Hydrobiologia 394: 83–91, 1999.© 1999Kluwer Academic Publishers. Printed in the Netherlands.

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Patterns in invertebrate and periphyton size distributions fromnavigation buoys in the St. Lawrence River

Vincent Mercier1, Chantal Vis2, Antoine Morin1 & Christiane Hudon21Ottawa-Carleton Institute of Biology, Univ. of Ottawa, P.O. Box 450, Stn. A, Ottawa, Ontario, Canada K1N 6N52St. Lawrence Centre, Environment Canada, 105 McGill St., 7th Floor, Montreal, Quebec, Canada H2Y 2E7

Received 25 February 1998; in revised form 24 December 1998; accepted 8 January 1999

Key words:Size distribution, invertebrates, periphyton, total phosphorous, river

Abstract

For the purposes of conducting environmental assessments, it has been suggested that benthic size distributionscould serve as complementary or alternative measures to traditional taxonomic descriptions, which are labourintensive and require much expertise. Consequently, temporal patterns of invertebrate and algal size distributions,along a trophic gradient from St. Lawrence River navigational buoys, were investigated in this study. It wasobserved that the size distributions were not significantly related to physical or chemical parameters of the river,although variability in the data may have been too high to detect trophic effects, as indicated by Monte Carlosimulations. Size spectra on buoys, despite the fact that protozoans were not accounted for, had striking similaritieswith other complete size distributions (containing algae, protozoans and invertebrates) from stream (Cattaneo,1993; characteristic size distributions of integral bethnic communities. Can. J. Fish aquat. Sci. 38: 1255–1263),lake (Cattaneo, 1987; Size distribution in periphyton Can. J. Fish. Aquat. Sci. 44: 2025–2028) and marine littoralzones (Schwinghammer, 1981; Size spectra of bethnic communities in Laurantian streams. Can J. Fish. aquat. Sci.50: 2659–2666). This suggests that size distributions, determined over broad size ranges, are relatively robust toenvironmental conditions and may be of limited use in assessing ecological degradation.

Introduction

The potential use of benthic size distributions forcomparing community structure among ecosystems(Cattaneo, 1993; Poff et al., 1993; Strayer, 1991;), forpredicting trophic transfers of contaminants and en-ergy (Borgmann, 1987; Boudreau & Dickie, 1992) andfor environmental assessments (Cattaneo et al., 1995;Morin et al., 1995) has often been stated. However, thescarcity of benthic size distributions data and predict-ive models have impeded the routine use of benthicsize distributions as an alternative or complementarymeasure to taxonomic descriptions of aquatic organ-isms. Quantification of seasonal and spatial variability,in relation to environmental characteristic is, there-fore, essential to the understanding and practical useof size distributions (Morin et al., 1995).

There are considerable differences in the amp-litudes and shapes of the few existing benthic size

distributions among studies and ecosystems. Unim-odal (Bourassa & Morin, 1995; Morin & Nadon, 1991;Rodriguez & Magnan, 1993; Strayer, 1986), bimodal(Cattaneo, 1987; Poff et al., 1993; Rasmussen, 1993)and trimodal distributions (Schwinghammer, 1981)with different peaks and troughs have been found inlake, stream and marine littoral ecosystems (see Cat-taneo, 1993). These differences may not only be dueto environmental constraints (Schwinghammer, 1981;Warwick & Joint, 1987) and/or evolutionary history(Strayer, 1991) but to methodological differences,such as organism size ranges examined (Poff et al.,1993), trophic groups sampled (i.e. zoobenthos only)and different graphical representations leading to vary-ing conclusions (i.e. non-logarithmicscales, Hanson etal., 1989). Some of these differences were reconciledin temperate North American streams, when size dis-tributions were constructed from organisms spanning a

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wide range of body sizes, which included protozoans,algae and macroinvertebrates (Cattaneo, 1987,1993;Morin & Nadon, 1991). These distributions werefound to be very similar to pelagic distributions (Shel-don et al., 1972; Sprules & Munawar, 1986; Ahrens &Peters, 1991) with roughly even biomass in logarith-mically increasing size classes and a normalised sizedistribution (log density per size class) with a slope ofapproximately−1.

Although size spectra for assemblages compris-ing algae, protozoa and invertebrates appear similaramong sites and across ecosystems, there is a sub-stantial amount of variability from the smooth lineartrend. It is this variability, according to Sprules &Munawar (1986), that may contain information aboutanthropogenic impacts. It is therefore important to ex-amine the effect of season and environmental factors,not only on the general negative trend between nor-malized biomass and organism body mass, but also onthe systematic deviations from this general trend.

In this paper, the seasonal patterns in invertebrateand algal size distributions from navigational buoyssampled over a trophic gradient in the Montreal area ofthe St. Lawrence River are investigated. Size spectraobserved on buoys are then compared to previouslydescribed size distributions. Lastly, their usefulness asdescriptors of aquatic communities for environmentalassessments is discussed.

Methods

Study area

The sampling sites were located in two distinct areasof the St. Lawrence River, in the Montreal (Quebec)area (45◦ 33′N, 73◦ 30′O – 45◦ 53′N, 73◦ 15′O) andthe Beauharnois Canal (45◦ 13′N, 74◦ 09′O), an areaapproximately 20 km upstream of Montreal (Figure 1).All sampling sites are in the green waters of the St.Lawrence River which outflow from the Great Lakeswatershed and constitute roughly 80% of the dischargedownstream of Montreal (7000 m3 s−1) (EnvironmentCanada, 1996). In this stretch of the river, the water isclear (average Secchi depth 3.2 m), strongly mineral-ised (average conductivity 266µS/cm), mesotrophic(average TP 16µg l−1) and generally fast flowing(average current velocity 0.9 m s−1).

Sites were chosen to encompass the widest trophicgradient possible in the Montreal area of the St.Lawrence River, a 5–28µg l−1 range in TP (Figure 1).

Therefore, two sampling sites (M104, M84) werelocated downstream of the Ile Charron wastewatertreatment plant, which serves the Longueil communityon the South shore and discharges domestic and in-dustrial wastes at an average rate of 3 m3 s−1 (CERS,1996). Other sites (M140, MA14, M132) were loc-ated downstream of the Montreal Urban Communitywastewater treatment plant (Ile aux Vaches) receiv-ing an average discharge of 23 m3 s−1 (MUC, 1996)from Montreal’s combined sewer system. M152 waslocated in the Montreal harbour which occasionallyreceived sewage from storm sewer overflows duringrainfall events (Deschamps et al., 1996). Lastly, thesampling site found in the Beauharnois Canal (C46)was not impacted by urban wastewater discharge andserved as a reference site.

Sampling

Sampling of biotic assemblages, in addition to meas-urements of the physical and chemical characteristicsof the water, was conducted at four buoys on May 7,five buoys on June 7, five buoys on August 1 and fivebuoys on September 26, 1995 (Table 1). Attached al-gae and invertebrates were removed from red metallicnavigational buoys, which ranged in diameter from 0.8to 1.4 m. These buoys are placed in the river by theCanadian Coast Guard each spring, in April or Mayjust after ice break-up, and remain in the water un-til late fall when they are retrieved to be cleaned andrepainted. On each buoy, triplicate samples of inver-tebrate and algal assemblages were collected 20 cmbelow the surface using a specially designed samplingapparatus, which has a 45.6 cm2 surface area (see Viset al., 1998a,b for detailed descriptions of samplingprotocol and apparatus used). Triplicate physical andchemical measurements taken at each site include:Secchi depth, suspended matter, pH, temperature, con-ductivity, current velocity, NO2 + NO3, NH4, TP, PO4,and SiO2 concentrations (see Vis et al., 1998a,b fordescription of methodology and instruments used).

Laboratory methods

In the laboratory, unsieved invertebrates were sortedfrom the collected material under a dissecting scopeat 12–25×. Samples that contained more than 200individuals were divided into smaller fractions us-ing a Folsom plankton splitter, until there remainedat least 100 individuals. Invertebrates were meas-ured using an image analysis system (±0.01 mm) in

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Figure 1. Location of sites sampled in the St. Lawrence River in the Beauharnois Canal area (reference) and in the Montreal area, downstreamof wastewater discharge points

order to estimate individual dry masses from allomet-ric equations. The equations used, for the followingtaxa, were obtained in the literature: Diptera, Chiro-nomidae, Ephemeroptera, and Trichoptera in Smock(1980) & Meyer (1989), Amphipoda in Marchant &Hynes (1981), Copepoda in Culver et al. (1985) andOligochaeta in Ladle & Bird (1984), Lafont (1987),and Lindegaard et al. (1994). Acarina and Nematodadry mass were estimated by DM (µg) =1 l3 (mm)(Morin and Nadon, 1991). A minimum of 600 algalcells were counted per sample at 640× and 160× inrandom fields using a Zeiss inverted microscope (Viset al., 1998b). Filamentous algae (e.g.Cladophora)were counted under a dissecting scope at 6.6× usingwhole samples (600 ml). The length and width of in-dividual cells were measured using an image analysissystem connected to the microscopes. Algal volumeswere calculated by approximation to known geometricshapes using mean species length and width (Wet-zel & Likens, 1991). Since each algal filament wasconsidered an individual, estimates of algal volume

reflect whole colonies and not individual cells withinfilaments. Some measurement error in the length ofindividuals are undoubtedly due to fragmentation offilaments from brushing and sonification (Ultrasoniccleaner at 50/60 Hz for 10 min). Filaments were there-fore counted as individuals of average length for eachsample (Cattaneo, 1987). Algal volumes were con-verted to wet mass by assuming a specific density of1 µg µm−3 (Cattaneo, 1993; Schwinghammer, 1981)and to dry mass by dividing wet mass by 4 (Cum-mins & Wuycheck, 1971). Density and biomass oforganisms was determined by dividing the numberof organisms and total dry mass of organisms by thesampler surface area (0.0045 m2).

Invertebrates and algae were grouped into 11 log-arithmic size classes which corresponded to an 8-fold increase in individual dry mass or a doublingof ‘equivalent spherical diameter’ (ESD) (Cattaneo,1993) and ranged in size from 10−6 to 104µg DM (drymass). Large size classes were used to quantify sizedistributions in order to reduce noise due to measure-

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Table 1. Sampling schedule of buoys and some of their physico-chemicalcharacteristics (average of three replicates per buoy).

Buoy Sampled TP Temp. Suspended Current

(µg l−1) (◦C) matter velocity

(mg l−1) (m s−1)

M104 May 7 8 8.8 2.9 1.01

M132 May 7 14 9.1 3.2 0.88

C46 May 7 5 8.5 0.9 0.84

June 7 10 15.6 2.1 0.89

August 1 13 23.4 1.9 0.95

September 26 12 15.5 0.9 0.80

M140 May 7 20 9.5 3.2 1.19

June 7 28 16.3 6.2 1.15

August 1 27 23.7 4.5 1.39

September 26 25 15.5 2.5 1.21

M84 June 7 17 15.9 5.0 1.31

August 1 22 23.6 7.2 0.93

September 26 14 15.6 2.4 0.89

M152 June 7 26 16.4 6.2 0.95

August 1 16 23.8 3.0 1.03

September 26 11 15.5 2.3 0.95

MA14 June 7 19 16.3 4.6 0

August 1 21 23.9 2.8 0.34

September 26 13 15.7 1.8 0.24

ment error (Ahrens & Peters, 1991) and to calculatingalgal density per size class based on average speciessize rather than individual size.

Statistical analyses

The effects of environmental factors on the amplitudeand shape of the size distributions of the invertebratesand algae were assessed by fitting their distributionsto polynomial regression models (Bourassa & Morin,1995). These models included terms such as TP andsuspended matter to test for physical and chemical ef-fects of the water on density, regardless of size class,as well as interaction terms between log10DM (drymass) and environmental factors which test for sizedependent changes in density. The models tested alsoincluded terms such as sampling date and the interac-tion between date and log10DM to test for temporalchanges in size distributions.

Monte Carlo simulations were run to estimate ourability to detect changes in size distributions in re-sponse to TP. To generate simulated data sets, we usedthe regression model to first predict the log10 densityin each size class for each buoy. We then added a nor-

mally distributed error term with a mean of 0 and avariance equal to the RMS of the regression model.To this randomised predicted density, we finally ad-ded hypothetical trophic effects in various scenarios.In one of these scenario, for example, we increaseddensity in all size classes by 50%, over the observed5–28µg TP gradient. For each analysis, we generated500 simulated data sets and counted the percentage oftimes TP was significant in the regression models.

Statistical analyses and simulations were conduc-ted with the statistical software package Systat 7.0(Wilkinson et al., 1996).

Results and discussion

Algae and invertebrates measured to describe sizespectra, ranged in size from 10−6 to 104 µg DM(Figure 2). Diatoms (Bacillariophyceae) and blue-green algae (Cyanophyceae) occupied the smallestsize classes (10−6 to 10−3 µg), whereas green algae(Chlorophyceae) spanned 6 orders of magnitude insize from 10−5 to 10µg. The filamentous algaClado-

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Figure 2. Sizes range for dominant groups of organisms foundcolonising navigation buoys. The Chlorophyceae group does notincludeCladophora

phorawas found in the largest classes (10 to 104 µg).Invertebrates occupied the 10−1 to 103µg size classes.

Biomass was not evenly distributed among sizeclasses but average values in most intervals were gen-erally within an order of magnitude of the geometricmean biomass for each sampling date (Figure 3). Ateach sampling date, biomasses in three size classeswere consistently lower than the geometric meanbiomass, corresponding to organisms≤ 10−6 andbetween 0.004 and 0.260µg DM for each samplingdate. This trough between large non-filamentous al-gae and small invertebrates may correspond to ciliates,which can be abundant in streams (Bott & Kaplan,1989; Cattaneo, 1993) and ranged in size from 10−4 to0.26µg DM in Laurentian streams (Cattaneo, 1993).Flagellates may also have overlapped in size with thesmallest algae (≤ 10−6 µg DM) (Bott & Kaplan,1989). Unfortunately, ciliates and flagellates were notquantified in this study but their presence on buoyswere observed when algae samples were counted (C.Vis, personal communication); it is unknown whetherinclusion of protozoans would have produced moreeven biomass distributions.

Seasonal changes in biomass occurred betweenspring and fall of 1995. Some of the successionalevents which can be related to these changes include

slower invertebrate colonisation relative to unicellu-lar algae (in May), potential grazing of algae byinvertebrates (June) and the development of filament-ous algae, predominantlyCladophora, in August andSeptember (Figure 3). In May, overall biomass washigh (601±223 mg m−2) and mainly composed ofnon-filamentous algae (585±214 mg m−2). In June,algal biomass was much lower (141±104 mg m−2),whereas invertebrate biomass was considerable higher(277±142 mg m−2) than in May (see large sizeclasses, Figure 3). In August and September, fila-mentous algae (mainlyCladophora) had developedresulting in an increase of biomass in the large sizeclasses (> 16µg DM). Overall biomass levels in Au-gust and September (651±177 and 685±95 mg m−2,respectively) were again comparable to those found inMay.

Density distributions (or normalized biomass spec-tra) of buoy assemblages, for each of the samplingdates, were best described by fourth and fifth or-der polynomials (Figure 4). These distributions hadsimilar shapes throughout the sampling period andas such, polynomial regression analyses did not re-veal any significant relationships between densityper size class and sampling date or the samplingdate•log10DM interaction. Furthermore, variability indensity per size class increased in the mid to largesize classes (> 10−3 µg) with time. The difficultiesin estimating the density of algal filaments and as-signing them to their proper size classes undoubtedlycontributed significantly to this variability. The fourth-and fifth-order curves that were fitted to these data areprobably not representative of the curves that wouldnormally be observed if all organisms (including pro-tozoans) within the size ranges sampled, had beenquantified. Cattaneo (1993) fitted linear regressionsto benthic distributions, as in many pelagic studies(e.g. Ahrens & Peters, 1991; Sheldon et al., 1972;Sprules & Munawar, 1986) , and had slopes rangingfrom –0.97 to –0.81. However, regression residualswere probably highly correlated due to the presenceof peaks and troughs in those distributions (Morin etal., 1995; Rasmussen, 1993), therefore, higher orderpolynomial regressions may have provided better fit insome cases.

Environmental conditions apparently were not im-portant in determining size distributions since noneof the physical or chemical parameters significantlyimproved the fit of polynomial models describingsize distributions (Table 2). Monte Carlo simulationsrevealed that our ability to detect changes in size dis-

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Figure 3. Average dry biomass (±SE) of orgamisms per size class (left panel) and average biomass of invertebrates (empty bars) and algae(filled bars) per size class (right panel) (n=4 in May, n=4 in June,n=5 in August andn =5 in September). The reference line represents thegeometric mean biomass for each date.

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Figure 4. Destiny of invertebrates and algae per size class colonising buoys for each sampling date (n=4 in May, n=4 in June,n=5 in Augustandn=5 in September). The lines represent fourth-and fifth-order linear regression. The symbols•,◦,N, 4 and� are in order of increasingtrophic conditions.

tribution related to TP was low. Density in all sizeclasses would have to increase by 3-fold over the ob-served TP range for 50% chance of detection, and by5-fold if only the five larger size classes were affected.When applying the model developed by Bourassa &Morin (1995) for Eastern Canadian streams over ourobserved 8 – 26µg l−1 range of total phosphorous,only a 1.1–1.8 fold increase in density, in response

to this increase in TP, would be expected. Therefore,given the residual variation of our polynomial mod-els, the chance of detecting trophic effects on sizedistributions, if they exist, were low.

Size distributions of algae and invertebrates col-onising buoys were visually compared with the mostcomplete published benthic distributions, which con-tained protozoans, macroinvertebrates and epiphyton

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Table 2. Polynomial regression model parameters de-scribing invertebrate and algae log10density per sizeclass (ind. m−2) as a function of dry mass (M,µg ) (adj.R2=0.83, RMS=1.23,n=198). Note that TP and the inter-action term TP•M were not significant parameters in themodel.

Independent variable Coefficient P

(standard error)

Intercept 3.22 (0.25) <0.0001

Log10 m −0.52 (0.11) <0.0001

(Log10 m)2 0.48 (0.04) <0.0001

(Log10 m)3 −0.02 (0.01) <0.0001

(Log10 m)4 −0.02 (0.00) <0.0001

TP 0.91

TP•log10M 0.67

Figure 5. Size distributions of invertebrates and algae from St.Lawrence River buoys and protozoans, algae and, macroinverteb-rates from lakes (Cattaneo, 1987), marine littoral zones (Schwing-hammer, 1981) and temperate streams (Cattaneo, 1993).

found in lakes (Cattaneo, 1987), streams (Cattaneo,1993) and marine littoral areas (Schwinghammer,1981) (Figure 5). Our size distribution had similarabundance in most size classes, except for the ob-vious troughs in the smallest class and the middlesize classes, which probably correspond to missingflagellates and ciliates, respectively. These strong sim-ilarities despite clearly different species assemblages,across ecosystems and substrates, suggest strong size-based constraints on community organisation and re-source allocation (Peters, 1983). Environmental con-ditions, therefore, seem to be of lesser importance indetermining benthic size distribution, especially whenquantifying a broad size range of organisms.

The difficulties in detecting systematic deviationsrelated to Montreal area trophic gradients (or wastewa-ter discharge), along with the apparent similarity ofsize spectra across ecosystems, imply that they are notvery useful or practical community measures to assessecological changes in the St. Lawrence River. In or-der to obtain higher measurement precision to increasethe chances of detecting trophic effects, if indeed theyexist, more data and/or more precise data (e.g. re-ducing measurement error ofCladophorafilaments)would be required for statistical analysis. The onlyway to achieve this level of precision would be to con-duct more field sampling and organism measurements,even in studies such as this one where standardisedhabitats were used to reduce sampling noise (Rosen-berg & Resh, 1982). However, these efforts may bemuch more labour intensive than measuring other po-tentially more sensitive components of the benthicassemblages, particularly in situations of low levels ofpollution which elicit subtle responses. For example,size distributions of particular trophic groups (e.g. epi-phyton or invertebrates) may be more responsive toenvironmental conditions (see Cattaneo et al., 1995;Bourassa & Morin, 1995; Mercier, 1998) becausethese organisms share similar ecological limitations(e.g. particulate organic matter for invertebrates) orare physiologically susceptible to particular stressors(e.g. herbicides). Alternatively, the use of size dis-tributions of particular taxa (e.g. Baetidae family ofEphemeroptera) which are expected to be either partic-ularly tolerant or sensitive to changes in water quality,would probably be more sensitive and less labour in-tensive (S. McKee, personal communication). Futureresearch in integrating size distributions with environ-mental assessments should probably focus on the lastapproach to maximise our ability to detect ecosystemperturbations.

Acknowledgements

We are grateful to Kim Desrochers, ThierryDanserault, Sophie Wong and Aline Sylvestre forproviding technical assistance in the lab and the field.This study was funded by an Ontario Graduate Schol-arship Award to V. Mercier, an operating grant anda grant from the National Sciences and EngineeringResearch Council of Canada to A. Morin and C. Vis,respectively. The St. Lawrence Centre was invaluablein providing labour and equipment for field samplingand physico-chemical analyses of the water.

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References

Ahrens, M.A. & R.H. Peters. 1991. Patterns and limitations in lim-noplankton size spectra. Can. J. Fish. aquat. Sci. 48: 1967–1978.

Borgmann, U., 1987. Models on the slope of, and the flow up, thebiomass size spectrum. Can. J. Fish. aquat. Sci. 44 (Suppl. 2):136–140.

Bott, T.L. & L.A. Kaplan, 1989. Densities of benthic protozoa andnematodes in a Piedmont stream. J. n. am. Benthol. Soc. 8: 187–196.

Boudreau, P. R. & L. M. Dickie, 1992. Biomass spectra of aquaticecosystems in relation to fisheries yield. Can. J. Fish. aquat. Sci.49: 1528–1538.

Bourassa, N. & A. Morin, 1995. Relationships between sizestructure of invertebrate assemblages and trophy and substratecomposition in streams. J. n. am. Benthol. Soc. 14: 393–403.

Cattaneo, A., 1987. Size distribution in periphyton. Can. J. Fish.aquat. Sci. 44: 2025–2028.

Cattaneo, A., 1993. Size spectra of benthic communities in Lauren-tian streams. Can. J. Fish. aquat. Sci. 50: 2659–2666.

Cattaneo, A., G. Méthot, B. Pinel-Alloul & T. Niyonsenga, 1995.Epiphyte size and taxonomy as biological indicators of ecolo-gical and toxicological factors in Lake Saint-François (Québec).Envir. Pollut. 87: 357–372.

CERS, 1996. Monthly summary report for 1994–1995. Aquacers.Société de gestion du CERS (Centre d’Épuration de la Rive Sud)Inc, Longueil, Quebec, Canada.

Culver, D.A., M.M. Boucherle, D.J. Bean & J.W. Fletcher, 1985.Biomass of freshwater crustacean zooplankton from lengthweight regressions. Can. J. Fish. aquat. Sci. 42: 1380–1390.

Cummins, K.W. & J.C. Wuycheck. 1971. Caloric equivalents forinvestigations in ecological energetics. Int. Ver. Theor. Angew.Limnol. Mitt. 18: 1–158.

DesChamps, G., C. Juteau & P. Cejka, 1996. Bilan Sommairedes activités du réseau de suivi écologique de 1990 à 1995.Communauté Urbaine de Montréal, Montreal, Quebec, Canada.

Environment Canada, 1996. Daily flow values for the St. Lawrence.Hydrology Section, Atmospheric Environment Service, QuebecRegion, Montreal, Quebec.

Hanson, J.M., E.E. Prepas & W.C. MacKay, 1989. Size distributionof the macroinvertebrate community in a freshwater lake. Can. J.Fish. aquat. Sci. 46: 1510–1519.

Ladle, M. & G.J. Bird, 1984. The biology ofPsamoryctides bar-batus (Grube) in English chalk streams. Hydrobiologia 115:109–112.

Lafont, M., 1987. Production of Tubificidae in the littoral zone ofLake Léman near Thono-les-Bains: a methodological approach.Hydrobiologia 155: 179–187.

Lindegaard, C., K. Hamburger & P.C. Dall, 1994. Populationdynamics and energy budget ofMarionina southerni(Cernos-vitov) (Enchytraeidae, Oligochaeta) in the littoral of Lake Esrom,Denmark. Hydrobiologia 278: 291–301.

Marchant, R. & H.B.N. Hynes. 1981. Field estimates of feeding ratefor Gammarus pseudolimnaeus(Crustacea: Amphipoda) in theCredit River, Ontario. Freshwat. Biol. 11: 27–36.

Mercier, V., 1998. Relationships between composition andsize distribution of invertebrates colonising navigation buoysand physico-chemical parameters of the St. Lawrence River,Montreal (Quebec). M.Sc. thesis, University of Ottawa, Canada.

Meyer, E., 1989. The relationship between body length parametersand dry mass in running water invertebrates. Arch. Hydrobiol.117: 191–293.

Morin, A. & D. Nadon, 1991. Size distribution of epilithic loticinvertebrates and implications for community metabolism. J. n.am. Benthol. Soc. 10: 300–308.

Morin, A., M. Rodriguez & D. Nadon, 1995. Temporal and environ-mental variation in the biomass spectrum of benthic invertebratesin streams: an application of thin-plate splines and relative warpanalysis. Can. J. Fish. aquat. Sci. 52: 1881–1892.

MUC, 1996. Monthly summary reports for 1994-1995. MontrealUrban Community, Environment Services, Montreal, Quebec,Canada.

Peters, R.H., 1983. The Ecological Implications of Body Size.Cambridge University Press, Cambridge, U.K. 329 pp.

Poff, N.L., M.A. Palmer, PlL. Angermeier, R.L. Vadas Jr., C.C.Hakenkamp, A. Bely, P. Arensburger & A.P. Martin, 1993. Sizestructure of the metazoan community in a Piedmont Stream.Oecologia. 95: 202–209.

Rasmussen, J.B., 1993. Patterns in the size structure of littoralzone macroinvertebrate communities. Can. J. Fish. aquat. Sci. 50:2192–2207.

Rodriquez, M.A. & P. Magnan, 1993. Community structure oflacustrine macrobenthos: do taxon-based and size-based ap-proaches yield similar insights? Can. J. Fish. aquat. Sci. 50:800–815.

Rosenberg, D.M. & V.H. Resh, 1982. The use of artificial substratesin the study of freshwater benthic macroinvertebrates,In: CairnsJ. (ed.), Artificial Substrates. Ann Arbor Science Publishers, AnnArbor, MI. 175–235.

Schwinghammer, P., 1981. Characteristic size distributions of integ-ral benthic communities. Can. J. Fish. aquat. Sci. 38: 1255–1263.

Sheldon, R.W., A. Prakash & W.H. Sutcliffe Jr, 1972. The sizedistribution of particles in the ocean. Limnol. Oceanogr. 17:327–340.

Smock, L.A., 1980. Relationships between body size and biomassof aquatic insects. Freshwat. Biol. 10: 375–383.

Sprules, W.G. & M. Munawar, 1986. Plankton size spectra in re-lation to ecosystem productivity, size and perturbation. Can. J.Fish. aquat. Sci. 43: 1789–1794.

Strayer, D., 1986. The size structure of a lacustrine zoobenthiccommunity. Oecologia 69: 513–516.

Strayer, D., 1991. Perspectives on the size distributions of lacustrinezoobenthos, its causes, and its consequences. J. n. am. Benthol.Soc. 10: 210–221.

Vis, C., A. Cattaneo & C. Hudon, 1998a. Periphyton in the clearand colored water masses of the St. Lawrence River (Quebec,Canada): a 20-year overview. J. Great Lakes Res. 24: 105–117.

Vis, C., C. Hudon, A. Cattaneo & B. Pinel-Alloul, 1998b.Periphyton as an indicator of water quality in the St. LawrenceRiver (Québec, Canada). Envir. Pollut. 101: 1–12.

Warwick, R.M. & I.R. Joint, 1987. The size distribution of organ-isms in the Celtic Sea: from bacteria to metazoa. Oecologia 73:185–191.

Wetzel, R.G. & G.E. Likens, 1991. Limnological Analyses. 2nd edn.Springer-Verlag, New York: 391 pp.

Wilkinson, L., G. Blank & C. Gruber, 1996. Desktop Data Analysiswith SYSTAT. Prentice-Hall, New Jersey: 798 pp.