INFLUENCE OF LOW LEVEL RADIOACTIW WASTE MANAGEMENT SITES ON BIOTIC AND ABIOTIC COMPONENTS OF WETLANDS

IN CHALK RIVER, ONTARlO

A Thesis submitted to the Cornmittee on Graduate Studies

in Partial Fulfiiment of the Requirements for the

Degree of Master of Science

in the Facuity of Arts and Science

TRENT UNIVERSITY

Peterborough, Ontario, Canada

0 Copyright by Mary Loretta Hardwick 1998

Watershed Ecosystems M.Sc. Program

June 1999

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InfIuence of Low Lem1 Radioactive Waste Management Sites on Biotic and Abiotic Compoaents of Wetiands m Chalk River, Ontario

Four wetlands near Chalk River, Ontario, are wntaminated by leachate h m low-level

radioactive waste management sites, The piirpoçc of this saidy is to 1) evaluate the

extent of LLRW contamination m wetland s d k e water and sedÛnnt; 2) detetmme the impact

of LLRW on wetland bathic macruinvertcbratc comunity stnrcture; and 3) d e t e e whether

invertebrate community structure (i.e. M y and gcnus) or fimctim (i-e. feedmg group) is a

better predictor of habitat quality. Surface water, sedimeent and benhic macroinvertebrates were

collected in June 1995, for a detailcd snidy of one wetland @uke Swamp) and in October for a

comparative study of 14 weîiands Contamhants detected in the wetfands include '37~s7 I 4 c and

SO~o in the sediments and '8, tm organic cbtmicds (measured in Duke Swamp ody), mainiy

TCE and 1,l-DCA, and Zn m the SUfface water. Other metais that were detected at hïgh

concentrations (Fe, fi, Al) are not unwual in wdands in the area and surface water metal

concentrations were related to wetland type and water pH, The structure of the benthic

invertebrate communïty was not reiated to the conceotrations of leachate contaminants, but was

strongly related to wetland water chemistry and most of the variation between wetlands was

easily explained by ciifferences in wetland type. The nutnent-rich wetlands had less diverse

aquatic invertebrate fauna dominated by Chimnomus chironamids and tubincid oligochaetes.

Witbin Duke Swamp, sites with low pH, alkalini-ty and DOC and high metal concentrations also

had less diverse invertebrate h a dominateci by Chironomw, mosquitoes and false ctaneflies.

Sites with higher pH, alkaIinity and DOC and Iowa concentrations of Fe, Mn and Ai were

charactensed by Asellus isopods, and the gastropods -sella and Valvata. Correspondence

Analysis ordination of wetlands using bemthic macroinvertebrates identified to lowest taxonomie

unit ( L m and also to family level gave very similar d î s . Ordination to the f d y level

provided a clearer separattion of swamps h m fens- However, ordination to LTU was more

strongly related to water chemistry, Ordination of functional feeding gmups was related to

wetland water chemïsûy in the sprïng, but the relationship was much weaker than the previous

ordinations, and was unrelateci to water chemïstry in the fall. Ordination of FFG is imlikely to be

an effective ïndicator of chernical contamination in wetlands-

This project could not bave been completcd wïthout the help ofa number of individuais,

First 1 wouid like to tbank my thesis supcrvi*sor, Dr, Ron Hall, for his constant encouragement

and support. Dr. MaIwIm Stephenson provided mdispensable support and advice, as well as

Company whiie slogging through wetlands. 1 aiso tbank Dr- rack Comett and Dr- Tom W h ï h s

for k i r advice and cntical comments on the thesis.

1 would aiso Wre to thanlc all those at Chalk River Laboram-es who helped on this

project, in particular, Bruce Lange for supporthg this project Thanks also to Doug Killey and

Karen KÏng for advice and help m Duke Swamp. In addition, 1 want to thank those at Dorset

Research CentreT Ontario Mimsstry of the Envlnnmient, who helped with this reseafch, including

Dr. Keith Somers, who gave valuable advice on statistical analysk of the data.

1 want to thank ali who were mvolved with chemîcal or fadiological anaiysis of water

ancüor sedirnents for this project h m AECL Ch& River Laborataies, AECL Whïtesheii

Laboratories, and OMEE Dorset Rtstarch Centre-

Fmding for this p j e c t was provided by Atomic Energy of Canada, Limited, Chak

River Lahtories- F i n a n d support was provided through a Natural Sciences and Engineering

Research Council postgraduate scholarship.

1 also thank my farnily for encouragement and support thmughout this long, long haul. 1

am especially grateful to Chris and Stephen who stood by me through it ail, and provided love

and support,

TabIe of Contents

.................................................................................. 1.1 Low-Level Radioactive Waste I

1.2 LLRW Leachate ............................................................................................... 2

1.3 Ecological Assessrnent ........................................................................................... 3

1.4 Choice of Biological Measure of Degradation ......................................................... 4

1.5 Objectives .............................................................................................................. 6

.................................................................................. 2.1 -0 Sampling of Duke Swamp 6 2.1.1 Study Ara ................................................................................................. 6 2.1 -2 Water and Sediment Collection ................... ............. .................................. 12 2.1.3 Invertebrate Collection .................................................................................... 13

............................................ 2.2.0 Sampling of Chalk River and Petawawa Wetlands 14 2.2.1 Study Ares .................................................................................................... 14

...................................................................... 2.2.2 Water and Sediment Chemistry 18 2.2.3 hvertebrate Collection ............................................................................... 18

2.3.0 Data Analysis .........................................................................................~.......~.... 21 2.3.1 Waterandsediment Data ............................................ 21 2.3.2 Xnvertebrate Data .................................................................................... 2 3

............................................................... 2.3 -4 Biota - Enviromnent Relationship 2 4

3.1.0 Duke Swamp ........................................................................................................ 25 3 .1.1 Wetland Chemistry ................... .. .................................................................. 25

......................................................................................... 3.1.2 Tnvertebrate Me- 26 ................................................................................ 3 -1 -3 Correspondence Analy sis -33

............................................................ 3.1 -4 Summary of Results for M e Swamp 47

................................................................. 3.2.0 Chalk River and Petawawa Wetlands 4 8 32.1 Water Chemistry ....................... .. ............................................................... 48

3.2.2 Sediment Chemistry.. ................................................................................. 5 3 3.2.3 Invertebrate Metrics ......................................................................................... 54

............................................................................... 3.2.4 Conespondence Aoalysis 5 8 ....................... 3.2.5 Summay of R e d t s for Chalk River and Petawawa Wdands 78

4.1 . 0 Extent of Contamination of Wetiand Surface Water and Sediment .................... 79 4.1.1 RadionucIides ...........*....... .... ................................................................... 79 4.1.2 Non-Radiological Contaminant9 - Volatile ûrganic Compounds ................... 80 4.1.3 Non-Radiological Contaminants - Metals ....................................................... 82

...................................................................................... 4.2.0 Wetland Aquatic Habitat 83 4.2.1 Duke Swamp .................................................................................................... 84

.............................................................. 4.2 -2 Chalk River and Petawawa Wetiands 85

.................................. 4.3.0 Benthic Invextebrate Community and Wetland Chemktry 86 4.3.1 Leachate Impact on Wetlands . Radionuclides ................................................ 87 4.3.2 Leachate Impact on M e Swamp - Volatile Organic Compomds ................ 88 4.3.3 pH and Metals .................... .... ................................................................. 90 4.3 -4 Benthic Community and Wetiand Type ......................................................... 92

............. 4.4.0 Benthic hvertebrate Community as Wetland Water Quaiity Indicator .. 94 4.4.1 Biotic Indices ............................................................................................. 9 5 4.4.2 Multivariate Ordination .................... .. .......................................................... 96

............................................ 4.4.3 Taxonomie Level ............................................ 98

....................... 4.4.4 Cornparison with Other Indicators of Wetland Water Quality ... 98

Literature Cited mœoœaoaooeoœooooaomaaoaaœœ~eœooommoaœooeooœemeoooeoa~œœœmaooœaœœmaœœœaaœaœœoœœœœœaœœaœœaaœmaaaœœaœaaœ 103

Appendix 1 . Physicochemical parameters analysed for Duke Swamp surface water and ................................................................................ sediment collecteci June. 1995 1 18

Appendix 2 . hvertebrates collecteci by core sampler in Duke Swamp. lune. 1995 ..... 121

Appendix 3 . S d a c e water and sediment physicochernical parameters detected in ................................................... samples collected in 14 wetlands. ûctober, 1995 123

Appendix 4 . Invertebrates collectecl by artincial substrate in 14 wetlands sampled in October. 1995 .......................................................................................................... 126

Appendix 5 . CA plot of invertebrates identified to LTU for aU 14 wetlands sampled in October. 1995 ............... .... ................................................................................. 129

LIST OF TABLES

............... ........ Table 2.1. Taxonomie Iml and keys used to identify invertebrates., .. 14

Table 2.2. Latitude and longitude ofwetlands studied at Chaik River Laboratories and Petawawa Forestty Mtute in Jime and October 1995. ...............................~........... 15

Table 3.1. Means, ranges and transfomations for water and sediment chemicai parameters ïncluded in PCA ofDuke Swamp environmental variables for the sites DS 1 to DS7, DS9, DS 10, and DS2lto DS25. ............................................................ 27

Table 3.2. Loading of Duke Swamp water and sediment chemical parameters onto the principal compomnts b m a PCA of a correlation ma- ........-............................. 28

Table 3.3. Means and ranges of invertebrate m d c s for Ddce Swamp separateci into fcn (n=5) and swamp ( ~ 1 0 ) .......-...........................................-.....~-............................... 3 1

Table 3.4. Pearson and Spearman correlation coefficients between Duke Swamp invertebrate metncs and the principal components of Duke Swamp water and sediment chexnical variables (n=14; five fens and nine swamps combined). ...-....... 3 1

Table 3 S. Summary of a backwards stepwise multiple hear regression of invertebrate metrics on principal components h m a comlation matrix of water and sediment chemistry parameters of M e Swamp. ............................................................ 3 2

Table 3.6. Peatson and Speannan correlation coefficients between the axes h m three CAS of M e Swamp iavertebrates and the principal components from a PCA of M e Swamp water and sediment chernical variables (n = 14). ............................... 46

Table 3.7. Summary of a backwards stepwise multiple linear regsession analyses of the major dimensions fimm cornespondence analyses of invatebrate data on principal components fiom a correlation matrix of water and sediment chemistry parameters of Duke Swamp ......................................................................................................... 46

Table 3.8. Means, ranges and transformations for water chemical parameters included in PCA of 12 wetlands (FDS1, BSI, BS2, UBL, TC, ES, PP, MS, and PF1 to PF4) ... 49

Table 3.9. Loachg of water chernistry parameters h m the 12 wetlands (.S 1, BS 1, BS2, UBL, TC, ES, PP, MS, and PFI to PF4) onto the two principal components nom a PCA of a correlation matrjx. Scores 2 1 0.05 1 are underlïned ...................... 50

Table 3.10. Means, ranges and tninsfonnatiom for sediment chemisûy parameters included in PCA of 12 wetlaads (FDS1, BSl, BS2, UBL, TC, ES, PP, MS, and PF1 to PF4) ...-.......................................................................--........--................................ 53

Table 3.1 1. Loading of sediment chemistry parameters onto the components h m a PCA of a comlation ma* of 12 wetlands (FDSI, BSI, BS2, UBL, TC, ES, PP, MS, and PF 1 to PF4).. ...................... ... ....... ... ..................................................................... 54

Table 3.12. Pearson and Speannan correlation coe~cients between invertebrate mehics and components h m a correlation ma& of the water chemistry parameter from 12

.................... wetlands (FDS 1, BS 1, BS2, UBL, TC, ES, PP, M!3, and PF1 to PF4). 57

TabIe 3.13. Summary of bacbards stepwise multiple hear regressïon analyses of invertebrate metrics vs. principal componmt scores h m a PCA of water chemîstry variables for 12 wetlands (FDS1, BS1. BS2, UBL, TC, ES. PP, MS, and PFl to

........................................................................................................................... PF4) 58

Table 3.14. Pearson and Spearman correlation coefficients between the three dimensions h m CAS of the invettebrate data and p ~ c i p a i components h m a PCA of a correlation matrix of the water chetnistry parameters h m 12 of the 14 wetlands.. 72

Table 3.15. Pearson and Spearman comlation coefficients betweai the three dimensions h m CAS of the invertebrate data and the principal components h m a PCA of a comlation matrix of the secliment chemistry parameters h m 12 of the 14 wetlands. ............................... ... ................................................................................................ 72

TabIe 4.1. Pubfished acute torricity to Wwater organjsms of individuai organic ................................................. chernicals detected in Duke Swamp surfiace water., 90

Table 42. Published studies for cornparison with CRL wetland benthic . - macroinvertebrate community compo~ihon. ............................................................ 95

LIST OF FIGURES

Figure 2.1. Location of the 14 Chaik River wetland sites sampled for water and Sediment ............................................ chemistry and Znvertebrate coUection, November 1995- 8

Figure 2.2 Fifteen sites in Duke Swamp that were sampled for water and sediment chemistry and invertebrate coilectioa, hue 1995. .................................................... 10

Figure 2.3. Location of the four Petawawa Fo- Institute @FI) wetlaml sites sampled for water and sediment chemistry and invertebrate colkction, November 1995. .... 16

Figure 3.1. Graph matrix of principal CompOnent scores for the four p ~ c i p a i cornponents h m a PCA of a correlation mat& of Duke Swamp water and sediment chemistry for the 14 study sites for which complete water chemistry data was available.. .................................................................................................................. 29

Figure 3.2. Plots of site scores h m a CA of DS invertebrate àata (95% by abundance) showing: (A) the second dimension (CM) against the fjrstdimension (CAl); (B) the third dimension (CA3 against the second dimension; and (C) the third dimension against the first dimension. .................... ..., ......................................... 36

Figure 3.3. Plots of taxa scores h m a CA of DS invertebrate data (95% by abundance) showing: (A) the 2nd dimension (CM) agaiast the 1st dimension (CAl); (B) the 3rd dimension (CA9 against the 2nd dimension; and (C) CA3 against CA1. ............... 38

Figure 3.4. Plots of site scores h m a comspondence analysis of M e Swamp invertebrate data (family level) showing: (A) the second dimension (CAF&) against the nrSt dimension (CAF,~); (8) the third dimension (CAF&) against the second

...................... dimension; and (C) the tbird dimension against the nrSt dimension. 40

Figure 3 .S. Plots of taxa scores h m a CA of DS invertebrate data (family level) showing: (A) the 2nd dimension (CA&) agak t the 1st dimension (CAFml); (8) the 3rd dimension (CAF,3) against the 2nd dimension; and (C) CAF& agahst the

Figure 3.6. Plots of the second dimension ( C A F ~ ~ ~ ) against the f b t dimension (CAFFG 1) fiom a CA of DS invertebrate hctional feeding group data showing: (A) site

..................................... ............. scores; (B) functional feeding group scores .... 44

Figure 3.7. Plots of principal component scores for PC2 versus PC1 from a PCA of a correlation matrix of water chemlstry for 12 wetlands.. ........................................... 5 1

Figure 3.8. Plots of principal component scores for PC2 versus PCl h m a PCA of a ............. ................... correlation maûk of sediment chemistry for 12 wetlands. .. 55

Figure 3.9. Plots of site scores h m a comspondence analysis of invertebrate data (95% by abundance) h m 12 wetlands showing: (A) the second dimension (CA2) against the Grst dimension (CA1); (B) the third dimension (CM) against the second

................... dimension; and (C) the third dimension against the fïrst dimension. .... 6 1

Figure 3.10. Plots of taxa scores h m a correspondaice analysis of invertebrate data (95% by abundance) h m 12 wetlands showing: (A) the second dimension ( C M )

against the first dimension (CAL); (B) the third dimension (CM) against the second ...................... dimension; and (C) the third dimension agakt the £bt dimension.. 63

Figure 3.1 1. Plots of site scores 6rom a comespondence andysis of invertebrate data ( f d y level) h m 12 wetlands showing: (A) the second dimension ( C M ) against the fkst dimension (CAé,l); @) the tbïrd dimension (CAFA) against the

.......... second dimension; and (C) the thùd dimension against the first dimensioa. 65

Figure 3.12. Plots of taxa scores h m a CA of invertebrate data ( f d y ) h m 12 wetlands showing: (A) the 2nd dimension (CA&l) against the 1st dimension (Ch-1); @) the 3rd dimension (CAF& a g a the 2nd dimension; and (C)

............................................................................................. CAh3 against C h 1 67

Figure 3.13. Plots of the second dimension ( C h 2 ) again& the fb t dimension ( C k G l ) h m a correspondence analysk of hvertebrate tùnctionai feeding group data fb1.112 wetlands showing: (A) site scores; and @) fhctional feeding gmup C O . . ............................................................................................................. 6 9

Figure 3.14. Plots of site scores h m a comspondence analysis of invertebrate data ( f d y level) h m 10 wetlands showing: (A) the second dimension (CArM2) against the first dimension (CAFml); (8) the third dimension (mM3) against the

........... second dimension; and (C) the third dimension against the ntSt dimension. 74

Figure 3.15. Plots of taxa scores h m a CA of învertebrate data (family) h m 10 wetlands showing: (A) the 2nd dimension (CAF&) against the 1st dimension ( C A F ~ ~ ~ ) ; (B) the 3rd dimension (CAF&) against the 2nd dimension; and (C)

........................................................................................ CAF,& against CAFAMI.. 76

1.0 INTRODUCTION

1.1 Low-Level Radioactive W d e

Historicaîly, in Noah America Law-level radioactive waste (URW) has been

disposed of by two prhmqr methods, ocean dumping and shallow land burial (Gershey et

al. 1990). Ocean dumping is no longer widely practised and, although sewage disposal

and incineration are acceptable altematives in the US. (Eisenbud and Gesell 1997).

shailow land burial rem- as the most fkpentiy used method of managing LLRW

(Gershey et al. 1990, Kim et al. 1993). In Canada, LLRW has historically been buried in

municipal lannfitls or dumped on unusecl land (Fawcett 1993). Presentiy, Canada allows

only interim storage of LLRW Comk and Buckley 1988, Gershey et ai. 1990). Much of

the waste that is presentLy grneratecl is shipped to AECL Chalk River Laboratories (CRL)

for storage and eventual disposal (Fawcett 1993).

LLRW is composed of radioactively contaminateci refbse onginating h m nuclear

facilities, industry, hospital and research institutions. The classification of radioactive

waste as low-level is based on the level of radiation (cl00 mWh) detected at 30 cm k m

the package (Killey and Munch 1986). Low-Ievel wastes can include any radioactively

contaminated trash, including clothing, paper, plastic, biologicd waste and metais

(Eisenbud and Gesell 1997). Chemical characterisation of LLRW is difficuit due to lack

of waste inventory in the past (Tomk and Buckley 1988, Lipschutz 1980). However, in

addition to radioactivity, LLRW may contain toxic compounds such as heavy metals

(Fawcett 1993) and solvents (Francis et al. 1980).

1.2 LLRW Leachate

Waste disposal by burial in soil has been implicated in the contamhtion of

adjacent waters @amis et al. 1980, Gershey et al. 1990, LeSage et al. 1990, Westlake

1995, Visvanathan 1996). Precipitation percolating through waste disposal sites cornes

into contact with the waste and f o m leachate, usually a complex mixture of chernlcals.

The leachate c m then migrate out of the disposal site into groundwaier or d a c e water.

Migration of radionuclides h m near-surface LLRW bacial sites into ground and d a c e

waters has been reporteci at waste management sites across North America (e-g.,

Lipschutz 1980, Philipose 1992, Eisenbud and Gesell 1997).

Due to the diversity of materials that have been buriexi in LLRW sites, leachate

may be expected to display chernical characteristics similar to those of leachate h m

non-radiological waste sites. For example, the pH of leachate h m municipal l m n s

(Atwater 1980) and radioactive waste sites (Torok and Bucldey 1988) is highly variable.

Also, meds and organic solvents, commonly detected in municipal landfill leachate

(Qasim and Chiang 1994, Assmuth 1996), have also been detected almg with

radionuclides migrahg h m radioactive waste sites (Francis et al. 1980, Lipschutz

1980).

Assesmient of the impact of radioactive materials on aquatic biota generally deals

with short-term exposure ta high doses. However, a limited number of studies on chronic

exposure to low levels of radioactivity on aquatic organïsrns have shown that naturai

populations may not exhibit adverse effects (Ophel1979), or an increase in mortality may

be offset by an increase in fecundity (Blaylock and Trabalka 1978). Studies addressing

the impact of non-radiological components of LLRW leachate on aquatic commdties

wae not found in the published litetafiue. Nevertheiess, leachate h m LLRW sites may

contain non-radiological pollutants that can have sipnincant effects on a q d c organisms

and communities. Bioassays have shown municipal l a n a leachates, ofvarying

chemical composition, to be toxic to a variety o f m w a t e r o ~ g a ~ s m s iduding bacteria

(Schrab et al. 1993), dgae (Cheung et al. 1993. Rutherford 1995). zooplankton (Cameron

1982, Assmuth and Penttila 1995) and Mwater fish (Cameron 1982, Wong 1989).

Altered quatic macromvertebrate community structure has been observed in M w a t e r

systems receiving leachate h m laadfilis (Cingolani and Morosi 1992, Rutherford 1995)

and hazardous waste sites (Siewert et al. 1989, Kappehan 1993). Because LLRW sites

may be expected to contain toxicants smiilar to those foimd in municipal waste, the

effects of non-radiological components of LLRW leachate on both the chemical and

biological characteristics of receiving waters needs to be addresseci.

1.3 Ecologicai Assessrnent

An assesment of the extent of effects of poUutants on aquatic systems should

uivolve the measurement of both chemical and biologicd parameters (Courtemanch

1989, Karr 1993). Chernical assessments identify individual contaminants, signal the

presence of priority poliutants and deteunine whether contaminant concentrations exceed

regdatory limits or guidelines. GuideLines and criteria are generally developed for

individual chemicals based upon published toxicity information (CCME 1995) and do not

take into account the presence of a mixture of chemicals. The toxicological effects of a

mixture of chemicals, at individual levels below water quality criterîa, may be additive

(Enserink et al. 1991). In addition, chemical criteria do not reflect site specific

characteristics. For example, metals rnay be present at concentrations above

recommended water quality criteria, yet toxicological effects may not be obsnved due to

binding ofthe met& to organic co~irpounds present at the site (e-g., Assmuth and Penttila

1995). The use of only physico-chemicai paratfletets in the assessrnent of efological

effects may eitha over- or underestim;ite the amomt of stress on the systenz.

Biological assessments detect the existence of d e m o n and evaluate the extent

and na- of damage to the biota Biological communïties combine the effeds of

contamhants and serve as a composite measure of impact- Synergistic effects of a

complex mixture of contamhmts present in low concentrations rnay be revealed by

changes in the biota (Hellawell1986, Seager et al. 1992). Altematively, the toxicity of

contatnïnants may be ameiiorated by components of the water chemistry, for example,

the toxicity of copper to populations of Daphnia magna was decreased in humic water

(Oikari et al. 1992). The presence or absence of a meanirable biological efféct when

there is a known con taminant in the systwi wiU be important in the consideration of

remedial action. Measurement of water chemistry and biota, and the correlation between

them, are necessary to assess the degree of disturbance and decide upon regulatory or

remedial action (Hemond and Benoit 1988).

1.4 Choice of Biological Measure of Degradation

Exposure and sensitivity of biota to the contaminant of concem are primary

considerations in the choice of a biological measure of degradation (Adamus 1992).

Aquatic macroinvertebrates have been used extensively to assess the impact of a range of

stresses (HeIIawell1986). Benthic maminvertebmtes are directly exposed to aquatic

con taminants îhmugh contact with both the water and the sediments and tbrongh

ingestion of particdates. The seLlSitivity of aquatic ùivertebrates to metal contamination

has been extdvely studied (rdewed by Wren and Stephenson 1991, Hare 1992 and

Gerhardt 1993). Toxïcity tests have shown that quatic invertebrates are ais0 sensitive to

individual organic solvents (Slooff et al. 1983) and combinations of poiiutants (Chapman

et al. 1982).

Althou& the impact of toxk contamhants may be observed at Iower IeveIs of

biological organisation, such as cellular or organismal, measures of community structure

may provide better integration of the effects of complcx mktmes of con taminanta

(Pl& et al. 1989). Alteration of benthic macroinvertebrate commmïty structure has

been found to occur as a result of exposure to toxic metals in streams (Winner et al. 1980,

Clements et al. 1988, Rasmussen and Lîndegaard 1988, Gower et ai. 1994). In addition,

complex leachates h m landf3ls (Cingolani and Morosi 1992, Rutherford 1995) and

hazardous waste sites (Siewert et aL 1989, Kappelman 1993) have been irnpücated in the

alteration of aquatic maaoinvertebrate community structure in receiving waters.

Commonly used measures of invertebrate community include univariate metrics,

such as abundance, diversity, taxonornic richness and % taxonomic composition, as well

as multivariate ordination techniques. Invertebrate community structure may ais0 be

summarised using ciiffirent levels of taxonomic discrimination, for example, genus or

family, or fùnctional p u p associations (Cranston 1990). Since community mehics may

not respond in the same manner to dtEerent contaminants, a combination of metrics may

be better able to detect the influence of a complex mixture of contamïnants.

1.5 Objectives

The pinpose of this research was to assess the impact of leachate h m a LLRW

waste management site on the benthic macminvertebrate community of a receiving

wetland. The specinc objectives of this study were as follows:

1) evaluate the extent of landfill leachate contamination in wetland suffie water and

sediment;

2) detemiine the impact ofLLRW Ieachate on wetrand benthic macroinvertebrate

community structure; and

3) determine whether invertebrate community structure (Le. f d y and genus) or

hct ion (i.e. fecding group) is a better tool for asessing habitat degradation.

2.0 METHODS

2.1.0 Sampiing of Duke Swamp

2.1.1 Study Area

Duke Swamp @S) is located in Chalk River, Ontario, in the AECL Ch&

River Laboratones Outer Ana between Waste Management Area C (WMA-C) and

Maskinonge Lake (Figs. 2.1,2.2). Since 1963, WMA-C has received industrial, hospital

and university solid wastes contaminated with low levels of radioactivity (Killey and

Munch 1986). Killey and Munch (1986) describe the geology of the area. Seepage h m

Lake 233 flows beneath WMA-C to DS (Fig. 2.2). Radionuclide monitoring has been

conducted since 1971 and the data show the advancement of a tritium plume h m WMA-

C (Philipose 1992). The leachate plume h m WMA-C extends into the north-eastem

edge of DS (Fig.22). More intensive sampling of IMWk-C revealed the presence of the

foUowing eight volatile organk compounds (VOCs): 1,4dioxane, trichloroethene (Ta),

1 , 1,l-trichlomethane WA), carbon tetrachlotide (CLE'f"I', cchlmform, 1,l-

dichloroethane (1, I-DCA), 1,2-dichloroetûaue (13-DCA), and tram 1,2-dichIomethene

(13-XE)-

The wetland wnsïsts of two habitat types, based on the vegetation. The no& end (Fig

2.2, DS-1 to DS-5) is an open shrub fm wiih no tree cover, dominatal by grases and

sedges, with Tjphu and Sphagnum present- The south end (Fig. 2.2, DS-6 to DS-25) is a

wooded swamp with 10% to 96% shade (mean 76%). Grasses, ffems and Sphagnum

dominate the ground vegetation on the south end of the swamp. The leachate plume has

been identifieci entering the swamp on the south end of DS, but not the fen on the north

end,

Figure 2.1. Location of the 14 C M k River wethnd sites sampled fbr water and sediment chemistry and invertebrate collection, November 1995. The five fens are Duke Swamp fen (FDS l), Upper Bass Lake (UBL), Bnlk Storage (BSl), B Management PM) and Main Stream (MS). The five swamps are Duke Swamp (FDS22), Pitchet Plant (PP), Bulk Storage Swamp (BS2), Toussaint Creek (TC) and East Swamp (ES).

Figure 2.2 FiAeen sites in Duke Swamp that were sampled for water and sediment chemistry and invertebrate collection, June 1995. Map includes waste management Area C and leachate plume. A r r o w s indicate direction of water flow. Fen sites are DS1, DS2, DS3, DS4 and DS5. Swamp sites are DS6, DS7, DS8, DS9, DSIO, DS21, DS22, DS23, DS24 and DS25.

Lake Waste Management Area

4 Sampling Site

~ z s ~ Subsurface Tritium Plume

2.1.2 Water and Sediment Coiidon

Water and sediment sample collection was c h e d out h m Jime 5 to Jime

9, 1 995. Along north-south transects in the swamp, ten sampling sites were selected that

had sufncient open water for sarnpling water and benthic invertebrates. In the fen the

d a c e water foms channels that flow through the wetland, and five samphg sites were

chosen to be representative of the open water areas.

At each sampling site, 1 1 containers were filled with surface water b m the

wetlané Surface water was collected approximately mid-channel in the fen and mid-

pool in the swamp. One 500-mL polyethylene bottle was filleci for analysis of

radionuclide activity. Two 100-mL brown glass bonles with gas seal caps were med for

analysis of trace organic con taminants. Two polyethylene bottles were nUed for analysis

of conductivity , colour , hardness, dissolved organic carbon @OC), silica (Si), calcium

(Ca), rnagnesium (Mg), sodium (Na), potassium (Q, fluonde O, chloride (Cl), sulphate

(S04), ammonia (NIE3) and ammonium (NIt), nitrate @IO3) and nitrite (NO), and total

Kjeldal nitrogen (TIEN). One 250-mL brown nalgene bottle was filled for analysis of pH

and alkalinityty Two 100 mL borosilicate test tubes with Teflon coated caps were nIled

for analysis of total phosphorus (TP). One 1ûûm.L glass test tube with gas seal cap was

filled for anaiysis of dissolved inorganic carbon PIC). One 500-mL glass bottle with a

gas seal cap was filled for analysis of dissolved oxygen @O). One 100-mL bottle was

med for analysis of iron (Fe) and manganese m). One clea., new 500-rnL

polyethylene bottle was used for analysis of trace metals. Since the botties for trace

metal analysis had not been acid washed pnor to sampling, three clea., new polyethylene

bottles were nIled with distilleci watet and sent for analysis with the others to test for

contamination k m the bottles- Laborittory d t s showed that ail trace metals were

below the detection Iimit in the distilled water h m the test bottles,

Water sarnples were collected fjist to auun that sediment and

invertebrate sampling did not &ect the water chemistry- Water was fïitered through a

250-pm Nitex mesh to remove large particulaie matter for all bottles except the DO

bottle. The water sample for DO analysis was nxed promptly with W i e r reagents.

Water samples were stored on ice mitil deiivered to the labs for analyses. Radionuclide

analysis was perfionned at Atomic Energy of Canada Limited (AECL) Ch& River

Laboratorks, Chalk River, Ontano. Trace organic analysis was performed at AECL

Whiteshell Laboratones, Pinawa, Manitoba AU other water chemistry samples were sent

to the Ontario Ministry of Enviromnent and Energy (OMEE) laboratones in Dorset,

Ontario for analyses-

At each site one 125-mL polyethylene bottle was used to sample the

sedirnents for organic carbon content, radionuclide activity, and trace metals. Sediments

were taken in the same a m as the water samples, h m the top 5 cm of sediment, and

excess water was removed fiom the sample. Sediments were stored in a deep fieeze until

analysed. Sediment analyses were performed at AECL, Chalk River Laboratories.

2.1.3 Invertebrate Collection

Invertebrate collection was carried out fmm June 5 to June 9,1995, dong

with water and sediment coîiection. Sampiing was perfonned with a core sampler that

penetrated to a sediment depth of 4 cm. The area covered by each core was 44 cm2.

Twelve replicate cores were taken at each site. Each sample was rinsed through 200-pm

screen to remove sediments, and returned to a field laboratory for handsorting the Iive

invertebrates. hvertebrate samples were sorted w i h 24 hours. Whai this was not

possible, the sample was fïxed in 4% fornialin, and then rnised and fiiled with 90%

ethanol. Preserved samples were later stained and bandsortd The levd of taxonomic

identification for each class of invertehaîes, and the taxonomic keys used for

identification, are given in Table 2.1.

Table 2.1. Taxonomie level and keys used to iden* invertebrates.

Taxa Taxmomïc Level Identitication Keys

Bivalvia genus Clarke (198 1); Mackie, White and Zdeba (1 980) -0poda genus Burch (1982); Clarke (198 1) Oligochaeta family Loveridge (1976) Hinidinea genw Sawyer (1972)

C O P ~ P ~ ~ subarder Pennak (1978) Isopoda gent= Williams (1972) Amphipoda Pennak (1 978) Arachnida class Pemiak (1978) Insecta genus* M h t t and Crmunins (1984)

Trichoptera genus* Wiggbs (1977) Diptera: Chironomidae genus Oliver and Roussel (1983); Kowalyk (1981) Diptera: Culicidae genus Wood, Dang and Ellis (L979)

* Some oniy identitied to family

2.2.0 Sampïing of Chak River and Petawawa Wetlmds

2.2.1 Study Area

In October 1995, 14 wetland sites were sampled, including both sides of DS @S-

1 and DS-22). Ten of the wetlanci sites were Iocated in the CRL ûuter h a ; the

remaining four wetlands were located in the Petawawa Forestry Institute (PFI) area. The

wetlands were chosen to represent the two habitat types found in DS. Seven of the

wetlands (Fig. 2.1, FDS 1, BS I, BM, MS and UBL; Fig. 2.3, PF1 and PF2) were open

fen, with grasses, sedges and TJpho, and with water flowhg slowly in cbanneis. The

remaining seven wetlands (Fig. 2.1, FDS22, BS2, ES, PP and TC; Fig. 2.3, PF3 and PF4)

were wooded s w q s with d pools of standing water. Table 23 gives the Latitude

and longitude for each of the wetlands studied

Table 2.2. Latitude and Iongitade ofwetlands snidied at Chak Riva Laboratories and Petawawa Forestry lnstitute in Jme and October 1995.

Wetiand Abbieviation Typt Latitude Longitude

Duke Swamp

BuIk Storage Swamp

Toussaint Creek

Pitcher Plant

Upper Bass Lake

East Swamp

B Management

Main Stream

Petawawa 1

Petawawa 2

Petawawa 3

Petawawa 4

DS1 to DSS; FDSl DS6 ta DS25; FDS22

Fen swamp

Swamp

swamp Fen

Swamp Fen

Fen

Fen

Fen

Figure 2.3. Location of the four Petawawa Forestry Institute @FI) wetland sites sampled for water and sediment chemistry and invertebtate collection, November 1995. The map indicates Ch& River t o m limits and PFI boundary. The two fens are PFl and PF2, The two swamps are PF3 and PF4.

----- Town Limits

Lake - - -- PFI Boundary

O 2000 m f Sampling Site , ,

2.2.2 Water and Scdimeot Chemistry

Water and Sediment chexnisîry was coîiected fbm ûctober 23 - 27,1995. The

water and sediment collection was Camed out in the same way as desaibed for the sprhg

collection, with some exceptions. At each sampling site, duplicate sets of 9 containers

were filled wîth surface wata h m the wetland- Except for the use of a s m a k mesh

filter (54 pl, the sampling procedure for the foUowuig aaalyses was carrieci out as

demibed for the s p ~ g regime: conductivity, colour, hardnesq W. C, Si, Ca, Mg, Na,

K, F, Ci, S04, annnonia and ammonium, nitrate and nitrite, TKN, pH. allcalinity, TP,

DIC. Fe, Mn, and trace metals. One 4-L container was nIled for analysis of radionuclide

activity.

At each site, two 125-mL polyethylene bottles were used to sample the sediments

for organic carbon content, radionuclide activity, and trace metals. Sediments were t h

fiom the top 5 cm and excess water was removed h m the sample. Sediments were

stored in a deep fhze until analysed. Sediment analyses were perfiomed at AECL,

Chalk River Caboratories.

2.2.3 Invertebrate Collection

Artificial Substrates

Due to substrate heterogeneity and the shailowness of some of the water bodies,

neither a corer nor a dip net was suitable for invertebrate sampling in alî of the wetlands.

To staaâardise the sample unit for aü the wetlands, an &cial substrate was chosen.

Three potential substrates were tested in a smaii wetland near Trent University,

Peterborough, Ontario: atüïcial tmf; mop hea&, and burlap sackingg Two of each of the

substrates were submaged in a w e t i d and aüowed to be colonisai for tbree weeks,

after which they wëre co11ected and thoroughly nnSed to remove the invertebrates. Of

the three substrates, the burlap sacking was easier to submerge and also easier to remove

the invertebrates h m afta collecting. Invertebrate sarnples h m each substrate were

qualitatively similar so the burlap was chosen for its ease of use.

The burlap substrates were assemb1ed by foldïng 100 cm x 30 cm strips of buriap

into 25 cm x 30 cm rectangles, Smaii sand-filied cotton bags were tied with jute t e e to

the four corners of each burIap sampkr to keep the sampler in place. Ten substrates were

set in each of the 14 wetlands on August 29, 1995- The burlap substrates were leR to be

coloniseci for two montlu. In a review by Rosenberg and Resh (1982) of the use of

artificial substrates to sample khwater benthic macroinvertebrates, reported lengths of

time to reach population equilibrium were less than 49 days, usuaiiy less than 30 days.

However, the studies examined were almost excluçively in rivers or streams, a variety of

measures of population were included, and a variety of mirent substrates were used.

Findlay et al. (1989) found colonisation of ceramic t i la by chironomids in a fieshwater

wetland reached equilibrium after less than two weeks.

M c i a l substrates were cokted h m October 23 - 27,1995. Each substrate

was collecteci by grabbing it with a hook on the end of a b m m handle and slipping a

nylon mesh bag (pantyhose) stretched over a fishing net under the substrate. Tying off

the legs of pantyhose and using the upper portion to enclose the substrate fonned the

nylon mesh bag. Each bag enclosed one substrate. The substrates were then taken to the

field station for rinsing. Each substrate was thoroughly rked in three consecutive

buckets of tap water. The sample was combined and wncentrated by sievïng the three

buckets thmugh 2Oû-p mesh. Spnples were nxed in 4% formalin for 2-3 days,

and then rinsed and preserved in 75% ethanol Sarnples were stained with a mixture of

water-soluble Eosin B and Biebrich Scarlet and handsortd

Sorting

Because uuequal numbem of substrates were successfidly retrieved, and to cut

down on the processing time of the samples, four sites were completely sorted and the

abundance of invertebrates wliected at each of these sites was used to calculate the

required number of samples. The following equation for the estimation of requùed

sample size formulated by Morin (1985) takes into account both sampler size and

aggregation of benthos:

Where: n = sample size = mean density

Q = size of sampler p = precision of estimate

The estimated sample size calcuiated fiom the mean abundance at each of the four

sites ranged from 5-6 for a 20% precision level, that is a sample of 5-6 replicates would

estimate the mean within 20%. A precision level of 20% is satisfactory for benthic

macroinvertebrate communities, which are typicaily highly aggregated (see Downing

1979, Morin 1985, Resh and McEhvy 1993)-

Subsampïing

Due to the high abundance of Bivalvia, Oligochaeta, Amphipoda, Isopoda,

Chironomidae and Ceratopogonidae in some of the samples, subsampling was carried out

when there were more than 200 hdivïduais per sample. The subsampling procedure was

cmïed out by evenly spreading the invertebrates over the bottom of a 4 cm x 6 cm

rectanguiar dish that was marked with 0.5 cm gridlines. Invertebrates were picked h m

0.25-cm squares, whose X and Y co-ordiaates were generated h m a random numbers

table, until at least 50 individuais were chosen (Gimwc 1994).

2.3.0 Data Anaïysis

2.3.1 Water and Sediment Data

Of the 15 sites sampled in DS. one @S8) gave unreliable water chemistry fesults;

therefore it wes excluded h m absequent analyses. Preliminary inspection of the Duke

Swamp SUTface water and sediment chemistry panuneters by a Pearson Roduct-Moment

correlation matrix tevealed a high degree of correlation between the two, therefore they

were combined for subsequent analyses. Due to the many chernical parameters and the

correlations between thern, a p ~ c i p a l components analysis @CA) was carrieci out to

reduce the number of original variables. The purpose of PCA is to fkd combinations of a

number of variables, providing a smaller number of uncorrelated variables that will

accolmt for most of the variation in the original variables @lady 1986).

To meet the assumptions ofmultivariate nomality and lïnearity for PCA, biplots

of al1 possible combinations of the variables were examined for linearity and the Shapim-

Wilks W test of nonnallty was perfomed on each of the variables (StatSoft Inc. 1995).

When necessary, vaxiables were log@) or log(x+l) transfomed.

Of the 14 wedands sampled in the fa two (BM and FDS22) were not analysed

for water chemisûy by OMEE and therefore were dropped h m M e r statistical

analysis. A prelimùiary analysis of the fd wetland water and sediment chemistry

variables by a Pearson Roduct-Moment correlation maîrix showed correiations.

However, there was littie correlation between wata parameters and sediment parameters,

therefore they were analysed as separate matrices. A PCA was carrieci out on each of the

water and sediment chemistry matrices. The Sediment chemistry data were available for

aii of the 14 wetlands, but to aid cornparisons between the water and sediment chemistry

analyses, oniy the 12 wetlands for which water chemisûy data were available were used

in the sediment chemistry analysis.

Prïor to pedorming PCA, each variable was standardlsed to 2-scores by

subtracting the mean and dividing by the standard devïation. PCA was carried out using

correlation matrices of 2-scores. Althougb the number of sites (14) is much smailer than

the number of chemical variables (35). Legendre and Legendre (1983) indicate that the

nrst few eigenvalues are robust to this violation. Each chemical parameter is assigned to

one component based upon the absolute value of its loading being greater than or equal to

0.5.

The Broken Stick model (Legendre and Legendre 1983) was used to determine

the non-trivial axes. This method identifies the number of components that explai. any

more variation than expected by chance (Legendre and Legendre 1983). Though there

are a varïety of stopping d e s for detemunùig the number of components to retain in a

PCA, Jackson and Harvey (1995) suggest that the Broken Stick model is the most

consistent.

For each of the wetland sites, the following ten invertebrate metrics were

calculateck

Total invertebrate abundance - totd number of individuai invertebrates at each site

Taxa richness - number of taxa identïfïed at each site

F d y richness - number of families identifsed at each site

Trichoptera nchness -number ofTrichoptera taxa identifÏed at each site

Chironornid n c h s - number of Chironomid taxa identifid at each site

% Chironomidae - Chbuornid fiaction of the totai number of invertebrates

% Tanytarsini - fhction of chironomids identifid as Tanytarsioi

% Chironomini - &action of cbironomidS identifid as Chironomini

% Oligochaeta - fbction of invertebrates identified as Oligochaeta

Shannon's H' diversiîy index

Each of the invertebrate taxa was assigned to a hctional feeding gcoup as

outlined in Merritt and Cummins (1984). Each of the taxa was assigned to one of five

groups (shredders, scrapers, collectors, predators, or macrophyte pieners) based upon

their hown ecology (see Appendices 2 and 4). Taxa for which little information was

available were desigxîted as unknown.

The invertebrate data were log (x + 1) transfomed and analysai by

Correspondence Anaiysis (CA). CA is simila to PCA, providing a smailer number of

derived variables that explain most of the variation in the original variables, but uses a

chi-square measure of association rather than major-axis regcession (Jongman et al.

1987). Three separate CAS were carrieci out on invertebrate data h m each sampling

period. The b t was canïed out on 95% of the taxa by abundance, identified to the

lowest taxonomie level. The second CA was canied out on di the taxa identifseci to the

f;lmily level The third CA was carrieci out on the fiinctiond feedhg groups.

The fa11 invertebrate data h m the 14 wetlands was analysed m the same way

mth three separate CAS. Each CA was carrieci out on the data for ail 14 wetlands and, to

enable correlation analysis with the chernkîry data, CAS were ;th ccatculated for 12

wetlands-

2.3.4 Biota - Environment Relationship

Pearson and Spearman comlation coefficients were calculated between each of

the invertebrate metncs and the non-trivial principal components o f the water and

sediment chernid variables- As weil, for each of the CAS, Pearson and Spearman

correlation coefficients were calculated between the retained CA axes and the non-trivial

principal components of the chexnical variables. To correct for the number of correlation

coefficients calculated for metrics and CAS the sequential Bonf in i method was applied

(Rice 1989).

A backward stepwise multipb linear regression (MLR) was nui for each

invertebrate metric and retained CA axis as the dependent variable and the non-trivial

principal components as the independent variables. Alpha-to-enter and alpha-to-remove

values were set at 0.05.

3.1.0 Drike Swamp

3.1.1 Weüand Chemistry

Thuty-five parameters were included in the principal components arialysis (Table

3.1). Only the nrst three components (Table 3.21, accounturg for 64% of the variance,

explain any more variation than expected by chance based on the Broken Stick method

(Legendre and Legendre 1983). However, since the tnchlomethene and 1.1-

dichloroethane represent the plume-impacted sites with the highest concentrations of

trace organics and do not Ioad highly on the hrst three components, the fourth component

is also included. The four principal componmts account for 73% of the total variance.

Most of the variation for the fint component was explained by Cl, conductivity,

base cations (Mg, Na, Ca, Ba) and tritium ( 3 ~ having a strong positive influence and

sediment Cr having a strong negative influence (Table 32). The fen sites @SI to DS5)

separate nom the swamp sites @S6 to DS25) along PC1 (Fig. 3.1). the former being

located towards the negative end of the vector, the latter sites along the positive end.

Conductivity generally separates the two habitat types. nie second component explains

23% of the variance, and is positively influenceci by pH, alkalinity and DOC, and

negatively by Mn, Fe and Al (Table 3.2). PC2 clearly deheates the swamp sites closer

to the entrance of the plume into the swamp (Fig. 3.1, DS22, DS25, DS21, DS6. DS7)

compared to ai l other sites (Fig. 3.1, DS9, DSlO, DS23, DS24). The chemical parameters

that b a t explain the variation of the third component are S04, K, nitrate plus nitrite, and

DO on the negative end, and F and TP on the positive end (Table 3.2). The fourth

component represents increased Si04, sediment Mn and "'CS on the positive end and

trîchloroethene and 1.1-dichloroethane on the negative end mie 3.2).

3.1.2 Invertebrate Metrics

Ten invertebrate metrics were measured in Duke Swamp (Table 3 -3). Overd,

mean numbers for chironomid metrics were higher in fen habitats relative to swamps.

Means for H' were similar between habitats.

Pearson (pacametric) and Spearman (nonparametric) correlation coefficients

between DS invertebrate and PC metrics for water and sediment chernical variables

(Table 3.4) generally agree weU with each other. Invertebrate abundance and PC2 show

a high positive Pearson conelation coefficient (0.74) as well as a high positive Speannan

conelation coefficient (0.77) that is si@cant 0, S 0.05). PC2 appears to be correlated

with al1 the invertebrate metrïcs, except %Chironomidae, though none of the Pearson

correlation coefficients are signincant, The difference in correlations between the two

tests is likely the result of low sample size. AU the richness measures (TaxaR, Fard%,

TnchR, and ChirR), as weil as H' diversity, show a positive correlation with PC2. Of the

percent composition metrics, only %Tannytarsini is positively correlated with PC2, while

%Chuonornini and %Oiigochaeta show a negative correlation with that component.

Table 3.1. Means, ranges and transformations for water and sediment chernical parameters included in PCA of Duke Swamp environmental variables for the sites DS 1 to DS7, DS9, DS10, and DS2lto DS25.

Sediment: Carbon 14 mq.g-' C) Cesium 137 (Bq-g-') Carbon (%) Cr (mg.kg-') Fe (mg.kg-') Mn (mg.kg-') Pb (mg.kg-')

Table 3.2. Loading of Duke Swamp water and sediment chernical parameters ont0 the principal components h m a PCA of a comIatioa mat&- Scores L 1 0.5 1 are underlined.

Chloride Conductivity Magnesium Sodium Sediment Chromium Calcium Barium Tritium Ammonia + Ammonium Sediment Lead Lron zinc Strontium Dissolved Oxygen PH Alkalinity Manganese Dîssolved Organic Carbon Sediment 14carbon Sediment Zinc Aluminum Colour Sulphate Potassium Fluoride Nitrate + Nitrite Silicate Sediment %Carbon 1, l -Dichloroethane Sediment Iron Sediment '37~esium '37~esîum Total Phosphorous Tricldomethene Sediment Manganese

Figure 3.1. Graph rnatnx of principal cornponent scores for the four principal components h m a PCA of a correlation matrix of Ddce Swamp water and sedunent chemistry for the 14 study sites for which complete water chemistry data was available. DSI to DSS represent fens (O); DS6 to DS25 represent swamps (4 -

Table 3.3. Meam and ranges of invertebrate metrics for Duke Swamp qarated into fen (n=5) and swamp @=IO).

Fen SV Mean Min Max Mean Min Max

Abundance 21 -0 6 41 233 6 64 Taxa Rï~hness 17-8 8 28 18-4 10 29 Family Richness 9.4 6 12 13.6 7 19 Trichoptera Ricimess 7.2 2 12 4.4 O 10 Chironomid Richness 2 2 O 4 0.5 O 2 %Chiton&dae 14-7 0.8 53 -9 8.3 O 23.3 %Tanytarsini 423 3.1 91-6 8.8 O 26- 1 %Chironomini 149 O 46-6 92 O 30-1 %Oligochaeta 9-4 1.9 17.4 14-0 O 34.0 H' diversity O S O. 19 0.75 0.5 0.32 0.73

Table 3 -4. Pearson and Spearman correlation coefficients between Duke Swamp Uivertebrate meûics and the principal components of hiLe Swamp water and sediment chemical variables (n=14; five fens and nine swamps combined).

Pearson Spearman

PC1 PC2 PC3 PC4 KI PC2 PC3 PC4 --

Abundance -0.10 0.74 -0.13 -0.07 -0.17 0.77' -021 025

Taxa Richness -0.18 0.67 -0.11 -0.02 -0-16 0.67 -0-14 0.24

Family Richness 0.27 0.71 0.04 -0-14 0.26 0.60 0.02 -0.10

Trichoptera Richness -0.48 0.45 -0.21 0.02 -0.45 0.59 -0.33 0.36

Chir0110mid R ~ C ~ I I ~ S S -0-43 0.53 -022 -0.07 -0.45 0.59 -033 0.36

%Chironomidae -0.56 0.07 -0.21 0.23 -0.46 0.3 t -028 0.45

%Tanytarsini -0-13 0.57 0.09 -0.14 -0.18 0.55 -0.05 0.21

%Chironomuii 4-04 -0.64 0-55 -0.05 0.12 -0-66 0.58 -0.40

%Oligochaeta 0-13 -0.54 0.62 -0.09 0-12 -0.66 0.58 -0.40

H' diversity -0.33 0.57 -0.14 0.13 -0.32 0.65 -0.28 0.35

* p 1 0.05; Bonférroni adjusted

A backwatd stepwise MLR on hvertebrate metriCs with the four PC's retained

PC2 (pH, aikaiinity, DOQ as a predictor for 6 of the invertebrate metrics while PCL

(conductivity) and PC3 (nutrients) were each retaineâ for only one flab1e 3.5). PC4

(solvents) was not retained as a pmiictor for any metric. None of the components were

retained for Trichoptera Riclmess, chuonornid Richness, or %Oligocbaeta The model

for invertebrate abundance retained PC2 and explained 50% of the variation (Table 3.5).

PC2 was also retained- in the Taxa Richness, F d y Richness, %Taq&mmr - *

, a d

H'diversity regrasion models, which explained Wh, 46%. 27%. and 27% of the

variances, respectively. The model for %Chironomini retained PC3 as weU as PC2,

explaining 65% of the variance. The model for %Chironomidae retained PC1, explaining

26% of the variance.

Table 3 .S. Summary of a backwards stepwise multiple linear regression of invertebrate meûics on principal components h m a correlation mat& ofwater and sediment chemisûy parameters of Duke Swamp.

Standardised Regressîon Coefficient @-value) R'~ ANOVA

Metric PC1 PC2 PC3 PC4 P

Abundance 0.74 (0.003) 0.50 0.003 Taxa Richness 0.67 (0.009) 0.40 0.009 Family Richness 0.71 (0.005) 0.46 0,005 Tnchoptera Ricbness - - Chironomid Richness - - %Chironomidae -0.56 (0-04) 0.26 0.04 %Tanytarsini 0.57 (0.03) 0.27 0.03 %Chironommi -0.64 (0.003) 0.54 (0.007) 0.65 0.001 %Oligochaeta - - H' diversity 0.57 (0.03) 0.27 0-03

3.1.3 Correspondenec Anmis

For the first CA on abundance, three axes accounted for 65% of the variance. The

first dimension, with an eigenvdue of 0.540 and representing 30% of the explained

variance, separafes most of the fen sites (Fig. 3.2 A and B, DS1 to DSS) h m the swamp

sites (Fig- 3.2 A and B, DS6 to DS25). Jongman et al. (1987) suggest that axes with

eigenvalues greater than 0.5 separafe the species wek The important taxa for sites DS 1

and DS5 are the chùonomids Pwatendipes sp. and Micmpsectra sp. as weU as the

caddisfly Limnephilo sp. and an unknown caddisfly. (Fig-3.3 A and B). The stonefly

Leuctra sp. is most important for DS3 and the chkonomid Retemîanytarw sp. is most

important for DS2 (Fig.3.3). The second and third dimensions, with eigaivalues of 0.331

and 0.292 respectively, do not provide as wide a separation ofthe taxonomie data as the

fïrst axis (Fig. 3.3 B and C).

The first three axes best represent the CA of the invertebrate data groupeci to the

f d y level dm, accounting for 62% of the variance. Similar to the resuits for total

abundance, the hrst dimension, with an eigenvalue of 0.366, accounts for 26% of the

variance and separates ail the fen sites (Fig. 3.4 A and B, DS1 to DSS) fkom the swamp

sites (Fig. 3.4 A and B, DS6 to DS25). The important taxa for fen sites DS 1 and DSS,

which group togethet, are the Sialidae, Cordulegastridae and the Copepoda, whiie DS4 is

infiuenced by chiranomids, tubincids and planorbids (Fig.3.5 A and B). The stonefly

family Leuctridae is still most important for DS3, but now the caddisfly family

Polycentropidae distinguishes DS2 (Fig.3.5) as compared to the CA of total abundance

(Fig. 3.3). The sccond dimension, eigenvalue 0.260, separates DS2 and DS3 from

DS 1 and DS5 (Fig.3.4 A). On both the first and second dimensions the swamp sites are

groupeci together? but the third dimension, eigenvdue 0.236, spreads them out into three

groups (Fig. 3.4 B and C). Swamp sites DS9 and DSlO form one group separateci h m

the rest of the swamp sites (DS6, DS7, ancl DS21 to DS25) by the influence of the

chaoborids, culicids, and ptychoptends (Fig. 3.5 B and C). DS7, DS6 and DS25 form

another group influenced by the Physidae, Vai~atidae~ Halipfidae, and Glossiphonidae

pig 3 -5 B and C). The iffnaùiing swamp sites @SZl to DS24). as weU as the fen site

DS4, form a group mund the origin, making it dIfficult to interpret the importance of

any of the taxa to these sites-

The CA on ùivertebrate fiuictiod feaiing group was weli describeci by only one

dimension (5 1% of variance), though both the first and second dimensions are depicted in

Figure 3.6. The nrSt dimension (eigenvalue 0.23 1) separates the fen sites h m the

swamp sites, though there is overlap mg. 3.6 A). Shredders and taxa of unlmown

feeding group (Fig. 3.6 B) m d y influence DS 1, DS2, and DS3. Scrapers and

macrophyte piercers influence DS6 and DS25, while the rest of the sites are maidy

iduenced by coktors (Fig. 3 -6 B). The second dimension (eigenvalue 0.106) does not

s eparate sites well.

Pearson and Speman correlation coefficients were calculated between the first

three dimensions h m a CA of DS invertebrate data (95% of total) and the four PC's of

DS water and sediment chernical variables (Table 3.6). CAL appears to be negatively

correlated with PC1 and PC3; CA2 is negatively correlated with PC2; and CA3 is

positively correlated with PC3, However, none of these correlations are significant at the

Bonfmni adjusteci a S 0.05, except the Speamian coefficient between CA1 and PC1. A

backward stepwise multiple linear regression was ~n for each of CAL, CA2, and CA3

with the CA dimension as the dependent variable and the four principal components as

the predictor variables. The d t s are summarised in Table 3.7. As expected h m the

Pearson correlations, the model for CA1 retained both PCl and PC3, explaining 74% of

the variance. The model for CA2 retained PC2, explaining 35% of the variance, and the

mode1 for CA3 retained PC3, explainhg 3 1% of the variance. PC4 was not retained for

any of the CA dimensions.

Pearson and Spcamüin correlation between CA dÏmeLlSi011~ of DS invertebrates

( f d y level) and PCs of DS water and sediment chernicd variables fiable 3.6) showed

that C h 1 was negatively conelated with PC1 and PC3; C&&2 is not correiated with

any of the PCs; and C&& is negatively wmlated with PC2- However, correlations

were not signiscant at the Bonfmni adjusted a 9 0.05. except for Spearman rank

coefficient between C & a and PC2. A backward stepwise multiple linear regression

between CA-1, CAF&, and C&3 and the four PCs showed that CA-I retained

PCl, PC2, and PC3, explaining 77% of the vaziance (Table 3.7). The model for CAF&

retained PC2 and PC3. explaining 57% of the variance. PC4 was not retained for any of

the dimensions-

Pearson and Speannan conelations between CA dimensions of DS invertebrate

functional feeding groups and the PCs of Duke Swamp water and sediment chernical

variables (Table 3.6) showed weak positive correlations between CAwGl and PC1 and

PC2, but these were not signScant (p s 0.05; Bonferroni adjusted). A backward

stepwise multiple hear regression for each CAmoland CAmG2 with CA dimension as

the dependent variable and the four PCs as the predictor variables resulted in CAmGl

retaining both PC1 and PC2 explaining 46% of the variance uable 3 -7).

Figure 3.2. Plots of site scores h m a CA of DS invertebrate data (95% by abundance) showing: (A) the second dimension (CA2) against the k t dimension (CAl); (B) the third dimension (CM) against the second dimension; and (C) the third dimension agaiast the ht dimension. The five feo sites are DS 1 to DSS (@); the nine swamp sites arc DS6 to DS25 (A).

CA1 (Eigenvalue 0.540)

CA1 (Eigenvafue 0.540)

-0.5 0.0 0-5

CA2 (Eigenvalue 0.331 )

Figure 3.3. Plots of taxa scores h a CA of DS invertebrate data (95% by abundance) showïng: (A) the 2nd dimension (042) against the 1st dimension (CAI); (B) the 3rd dimension (CA3 against the 2nd dimension; and (C) CA3 against CAL The foliowing abbdations are used: Muscd = M1c~cuiiunz; Tubif = Tubificidae; Erpob = Erpobdellidae; B-moxph = Bittacomorpha; L h e = Limnephiius; Lim- 0th = unidentifid Limnephilidae; Chiron = Cliimnomus; Edûe = E ~ k f @ ~ e l Z a ; H-tany = Heterotanytmsus; H-triss = Hererotriisociaditcp; P-tend = Paratendees; Microp = Micropsecîra; Proclad = Procludius; Then = Thienernannenyïa; Psect = Psectrocladius.

CA1 (30% of variance)

CA1 (30°h of variance)

-2.0 -1 .S O -0.5 0.0 0.5 1.0 1.5 2 0

CA2 (1 8% of variance)

Figure 3.4. Plots of site scores fiom a conespondence analysis of Duke Swamp invertebrate data (famiiy level) showing: (A) the second dimension (Ch&) agak t the fkst dimemion (CA-1); (8) the third dimension (CAcm3) against the second dimension; and (C) the third dimension against the first dimension. The five fen sites are DSl to DS5 (@); the nine swarnp sites are DS6 to DS25 (4-

Figure 3 .S. Plots of taxa scores h m a CA of DS invertebrate data ( f d y level) showing: (A) the 2nd dimension ( C A F ~ ) against the 1st dimension (Chm1); (B) the 3rd dimension (CAF&) against CAF, 1 ; and (C) C A F A a g a CAF&. The following abbreviations are use& Valvat = valvatid; Glossi = glossiphonid; Cerato = caatopogonid; Polycen = polycentropid; Chiron = chironomid; Planorb = planorbici; Cordul = comlegastrid; Chaobo = chaoborid; Ptychop = ptychopterid; Ostrac = ostracod; Collem = ColIemboUa; Limne = Limnephilid.

Cka,l (26% of variance)

4 O 1 2 3

CA,=-, 1 (26% of variance)

G-e'-t : aii id

CA&! (19% of variance)

Figure 3.6. Plots of the second dimension (C&&) against the fht dimension (CAFFG 1) h m a CA of DS invertebraie fiinctional feedug p u p data showing: (A) site scores; (B) hctional feeding group scores. The five fen sites are DS1 to DS5 (O); the nine swamp sites are DS6 to DS25 (A).

-1 .O 4.5 0.0 0.5 1 -0

C+,J (Eigenvaiue 0.231 )

-1.0 4.8 -0.6 4.4 4.2 0.0 0 2 0.4 0.6 0.8 1.0 1.2 1.4

1 (51 % of variance) ' 9~43

Table 3.6. Pearson and Spearman com1ation coefficients between the axes h m three CAS of Duke Swamp invertebrates and the principal components h m a PCA of Duke Swamp water and sediment chemical variables (n = 14).

Table 3.7. Summary of a backwards stepwise multiple linear regression analyses of the major dimensions h m co~~espondence analyses of hvertebrate data on principal components fiom a coirelation matrix of water and sediment chemistry parameters of Duke Swamp.

Regcessian Coefficient @-value) R2&j

ANOVA Variable PCl PC2 PC3 PC4 P

3.1.4 Siunmuy of Resiilts for Duke Swamp

A PCA of the DS water and sediment chemistry separates the swamp sites @S6

to DS25), which have higher conductivity measares and higher concentrations of Ci, base

cations, and tritium, h m the fm sites @SI to DS5). The swamp sites closer to the

entrance of the leachate plume @S6, DS7, DS21, DS22 and DS25) also separate k m

the other sites based on thek higher levds of pH, allalinity, and DOC. ANOVA results

suggest that F d y , Trichoptera, and Chironomid richness and %Chùonomidae were

usefirl measmes for detecting ciifferences between fen and swamp habitats. However,

correlation with PCs indicates that total invertebrate abundance, taxa nchness fimily

n'chness, %Tanytarsini, %Chironomini, and H' diversity may be good measutes for

detecthg pH related influence on wetland invertebrate communities. AU these mebics

increased with pH, altralinity, and WC and a corresponduig decrease in Mn, Fe, and Ai,

except %Cbironomini which decreased.

A CA based on organisms mostly identiflecl to genus gave very similar results to

one based on invertebrates identifiecl only to family. Both levels of taxonomie resolution

allowed separation of the swamp sites b m the fen sites. Identincation to the genus level

gave better separation of the swamp sites close to the plume entrance h m the rest of the

sites comparai to taxa identifid to f d y . The CA based on fûnctional feeding group

was only able to detect diffaences between swamp and fen sites. AU the CAS that

distinguished between fen sites @SI to DSS) and swamp sites @S6 to DS25) were

strongly related to conductivity and nutrient status of the sites. The CA that most clearly

separated the plume influenced sites (DS6, DS7, DS21, DS22 and DS25) h m the rest

@S 9, DS 10, DS23 and DS24) was related to pH and alkalinity.

3.2.0 Ch& River and Petawawa Wetlrads

3.2.1 Water Chemistry

Complete water chemistry data were availabIe for only 12 (FDS 1, UBL, PP, BS1,

BS2, ES, TC, MS, PF1, PF2, PF3, PF4) of the 14 wetlands sampld Only radiological

parameters and same anions and cations were measured in B M and FDS22 water

samples. Twenty-five parameters were included in the PCA of the 12 wetlands for which

complete water chemistry data were avdable (Table 3 -8).

M y the nrst two components of the PCA, accounting for 61% of the variance

(Table 3.9), explain any more variation than expected by chance based on the Broken

Stick rnethod (Legendre and Legendre 1983). The ht component (PCI), explainhg

36% of the variance, has 16 chernical parameters with absolute values greater than 0.5.

On PC1 the parameters Zn, hardIless, Ca, conductivity, Sr, K, Cl, Mg, Na, and colour

have a strong positive inauence and DO and pH have a strong negative influence (Table

3.9). The second component is positively Muenced by TKN, Total P, nitrate plus

nitrite, Al, Fe, and S 0 4 and negatively by tritium. The fen sites (FDS 1, BS 1, UBL, PF1,

PF2, MS) and swamp sites (TC, PP,ES, BS2, PF3, PF4) are sepatated, with some overlap,

dong PCL (Fig 3.7). The second axis, PC2, separates the four Peuwawa sites Fig. 3.7,

PF1 to PF4) h m the eight Chalk River sites (Fig. 3.7, D L , TC, FDSl, BS1, BS2, PP,

ES, MS).

Table 3.8. Means, ranges and transfoLmafions for wata chemical parameters included in PCA of 12 wetlauds (FDS1, BS 1, BS2, UBL, TC, ES. PP, MS, and PF1 to PF4).

Table 3.9. Loading of water cbemistry parameters h m the 12 wetlands (FDS 1, BS 1, BS2, UBL, TC. ES, PP, MS, and PFl to onto the two principal components b m a PCA of a correlation matrk Scores L 1 are mderiined.

Pc l PC2 Zinc - 0.868 0,100 Hardness - 0-844 -0.452 Calcium - 0.837 -0,464 Conductivity - 0,802 - -0.582 Strontium - 0,789 -0.483 Potassium - 0-786 0-145 Chioride - 0,784 -0558 Magnesium 0.750 -0383 Sodium - 0.738 4.636 Dissolved ûxygen 4.644 -0.480 Colour - 0,617 - 0.583

PH - -0.576 - -0.505 Manganese - 0.564 0.371 Ammonia + Ammonium - 0.561 0.389 Tritium 0.524 -0.661 Dissolved ûrganic Carbon - 0.512 0.427 Total KjeldahK Nitrogen 0.481 - 0,808 Total Phosphorus 0,412 0.78 1 Nitrate + Niûite 0.048 - 0-702 Aluminum 0 -435 - 0.621 Iron 0.395 0.426 Fluoride 0.403 0.355 Barium 0.461 0.3 10 Alkalinity -0.163 -0.093 Sulphate 0.073 0.251 Silicate 0.320 -0.474

% of Variance Explained 36

Figure 3.7. Plots of principal component scores for PC2 versus PC1 fbm a PCA of a correlation matrEr ofwater chemistry for 12 wetlands. The six fens (0) are Duke Swamp fw (FDS 1). Upper Bass Lake (UBL), Bulk Storage (BS l), , Main Stream (MS), and Pebwawa Forestry Mtute sites PF1 and PF2. The six swamps (A) are Pitcher Plant PP), Bulk Storage Swamp @S2), Toussaint Creek (TC), East Swamp (ES), and Petawawa For~stry Institute sites PF3 and PF4-

B

8 PF4

2 8 I A B

B

P :

. PF2 b ~ 3 1 . a i A

PF1 i APP . 8

a 8

rn MS O ---HI-- * ----O,-: -----.--- -----.-----.

I *-- b . FDSI BS]

A 0 ; ES -1 . a rn A ~ s 2 . B

B A 8 8

I 8

-2 O 1 2

PC1 (36 % of variance)

Sediment chemistry data wem available for ail of the fourteen wetlands sampled,

however, to make comparisons between the water and sediment analyses only data h m

twelve wetlands @Sl, BSI, BS2, UBL, TC, ES, PP, MS, and PF1 to PF4) were u s d

The 10 variables included in the analysis are shown in Table 3.1 O, dong with their

means, ranges, and the transformation used to normalise them There were two non-

trivial axes by the Broken Stick Model. The k t component accounts for 36% of the

variance, with Zn, Pb, Mn, and exerting a strong positive influence, and Cr a

negative innuence (Table 3.1 1). The second component accounts for 32% of the

variance, representing 14c, Ni, and Fe on the positive end and %C and "'CS on the

negative end (Table 3.11). Neither of the axes separates the fens fiom the swarnp sites,

but there appear to be two gcoups with BS2 and UBL as outliers (Fig. 3.8).

Table 3.1 O. Means, ranges and transfonnatioos for sediment chemistry parameters inc1uded in PCA of 12 wetlands (FDS 1, BS 1, BS2, UBL, TC, ES, PP, MS, and PF1 to PF4).

Chernicd Parameter Mean Range Transformation Carbon (%) 28.1 1.55 - 51.7 none Fe (mg.g-') 46.8 2.30 - 245 h3

Mn (mg-') 1.48 0.051 - 13.9 log Zn (mg-g-'1 0.037 0.021 - 0.071 none Cr (mg-g-') 0.062 0.0039 - 037 log Ni (mg.g-') 0.032 0.005 - O. 150 log Pb (mg&) 0.021 0.005 - 0.053 none Carbon 14 C) 0.15 0.039 - 0.366 log Cobalt 60 2.23 O - 20.9 log Cesium 137 ( ~ ~ . g - ' ) 0.71 0.05 - 1.55 log

Table 3.1 1. Loading of sediment chemistry parameters onto the components h m a PCA of a correlation mahix of 12 wetIands (.S 1, BS 1, BS2, UBL, TC, ES, PPy MS, and PF 1 to PF4). Scores 2 1 0.05 1 are underiind

% of Variance Explained 36 32

3.23 Invertebrate Metrics

Pearson and Spearman correlation coefficients showed that PC1 was negatively

correlated with ab~fldance~ ai i four richness measUres, and %Tanytarsini, and positively

correlated with %Chironomini (Table 3.12); none of the Pearson correlation coefficients

are significant. PC2 is signincantly negatively correlated with Taxa Richness (r = -0.78),

Family Richness (r = -0.8 1) and H' divmity (r = -0.83). Trichoptera Richness,

Chironomid Ricbness, %Chironomidae and %Tanytarsini are also negatively correlated

with PC2, and %OLigochaeta is positively conelated with PC2, though these coefficients

are not statistically sigdicant. The Spea~nan coefficients are similar to the Peanon

coefficients.

Figure 3.8. Plots of principal component scores for PC2 versus PC1 h m a PCA of a comlation matrix of sedimmt chernktry for 12 wetlands. The six fnis (e) are Duke Swamp fen (FDSl), Upper Bass Lake (UBL), Bulk Storage @SI), , Main Stream (MS), and Petawawa Forestry M u t e sites PF1 and PF2. The six swamps (A) are Pitcher Plant (PP), Bulk Storage Swamp (BS2). Toussaint Creelc (TC), East Swamp (ES), and Petawawa Forestry Institute sites PF3 and PF4-

-2 -1 O 1 2 3

PC end 1 (36% of variance)

A MLR showed thaî none of the pfedlfedlctor variables were rrt?ined for abundance

or Trichoptera Fable 3.13). BothPCl and PC2 were retained for Taxa Richness @t2& =

0.72), Famiiy Richness = 0.74). and Chironomid Richness w2* = 0.53). PC1 was

ais0 retained for %Tannytarsni @2dj = 0.29) and %Chironomini (jtza = 0.3 1). As weli,

PC2 retained for %Chin,nomidae (Et2* = 0.29). %Oligochaeta w2dj = 0.57). and H'

diversity (R2* = 0.65).

Pearson and Spearman correlation coefficients wae aiso calculateci between each

of the invertebrate metncs and the two principal compoaents h m the sediment

chemistry PCA. None of the coefficients were found to be signifïcant. Bachard

stepwise MLR did not retain either of the sediment components for any of the

invertebrate metrics.

Table 3 -12. Pearson and Spearman correlation coefficients between invertebrate metncs and components h m a correlation r n a e of the water chemistry parameter b m 12 wetlands (FDS 1, BS1, BS2. UBL, TC, ES, PP. MS, and PF1 to PF4).

Pearson speannan PC1 PC2 PCl PC2

Abundance -0.57 -0.05 -0-43 -0.15 Taxa Richness -0.45 -0.78 * -0.46 -0.76 Farnily Richness -0-43 -0.81 * -0.36 -0.80 * Trichoptera Richness -0.49 -0.54 -0.46 -0.62 Chironomid Richness -0.54 -0.63 -0-61 -0.63 %Chironomidae -0.32 -0.58 -0.33 -0.59 %Tanytarsini -0.66 -0.49 -0.65 -0-46 %Chironomini 0.66 0.36 0.71 0.35 %Oligochaeta 021 0-68 0-1 1 0.55 EF diversity -0.24 -0.83 * -0.25 -0-77 * p 5 0.05; B o n f i n l adjusted

Table 3.13. S- of backwards stepwise multiple linear regression analyses of inverîebrate metnca vs. principal component scores h m a PCA of waterchemistry variables for 12 wetlands @?DS1, BS1, BS2, UBL, TC, ES, PP, MS, and PF1 to PF4).

Re-on Coefficient @-value) VariabIe R~* ANOVA

PC1 PC2 P Abundance - Taxa Richness -038 (0.04) -0.79 (0.001) 0.72 0.00 1 Family Ricimess -0.36 (0.04) -0.81 (0.000) 0-74 0.00 1 Trichoptera Riclmess - Chircmom.id Richness 4-47 (0.05) -0.63 (0.01) 0.53 0.0 1 %ChÏronomidae -0.60 (0-04) 029 0.04 %Tanytarsini -0.60 (0.04) 029 0.04 %Chnonomini 0.61 (0.04) 03 1 0.04 %Oligochae ta 0.78 (0.003) 0.57 0.003 H' diversity -0.83 (0.001) 0-65 0.00 1

33.4 Correspondence Anriiysis

For the first CA, on abundance of 95% of the taxa identifid to lowest taxonornic

unit, three axes account for 65% of the vanance. The first dimension, with an eigenvalue

of 0.569 and representing 30% of the explained variance, separates the wetlands into two

groups, but not the fens (Fig. 3.9, FDS 1, BS 1, UBL, PF 1, PF2, MS) h m the swamps

(Fig. 3.9, TC, BS2, ES, PP, PF3, PF4). The taxa that are associated with the separation

almg CA1 are the chironomid Chironomus sp. and the Tubificidae, as weU as the

chironomid Procludius sp. and the Sphuenum sp. and Pkidiwn sp. clams (Fig.3.10). The

second dimension, eigenvalue 0.460, mainly separates the fen BS 1 h m the rest of the

sites, based on the abundance of the amphipod Hyaiella sp., the stonefly Somatochloru

sp. and the mail Gyruulus sp. at that site. The third dimension, eigenvdue 0.418,

separates the fen PF1 h m the r a t of the sites, mainly due to the abundance of the

may£iy Lqtophlebiu sp-

The nrSt three axes also best represent the CA of the invertebrate data groupeâ to

the f d y leveI, accoimtmg for 56% of the variance. The first dimension, with an

eigenvalue of 0.337, accounts for 23% of the variance aud separates the fen BS 1 h m the

other sites (Fig. 3.1 1). The separation of BS 1 dong the fbt dimension is driven by the

abundance of taxa in the m e s Baetidae (mayflïes), Aeshnidae and Cordulegastndae

(dragonflies), Talitridae (amphipods), Physidae and Planorbidae (mails), and Haliplidae

(beeties), as weli as the Zygoptera (damselflies) (Fige 3.12). The second dimension,

eigenvalue 0.302, separates the fen MS and the four Petawawa wetlands (PF1 to PF4)

h m the rest on one end, as weli as the swamp TC on the other end (Fig. 3.11). The

separation of TC on the second dimension (Fig. 3.12) is due to the abundance of

Polycentropidae and Lepidostomatidae (caddisfiies), Dixidae and Empididae (dipterans),

and EphemereUidae (mafles). Hydropsychidae (caddisfies), Corydalidae (dobsonfiies),

Erpobdellidae (leeches), Tubificidae (worms), Leptophlebidae (ma*=), Culicidae

(mosquitoes), and Chrysomelidae (beetles) drive the separation of the Petawawa sites and

MS fiom the rest. The third dimeosion, eigenvalue 0.185, separates the swamp PP h m

the other sites based on the abundance of the beetle family Scirtidae (marsh beetles) Figo

3.12)-

The CA on invertebrate fimctionsir feeding group was weli described by only one

dimension (57% of variance), though both the fïrst and second ~ e n s i o n s are depicted in

Figure 3.7. The fust dimension (eigcnvalue O. 173) separates the four Petawawa sites and

the fen MS h m the other sites (Fig. 3-13), mauùy due to the predators and collectors.

The first dimension also separates the fen BS 1 based on macmphyte piercers and scrapers

(Fig. 3-13). The second dimension (eigemralue 0.084) Qes not separate sites weli (Fig.

3-13)-

Pearson and Spearman correlation betweetl the CA dimensions of invertehate

data (95% of total) and the two PCs of water chemistry variables (Table 3-14) showed

that CA1 is positively comlated with PCl and PC2, though only the conelation with

PC2 is sigaincant. CA2 and CA3 are not codated with either PC. A bachard

stepwise MLR between CA1, CA2, and CA3 (dependent variables) and the two PCs

(predictor variables resulted in CA1 retainin - -

g PC1 with a coefficient of 0.34 (p = 0.001)

and PC2 with a coefficient of 0.67 (p = 0.000). explaining 91% of the variance. Neither

of the PCs was retained for CA2 or CA3.

Two of the wetlaads influenced by LLRW management sites, Duke Swamp

(FDS22) and B Management (BM), were not included in analysis of the environmental

data due to the lack of complete water chanistry data. Likewise, FDS22 and BM were

excluded nom the invertebrate data analysis to aid in cornparison between the biota and

environmental parameters. To examine a possible influence of LLRW on the

macroinvertebrate community structure in BM and FDS22, ordination by CA was

performed on a mahix including ail 14 wetlands (Appendix 7) for comparison with an

ordination on only 12 wetlands (FDSl, BSl, BS2, UBL, TC, ES, PP, MS, and PF1 to

PF4). CA plots for the 12 wetlands were very similar to the CA plots for the 14

wetlands; BM and FDS22 do not stand out fmm the rest of the wetlands (FDS 1, BS 1,

BS2, UBL, TC, ES, PP, MS, and PFI to PF4) on any of the nrst three componentsts

Figure 3.9. Plots of site scores h m a conespondence aualysis of invertebrate data (95% by abundance) nOm 12 wetlands showing: (A) the second dimension ( C M ) against the nrst dimension (CAL); (B) the third dimension (CA3 a g d the second dimension; and (C) the third ctimension a g a the fkst dimension. The six fens (O) are Duke Swamp fen (FDSl), Upper Bass Lake (UBL), Buik Storage (BS l), Main Stream (MS), and Petawawa Forestry Institute sites PF1 and PF2. The six swamps (A) are Pitcher Plant (PP), Bulk Storage Swamp (BS2), Towaint Creek (TC), East Swamp (ES), and Petawawa Forestry uistitute sites PF3 and PF4.

Pn a

UBL : @PF1 MS PF3 pF4

-....*.*...*.....--.---*---.---.-*-*-**-~--*.,"*.-.*.*.*-..*-*. i"""".""".

CA1 (Eigenvalue 0-569)

6.5 0.0 0.5

CA2 (Eigenvalue 0-460)

Figure 3.10. Plots of taxa scores h m a correspondaice analysis of invertebrate data (95% by abundance) b m 12 wetlands showing: (A) the second dimension (CM) against the first dimension (CA1); (B) the third dimension (CA3 against the second dimension; and (C) the third dimension against the nrst dimension. The foilowùrg abbreviations are used: Somato = Somatochlora; Kreno = Krenopelopia; Lepto = Leptophlebia; Psectro = Psectrocludius; Thien = Thienmunnemyta; Eukie = Eukï~erïefla; H-triss = Heterotn3îsocladiu.s; P-tend = Pwatendipes; Soyed = Soyedina; Polycen = Polycentrp; Paipom = Paipomyia; Pisid = Pisidim; Proclad = Plocladius; Chiron = Cikironornus; Zavrel = 2àvrelemyiiz; Cyclop = Cyclopoida; Sphaer = Sphaerium; M-psect = Micropsectru; Lumbn = Lumbriculidae.

- -

-20 -15 -1.0 a.!j 0.0 OS 1.0 1 5 20

CA1 (29?h of variance)

CA2 (1 9% of variance)

Figure 3.1 1. Plots of site scores h m a correspondence analysis of invertebrate data ( f d y level) firom 12 wetlands showing: (A) the second dimension (CAF&) against the nrst dimension (CAF,~); (B) the third dimension (CAF,3) against the second dimension; and (C) the tbird dimension against the h t dimension. The six fens (O) are Duke Swamp fa (FDS l), Upper Bass Lake (UBL), Bulk Storage @SI), Main Stream (MS), and Petawawa Forestry Institute sites PFL and PF2. The s u swamps (A) are Pitchet Plant PP), Bulk Storage Swamp (BS2), Toussaint Creek (TC), East Swamp (ES), and Petawawa Forestry Institute sites PF3 and PF4.

-0.5 0.0 0.5

CAFI& (Egenvalue 0.302)

Figure 3.12. Plots of taxa scores h m a CA of hvertebrate &ta (family) h m 12 wetlands showing: (A) the 2nd dimension (CAF&) against the 1 st dimension (CAFual); (B) the 3rd dimension (C&,3) agaïnst the 2nd dimension; and (C) CAFA against CAFmI. The foliowing abbrewiations are used: Aesh=&d; Talit--talitd; Zygopt'Zygopttffd; Gammar=gafnmed; Cordul=corduiegastrid; Planor=planorbid; Glossi=glossiphonid; Nemamematode; Lepto=leptophlebiïd; C ~ ~ r y d a i i d ; H-psych=hydmpsychid; Erpob=erpobdeIlid; Tubin=tubincid; Chryso=chrysomelliid; Sphaer= sphaexid; Molann=molannid; H- phiiïd=hydmphilid; Hydra=Hydrace Lepid~lepidostomatid; Ephenmqhemerellid; Polyce=polcentropid; Ptycho=ptychopterid; Lymn=lymnaeid; Nernor- nemourici; Gamma~=gammarid; Ostramstracod.

CAF-1 (23% of variance)

I - -20 -1.5 O 0.5 0.0 0.5 1.0 1.5 20

CL.2 (21 % of variance)

Figure 3.1 3. Plots of the second dimension ( C h G 2 ) against the first dimension (CAwGl) h m a correspondence analysis ofinvertebrate functional feedùg g r o g data fiom 12 wetlands showing: (A) site scores; and (B) hctional feeding group scores. The six f a (a) are Duke Swamp fm (FDSl), Upper Bass Lake (UBL), Bulk Storage (BS l), Main Stream (MS), and Petawawa Foresîry Lastitute sites PF1 and PF2. The six swamps (A) are Pitcher Plant (PP), Bulk Storage Swamp (BS2), Toussaint Creek (TC), East Swamp (ES), and Petawawa Forestry htitute sites PF3 and PF4.

-1 .O -0.5 0-0 0.5 1 *O

CbFG1 (Eigenvalue O. 1 73)

-1 -5 -1 .O -0.5 0.0 0.5 1 .O 1.5

C&,,1 (57% of variance)

Pearson and Speamian correlation betwee~l the CA dimensions ofinvertebrates

(family level) uad the two P O ofwater chemistry variables (Table 3 -14) revealed that

CAFsml and CAFA are not correlateci with either PC. CA&2 is significantly positively

comdated with Pa. A backward stepwise MLRbetween Chml, CAF&, and CAF-~

and the two PC's showed thaî Fam2 retained PC2 with a coefficient of 0.48 @ = 0.002),

e x p l d g 60% of the variation. As expected, neither PCl nor PC2 were retained for

CAFmI a d CAF&.

Pearson and Spearman coefficients between the CA dimensions of invertebrate

hctional feedùig p u s and the PCs ofwater chemistry variables (Table 3.14) reveal

littie correlation between CAFFGl with either PC. CA-2 is negatively, but not

signincantly, correlateci with PC2. A backward stepwise MLR for each of CAm~land

CAmo2 with CA dimension as the dependent variable and PCs as the predictor variables

resulted in neitha PC being retained for e i t k or CAF&.

No signficant correlation was found betsveen any of the CA dimensions and the

PCs sediment chemistry vanables (Table 3.15). A backward stepwise MLR did not

retain any of the PCs.

Table 3.14. Pearson and Speaxman correlation coefficients betweeri the three dimensio~~~ fimm CAS of the invertebratë daîa aud principal components fiom a PCA ofa correlation rnatrix of the water ch- parameters h m 12 ofthe 14 wetlands.

Ptarson sptarman PCI PC2 PC1 PC2

CAL 0.50 0.83 * 0.42 0.76 * CA2 4-17 0-17 -0.28 0.45 CA3 -0.47 0.04 -0.33 -0.17

Table 3.15. Pearson and Spearman correlation coefficients between the three dimensions fiom CAS of the kvertebrate data and the principal wmponents h m a PCA of a correlation matrix of the sediment chemistry panuneters h m 12 of the 14 wetlands.

Pearson spearman PCl Pc2 PC1 PC2

CA1 -0.28 -0.13 -0.47 -0.17

CAFFG~ 0.05 -0.32 0.14 -0.38 C&d 0.35 0.22 0.40 0.3 1 * p ( 0.05; B o n f i i adjusted

Visual m o n of Fig. 3 9 suggests that BS 1 and PFI may be outliers, possibly

exerting undue influence on the regnssion analyses. To examine this possibility these

two sites were removed h m the data matrix and PCA, CA, and MLR were wned out

on the remaining 10 sites. Neither the PCA nor the CA showed any substantial

deviations h m the ordinations with 12 sites- A backwards stepwise MLR betwee~l the

new CA dùnensiom and the PC's based on 10 sites gave similar results as previously,

with CA1 retaining PC1@ = 0.36, p = O.O3), and PC2 (p = 0 . 8 7 , ~ = O-ûûû), explauung

85% of the variance (ANOVAp = 0.001). Again, CA2 and CA3 did not retain either of

the PC's.

Inspection of Fig. 3.11 shows that BS1 and PP are outliers, and as above, these

sites were removed h m the data marrix and PCA, CA, and regression analyses were

canied out on the duced data set. The PCA was essentially the same as with the 12

sites, but with BS 1 and PP removed the second dimension of the original CA (CAFAM2)

became the h t dimension (CAFml) of the new CA @gs. 3.14 and 3.15). The MLR

now indicates that CAFM1 =tains PC2 (p = 0.78, p = 0.007) explaining 56% of the

variance (ANOVAp = 0.007). Obviously, this srnail change will not influence inferences

drawn Erom the results.

Figure 3.14. Plots of site scores h m a comespondence analysis of invertebraîe data (family b e l ) h m 10 wetlands showing: (A) the second dimension (CkM2) against the nrst dllnension (CAFM1); (8) the third dimension (CAFAM3) against the second dimension; and (C) the third dimension against the first dimension. The five feas (a) are Duke Swamp fai (j?DSl), Uppa Bass Lake (UBL), Main Stream (MS), and Petawawa Forestcy Institute sites PF1 and PM. The five swamps (A) are Bulk Storage Swamp (BS2), Toussaint Creek (TC)? East Swamp (ES)? and Petawawa Forestry Iastitute ates PF3 and PF4.

C&aml (Eigenvalue 0.323)

CIC,,l (Eigenvalue 0.323)

-12 2 -1 2 4-8 4 4 0.0 0.4 0.0 1 1

CA,=& (Eigenvalue 0.21 3)

Figure 3.15. Plots of taxa scores h m a CA of invextebrate data ( f d y ) h m 10 wetlands showing: (A) the 2nd dimension (ChM2) against the 1st dimension (CAFMl); (B) the 3rd dimension (cAFAM3) a g a the 2nd dimension; and (C) CACm3 against C h m 1. The following abbreviations are used: Asel=ase1lid; Capn=capnüd; Cbry~hrysomellid; CoU=Collembola; Cordul=corddegastrid; C ~ ~ o r y d a l i d ; Culic=culici& id;s-dytiscid; Empid=empidid; Ephe=ephemereilïd; Epo=apobdellid; Gam=gammarid; Glos=glossiphonid; Halip=haliplid; H-phil=hydrophiliid; H-psyc=hydropsychid; H-til=Hydn,ptifid; Kydra=Hydracarina; Lepid=lepidostomatid; Jkpt+ptophlebiid; Leuci-leuctrid; Lumb=lumbricuiid; Lymn4ymnaeid; Mola-olaanid; Naid=naidid; Nema=nematode; N e w nemourid; Ostra=ostracod; Phryg=phrygnaid; Plan=planorbid; Poly=polce~1tropid; Ptyc=ptychopterid; Sial=sialid; Sph= sphaerid; Tab= abanid; TaliMalitrid; Tipul=tipulid; Tubi=tubificid.

C+&1 (29% of variance)

CA,-J (16% of variance)

Similar to the DS study, PCA of water chemistry separates fens (FDS 1, BSl,

UBL, MS, PF1, PF2) k m the swamps (BSZ, ES, PP, TC, PF3, PF4), which are higher in

conductivity, Zn, Sr, CI, base cations, and colour, and lower in pH and DO. The

Petawawa wetlaads (PF1 to PF4) tend to be disthguished h m the CRL wetiands PSI,

BSZ, BS2, UBL, MS, PP, ES, TC) based on theV higher levels of nutrients m, TP, and N a t Na) as weli as Fe, Ai, and S04, and lower tritium levels- Taxa richness,

famiy nchness, chironomid nchness, and H' diversity were negatively influenced by

nutrient levels in the wetlands, while %Oligochaeta is positively iduenced. High levels

of conductivity, base cations, Zn, Cl, and Sr, combined with Iow DO and pH also exerted

a negative Muence on the richness measures ma, F d y , Chitonornid), as well as

%Tanytarsini, and a positive influence on %Chironomini. hvertebrate richness and

diversity tended to be lower in the high nutrient, low pH, oxygen depleted sites.

Simila, to the DS results, a CA based on identification to the genus level (for the

most part) gave very similar results to one based on invertebrates identifIed only to the

family level. Aowever, neither level of taxonomie resolution provided good partitioning

of the swamp sites h m the fim sites. The PFI wetlands (PF1 to PF4) were separated

h m the CRL wetlands (FDS1, BSI, BS2, UBL, MS, PP, ES, TC) and this correlated

with the water chernistry PC associateci with nutrients and tritium. The CA based on

fiinctional feeding group also detected clifferences between PFI and CRL wetlands, but

this only weakly comlated with water chemistxy.

PCA ofsediment chemistry did not distinguish between habitats (swamp and fen)

or PET and C R . sites, and sediment PC's were not correIated with any of the invertebrate

measures.

4.0 DISCUSSION

4.1-0 Extent of Contrminrtian of Wetltnd Surface Water and Sediment

4.1.1 Radionuclides

A primary concem o f radioactive waste management is the potential for migration

of radionuclides h m the waste management site into the surrounding environs.

Radionuclide deases h m low-level waste management sites into soils, d a c e water

and groundwata have been documented (see NRCC 1983). Migration of 'H, 6 0 ~ o

(Wey and Munch 1986) and 14c @'hifipose 1992) h m waste management Area C

(WMA-C) were previously identined in Duke Stream, which drains Duke Swamp @S).

In this study 'H was detected in DS swamp smfke water and I4c, 1 3 7 ~ ~ and 6 0 ~ o h DS

sediments. These were found at higher levels in the swamp side of DS than the fin side

(Appendix 1). The levels of tritium in di of the DS swamp sites are higher than the

40,000 BqIL Canadian Water Quality Guideline (CWQG) and Ontario Provincial Water

Quality Objective (PWQO) for drdchg water (CCME 1995, MOEE 1994).

Plumes nom three waste management areas are contaminahg fou. (Fig. 2.2) of

the fourteen wetiands investigated in the faIl, Duke Swamp (DS), Bulk Storage (BS l), B

Management (BM) and East Swamp (ES). Of these four wetlands, the fen BM was the

most highly contaminated with doactive material (Appendix 4) with 3~ and ?Sr above

CWQG and PWQO of 40,Oûû Bq& and 10 Bq/L, respectively (CCME 1995, MOEE

1994). BM teceives a emanating h m waste management Area-B (WMA-B),

a site used for storage oflow-Ievel Radioactive waste (LLRW) similar to WMA-C. A

bifiircated tritium plume h m WMA-C d e s leachate into the DS and BS2 swamps

@g. 2.1). DS swamp (FDS22) has tritium levels above CWQc and PWQO.

Radionuclide escape h m the Liquid Dispersal &a, which receives low fevel

radioactive liquid waste, into ES was b t observecl in 1955 (Ophel and Fraser 1957). ES

also has elevated levels of tritium and (Appendix 4)-

4.1.2 Non-Radiologicaî Contaminanb - VolaüIe Orgmic Compoanàr

Although not as widely addressed as the issue of radionuclide teleases, LLRW

sites may also contain wn-radiological wmponents that can migrate into su~ounding

soils and waters. Volatile organic compounds (VOCs) have been detected in leachate

h m some waste disposa1 sites migmting into groundwater (Sawhney 1989). Monitoring

for non-radiological contamïnants at Chalk River Laboratones (CRL) was initiated in

1990 (Turner 1993). An investigation of the chemical character of the groundwater

beneath WMA-C revealed high concentrations of volatile hybcarbons, primarily TCE,

1,1 DCA and chlorofonn (Donders et al. 1996).

Some volatile organics are not easily broken dom by naturaily occwring

microorganisms and chemical reactions, and exhibit high mobility, migrating with

leachate and groundwater. Degradation of VOCs may occur under anaerobic conciîtiotls

(Lesage et al. 1993, Semprini et ai. 1995), however, degradation may be quite slow

(Fogell986, JafYert and W o E 1987) and may pioduce equally toxic pducts, such as

vinyl chlonde @eopfn et al. 1985, Semprini et al. 1995). In a laboratory study, TCE,

l,ZDCA, and chloroform were highly mobile in sandy soil and were not degraded

(Wilson et al. 1982). In the sandy aquifer beneath WMA-C, VOCs are mobile, sinfacing

in DS swamp.

VOCs were detected in DS swamp smface water (Appendix 1). The highest

concentrations wem foundh the immediate discharge zone of the swamp pig- 2.1) and

concentraîions decreared farther h m the entrance of the plume to the swamp, to below

detection at the farthest site. No VOCs were detected in the fen sites. The VOCs found

in DS were primarily represented by I,l-dichloroethane (DCA) and ûichloroethene

(TCE), which were widely distriiuted md at highn concentrations than other identifiecl

VOCs (Appendix 1). rii a survey of 35 Finnish waste sites, TCE and DCA were

identined among the chernicals that most kquently exceed drinkïng water guidelines

(Assmuth 1996). TCE concentrations in DS (up to 496 &L) were much higher than

generally found in surface waters in Canada (Moore et al. 1991), and higher than the

interi. CWQG and PWQO of 20 pgL (CCME 1995, MOEE 1994), and were similiil: to

concentrations often found in groundwater near landfSIls and industrial sites. VûCs are

fiequently detected in leachates and runoff h m waste disposai sites (e.g., Assmuth 1996,

Schrab et al. 1993, Lesage et ai. 1990). Concentrations of TCE ùi DS swamp surface

water (6.6 to 496 pg/L) reached into the high end of the range of concentrati011~ fond in

groundwater at the Gloucester Landfiil hazardous waste site near Ottawa, Ontario

(Lesage et al. 1990).

4.1.3 Non-Radiologid Contimhants - Me- Contamination ofaquafIuafIc habitats with toxic met& is also a potential pmblem

associated with the management of LLRW. Dondas et al. (1996) detected Zn, Ni and Co

in gromdwater b e n e . WMA-C. Previous analysk o f surface waters aBected by CRL

waste management area leachate plumes, including hike Stream, which dr- DS,

indicated that metais, except Fe, were not elevated above CWQG Fumer 1993).

However, the WMAC plume passes unda and üuough DS and BS2 swamps before

reaching Duke Stream. The wetland soils may immobilise metals tesulting in an

accumulation in the wetlands (reviewed in GambeU 1994).

DS swamp s&e water had high levels of Zn in some sites, exceeding CWQG

and PWQO cif 30 pg/L (CCME 1995, MOEE 1994) at one site (Appendix 1). Zn was not

more abundant in the swamp sediments wmpared to the fm and was not higher than Zn

levels in the reference wetland sediments. The only metais measured in DS sediments at

levels above interim assesment Cnteria for soi1 were Cr and Ni (Appendùr 1). However,

both metais were only elevated in the fen sediments and probably did not originate from

WMA-C. Concentrations of Cr and Ni in DS fen sediments were at least 2.5-3 times

higher than in any of the other wetland sediments. Elevated concentrations of Cr (64

pgL) and Ni (9 p@L) were detected in groundwaterdown-gradient of the Acid-

Chernical-Solvent pits (Kuig and Killey 1992) situateci near the northem end of WMA-C,

and these metals may have migrated h m the acid pit into DS fin.

Al and Fe were detected at concentrations above the CWQG of 0.005 to 0.1 m a

and 0.3 mg& (CCME 1995), respectively, in the sutface waters of both the fen and the

swamp sites of DS, though the swamp sites had the highest levels of both metals

(Appendix 1). Al and Fe also exceeded PWQO (15 pfl and 300 Ccgn, respectively)

(MOEE 1994) in DS surfàce water. However, both Ai and Ft wae higher in

concentration M e r h m the plume entrance and surfme waterconcentratious were

negatively cornlatexi with pH- A similar relatioaship between Fe and Ai concentration

and pH has been obsewed by the experimental acidification o f lakes (King et al. 1992;

Schinder and Tunier 1982; Schïndler et al. 1980) and stnams morton et aL 1992; Hall et

al. 1980) and i s consistent with observations in 0th wetland -es (BIancher and

McNicoi1987; Spariing 1966). Fe lweis in the sediments were not signincantly higher in

the swamp cornpared to the fen sites, and were similar to levels measured in sediments of

reference wetlands. High concentrations of Fe and Al are not unusuai in wetland waters.

A shidy of bogs in northeastern North Amaica revealed a wide range of Fe and Al

concentrations, with peat sorption and release, evapotranspiration and atmospheric

deposition being important factors in &ace water concentrations (trrban et al. 1987).

4.2.0 Wetland Aquatic Habitat

Chanical conditions in wetlands can have a strong influence on the potential

toxicity o f wntaminants. The bioavailability and toxicity of metals Ire dependent on

environmental fàctors, such as pH, DOC, and organic carbon content of sediments

(reviewed by Wren and Stephenson 1991, Gerhardt 1993, Gambre111994). In addition,

BendeU-Young et al. (1994) found that metal avaiIabiiity to benthic invertebrates differed

between wetlands with inmaseci bioavailability of 2h, Fe and Mn in minerakich fens

with low organic matter. The present study included fens and swarnps.

The chemical character of LLR.W leachate may be highly variable (e.g. Husain

1979), influencing the mobility of w n îaminants as welî as the chemistry of recipient

waters. WM2L-C gromdwater anaiysis by Donders et aL (1996) indicated that pH and

major auions and cations (except K) were simila to levels up-gradient of the site.

Howevery there was some evidence of efevated concentrations of DIC and DOC and

higher allralinity mmpared up-gradient sites-

A p W a b h i t y gradient was apparent in DS swamp, with a corresponding

gradient in metals m l e 3.2, Fig. 3.1). The swamp sites that were closa to the leading

edge of the plume were distinguished h m the rest of the swamp sites due to higher pH

and aikahity, and higher concentrations of DOC (Figs. 2.1 and 3.1). The sites fiirther

h m the plume had lowa pH and bigher concentrations of Mn, Fe, and Al. Studies of

lake and river waters have show that Mn, Fe, and Ai concentrations are negatively

correlated with pH (King et al. 1992, Urban et al. 1990, Underwood et al. 1986). Urban

et al. (1990) found that Al, Fe, and Mn concentrations in Nova Scotia lakes and ponds

were inversely correlated with pH over a pH range of 4-01 to 6.08. Experjmental

acidification of enclosures in a Mwate r lake resuited in Al, Fe, and Mn increasing in

the treated enclosures @H 5.1 and 5.7) compared to the controls (pH 6.7-6.8) (Schindler

et al. 1980). The pH range observeci in DS was 4.8 to 6.6 (Appendix 1).

The pH and ionic content of DS d a c e water chemistry were within the range of

values found in mïnerotrophic wetlands of B o r d Canada (see Zoltai et al. 1988) and

eastem Canada (Comeau and Beilamy 1986). The fen sites were partiaiiy disthguished

fiom the swamp sites based on Cl, conductivityy and cations (Fig. 3.1). These kdings

are consistent with Zesults h m wetland studies on the relationship between water

chemistry and vegetation structure in bogs, fms and swamps by Kudray and Gale (1997)

in Alberta, as weli as Nicholson (1995) and Schwintzer and Tomberlui (1982) in

Michigan. The overiap between fen ami swarnp sites based on water chemistry was also

observed by Kudray and Gale (1997) and Schwintzer and Tomberlin (1982). The fens

were higher in DO Fig- 3 -1, Appendix t), which can be explained by the hcreased water

flow observeci in the fen sites compared to the pools of standing water in the swamp sites.

In a study on water movement and water chemistry in wetlands of eastem Canada,

Sparling (1966) found wetlands with higher fïow rates consistently had lower

concentrations of ions and bigher pH.

4.2.2 Ch- River and Petawawi Wetiands

In the fa, two of the four wetlands that receive leachate were included in water

chemistry analysis. These two wetlands, both swamps (ES and BS2), were disthguished

fbm the 0 t h swamps mainly by lower levels of nutnents and higher levels of tritium

(Appendur 4). Otherwise, the swamps were alike in water chemistry-

Generaliy the water chemistry divides the wetlands into fens with higher DO, pH

and alkalinity, and swamps with higher conductivity and colour (Table 3 9 , Fig. 3 -7).

This is consistent with DS and previous wetland studies (e-g., Kudray and Gale 1997,

Nicholson 1995, Schwintzer and Tomberlin 1982). Increased plant litter input can

explah the difference between fens and swamps in conductivity- The swamps were a i i

forested and sampling was camed out in the fd, when there was a large input of fden

leaves. Leaching of plant litter increases the cation concentration, as weil as Al and Fe

concentrations in wetlands @avis and van der Vak 1978). In wntrast, the fens were

open, and, with the exception ofMS, and to a lesser extent PF2, the water samples were

taken h m opai. flowing channels with few macz~phytes and Little canopy wver. A

substantially lower input of terrestrial plant litter into the feas. and increased flow of

water, couid account for the water cherni- àifSereflce between fens and swamps.

MS stands out h m the rest of the wetlands due to elevated concentrations of N,

P, conductivity, Cl, Al, Zn, and tie, and lower pK and alkahity (Fig- 3-7, Appendix 4).

The presence in MS of relatively dense stands of emergent macrophytes, mallily Typha,

as weli as conspicuous water-level fluctuation may contribute to the difference between

MS and the otha fens. Breakdown of plant litter would contniute to increased

conductivity, giving MS "swamp-like" water chemistry. Another factor in the increased

conductivity found in MS would be its proximity to the plant road. Iircreased Ievels of Cl

fkom de-king salts used on the road in winter would influence the positioning of MS on

PC 1. As well, BS2, and possibly also ES, may receive niwff of salts h m the nearby

road.

TC was found to be more like the fens than like the other swamps. Sorne water

movement was apparent in TC, which was situatecl on a slope. Lacreased DO and lower

conductivity due to higher water flow could explain the grouping of TC with the fens.

4.3.0 Benthic Iivertebrate Community and Wetland Chemistry

Of the leachate contamhants entering DS swamp, ody TCE exceeds the CWQG

and PWQO of 0.02 m a for protection of aquatic Life (CCME 1995, MOEE 1994).

CWQG and PWQO for protection of aquatic life have not been developed for

radiolopical parameters, however, tritium exceeds the CWQG and PWQO o f 40,OOO

Bq/L for drinking water (CCME 1995, MOEE 1994). There was no apparent eff i t on

the benthic commrmity of DS that w d d be attributed to leachate con bmhnts h m

WMA-C. VOCs did not ex& a detectable influence on measures of community

composition. Although the biotic com~~~unity was stn,ngly wrrelated with water

chemistry, the chernical character o f the wetiand sudiace water was pmbabIy much more

important in shaping the comunity structure than was the presence of leachate

contaminants. Tritium activity in DS was positively correlated with conductivlty and

related parameters? and negatively correlated with sediment Cr (Fig. 3.1). Although this

factor appeared to have a strong influence on commUILity composition, it can be more

easily explaineci by such ciifferences in habitat type as canopy cover, DO and nutrients.

Within DS swamp, biotic measmes comlated most strongly with pH and the metals Fe,

Al and Mn, which are not likely to have originated as contambants h m WMA-C.

However, there was a gradient away firom the leachate plume (Fig. 2.1 and 3.1).

4.3.1 Leachate Impact on Wetlands - Radionuclides

There was no apparent correlation between benthic invertebrate community and

radioactivity measureâ in CRL wetlands. However, the two CRL wetlands that had the

highest levels of radioactivity, B Management fen PM) and Duke Swamp mS22) were

not included in the regression analysis. Nevertheless, cornparison o f correspondence

analysis ordinations including BM and FDS22 (Appendur 7) and excluding BM and

FDS22 (Fig. 3.9) shows that the macrohvertebrate community structure does not stand

out h m community structure h the rest of the wetlanùs. Although both BM and DS

swamp containeci )H exceeding CWQG, the guideïine is for drialring water and has been

derïved for the pmtection of humans. Câniidh guidehes for the protection of aquatic

life have not been developed for radi010gical parameters.

Benthic macroinvertebrate community may not be a sensitive incikator o f c b n i c

exposure to low level radiation- Changes in community or a reduction in diversity may

only be perceived af€er exposure to very high doses of radiation (Whicker and Schultz

1982). White Oak Lake, a settling barin for radioactive efmients of Oak Ridge Nuclear

Laboratories, has been abject to chronic radiation contamination since 1943, and

supports a natural population of aquatic organisrns (Blaylock aad Trabalka 1978). Waste

disposal practices at the Hadord Site in Washington, have been implicated in the reiease

of radionuclides, metals and solvents into the Columbia River, yet studies conducted at

H d o r d did not reveal any changes in the benthic community (Becker 1990).

4.3.2 Leachate Impact on Duke Swamp - Volatiie Organic Compoands

While invertebrate community composition was strongly correlated with DS

water chemistry components, volatile organic compounds (VOCs) h m WMA-C

leachate did not have any apparent influence on community stmctwe (Tables 3.6 and 3.8,

PC4). Few studies were found that utilise community composition to evaluate the effects

of leachates or VOCs on aquatic fauna In a field experirnent, Duphnia abundance was

reduced significantly at TCE concentrations of 25.0 mg& (Lay et al. 1984). Benthic

macroinvertebrate taxa nchne~s was lower in and below the discharge zone of streams

receiving leachate h m a municipal landnu (Cingolani and Morosi 1992) and a

hazardous waste site (Siewert et al. 1989). Leachate h m a landnll in Sackville, NS, was

associated with d m benthic mstc~oinvertebrate diversïty in the Sackville River, but

only in the immediate vicinity ofthe point ofdischarge (Rutherford 1995). The taxa that

were most sensitive in these stream shdies (e-g., znaynies, stoneflies, dragonnies and

amphipods) were not fomd in DS swamp sites (Appcndur 3).

Ln contrast to studies involvhg community composition, leachate toxicity

bioassays with individual organisns have received more attention. Ground and d a c e

waters h m municipal aud hazardous waste sites have been found to elicit lethal toge

effects on hhwater fish (PlotlOn and Ram 1984, Wong 1989). zooplankton (Plotkin and

Ram 1984, Assmuth and Penttila 1995) and algae (Plotkin and Ram 1984, Cheung et al.

1993). However, ECSO and LCSO values exhibit a broad range of concentrations within

each group of organisms. A number of factors will influence the concentration of

leachate r e q d to effect a response, incfuding chernical composition of the leachate,

concentration of chernicals in the leachate and organisns used in toxicity test. AU of the

waste site leachates cited above had concentrations of trace metals at least an order of

magnitude higher thau those observed in D S (Appendix 1).

Table 4.1 shows published concentrations of the VOCs detected in DS that are

lethal to fkshwater fa- Other than TCE, the VOCs detected in DS (Appendix 1) were

at least 2-3 orders of magnitude lower ui concentration than the lowest reporteci to be

lethal (Table 4.1). Even TCE, which was detected at concentrations one to two orders of

magnitude higher than any of the other VOCs in DS, was less than one tenth of the lowest

concentration found to be toxic (Table 4.1). Although TCE was detected at

concentrations exceeding the interim CWQG, the guideiine was set for the protection of

brook trout (SalveIinw fonterrcrIrS), amently considered to be the most sensitive

freshwater organism (Moore a al 1991). hvertebnites tend to be slightly Iess sensitive.

Table 4.1. Pubiisbed acute toxîcity to M w a t e r organisms of individual organic chernicals detected in Duke Swarnp d c e water with obscrved concentrations. Ail values are m a .

chlorofonn

chlotomethane

1,l-DCA

12-DCA

tetrachloroethene

TCE

benzene

ethylbenzcne

toluene

m&p-xylenes

' 24hI-C~ $9-50

48hL.C~ # 7d& S skitic test F flow thrwgh test 1 Leblanc 1980 2 Richter et al. 1983 3 Abeniathy et al. 1986

4 C n t c m & k l ~ ~ ~ 1978 5 Slooff et aï. 1983 6 TNCW et ai- 1983 7 Bobra et al- 1983 8 Gaîassi et d. 1988 9BerryBtBnmmcrL977 10 Holcornbe et ai- 19û7 1 L Veith et ai. 1983 12 Wdbndge n al. 1983

13 Geigcret ai- 1985 (citcd m WHO 1995) 14 Bucfzfiisco etal- 1981 ISAlexanderetaL 1978 16 DeGracvc et ai. 1982 17 Dcviin et al- 1982 18 Blackct al. 1982 19 SIooff 1983 20 Kiinerrpnn 1981 21 Vcrschuenm 19û3

4.3.3 pH and Metrils

Of the met& detected in DS surface water, Al and Fe reached concentrations that

may be toxic to benthic macroinvertebrates. Al concentrations in DS swamp were up to

400 pgL (pH 4.8) and Fe reached up to 16.8 mgL (pEI 4.94) (Appendix 1). However,

these metals are withui the range o f concentrations meswmd in r e f m c e wetlands.

Within DS swamp, a watn chernistry gradient comprising pH, allraliniîy, WC

and metals Fe, Mn, and Al) was stn>ngly reiated to the benthic commUILity (Tables 3.6

and 3.8, PC2). In the ordination of the swamp taxa @igsgs 3 2 and 3 -3). the isopod

Asellus, a cchironomid and thm moihscs, Physella, Valvata and M i l i ~ m , distingukh

the sites with hïgherpH, akahity and WC. Burton and AIian (1986) determined

experhentaiiy that both A s e h and Physellla w m sensitive to pH, with Asellus

aquaticus sumival decreased at pH 5, and survival of both taxa decreased at pH 4. In

contrast, Maltby et al. (1987) did not obsetve a signincant Merence in sumival of A.

aquaticus over a pH range of 7.5 to 3.5.

In hiLe Swamp Asellus and the moiiuscs were absent h m swamp sites with

higher levels of Fe and Al. The physiological influence of Iowered pH is related to

dimption ofionic regdation in aquatic biota, which for some metals r d t s in increased

uptake (Gerhardt 1993). An increase in uptake, coupled with an increase in availability

due to lower pH, may result in greater mortality for sensitive biota. Burton and Man

(1 986) observed signifiant mortality in Aseilw in response to Al concentrations of 500

pg/L at pH 4. Martin and Holdich determined the 48 hour LC50 for A. aqzuzticus to be

144 mg/L when exposed to Fe at pH 6.8. Maltby et al. (1987) suggest that for A.

aquaticus mortality is increased with the addition of Fe at pH 4.5 compared to pH 6, with

LCc0s calculateci to be 300429 mg/L and 419-467 mg& respectively. However, A.

aquaticus has been foued to be sensitive to concentrations of Fe as Low as 3.0 mgL. As

well, in a Danish stream having Fe concentrations up to 3 1.8 mgL and cùcamneutral pH,

A. a q u u t i ~ was absent h m sites with greater than 2 mgL (Rasmussen and Lhdegaard

1988)- The mosquito Aedes, the false crime fly Bitfocomo~pha and the marsh beetle

Scirtes distbguish the low pH sites with increased Mn, Fe and A l (Fip. 3 2 and 3.3).

Additionally, famiy richness, total invertebrate abundance, richness and divefsity

were lower in sites with Iow pH and high levels of Fe, Mn and Ai (Table 3.5, PC2).

Comparable observations have been made in lakes and streams. In experimentally

acidifiecl streams in Ontario, Hall (1994) observed au increase in total and inorganic AL,

and a corresponding reductîon in benthic invertebrate abundance and diversity. Taxa

richness has also been comlated with pK in Welsh streams (Wade et al. 1989) and

Swedish streams (Otto and Svensson 1983).

Similar invertebrate responses to Fe have been observed in streams. Wellnitz et

al. (1 994) fomd invertebrate abundance and H' diversity were reduced at Fe

concentrations of up to 3.46 m f i in a Vermont Stream. In streams rauging in pH firom

6.7-8.8, Fe concentrations greater than 0.3 mg/L lead to a decrease in taxa nchness, and

at Fe concentrations of 10 m e the fauna was considerd to be typical of an organically

poiiuted site @asmussen and Lindegaard 1988).

4.3.4 Benthic Commnnity and Wetland Type

Community composition was strongiy related to wetiand water chemistry. The

clear separation in Duke Swamp of fen and swamp sites based on invertebrate

community composition (Figs. 3.2 and 3.4) may be largely due to a ciifference in

hydrology, in addition to conductivity and nutrients. The taxa found to be important in

the swamp sites include primarily lentic taxa, such as the mosquito Aeder, the midge

CIiironmus, the marsh beetle Scl'rtes, and the water &le Laccobitrr, as weU as those

associated with Ca-rich habitats (the iJopod Asellur, the snails Pljisella and Valvata and

the fïngemail clam Mwc~(Iim). Lotic taxa, such as the stonefiy, Leucha, limnepmd

caddisflies and chironomids (Tl>ienemannentyla, Heterotanytcnsus, HeterotriksocIadius

and ProcIadi~~~) are all all iated with the higher DO, flowing-water fen sites.

The nutnent enrîched wetlands with low DO, pH and alkahity (PF3, PF4, MS,

PP) were chaacterised by ~ o n o m w and Tubificidae. Nuûient enrichnient was most

strongly related to communïty composition (Table 3.13, PC2). Similady, strong

relationships between invertebrate comrnunity composition and nutnent enrichment were

found in AUSffalian wetlands (Balla and Davis 1995, Growns et al. 1992). Benthic

invertebrate commwiity structure was also related to DO and p K pH has been found to

be strongly correlated with .Stream invertebrate community composition (Gower et al.

1994, Wade et al. 1989, Omierod 1987). Stream study cornparisons by LaPoint et al.

(1984) show that increased proportions of Chironominae and Tubincidae are associated

with hi& N and P (nutrient enriched) and low DO. Chironornini (eg. Chironomus) and

Tubincidae are generally wnsidered to be pollution tolerant (Plafkin et al. 1989).

For the 12 wetlands sampled in the fall, DO, pH and conductivity gradients, dong

with nutrient statw, were good predictors of invertebrate richness (Taxa Richness: ~~d~ =

0.72, p = 0.001; Family Richness: R~~ = 0.74, p = 0.001) (Table 3.13). Wetlands with

hi& DO, pH, and aIlralinity, as weU as low nutnents and conductivity, were higher in

total taxa riclmess, family richness, and chironomid richness. Invertebrate EI' diversity

and %Chironomidae were higher in the low nutrient sites. % Oligochaeta was higher in

the nutnent enriched sites. In a study of 33 Australian wetlands, species ricbness was

highest in mderateIy uinched sites and lower in the most and least enriched sites

(Gmwns et al 1992). Iil the Horida Everglades, P and N enricheci sites were

characteriseci by Iow DO and low hvertebrate diversity (Ratier and Richardson 1992).

In a Gaman study, increased leveis of leaf Iitter in anail pools were associateci

with lower richness and diversity and increased chimnomid abundance (Schleuter 1986).

As well, a study of streams in North Carolina showed that demer canopy cover is

associated with a decrease m total invertebrate richness and chironomid ricbness (Lenat

1983). in con- an Oregon stream study mealeci that campy cover iduenced

invertebrate abundance, but not richness (Hawkins et al. 1982). In a cornparison of

stream studies n0m across the U.S., LaPoint et al. (1984) found that high nutrient, low

DO streams have reduced diversity of invertebrates and higher proportions of

chironomids and tubificid oligochaetes.

The proportion of Chironomini was higher in the swamps while the proportion of

Tanytarsini was higher in the fens (Table 3.13, PC1 and Fig. 3.7). Tanytarsini are

important chironomid taxa in pools wîth low leaf litter input whereas Chironomini are

important chironomid taxa in pools with high leaf iitter input (Schleuter 1986)-

4.4.0 Benthic Invertebrate Community as Wetlrird Water Quaîity Indicator

A useful indicator of adveme envixonmental impact should be sensitive to the

contaminant of interest and should provide a predictable rqonse. Taxa richness and CA

ordination of benthic invertebrates lowest taxonomie unit were the most consistent

measures of wata q d t y in the wetlands studied Neiîher appears to be adversely

affecteci by LLRW contamination in CRL wetlands, yet both show potential to be usefiil

indicators of nutrient enrichment and/or acidification/metals stress.

The benthlc invertebrate cornmunities measured in CRL wetlands are M a r to

those found in d i u systems. Eight studies weze found that describe wetland

rnacroinvertebrate community, or at l e s t a component of it (Table 4.2). Over 70% of the

taxa coliected in either spring or fa m CRL wetlands (Appendices 3 and 6) have bew

record& occrring m these studies.

Table 4.2. Published studies for cornparison with CRL wetland benthic macminvertebrate cornmUXUty composition Authors Wear) Habitat Community Sunpling metbal Chouinard (1993) fens, bogs benthos, chnonomids grabs, emefgence traps

Ennan & Erman (1975) fens benthos corer Gladden & Smock (1990) forestcd flooûplains benthos, nekton corer, sweep net Rosenberg, Wiens & Biiyj (1 988) fens chironomïds emcrgence traps

S b v e r et al. (1996) wetlands* benîhos coter Voigts (1976) marshes nekton dip net Wiggins et al (1980) p0oh benthos, nekton sweep net WilIiams (1983) pond benthos, n e b n sweep net

d c s c n i as '%crb;tfeous fhshwaîer wetlands"

4.4.1 Biotic Indices

Measu~es of nchness at lowest taxonomie unit and family level respond in a

consistent manner to wetland water chemistry, both in DS in the spring and in the fall

wetlands (Tables 3.6 and 3.14). Decreased nchness was associated with lower pH and

higher concentrations of metals, although in the fd, hi& nutrient levels also have a

strong influence on the reduced nchness. %Chironoomini also responded consistently in

both seasons, being higher in sites with reduced pH and DO. Other indices used

(Trichoptera nchness, chironomid nchness, %Chironomidae, %Tanytarsi.t& and

Shannon's H' diversity) either failed to show a clear relationship to the measured

environmental gradients, or did not respond eonsistently in both seasons.

Support for these r d t s can be found in strpam studies. Richness was found to

be more consistent than Hf diversity as an indicator of water quafity in agriculturally

impacted streams (Barton and MetcalfcSmith 1992). HowmiUer and Scott (1977) found

diversity indices to be inadeqyate for monitoring pollution, but may be a measme of

trophic statas. Wmnet et aI. (1980) suggest that %Chuonomidae is a good indicator of

metai pollution in heavily cont?iminated strearns, but is not predictable under Iess severe

conditions.

4.4.2 Multivariate Ordination

Ordination of the benthie invertebrate community identified to lowest taxonomie

unit performed consistently between seasons as an indicator of wetland water quality, and

appears to be sensitive to a gradient of pH/akahity/metaIs in DS where this gradient is

distinct (Table 3.7, PC2). In cornparison, while ordination of f d y was also reiated to

water chemistry, the response is not consistent between seasons, and the in£luence of

pWmetals is less clearly defmed -le 3 -7).

Ordination of DS by CA (Fig. 3.2) provides a more distinct separation of fen sites

fiom swamp sites than that by PCA of physicochemical variables (Fig. 3.1). Regression

analysis revealed a strong relationship between community composition and

physicochemical parameters (CA1 : R~~ = 0.74, p = 0.00; CA2 ad^ = 0.3 5, p = 0.0 15;

C A 2 R~~~ = 0.3 1, p = 0.02) (Table 3 -7). Balia and Davis (1995) obtained similar resuits

by classiQing Australian wetlands, with invertebrate cornmunity composition pmviding

greater separation than physicochemical variables, and arhiiting a strong relationship

between the two ordinations. Iri contrast to DS, for the 12 wetlands sampled in the fd,

separation of fms and swamps was mom clearly demonstrated by the physi~~~hemical

variables Fig- 3-7). The invertebraîe community composition did not clearly establish a

division between habitats (Figs. 3 9,3.11,3.13). Dinerences in community composition

between fais and swamps may have been bettn explained by additionai variables that

were not measinsd. such as substrate particle size, morphometry, vegetational

composition and structure, and hydrological fluctuation.

Ordination of fiuictional feeding group @TG) was effective for separating the fen

sites fiom the swamp sites in DS (Fig. 3.6). Collectors, shredders7 piercers and predators

have been found to be more abundant in unshaded streams, while scrapers did not differ

between streams with differing canopy cover (Hawkins et al. 1982). In DS, whiie

shredders and predators were more abundant in unshaded fen sites, scrapers and piercers

were more abundant in shaded swamp sites. Howevex, in DS, the pWmetais gradient was

also wrrelated with FFG (Table 3.7, PC2). Otto and Svensson (1983) also found the pH

of stream water infiuenced FFG composition, with shredders more abundant in acid

streams, scrapers in non-acid stieams, and coUectors and predators equally abundaut.

Ordination of FFG does not appear to be as useful as a tool for water @ty

assessrnent A relationship to water chemistry was not consistent and FFG was not

particularly useful for distinguishing between the swamp sites closer to and f d e r fbm

the plume (Fig. 3.6). Palmer et ai. (1996) also fomd Canonicd Correspondence Andysis

of FFG was not effective for distzaguishing stream sites with known urban/industrial

impacts, despite showing a pronouaced upstream/downstxeam gradient.

Warwick (1988) suggested that anthropogenic influences on benthic communities

may be more readily detected at higher levels of taxonomic organisation. In the present

investigation, invertebrate wmmmnty was d e s c r i i at the family level as well as L m

(generally genus). Menrics that wexe calculated for both taxonomk levefs responded

comparably to environmental gradients-

Taxa and fa- richneas showed strong relationship with the water chemistry in

both the s p ~ g and the fdl ('ïables 3.6 and 3.14). Hughes (1978) obsened that

macroinvertebrate diversity meanaed at the family level revealed a pattexn that was very

much iike that measured at the genus or species level, and was able to distinguish

between stream sites with only a small l o s of information.

CA ordination of family level gave similar results compareci to ordination of

lowest taxonomie unit (mostly genus) (Egs. 3 2 and 3.4). DS fen and swamp sites were

more clearly separated than at the genus level. However, the relationship to water

chemïstry was stronger at the genus level. In cornparison, nonmetric muItidimdond

scaling (NMDS) of sewage impacted streams in Austrajia revealed the diffefence

between clean and polluted sites at both species and f d y levels (Wright et al. 1995).

4.4.4 Cornparison with Other Indicators of Wetland Water Quality

In a swamary of biological indicators used to assess wetland habitat, heibaceous

vegetation and invertebrate communities appear to be the most widely used and respond

strongly to both enrichment and contamination by heavy metals or organic contaminants

(Adamus and Brandt 1990). Ambient acidity of soils and water has also been shown to

alter herbaceous vegetation wmmUIUties in wetlandsv but reports of acid16cation effects

on wetland hvertebrates are primarily based upon strram and 1&e studies ( A h u s and

Brandt 1990).

Few pubiïshed studies were found that wuld be adequately compared with this

study. Studies that examineci the relationship between weüand water qualïty and

biologicd communities in wetlands included aigae (Gabor et al. 1994, MudEin et al 1994,

Rader and Richardson 1992). amph'bians Pecnar and M'Closlcey 19963, benthic

macmiavertebrates (Wan et al. 1994, Growns et al. 1992, Rosenberg et al. 1995, Jones et

al. 1995, Gabor et al. 1994, Murkin et al 1994). vegetation (Jeglum 1971, Glaser et al.

198 1, Nichohon 1995, Zogg and Barnes 1995, Jeglum and He 1995) or hhwater fish

(Wan et al. 1994) as the biotic component. Few of these studies quantifieci the

relationship between biotic and abiotic components using correlation or regression

analysis -

The most directly comparable study was presented by Jeglum and He (1995)-

Using Canonical Comspondence Analysis to examine the vegetation-envkonment

relationsbip in an Ontario wetland, le* and He (1995) found that 8 1% of the variance

in the vegetation data set descrïibed by the nrSt two axes were explained by the measured

environmentd variables. The explanatory environmentai variables were interpreted as

peat depthlrnoisture and pWcalcium gradients (Jeglum and He 1995). A study of

amphibian communiîy composition in relation to pond water chemistry in Ontario

concluded that water chemisîry was not a good predictor of amphibian species richues,

with only 19% of the variance in the amphibian data explained by the water chemistry

(Hecnar and M'Closkey 1996).

Muitivariate ordination ofbenthic mac~oinvertebrate data was strongly related to

environmental vanables in Chalk River. In Duke Swmp 74% of the variance in the nrst

axis derived h m macroinvertebrate data was explained by mvironmentd variables

(Table 3.7). In the analysis ofthe 12 wetlands, 91% of the variance in the fin$ axis was

explained by environmental variables (Section 3.2.4). Environmental variables explained

more of the variation in multivananate ordination axes of the invertebrate data (Table 3.7

and Section 3.2.4) than in univariate metrics of hvertebrate structure (Tables 3.5 and

3.16).

Con taminants h m WMA-C were detected in surface water in the southwest end

of DS. Radiological contaminants consisted of 3~ in the water column and 13'cs, I 4 c and

@CO in the sediments. Non-fadio1ogical contaminants consisted of ten organic

chemicals, of which the volatile organics TCE and 1,l-DCA were the most widely

distributed and at higher concentrations. In addition to VOCs and radionuclides slightly

elevated levels of Zn in the swamp may also be attri'buted to WMA-C leachate. Other

metais that were substantially elevated in DS (e-g., Fe, Mn, Al) probably did not originate

fiom WMA-C. Elevated concentrations of these metals are not musual in wetlands in

the area and surface water concentrations were related to wetland type and water pH.

An iufiuence of L m W leachate on wetland aquatic fama was not detected.

Although 'H and "C were constituents of environmental components that were related to

wetland fauna, the structure of the benthic invertebrate community was not clearly

correlated to leachate contaminants in DS. Published toxicity studies have shown that

aquatic invertebrates are sensitive to much higher concentrations of VûCs than were

detected in DS. In studïes of radiological con tamimmts, low levels of% and "C have

not been found to influence aquatic invertebrate community composition.

Benthic invertebrate communïty structure was strongiy related to wetland water

chemistry. Most of the in invertebrate commutulty structure between wetlands

was easily explaineci by differences in wetland type. nie nutrient-cich wetlands with

highly colod water were charactexised by a less &verse aquatic invertebrate fima

dominated by Chironomur chuonornids and tubificid oligochaetes. Elevated

concentrations of Al In the d a c e waters were also associatecl with these nuüient-nch

wetlands. Within DS swamp, a pWalLalinity/DOCfmetals gradient was reflected in the

Invertebrate community. Sites with low pEE, atkalini-ty and DOC and hi& metal

concentrations were charactdsed by a less diverse invertebrate fauna dominated by

Chironomus chironomids, mosquitoes and false craneflies. Sites with higher pH,

akalinity and DOC d lower concentrations of Fe, M n and Al were cbaracterised by

Aiellus isopods, and the gastropads PhyseZIu and Valvata. Published studies have found

that Asellus and Physellla ara sensitive to pH, Fe and Al.

Univariate and multivariate meaSUTes of wetland benthic invertebrates related to

wetland type and nutrient status in a similar fashion for the wetlands sampled in the fd.

Wi'ithin DS, sampled in the spring, univariate measures of richness and diversity were

more strongly related to pHlalkahity/metais gradient, while CA ordination reflected

both the pH gradient as well as the wetland type (swamp or fa).

CA ordination of wetlands using benthic macroinvertebrates identifid to lowest

taxonomie unit &TU) and also to f d y level, in the spring and in the fail, gave very

similar d t s - Ordination to the M y level provided a clearer separation of swamps

h m fens Howmr, ordination to LTU was more strongly related to water chemistry

and therefore may be more usefiil as an indicator of chemical contamination in wetlands.

Ordination of FFG was related to wetiaad water chemïstry in the spring, but the

relationship was much weaker than the pmvious ordinations9 and was unrelated to water

chernistry in the fa- Ordination of FFG is unlikely to be an effective indicator of

chemical con tamhmtion Ùz wetlands

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Appendix 1. Physicochemical parameters analysed for Duke Swamp surface water and sediment coUected h e , 1995.

1 4 3 2-84

10-7 5.76 9.18 3.80 360 143

8 41.8 48.7

1-1 9-50

1150 393 14.0 035 154 0.4 12

50.0 4 .1 a2 a2

<S 8500 517 024 020

46 0-10 1 .O 79

LOO 0.009 <DL 4 2 <DL <DL <DL 10.6 <DL <DL <DL <DL

24- 1 O251 0.87

O 5-120

Appendix 2. Invertebrates coiiected by core sampler in Duke Swamp, June, L 995, and fiincti0m.l fecding group.

Valvatidac Volvata

Physidae PhyselIa

Planorbidae G y r d = Helisoma

Lirnnaeidae Fossana

Oligocbicta Lumbriculidac Naididae Tubificidac

Hirudiner Erpobdellidae GIossiphonudaç

In secta Collembola Odonata

Corduiegasaidae Cordutegaier

Plecoptcra Leuctrïdac

Leucrra Megaioptera

SiaIidae Sidis

Coleoptera Dytiscidae

Celina Hydah'crss LuccornrS Lhtarus

Halip tidae IIal@Ius

Scirtidat Cjphon

Hydrophilidac Laccobiuf

Collcctor ColIector

scraper

scra~cr

scra~er S m

Srraper

Collecter Colïcctor Collcctor

Mator Rtdator

Predator

Shredder Redator Collcctor

Collector

Predator

S hredder

Predator

Predator Predator Predator Redator

Shredder

Shredder Sbredder

Piercer

S M a

llnlrnow~ Sbreddct Unhown Shrcdder unrcnown CoUcctor

Prcdator Unkown

Predator

Prcdator ncdator Predator nedator nedator

Colltctor Collcctor Shrcdder CoUcctor Unlcnown ColIector Collector Colltctor

Collector

Collemr Unknown Collector

Collector Shredder Collector Shredder

Collector

Collector

Collcctor

couector Predator

udcown nedator Redator unlrown

Appendix 3. Surface water and sediment physicochemicai parameters detected in samples wllected in 14 wetlands, October, 1995. The seven fais are Duke Swamp fien @Si), B Management fen (BM), Upper Bass Lake (UBL), Buïk Storage @S l), Main Stream (MS), and Petawawa Forestry Iiistitute sites PF1 and PF2. The six swamps aie Duke Swamp (-S22), Pitcha Plant (PP), Buik Storage Swamp (BSZ), Toussaint Credc (TC), East Swamp (ES), and Petawawa Forestry Iiistitute sites PF3 and PF4.

'''sr 2263 Anions and Cltioris Na (rng/L) 332 274 034 23-04 130 0.48 124 analyzedatCRLr K ( M ) 098 1.68 0.8 1 898 139 023 133

M g 297 1-73 1.72 4.53 3.83 132 1-80 -mm 6.12 6.45 4 1247 9.04 4.06 4.99 F Cm&) 0.14 0.11 0.06 0.04 0.11 0.09 0.10 (3 (6) 455 L273 0.73 11032 282 025 0.75 NO3 hdLJ O 0 9 O O 0.82 0.83 O so, i d j 30.67 6.84 2.68 1.75 7.49 22s 1 -72

Sediment: %C 23.0 9.1 18-1 51.7 40.6 15 41.1

Anions and Cations Na (m) 524 3136 1274 1.05 119 0.86 058 analyzed at CRL: K (mg/L) 1 -47 2-74 0.00 0.75 1-10 0.86 3.07

Mg h!m 394 4-10 259 157 153 1.50 1 -72 10.66 11.80 838 6.78 5.73 433 4.73

F 0-19 038 0.24 0.05 0.09 0.09 0.07 cl O&-) 34.69 113.81 67.09 0.69 4.07 1.12 0.70

Appendix 4. Invertebrates wiiected by artincial substrate in 14 wetlands sampled in October, 1995, and fhctional feeding group. The seven fens are Duke Swamp fen (FDS 1), B Management fen (BM), Upper Bass Lake (UBL), Bulk Storage @S 1). Main Stream (MS), and Petawawa Forestry Institute sites PF1 and PF2, The six swamps are M e Swamp (FDS22), Pitcher Plant (PP), BuIk Storage Swamp @S2), Toussaint Creek (TC), East Swamp (ES), and Petawawa Forestry Mtute sites PF3 and PF4.

DSI DS2 DS3 D M DSS D DS7 RS8 DS9 DSlO DS2l DS22 Ils23 DS24 DS2S

NR) NID NID Nrn

N D

NO I 20

0.002

<DL

20.4

<DL <DL

I A I4.2

<DL

<DL

<DL

<DL

29.6

0,598

0.88

O 3.5 15

344 10

10,35

1 12888

357.6 15.49

30,91

63,40

Imcch ColltmboIa Odonata

Cordulcgasîridae CorciicIegtuer

Corduiiidae somolochlora

Aeshnidae Anmc

ZY~OP- P I e c o p ~

Leucbidae Leuc!m

Ntmoundat soyedaio

Capniidat

Colkcuir Colkcbr Calkdor

Coi fcdor Rtdpror

Rcdlor Shreddcr Unhiown Prrdstor

Unknowa Stuc4î&r Drrdawr

Redator RcdPtor Rdator RcQior Redaor Redanor Rtdator

unlaw,wn Shrcddcr CoIIector Coltemr unknown couector Collcctor Colledot couCCtOr couedor Collcdor Unbw,wn

Coilcctor

Collcctor

CoUedor URbw,wn Unkw,wn Collector

Collector Collcaor Shttdder Collcctor Collcctor Colltctor Shrcddu Collcctor Rcdator

Appendix 5. CA plot of invertebrates identifïed to LTU for aU 14 wetlands sampled in October, 1995. The seven feas (0) are Duke Swamp fen (FDSL), B Management fen (BM). Upper Bass Lake WL), Buik Storage (SS l), Main Stream (MS), and Petawawa Forestry Mtute sites PFl and PF2. The six swarnps (A) are Duke Swamp (FDS22). Pitcha Plant (PP), Buik Storage Swamp (BSZ), Toussaint Creek (TC), East Swamp (ES). and Petawawa Forestry Institute sites PF3 and PF4.

CA1 (26% of variance)

-1 -5 -1 .O -0.5 0.0

CA1 (26% of variance)

0.0 0.5

CA2 (19% of varianœ)

CA1 (26% of variance)

-0.5 0.0 0.5 1.0

CA2 (1 9% of variance)