determine how much
additional information
the fine fraction adds to
BMI community
analyses.
•
The abundance of
select taxa will
increase with the
addition of the fine
fraction, but no
overall significant
effects will be
observed.
•
Determine if BMI
community
composition is
determined by the level
of deposited sediment
in PEI streams.
•
As % fine
sediment cover
increases, BMI
taxa intolerant of
fine sediment will
disappear and
tolerant taxa will
increase in
abundance.
•
Determine which
biomonitoring metrics
are sensitive or respond
to fine sediment
deposition.
•
Metrics
incorporating
tolerant taxa will
respond to
increased
sedimentation
positively. Metrics
using intolerant
taxa will respond
negatively to
increased sediment
deposition.
•
Reducing benthic
invertebrate sample
processing time and
costs using coarse sieve
subsampling.
3
Determination of
benthic invertebrate
biomonitoring metrics
responsive to fine
sediment deposition.
10
4
•
Determine which
physical, behavioural,
or functional traits are
most sensitive to fine
sediment deposition.
•
Biological traits
benefical in areas
of increased
sediment will be
positively
associated with
increased
sediment.
•
Develop a list of
sediment tolerant traits.
•
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.
Using species traits as
a diagnostic
bioassessment tool.
• Determine sediment
tolerance of select
BMI taxa.
• 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 810 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 subsamples 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 twodimensional 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 samplesize-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.
2.6 LITERATURE CITED
Barbour, M.T., and J. Gerritsen. 1996. Subsampling of benthic samples: a defense of the
fixed-count method. Journal of the North American Benthological Society 15(3):
386-391.
Cao, Y., D. Williams, and N.E. Williams. 1998. How important are rare species in
aquatic community ecology and bioassessment. Limnology and Oceanography
43(7): 1403-1409.
Carter, J.L., and V.H. Resh. 2001. After site selection and before data analysis: sampling,
sorting, and laboratory procedures used in stream benthic macroinvertebrate
monitoring programs by USA state agencies. Journal of the North American
Benthological Society 20(4): 658- 682.
Ciborowski, J.J.H. 1991. Estimating processing time of stream benthic samples.
Hydrobiologia 222: 101- 107.
Clarke, K.R., and R.N. Gorley. 2001. PRIMER v5: User manual/tutorial. Plymouth
Marine Laboratory, Plymouth, UK.
Courtemanch, D.L. 1996. Commentary on the subsampling procedures used for rapid
bioassessments. Journal of the North American Benthological Society 15(3): 381385.
Doberstein, C.P., J.R. Karr, and L.L. Conquest. 2000. The effect of fixed-count
subsampling on macroinvertebrate biomonitoring in small streams. Freshwater
Biology 44: 355-371.
Growns, J.E., B.C. Chessman, J.E. Jackson, and D.G. Ross. 1997. Rapid assessment
of Australian rivers using macroinvertebrates: cost and efficiency of 6 methods
of sample processing. Journal of the North American Benthological Society.
16(3): 682-693.
King, R.S., and Richardson, C.J. 2002. Evaluating subsampling approaches and
26
macroinvertebrate taxonomic resolution for wetland bioassessment. Journal of the
North American Benthological Society 21(1): 150-171.
Larsen, D.P., and A.T. Herlihy. 1998. The dilemma of sampling streams for
macroinvertebrate richness. Journal of the North American Benthological Society
17(3): 359-366.
Lorenz, A., L. Kirchner, and D. Hering. 2004. ‘Electronic subsampling’ of macrobenthic
samples: how many individuals are needed for a valid assessment result?
Hydrobiologia 516: 299-312.
Morin, A., J. Stephenson, J. Strike, and A.G. Solimini. 2004. Sieve retention probabilities
of stream benthic invertebrates. Journal of the North American Benthological
Society 23(2): 383-391.
Scrimgeour, G.J., J.M. Culp and N.E. Glozier. 1993. A new technique for sampling lotic
invertebrates. Hydrobiologia 254: 65-71.
Sebastien, R.J., D.M. Rosenberg, and A.P. Wiens. 1988. A method for subsampling
unsorted benthic macroinvertebrates by weight. Hydrobiologia 157: 69-75.
Vinson, M.R., and C.P. Hawkins. 1996. Effects of sampling area and subsampling
procedure on comparisons of taxa richness among streams. Journal of the North
American Benthological Society 15(3): 392-399.
Walsh, C.J. 1997. A multivariate method for determining optimal subsample size in the
analysis of macroinvertebrate samples. Marine and Freshwater Research 48: 241248.
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
North Brook Lower
Watershed
Dunk
Latitude
N 46º 20’ 48.6”
Longitude
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
NBR Up
R- value
0.074
Significance level
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
BKV Lo
BKV Up
Dunk Lo
Dunk Up
EasBr Lo
EasBr Up
NBR Lo
NBR Mid
NBR Up
SWB Lo
SWB Mid
SWB Up
Wil Up
SS
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
16.67
10.67
27.33
32.67
36.67
69.33
6.0
73.33
79.33
13.5
1.33
14.83
6.0
25.33
31.33
16.67
20.67
37.33
24.0
25.33
49.33
28.17
6.67
34.83
32.67
37.33
70.0
13.50
23.33
36.83
42.67
21.33
64.0
2.67
30.67
33.33
13.5
7.33
20.83
df
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
F
6.25
p
0.067
3.56
0.132
0.327
0.598
40.5
0.003 *
0.947
0.386
3.23
0.147
3.79
0.123
16.9
0.015 *
3.5
0.135
2.31
0.203
8.0
0.047 *
0.348
0.587
7.36
0.053 *
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
BKV Up
Dunk Lo
Dunk Up
EasBr Lo
EasBr Up
NBR Lo
NBR Mid
NBR Up
SWB Lo
SWB Mid
SWB Up
Wil Up
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
4.09
1
0.043 *
2.63
1
0.105
0.429
1
0.513
2.63
1
0.105
1.82
1
0.178
1.82
1
0.178
0.784
1
0.376
0.833
1
0.361
2.72
1
0.09
2.72
1
0.09
2.33
1
0.127
0.22
1
0.637
3.97
1
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
BKV Lo
BKV Up
Dunk Lo
Dunk Up
EasBr Lo
EasBr Up
NBR Lo
NBR Mid
NBR Up
SWB Lo
SWB Mid
SWB Up
Wil Up
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
SS
0.012
0.033
0.045
0.101
0.048
0.149
0.015
0.017
0.032
0.103
0.013
0.116
0.014
0.060
0.074
0.013
0.015
0.028
0.001
0.061
0.063
0.023
0.011
0.034
0.004
0.101
0.106
0.000
0.038
0.038
0.006
0.101
0.016
0.007
0.014
0.021
0.057
0.005
0.062
df
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
F
1.39
p
0.303
8.36
0.044*
3.37
0.140
30.83
0.005*
0.963
0.382
3.48
0.136
0.072
0.801
8.70
0.042*
0.175
0.697
0.002
0.963
2.61
0.181
2.09
0.222
49.47
0.002*
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
BKV Up
Dunk Lo
Dunk Up
EasBr Lo
EasBr Up
NBR Lo
NBR Mid
NBR Up
SWB Lo
SWB Mid
SWB Up
Wil Up
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
Chi-Square
df
p
3.86
1
0.05
0.429
1
0.513
3.86
1
0.05
3.86
1
0.05
1.19
1
0.275
0.048
1
0.827
3.97
1
0.046 *
3.86
1
0.05
0.429
1
0.513
1.19
1
0.275
1.19
1
0.275
1.19
1
0.275
0.429
1
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
BKV Lo
BKV Up
Dunk Lo
Dunk Up
EasBr Lo
EasBr Up
NBR Lo
NBR Mid
NBR Up
SWB Lo
SWB Mid
SWB Lo
Wil Up
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
SS
0.013
0.011
0.023
0.006
0.305
0.311
0.004
0.016
0.020
0.009
0.051
0.06
0.005
0.007
0.012
0.011
0.09
0.101
0.002
0.064
0.066
0.014
0.013
0.027
0.003
0.081
0.084
0.004
0.009
0.013
0.004
0.023
0.027
0.002
0.062
0.064
0.016
0.037
0.053
df
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
1
4
5
F
4.65
p
0.09
0.08
0.791
0.89
0.40
0.67
0.46
3.03
0.157
0.485
0.524
0.098
0.770
4.49
0.101
0.161
0.709
1.90
0.240
0.679
0.456
0.119
0.747
1.71
0.261
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
350
{
*
*
{
Number of Individuals
300
Coarse
Total
250
200
150
100
50
Ep
he
m
er
op
te
ra
Pl
ec
op
te
ra
Tr
ic
ho
pt
er
a
D
ip
te
ra
C
ol
eo
pt
C
er
hi
a
ro
no
m
id
ae
O
lig
oc
ha
et
a
0
Taxa
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
0.50
Coarse
Total
0.45
*
0.35
{
Relative Abundance
0.40
0.30
0.25
0.20
0.15
0.10
0.05
0.00
m
he
p
E
er
a
a
a
a
a
ae
et
er
er
er
er
d
t
t
t
t
i
a
p
p
op
op
ch
Di
om
co
h
e
o
l
n
c
e
i
lig
Pl
Co
iro
Tr
O
h
C
a
er
t
op
Taxa
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
30
Coarse
Total
*
{
{
*
20
*
{
Number of families
25
15
10
5
0
Up Mid R Lo B Up Mid B Lo k Up k Lo il Up r Up r Lo V Up V Lo
R
n W sB sB K BK
B
R
n
B
NB NB NB SW SW SW Du Du
Ea E a
Site
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
16
Coarse
Total
12
10
*
{
*
{
Number of EPT families
14
8
6
4
2
0
Up Mid Lo Up Mid B Lo k Up k Lo il Up r Up r Lo Up V Lo
B B
R R BR
n W sB sB KV K
n
B
a
B
NB NB N SW SW SW Du Du
Ea E
Site
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
# Individuals in Taxa/ Total # Individuals
50
Coarse
Total
40
*
30
20
10
0
% EPT
% Chironomidae
Metric
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
2.00
1.75
Coarse
Total
1.50
1.00
0.75
*
{
Value
1.25
0.50
0.25
0.00
Evenness
Shannon Diversity
Taxa
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
1.0
Coarse/Total
0.8
0.6
0.4
0.2
0.0
ra
ra
ra
pte
p te
pte
o
o
o
r
h
le c
me
ric
P
e
T
h
Ep
ter
p
i
D
a
e
ta
ra
ida
ae
pte
h
m
o
o
oc
le
on
lig
r
Co
i
O
Ch
Taxa
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
0.9
Coarse/Total Abundance
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
p
o
o
p
p
p id
o
o
p
p id
o
k U unk L BR U R M BR L B U B M WB L Wil UsBr U sBrL KV U KV L
n
u
a
W
B
B
N S SW S
B
N N
D
D
Ea E
Site
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 MarshMc 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 preashed 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 Unet 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.
3.6 ACKNOWLEDGEMENTS
64
I would like to acknowledge the help and support of the lab. I am particularly
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.
3.7 LITERATURE CITED
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.
Brinkhurst, R.O. 1993. Future directions in freshwater biomonitoring using benthic
macroinvertebrates. Pages 442-459 in D.M. Rosenburg and V.H. Resh (editors).
Freshwater biomonitoring and benthic macroinvertebrates. Chapman and Hall,
New York, New York.
Brusven, M.A, and Prather, K.V. 1974. Influence of stream sediment on distribution of
Macrobenthos . Journal of the Entomological Society of British Columbia. 71:
25- 32.
Cairns, D.K. 2002. Land use and aquatic resources of Prince Edward Island streams and
estuaries: an introduction. Pages 1-13 in D.K. Cairns. Effects of land use practices
in fish, shellfish, and their habitat on Prince Edward Island. Canadian Technical
Report of Fisheries and Aquatic Sciences No. 2408.
Caissie, D. and E. Arseneau. 2002. Substrate composition in the Morell River and other
Maritime Provinces rivers: implications for Atlantic salmon fry emergence. Pages
26-34 in D.K. Cairns. Effects of land use practices in fish, shellfish, and their
habitat on Prince Edward Island. Canadian Technical Report of Fisheries and
Aquatic Sciences No. 2408.
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
65
fine sediment deposition versus transport. Canadian Journal of Zoology.
64 : 1345- 1351.
Culp, J.C., K.J. Cash, N.E. Glozier, and R.B. Brua. 2003. Effects of pulp mill effluent
on benthic assemblages in mesocosms along the Saint John Rivers, Canada.
Environmental Toxicology and Chemistry 22(12): 2916- 2925.
Curry, R.A., J.C. Culp, D.J. Baird, M. Gautreau, and O. Logan. 2006. The potential
impacts of surface water withdrawal on stream ecosystems of Prince Edward
Island: Phase I (2004- 05). Prepared for Canada- PEI Water Program.
Degrace, . 1989. The geology of Prince Edward Island. Pg 1-9 in the Proceedings of the
Second Regional Workshop on Atlantic Shorelines, Charlottetown, PEI, May 1617. NRCC- 31101.
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.
Grost, R.T., W.A. Hubert, and T.A. Wesche. 1991. Field comparison of three devices
used to sample substrate in small streams. North American Journal of Fisheries
Management 11: 347- 351.
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.
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.
Kaller, M.D. and K.J. Hartman. 2004. Evidence of a threshold level of fine sediment
accumulation for altering benthic macroinvertebrate communities.
Hydrobiologia 518: 95- 104.
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.
66
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.
Rosenberg, D.M. and A.P. Wiens. 1980. Responses of Chironomidae (Diptera) to
short-term experimental sediment additions in the Harris River, Northwest
67
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. Habitatspecific 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).
% < 2mm
% < 4mm
% Gravel
% Pebble
% Deposited
Sediment
%
Embeddedness
Total
Suspended
Solids
Correlation Coefficient
0.639 **
0.115
- 0.105
p- value
< 0.001
0.503
0.544
Correlation Coefficient
0.641 **
0.119
- 0.125
p- value
< 0.001
0.489
0.469
Correlation Coefficient
- 0.152
0.026
- 0.111
p- value
0.375
0.88
0.52
- 0.542 **
- 0.067
-0.111
< 0.001
0.697
0.519
1.00
0.335 *
0.109
< 0.001
0.046
0.528
0.335 *
1.00
0.229
0.046
< 0.001
0.18
Correlation Coefficient
p- value
% Deposited
Sediment
Correlation Coefficient
p- value
Correlation Coefficient
%
Embeddedness
p- value
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).
Water
Temperature
% Deposited
Sediment
%
Embeddedness
Total
Suspended
Solids
Correlation Coefficient
- 0.143
- 0.06
0.755 **
p- value
0.405
0.729
< 0.001
Correlation Coefficient
-0.12
0.107
0.611 **
p- value
0.485
0.535
< 0.001
Correlation Coefficient
- 0.136
- 0.05
- 0.581 **
p- value
0.431
0.774
< 0.001
Correlation Coefficient
- 0.227
- 0.13
- 0.084
p- value
0.183
0.449
0.627
Correlation Coefficient
- 0.279
0.013
0.597 **
p- value
0.099
0.941
< 0.001
Correlation Coefficient
- 0.485 **
- 0.275
- 0.413 *
p- value
0.003
0.104
0.012
Correlation Coefficient
- 0.25
- 0.016
- 0.479 **
p- value
0.141
0.928
0.003
pH
Dissolved
Oxygen
Conductivity
Flow
% Slope
Chlorophyll- a
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).
% < 2mm
% Deposited
Sediment
%
Embeddedness
Total
Suspended
Solids
0.393 *
0.464 **
0.192
0.018
0.004
0.262
0.398 *
0.475 **
0.196
p- value
0.016
0.003
0.252
Correlation Coefficient
0.289
0.055
- 0.035
p- value
0.087
0.749
0.840
- 0.481 **
- 0.499 **
- 0.173
p- value
0.003
0.002
0.314
Correlation Coefficient
1.00
0.670 **
0.211
p- value
0.000
< 0.001
0.216
0.670 **
1.00
0.043
< 0.001
0.000
0.805
Correlation Coefficient
p- value
% < 4mm
% Gravel
% Pebble
% Deposited
Sediment
Correlation Coefficient
Correlation Coefficient
Correlation Coefficient
%
Embeddedness
p- value
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).
Water
Temperature
% Deposited
Sediment
%
Embeddedness
Total
Suspended
Solids
Correlation Coefficient
0.077
- 0.077
0.231
p- value
0.655
0.969
0.175
Correlation Coefficient
- 0.159
0.033
0.014
p- value
0.353
0.847
0.934
Correlation Coefficient
- 0.035
0.033
- 0.614 **
p- value
0.852
0.849
< 0.001
Correlation Coefficient
- 0.209
0.108
0.084
p- value
0.22
0.532
0.627
Correlation Coefficient
- 0.523 **
- 0.472 **
- 0.149
p- value
0.001
0.004
0.386
Correlation Coefficient
- 0.263
- 0.221
- 0.608 **
p- value
0.121
0.196
< 0.001
Correlation Coefficient
- 0.012
0.283
- 0.106
p- value
0.944
0.099
0.543
pH
Dissolved
Oxygen
Conductivity
Flow
% Slope
Chlorophyll- a
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).
%
Burrower
%
Climber
%
Sprawler
%
Clinger
%
Swimmer
%
Gatherer
%
Filterer
%
Scraper
%
Predator
%
Shredder
% Deposited
Sediment
%
Embeddedness
Total
Suspended
Solids
Correlation Coefficient
0.507 **
0.404 **
- 0.235
p- value
0.002
0.014
0.168
Correlation Coefficient
0.194
0.003
0.24
p- value
0.256
0.984
0.158
Correlation Coefficient
0.347 *
- 0.073
- 0.094
p- value
0.038
0.672
0.587
Correlation Coefficient
- 0.13
- 0.24
0.500 **
p- value
0.45
0.159
0.002
Correlation Coefficient
- 0.436 **
- 0.214
0.087
p- value
0.008
0.211
0.615
Correlation Coefficient
- 0.513 **
- 0.219
- 0.031
p- value
0.001
0.2
0.858
Correlation Coefficient
0.577 **
0.550 **
0.416 *
p- value
< 0.001
0.001
0.012
Correlation Coefficient
- 0.054
- 0.006
0.524 **
p- value
0.755
0.97
0.001
Correlation Coefficient
0.339 **
- 0.022
- 0.234
p- value
0.043
0.899
0.17
Correlation Coefficient
0.196
- 0.199
- 0.255
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).
Taxa
Richness
% Deposited
Sediment
%
Embeddedness
Total
Suspended
Solids
Correlation Coefficient
- 0.247
- 0.048
0.370 *
p- value
0.146
0.78
0.027
Correlation Coefficient
0.483 **
0.049
- 0.059
p- value
0.003
0.778
0.731
0.24
- 0.014
0.146
0.142
0.933
0.394
0.037
- 0.182
0.131
p- value
0.821
0.288
0.446
Correlation Coefficient
- 0.287
0.061
- 0.265
p- value
0.089
0.724
0.118
Correlation Coefficient
- 0.123
0.036
0.440 **
p- value
0.473
0.837
0.007
Correlation Coefficient
- 0.711 **
- 0.239
- 0.053
p- value
< 0.001
0.161
0.76
Correlation Coefficient
- 0.612 **
- 0.294
- 0.271
p- value
< 0.001
0.082
0.11
Correlation Coefficient
- 0.431 **
0.092
- 0.084
p- value
0.009
0.594
0.627
Correlation Coefficient
0.453 **
- 0.015
0.007
p- value
0.006
0.931
0.967
Evenness
Correlation Coefficient
Shannon
Diversity
p- value
Correlation Coefficient
Chironomid
Richness
Chironomid
Abundance
EPT
Richness
EPT
Abundance
% EPT
Taxa
%
Orthocladiinae
%
Chironominae
77
% Dominant
Taxa
Ephemeroptera
Richness
Plecoptera
Richness
Trichoptera
Richness
% Deposited
Sediment
%
Embeddedness
Total
Suspended
Solids
Correlation Coefficient
- 0.315
- 0.025
- 0.163
p- value
0.061
0.884
0.343
Correlation Coefficient
- 0.244
- 0.156
0.520 **
p- value
0.151
0.365
0.001
Correlation Coefficient
- 0.206
- 0.308
- 0.054
p- value
0.229
0.068
0.753
Correlation Coefficient
0.012
0.135
0.163
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).
%
Burrower
%
Climber
%
Sprawler
%
Clinger
%
Swimmer
%
Gatherer
%
Filterer
%
Scraper
%
Predator
%
Shredder
% Deposited
Sediment
%
Embeddedness
Total
Suspended
Solids
Correlation Coefficient
0.334 **
0.138
0.081
p- value
0.046
0.424
0.637
Correlation Coefficient
0.22
0.031
0.206
p- value
0.198
0.857
0.228
Correlation Coefficient
0.126
0.211
- 0.329 *
p- value
0.465
0.218
0.05
Correlation Coefficient
- 0.111
- 0.281
0.216
p- value
0.518
0.097
0.206
Correlation Coefficient
- 0.203
0.079
- 0.195
p- value
0.234
0.649
0.254
Correlation Coefficient
- 0.286
- 0.129
- 0.057
p- value
0.091
0.454
0.74
Correlation Coefficient
0.142
- 0.101
0.146
p- value
0.409
0.557
0.394
Correlation Coefficient
- 0.166
- 0.451 **
0.16
p- value
0.334
0.006
0.351
Correlation Coefficient
- 0.049
- 0.301
0.046
p- value
0.775
0.075
0.791
Correlation Coefficient
- 0.174
0.033
- 0.412 *
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).
Taxa
Richness
% Deposited
Sediment
%
Embeddedness
Total
Suspended
Solids
Correlation Coefficient
- 0.033
- 0.212
0.035
p- value
0.85
0.215
0.839
Correlation Coefficient
0.193
- 0.067
- 0.005
p- value
0.258
0.698
0.975
Correlation Coefficient
0.192
- 0.16
0.033
p- value
0.262
0.351
0.847
Correlation Coefficient
0.134
- 0.097
- 0.209
p- value
0.434
0.575
0.221
Correlation Coefficient
- 0.288
- 0.183
- 0.136
p- value
0.088
0.285
0.429
Correlation Coefficient
- 0.286
- 0.409 *
- 0.221
p- value
0.091
0.013
0.196
Correlation Coefficient
- 0.613 **
- 0.453 **
- 0.067
p- value
< 0.001
0.006
0.699
Correlation Coefficient
- 0.678 **
- 0.593 **
- 0.229
p- value
< 0.001
< 0.001
0.18
Correlation Coefficient
- 0.770 **
- 0.560 **
- 0.575 **
p- value
< 0.001
< 0.001
< 0.001
Correlation Coefficient
0.669 **
0.562 **
0.448 **
p- value
< 0.001
< 0.001
0.006
Evenness
Shannon
Diversity
Chironomid
Richness
Chironomid
Abundance
EPT
Richness
EPT
Abundance
% EPT
Taxa
%
Orthocladiinae
%
Chironominae
80
% Dominant
Taxa
Ephemeroptera
Richness
Plecoptera
Richness
Trichoptera
Richness
% Deposited
Sediment
%
Embeddedness
Total
Suspended
Solids
Correlation Coefficient
- 0.276
- 0.049
- 0.043
p- value
0.103
0.778
0.802
Correlation Coefficient
- 0.167
- 0.25
0.102
p- value
0.33
0.142
0.555
Correlation Coefficient
- 0.054
- 0.359 *
- 0.053
p- value
0.752
0.032
0.758
Correlation Coefficient
- 0.342 *
- 0.364 *
- 0.236
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
- 0.276
0.099
- 0.242
0.156
0.079
0.646
- 0.224
0.189
- 0.416 *
0.027
- 0.489 **
0.002
0.614 **
< 0.001
- 0.526 **
0.001
0.108
0.53
- 0.077
0.657
0.088
0.609
0.03
0.86
- 0.324
0.054
- 0.101
0.56
0.753 **
< 0.001
- 0.473 **
0.004
- 0.294
0.07
- 0.198
0.248
0.539 **
0.001
- 0.418 *
0.011
- 0.312
0.064
0.191
0.264
- 0.005
0.979
- 0.02
0.907
Correlation Coefficient
Water
Temperature
p- value
Correlation Coefficient
pH
p- value
Correlation Coefficient
Dissolved
Oxygen
p- value
Correlation Coefficient
Conductivity
p- value
Correlation Coefficient
Flow
p- value
Correlation Coefficient
% Slope
p- value
82
Correlation Coefficient
Chlorophyll- a
p- value
0.308
0.068
- 0.192
0.262
- 0.106
0.539
- 0.08
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).
Water
Temperature
pH
Dissolved
Oxygen
Conductivity
Flow
Evenness
EPT
abundance
%
EPT
%
Orthocladiinae
%
Chironominae
Correlation
Coefficient
- 0.214
0.195
-0.068
-0.141
- 0.092
p- value
0.21
0.255
0.695
0.413
0.594
Correlation
Coefficient
- 0.482 **
0.171
0.047
0.223
-0.105
p- value
0.003
0.317
0.785
0.192
0.542
Correlation
Coefficient
0.045
-0.154
0.115
0.386 *
- 0.102
p- value
0.793
0.37
0.505
0.02
0.555
Correlation
Coefficient
- 0.382 *
0.337 *
0.286
0.057
0.063
p- value
0.021
0.044
0.091
0.741
0.714
Correlation
Coefficient
- 0.343 *
0.308
0.045
-0.048
0.021
p- value
0.04
0.068
0.793
0.781
0.905
84
% Slope
Chlorophyll- a
Correlation
Coefficient
- 0.168
0.482 **
0.528 **
0.353 *
- 0.526 **
p- value
0.327
0.003
0.001
0.035
0.001
Correlation
Coefficient
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
Water
Temperature
pH
Dissolved
Oxygen
Conductivity
Flow
EPT
abundance
%
EPT
%
Orthocladiinae
%
Chironominae
Trichoptera
Richness
- 0.206
- 0.268
- 0.101
0.024
Correlation
Coefficient
- 0.408 *
p- value
0.013
0.971
0.228
0.114
0.56
0.891
Correlation
Coefficient
- 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
Correlation
Coefficient
- 0.054
0.114
0.07
0.267
- 0.437 **
0.089
p- value
0.756
0.508
0.684
0.115
0.008
0.607
Correlation
Coefficient
- 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
Correlation
Coefficient
- 0.475 **
0.501 **
0.586 *
0.289
- 0.188
0.340 *
p- value
0.003
0.002
0.087
0.273
0.042
0.006
< 0.001
86
% Slope
Chlorophyll- a
Correlation
Coefficient
- 0.212
p- value
0.214
Correlation
Coefficient
p- value
0.082
0.365 *
0.430 **
- 0.326
0.616 **
0.636
0.029
0.009
0.053
< 0.001
0.390 *
0.028
- 0.082
0.101
0.131
- 0.038
0.02
0.874
0.64
0.564
0.453
0.83
87
3.9 FIGURES
90%
80%
70%
Percent
60%
% Agriculture
50%
% Forestry
40%
% Other
30%
20%
10%
0%
Dunk River
North Brook
Southwest
Brook
Tryon
River(East
Branch)
West River Wilmot River
(Brookvale)
Watershed
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
% Sprawler
35
80
70
60
50
40
30
20
Sr = 0.507
p = 0.002
10
0
0
10
20
30
40
50
60
70
80
90
Sprawler abundance/ Total abundance
Burrower abundance/ Total abundance
% Burrower
Sr = 0.347
p = 0.038
30
25
20
15
10
5
0
100
0
10
20
% Deposited Sediment
30
40
60
70
80
90
100
% Deposited Sediment
% Predator
% Swimmer
40
80
Sr = - 0.436
p = 0.008
60
40
20
0
Predator abundance/ Total abundance
Swimmer abundance/ Total abundance
50
Sr = 0.339
p = 0.043
35
30
25
20
15
10
5
0
0
10
20
30
40
50
60
70
% Deposited Sediment
80
90
100
0
10
20
30
40
50
60
70
80
% Deposited Sediment
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.
90
100
92
% Gatherer
Evenness
0.9
90
0.8
80
70
0.7
Evenness
Gatherer abundance/ Total abundance
100
60
50
40
0.5
30
20
Sr = - 0.513
p = 0.001
10
Sr = 0.483
p = 0.003
0.4
0
0.3
0
10
20
30
40
50
60
70
80
90
100
0
10
20
30
40
50
60
70
% Deposited Sediment
% Deposited Sediment
EPT Abundance
% EPT
1000
80
90
100
100
800
EPT abundance/ Total abundance
Sr = -0.711
p = < 0.001
900
No. of EPT Individuals
0.6
700
600
500
400
300
200
100
0
Sr = -0.612
p = < 0.001
80
60
40
20
0
0
10
20
30
40
50
60
70
% Deposited Sediment
80
90
100
0
10
20
30
40
50
60
70
80
90
% Deposited Sediment
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
100
93
Orthoclad abundance/ Chironomid abundance
% Orthocladiinae
120
100
Sr = -0.431
p = 0.009
80
60
40
20
0
0
10
20
30
40
50
60
70
80
90
100
80
90
100
% Deposited Sediment
Chironominae abundance/ Chironomid abundance
% Chironominae
120
Sr = 0.453
p = 0.006
100
80
60
40
20
0
0
10
20
30
40
50
60
70
% Deposited Sediment
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
EPT Abundance
% Burrower
2200
Sr = 0.334
p = 0.046
70
Sr = -0.613
p = < 0.001
2000
1800
60
No. EPT individuals
Burrower abundance/ total abundance
80
50
40
30
20
1600
1400
1200
1000
800
600
400
200
10
0
-200
0
0
10
20
30
40
50
60
70
80
90
0
100
10
20
40
50
60
70
80
90
100
% Deposited Sediment
% Deposited Sediment
% Orthocladiinae
100
Sr = -0.678
p = <0.001
90
80
70
60
50
40
30
20
Orthoclad abundance/ total abundance
% EPT
EPT abundance/ Total abundance
30
120
Sr = -0.770
p = < 0.001
100
80
60
40
20
0
-20
-40
10
0
10
20
30
40
50
60
70
% Deposited Sediment
80
90
100
0
10
20
30
40
50
60
70
80
90
% Deposited Sediment
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.
100
95
Chironominae abundance/ total abundance
% Chironominae
120
Sr = 0.669
p = < 0.001
100
80
60
40
20
0
-20
0
10
20
30
40
50
60
70
80
90
100
% Deposited Sediment
Trichoptera Richness
8
Sr = -0.342
p = 0.041
No. Trichoptera taxa
7
6
5
4
3
2
1
0
0
10
20
30
40
50
60
70
80
90
100
% Deposited Sediment
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, platelike 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
103
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
104
(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
105
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; pvalue 0.015), flow (SR - 0.664; p-value 0.018) and total suspended solids (SR – 0.923; pvalue <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
106
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
107
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
108
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; pvalue 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
109
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
110
% 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/ freemoving (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.
111
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,
112
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 freemoving 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.
113
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, slowseasonal 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 slowflowing 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.
118
4.5 ACKNOWLEDGEMENTS
I would like to acknowledge the help and support of the lab. I am particularly
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.
4.6 LITERATURE CITED
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.
Archaimbault, V., P. Usseglio- Polatera and J-P. Vanden Bossche. 2005. Functional
differences among benthic macroinvertebrate communities in reference streams of
same order in a given biogeographic area. Hydrobiologia 55: 171- 182.
Cairns, D.K. 2002. Land use and aquatic resources of Prince Edward Island streams and
estuaries: an introduction. Pages 1-13 in D.K. Cairns. Effects of land use practices
in fish, shellfish, and their habitat on Prince Edward Island. Canadian Technical
Report of Fisheries and Aquatic Sciences No. 2408.
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.
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.
Degrace, . 1989. The geology of Prince Edward Island. Pg 1-9 in the Proceedings of the
Second Regional Workshop on Atlantic Shorelines, Charlottetown, PEI, May 1617. NRCC- 31101.
119
Doledec, S., B. Statzner and M. Bournard. 1999. Species traits for future biomonitoring
Across ecoregions: patterns along a human- impacted river. Freshwater Biology.
42: 737- 758.
Doledec, S., N. Phillips, M. Scarsbrook, R.H. Riley and C.R. Townsend. 2006.
Comparison of structural and functional approaches to determining landuse
effects on grassland stream invertebrate communities. Journal of the North
American Benthological Society. 25(1): 44- 60.
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.
Finn, D.S. and N.L. Poff. 2005. Variability and convergence in benthic communities
along the longitudinal gradients of four physically similar Rocky Mountain
streams. Freshwater Biology 50: 243- 261.
Gayraud, S; Statzner, B; Bady, P; Haybachp, A; Scholl, F; Usseglio- Polatera, P, and
Bacchi, M. 2003. Invertebrate traits for the biomonitoring of large European
rivers: an initial assessment of alternative metrics. Freshwater Biology.
48: 2045- 2064.
Gray, L.J., and J.V. Ward. 1982. Effects of sediment releases from a reservoir on
stream macroinvertebrates. Hydrobiologia. 96: 177- 184.
Heino, J. 2005. Functional biodiversity of macroinvertebrate assemblages along major
ecological gradients of boreal headwater streams. Freshwater Biology 50: 15781587.
Hilsenhoff, W.L. 1987. An improved biotic index of organic stream pollution. The Great
Lakes Entomologist. 20(1): 31- 39.
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.
Lamoroux, N.; S. Doledec and S. Gayraud. 2004. Biological traits of stream
macroinvertebrate communities: effects of microhabitat, reach, basin filters.
Journal of the North American Benthological Society. 23(3): 449- 466.
Leps, J. and P. Smilauer. 2003. Multivariate analysis of ecological data using CANOCO.
Cambridge University Press. Cambridge, United Kingdom.
Lemly, A.D. 1982. Modification of benthic insect communities in polluted streams:
combined effects of sedimentation and nutrient enrichment. Hydrobiologia.
87: 229- 245.
120
Lenat, D.R., D.L. Penrose, and K.W. Eagleson. 1981. Variable effects of sediment
addition on stream benthos. Hydrobiologia 79: 187- 194.
Mangum, F.A; R.N. Winget. 1991. Environmental profile of Drunella (Eatonella)
doddsi (Needham) (Ephemeroptera: Ephemerellidae). Journal of Freshwater
Ecology. 6: 11- 22.
McClelland, W.T. and M.A. Brusven.1980. Effects of sedimentation on the behaviour
and distribution of riffle insects in a laboratory stream. Aquatic Insects 2: 161169.
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. 1997. Landscape filters and species traits: towards mechanistic understanding
And prediction in stream ecology. Journal of the North American Benthological
Society. 16: 391- 409.
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.
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.
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.
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-
121
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.
Scarsbrook, M.R. and C.R. Townsend. 1993. Stream community structure in relation to
spatial and temporal variation: a habitat templet study of two contrasting New
Zealand streams. Freshwater Biology 29: 395- 410.
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., K. Hoppenhaus, M.F. Areans and P. Richoux. 1997. Reproductive traits,
habitat use and templet theory: a synthesis of world- wide data on aquatic insects.
Freshwater Biology 38: 109- 135.
Statzner, B; Doledec, S, and Hugueny, B. 2004. Biological trait composition of
European stream invertebrate communities: assessing the effects of various trait
filter types. Ecography. 27: 470- 488.
ter Braak & Smilauer, 1998
Townsend, C.R. 1988
Townsend, C.R. and A.G. Hildrew. 1994. Species traits in relation to a habitat templet for
river systems. Freshwater Biology 31: 265- 275.
Townsend, C.R., S. Doledec and M. Scarsbrook. 1997. Species traits in relation to
temporal and spatial heterogeneity in streams: a test of the habitat templet theory.
Freshwater Biology 37: 367- 387.
Usseglio- Polatera, P; Bournaud, M; Richoux, P, and Tachet, H. 2000a. Biological and
ecological traits of benthic freshwater macroinvertebrates: relationship and
definition of groups with similar traits. Freshwater Biology. 43: 175- 205.
Usseglio- Polatera, P., Bournaud, M; Richoux, P, and Tachet, H. 2000b. Biomonitoring
through biological traits of benthic macroinvertebrates: how to use species traits
databases? Hydrobiologia 422/ 423: 153- 163.
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.
U.S. Geological Survey Data Series 187, http://pubs.water.usgus.gov/ds187.
Waters, T.F. 1995. Sediment in streams: sources, biological effects and control.
Monograph 7. American Fisheries Society, Bethesda, Maryland.
Zanetell, B.A., and B.L. Peckarsky. 1996. Stoneflies as ecological engineers- hungry
122
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
U.S. Geological Survey Data Series 187, http://pubs.water.usgus.gov/ds187.
Williams, D.D. and I.D. Hogg. 1988. Ecology and production of invertebrates in a
Canadian coldwater spring-springbrook system. Holarctic Ecology. 11: 41- 54.
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
Collector filterer
Scraper
Predator
Shredder
Feed 1
Feed 2
Feed 3
Feed 4
Feed 5
Burrower
Climber
Sprawler
Clinger
Swimmer
Skater
Habi 1
Habi 2
Habi 3
Habi 4
Habi 5
Habi 6
Free moving
Sedentary
Both
Mobi 1
Mobi 2
Mobi 3
Depositional
Erosional/ Depositional
Erosional
Rheo 1
Rheo 2
Rheo 3
Semivoltine
Univoltine
Bi- Multivoltine
Volt 1
Volt 2
Volt 3
None
Weak
Strong
Swim 1
Swim 2
Swim 3
Small (< 9mm)
Medium (9- 16mm)
Large (> 16mm)
Size 1
Size 2
Size 3
Flattened
Slightly Flattened
Streamlined
Cylindrical
Body 1
Body 2
Body 3
Body 4
Habit (mode of existence)
Mobility
Rheophily
Voltinism
(Lifecycle)
Swimming Ability
Body Size
Body Form
125
Sub-cylindrical
Elongate
Body 5
Body 6
None
Some
Good
Arm 1
Arm 2
Arm 3
Tegument
Gills
Plastron
Resp 1
Resp 2
Resp 3
Present
Absent
Gill 1
Gill 2
Lateral
Abdominal
Thorax
Sub-mental
Anal
None
Plac 1
Plac 2
Plac 3
Plac 4
Pac 5
Plac 6
Lanceolate
Plate-like
Filament (single, branch)
Operculate (semi, full)
Tufts
None
Form 1
Form 2
Form 3
Form 4
Form 5
Form 6
None
Sceloritized (some)
Case (good)
Arm 1
Arm 2
Arm 3
Fast Seasonal
Slow Seasonal
Non-Seasonal
Devel 1
Devel 2
Devel 3
Armouring
Respiration Technique
Gills
Gill Placement
Gill Form
Armouring
Development
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).
Water
Temperature
pH
Dissolved
Oxygen
Conductivity
Flow
% Deposited
Sediment
%
Embeddedness
% Slope
Total
Suspended
Solids
Chlorophyll- a
% < 2mm
Axis 1
Axis 2
Axis 3
Axis 4
Correlation
Coefficient
- 0.692*
- 0.168
- 0.469
0.210
p- value
0.013
0.602
0.124
0.513
Correlation
Coefficient
- 0.678*
- 0.510
- 0.119
- 0.210
p- value
0.015
0.09
0.713
0.513
Correlation
Coefficient
0.566
- 0.161
0.636*
- 0.154
p- value
0.055
0.618
0.026
0.633
Correlation
Coefficient
0.210
- 0.406
- 0.021
- 0.364
p- value
0.513
0.191
0.948
0.245
Correlation
Coefficient
- 0.664*
- 0.476
0.035
- 0.021
p- value
0.018
0.118
0.914
0.948
Correlation
Coefficient
- 0.189
0.722**
- 0.088
- 0.536
p- value
0.556
0.008
0.787
0.073
Correlation
Coefficient
- 0.455
0.594*
0.343
- 0.210
p- value
0.138
0.042
0.276
0.513
Correlation
Coefficient
0.357
- 0.133
0.105
0.580*
p- value
0.255
0.681
0.746
0.048
Correlation
Coefficient
- 0.923**
0.014
- 0.224
0.077
0.000
0.966
0.484
0.812
p- value
0.420
0.175
- 0.070
0.829
0.343
0.276
0.049
0.880
Correlation
Coefficient
0.021
0.49
- 0.455
- 0.762 **
p- value
Correlation
Coefficient
127
% < 4mm
Bankfull Tractive
p- value
0.948
0.106
0.138
0.004
Correlation
Coefficient
- 0.028
0.503
- 0.517
- 0.713 **
p- value
0.931
0.095
0.085
0.009
Correlation
Coefficient
0.14
- 0.252
0.168
0.671 *
p- value
0.665
0.43
0.602
0.017
Correlation
Coefficient
- 0.119
0.049
0.329
0.329
p- value
0.713
0.88
0.297
0.297
Force
Tractive Force
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).
Water
Temperature
pH
Dissolved
Oxygen
Conductivity
Flow
% Deposited
Sediment
%
Embeddedness
% Slope
Total
Suspended
Solids
Chlorophyll- a
Axis 1
Axis 2
Axis 3
Axis 4
Correlation
Coefficient
- 0.692 *
- 0.18
- 0.469
0.21
p- value
0.013
0.602
0.124
0.513
Correlation
Coefficient
- 0.678 *
- 0.51
- 0.119
- 0.21
p- value
0.015
0.09
0.713
0.513
Correlation
Coefficient
0.566
- 0.161
0.636 *
p- value
0.055
0.618
0.026
0.633
Correlation
Coefficient
0.21
- 0.406
- 0.021
- 0.364
p- value
0.513
0.191
0.948
0.245
Correlation
Coefficient
- 0.664 *
- 0.476
0.035
- 0.021
p- value
0.018
0.118
0.914
0.948
Correlation
Coefficient
- 0.189
0.722**
- 0.088
- 0.536
p- value
0.556
0.008
0.787
0.073
Correlation
Coefficient
- 0.455
0.594*
0.343
- 0.21
p- value
0.138
0.042
0.276
0.513
Correlation
Coefficient
0.357
- 0.133
0.105
0.580*
p- value
0.255
0.681
0.746
0.048
Correlation
Coefficient
- 0.923**
0.014
- 0.224
0.077
0.00
0.966
0.484
0.812
0.42
0.175
- 0.07
0.829
0.343
0.276
0.049
0.88
- 0.154
p- value
Correlation
Coefficient
p- value
129
% < 2mm
% < 4mm
Bankfull Tractive
Correlation
Coefficient
- 0.287
0.643 *
0.007
- 0.427
p- value
0.366
0.024
0.983
0.167
Correlation
Coefficient
- 0.287
0.643 *
0.007
- 0.427
p- value
0.366
0.024
0.983
0.167
Correlation
Coefficient
0.14
- 0.252
0.168
0.671 *
p- value
0.665
0.43
0.602
0.017
Correlation
Coefficient
- 0.119
0.049
0.329
0.329
p- value
0.713
0.88
0.297
0.297
Force
Tractive Force
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).
Water
Temperature
pH
Dissolved
Oxygen
Conductivity
Flow
% Deposited
Sediment
%
Embeddedness
% Slope
Total
Suspended
Solids
Chlorophyll- a
Axis 1
Axis 2
Axis 3
Axis 4
Correlation
Coefficient
- 0.287
0.669*
0.224
- 0.182
p- value
0.366
0.017
0.484
0.572
Correlation
Coefficient
0.287
0.872**
0.175
- 0.056
p- value
0.366
0.00
0.587
0.863
Correlation
Coefficient
0.462
- 0.459
- 0.126
0.042
p- value
0.131
0.134
0.697
0.897
Correlation
Coefficient
0.832**
0.312
0.056
0.441
p- value
0.001
0.324
0.863
0.152
Correlation
Coefficient
0.259
0.764**
0.084
- 0.014
p- value
0.417
0.004
0.795
0.966
Correlation
Coefficient
- 0.385
- 0.044
- 0.242
0.504
p- value
0.216
0.892
0.449
0.094
Correlation
Coefficient
- 0.427
- 0.109
- 0.161
- 0.028
p- value
0.167
0.737
0.618
0.931
Correlation
Coefficient
0.035
- 0.406
0.559
- 0.238
p- value
0.914
0.19
0.059
0.457
Correlation
Coefficient
- 0.322
0.718**
0.217
0.091
0.308
0.009
0.499
0.779
0.098
0.762
- 0.396
0.203
- 0.357
0.255
- 0.266
0.404
p- value
Correlation
Coefficient
p- value
131
% < 2mm
% < 4mm
Bankfull Tractive
Correlation
Coefficient
0.021
0.49
- 0.455
- 0.762 **
p- value
0.948
0.106
0.138
0.004
Correlation
Coefficient
- 0.028
0.506
- 0.517
- 0.713 **
p- value
0.931
0.095
0.085
0.009
Correlation
Coefficient
0.14
- 0.252
0.168
0.671 *
p- value
0.665
0.43
0.602
0.017
Correlation
Coefficient
- 0.119
0.049
0.329
0.329
p- value
0.713
0.88
0.297
Force
Tractive Force
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).
Water
Temperature
pH
Dissolved
Oxygen
Conductivity
Flow
% Deposited
Sediment
%
Embeddedness
% Slope
Total
Suspended
Solids
Chlorophyll- a
Axis 1
Axis 2
Axis 3
Axis 4
Correlation
Coefficient
0.678*
0.448
- 0.308
0.51
p- value
0.015
0.145
0.331
0.09
Correlation
Coefficient
0.329
0.497
- 0.741**
0.154
p- value
0.297
0.101
0.006
0.633
Correlation
Coefficient
- 0.650*
- 0.224
0.196
- 0.643*
p- value
0.022
0.484
0.542
0.024
Correlation
Coefficient
- 0.322
- 0.168
- 0.42
- 0.126
p- value
0.308
0.602
0.175
0.697
Correlation
Coefficient
0.385
0.538
- 0.42
0.091
p- value
0.217
0.071
0.175
0.779
Correlation
Coefficient
0.284
- 0.511
0.102
- 0.165
p- value
0.372
0.089
0.753
0.609
Correlation
Coefficient
0.483
- 0.098
0.371
- 0.455
p- value
0.112
0.762
0.236
0.138
Correlation
Coefficient
- 0.294
0.07
0.063
0.322
p- value
0.354
0.829
0.846
0.308
Correlation
Coefficient
0.811**
0.336
- 0.203
0.28
0.001
0.286
0.527
0.379
- 0.238
0.457
0.084
0.795
0.308
0.331
- 0.28
0.379
p- value
Correlation
Coefficient
p- value
133
- 0.189
% < 2mm
% < 4mm
Bankfull Tractive
Correlation
Coefficient
0.538
- 0.259
0.294
p- value
0.071
0.417
0.354
0.557
Correlation
Coefficient
0.538
- 0.259
0.294
- 0.189
p- value
0.071
0.417
0.354
0.557
Correlation
Coefficient
- 0.154
0.28
0.014
0.357
p- value
0.633
0.379
0.966
0.255
Correlation
Coefficient
0.049
0.308
- 0.007
0.231
p- value
0.88
0.331
0.983
0.471
Force
Tractive Force
134
Table 4.6: Species tolerances for July 2005. (Tolerant = 3, moderately tolerant = 2,
sensitive = 1).
Taxa
Pelecypoda
Dicranota
Pseudolimnophila
Chironominae
Limnophila
Oligochaeta
Prodiamesinae
Onocosmoecus
Rhyacophila
Parapsyche
Sialis
Tabanus
Psychoglypha
Nematoda
Sweltsa
Attenella
Nemoura
Neophylax
Tanytarsini
Lepidostoma
Isogenoides
Curculionidae adult
Leuctra
Paraleuctra
Tanypodinae
Amphinemura
Epeorus (Iron)
Dannella
Serratella
Elmidae larva
Elmidae adult
Ephemerella
Heptagenia
Mallochohelea
Simulium
Tipula
Hesperophylax
Chelifera
Apatania
Prosimulium
Species Score
-0.7208
-0.7012
-0.519
-0.5174
-0.4957
-0.4839
-0.4584
-0.4548
-0.4522
-0.3817
-0.3613
-0.3015
-0.2929
-0.248
-0.2337
-0.2248
-0.17
-0.1335
-0.1275
-0.1242
-0.0739
-0.0511
-0.0482
-0.0243
-0.0149
0.0012
0.0022
0.0189
0.0196
0.0251
0.0413
0.0542
0.0615
0.0798
0.1063
0.1457
0.2186
0.2384
0.2551
0.2723
Tolerance Value
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
135
Diura
Micrasema
Atopsyche
Paraleptophlebia
Cinygmula
Isoperla
Drunella
Limnophora
Hydroptila
Antocha
Baetis
Orthocladinae
0.2819
0.3157
0.3157
0.3332
0.3674
0.4442
0.4528
0.4944
0.5263
0.5286
0.598
0.7889
1
1
1
1
1
1
1
1
1
1
1
1
136
Table 4.7: Species tolerances for October 2005. (Tolerant = 3, moderately tolerant = 2,
sensitive = 1).
Taxa
Species Score
Dicranota
Oligochaeta
Chironominae
Simulium
Pseudolimnophila
Glossosoma
Elmidae adult
Hirudinea
Sweltsa
Leptophlebia
Nematoda
Pelecypoda
Rhyacophila
Baetis
Prodiamesinae
Tipula
Brachycentrus
Lepidostoma
Parapsyche
Molophilus
Limnophora
Antocha
Nemoura
Neophylax
Heptagenia
Tanypodinae
Paracapnia
Ephemerella
Elmidae larvae
Serratella
Mallochohelea
Tanytarsini
Hydroptila
Chelifera
Epeorus (Iron)
Apatania
Orthocladinae
Leuctra
Isoperla
Platyhelminth
-0.4911
-0.4781
-0.4691
-0.4189
-0.3771
-0.3117
-0.2906
-0.2719
-0.2651
-0.2495
-0.2485
-0.2078
-0.1399
-0.1223
-0.0882
-0.0644
-0.0639
-0.0182
0.02
0.0321
0.0978
0.1196
0.1312
0.1373
0.1427
0.1446
0.1531
0.1672
0.181
0.1883
0.205
0.2058
0.2106
0.2159
0.2389
0.2738
0.3222
0.3295
0.3297
0.3297
Tolerance Value
3
3
3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
137
Cinygmula
Paraleptophlebia
Hydropsyche
Micrasema
0.3752
0.3927
0.4379
0.5402
1
1
1
1
138
4.8 FIGURES
Axis 1
1
2
3
4
5
6
7
8
9
10
11
12
13
16
18
20
21
22
Acerpenna
Baetis
Attenella
Dannella
Drunella
Ephemerella
Serratella
Cinygmula
Epeorus
Heptageniidae
Leucrocu
Leptophl
Paralept
Paracapnia
Sweltsa
Leuctra
Paraleuctra
Amphinemura
23
24
25
26
27
29
30
31
34
35
37
38
39
41
45
46
47
48
Nemoura
Diura
Isogenoi
Isoperla
Apatania
Micrasema
Glossosoma
Atopsyche
Parapsyche
Hydroptila
Lepidost
Hesperop
Onocosmo
Psyhogl
Rhyacophilidae
Neophylax
Curculio
Agabus
49
50
53
54
55
56
57
58
59
60
61
62
64
65
66
67
69
70
Elmidae larve
Elmidae adult
Atherix
Mallocho
Chironom
Orthocladiinae
Prodiame
Tanypodi
Tanytars
Dixa
Chelifera
Limnophora
Simulium
Prosimulium
Tabanus
Antocha
Dicranota
Limnophila
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
88
Molophilus
Pseudolimnophila
Tipula
Corixida
Gerridae
Sialis
Pyralida
Oligochaeta
Hirudine
Platyhelminthes
Nematoda
Pelecypoda
Gastropoda
Ostracoda
Copepoda
Hydracar
Collembolla
Figure 4.1: RDA ordination of average species relative abundance and environmental
variables significantly correlated with PCA axes 1 and 2, July 2005.
139
Axis 1
2
3
6
7
8
9
10
12
13
14
15
16
18
20
22
23
Baetis
Attenella
Ephemerella
Serratella
Cinygmula
Epeorus
Heptagenia
Leptophlebia
Paraleptophlebia
Capnura
Isocapnia
Paracapna
Sweltsa
Leuctra
Amphinemura
Nemoura
25
26
27
28
29
30
33
34
35
37
42
43
44
45
46
47
Isogenoides
Isoperla
Apatania
Brachycentrus
Micrasema
Glossosoma
Hydropsyche
Parapsyche
Hydroptila
Lepidostoma
pycnopsyche
Chimarra
Oligostomis
Rhyacophila
Neophylax
Curculionidae
49
50
52
53
54
55
56
57
58
59
61
62
63
64
66
67
Elmidae larvae
Elmidae adult
hydrophilidae
Atherix
Mallochohelea
Chironominae
Orthocladiinae
Prodiamesinae
Tanypodinae
Tanytarsini
Chelifera
Limnophora
Pericoma
Simlium
Tabanus
Antocha
69
70
71
72
73
78
79
80
81
82
83
84
85
86
87
89
Dicranota
Limnophila
Molophilus
Pseudolimnophila
Tipula
Oligochaeta
Hirudinea
Platyhelminthes
Nematoda
Pelecypoda
Gastropoda
Ostracoda
Copepoda
Hydracar
Isopoda
Collembola
Figure 4.2: RDA ordination of average species relative abundance and environmental
variables significantly correlated with PCA axes 1 and 2, October 2005.
140
Tolerant
Sensitive
Axis 1
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).
141
Tolerant
Sensitive
Axis1
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).
142
Figure 4.5: RDA ordination of species traits and environmental variables correlated with
PCA axes 1 and 2, July 2005.
143
Axis 1
Figure 4.6: RDA ordination of species traits and environmental variables correlated with
PCA axes 1 and 2, October 2005.
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.
5.3 LITERATURE CITED
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
Acerpenna
Baetis
Attenella
Dannella
Drunella
Ephemerella
Serratella
Cinygmula
Epeorus (Iron)
Heptagenia
Leucrocuta
Leptophlebia
Paraleptophlebia
Capnura
Isocapnia
Paracapnia
Sasquaperla
Sweltsa
Utaperla
Leuctra
Paraleuctra
Amphinemura
Nemoura
Diura
Isogenoides
Isoperla
Apatania
Brachycentrus
Micrasema
Glossosoma
Atopsyche
Trophic
1
1
1
1
3
1
1
3
1
3
3
1
1
5
5
5
0
4
5
5
5
5
5
3
4
4
3
2
5
3
4
Habit
4
4
4
4
4
4
4
4
4
4
4
5
5
3
3
3
4
4
4
1
3
3
3
4
4
4
4
4
4
4
4
Rheo
3
3
2
2
2
2
2
3
3
3
2
3
3
2
3
2
3
3
2
2
2
2
3
3
2
3
3
3
3
3
3
Volt
3
3
2
2
2
2
2
2
2
2
2
2
2
2
1
2
0
1
2
2
2
2
2
2
2
2
2
2
2
3
0
Respir
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
2
1
1
2
1
2
2
2
1
2
Gills
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
1
2
2
1
2
1
1
1
2
2
Gill
Form
2
2
2
4
2
4
2
2
2
2
2
1
1
6
6
6
6
6
6
6
6
5
6
6
3
6
3
3
3
6
3
Gill
Placement
2
2
2
2
2
2
2
1
1
1
1
2
2
6
6
6
6
6
6
6
6
3
6
6
4
6
2
2
2
6
2
Body
Form
3
3
1
1
1
1
1
1
1
1
1
2
2
4
4
4
4
4
4
4
6
5
5
1
4
4
4
4
4
4
4
Mobility
1
1
1
1
1
1
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
3
3
1
1
1
3
3
3
1
3
152
Homoplectra
Hydropsyche
Parapsyche
Hydroptila
Theliopsyche
Lepidostoma
Hesperophylax
Onocosmoecus
Pseudostenophylax
Psychoglypha
pycnopsyche
Chimarra
Oligostomis
Rhyacophila
Neophylax
Curculionidae adult
Agabus
Elmidae larva
Elmidae adult
Psephenidae
hydrophilidae
Atherix
Mallochohelea
Chironominae
Orthocladinae
Prodiamesinae
Tanypodinae
Tanytarsini
Dixa
Chelifera
Limnophora
Pericoma
Simulium
2
2
5
3
5
5
5
5
5
5
5
2
4
4
3
5
4
1
1
3
4
4
4
1
1
1
4
2
1
4
4
1
2
4
4
4
4
2
2
3
3
3
3
3
4
4
4
4
4
5
4
4
4
2
3
1
1
1
1
3
1
5
3
1
1
4
3
3
3
2
3
2
2
1
2
2
1
3
2
3
3
0
2
3
3
3
1
2
1
1
2
3
2
2
2
1
3
1
3
2
2
2
2
2
2
2
2
2
2
2
2
0
2
2
0
1
1
1
1
1
2
2
3
2
2
2
0
2
2
0
2
3
2
2
2
2
2
2
2
2
2
2
2
1
2
2
2
0
3
1
1
2
0
1
1
2
2
2
2
0
0
1
3
2
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
2
2
2
2
1
2
1
2
1
1
1
1
0
0
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
6
3
0
3
6
6
6
6
0
6
0
6
3
3
3
3
0
0
6
6
6
6
2
2
2
2
2
2
2
2
2
2
2
6
2
0
2
6
6
6
6
0
6
0
6
0
0
0
0
0
0
6
6
6
6
4
4
4
1
4
4
4
4
4
4
4
4
4
4
4
0
1
4
4
1
0
6
6
4
4
4
4
4
4
4
0
4
4
3
3
3
3
3
3
3
3
3
3
3
3
0
1
3
0
1
1
1
3
0
1
1
1
1
1
1
0
0
1
0
1
3
153
Prosimulium
Tabanus
Antocha
Arctoconopa
Dicranota
Limnophila
Molophilus
Pseudolimnophila
Tipula
Sialis
Pyralidae
Genus
Acerpenna
Baetis
Attenella
Dannella
Drunella
Ephemerella
Serratella
Cinygmula
Epeorus (Iron)
Heptagenia
Leucrocuta
Leptophlebia
Paraleptophlebia
Capnura
Isocapnia
2
4
1
5
4
4
0
0
5
4
5
Swimming
Ability
3
3
2
2
2
2
2
2
2
3
2
3
2
2
2
4
1
4
1
3
1
1
1
1
1
1
3
2
3
2
2
1
1
1
2
2
1
2
2
3
0
2
0
0
0
1
2
2
1
3
2
0
3
3
3
3
0
1
0
Armouring
Development
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
0
2
2
2
1
1
1
1
2
1
1
1
2
2
1
0
2
2
2
2
0
2
2
Max Body Size
1
1
1
1
2
1
1
2
2
2
1
2
2
1
1
6
6
3
0
6
6
6
6
0
6
6
6
6
5
0
6
6
6
6
0
6
6
4
0
6
6
6
6
6
6
0
6
0
3
0
0
0
0
0
0
0
0
1
0
154
Paracapnia
Sasquaperla
Sweltsa
Utaperla
Leuctra
Paraleuctra
Amphinemura
Nemoura
Diura
Isogenoides
Isoperla
Apatania
Brachycentrus
Micrasema
Glossosoma
Atopsyche
Homoplectra
Hydropsyche
Parapsyche
Hydroptila
Theliopsyche
Lepidostoma
Hesperophylax
Onocosmoecus
Pseudostenophylax
Psychoglypha
pycnopsyche
Chimarra
Oligostomis
Rhyacophila
Neophylax
Curculionidae adult
Agabus
2
0
2
2
2
2
1
1
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
3
1
1
1
1
1
1
1
1
2
2
2
3
3
3
3
2
3
3
3
3
3
3
3
3
3
3
3
3
3
0
3
0
2
2
0
2
2
1
1
1
2
2
2
2
2
2
2
2
0
2
2
2
2
0
0
2
2
2
2
0
2
0
2
2
0
2
1
0
2
1
1
1
1
1
0
3
2
2
2
1
1
0
2
0
2
1
1
2
3
3
3
3
3
0
3
2
3
1
1
155
Elmidae larva
Elmidae adult
Psephenidae
hydrophilidae
Atherix
Mallochohelea
Chironominae
Orthocladinae
Prodiamesinae
Tanypodinae
Tanytarsini
Dixa
Chelifera
Limnophora
Pericoma
Simulium
Prosimulium
Tabanus
Antocha
Arctoconopa
Dicranota
Limnophila
Molophilus
Pseudolimnophila
Tipula
Sialis
Pyralidae
1
1
1
0
1
3
1
1
1
3
1
0
1
0
1
1
1
0
0
0
0
0
0
0
0
1
0
2
2
2
0
2
2
1
1
1
1
0
0
1
0
2
1
1
0
1
1
1
1
1
1
1
2
0
3
3
2
0
2
1
1
1
1
1
0
0
2
0
1
1
1
0
2
2
2
2
2
2
2
2
0
1
1
1
1
0
2
0
0
2
0
0
0
1
0
0
1
2
3
0
0
0
0
0
0
0
3
0
1
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF

advertising