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EFFECTS OF FINE SEDIMENT DEPOSITION ON BENTHIC INVERTEBRATE COMMUNITIES
By
Olivia D. Logan
BSc University of New Brunswick (Saint John) 2004
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
Masters of Science
In the Graduate Academic Unit of Biology
Supervisor: R.A. Curry, Ph.D., Canadian Rivers Institute, UNB (Fredericton) Joseph Culp, Ph.D., Canadian Rivers Institute, UNB (Fredericton) Examining Board: Les Cwynar, Ph.D., Department of Biology, UNB Fredericton,
Chair Donald Baird, Ph.D., Department of Biology, UNB Glenn Benoy, Ph.D., Environment Canada and Agriculture and Agri-Food Canada
This thesis is accepted by the Dean of Graduate Studies
3.2 MATERIALS AND METHODS ....................................................... 47
3.2.1 Site Selection and Location .................................................................... 47 3.2.2 Site Descriptions ................................................................................. 48
3.2.3 Field Sampling ................................................................................... 48
3.2.4 Algal Biomass (Chlorophyll a) ............................................................... 49
4.2 MATERIALS AND METHODS ..................................................... 100
4.2.1 Site Selection and Location .................................................................. 100 4.2.2 Site Descriptions ............................................................................... 101
4.2.3 Field Sampling ................................................................................. 101
5.3 LITERATURE CITED....................................................................... 148
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LIST OF TABLES
CHAPTER 1: INTRODUCTION...................................................................... 10 Table 1.1: Objectives and hypotheses by chapter................................................10 CHAPTER 2: REDUCING BENTHIC INVERTEBRATE SAMPLE PROCESSING TIME AND COSTS USING COARSE SIEVE SUBSAMPLING ....................... 27-33
Table 2.1: Stream sites and GPS coordinates sampled from the central portion of Prince Edward Island, Canada, 25- 27 July 2005 ................................................27 Table 2.2: Results of the analysis of similarity (ANOSIM) tests performed on the coarse/total replicates for individual sites............................................................28 Table 2.3: Results of 1-way ANOVA comparing coarse and total family richness values within individual sites...............................................................................29 Table 2.4: Results of 1-way Kruskal-Wallis nonparametric test comparing coarse and total EPT richness values within individual sites..........................................30 Table 2.5: Results of 1-way ANOVA comparing coarse and total % chironomidae values within individual sites...............................................................................31 Table 2.6: Results of 1-way Kruskal-Wallis nonparametric test comparing coarse and total evenness values within individual sites ................................................32 Table 2.7: Results of 1-way ANOVA comparing coarse and total % EPT values within individual sites ..........................................................................................33
CHAPTER 3: DETERMINATION OF BENTHIC INVERTEBRATE BIOMONITORING METRICS RESPONSIVE TO FINE SEDIMENT DEPOSITION ............................................................................................................................... 68- 85
Table 3.1: Study sites sampled on Prince Edward Island, including their watershed and geographical location ....................................................................................68 Table 3.2: sizes used during substrate particle size distribution analysis ............69 Table 3.3: Biomonitoring metrics tested for a response to sediment deposition .70 Table 3.4: Spearman rank correlation coefficients and associated p- values for deposited sediment and substrate measures for July 2005...................................71
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Table 3.5: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and environmental variables for July 2005 ..........72 Table 3.6: Spearman rank correlation coefficients and associated p- values for deposited sediment and substrate measures for October 2005 ...........................73 Table 3.7: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and environmental variables for October 2005 ....74 Table 3.8: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and % habit/ feeding group metrics for July 2005..............................................................................................................................75 Table 3.9: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and biomonitoring metrics for July 2005. ............76 Table 3.10: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and % habit/ feeding group metrics for October 2005...............................................................................................................................78 Table 3.11: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and biomonitoring metrics for October 2005.......79 Table 3.12: Spearman rank correlation coefficients and associated p- values for habit/ feeding group metrics responsive to deposited sediment and environmental variables for July 2005.........................................................................................81 Table 3.13: Spearman rank correlation coefficients and associated p- values for metrics responsive to deposited sediment and environmental variables for July 2005......................................................................................................................83 Table 3.14: Spearman rank correlation coefficients and associated p- values for metrics responsive to deposited sediment and environmental variables for October 2005......................................................................................................................85
CHAPTER 4: USING SPECIES TRAITS TO DETERMINE THE EFFECTS OF FINE SEDIMENT DEPOSITION............................................................125- 36
Table 4.1: Biological traits and categories examined along a fine sediment deposition gradient ...........................................................................................125 Table 4.2: Spearman rank correlation coefficients and associated p- values for PCA axis site scores of average benthic invertebrate relative abundance data and environmental variables for July 2005..............................................................126
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Table 4.3: Spearman rank correlation coefficients and associated p- values for PCA axis site scores of average benthic invertebrate relative abundance data and environmental variables for October 2005. .....................................................128 Table 4.4: Spearman rank correlation coefficients and associated p- values for PCA axis site scores of average species trait relative abundance data and environmental variables for July 2005......................................................................................130 Table 4.5: Spearman rank correlation coefficients and associated p- values for PCA axis site scores of average species trait relative abundance data and environmental variables for October 2005................................................................................132 Table 4.6: Species tolerances for July 2005......................................................134 Table 4.7: Species tolerances for October 2005 ...............................................136
LIST OF FIGURES
CHAPTER 2: REDUCING BENTHIC INVERTEBRATE SAMPLE PROCESSING TIME AND COSTS USING COARSE SIEVE SUBSAMPLING ....................... 34-43
Figure 2.1: Location of watersheds sampled for benthic invertebrates, 25- 27 July 2005......................................................................................................................34
Figure 2.2: Ordination of benthic invertebrate assemblages within sites from the coarse and total samples.......................................................................................35
Figure 2.3: Abundance (mean ± 1SE) of benthic invertebrate taxa from total and coarse fraction samples ........................................................................................36
Figure 2.4: Relative abundance (mean ± 1SE) of benthic invertebrate taxa from total and coarse fraction samples .........................................................................37 Figure 2.5: Family Richness (mean ± 1SE) of total and coarse samples at all 13 sites..............................................................................................................................38 Figure 2.6: EPT Richness (mean ± 1SE) of total and coarse samples at all 13 sites. Sites values are the average of all three replicates...............................................39 Figure 2.7: Percent EPT and percent chironomidae (mean ± 1SE) of benthic invertebrate taxa from total and coarse fraction samples.....................................40 Figure 2.8: Shannon Diversity and Evenness (mean ± 1SE) of benthic invertebrate data from coarse fraction and total samples.........................................................41
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Figure 2.9: Mean percent capture (number in coarse/ total number of individuals) of different taxa by the coarse sieve (2mm).............................................................42 Figure 2.10: Mean percent capture (number in coarse/ total number of individuals) of Chironomids by site in the coarse sieve (2mm) ..............................................43
CHAPTER 3: DETERMINATION OF BENTHIC INVERTEBRATE BIOMONITORING METRICS RESPONSIVE TO FINE SEDIMENT DEPOSITION............................................................................................................................... 87- 95
Figure 3.1: Percentage of different land-use types within the six watersheds sampled in the summer (25- 27 July) and fall (19-21 October) of 2005 on Prince Edward Island, Canada ........................................................................................87 Figure 3.2: A.) 1m2 Quadrat used to designate area from which samples were to be collected. B.) Placement of algal substrate within the quadrat ............................88 Figure 3.3: A.) Artificial algal substrate used for Chlorophyll- a analysis. B.) Algal substrate deployed in river ...................................................................................89 Figure 3.4: Site- Season biplot diagram summarizing the effect of season on benthic invertebrate community composition ..................................................................90 Figure 3.5: Relationships between % habit/ functional feeding group metrics and % deposited fine sediment that had significant correlations in July 2005 ...............91 Figure 3.6: Relationships between biomonitoring metrics and % deposited fine sediment that had significant correlations in July 2005.......................................92 Figure 3.7: Relationship between chironomidae metrics and % deposited fine sediment that had significant correlations in July 2005.......................................93 Figure 3.8: Relationships between metrics and % deposited fine sediment that had significant correlations in October 2005..............................................................92 Figure 3.9: Relationships between metrics and % deposited fine sediment that had significant correlations in October 2005..............................................................95
CHAPTER 4: USING SPECIES TRAITS TO DETERMINE THE EFFECTS OF FINE SEDIMENT DEPOSITION ............................................................... 138- 145
Figure 4.1: RDA ordination of average species relative abundance and environmental variables significantly correlated with PCA axes 1 and 2, July 2005..............................................................................................................................138
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Figure 4.2: RDA ordination of average species relative abundance and environmental variables significantly correlated with PCA axes 1 and 2, October 2005......................................................................................................................139 Figure 4.3: RDA ordination of benthic invertebrate taxa tolerances to deposited sediment (axis 1), July 2005 ................................................................................140 Figure 4.4: RDA ordination of benthic invertebrate taxa tolerances to deposited sediment (axis 1), October 2005 ..........................................................................141 Figure 4.5: RDA ordination of species traits and environmental variables correlated with PCA axes 1 and 2, July 2005 .......................................................................142
Figure 4.6: RDA ordination of species traits and environmental variables correlated with PCA axes 1 and 2, October 2005.................................................................143 Figure 4.7: RDA ordination of average trait relative abundances and sediment variables July 2005 ..............................................................................................144 Figure 4.8: RDA ordination of average trait relative abundances and sediment variables, October 2005 .......................................................................................145 CURRICULUM VITAE APPENDIX A
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1 GENERAL INTRODUCTION 1.1 Background
In 1998 the US Environmental Protection Agency (EPA) identified deposited
sediment as the number one source of stream impairment and habitat degradation
nationwide (Zweig 2000). Sediment impacting rivers and streams can be suspended or
deposited (Waters 1995). Suspended sediment is defined as the particles that are carried
in the water column, while deposited sediment refers to the particles covering the
streambed (Waters 1995; Wilcock 2004). Sediment is any organic/ inorganic particulate
matter that can be transported and deposited in aquatic environments. Suspended
sediments impair respiration, feeding and visual foraging of aquatic biota (Waters 1995;
Wood and Armitage 1997). Sediment deposition degrades benthic habitat used by algae,
benthic invertebrates and fish (Waters 1995). The accumulation of excess fine sediment
on streambeds decreases substrate particle size, resulting in a change from a
heterogeneous substrate with complex habitats to a more homogenous sand/silt substrate
Unfortunately, existing studies focused on fine sediment addition and a short range of
sedimentation conditions. For example, Angradi (1999) examined experimentally added
fine sediment between 0 and 30% surface cover.
Identifying benthic invertebrate community response patterns to fine sediment
deposition, which would allow for identification of the source of impairment across
communities differing in taxonomic composition, is highly desirable for biomonitoring
purposes (Poff et al. 2006; Vieira et al. 2006). Biological traits, which are the functional
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attributes , e.g., morphological, physiological, behavioural and ecological characteristics,
of a species may provide a more generalized metric for use in any stream ecosystem
(Vieira et al. 2006). Statzner et al. (2004) observed that “habitat characteristics are filters
for the biological traits of organisms” or they are forces that act on the trait composition
of communities such as fine sediment deposition. In areas where environmental
conditions are constant, the community trait composition should be similarly constant
because the suite of biological traits an individual can draw from remains stable across
different taxonomic groups (Southwood 1977, 1988). For that reason biological traits can
be used for assessing effects of sediment deposition over many geographic areas without
refinement across communities with different taxonomic compositions. Differences in
morphology, feeding mechanism and mode of respiration along with other biological
traits may also help account for the varying degrees of sediment tolerance observed in
benthic invertebrate taxa.
1.3 Aims and Objectives
My thesis examines communities of benthic invertebrates in sediment impacted
streams to determine if biological traits, individually or in combination can explain
differences in community structure. By sampling multiple streams from the same
geographic area to take into account intra and inter-stream variability, I hope to be able to
extend my results beyond a single stream. My ultimate goal is to begin the process of
establishing a trait-based metric for assessing the impact of deposited sediment on a
stream’s benthic ecosystem.
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The primary objectives of my study are to determine the sensitivity of select
benthic invertebrate taxa from small streams on Prince Edward Island (PEI), Canada to
deposited fine sediment (< 2mm) to be used in a deposited sediment index and the
development of a list of benthic invertebrate biological traits (e.g. life history,
morphological, behavioural, or functional) indicative of fine sediment deposition (Table
1.1). My hypothesis is that certain taxa are more tolerant, because they possess life
history, physical, behavioural, or functional traits more adapted to fine sediment habitats.
My hypothesis is that biological traits in invertebrate communities are related to levels of
fine sediment deposition, and therefore some traits will become identifiable as percent
fine sediment deposition increases in a stream. A secondary objective was to determine
biomonitoring metrics that respond to changes in deposited sediment and therefore can be
used to assess the impacts of deposited fine sediment on benthic invertebrate
communities (Table 1.1). My hypothesis is that several metrics will respond positively or
negatively to increased sediment deposition in streams.
1.4 Significance of study
This study will add to the knowledge of the relationship between individual
benthic invertebrates/communities and fine sediment deposition in agricultural areas. A
deposited sediment index for PEI would be of benefit to bioassessment approaches,
because it is a high sediment risk area and may be indicative of Canadian patterns of
distribution and abundance in areas of sedimentation. Identification of biomonitoring
metrics responsive to sedimentation will allow for more expedited source of impairment
identification and remediation. Identification of a suite of biological traits that are
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responsive or indicative of fine sediment deposition will improve our ability to monitor/
diagnose problems in aquatic environments. Assessment of biological traits will also
allow us to become more predictive, by give a-priori predictions of trait compositions, so
the impacts can be identified sooner and reduced.
1.5 LITERATURE CITED
Allan, J.D. 2004. Landscapes and riverscapes: The influences of land use on stream ecosystems. Annual Review of Ecological Systems 35: 257- 284. Angradi, T.R. 1999. Fine sediment and macroinvertebrate assemblages in Appalachian
streams: a field experiment with biomonitoring applications. Journal of the North American Benthological Society 18(1): 49- 66.
Brusven, M.A., and K.V. Prather. 1974. Influence of stream sediment on distribution of Macrobenthos . Journal of the Entomological Society of British Columbia 71:
25- 32. Cobb, D.G., T.D. Galloway and J.F. Flannagan. 1992. Effects of discharge and substrate stability on density and species composition of stream insects. Canadian Journal of Fisheries and Aquatic Sciences. 49: 1788- 1795. Cordone, A.J., and D.W. Kelley. 1960. The influences of inorganic sediment on the aquatic life of streams. California Department of Fish and Game. Culp, J.M., F.J. Wrona and R.W. Davies. 1986. Response of stream benthos and drift to fine sediment deposition versus transport. Canadian Journal of Zoology. 64 : 1345- 1351. Fairchild, J.F., T. Boyle, W.R. English and C. Rabeni. 1987. Effects of sediment and contaminated sediment on structural and functional components of experimental stream ecosystems. Water, Air, and Soil Pollution. 36: 271- 293. Gray, L.J., and J.V. Ward. 1982. Effects of sediment releases from a reservoir on stream macroinvertebrates. Hydrobiologia. 96: 177- 184. Henley, W.F., M.A. Patterson, R.J. Neves and A.D. Lemly. 2000. Effects of
sedimentation and turbidity on lotic food webs: a concise review for natural resource managers. Reviews in Fisheries Science. 8(2): 125- 139.
Jowett, I.G. 2003. Hydraulic constraints on habitat suitability for benthic invertebrates
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in gravel- bed rivers. River Research and Applications. 19: 495- 507. Kaller, M.D., K.J. Hartman, and T.R. Angradi. 2001. Experimental determination of benthic macroinvertebrate metric sensitivity to fine sediment in Appalachian streams. Proc Annu Conf SEAFWA 55: 105-115. Kreutzweiser, D.P., S.S. Capell and K.P. Good. 2004. Effects of fine sediment inputs
from a logging road on stream insect communities: a large- scale experimental approach in a Canadian headwater stream. Aquatic Ecology. 00: 1- 12.
Lemly, A.D. 1982. Modification of benthic insect communities in polluted streams: combined effects of sedimentation and nutrient enrichment. Hydrobiologia. 87: 229- 245. Lenat, D.R., D.L. Penrose and K.W. Eagleson. 1981. Variable effects of sediment addition on stream benthos. Hydrobiologia. 79: 187- 194. McClelland, W.T., and M.A. Brusven. 1980. Effects of sedimentation on the behavior and distribution of riffle insects in a laboratory stream. Aquatic Insects.
2(3): 161- 169. Minshall, G.W. 1984. Aquatic insect- substratum relationships. Pages 358- 400 in Resh, V.H., and Rosenburg, D.M. The ecology of Aquatic Insects. Praeger Publishers, One Madison Ave. New York, NY. Nuttall, P.M. 1972. The effects of sand deposition upon the macroinvertebrate fauna of the River Camel, Cornwall. Freshwater Biology 2: 181- 186. Parkhill, K.L., and J.S. Gulliver. 2002. Effect of inorganic sediment on whole- stream productivity. Hydrobiologia 472: 5- 17. Poff, N.L., J.D. Olden, N.K.M. Vieira, D.S. Finn, M.P. Simmons and B.C. Kondratieff. 2006. Functional trait niches of North American lotic insects: trait- based ecological application in light of phylogenetic relationships. Journal of the North American Benthological Society 25(4): 730- 755. Relyea, C.D., G.W. Minshall and R.J. Danehy. 2000. Stream insects as bioindicators of fine sediment. Watershed Management 2000 Conference, Water Environment Federation. Richards, C., R.J. Haro, L.B. Johnson and G.E. Host. 1997. Catchment and reach- scale properties as indicators of macroinvetebrate species traits. Freshwater Biology. 37: 219- 230. Roy, A.H., A.D. Rosemond, D.S. Leigh, M.J. Paul and J.B. Wallace. 2003. Habitat- specific responses of stream insects to land cover disturbance: biological
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consequences and monitoring implications. Journal of the North American Benthological Society 22(2): 292- 307. Southwood, T.R.E. 1977. Habitat, templet for ecological strategies. Oikos 46: 337-365. Southwood, T.R.E. 1988. Tactics, strategies and templets. Oikos 52: 3-18. Statzner, B., S. Doledec and B. Hugueny. 2004. Biological trait composition of European stream invertebrate communities: assessing the effects of various trait filter types. Ecography. 27: 470- 488. Sylte, T., and C. Fischenich. 2002. Techniques for measuring substrate embeddedness. ERDC TN- RMRRP- SR- 36, September 2002. Vieira, N.K.M., N.L. Poff, D.M. Carisle, S.R. Moulton, M.L. Koski, and B.C. Kondratieff. 2006. A database of lotic invertebrate traits for North America. U.S. Geological Survey Data Series 187, http://pubs.water.usgs.gov/ds187. Waters, T.F. 1995. Sediment in streams: sources, biological effects and control. Monograph 7. American Fisheries Society, Bethesda, Maryland. Wood, P.J., and P.D. Armitage. 1997. Biological effects of fine sediment in the lotic environment. Environmental Management. 21(2): 203- 217. Wood, P.J., and P.D. Armitage. 1999. Sediment deposition in a small lowland stream- management implications. Regulated Rivers: Research & Management. Zanetell, B.A, and B.L. Peckarsky. 1996. Stoneflies as ecological engineers- hungry predators reduce fine sediment in stream beds. Freshwater Biology. 36: 569- 577. Zweig, L.D. 2000. Effects of deposited sediment on stream benthic macroinvertebrate communities. MSc Thesis, University of Missouri- Columbia. Zweig, L.D. and C.F. Rabeni. 2001. Biomonitoring for deposited sediment using benthic
invertebrates: a test on 4 Missouri streams. Journal of the North American Benthological Society 20(4): 643- 657.
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1.6 TABLES
Table 1.1 Chapter Objectives and Hypotheses
Chapter Objectives Hypotheses 2
Reducing benthic invertebrate sample processing time and
costs using coarse sieve subsampling.
• Determine if removal of the fine fraction changes BMI community trends.
• Determine how much
additional information the fine fraction adds to BMI community analyses.
• Trends observed in BMI community structure/ composition will not significantly change.
• The abundance of
select taxa will increase with the addition of the fine fraction, but no overall significant effects will be observed.
3
Determination of benthic invertebrate
biomonitoring metrics responsive to fine
sediment deposition.
• Determine if BMI community composition is determined by the level of deposited sediment in PEI streams.
• Determine which
biomonitoring metrics are sensitive or respond to fine sediment deposition.
• As % fine sediment cover increases, BMI taxa intolerant of fine sediment will disappear and tolerant taxa will increase in abundance.
• Metrics incorporating tolerant taxa will respond to increased sedimentation positively. Metrics using intolerant taxa will respond negatively to increased sediment deposition.
10
4
Using species traits as a diagnostic
bioassessment tool.
• Determine which physical, behavioural, or functional traits are most sensitive to fine sediment deposition.
• Develop a list of
sediment tolerant traits.
• Determine sediment tolerance of select BMI taxa.
• Biological traits benefical in areas of increased sediment will be positively associated with increased sediment.
• Biological traits
will become identifiable as % fine sediment cover increases. Trait composition will shift and the frequency of maladaptive traits will decrease as sediment deposition increases.
• BMI taxa with
tolerant traits will be more tolerant of deposited sediment.
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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.
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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
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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
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amount and diversity of benthic invertebrates are retained. The main objective was to
determine if removal of the subsampled fine fraction (< 2 mm) from the sample analysis
significantly changed the interpretation of observed trends in community composition. In
addition, we determined the effect of the inclusion of the fine fraction (in the total
sample) on the performance of various biomonitoring metrics. Finally, the percentage of
different taxonomic groups captured by the coarse sieve was investigated in order to
identify any taxonomic-specific biases related to size-fraction subsampling as this type of
bias could confound data interpretation.
2.2 MATERIALS AND METHODS 2.2.1 Sample Collection
Benthic invertebrate samples were collected on 25- 27 July 2005 from six 2nd and
3rd order streams located in the central portion of Prince Edward Island, Canada (Figure
2.1). Sample sites were selected from small streams within agricultural areas to obtain a
gradient of fine deposited sediment conditions, but which also had a narrow range of
other environmental variables (e.g. temperature, dissolved oxygen, pH) that could affect
benthic community composition. A total of 13 sites were sampled from the 6 streams
(Table 2.1).
Benthic invertebrates were sampled from riffle/ run areas using a U-net
(Scrimgeour et al. 1993; 30 cm opening, 400 µm mesh, 0.055 m2). The U-net was placed
on the stream substrate and the bed material within the area was disturbed to a depth of 8-
10 cm by hand for 3 min. A second U-net was then taken from within the same riffle and
combined with the contents of the first to produce a single sample. A total of 3 replicate
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samples (area of 0.11 m2 each) were taken in this way at each site. Once collected the
samples were poured onto a 250 µm mesh sieve, rinsed, placed into containers, preserved
with 10 % formalin and returned to the laboratory for identification.
2.2.2 Sample Processing
Benthic invertebrate samples were transferred into 70 % ethanol in the laboratory.
For sorting purposes the samples were separated into a coarse fraction (> 2 mm) and a
fine fraction (> 250 µm) by passing the material through sieves. Coarse material was
sorted, counted, and identified to the lowest practical taxonomic level. In contrast the fine
fraction was subsampled using an Imhoff cone (Wrona et al. 1982). At least 3 sub-
samples containing 200 individuals were sorted, counted, and identified to the lowest
practical taxonomic level. Precision between subsamples and precision of the cone were
evaluated (> 20% error whole sample sorted), done by checking whether number of
invertebrates in each subsample was similar. Elutriation was used to separate the organic
matter from the fine sediment in the samples before subsampling. The remaining
sediment was then checked for invertebrates. The organic fine fraction was then
subsampled and sorted.
2.2.3 Statistical Analyses
Ordination analysis was used to determine if trends in benthic invertebrate
communities among the sites differed between data sets which included either the total
sample (i.e., coarse and fine fractions combined) or only the coarse fraction. The two-
dimensional ordination was performed in the PRIMER software package using nonmetric
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).
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2.4 DISCUSSION
2.4.1 Community Composition
A major assumption of subsampling is that abundance and composition of
subsamples provides an unbiased, adequate representation of the benthic invertebrate
community in the original total sample. Our results show that subsampling using the
coarse fraction of a benthic invertebrate sample did not significantly change estimates of
the invertebrate composition. Even though information from the fine fraction was lost,
the sites were adequately represented as seen in the ANOSIM tests (Table 2.2). Thus, the
exclusion of the fine fraction did not have a significant effect on the overall sample
invertebrate composition, even though total taxa richness was significantly different
between the coarse and total samples (Figures 2.5 and 2.6). This indicates that the taxa
being lost were less abundant and arguably less important in the analysis. Further
examination of the data revealed many of the new taxa being acquired in the fine fraction
were relatively rare and therefore possibly excludable from the analysis. Previous studies
have found the inclusion of rare taxa contributes very little new information in terms of
community composition (Barbour and Gerritsen 1996). An alternative explanation of
why these differences in taxa richness did not affect community composition may be
attributable to nMDS being robust to the inclusion of rare taxa in the fine fraction, such
that their inclusion would have little effect on the multivariate patterns (Walsh 1997).
These results indicate the coarse fraction of a benthic invertebrate sample provides
sufficient information to interpret differences along a stressor gradient.
2.4.2 Metrics
21
Subsampling using the coarse fraction of a benthic invertebrate sample had an
effect on several metrics (taxa richness, EPT richness, % Chironomidae, Evenness), but
little effect on others (% EPT, Shannon Diversity). Few studies have examined the effect
of benthic subsampling on a suite of metrics (e.g. Doberstein et al. 2000). Our results
demonstrate use of the coarse fraction alone can result in an underestimation of taxa
richness and EPT richness (Figures 2.5 and 2.6). This is consistent with the findings of
previous subsampling studies, which indicate taxa richness increases as the total number
of individuals sorted increases (Courtemanch 1996; Vinson and Hawkins 1996; Larsen
and Herlihy 1998). As the total number of individuals counted increases, taxa of lower
proportions in the sample are more likely to be encountered (Courtemanch 1996). In fact,
the accumulation of taxa is a function of sampling effort with an initial rapid increase in
new taxa followed by a drop off in the number of new taxa encountered (Larsen and
Herlihy 1998). Vinson and Hawkins (1996) found taxa richness increased rapidly up to
200 organisms after which new taxa were encountered at a much slower rate.
Additionally, Growns et al. (1997) found both family and EPT richness to be sample-
size-dependent (number of individuals counted) with the highest values being obtained
when the whole sample was processed. The question then is whether the information
gained from processing whole samples to estimate taxa richness is worth the increased
sample processing effort in terms of time and cost? The problem as Vinson and Hawkins
(1996) stated is “we can never completely census a taxonomic assemblage or entire
community; we have to rely instead on estimates that describe some portion of the real
taxa richness of an assemblage.” Although use of the coarse fraction as a subsampling
method will underestimate taxa richness due to not being retained in the coarse sieve and
22
low probability of encounter, we conclude the cost-saving benefit outweighs the potential
information loss, because even whole samples are estimates of the assemblage and will
therefore underestimate taxa richness. Vinson and Hawkins (1996) found that although
subsampling by a fixed count underestimated taxa richness, statistical tests for differences
in richness between sites was as sensitive as analyses based on whole samples. Therefore
coarse sieve subsampling should be able to distinguish and allow for comparison of
relative differences in taxa richness along a disturbance gradient.
Metrics based on counts of Chironomids are also affected by the removal of the
fine fraction from analyses. The abundance and relative abundance of Chironomids was
significantly greater in the total samples when compared to coarse fraction values. The
coarse sieve fails to retain a large amount of the smaller Chironomids, leading to the
lowest mean capture efficiency (< 40 %) of all taxa. Because the coarse sieve
underestimates Chironomid abundance, any metric based on their relative abundance
(e.g. % Chironomidae) will also be underestimated if a large amount of small
chironomids are present in the sample. The metrics of Evenness and Shannon diversity
were only significantly different between the coarse and total samples at one site. Many
coarse and total metric values were significant overall, but not at the individual site level.
Previous studies have found that metric values calculated from fixed count subsampling
are able to distinguish sites based on ecological impairment and are stable when
compared to larger samples (Growns et al. 1997). In summary, results produced from
coarse fraction subsampling are a good representation of the original sample.
23
2.4.3 Conclusions
In conclusion, coarse fraction subsampling is a simple and cost-effective method,
which gives an adequate representation of the benthic invertebrate community. The
additional information gained from use of the coarse fraction as well as the fine fraction
of a benthic invertebrate sample produced only marginally different results. Thus, for
environmental gradient analyses such as that described here, processing the entire sample
would appear to be unnecessary and certainly not worth the increase in processing costs.
Subsampling using a coarse sieve can potentially cut sample sorting time by half. As well
coarse sieves retain a large percentage of the total number of individuals in a sample,
including both large and smaller invertebrates. Morin et al. (2004) found the proportion
of the biomass retained in coarse sieves is very high and they have a tendency to retain a
large amount of the smaller organisms as well. We found > 60 % of the total number of
individuals of the major taxonomic groups (with the exception of Chironomidae) were
captured in the coarse sieve. Benthic invertebrates not retained in the coarse sieve were
smaller and earlier instars that are often difficult to identify and lead to greatly increased
processing time and effort. Family and EPT richness were underestimated by this
subsampling method, however, many of the taxa being added from the fine fraction
appear to be rare and their inclusion contributes very little information to community
analyses.
In benthic research there are often trade-offs between sample processing time,
effort and the number of samples collected. Our results suggest that, coarse sieve
subsampling is an acceptable trade-off for reducing sample processing time and cost to
24
allow for more sites to be sampled. We have shown that subsampling using the coarse
fraction does not affect sample community composition. Thus, the collection of more
benthic invertebrate samples to increase statistical power and sample size to monitor the
invertebrate community is a better option than whole sample processing.
Coarse sieve subsampling is appropriate in bioassessment and ecological studies,
since the ability to discriminate sites along a stressor gradient is not compromised.
Although taxa and EPT richness are underestimated, coarse sieve subsampling is robust
enough to test for differences along a stressor gradient using relative richness differences
(Vinson and Hawkins 1996). In addition coarse sieves capture large, rare taxa that have
been deemed important for bioassessment and ecological information (Courtemanch
1996; Vinson and Hawkins 1996; Cao et al. 1998). However, coarse sieve subsampling
does have limitations and therefore may not be suitable for all studies. For example,
studies that require both small and large benthic invertebrates, such as life history and
size distribution studies would not be suitable for coarse sieve subsampling. These
studies often require all instars of the taxa of interest including the smaller, earlier instars
usually found in the fine fraction. We conclude the use of the coarse fraction of a benthic
invertebrate sample is a simple and cost-effective subsampling method viable for certain
studies. Coarse fraction subsampling reduces sample processing time allowing more sites
to be sampled and more replicates to be collected to better assess the benthic community.
2.5 ACKNOWLEDGEMENTS
I would like to acknowledge the help and support of the lab. I am particularly
indebted to Megan Finley for her help with the sorting of numerous benthic invertebrate
25
samples. I would also like to thank Allen Curry, Joseph Culp and Kristie Heard for their
guidance and help.
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): 381-385.
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: 241-248.
Wrona, F.J., J.M. Culp, and R.W. Davies. 1982. Macroinvertebrate subsampling: A
simplified apparatus and approach. Canadian Journal of Fisheries and Aquatic Sciences 39: 1051-1054.
27
2.7 TABLES
Table 2.1: Stream sites and GPS coordinates sampled from the central portion of Prince Edward Island, Canada, 25- 27 July 2005. Site Watershed Latitude Longitude North Brook Lower Dunk N 46º 20’ 48.6” W 63º 37’ 54.8”
North Brook Middle Dunk N 46º 21’ 32.8” W 63º 36’ 53.7”
North Brook Upper Dunk N 46º 21’ 51.2” W 63º 36’ 50.0”
Southwest Brook Lower Dunk N 46º 20’ 28.6” W 63º 38’ 13.0”
Southwest Brook Middle Dunk N 46º 20’ 05.2” W 63º 37’ 48.6”
Southwest Brook Upper Dunk N 46º 18’ 46.3” W 63º 35’ 55.5”
Dunk River Lower Dunk N 46º 21’ 18.6” W 63º 33’ 57.2”
Dunk River Upper Dunk N 46º 21’ 05.6” W 63º 29’ 20.4”
Wilmot River Upper Wilmot N 46º 23’ 32.3” W 63º 33’ 16.2”
Wilmot River Lower Wilmot N 46º 24’ 29.3” W 63º 35’ 46.1”
East Branch Lower Tryon N 46º 17’ 50.8” W 63º 31’ 39.0”
East Branch Upper Tryon N 46º 15’ 45.4” W 63º 31’ 45.2”
Brookvale Lower West N 46º 17’ 02.4” W 63º 24’ 29.4”
Brookvale Upper West N 46º 19’ 58.8” W 63º 25’ 19.5”
28
Table 2.2: Results of the analysis of similarity (ANOSIM) tests performed on the coarse/total replicates for individual sites. North Brook Mid was the only site with the coarse and total samples well separated (different) (R- value >0.75).
Site R- value Significance level NBR Up 0.074 40%
NBR Mid 0.815 10%
NBR Lo -0.185 80%
SWB Up -0.296 70%
SWB Mid -0.037 70%
SWB Lo 0.037 60%
Dunk Up -0.111 70%
Dunk Lo -0.259 80%
Wil Up 0.148 40%
BKV Up 0.037 50%
BKV Lo 0.185 30%
EasBr Up 0.037 60%
EasBr Lo -0.074 70%
29
Table 2.3: Results of 1-way ANOVA comparing coarse and total family richness values within individual sites. Stars denote significant difference ( p <0.05). Site SS df F p BKV Lo Between groups 16.67 1 6.25 0.067
Within groups 10.67 4 Total 27.33 5
BKV Up Between groups 32.67 1 3.56 0.132 Within groups 36.67 4 Total 69.33 5
Dunk Lo Between groups 6.0 1 0.327 0.598 Within groups 73.33 4 Total 79.33 5
Dunk Up Between groups 13.5 1 40.5 0.003 * Within groups 1.33 4 Total 14.83 5
EasBr Lo Between groups 6.0 1 0.947 0.386 Within groups 25.33 4 Total 31.33 5
EasBr Up Between groups 16.67 1 3.23 0.147 Within groups 20.67 4 Total 37.33 5
NBR Lo Between groups 24.0 1 3.79 0.123 Within groups 25.33 4 Total 49.33 5
NBR Mid Between groups 28.17 1 16.9 0.015 * Within groups 6.67 4 Total 34.83 5
NBR Up Between groups 32.67 1 3.5 0.135 Within groups 37.33 4 Total 70.0 5
SWB Lo Between groups 13.50 1 2.31 0.203 Within groups 23.33 4 Total 36.83 5
SWB Mid Between groups 42.67 1 8.0 0.047 * Within groups 21.33 4 Total 64.0 5
SWB Up Between groups 2.67 1 0.348 0.587 Within groups 30.67 4 Total 33.33 5
Wil Up Between groups 13.5 1 7.36 0.053 * Within groups 7.33 4 Total 20.83 5
30
Table 2.4: Results of 1-way Kruskal-Wallis nonparametric test comparing coarse and total EPT richness values within individual sites. Stars denote significant difference ( p <0.05). Site BKV Lo Chi-Square 4.09 df 1 p 0.043 * BKV Up Chi-Square 2.63 df 1 p 0.105 Dunk Lo Chi-Square 0.429 df 1 p 0.513 Dunk Up Chi-Square 2.63 df 1 p 0.105 EasBr Lo Chi-Square 1.82 df 1 p 0.178 EasBr Up Chi-Square 1.82 df 1 p 0.178 NBR Lo Chi-Square 0.784 df 1 p 0.376 NBR Mid Chi-Square 0.833 df 1 p 0.361 NBR Up Chi-Square 2.72 df 1 p 0.09 SWB Lo Chi-Square 2.72 df 1 p 0.09 SWB Mid Chi-Square 2.33 df 1 p 0.127 SWB Up Chi-Square 0.22 df 1 p 0.637 Wil Up Chi-Square 3.97 df 1 p 0.046 *
31
Table 2.5: Results of 1-way ANOVA comparing coarse and total % chironomidae values within individual sites. Stars denote significant difference ( p <0.05). Site SS df F p BKV Lo Between groups 0.012 1 1.39 0.303 Within groups 0.033 4 Total 0.045 5 BKV Up Between groups 0.101 1 8.36 0.044* Within groups 0.048 4 Total 0.149 5 Dunk Lo Between groups 0.015 1 3.37 0.140 Within groups 0.017 4 Total 0.032 5 Dunk Up Between groups 0.103 1 30.83 0.005* Within groups 0.013 4 Total 0.116 5 EasBr Lo Between groups 0.014 1 0.963 0.382 Within groups 0.060 4 Total 0.074 5 EasBr Up Between groups 0.013 1 3.48 0.136 Within groups 0.015 4 Total 0.028 5 NBR Lo Between groups 0.001 1 0.072 0.801 Within groups 0.061 4 Total 0.063 5 NBR Mid Between groups 0.023 1 8.70 0.042* Within groups 0.011 4 Total 0.034 5 NBR Up Between groups 0.004 1 0.175 0.697 Within groups 0.101 4 Total 0.106 5 SWB Lo Between groups 0.000 1 0.002 0.963 Within groups 0.038 4 Total 0.038 5 SWB Mid Between groups 0.006 1 2.61 0.181 Within groups 0.101 4 Total 0.016 5 SWB Up Between groups 0.007 1 2.09 0.222 Within groups 0.014 4 Total 0.021 5 Wil Up Between groups 0.057 1 49.47 0.002* Within groups 0.005 4 Total 0.062 5
32
Table 2.6: Results of 1-way Kruskal-Wallis nonparametric test comparing coarse and total evenness values within individual sites. Stars denote significant difference ( p <0.05). Site BKV Lo Chi-Square 3.86 df 1 p 0.05 BKV Up Chi-Square 0.429 df 1 p 0.513 Dunk Lo Chi-Square 3.86 df 1 p 0.05 Dunk Up Chi-Square 3.86 df 1 p 0.05 EasBr Lo Chi-Square 1.19 df 1 p 0.275 EasBr Up Chi-Square 0.048 df 1 p 0.827 NBR Lo Chi-Square 3.97 df 1 p 0.046 * NBR Mid Chi-Square 3.86 df 1 p 0.05 NBR Up Chi-Square 0.429 df 1 p 0.513 SWB Lo Chi-Square 1.19 df 1 p 0.275 SWB Mid Chi-Square 1.19 df 1 p 0.275 SWB Up Chi-Square 1.19 df 1 p 0.275 Wil Up Chi-Square 0.429 df 1 p 0.513
33
Table 2.7: Results of 1-way ANOVA comparing coarse and total % EPT values within individual sites. Stars denote significant difference ( p <0.05). Site SS df F p BKV Lo Between groups 0.013 1 4.65 0.09 Within groups 0.011 4 Total 0.023 5 BKV Up Between groups 0.006 1 0.08 0.791 Within groups 0.305 4 Total 0.311 5 Dunk Lo Between groups 0.004 1 0.89 0.40 Within groups 0.016 4 Total 0.020 5 Dunk Up Between groups 0.009 1 0.67 0.46 Within groups 0.051 4 Total 0.06 5 EasBr Lo Between groups 0.005 1 3.03 0.157 Within groups 0.007 4 Total 0.012 5 EasBr Up Between groups 0.011 1 0.485 0.524 Within groups 0.09 4 Total 0.101 5 NBR Lo Between groups 0.002 1 0.098 0.770 Within groups 0.064 4 Total 0.066 5 NBR Mid Between groups 0.014 1 4.49 0.101 Within groups 0.013 4 Total 0.027 5 NBR Up Between groups 0.003 1 0.161 0.709 Within groups 0.081 4 Total 0.084 5 SWB Lo Between groups 0.004 1 1.90 0.240 Within groups 0.009 4 Total 0.013 5 SWB Mid Between groups 0.004 1 0.679 0.456 Within groups 0.023 4 Total 0.027 5 SWB Lo Between groups 0.002 1 0.119 0.747 Within groups 0.062 4 Total 0.064 5 Wil Up Between groups 0.016 1 1.71 0.261 Within groups 0.037 4 Total 0.053 5
34
2.8 FIGURES
Figure 2.1: Location of watersheds sampled for benthic invertebrates, 25- 27 July 2005. A total of 13 sites were sampled from within the 6 streams.
35
Figure 2.2: Ordination of benthic invertebrate assemblages within sites from the coarse and total samples. The nonmetric multidimensional scaling (MDS) is based on log transformed abundances and Bray-Curtis similarities (stress = 0.2).
36
TaxaEph
emero
ptera
Plecop
tera
Tricho
ptera
Diptera
Coleop
tera
Chiron
omida
e
Oligoc
haeta
Num
ber o
f Ind
ivid
uals
0
50
100
150
200
250
300
350
Coarse Total {{* *
Figure 2.3: Abundance (mean ± 1SE) of benthic invertebrate taxa from total and coarse fraction samples. Stars denote significant differences in abundance (p<0.01) in comparison of the coarse and total sample fractions.
37
TaxaEphemeroptera
Plecoptera
Trichoptera
Diptera
Coleoptera
Chironomidae
Oligochaeta
Rel
ativ
e A
bund
ance
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
Coarse Total
{*
Figure 2.4: Relative abundance (mean ± 1SE) of benthic invertebrate taxa from total and coarse fraction samples. Stars denote significant differences in relative abundance (p <0.01) in comparison of the coarse and total sample fractions.
38
Site
NBR Up
NBR MidNBR Lo
SWB Up
SWB MidSWB Lo
Dunk UpDunk Lo
Wil Up
EasBr Up
EasBr LoBKV Up
BKV Lo
Num
ber o
f fam
ilies
0
5
10
15
20
25
30
Coarse Total
{
{
{ **
*
Figure 2.5: Family Richness (mean ± 1SE) of total and coarse samples at all 13 sites. Family richness was significantly greater in the total samples overall and for each site. Stars denote significant differences (p <0.05) between coarse and total sample family richness values.
39
Site
NBR Up
NBR Mid
NBR Lo
SWB Up
SWB Mid
SWB Lo
Dunk Up
Dunk LoWil U
p
EasBr U
p
EasBr L
o
BKV Up
BKV Lo
Num
ber o
f EP
T fa
mili
es
0
2
4
6
8
10
12
14
16Coarse Total
{ *{*
Figure 2.6: EPT Richness (mean ± 1SE) of total and coarse samples at all 13 sites. Sites values are the average of all three replicates. Stars denote significant differences in EPT richness (p<0.05) in comparison of the coarse and total sample fractions.
40
Metric
% EPT % Chironomidae
# In
divi
dual
s in
Tax
a/ T
otal
# In
divi
dual
s
0
10
20
30
40
50
Coarse Total
*
Figure 2.7: Percent EPT and percent chironomidae (mean ± 1SE) of benthic invertebrate taxa from total and coarse fraction samples. Star denote significant difference (p<0.01) in comparison of the coarse and total sample.
41
Taxa
Evenness Shannon Diversity
Valu
e
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
Coarse Total
{ *
Figure 2.8: Shannon Diversity and Evenness (mean ± 1SE) of benthic invertebrate data from coarse fraction and total samples. Star denotes significant difference (p<0.05) in comparison of the coarse and total sample.
42
TaxaEphemeroptera
Plecoptera
TrichopteraDiptera
Coleoptera
Chironomidae
Oligochaeta
Coa
rse/
Tota
l
0.0
0.2
0.4
0.6
0.8
1.0
Figure 2.9: Mean percent capture (number in coarse/ total number of individuals) of different taxa by the coarse sieve (2mm). Percent capture values are the average for all sites and replicates.
43
Site
Dunk UpDunk Lo
NBR Up
NBR MidNBR Lo
SWB Up
SWB MidSWB Lo
Wil Up
EasBr UpEasBrLo
BKV UpBKV Lo
Coa
rse/
Tota
l Abu
ndan
ce
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Figure 2.10: Mean percent capture (number in coarse/ total number of individuals) of Chironomids by site in the coarse sieve (2mm). Capture values (mean ± 1SE) are the average of sites replicates.
44
3.0 ABSTRACT
This study examines the response of several commonly used biomonitoring
metrics to a gradient of deposited fine (< 2 mm) sediment conditions to determine metrics
useful for identifying and quantifying the effects of increased fine sediment deposition.
Thirteen sites within six agricultural streams on Prince Edward Island, Canada were
sampled during the summer and fall of 2005. Fine sediment deposition was quantified by
three methods: a visual estimate of percent cover of deposited fine sediment, calculated
% embeddedness and % fines < 2 mm from a substrate sample.
Several metrics were significantly correlated and responded consistently to
increasing fine sediment deposition across seasons. The metrics % burrower and %
Chironominae of Chironomidae responded positively to increasing fine sediment
deposition. While the metrics % EPT, EPT abundance and % Orthocladiinae of
Chironomidae responded negatively to increasing fine sediment deposition. Indicating
several commonly used metrics can help to detect the impact of fine sediment deposition.
45
3.1 INTRODUCTION
The amount and rate at which fine sediment enters aquatic environments has been
greatly increased by anthropogenic activities such as agriculture, forestry, mining, road
construction and urbanization (Waters 1995). Increased sediment rates have overloaded
many rivers and streams, exceeding their ability to flush sediment (Relyea et al. 2000),
resulting in sedimentation and impairment of benthic habitat. In fact, the US
Environmental Protection Agency has identified sediment as the number one source of
substrate impairment to streams nationwide (Zweig 2000; Zweig and Rabeni 2001).
Deposited fine sediment reduces benthic substrate quality and quantity by: increasing
substrate embeddedness (Brusven and Prather 1974; Sylte and Fischenich 2002), altering
substrate particle size distribution (Waters 1995), clogging interstitial spaces (Zanetell
and Peckarsky 1996), as well as decreasing streambed stability (Cobb et al. 1992; Roy et
al. 2003) and heterogeneity (Minshall 1984). Habitat alterations have major impacts on
riverine biotic communities, however we lack specific methods that quantify the extent of
impairment due to increased deposited sediment in streams.
Biomonitoring often involves the use of benthic invertebrates to determine the
quality or extent of impairment of aquatic environments. Benthic invertebrates are
popular choices because they are ubiquitous, semi- sedentary, long lived and speciose
(Rosenberg and Resh 1993). Benthic invertebrates live in close contact with the substrate
and substrate quality is one of the primary factors regulating their abundance and
distribution. This makes benthic invertebrates ideal candidates for evaluating the impact
of increased sediment deposition in streams (Minshall 1984; Zweig and Rabeni 2001).
46
Despite numerous studies investigating the relationship between deposited sediment and
benthic invertebrates, few response patterns are known.
Excessive deposited fine sediment affects benthic invertebrate behaviors such as:
drift (McClelland & Brusven 1980; Culp et al. 1986; Fairchild et al. 1987), feeding
respiration (Lemly 1982). At high levels of sediment deposition, decreases in density,
overall abundance and community diversity have been observed (Nuttall 1972; Lenat et
al. 1981; Minshall 1984; Henley et al. 2000; Roy et al. 2003). Changes in community
composition have also been reported (Cordone & Kelley 1961; Lenat et al. 1981;
Kreutzweiser et al. 2004). Certain sensitive taxa, mainly in the orders Ephemeroptera,
Plecoptera and Trichoptera, decrease in abundance as fine sediment deposition increases
(Waters, 1995). In contrast, other taxa, such as chironomids and oligochaetes can increase
in abundance and richness (Gray and Ward 1982). Few studies have examined the
sensitivity of benthic invertebrate taxa and biomonitoring metrics along a gradient of
deposited sediment conditions. I know of only four other studies which attempted to
empirically determine metric sensitivity or responsiveness to fine sediment deposition
(Angradi 1999; Relyea et al. 2000; Kaller et al. 2001; Zweig and Rabeni 2001). Two of
these studies focused on fine sediment addition and a small range of sedimentation
conditions, so the results are not broadly applicable (Angradi 1999; Kaller et al. 2001).
Thus, the responsiveness of biomonitoring metrics under natural conditions along a
deposited sediment gradient warrants further examination.
Rivers draining agricultural lands such as those found on Prince Edward Island
(PEI, Canada), are ideal to investigate these relationships as sediments are continually
47
being added and a range of deposited sediment conditions exist. Many PEI streams are
impacted by high sediment loads, caused by agricultural activities (Resource Inventory
and Modeling Section, Department of Agriculture and Forestry 2003). Each year, as
much as 14 tons of topsoil can be lost from an acre of farmland on PEI (InfoPEI 2006).
Extensive agriculture, sandy soil and the fact that most streams are groundwater fed
makes PEI aquatic environments especially vulnerable to increased sediment deposition
through erosion and run-off (Caissie and Arseneau 2002; Curry et al. 2006).
The objective of the present study was to test for relationships between
biomonitoring metrics and changes in fine (< 2 mm) sediment deposition in small
agricultural streams and to identify a set of metrics to be used in monitoring aquatic
environments for sediment impacts. I hypothesized certain metrics would respond
positively to increases in deposited fine sediment, while others would have a negative
response. More specifically, metrics incorporating taxa known to be sensitive (e.g. EPT
taxa) will respond negatively, while metrics using tolerant taxa (e.g. chironomids) will
respond positively to increases in fine sediment deposition.
3.2 M ATERIALS AND METHODS 3.2.1 Site Selection and Location Study sites were selected within six, 2nd and 3rd order streams located in the
central portion of Prince Edward Island, Canada (Figure 2.1). The island covers a land
area of approximately 5660 km2 (Cairns 2002) with predominantly sandstone and
siltstone bedrock geology (Purcell 2003). The overall topography varies, with the central
portion being more elevated than the eastern and western sections of the island (DeGrace
48
1989; Purcell 2003). On PEI, 39% of the land is devoted to agriculture, 45% is forested
and 8% is urban land use (Resource Inventory and Modeling Section, Department of
Agriculture and Forestry 2003).
3.2.2 Site Descriptions
A total of 13 sites within six streams (Table 3.1) were sampled during the summer
(25-27 July) and fall (19-21 October) of 2005. Sample sites were selected from small
streams within agricultural areas (Figure 3.1) to obtain a gradient of fine deposited
sediment conditions (10-90 %), but which also had a narrow range of other
environmental variables (e.g. temperature, pH). The construction of a beaver dam
necessitated the removal of the Southwest Brook Upper site from the fall sampling.
Similarly, North Brook Middle samples were taken further upstream during the fall due
to the construction of a beaver dam. Data from the Southwest Brook Upper site for the
summer sampling was removed from the analyses as the total suspended solids levels
were 4 times that of the other streams, resulting in spurious correlations.
3.2.3 Field Sampling
Three replicates were taken of all measurements in riffle/ run habitats at each site.
First a 1L water sample was collected for total suspended solids (TSS) analysis in the
laboratory. A 1 m2 quadrat was then placed around an artificial algal substrate deployed
prior to sampling to designate the area from which all measurements for that replicate
were to be taken (Figure 3.2). A YSI ® 556 MPS multimeter was used to measure water
temperature, pH, dissolved oxygen, and conductivity. Flow was measured using a Marsh-
Mc Birney ®, model 2000 portable flowmeter. These measurements were taken to ensure
similar and typical conditions at all sites. Additionally measurements of percent fine
49
sediment cover and substrate embeddedness along with benthic macroinvertebrate,
Chlorophyll a and streambed substrate samples were collected from within the 1 m2
quadrat (Figure 3.2). Channel measurements, such as bankfull and wetted width as well
as slope of the sample site were also collected.
3.2.4 Algal Biomass (Chlorophyll a)
Preliminary sampling in October 2004 revealed that suitable rocks for chlorophyll
a sampling were not present at all sites. Chlorophyll a measurements were used to
estimate algal biomass. This necessitated the deployment of artificial algal substrates
prior to the two sampling periods in 2005. Each artificial substrate consisted of 3 small
ceramic tiles (48.25 mm x 48.25 mm) affixed to a cement brick with silicon and plastic
cable ties (Figure 3.3). Twenty days prior to sampling in the summer and fall, 3 replicate
bricks were deployed at each site. The artificial substrates were placed in riffle/ run
habitats, and dug into the substrate a few cm to ensure they remained submerged.
Algal samples for chlorophyll a analysis were obtained by scraping all algae from
the tiles on a brick with a scalpel into a scintillation vial containing water. Samples were
then frozen until analysis. In the laboratory samples were blended and filtered with a pre-
ashed glass-microfiber filter. Chlorophyll a was extracted by placing the filters in a 80 °C
bath of 90 % ethanol for 5 min. Chlorophyll a fluorescence was measured using a Turner
Designs® (Sunnyvale, CA, USA) 10- AU Fluorometer equipped with a red sensitive
photomultiplier tube, a round 680 -nm emission filter, and a reference filter (Culp et al.
2003).
50
3.2.5 Benthic Macroinvertebrates
Benthic invertebrates were collected from within the 1 m2 quadrat using a U-net
(Scrimgeour et al. 1993; 30 cm opening, 400 µm mesh, 0.055 m2; Figure 3.2). The U-net
was placed on the stream substrate and the bed material within the area was disturbed to a
depth of 8-10 cm by hand for 3 min. A second U-net was then taken from within the
same quadrat and combined with the contents of the first to produce a single sample. A
total of 3 replicate composite samples (area of 0.11 m2 each) were taken in this way at
each site. Once collected the samples were poured onto a 250 µm mesh sieve, rinsed,
placed into containers, preserved with 10 % formalin and returned to the laboratory for
identification.
Benthic invertebrate samples were transferred into 70 % ethanol in the laboratory.
For sorting purposes the samples were separated into a coarse fraction (>2 mm) and a
fine fraction (>250 um) by passing the material through stacked sieves. All coarse
material was sorted, counted, and identified to the lowest practical taxonomic level.
3.2.6 Total Suspended Solids
A 1L water sample was collected at each site and kept cool until analyzed back in
the laboratory for total suspended solids (TSS). Sample bottles were shaken for 1 min by
hand to re-suspend settled sediment particles in the laboratory. Sample volume was
measured using a graduated cylinder. The contents of the graduated cylinder where then
filtered onto a pre-weighed and ashed glass-microfiber filter. All filters were then
weighed and recorded. The filters were placed in a drying oven for 8 hours and then
51
ashed for 4 hours. Filters were reweighed and TSS per unit volume (mg/L) was
calculated.
3.2.7 Sediment Measures
For the purposes of this study, fine sediment was classified as particles < 2 mm,
which encompasses the particle categories of clay, silt and sand (Waters 1995). Fine
sediment deposition was quantified two ways and a substrate sample was collected at
each replicate per site.
Percent deposited fine sediment
The percentage of the streambed surface covered by fine sediment within a 1 m2
quadrat was visually estimated in 10 % increments prior to subsequent sampling (Zweig
2000; Zweig & Rabeni 2001).
Substrate embeddedness
From within the 1 m2 quadrat 10 rocks with a diameter > 8 mm were removed
from the streambed. The total height of the rock based on its orientation to the streambed
was measured with a ruler and recorded. The height to the silt line on the rock was then
measured and recorded. The percent embeddedness of each rock was calculated and a
sample average determined (Zweig 2000; Sylte & Fischenich 2003).
Substrate particle size distribution
Substrate samples were collected from within the quadrat using a shovel similar to
the methods of Grost et al. 1991 and Zweig 2000 (Figure 3.3). The method involved
pushing the shovel into the substrate at roughly a 40º angle and then lifting it from the
water. A U-net (400 µm mesh) placed downstream of the sample area caught the
52
resuspended fine sediment. Two shovels of substrate and fine sediment caught in the U-
net were placed in sample bags for analysis in the laboratory. Substrate samples were air
dried in the laboratory for 24 hours and organic material (leaves and woody debris) was
removed. Samples were further dried in an oven at 120 ºC for 12- 24 hours (depending on
the amount of water). The samples were then shaken through a series of coarse (25, 19,
12.5, 9.5, 6.3, 4.75 and 4 mm) and fine sieves (2, 1, 0.5, 0.25, 0.125, 0.038 mm and silt)
(10 min each; Table 3.2). Each size fraction was weighed and recorded. Percent < 2mm,
% < 4 mm, % gravel (2- 16 mm) and % pebble (16- 64 mm) were also calculated for each
replicate sample.
3.2.8 Biomonitoring metrics
A variety of commonly used biomonitoring metrics were calculated from the
benthic invertebrate data (Table 3.3). Information on benthic invertebrate habit and
functional feeding group for metric calculations was obtained from Merritt & Cummins
(1996) and Poff et al. (2006).
3.2.9 Statistical Analysis
Due to seasonal variance in the benthic invertebrate composition of the samples,
analyses were performed separately on the two sampling periods (summer n = 39 and fall
n = 36) (Figure 3.4). The relationships between calculated biomonitoring metrics,
deposited sediment metrics and environmental variables were examined using correlation
analysis (SPSS 13.0; Chicago, IL, USA). Data normality (Kolmogorov-Smirnov test) and
variance homogeneity (Levene’s Test) was tested prior to analysis (SPSS 13.0; Chicago,
IL, USA). Since many of the sediment measures and metrics did not meet these
53
assumptions even after transformation, the nonparametric Spearman’s Rank Correlation
test was used in lieu of the Pearson Correlation test. Scatterplots were constructed for
biomonitoring metrics found to be correlated with deposited sediment measures to
determine their sensitivity to increasing fine sediment deposition using all data points (no
averaging of replicates) to obtain a better visual representation of the relationships.
Spearman’s rank correlation coefficients (Sr) are reported along with their p-values.
3.3 RESULTS
Relationships between biomonitoring metrics, sediment measures and
environmental variables were evaluated for both summer 2005 and fall 2005. The
relationship between biomonitoring metrics and sediment was examined using only
percent surface cover of deposited fine sediment.
3.3.1 Sediment Measures and Environmental Variables
Summer 2005
In the summer (2005) sampling, percent deposited fine sediment was highly
correlated with calculated percent embeddedness (Table 3.4). Percent deposited fine
sediment was also highly correlated with smaller substrate particle classes (from shovel
cores) such as % < 2 mm and % < 4 mm (Table 3.4). Neither percent embeddedness nor
TSS was significantly correlated with sediment sample particle size classes. In the
summer the deposited sediment gradient ranged from 10 % (e.g. North Brook Low) to 90
% (e.g. Brookvale Up) surface cover of fine sediment (< 2 mm).
54
Percent deposited fine sediment was negatively correlated with percent slope of
the sampling sites in the summer sampling (Sr = -0.485, p = 0.003; Table 3.5). However,
upon examination of the scatter plots it was found the correlation was spurious due
partially to one site having a slightly higher slope. Percent embeddedness was not
significantly correlated with any of the environmental variables measured (Table 3.5)
TSS was correlated with many of the water chemistry variables such as water
temperature, pH, dissolved oxygen and flow (Table 3.5).
Fall 2005
As in the summer, deposited fine sediment and percent embeddedness were
highly correlated in the fall. Both percent deposited fine sediment and percent
embeddedness were significantly correlated with substrate particle size classes (e.g. % <
2mm, % < 4mm and % pebble; Table 3.6). TSS was not significantly correlated with any
of the other sediment measures (Table 3.6). In the fall the deposited sediment gradient
ranged from 10 % (e.g. Dunk Low) to 100 % (e.g. North Brook Mid) surface cover of
fine sediment (< 2 mm).
Unlike in the summer, percent deposited fine sediment and percent embeddedness
were negatively correlated with flow in the fall (Sr = -0.523, p= 0.001 and Sr = -0.472, p=
0.004; Table 3.7). Spearman’s correlations showed TSS was negatively correlated with
percent slope and dissolved oxygen (Sr = -0.608, p= <0.001 and Sr = -0.614, p= <0.001).
The correlation between percent slope and TSS was the result of one site (Brookvale Up)
having a higher slope than the other sites sampled.
3.3.2. Biomonitoring Metrics
55
Several biomonitoring metrics responded to various sediment measures (Tables
3.8 - 3.14). However, since percent deposited sediment and percent embeddedness were
highly correlated and TSS was not correlated with any other sediment measures, relations
between percent deposited fine sediment (< 2 mm) and the biomonitoring metrics will
only be examined. Additionally percent deposited fine sediment was significantly
correlated with only one environmental variables in each season, while TSS was
correlated with the majority of environmental variables (Tables 3.5 and 3.7).
Summer 2005
In the summer the habit/ feeding group metrics: % burrower, % sprawler, %
swimmer, % gatherer, % filterer and % predator were significantly correlated with
deposited fine sediment (Table 3.8). However, upon examination of the scatterplots, the
correlation between percent filterer and percent deposited fine sediment was found to be
spurious as no relationship was apparent. Percent burrower, % sprawler and % predator
were all positively correlated with percent deposited fine sediment (Sr = 0.507; p = 0.002;
Sr = 0.347; p = 0.038 and Sr = 0.339; p = 0.043 respectively; Figure 3.5). Percent
swimmer and % gatherer were both strongly and negatively correlated with percent
deposited fine sediment (Sr = -0.436; p = 0.008 and Sr = - 0.513; p = 0.001 respectively;
Figures 3.5 and 3.6).
The following metrics were also significantly correlated with deposited fine
sediment: evenness, EPT abundance, % EPT, % Orthocladiinae of Chironomidae and %
Chironominae of Chironomidae (Table 3.9). Evenness and % Chironominae of
Chironomidae were positively correlated with percent deposited sediment (Sr = 0.483; p
= 0.003 and Sr = 0.453; p = 0.006 respectively; Figures 3.6 and 3.7). The metrics EPT
56
abundance, % EPT and % Orthocladiinae of Chironomidae were strongly and negatively
correlated with percent deposited fine sediment (Sr = - 0.711; p = < 0.001; Sr = - 0.612; p
= < 0.001 and Sr = - 0.431; p = 0.009 respectively; Figures 3.6 and 3.7).
Fall 2005
In the fall, % burrower was the only habit/functional feeding group metric
significantly correlated with percent deposited fine sediment (Sr = 0.334; p = 0.046;
Table 3.10). Increased surface cover of deposited fine sediment resulted in an increase in
the percentage of burrowers (Figure 3.8). Additionally the metrics: EPT abundance, %
EPT, % Orthocladiinae of Chironomidae, % Chironominae of Chironomidae and
Trichoptera richness were significantly correlated with deposited fine sediment (Table
3.11). Percent Chironominae had a strong positive correlation with percent deposited
fine sediment (Sr = 0.669; p = < 0.001; Figure 3.9). EPT abundance, % EPT, %
Orthocladiinae and Trichoptera richness were negatively correlated with percent
deposited fine sediment (Figures 3.8 and 3.9).
3.3.3 Biomonitoring metrics and environmental variables
Metrics significantly correlated with percent deposited fine sediment were
examined for correlations with environmental variables.
Summer 2005
In the summer, % burrower, % sprawler, % swimmer, % predator and evenness
were significantly correlated with water pH (Table 3.12 and 3.13). Percent swimmer, %
predator and evenness were also significantly correlated with conductivity and flow
57
(Table 3.12). Percent EPT, EPT abundance, % Orthocladiinae and % Chironominae were
significantly correlated with local slope (%) of the study sites (Table 3.13).
Fall 2005
In the fall, % burrower, EPT abundance, % EPT and Trichoptera richness were
significantly correlated with flow (Table 3.14). As in the summer, several metrics were
significantly correlated with local slope (%) (Table 3.14). Significant correlations also
existed between various other biomonitoring metrics and environmental variables (e.g.
EPT abundance and pH; Sr = 0.378; p = 0.023).
3.3.4 Overall metric responses to deposited fine sediment
Several biomonitoring metrics were consistently significantly correlated with
percent surface cover of deposited fine sediment. The metrics percent burrower and %
Chironominae had a significant positive correlation with deposited fine sediment in both
the summer and fall (Figures 3.5, 3.7, 3.8 and 3.9). Percent EPT, EPT abundance and %
Orthocladiinae of chironomidae were highly negatively correlated with deposited fine
sediment in both seasons (Figures 3.6, 3.7 and 3.8).
3.4 DISCUSSION
In biomonitoring, response patterns of benthic invertebrate metrics are often used
to identify and quantify stream ecosystem impairment (Rosenberg and Resh 1996).
Therefore, understanding how specific metrics respond to individual stressors is of great
value. This study examined the response of several commonly used biomonitoring
metrics to a gradient of deposited fine (< 2 mm) sediment conditions to determine metrics
58
useful for identifying and quantifying the effects of increased fine sediment deposition.
Several metrics were significantly correlated and responded consistently to increasing
fine sediment deposition across seasons. The metrics % burrower and % Chironominae of
Chironomidae responded positively to increasing fine sediment deposition. While the
metrics: % EPT, EPT abundance and % Orthocladiinae of chironomidae responded
negatively to increasing fine sediment deposition.
Sediment Measures
For this study fine sediment deposition was quantified by three methods: a visual
estimate of percent cover of deposited fine sediment, calculated % embeddedness and %
fines < 2 mm from a substrate sample (shovel method). However, only the percent fine
sediment deposition was used to investigate the response of the biomonitoring metrics to
increasing fine sediment. This measure was used because it was highly correlated with %
embeddedness and small particle size classes. Percent fine sediment deposition was also
correlated with only a few environmental variables. Additionally, it is relatively easy and
the least time consuming method for quantifying deposited fine sediment.
Metric Responses
The percentage of burrowers was positively correlated with percent cover of
deposited fine sediment. This result was expected since as fine sediment is deposited the
interstitial spaces are filled reducing the habitat available to non-burrowing benthic
invertebrates (McClelland and Brusven 1980; Waters 1995; Wood and Armitage 1997).
Previous studies have found sensitive taxa, such as the EPT taxa decrease in abundance
as fine sediment deposition increases (Waters, 1995), while other taxa, such as burrowing
chironomids, oligochaetes and bivalves increase in abundance and richness (Gray and
59
Ward 1982). Rabeni et al. (2005) found significant increases in burrower and climber
densities as fine sediment increased, while density decreases were seen for clingers and
sprawlers. In my study additional functional feeding and habit group metrics were
correlated with percent deposited fine sediment. However, these correlations were only
apparent in the summer and not in the fall. The lack of correlations in the fall may be due
to the fact that sampling occurred when water levels had receded following a large rain
event, adding large quantities of fine sediment to the sample sites. All taxa groups were
probably drastically reduced and the community may not have had sufficient time to
recover when sampling occurred.
Percent Orthocladiinae of chironomidae decreased as percent deposited fine
sediment increased, while % Chironominae of chironomidae increased (Figures 3.8, 3.9
and 3.10). This is contrary to the findings of Lenat et al. (1981) and Angradi (1999),
which found % Orthocladiinae to increase and % Chironominae to decrease as percent
fine sediment increased. Chironomids are generally viewed as pollution-tolerant taxa,
however at the species level large variation in tolerance is often observed (Brinkhurst
1993). The difference in metric responses between the two studies may be due in part to
the two subfamilies being composed of species with different pollution or sediment
tolerances. Rosenberg and Wiens (1978) found three chironomid taxa to be unaffected by
fine sediment addition, while four taxa were affected. Additionally chironomid species
present in these studies may belong to different functional feeding groups, which can
affect their distribution and abundance. Chironominae are known to prefer depositional
areas only, while Orthocladiinae use both depositional and erosional areas (Merritt and
Cummins 1996). Therefore Chironominae is the dominant chironomid in areas of fine
60
sediment, while Orthocladiinae dominate in areas composed of cobble and gravel (Pinder
1986). The metrics chironomid richness and abundance were not significantly correlated
with percent deposited sediment, which coincides with the results of Zweig and Rabeni
(2001). While Angradi (1999) found chironomidae abundance decreased as deposited
fine sediment increased, his study only examined a short range of sediment levels (0 –
30%) and correlations were only weak. The difference in metric response between
Angradi’s (1999) study and mine may be because a larger natural gradient of sediment
conditions was used and therefore real impacts were observed in my study. Angradi’s
(1999) results may be incorrect since only a short gradient of artificially deposited
sediment conditions were examined. The fact that chironomid abundance and richness
were not significantly correlated with deposited sediment lends evidence to the fact that
the tolerance of individual species varies greatly (Zweig and Rabeni 2001). The
abundance of non-sediment tolerant species may have decreased, while the abundance of
more sediment tolerant species increased to compensate. Further studies are needed to
determine the true response patterns of chironomid metrics.
The metric EPT abundance was significantly, negatively correlated with percent
deposited fine sediment. Zweig and Rabeni (2001) found EPT density to consistently
decrease as surface cover of deposited fine sediment increased. Additionally the metric %
EPT had a significant negative correlation with percent deposited fine sediment in our
study. The decrease in % EPT and abundance of EPT taxa is probably the result of a
reduction in the heterogeneous substrate preferred by these taxa (Lenat 1981; Minshall
1984; Waters 1995). Despite the significant negative correlation with EPT abundance
deposited fine sediment was not significantly correlated with EPT richness in either
61
season. My results do not concur with previous studies that found EPT richness decreases
as deposited fine sediment increases (Angradi 1999; Zweig and Rabeni 2000; Kaller et al.
2001 and Kaller et al. 2004). The lack of correlation between EPT richness and deposited
fine sediment is puzzling, as generally the taxa in these three orders are sensitive to
pollution (Lenat 1988). However, the EPT taxa present in our samples may have
ecological or physiological traits that allow for higher sediment tolerances than the taxa
sampled in previous studies. It is highly likely that EPT taxa intolerant to sediment
declined, however this was compensated for by an increase in sediment tolerant taxa
resulting in a lack of correlation between EPT richness and deposited fine sediment.
Relyea et al. (2000) found individual taxa of the EPT orders to have varying sediment
tolerances and the metric EPT richness not to be responsive to fine sediment as well. My
results indicate the metrics % EPT and EPT abundance are good indicator metrics of
increased fine sediment; however EPT richness does not show promise as the tolerance of
the taxa in these orders to sediment deposition varies.
Increased fine sediment deposition reduces critical habitat for many benthic
invertebrates, often lowering species diversity (Gray and Ward 1982). However our data
did not show a significant association between species diversity, measured as Shannon
Diversity and increased fine sediment deposition. Results of the present study agree with
Zweig and Rabeni (2001) and are contrary to the results of other studies which have
found a reduction in diversity associated with increased fine sediment deposition (Nuttall
1972; Lenat et al. 1981; Henley et al. 2000; Roy et al., 2003). In the present study
evenness increased significantly in the summer, but richness, and therefore Shannon
diversity remained unchanged. Taxa richness has been shown to decrease in response to
62
deposited fine sediment (Nuttall 1972; Lemly 1982; Zweig and Rabeni 2001) in several
studies while no response has been observed in others (Angradi 1999). My results
suggest the metrics: Shannon Diversity, evenness and taxa richness are not effective at
quantifying the impact fine sediment deposition has on the benthic invertebrate
community. The lack of correlation may be the result of a shift in the community to more
sediment tolerant benthic invertebrates, therefore additional metrics or indices which take
into account the relative tolerance of the taxa present may be required.
Several response patterns of the metrics evaluated in this study differed from
those of previous work, which may be due to the fact that a larger natural deposited
sediment gradient was used in the present study instead of a smaller range of human
manipulated conditions. Sampling along a natural deposited sediment gradient increased
the range of sediment conditions, strengthened the observed relationships and allowed the
observation of real impacts. The significant correlations in my study have shown that
several commonly used biomonitoring metrics can be used in monitoring for the effects
of increased fine sediment deposition. However care must be taken using these metrics
when monitoring for sediment since the metrics were often correlated with one or more
other environmental variables. Therefore there may be problems in distinguishing
between the effects of fine sediment and other perturbations. New metrics or indices need
to be developed specially targeted at monitoring for the effects of fine sediment
deposition, since many of the commonly used metrics incorporate taxa that are known to
be sensitive to various pollutants, such as EPT taxa. Metrics taking into account the
relative tolerance and ecological/ physiological adaptations of individual taxa are
required to better detect and quantify the impact of fine sediment deposition. However
63
the metrics responsive to deposited fine sediment in this study can be used for detecting
the effects along a natural gradient.
3.5 CONCLUSIONS
In conclusion, my study has shown that several commonly used biomonitoring
metrics are responsive along a gradient of sediment conditions. Five biomonitoring
metrics were significantly correlated with percent deposited fine sediment. These metrics
generally showed a linear relationship with increasing percent deposited sediment
however, no sediment threshold was evident. The responses of the metrics were generally
consistent with results of previous studies, with the exception of the metrics, %
Chironominae and % Orthocladiinae. Percent Chironominae responded positively, while
% Orthocladiinae responded negatively to increasing fine sediment, which is contrary to
the results of Angradi (1999). Previous studies have shown reductions in EPT richness,
taxa richness and diversity as fine sediment deposition increased. However, percent fine
sediment deposition was not significantly correlated with these metrics in my study. The
lack of correlation may be the result of a shift in the community to more sediment
tolerant benthic invertebrates. Taxa intolerant to fine sediment probably decreased in
abundance or were replaced by taxa more tolerant to deposited fine sediment, resulting in
no change in the metric values even though a shift in the community had occurred.
However, it is also probable these metrics are more responsive to additional habitat or
environmental variables. Further testing spatially and temporally of the different metrics
along natural sediment gradients is necessary, since variation in metric responses has
been shown between studies.
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.
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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. Habitat- specific responses of stream insects to land cover disturbance: biological consequences and monitoring implications. Journal of the North American Benthological Society 22(2): 292- 307. Scrimgeour, G.J., J.M. Culp and N.E. Glozier. 1993. A new technique for sampling lotic invertebrates. Hydrobiologia 254: 65-71 Sylte, T., and C. Fischenich. 2002. Techniques for measuring substrate embeddedness. ERDC TN- RMRRP- SR- 36, September 2002. Waters, T.F. 1995. Sediment in streams: sources, biological effects and control. Monograph 7. American Fisheries Society, Bethesda, Maryland. Wood, P.J, and Armitage, P.D. 1997. Biological effects of fine sediment in the lotic environment. Environmental Management. 21(2): 203- 217. Zanetell, B.A., and B.L. Peckarsky. 1996. Stoneflies as ecological engineers- hungry predators reduce fine sediment in stream beds. Freshwater Biology. 36: 569- 577. Zweig, L.D. 2000. Effects of deposited sediment on stream benthic macroinvertebrate communities. MSc Thesis, University of Missouri- Columbia. Zweig, L.D. and C.F. Rabeni. 2001. Biomonitoring for deposited sediment using benthic invertebrates: a test on 4 Missouri streams. Journal of the North American Benthological
Society 20(4): 643- 657.
68
3.7 TABLES
Table 3.1: Study sites sampled on Prince Edward Island, including their watershed and geographical location Site Watershed Latitude (N) Longitude (W)
North Brook Lower Dunk 46º 20’ 48.6” 63º 37’ 54.8”
North Brook Middle Dunk 46º 21’ 32.8” 63º 36’ 53.7”
North Brook Upper Dunk 46º 21’ 51.2” 63º 36’ 50.0”
Wilmot River Upper Wilmot 46º 23’ 32.3” 63º 33’ 16.2”
Wilmot River Lower Wilmot 46º 24’ 29.3” 63º 35’ 46.1”
East Branch Lower Tryon 46º 17’ 50.8” 63º 31’ 39.0”
East Branch Upper Tryon 46º 15’ 45.4” 63º 31’ 45.2”
Brookvale Lower West 46º 17’ 02.4” 63º 24’ 29.4”
Brookvale Upper West 46º 19’ 58.8” 63º 25’ 19.5”
69
Table 3.2: Sieves sizes used during substrate particle size distribution analysis.
Coarse Sieves Fine Sieves
25 mm 2 mm
19 mm 1 mm
12.5 mm 0.5 mm
9.5 mm 0.25 mm
6.3 mm 0.125 mm
4.75 mm 0.038 mm
4 mm Silt
70
Table 3.3: Biomonitoring metrics tested for responsiveness to sediment deposition (Rosenberg and Resh 1993; Merrit and Cummins 1995). Metric
Chironomid Abundance
% Orthocladiinae
EPT Abundance
% Chironominae
Taxa Richness
% Burrower
EPT Richness
% Clinger
Chironomidae Richness
% Sprawler
Ephemeroptera Richness
% Climber
Plecoptera Richness
% Swimmer
Trichoptera Richness
% Filterer
Shannon Diversity
% Scraper
Evenness
% Gatherer
% EPT
% Shredder
% Chironomidae
% Predator
% Dominant Taxa
71
Table 3.4: Spearman rank correlation coefficients and associated p- values for deposited sediment and substrate measures for July 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * significant at the 0.05 level).
% Deposited
Sediment
%
Embeddedness
Total Suspended
Solids
% < 2mm
Correlation Coefficient
0.639 **
0.115
- 0.105
p- value
< 0.001
0.503
0.544
% < 4mm
Correlation Coefficient
0.641 **
0.119
- 0.125
p- value
< 0.001
0.489
0.469
% Gravel
Correlation Coefficient
- 0.152
0.026
- 0.111
p- value
0.375
0.88
0.52
% Pebble
Correlation Coefficient
- 0.542 **
- 0.067
-0.111
p- value
< 0.001
0.697
0.519
% Deposited
Correlation Coefficient
1.00
0.335 *
0.109
Sediment p- value
< 0.001
0.046
0.528
%
Correlation Coefficient
0.335 *
1.00
0.229
Embeddedness p- value
0.046
< 0.001
0.18
72
Table 3.5: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and environmental variables for July 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).
Table 3.6: Spearman rank correlation coefficients and associated p- values for deposited sediment and substrate measures for October 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).
% Deposited
Sediment
%
Embeddedness
Total Suspended
Solids
% < 2mm
Correlation Coefficient
0.393 *
0.464 **
0.192
p- value
0.018
0.004
0.262
% < 4mm
Correlation Coefficient
0.398 *
0.475 **
0.196
p- value
0.016
0.003
0.252
% Gravel
Correlation Coefficient
0.289
0.055
- 0.035
p- value
0.087
0.749
0.840
% Pebble
Correlation Coefficient
- 0.481 **
- 0.499 **
- 0.173
p- value
0.003
0.002
0.314
% Deposited
Correlation Coefficient
1.00
0.670 **
0.211
Sediment p- value
0.000
< 0.001
0.216
%
Correlation Coefficient
0.670 **
1.00
0.043
Embeddedness p- value
< 0.001
0.000
0.805
74
Table 3.7: Spearman rank correlation coefficients and associated p- values for deposited sediment measures and environmental variables for October 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).
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).
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).
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).
% Deposited Sediment
% Embeddedness
Total Suspended
Solids %
Correlation Coefficient 0.334 ** 0.138 0.081
Burrower p- value
0.046 0.424 0.637
%
Correlation Coefficient 0.22 0.031 0.206
Climber p- value
0.198 0.857 0.228
%
Correlation Coefficient 0.126 0.211 - 0.329 *
Sprawler p- value
0.465 0.218 0.05
%
Correlation Coefficient - 0.111 - 0.281 0.216
Clinger p- value
0.518 0.097 0.206
%
Correlation Coefficient - 0.203 0.079 - 0.195
Swimmer p- value
0.234 0.649 0.254
%
Correlation Coefficient - 0.286 - 0.129 - 0.057
Gatherer p- value
0.091 0.454 0.74
%
Correlation Coefficient 0.142 - 0.101 0.146
Filterer p- value
0.409 0.557 0.394
%
Correlation Coefficient - 0.166 - 0.451 ** 0.16
Scraper p- value
0.334 0.006 0.351
%
Correlation Coefficient - 0.049 - 0.301 0.046
Predator p- value
0.775 0.075 0.791
%
Correlation Coefficient - 0.174 0.033 - 0.412 *
Shredder p- value
0.31 0.847 0.013
79
Table 3.11 : Spearman rank correlation coefficients and associated p- values for deposited sediment measures and biomonitoring metrics for October 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at 0.05 level).
Table 3.12 : Spearman rank correlation coefficients and associated p- values for habit/ feeding group metrics responsive to deposited sediment and environmental variables for July 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).
% Burrower
% Sprawler
% Swimmer
% Predator
Water
Correlation Coefficient - 0.276
- 0.242
0.079
- 0.224
Temperature p- value
0.099 0.156 0.646 0.189
pH
Correlation Coefficient - 0.416 *
- 0.489 **
0.614 **
- 0.526 **
p- value
0.027 0.002 < 0.001 0.001
Dissolved
Correlation Coefficient 0.108
- 0.077
0.088
0.03
Oxygen p- value
0.53 0.657 0.609 0.86
Conductivity
Correlation Coefficient - 0.324
- 0.101
0.753 **
- 0.473 **
p- value
0.054 0.56 < 0.001 0.004
Flow
Correlation Coefficient - 0.294
- 0.198
0.539 **
- 0.418 *
p- value
0.07 0.248 0.001 0.011
% Slope
Correlation Coefficient - 0.312
0.191
- 0.005
- 0.02
p- value
0.064 0.264 0.979 0.907
82
Chlorophyll- a
Correlation Coefficient 0.308
- 0.192
- 0.106
- 0.08
p- value
0.068 0.262 0.539 0.642
83
Table 3.13 : Spearman rank correlation coefficients and associated p- values for metrics responsive to deposited sediment and environmental variables for July 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).
Evenness
EPT
abundance
%
EPT
%
Orthocladiinae
%
Chironominae
Water
Correlation Coefficient
- 0.214
0.195
-0.068
-0.141
- 0.092
Temperature p- value
0.21
0.255
0.695
0.413
0.594
pH
Correlation Coefficient
- 0.482 **
0.171
0.047
0.223
-0.105
p- value
0.003
0.317
0.785
0.192
0.542
Dissolved
Correlation Coefficient
0.045
-0.154
0.115
0.386 *
- 0.102
Oxygen p- value
0.793
0.37
0.505
0.02
0.555
Conductivity
Correlation Coefficient
- 0.382 *
0.337 *
0.286
0.057
0.063
p- value
0.021
0.044
0.091
0.741
0.714
Flow
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
Correlation Coefficient
- 0.168
0.482 **
0.528 **
0.353 *
- 0.526 **
p- value
0.327
0.003
0.001
0.035
0.001
Chlorophyll- a
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
EPT
abundance
%
EPT
%
Orthocladiinae
%
Chironominae
Trichoptera
Richness
Water
Correlation Coefficient
- 0.408 *
0.006
- 0.206
- 0.268
- 0.101
0.024
Temperature p- value
0.013
0.971
0.228
0.114
0.56
0.891
pH
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
Dissolved
Correlation Coefficient
- 0.054
0.114
0.07
0.267
- 0.437 **
0.089
Oxygen p- value
0.756
0.508
0.684
0.115
0.008
0.607
Conductivity
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
Flow
Correlation Coefficient
- 0.475 **
0.501 **
0.586 *
0.289
- 0.188
0.340 *
p- value
0.003
0.002
< 0.001
0.087
0.273
0.042
86
% Slope
Correlation Coefficient
- 0.212
0.082 0.365 *
0.430 **
- 0.326
0.616 **
p- value
0.214
0.636
0.029
0.009
0.053
< 0.001
Chlorophyll- a
Correlation Coefficient
0.390 *
0.028
- 0.082
0.101
0.131
- 0.038
p- value
0.02
0.874
0.64
0.564
0.453
0.83
87
3.9 FIGURES
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Dunk River North Brook SouthwestBrook
TryonRiver(EastBranch)
West River(Brookvale)
Wilmot River
Watershed
Perc
ent % Agriculture
% Forestry% Other
Figure 3.1: Percentage of different land-use types within the six watersheds sampled in the summer (25- 27 July) and fall (19-21 October) of 2005 on Prince Edward Island, Canada.
88
A.)
B.)
Figure 3.2: A.) 1m2 Quadrat used to designate area from which samples were to be collected. B.) Placement of algal substrate within the quadrat.
89
A.)
B.)
Figure 3.3: A.) Artificial algal substrate used for Chlorophyll- a analysis. B.) Algal substrate deployed in river, dug 1cm down in substrate.
90
Figure 3.4: Site- Season biplot diagram summarizing the effect of season on benthic invertebrate community composition. Sites separated into two distinct clumps based on season. Further analyses were performed on benthic invertebrate data for the fall and summer separately.
91
% Burrower
% Deposited Sediment
0 10 20 30 40 50 60 70 80 90 100
Burr
ower
abu
ndan
ce/ T
otal
abu
ndan
ce
0
10
20
30
40
50
60
70
80
Sr = 0.507 p = 0.002
% Sprawler
% Deposited Sediment
0 10 20 30 40 50 60 70 80 90 100
Spr
awle
r abu
ndan
ce/ T
otal
abu
ndan
ce
0
5
10
15
20
25
30
35
Sr = 0.347 p = 0.038
% Swimmer
% Deposited Sediment
0 10 20 30 40 50 60 70 80 90 100
Sw
imm
er a
bund
ance
/ Tot
al a
bund
ance
0
20
40
60
80
Sr = - 0.436 p = 0.008
% Predator
% Deposited Sediment
0 10 20 30 40 50 60 70 80 90 100
Pre
dato
r abu
ndan
ce/ T
otal
abu
ndan
ce
0
5
10
15
20
25
30
35
40
Sr = 0.339 p = 0.043
Figure 3.5: Relationships between % habit/ functional feeding group metrics and % deposited fine sediment that had significant correlations in July 2005. Spearman rank correlation coefficients and their associated p-values are shown.
92
% Gatherer
% Deposited Sediment
0 10 20 30 40 50 60 70 80 90 100
Gat
here
r abu
ndan
ce/ T
otal
abu
ndan
ce
0
10
20
30
40
50
60
70
80
90
100
Sr = - 0.513 p = 0.001
Evenness
% Deposited Sediment
0 10 20 30 40 50 60 70 80 90 100
Even
ness
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Sr = 0.483 p = 0.003
EPT Abundance
% Deposited Sediment
0 10 20 30 40 50 60 70 80 90 100
No.
of E
PT
Indi
vidu
als
0
100
200
300
400
500
600
700
800
900
1000
Sr = -0.711p = < 0.001
% EPT
% Deposited Sediment
0 10 20 30 40 50 60 70 80 90 100
EPT
abun
danc
e/ T
otal
abu
ndan
ce
0
20
40
60
80
100
Sr = -0.612p = < 0.001
Figure 3.6: Relationships between biomonitoring metrics and % deposited fine sediment that had significant correlations in July 2005. Spearman rank correlation coefficients and their associated p-values are shown
93
% Orthocladiinae
% Deposited Sediment
0 10 20 30 40 50 60 70 80 90 100
Orth
ocla
d ab
unda
nce/
Chi
rono
mid
abu
ndan
ce
0
20
40
60
80
100
120
Sr = -0.431 p = 0.009
% Chironominae
% Deposited Sediment
0 10 20 30 40 50 60 70 80 90 100
Chi
rono
min
ae a
bund
ance
/ Chi
rono
mid
abu
ndan
ce
0
20
40
60
80
100
120
Sr = 0.453 p = 0.006
Figure 3.7: Relationship between chironomidae metrics and % deposited fine sediment that had significant correlations in July 2005. Percent Orthocladiinae decreased, while % Chironominae increased. Spearman rank correlation coefficients and p-values are shown.
94
% Burrower
% Deposited Sediment
0 10 20 30 40 50 60 70 80 90 100
Burr
ower
abu
ndan
ce/ t
otal
abu
ndan
ce
0
10
20
30
40
50
60
70
80
Sr = 0.334 p = 0.046
EPT Abundance
% Deposited Sediment
0 10 20 30 40 50 60 70 80 90 100
No.
EPT
indi
vidu
als
-200
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
Sr = -0.613p = < 0.001
% EPT
% Deposited Sediment
0 10 20 30 40 50 60 70 80 90 100
EPT
abun
danc
e/ T
otal
abu
ndan
ce
10
20
30
40
50
60
70
80
90
100
Sr = -0.678 p = <0.001
% Orthocladiinae
% Deposited Sediment
0 10 20 30 40 50 60 70 80 90 100
Orth
ocla
d ab
unda
nce/
tota
l abu
ndan
ce
-40
-20
0
20
40
60
80
100
120
Sr = -0.770p = < 0.001
Figure 3.8: Relationships between metrics and % deposited fine sediment that had significant correlations in October 2005. Spearman rank correlation coefficients (Sr) and their associated p-values are shown.
95
% Chironominae
% Deposited Sediment
0 10 20 30 40 50 60 70 80 90 100
Chi
rono
min
ae a
bund
ance
/ tot
al a
bund
ance
-20
0
20
40
60
80
100
120
Sr = 0.669p = < 0.001
Trichoptera Richness
% Deposited Sediment
0 10 20 30 40 50 60 70 80 90 100
No.
Tric
hopt
era
taxa
0
1
2
3
4
5
6
7
8
Sr = -0.342 p = 0.041
Figure 3.9: Relationships between metrics and % deposited fine sediment that had significant correlations in October 2005. Spearman rank correlation coefficients and their associated p-values are shown.
96
4.0 ABSTRACT
This study examines the relationships between levels of deposited sediment and
benthic invertebrate species traits, to determine traits indicative of deposited sediment in
agricultural areas. The tolerance of individual benthic invertebrates to deposited sediment
is also examined. Thirteen sites within six agricultural streams on Prince Edward Island,
Canada were sampled during the summer and fall of 2005. Benthic invertebrate, sediment
and water chemistry samples were collected along a gradient of deposited sediment
conditions. Fine sediment deposition was quantified by three methods: a visual estimate
of percent cover of deposited fine sediment, calculated % embeddedness and % fines < 2
mm from a substrate sample. Benthic invertebrate taxa were assigned as tolerant,
moderately tolerant or sensitive to sediment based on their association with the deposited
sediment gradient. EPT taxa were represented in all sediment tolerance categories in both
seasons. Several traits were positively and negatively associated with increased deposited
sediment levels. Overall, several species traits were associated with deposited sediment
and helped to explain the tolerance of certain benthic invertebrate taxa. Species traits
positively associated with deposited sediment were plastron respiration, elongate body
form, depositional rheophily and predation. In contrast species traits negatively
associated were free-moving/ sessile, univoltine, abdominal gill placement, flattened
seasonal development, mulitvoltine, plate- like gills, strong swimmer, streamlined body
and erosional rheophily were negatively associated with increased sediment deposition
115
(Figures 4.7 and 4.8). The correlation between these traits and low levels of deposited
sediment was not unexpected as many of these traits are adaptations to areas of higher
substrate stability and flow or erosional areas where fine sediment is unlikely to be
deposited. The one exception however is multivoltinism, which refers to an organism
which produces more than two generations per year. This trait would be expected to be
more associated with high disturbance, such as deposited sediment areas where substrate
stability is lower. Whereas Doledec et al. (2006) found the univoltine trait to decrease
across a landuse gradient. However in the EPT orders voltinism is a less labile trait,
because some groups have little variation among genera, e.g. all Trichopterans are
univoltine (Poff et al. 2007). Lamoroux et al. (2004) and Doledec et al. (2006) found
small invertebrates with streamlined bodies to be associated with relatively coarse
substrates which agrees with our assignment of streamlined body forms as a sediment
sensitive trait.
Our results suggest benthic invertebrate taxa need not possess all tolerance traits
to be tolerant of deposited sediment. Therefore there may be degrees of sediment
tolerance with taxa that possess more of the tolerance traits being more tolerant to higher
levels of sediment deposition than others. Benthic invertebrate taxa classified as sediment
tolerant were found to possess one or more of the traits determined to be associated with
increased sediment deposition. The majority of sediment tolerant taxa also possessed
some traits that were thought to infer sediment tolerance, but were not found to be
positively associated with increased sediment (e.g. the habit burrower and fast- seasonal
development). The majority of sediment tolerant taxa were either predators or collector
gathers, which agrees with the results of Zweig (2000) who also determined these modes
116
of feeding to be unaffected by sediment deposition. Several of the tolerant taxa were also
shredders, which consume coarse particulate organic matter normally found in slow-
flowing areas where sediment is deposited. A large majority of the tolerant taxa (e.g.
Sweltsa and Glossosoma) also lacked gills and relayed on other respirations techniques,
which is consistent with the results from the trait analysis that showed gill respiration to
be sensitive to increased sediment deposition. However several of the tolerant taxa rely
on gill respiration, but they also posses other sediment tolerant traits such as burrowing
and sprawling.
The lack of response of some traits may be due to that fact that percent deposited
sediment was not the most important gradient, therefore traits may be responding to
additional environmental gradients. Furthermore traits expected to be associated with
increased sediment (e.g. burrowers) may be linked with other traits that are responding to
another gradient or they may be constrained by low evolutionary lability (Poff et al.
2006). As well a number of habitat trait filters may be acting upon the benthic
invertebrate community and therefore the trait composition hierarchically at multiple
scales (Poff 1997, Lamouroux et al. 2004).
4.5 CONCLUSIONS
In conclusion, our findings show that environmental gradients act as filters on benthic
invertebrates trait compositions as predicted by the habitat template model, which states
that habitat is the filter that acts on the trait composition. The positive and negative
correlations between certain species traits and increased sediment deposition gives
support to the use of species traits in the monitoring and assessment of sediment in lotic
117
environments. Our results have shown several species traits are correlated with deposited
sediment, however, a full understanding of the environmental variables responsible for
the presence/ absence and distribution of traits is necessary before trait monitoring tools
can be fully useful (Richards et al. 1997). Future work should focus on additional traits
and the response of traits to additional habitat characteristics. Once fully understood
species trait approaches may allow for a priori predictions of benthic invertebrate
responses and trait composition (Doledec et al. 2006). In contrast to taxonomic
composition, species traits approaches can be compared across geographic regions, as
well as determine most sensitive life history characteristics (Doledec et al. 2006). Our
results have also shown species traits can give an understanding of the presence and
absence of species in particular environments and may help to diagnose impacts by
observing response patterns. Benthic invertebrate tolerance to deposited sediment is not
dependent on a taxa possessing all sediment tolerant traits. Tolerant taxa possess a
combination of sediment sensitive and tolerant traits, therefore the degree of tolerance
may be dependent on the number of sediment tolerant traits possessed. Further work is
needed on linking benthic invertebrate tolerance and the species traits possessed.
Although a full understanding of trait responses to various human impact gradients and
reference conditions is required, our results demonstrate benthic invertebrate species
traits patterns show great promise as a more ecologically-relevant biomonitoring tool.
Therefore use of species traits patterns may be useful in the diagnosis of impacts during
the biomonitoring of lotic environments.
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.
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predators reduce fine sediment in stream beds. Freshwater Biology. 36: 569- 577. Zweig, L.D. 2000. Effects of deposited sediment on stream benthic macroinvertebrate communities. MSc Thesis, University of Missouri- Columbia. Zweig, L.D. and Rabeni, C.F. 2001. Biomonitoring for deposited sediment using benthic invertebrates: a test on 4 Missouri streams. Journal of the North American Benthological Society. 20(4): 643- 657. Species Trait Database References: Adler, P.H., D.J. Giberson and L.A. Purcell. 2005. Insular black flies Diptera: Simuliidae of North America: tests of colonization hypotheses. Journal of Biogeography. 32: 211- 220. Alba- Tercedor, J.and A. Sanchez- Ortega. 1991. Overview of the strategies of Ephemeroptera and Plecoptera. Proceedings of the VI th International
Ephemeroptera conference (24- 28 July 1989) and X th International symposium on Plecoptera (27- 30 July 1989), Granada, Spain. The Sandhill Crane Press, Inc. Gainesville, Florida, U.S.A.
Burks, B.D. 1953. The mayflies, or Ephemeroptera of Illinois. Bulletin of the Illinois Natural History Survey. 26(1): 216. Dobrin, M. and D.J. Giberson. 2003. Life history and production of mayflies, stoneflies,
and caddisflies (Ephemeroptera, Plecoptera, and Trichoptera) in a spring-fed stream in Prince Edward Island, Canada: evidence for population asynchrony in spring habitats? Canadian Journal of Zoology. 81: 1083- 1095.
Edmunds, G.F., S.L. Jensen and L. Berner. 1976. The mayflies of North and Central America. University of Minnesota. McAlpine, J.F., B.V. Peterson, G.E. Shewell, H.J. Teskey, J.R. Vockeroth and
D.M. Wood. 1981. Manual of Nearctic Diptera. Volume 1. Research Branch Agriculture Canada. Monograph No. 27. 674 pgs.
Merritt, R.W. and K.W. Cummins. 1996. An introduction to the aquatic insects of North America (3rd Edition). Kendall/Hunt Publishing Company. Dubuque, Iowa. Poff, N.L., J.D. Olden, N.K.M. Vieira, D.S. Finn, M.P. Simmons and B.C. Kondratieff. 2006. Functional trait niches of North American lotic insects: trait- based ecological application in light of phylogenetic relationships. Journal of the North American Benthological Society. 25(4): 730- 755. Vieira, N.K.M., N.L. Poff, D.M. Carlisle, S.R. Moulton, M.L. Koski and B.C. Kondratieff. 2006. A database of lotic invertebrate traits for North America.
123
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.
Table 4.1: Biological traits and categories examined along a fine sediment deposition gradient (Merritt and Cummins 1996; Usseglio- Polaters et al. 2000; Gayraud et al. 2003; Poff et al. 2007). Functional Feeding Group Collector gather Feed 1 Collector filterer Feed 2 Scraper Feed 3 Predator Feed 4 Shredder Feed 5 Habit (mode of existence) Burrower Habi 1 Climber Habi 2 Sprawler Habi 3 Clinger Habi 4 Swimmer Habi 5 Skater Habi 6 Mobility Free moving Mobi 1 Sedentary Mobi 2 Both Mobi 3 Rheophily Depositional Rheo 1 Erosional/ Depositional Rheo 2 Erosional Rheo 3 Voltinism (Lifecycle) Semivoltine Volt 1 Univoltine Volt 2 Bi- Multivoltine Volt 3 Swimming Ability None Swim 1 Weak Swim 2 Strong Swim 3 Body Size Small (< 9mm) Size 1 Medium (9- 16mm) Size 2 Large (> 16mm) Size 3 Body Form Flattened Body 1 Slightly Flattened Body 2 Streamlined Body 3 Cylindrical Body 4
125
Sub-cylindrical Body 5 Elongate Body 6 Armouring None Arm 1 Some Arm 2 Good Arm 3 Respiration Technique Tegument Resp 1 Gills Resp 2 Plastron Resp 3 Gills Present Gill 1 Absent Gill 2 Gill Placement Lateral Plac 1 Abdominal Plac 2 Thorax Plac 3 Sub-mental Plac 4 Anal Pac 5 None Plac 6 Gill Form Lanceolate Form 1 Plate-like Form 2 Filament (single, branch) Form 3 Operculate (semi, full) Form 4 Tufts Form 5 None Form 6 Armouring None Arm 1 Sceloritized (some) Arm 2 Case (good) Arm 3 Development Fast Seasonal Devel 1 Slow Seasonal Devel 2 Non-Seasonal Devel 3
126
Table 4.2 : Spearman rank correlation coefficients and associated p- values for PCA axis site scores of average benthic invertebrate relative abundance data and environmental variables for July 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).
Axis 1 Axis 2 Axis 3 Axis 4 Water
Correlation Coefficient
- 0.692*
- 0.168
- 0.469
0.210
Temperature p- value
0.013 0.602 0.124 0.513
pH
Correlation Coefficient
- 0.678*
- 0.510
- 0.119
- 0.210
p- value
0.015 0.09 0.713 0.513
Dissolved
Correlation Coefficient
0.566
- 0.161
0.636*
- 0.154
Oxygen p- value
0.055 0.618 0.026 0.633
Conductivity
Correlation Coefficient
0.210
- 0.406
- 0.021
- 0.364
p- value
0.513 0.191 0.948 0.245
Flow
Correlation Coefficient
- 0.664*
- 0.476
0.035
- 0.021
p- value
0.018 0.118 0.914 0.948
% Deposited
Correlation Coefficient
- 0.189
0.722**
- 0.088
- 0.536
Sediment p- value
0.556 0.008 0.787 0.073
%
Correlation Coefficient
- 0.455
0.594*
0.343
- 0.210
Embeddedness p- value
0.138 0.042 0.276 0.513
% Slope
Correlation Coefficient
0.357
- 0.133
0.105
0.580*
p- value
0.255 0.681 0.746 0.048
Total
Correlation Coefficient
- 0.923**
0.014
- 0.224
0.077
Suspended Solids
p- value
0.000 0.966 0.484 0.812
Chlorophyll- a
Correlation Coefficient
0.420
- 0.070
0.343
0.049
p- value 0.175 0.829 0.276 0.880
% < 2mm
Correlation Coefficient
0.021
0.49
- 0.455
- 0.762 **
127
p- value
0.948
0.106
0.138
0.004
% < 4mm
Correlation Coefficient
- 0.028
0.503
- 0.517
- 0.713 **
p- value
0.931
0.095
0.085
0.009
Bankfull Tractive
Correlation Coefficient
0.14
- 0.252
0.168
0.671 *
Force p- value
0.665
0.43
0.602
0.017
Tractive Force
Correlation Coefficient
- 0.119
0.049
0.329
0.329
p- value
0.713
0.88
0.297
0.297
128
Table 4.3: Spearman rank correlation coefficients and associated p- values for PCA axis site scores of average benthic invertebrate relative abundance data and environmental variables for October 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).
Axis 1 Axis 2 Axis 3 Axis 4 Water
Correlation Coefficient
- 0.692 *
- 0.18
- 0.469
0.21
Temperature p- value
0.013 0.602 0.124 0.513
pH
Correlation Coefficient
- 0.678 *
- 0.51
- 0.119
- 0.21
p- value
0.015 0.09 0.713 0.513
Dissolved
Correlation Coefficient
0.566
- 0.161
0.636 *
- 0.154
Oxygen p- value
0.055 0.618 0.026 0.633
Conductivity
Correlation Coefficient
0.21
- 0.406
- 0.021
- 0.364
p- value
0.513 0.191 0.948 0.245
Flow
Correlation Coefficient
- 0.664 *
- 0.476
0.035
- 0.021
p- value
0.018 0.118 0.914 0.948
% Deposited
Correlation Coefficient
- 0.189
0.722**
- 0.088
- 0.536
Sediment p- value
0.556 0.008 0.787 0.073
%
Correlation Coefficient
- 0.455
0.594*
0.343
- 0.21
Embeddedness p- value
0.138 0.042 0.276 0.513
% Slope
Correlation Coefficient
0.357
- 0.133
0.105
0.580*
p- value
0.255 0.681 0.746 0.048
Total
Correlation Coefficient
- 0.923**
0.014
- 0.224
0.077
Suspended Solids
p- value
0.00 0.966 0.484 0.812
Chlorophyll- a
Correlation Coefficient
0.42
- 0.07
0.343
0.049
p- value
0.175 0.829 0.276 0.88
129
% < 2mm
Correlation Coefficient
- 0.287 0.643 * 0.007 - 0.427
p- value
0.366
0.024
0.983
0.167
% < 4mm
Correlation Coefficient
- 0.287
0.643 *
0.007
- 0.427
p- value
0.366
0.024
0.983
0.167
Bankfull Tractive
Correlation Coefficient
0.14
- 0.252
0.168
0.671 *
Force p- value
0.665
0.43
0.602
0.017
Tractive Force
Correlation Coefficient
- 0.119
0.049
0.329
0.329
p- value
0.713
0.88
0.297
0.297
130
Table 4.4: Spearman rank correlation coefficients and associated p- values for PCA axis site scores of average species trait relative abundance data and environmental variables for July 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).
Axis 1 Axis 2 Axis 3 Axis 4 Water
Correlation Coefficient
- 0.287
0.669*
0.224
- 0.182
Temperature p- value
0.366 0.017 0.484 0.572
pH
Correlation Coefficient
0.287
0.872**
0.175
- 0.056
p- value
0.366 0.00 0.587 0.863
Dissolved
Correlation Coefficient
0.462
- 0.459
- 0.126
0.042
Oxygen p- value
0.131 0.134 0.697 0.897
Conductivity
Correlation Coefficient
0.832**
0.312
0.056
0.441
p- value
0.001 0.324 0.863 0.152
Flow
Correlation Coefficient
0.259
0.764**
0.084
- 0.014
p- value
0.417 0.004 0.795 0.966
% Deposited
Correlation Coefficient
- 0.385
- 0.044
- 0.242
0.504
Sediment p- value
0.216 0.892 0.449 0.094
%
Correlation Coefficient
- 0.427
- 0.109
- 0.161
- 0.028
Embeddedness p- value
0.167 0.737 0.618 0.931
% Slope
Correlation Coefficient
0.035
- 0.406
0.559
- 0.238
p- value
0.914 0.19 0.059 0.457
Total
Correlation Coefficient
- 0.322
0.718**
0.217
0.091
Suspended Solids
p- value
0.308 0.009 0.499 0.779
Chlorophyll- a
Correlation Coefficient
0.098
- 0.396
- 0.357
- 0.266
p- value
0.762 0.203 0.255 0.404
131
% < 2mm
Correlation Coefficient
0.021 0.49 - 0.455 - 0.762 **
p- value
0.948
0.106
0.138
0.004
% < 4mm
Correlation Coefficient
- 0.028
0.506
- 0.517
- 0.713 **
p- value
0.931
0.095
0.085
0.009
Bankfull Tractive
Correlation Coefficient
0.14
- 0.252
0.168
0.671 *
Force
p- value
0.665
0.43
0.602
0.017
Tractive Force
Correlation Coefficient
- 0.119
0.049
0.329
0.329
p- value
0.713
0.88
0.297
0.297
132
Table 4.5: Spearman rank correlation coefficients and associated p- values for PCA axis site scores of average species trait relative abundance data and environmental variables for October 2005. Stars denote significant results (** correlation is significant at the 0.01 level and * is significant at the 0.05 level).
Axis 1 Axis 2 Axis 3 Axis 4 Water
Correlation Coefficient
0.678*
0.448
- 0.308
0.51
Temperature p- value
0.015 0.145 0.331 0.09
pH
Correlation Coefficient
0.329
0.497
- 0.741**
0.154
p- value
0.297 0.101 0.006 0.633
Dissolved
Correlation Coefficient
- 0.650*
- 0.224
0.196
- 0.643*
Oxygen p- value
0.022 0.484 0.542 0.024
Conductivity
Correlation Coefficient
- 0.322
- 0.168
- 0.42
- 0.126
p- value
0.308 0.602 0.175 0.697
Flow
Correlation Coefficient
0.385
0.538
- 0.42
0.091
p- value
0.217 0.071 0.175 0.779
% Deposited
Correlation Coefficient
0.284
- 0.511
0.102
- 0.165
Sediment p- value
0.372 0.089 0.753 0.609
%
Correlation Coefficient
0.483
- 0.098
0.371
- 0.455
Embeddedness p- value
0.112 0.762 0.236 0.138
% Slope
Correlation Coefficient
- 0.294
0.07
0.063
0.322
p- value
0.354 0.829 0.846 0.308
Total
Correlation Coefficient
0.811**
0.336
- 0.203
0.28
Suspended Solids
p- value
0.001 0.286 0.527 0.379
Chlorophyll- a
Correlation Coefficient
- 0.238
0.084
0.308
- 0.28
p- value
0.457 0.795 0.331 0.379
133
% < 2mm
Correlation Coefficient
0.538
- 0.259
0.294
- 0.189
p- value
0.071
0.417
0.354
0.557
% < 4mm
Correlation Coefficient
0.538
- 0.259
0.294
- 0.189
p- value
0.071
0.417
0.354
0.557
Bankfull Tractive
Correlation Coefficient
- 0.154
0.28
0.014
0.357
Force
p- value
0.633
0.379
0.966
0.255
Tractive Force
Correlation Coefficient
0.049
0.308
- 0.007
0.231
p- value
0.88
0.331
0.983
0.471
134
Table 4.6: Species tolerances for July 2005. (Tolerant = 3, moderately tolerant = 2, sensitive = 1).
Taxa Species Score Tolerance Value Pelecypoda -0.7208 3
Figure 4.2: RDA ordination of average species relative abundance and environmental variables significantly correlated with PCA axes 1 and 2, October 2005.
Axis 1
140
Figure 4.3: RDA ordination of benthic invertebrate taxa tolerances to deposited sediment (axis 1), July 2005. Taxa within the red and green boxes are classified as tolerant (value = 3) or sensitive (value = 1), while taxa within the middle are considered to be moderately tolerant (value = 2).
Tolerant Sensitive
Axis 1
141
Figure 4.4: RDA ordination of benthic invertebrate taxa tolerances to deposited sediment (axis 1), October 2005. Taxa within the red and green boxes are classified as tolerant (value = 3) or sensitive (value = 1), while taxa within the middle are considered to be moderately tolerant (value = 2).
Tolerant Sensitive
Axis1
142
Figure 4.5: RDA ordination of species traits and environmental variables correlated with PCA axes 1 and 2, July 2005.
143
Figure 4.6: RDA ordination of species traits and environmental variables correlated with PCA axes 1 and 2, October 2005.
Axis 1
144
Figure 4.7: RDA ordination of average trait relative abundances and sediment variables July 2005.
145
Figure 4.8: RDA ordination of average trait relative abundances and sediment variables, October 2005.
146
5.0 DISCUSSION Several commonly used biomonitoring metrics were responsive along a gradient
of deposited sediment conditions. The metrics % burrower and % Chironominae of
Chironomidae responded positively to increasing fine sediment deposition. While the
metrics: % EPT, EPT abundance and % Orthocladiinae of Chironomidae responded
negatively to increasing fine sediment deposition. Additional metrics were significantly
correlated with deposited sediment however these results were not consistent, i.e., only
occurring in one of the seasons sampled. Diversity measures (Shannon Diversity, Taxa
richness and EPT richness) were not affected by increased fine sediment deposition,
indicating these metrics are not appropriate for testing the effects of a sediment stressor.
The results suggest sediment deposition causes a change in the benthic invertebrate
community under the conditions during this study, which can not be detected by current
metrics (e.g. Shannon Diversity, Species richness). Taxa intolerant to fine sediment may
have decreased in abundance or were replaced by taxa more tolerant to deposited fine
sediment, resulting in no change in the metric values even though a shift in the
community had occurred.
Species traits that are modifications or adaptations for living with fine sediment
were positively associated with increased sediment deposition. Tolerance traits included
plastron respiration, depositional preference, free moving, heavy armour and collector
gather. These traits allow benthic invertebrates to survive in fine sediment conditions or
escape (e.g. free moving). Tolerant benthic invertebrates were found to possess a
combination of tolerant and non-tolerant species traits, suggesting tolerance is not
dependant on the possession of all adaptive traits.
147
5.1 IMPLICATIONS OF RESULTS
Understanding how metrics respond to individual stressors is important for
biomonitoring purposes. The consistent responsiveness of several metrics over the two
seasons suggests they may be used for the detection of fine sediment deposition effects in
biomonitoring programs. The response patterns of metrics can be used to identify and
quantify the source of impairment if the response patterns of metrics to individual
stressors are known. The lack of correlations between certain metrics, previously found to
respond to deposited sediment, in my study shows a combination of metrics and indices
may be need to be incorporated into biomonitoring programs to detect impacts. Metrics
incorporating sediment tolerant and intolerant taxa, e.g. % EPT and % Orthocladiinae, as
well as deposited sediment biotic indices would be useful in biomonitoring.
Species traits offer a more ecologically relevant biomonitoring approach than
community composition and diversity measures. The results of this study have shown
that species traits are responsive to sediment stressors and may be used as an alternative
biomonitoring metric to determine the extent and source of the impact. Species traits can
help to explain the presences/ absences of benthic invertebrate taxa in particular
environments, as well as identify the source of impairment. For example, sediment
tolerant benthic invertebrates possessed one or more of the traits determined to be
associated with increased sediment deposition.
5.2 FUTURE RESEARCH
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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
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.