On the role of natural water level fluctuation in structuring littoral benthic

macroinvertebrate community composition in lakes

Michael S. White,a,1,* Marguerite A. Xenopoulos,b Robert A. Metcalfe,c and Keith M. Somersd

aWatershed Ecosystems Graduate Program, Trent University, Peterborough, OntariobDepartment of Biology, Trent University, Peterborough, Ontarioc Renewable Energy Section, Ministry of Natural Resources, Trent University, Peterborough, OntariodDorset Environmental Science Centre, Ontario Ministry of the Environment, Dorset, Ontario

Abstract

We used traditional hydrologic endpoints and extracted novel water-level fluctuation (WLF) characteristicsthrough principal components analysis (PCA), to determine the relationship between WLF and rocky littoralbenthic macroinvertebrate (BMI) richness in 16 boreal lakes. Yearly WLF amplitude (maximum minus minimumwater levels) ranged from 35.9 cm to 157.5 cm. Using PCA we derived a new variable D80-D210 (31 March minus01 August) as a surrogate for change in mean water level and potential habitat squeeze (loss). Analyses of BMIrichness with several physicochemical variables, including water quality (8), habitat variability (4), lake and basinmorphology (6), land classification (28), water temperature (8), hydrology (9), and PCA axes (10) resulted in onlythree significant relationships. We found a classic speciesarea relationship, as BMI richness increases withincreasing lake area (r2 5 0.38linear, r2 5 0.69unimodal). Similarly, as littoral slope increases macroinvertebraterichness decreases (r2 5 0.32linear). Most importantly, lower water levels, quantified using D80-D210, have highermacroinvertebrate richness (r25 0.38linear). Together these results suggest that a habitat squeeze in littoral areas isthe direct result of lower mean water levels and that relatively small changes in natural WLF can be associatedwith changes in BMI communities.

The response of organisms to changes in their environment(disturbance ecology) is a well-studied phenomenon in bothterrestrial and aquatic biomes (Mackey and Currie 2001). Inorder to better understand the effects of anthropogenicalterations and disturbance on biotic communities, research-ers must first recognize the role of natural variability. This isimportant so that unnatural disturbances can be assessedagainst naturally occurring levels of variability. Lack of athorough understanding of natural conditions limits thestrength of conclusions drawn from studies of effects inducedby humans (Bowman and Somers 2005).

One area in freshwater aquatic ecology where there islimited understanding of the natural variability and ecologicaleffects is the role of natural water-level fluctuation (WLF),particularly, in lake ecosystems (Wantzen et al. 2008;White etal. 2008). Considerable research has focused on studyingunnatural or manmade changes in water levels throughstudies of systems regulated by humans (e.g., hydroelectricreservoirs (Hill et al. 1998; Aroviita and Hamalainen 2008);however, we have a very poor understanding of natural WLFand its affect on aquatic communities.

In reservoirs and other manmade aquatic systems, WLFdecreases macrophyte diversity (Hill et al. 1998), macroin-vertebrate diversity (Aroviita and Hamalainen 2008), fishrecruitment (Fisher and Ohl 2005), and waterfowl recruit-ment (Sayler and Willms 1997). Regulated WLF inreservoirs often results in altered timing of flood events

and extremely low winter water levels (Aroviita andHamalainen 2008) compared with natural systems. There-fore, the usefulness of this WLF information fromreservoirs for making predictions on how changes innatural water levels will affect biological communities isquestionable. In addition, most reservoir studies focus ononly one WLF measure: amplitude (maximum minusminimum yearly water level). Although this basic metricoften produces significant results, effects are only found atlevels that exceed those of natural lakes (White et al. 2008).

While studies concerning the effects of WLF on aquaticbiota in hydroelectric reservoirs help us better understandthe effect of water regulation, these studies have a limitedcapacity to explain the role of natural WLF in structuringaquatic communities. Furthermore, climate scenariospredict more subtle changes in natural lake WLF (Magnu-son et al. 1997) than fluctuations observed in reservoirs. Asa result, to understand the potential effects of climatechange, a thorough understanding of natural WLF and itsecological relationships is necessary. The objective of thisstudy was to evaluate natural WLF character, to identify itspredictors, and to identify components of natural WLFthat correlate with nearshore stony littoral benthic macro-invertebrate (BMI) communities. We compared annualWLF and BMI communities in 16 relatively large BorealShield lakes. We hypothesized that landscape and climaticconditions influence WLF character which, in turn,structures littoral BMI communities.

Methods

Study siteSixteen lakes located in the Boreal Shieldecoregion of Ontario, Canada were used in this study

*Corresponding author: mwhite@laurentian.ca

1 Present address: Cooperative Freshwater Ecology Unit,Laurentian University, Department of Biology, Sudbury, Ontario,Canada.

Limnol. Oceanogr., 55(6), 2010, 22752284

E 2010, by the American Society of Limnology and Oceanography, Inc.doi:10.4319/lo.2010.55.6.2275

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(Fig. 1). This geographic area covers approximately300,000 km2, ensuring that study lakes experience a rangeof WLF due to climatic variability and geographic location.The dominant forest cover is black spruce (Picea mariana),white spruce (Picea glauca), white pine (Pinus strobes), jackpine (Pinus banksiana), poplar (Populus spp.), and birch(Betula spp.). Further, forests near the Great Lakes ofteninclude hardwoods such as maple (Acer spp.) and ash(Fraxinus spp.). In general, this region has a temperateclimate, receiving 600800 mm annual precipitation in thenorthwest (mean annual low temperature between 245uCand 240uC, mean annual high temperature between 26uCand 38uC) and 8001200 mm annual precipitation in thenortheast (mean annual low temperature between 240uCand 234uC, mean annual high temperature between 26uCand 38uC) (Natural Resources Canada 2004). The studylakes are part of the Ontario Water Level Lake Series(OWLLS) project; primarily funded by the OntarioMinistry of Natural Resources (OMNR). The focus ofOWLLS is to monitor and study water levels in lakes acrossthe province. Lake selection was based on the followingcriteria: surface area . 10 km2, mean depth , 10 m, nodam control structure upstream or immediately down-stream, accessible by road, and geographically dispersedacross the province.

Sampling protocolAll 16 lakes were outfitted withwater-level loggers in October 2005. Both SolinstH model2001 and HOBOH model U20-001-01 (Onset) loggers wereemployed, each logger was surveyed to a reference pin andset to record temperature and water level at hourly intervals365 d/yr. Biotic and abiotic sampling was conducted in latesummer of 2006 during a 4-week period (14 August to 11September), which limits temporal variation (Reid et al.1995). Each lake was sampled at one location only. At each

site the following water-quality measurements were takenusing a YSI 600QS SondeH: conductivity, pH, oxidationreduction potential (ORP), water temperature, and dis-solved oxygen (DO). A single 1-liter water sample wascollected for total phosphorus (TP), total dissolvednitrogen (TDN), and dissolved organic carbon (DOC). A500-ml subsample was filtered using a polycarbonate,0.2 mm-pore, 47-mm filter (GE Water and ProcessTechnologies) within 4 h of sample collection for TDNand DOC. Immediate processing of the water samples wasnot feasible; therefore, all water samples were stored at 4uCin the dark for 4 weeks until analysis in the lab waspossible.

Rocky littoral benthic macroinvertebrates were sampledin triplicate at each sampling location following the OntarioMinistry of the Environments (OMOE) lake benthossampling protocol (Jones et al. 2007). Each of three transectswere perpendicular to the shore and were kick sampledstarting at 1 m depth and proceeding back to shore until thewaters edge was reached. Kick sampling was standardizedby sampling for 1 min using a 500-mm mesh kick net. Totaltransect length was recorded along with GPS coordinatesand multiple habitat descriptors (% canopy coverage, threecobble size classes 6.525 cm, % woody debris, riparianvegetation type, and three attached algae classes) (Jones etal. 2007). Each kick-and-sweep transect was located 5 mapart. To reduce habitat variability, sampling locations wereconstrained to the following criteria: stony littoral substrateof 6.525 cm in length, west-facing shoreline, with fetch. 1 km. In situ measurements were taken for the above-mentioned parameters to account for any unexpectedinfluence that may be attributed to the site. Benthicmacroinvertebrate samples were preserved using 200 mL ofKahles solution (Martin 1977) and stored in 500-mLpolyethylene jars. In the lab, the BMI samples were sievedto 500 mm and all macroinvertebrates were removed usingstandard quality assurance and quality control (Jones et al.2007) procedures at 10X magnification. Due to the largegeographic extent of this study, heterogeneity between siteswas reduced by identifying BMI to the family level oftaxonomic resolution (Bailey et al. 2004). Water mites werethe only taxon not identified to family, and all mites wereidentified collectively as the Hydracarina. All BMI wereidentified using either the work of Merritt et al. (2008) or ofPeckarski et al. (1990).

Landscape analysisLand classification (28 land classi-fications) and delineation was determined using OntariosNatural Resources and Values Information System(NRVIS) database (OMNR 2002) and the Water Resourcesand Information Project (WRIP) (OMNR 2006). Thesedata were analyzed using ArcMapH (ESRI) to determinelake and watershed boundaries, watershed slope, and landuse. Lake morphology was extracted from the OntarioMinistry of Natural Resources (OMNR) lake database forthe following three variables: maximum depth, mean depth,and shoreline development (shape).

Hydrological data analysisHydrologic parameters werecalculated from water-level readings spanning the period

Fig. 1. Map of Ontario depicting locations of the 16study lakes.

2276 White et al.

from 01 March 2006 until 30 November 2006. However, ofthe calculated hydrologic parameters, it is expected thatonly those occurring before BMI collection (14 August2006 to 11 September 2006) will demonstrate meaningfulrelationships with BMI community structure. Standardiza-tion to a common WLF was accomplished by adjusting thewater level for each lake to identical starting heights at 01March 2006. This standardization date was selected due tothe fact that this date occurred at least a week before any ofthe 16 study lakes had started to exhibit a spring melthydrologic signature. Variation in hourly logger readingsdue to instrument sensitivity required that the hourly databe smoothed by taking a 12-h running average. Meandaily values were then calculated and used in calculatinghydrologic endpoints. The following metrics were calculat-ed from the water temperature data: mean temperature,minimum temperature, maximum temperature, coefficientof variation, and day of year at which a temperature of 4uC,10uC, 15uC, and 20uC was first attained. The followinghydrologic endpoints were calculated: water-level coeffi-cient of variation, amplitude (maximum minus minimumwater level), melt day (first day when water level exceedswater level during winter months), fastest 15-d flood rate(rate of water-level rise during spring), flood rate (melt dayto day of maximum water level), recessional limb rate (dayof maximum water level to 01 September), day of yearwhen maximum water level was attained (MWLD), water-level rise duration (WLRD, calculated as the number ofdays between melt day and MWLD), and mean annualrunoff. Mean annual runoff for each watershed wasestimated using Canada Gridded Climate Data 19611990(Hopkinson 2000) with ArcMapH by subtracting rastergrids of total annual potential evapotranspiration fromtotal annual precipitation, assuming no change in storage.Further, a novel hydrologic endpoint (D80-D210) wascalculated by subtracting the water level at day 80 from thewater level at day 210. Water level at day 80 is a surrogatefor the winter water level, and water level at day 210 isshown in the results to be predominantly important in thePCA extraction (see below) and precedes BMI sampling(day 226254). Thus, D80-D210 is a measure of the changein water level from ice-covered winter months to midsum-mer, representing an index of the change in habitatavailability.

Statistical analysisA principal components analysis(PCA) was used to summarize the water level of the 16lakes using Personal Computer Ordination (PC-ORD)multivariate analysis of ecological data (McCune andMefford 1999). The resulting ordination was then overlaidwith a second matrix containing landscape, lake morphol-ogy, water quality, BMI richness, and commonly measuredhydrologic endpoints to determine correlations with theextracted axes.

We used regression analysis (SPSS 2000) to evaluate therelationships among extracted principal components, com-monly measured hydrological variables, water temperature,water quality, lake morphology, basin morphology, habitatcharacteristics, and land classification with BMI richness.Where necessary, data was transformed to satisfy normality

assumptions. We also conducted stepwise multiple regres-sions using the same variables employed in regressionanalyses to determine which combination of variables bestexplained BMI richness (SAS 2001). Where necessary, atest of collinearity was conducted as described in Xeno-poulos et al. (2003) using SAS (SAS Institute 2001). Thecollinearities that resulted were all low and did not affectmodel outputs.

To identify taxa that are most affected by WLF, anindicator taxa analysis was conducted following themethod of Dufrene and Legendre (1997) using PC-ORD(McCune and Mefford 1999). This method combinesrelative abundance and relative frequency of occurrenceto identify indicator taxa.

Table 1. Limnological properties of the 16 study lakes. Waterquality was sampled from 14 August to 11 September 2006.

Lake properties (n516) Mean 6 SD Max Min

Lake area (km2) 31.52620.85 67.85 8.14Basin area (km2) 507.986571.71 2355.15 87.51Basin : lake area ratio 17.29614.29 56.27 3.87Elevation (m) 364.19657.15 437.00 252.00Max depth (m) 33.7467.33 48.80 16.20Mean depth (m) 8.5361.17 10.30 6.10Shoreline development (shape) 7.3662.69 12.19 3.05Littoral slope (u) 11.5864.16 21.80 6.30Fetch (m) 2667611146 5360 971DOC (mg L21) 7.3961.18 9.56 4.90TDN (mg L21) 0.2160.04 0.32 0.16TP (mg L21) 14.7565.20 23.71 4.72DO (mg L21) 8.6860.40 9.30 7.92Conductivity (mS cm21) 69.75646.34 167.00 23.00pH 7.6460.36 8.47 7.18ORP 85.15631.02 141.10 17.10

Fig. 2. Hydrograph showing mean water level (solid line) ofthe 16 study lakes 6 1 standard deviation (dashed line). Waterlevels were recorded at hourly intervals and were smoothed usinga 12-h running average. All analyses were conducting usingaverage daily values.

Water level and benthos 2277

For the indicator-taxa analysis, lakes were categorizedinto three groups based on the magnitude of change inwater level: the group with the highest change had D80-D210 values between +8.42 and 23.30 cm, n 5 5; the groupwith mid-range change had D80-D210 values between23.30 cm and 28.90 cm, n 5 5; and the group with thelowest change had D80-D210 values between 28.90 cm and224.20 cm, n 5 6. Significance of a taxon belonging toeither the high, mid, or low group was determined usingMonte Carlo randomization procedure using 1000 permu-tations (Dufrene and Legendre 1997).

Results

Lake morphology and water quality varied among lakes(Table 1). Similarly, amplitude ranged between 157.50 cmand 35.97 cm, with a mean and standard deviation of73.29 cm and 34.90, respectively (Fig. 2). Melt day variedfrom 11 March to 30 March 2006. The fastest 15-d floodrate ranged from 0.90 cm d21 to 7.83 cm d21, with mean 6standard deviation of 2.79 cm d21 6 2.05, respectively.Generally, lakes in Northeastern Ontario had largeramplitudes and greater flood rates.

We used PCA to extract empirical hydrologic endpointsassociated with WLF. A total of 10 axes were extracted; thefirst three axes explained 89.9% of the cumulative variance(Table 2). Based on day of year, axis 1 separated lakesalong day 210; axis 2 separated lakes between day 80 anddays 126147; and axis 3 separated lakes along day 300(Fig. 3a; axis 3 not shown). BMI richness was moststrongly correlated with axis 1 (r 5 20.51). D80-D210values ranged between +8.42 cm and 224.17 cm, with amean 6 standard deviation of 27.39 cm 6 7.97,respectively. A set of hydrologic endpoints was correlatedwith the WLF-derived principal components. We found

Table 2. Eigenvalues for the first 10 axes extracted throughPCA of WLF data from 01 March 2006 until 30 November 2006.Ordination of axis 1 and axis 2 presented in Fig. 3a.

Axis Eigenvalue % variance explained

1 102.66 41.062 85.01 34.003 36.10 14.444 11.86 4.755 5.27 2.116 2.96 1.187 1.58 0.638 1.48 0.599 1.13 0.4510 0.52 0.21

R

Fig. 3. Ordination of PCA showing (a) vector response of WLFwith axes 1 and 2, (b) vector response of measured hydrologic andtemperature variables with axes 1 and 2, and (c) vector response ofwater quality, landscape and lake morphology variables with axes1 and 2. Although 73 environmental variables were included inanalyses only variables with r values greater than 0.45 with eitheraxis 1, 2, or 3 are displayed. Vector length and direction is

proportional to its relationship with each axes. Open circlesrepresent lakes in northeastern Ontario, solid circles representlakes in northwestern Ontario, and solid triangles represent lakesin north-central Ontario (see Fig. 1).

2278 White et al.

that axis 1 had the highest correlation with D80-D210 (r 50.83); axis 2 was correlated with both amplitude (r 5 0.87)and flood rate (r 5 0.77), and recessional limb rate (r 520.84); and axis 3 was correlated with MWLD (r 5 20.74)and WLRD (r 5 20.58) (Fig. 3b; Table 3). All water-temperature data were most strongly correlated with axis 1.This could be due to the complementary effect of changesin mean water level adding increased variability to water-temperature readings: as the data logger records tempera-ture at a fixed position. Only three water-quality variablesdemonstrated strong correlations with axis 1, axis 2, or axis3: conductivity correlated with axis 1 (r 5 20.71); DOcorrelated with axis 2 (r 5 0.70); and ORP between axis 1and axis 2 correlated with r values of 0.44 and 20.47,respectively. Interestingly, no lake morphometry or land-class variables had strong correlations with axis 1. The fourparameters: basin area, basin : lake area ratio, deep water,and open fen showed the strongest correlations with axis 2,with r values of 0.68, 0.71, 20.61, and 0.52, respectively.Mean depth, lake area, and littoral slope had similarstrength correlations with both axis 1 and axis 3 (Table 3).

Linear regression of: principal components (n 5 10),hydrologic variables (n 5 9), water temperature (n 5 8),habitat characteristics and lake morphology (n5 10), waterquality (n 5 8), and land classification (n 5 28) with BMIrichness revealed four significant (p , 0.05) relationships:BMI richness decreased as axis 1 scores increased (r2 50.25); BMI richness decreased with increased D80-D210values (r2 5 0.38); BMI richness decreased with increasedlittoral slope (r2 5 0.32); and BMI richness increased withlake area (r2 5 0.38) (Fig. 4; axis 1 relationship not

displayed). A better fit (higher r2) of lake area with BMIrichness was found using a unimodal Gaussian (three-parameter) equation: f 5 a?exp(20.5?((x 2 x0)/b)2), p 50.001, r2 5 0.69 (Fig. 4b). Forward, stepwise, multiple-regression analysis resulted in lake area and littoral slopeexplaining BMI richness y 5 1.1764e27(lake area) 20.5069(littoral slope) + 26.0972 (p 5 0.03). Predicted versusactual BMI richness resulted in an r2 of 0.58. The inclusionof any predictor with D80-D210 did not yield significantresults. Analysis of collinearity confirmed that D80-D210,lake area, and littoral slope are not collinear with respect toBMI species richness because the variance inflation numberwas , 10 and condition index was , 30.

A total of 49 taxa were identified, and mean BMI richnessvalues ranged from 16 to 33, with mean 6 standarddeviation of 23.94 6 4.71, respectively. Indicator speciesanalysis identified four taxa with significant D80-D210group designation (Table 4). These taxa were the Gomphi-dae, Hydracarina, Leptoceridae, andMacromiidae and wereall determined to be indicative of mid D80-D210 water levels(23.30 cm to28.90 cm) and had p values of 0.01, 0.01, 0.02,and 0.03, respectively. Most taxa were designated to the midand low D80-D210 groups but had p values . 0.05.

Discussion

We found that natural WLF in large boreal lakes variedlittle (35.9157.5 cm) compared with reservoirs created forpower production, which vary several fold more (e.g., 17 m) (Aroviita and Hamalainen 2008). Although the WLFwere small, we still found significant effects on the BMI

Table 3. Pearson correlation coefficients (r) and p values for axis 1, axis 2, and axis 3 with environmental variables having r values. 0.45 with one of the first three axes. All p values , 0.05 are identified in bold.

Environmental variable

Axis 1 Axis 2 Axis 3

r p r p r p

D80-D210 0.83 0.0001 20.37 0.1582 0.31 0.2371Amplitude 20.34 0.1930 0.87 0.0001 0.26 0.3281Flood rate (15 d) 20.35 0.1863 0.76 0.0006 0.46 0.0714Flood rate 20.23 0.3892 0.77 0.0005 0.54 0.0326Recessional limb rate 0.45 0.0804 20.84 0.0001 20.20 0.4524Melt day 20.05 0.8449 0.64 0.0071 20.33 0.2140MWLD 20.33 0.2199 20.11 0.6904 20.74 0.0010WLRD 20.30 0.2646 20.42 0.1083 20.58 0.0185CV water level 20.30 0.2643 0.85 0.0001 0.41 0.1123CV water temperature 20.54 0.0326 20.25 0.3535 20.10 0.7029Mean temperature 0.59 0.0171 20.03 0.9187 20.16 0.5443Temperature to 4uC 20.76 0.0006 0.15 0.5875 0.22 0.4245Elevation 0.72 0.0018 20.28 0.2922 20.11 0.6757DO 20.37 0.1601 0.70 0.0024 0.10 0.7186Conductivity 20.71 0.0022 0.33 0.2152 20.01 0.9587ORP 0.44 0.0855 20.47 0.0654 0.03 0.9186Mean Depth 20.43 0.3845 20.30 0.5322 0.48 0.6635Basin area 20.26 0.3317 0.68 0.0038 20.15 0.5900Lake area 20.47 0.0659 20.05 0.8637 20.53 0.0363Basin : lake area ratio 20.12 0.6644 0.71 0.0021 0.35 0.1802Littoral slope 0.45 0.0792 20.10 0.7121 0.44 0.0907Open water 0.09 0.7510 20.61 0.0117 20.39 0.1403Open fen 20.33 0.2129 0.52 0.0398 20.07 0.8011Treed bog 0.16 0.5489 0.70 0.0025 0.04 0.8919BMI richness 20.51 0.0463 0.15 0.5701 20.45 0.0821

Water level and benthos 2279

community. As such, it appears that BMI are influenced bynatural WLF and our hypothesis was supported.

The two most prominent hydrologic features of lakeWLF, were amplitude and change in mean water levelmeasured as D80-D210. Of the many water-level param-eters measured, only the D80-D210 measure yieldedsignificant results with BMI richness. With the exceptionof one lake, our study lakes experienced a decrease in meanwater levels over the 20062007 period. This reduction inmean water level, quantified as D80-D210, ranged between+8.42 cm and 224.17 cm. We found that lakes with thegreatest decrease in water levels (at time of sampling)relative to winter water level had elevated BMI richness instony littoral habitats (Fig. 4c). The causality of thisrelationship is unclear. It is highly plausible that decreasedwater level reduces available littoral habitats resulting in ahabitat squeeze effect.

In addition to D80-D210, lake area and littoral slope weresignificantly correlated with BMI richness and, although notcollinear, all three environmental descriptors had highcorrelations. Although lakes were chosen based on aminimum size (. 10 km2), not having more stringent lake-size and littoral-slope criteria limit the strength of the studyresults: as lake area revealed a classic speciesarea curverelationship (Fig. 4b) and species richness declined withincreased littoral slope (Fig. 4a). The best multivariatemodel predicting BMI richness included lake area andlittoral slope. This indicates that although D80-D210explains some variance in BMI richness it may not be asimportant as lake area in predicting richness. The negativeinfluence of increased littoral slope on BMI diversity hasbeen shown in previous studies (Scheifhacken et al. 2007);however, its univariate relationship with BMI richness in ourstudy proved weaker than D80-D210 change in water level.

The linear relationships of D80-D210 and lake area withBMI richness had identical r2 values (0.38), but we alsofound that lake area exhibited a strong unimodal relation-ship with BMI richness (r2 5 0.69). The lake-arearelationship with BMI diversity is not surprising (Heino2008) and has been documented for many aquatic species(Dodson 1992; Griffiths 1997). However, evidence thatchanges in mean water level alter BMI richness regardlessof lake area or littoral slope is supported by White et al.(2008), who analyzed a long-term data set (20 yr) andfound similar results to this study: a long-term unimodalpattern between mean water level and BMI richness in onelake. We show here that the pattern holds across multiplelakes. Although our study focuses on large lakes, similarresults are expected in smaller lakes; however, it isimportant to recognize that smaller lakes have differentenvironmental controls that may further confound anassessment of the effects of WLF. For example, watertransparency is a good predictor of epilimnion depth insmall lakes (,5 km2), but no such pattern was found inlarge lakes (Fee et al. 1996).

We also found that the WLFBMI relationship isdictated by the region in which the lake is found. There isclearly a regional relationship among western, central, andeastern regions of Ontario (Fig. 4c). The water level of alake is indeed driven by regional, and to a lesser degree

Fig. 4. Regression analysis of (a) littoral slope with macroinver-tebrate richness (slope 5 20.64, p 5 0.02linear), (b) lake area withmacroinvertebrate richness (slope 5 0.14, p 5 0.01linear; p 50.0005unimodal), and (c) D80-D210 with macroinvertebrate richness(slope520.36, p5 0.01linear). Solid lines represent linear regressionand dashed line represents nonlinear regression employing aunimodal Gaussian (3 parameter) equation: f 5 a?exp(2.5?((x 2x0)/b)

2). Open circles represent lakes in northeastern Ontario, solidcircles represent lakes in northwestern Ontario, and solid trianglesrepresent lakes in north-central Ontario (see Fig. 1).

2280 White et al.

Table 4. Indicator taxa analysis of 49 identified BMI families with three D80D210 WLF groups. Relative abundance is the averageabundance of a given taxa in a given group of sites over the average abundance of that taxa in all sites, expressed as percentage. Relativefrequency is the percent of sites in a given group where the given taxon is present. Indicator value is the relative abundance multiplied bythe percent relative frequency. Monte Carlo significant tests of group designations were run using 1000 permutations. The high waterlevel group had D80-D210 values between +8.42 and 23.30 cm, n 5 5; the middle water level group had D80-D210 values between 23.30and28.90 cm, n5 5; the low water level group had D80-D210 values between28.90 and224.20 cm, n5 6. Taxa with significant p value(, 0.05) are in bold.

Taxa

Relative abundance Relative frequency Indicator valueMonte Carlo

significance test

High Mid Low High Mid Low High Mid Low Group p

Gomphidae 0 93 7 0 80 50 0 75 3 Mid 0.0097Hydracarina 18 60 23 100 100 100 18 60 23 Mid 0.0132Leptoceridae 16 56 28 100 100 100 16 56 28 Mid 0.0238Macromiidae 0 100 0 0 60 0 0 60 0 Mid 0.0341Ephemeridae 6 24 69 20 40 83 1 10 58 Low 0.0666Corixidae 0 0 100 0 0 50 0 0 50 Low 0.0697Chironomidae 11 64 25 100 100 100 11 64 25 Mid 0.1075Lumbriculidae 14 28 58 80 100 100 11 28 58 Low 0.1107Chaoboridae 0 77 23 0 40 17 0 31 4 Mid 0.1432Dyticidae 0 91 9 0 40 17 0 36 1 Mid 0.1432Helicopsychidae 3 41 56 20 60 83 1 25 46 Low 0.1918Empididae 21 62 17 40 100 100 9 62 17 Mid 0.2119Hydroptilidae 25 62 13 20 60 33 5 37 4 Mid 0.267Lymnaeidae 0 0 100 0 0 33 0 0 33 Low 0.2884Tabanidae 0 0 100 0 0 33 0 0 33 Low 0.2896Erpobdellidae 23 13 64 20 20 50 5 3 32 Low 0.3165Tubificidae 3 32 65 20 80 67 1 26 43 Low 0.3272Polycentropodidae 30 46 24 100 100 83 30 46 20 Mid 0.334Physidae 32 61 6 60 60 50 19 37 3 Mid 0.4399Planorbidae 2 75 23 40 80 100 1 60 23 Mid 0.4503Hydrobiidae 2 66 33 20 60 83 0 40 27 Mid 0.456Psephenidae 52 28 20 80 80 33 42 22 7 High 0.4722Elmidae 19 31 50 80 80 83 15 25 41 Low 0.4902Sphaeridae 11 41 48 100 100 100 11 41 48 Low 0.5225Caenidae 6 56 39 40 60 100 2 33 39 Low 0.5281Tipulidae 78 0 22 20 0 17 16 0 4 High 0.54Ancylidae 0 64 36 0 20 17 0 13 6 Mid 0.5455Hyalellidae 14 42 44 100 80 100 14 33 44 Low 0.5685Lepidostomatidae 35 45 19 40 60 33 14 27 6 Mid 0.5764Ceratopogonidae 16 50 34 60 80 83 10 40 29 Mid 0.6151Prostoma 100 0 0 20 0 0 20 0 0 High 0.6229Coenagrionidae 100 0 0 20 0 0 20 0 0 High 0.6242Aeshnidae 0 100 0 0 20 0 0 20 0 Mid 0.6267Baetiscidae 0 100 0 0 20 0 0 20 0 Mid 0.6323Perlidae 39 37 25 40 60 17 15 22 4 Mid 0.714Glossiphonidae 21 44 35 20 40 67 4 18 23 Low 0.7194Valvatidae 0 52 48 0 20 33 0 10 16 Low 0.7322Cambridae 79 12 9 20 20 17 16 2 2 High 0.7797Phygranidae 55 0 45 20 0 17 11 0 8 High 0.799Baetidae 19 35 46 60 80 67 11 28 30 Low 0.8203Naididae 36 36 28 100 100 100 36 36 28 High 0.8975Heptageniidae 40 27 33 100 100 100 40 27 33 High 0.8994Ephemerellidae 27 35 38 40 60 50 11 21 19 Mid 0.9288Leptophlebiidae 35 32 33 100 100 100 35 32 33 High 0.9891Capniidae 22 0 78 20 0 17 4 0 13 Low 1Chloroperlidae 0 0 100 0 0 17 0 0 17 Low 1Glossosomatidae 0 0 100 0 0 17 0 0 17 Low 1Psychomyiidae 0 0 100 0 0 17 0 0 17 Low 1Sisyridae 0 0 100 0 0 17 0 0 17 Low 1

Water level and benthos 2281

local, climatic influences (Bengtsson and Malm 1997).Interestingly, inspection of D80-D210 regression with BMIfamily richness (Fig. 4c) reveals that in both the northwestand the northeast BMI family richness increases withdecreasing D80-D210; however, this pattern does not holdwhen considering the north-central lakes separately. Theinconsistent pattern of north-central lakes is probably aconsequence of a low lake-sample size (three lakes) and thatall three lakes had similarly low D80-D210 water levels(216 cm to 224 cm), with no lakes exhibiting water levelssimilar to over-winter levels. Another regional effect mayresult from possible geospatial patterns in fish communitystructure, as top-down control of BMI communitystructure through fish predation probably occurs in ourstudy lakes (Vander Zanden and Vadeboncoeur 2002).Although we do not know the fish community structure ofour study lakes, it is expected that fish communities wouldbe similar across these large (. 10 km2) abiotically similarnorthern temperate lakes (Jackson et al. 2001). As such,slight differences in fish community structure wouldprobably only obscure detectability of BMIWLF rela-tionships across our study lakes.

Only 4 of the 49 identified taxa were found to besignificant indicator taxa and all were designated to the midD80-D210 water-level group. The four taxa designated tothis group (Gomphidae, Macromiidae, Hydracarina, andLeptoceridae) are relatively mobile species; however, there isno experimental evidence in the literature to suggest that achange of , 10 cm in mean water level would preferentiallybenefit the survival of these taxa. One possibility is that midwater levels concentrate detrital matter in rocky littoralhabitats, augmenting the proportionally dominant Chiro-nomidae taxon (Table 3) and resulting in increased forageefficiency, and thus survival, of predators such as Hydracar-ina (Proctor and Pritchard 1989) and Odonata (Merritt et al.2008). However, at extremely low water levels it is possiblethat disturbance is great enough that detrital matter isseverely agitated so that no taxon exhibits a preferencestrong enough to be statistically designated to the low water-level group (Table 3). Further, there is increased taxarichness in lakes designated to the mid and low D80-D210taxa groups, supporting the occurrence of a habitat squeeze(i.e., lakes with high water levels have lower taxa richnessand do not contain unique taxa that are not found in lakeswith mid and low water levels). Due to the qualitative natureof our sampling regime, relative frequency may be a moreaccurate measure of taxa response to changes in water levelthan indicator value: which is a product of relative frequencyand relative abundance.

Our study shows that relatively small changes in meanwater level (33 cm) can affect BMI richness. The majorityof WLF research in temperate lake systems involves thestudy of reservoirs with the conclusion that yearlyamplitude is a driver of biotic richness in littoral areas(Hill et al. 1998; Aroviita and Hamalainen 2008). Themagnitude of yearly amplitudes (minimum minus maxi-mum water level) reported in these studies varies severalfold but is almost always greater than 2 m. Our study showsthat a much smaller fluctuation (% 1 m) can be animportant factor structuring macroinvertebrate communi-

ties in natural lake ecosystems. This finding is importantbecause it highlights that even small incremental changes inwater quantity and water level associated with climatechange may alter benthic communities even without theconfounding effect of anticipated warmer water tempera-tures (Magnuson et al. 1997).

The sampling methodology of this study focused onmaximizing the number of lakes sampled at the cost ofreduced sampling precision of within lake variability (Baileyet al. 2004). By increasing the number of stony littoral sitessampled per lake, we would have increased the precision ofwithin lake variability (Reid et al. 1995), which wouldfurther strengthen the relationship between BMI familyrichness and the D80-D210 hydrologic measure. Neverthe-less, our sole focus on stony littoral habitats reduced habitat-related variability (Bailey et al. 2004) as well as our ability todraw conclusions for other types of littoral habitats. Sincestony littoral habitats are structured and maintained by thephysical process of wave action (Jackson et al. 2001), it issafe to assume that our results on the effects ofWLF on BMIare conservative. Stony littoral BMI communities arecomprised of taxa that are likely more tolerant of physicalwater-level disturbance. It is possible that other habitats(macrophyte, mud, woody debris) are composed of macro-invertebrate taxa that are more sensitive to WLF comparedto those in stony littoral areas.

In our study, many landscape variables can be used toinfer predictors typically extracted from a water balanceapproach (Fig. 3). For example, an indicator of yearlyamplitude is basin area, or more specifically, as the basin-to-lake-area ratio increases so does amplitude. Thisrelationship is well known (Bengtsson and Malm 1997),but many other factors also contribute to the quantity ofwater in lake systems: depth of snow pack (Nijssen et al.2001), frost depth (Bayard et al. 2005), precipitation,evaporation, preceding water content in surface storesand soils (Metcalfe and Buttle 1999), and seepage at thelake bottom (Sebestyen and Schneider 2001). Interestingly,we found a positive correlation between amplitude andpercentage of treed bog and open fen in the catchment,whereas amplitude was negatively correlated with openwater. This is surprising because it has been shown thatswamps seemingly do not influence or regulate seasonalrunoff in boreal areas (Devito et al. 1996); however, theinfluence of the landscape to reduce runoff is dependantupon the previous years hydrologic inputs (Metcalfe andButtle 1999). Boreal lake water levels in our study regionfollowed a 812-yr cycle that reached minimum levels in2003 (White et al. 2008). Thus in our study lakes, which weresampled in 2006, it is possible that open fen and treed bogwetlands had recharged to maximum water levels, becomingsupersaturated and therefore increasing water runoff ratherthan acting as a sink for spring melt. Conversely, open water(lakes) had slower recharge responses to precipitationcompared to treed bog and open fens.

We found that large lakes (in both area and mean depth)were at lower elevations and reached a temperature of 4uCearlier in the year but nevertheless had lower meantemperatures compared with smaller lakes found at higherelevations. The larger low-elevation lakes had lower D80-

2282 White et al.

D210 water levels than smaller high-elevation lakes. Theearlier rise in water temperature of the lower larger lakes isexpected, as air temperatures are higher at lower elevationsand air temperature is the largest predictor of ice breakup(Assel and Robertson 1995). Although ice breakup isdelayed in the higher elevation lakes, these lakes probablyreach a higher mean temperature due to their smaller watervolume and heat capacity. Direct linkages betweenincreased lake areas having low D80-D210 water levelsare unclear. Possible explanations include landscapeposition (Webster et al. 1996), increased evaporation, orincreased seepage (Sebestyen and Schneider 2001).

Out of eight water-quality parameters that we examined,only three had strong correlations with hydrologic charac-ter: DO, ORP, and conductivity. The separation of DO andORP along axis 2 and their relationship with amplitudemay be a result of increased water replenishment (Am-brosetti et al. 2003). During spring snowmelt lakes withlarger amplitudes receive larger inputs of melt waterrelative to the lakes surface area, which may increase theflushing and mixing of deoxygenated water (Ambrosetti etal. 2003) accumulated over the winter months (Jarvinen etal. 2002). The response of conductivity along axis 1 is adirect effect of north-central Ontario lakes having elevatedconductance due to underlying glacial deposits.

We found evidence that a habitat squeeze may result fromsmall changes in water level (33 cm). The high productivityof littoral areas warrants further investigation toward thishabitat squeeze hypothesis. Particularly, the responsivenessof nutrient cycling processes to changes in natural waterlevel in boreal lacustrine environments needs to be betterunderstood. The biotic implications of a habitat squeeze maylead to particularly insightful intra- and interspecificcompetition research. A focused examination of trophicdynamics under habitat squeeze may reveal significanteffects on higher trophic fauna that have large societalinfluence such as birds and fish. Finally, while much researchhas been devoted to understanding climatic, geomorpholog-ical, and landscape controls of WLF in lentic ecosystems,what is needed is research that undertakes a long-term (1020 yr), multidisciplinary approach that combines detailedwater balance study and aquatic community structure acrossmultiple lake and watershed basins.

AcknowledgmentsWe thank Ryan Stainton and Stephanie Lyons for their

assistance in the field and in the lab. We also thank Mark Hansonand two anonymous reviewers whose suggestions improved thismanuscript. This work was supported by funding from theOntario Ministry of Natural Resources through the Institute forWatershed Science, Trent University and from Canadas NaturalSciences and Engineering Research Council (NSERC) Discoverygrant and NSERC University Faculty Award to M.A.X.

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Associate editor: Alexander D. Huryn

Received: 23 November 2009Accepted: 15 June 2010Amended: 27 July 2010

2284 White et al.