ARTICLE

A Great Lakes Coastal Wetland InvertebrateCommunity Gradient: Relative Influence of FloodingRegime and Vegetation Zonation

Joseph P. Gathman & Thomas M. Burton

Received: 13 April 2010 /Accepted: 21 December 2010 /Published online: 17 February 2011# Society of Wetland Scientists 2011

Abstract Wetland invertebrate community composition isaffected by habitat conditions associated with floodingregimes and vegetation characteristics, yet distinguishingamong these influential factors is difficult because they tendto co-vary spatially. We studied a Great Lakes coastal wetlandinvertebrate community along an elevation gradient as LakeHuron water level rose and fell over a three-year period. Thishydrologic variation caused changes in the gradient offlooding conditions, while plant zonation remained relativelyunchanged. Multivariate analysis indicated that the inverte-brate community varied continuously along the elevationgradient, and that it changed substantially within vegetationzones as water level changed. Variation across the gradientdecreased because the high-elevation wet meadow communi-ty became more similar to lower-elevation communities as aresult of upslope expansion in distributions of many taxa, andsubstantial increases in dominance of a subset of taxa.Invertebrate density increased dramatically with high-waterconditions, and diversity decreased in general. Resultssuggested that invertebrate community composition wasinfluenced by flooding conditions more than vegetation.These results may have important implications for conserva-tion of high-elevation wetland zones as high-water refuges forwet-meadow invertebrates, and for coastal wetland monitor-ing schemes based on vegetation zones as habitat forparticular invertebrate assemblages.

Keywords Hydroperiod . Littoral . Marsh . PlantWater level

Introduction

Great Lakes coastal wetlands are exposed to natural water-level changes on various time scales. Annual lake-leveloscillations produce a continuum of flooding conditionsfrom short-term flooding at higher-elevation portions ofwetlands, down to permanent flooding at lower elevations(Krieger 1992; Gathman et al. 1999). This flooding-regimegradient shifts up and down the elevation gradient overtime because Great Lakes water levels rise and fallsomewhat unpredictably over multi-year periods (Burton1985). Where wetland slopes are gentle, as in our studysite, small vertical changes in water level cause largehorizontal movements of the water’s edge, alternatelyflooding and draining large areas. Literature reviews(Gathman et al. 1999; Brady and Ruzycki 2009) haverevealed that very little work has been done to determinethe effects of natural water-level fluctuations on inverte-brate communities in Great Lakes coastal wetlands.

Many studies have shown that aquatic invertebratecommunities vary substantially among different wetlandswith different flooding regimes (e.g., Wiggins et al. 1980;Jeffries 1994; Schneider and Frost 1996). Flooding regimesdirectly affect invertebrates by restricting communitymembership to taxa with life history and behavioraladaptations to the flooding conditions of each habitat(Williams 1987; Schneider and Frost 1996; Tronstad et al.2005), but also affect invertebrate communities indirectlythrough their effects on other habitat conditions. Infreshwater coastal wetlands, flooding gradients can causecharacteristic wetland vegetation zonation, with shrub-

J. P. Gathman (*) : T. M. BurtonDepartment of Zoology, Michigan State University,East Lansing, MI 48824, USAe-mail: joseph.gathman@uwrf.edu

Present Address:J. P. GathmanDepartment of Biology, University of Wisconsin River Falls,River Falls, WI 54022, USA

Wetlands (2011) 31:329–341DOI 10.1007/s13157-010-0140-9

meadow and wet-meadow communities at higher elevationsand marsh and submergent communities at lower elevations(Minc 1996; Keough et al. 1999). Burton et al. (2004)found that aquatic-invertebrate community compositionvaried among these vegetation zones. The zones shift alongthe elevation gradient in a time-lagged response to themulti-year water-level changes (Burton 1985), stronglyaltering habitat conditions for invertebrates at any givenelevation (Wilcox et al. 2002). Invertebrate communitiescan be affected by structural complexity of vegetation(Krecker 1939; Jeffries 1993) and stem density (Voigts1976; McLaughlin and Harris 1990; Batzer and Resh1992), as well as detritus characteristics, (Barlocher et al.1978; Nelson et al. 1990; Cooper et al. 2007). Furthermore,vegetation attenuates wave energy, reducing pelagic/littoralmixing in the shoreward direction, and creating water-chemistry discontinuities (Suzuki et al. 1995) that can affectinvertebrate community composition (Cardinale et al.1997). Variation in vegetation communities can also affectfish predation on invertebrates (Gilinsky 1984; French1988; Swisher et al. 1998).

Co-variation of so many factors along coastal wetlandelevation gradients makes it difficult to gauge the relativeimportance of each when regulating aquatic animal commu-nity composition. It would be very difficult to run fieldexperiments controlling for all the covarying factors present inthese systems, so we were fortunate to be able to examineaquatic invertebrates during a three-year period in which LakeHuron’s water level rose and then fell before vegetation zonescould respond (Gathman et al. 2005). As a result, floodingand vegetation conditions were temporarily de-coupledduring a high-water year because the previously seasonally

flooded wet meadow was flooded year-round in the secondyear of the study, and then gradually drained in the thirdyear. If flooding-related factors were more important thanvegetation-related factors in determining aquatic invertebratecommunity composition, we expected invertebrates to moveup and down the slope with the water, even if it meantinhabiting different plant zones. But if vegetation-associatedfactors were more important, we expected fauna to remainhorizontally fixed within the vegetation complex, showingfidelity to particular plant communities regardless of water-level changes and the availability of newly or more-deeplyflooded wetland area.

Methods

Study Site

The study site was a coastal wetland on the north shore ofLake Huron in Mackinac Bay, on Michigan's upperpeninsula (Fig. 1). The bay was very well protected fromoff-shore wave activity by a complex shoreline and islands,and had a gradual, uninterrupted slope, with a typicalvegetation-zonation pattern along the flooding-regimegradient (Fig. 2). From high elevation to low, there was:1) the “Hummock Zone” (HZ), an infrequently floodedcommunity of hummock-forming graminoids with scatteredshrubs, 2) “Wet Meadow” (WM), a broad and diversesedge-meadow community, 3) “Transition Zone” (TZ), anarrower zone in which cattails (Typha) were mostconspicuous, although the majority of the communityconsisted of a subset of species from contiguous zones,

Mackinac

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Highway

Sampletransects

Wet MeadowTransition Zone

Emergent Marsh

Lake Huron

Lake Mich

igan

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igan

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Lake Huron

Lake Mich

igan

Mackinac

Bay

Highway

Wet MeadowTransition Zone

Emergent Marsh

Lake Huron

Lake Mich

igan

1 Km

Sampletransects

Fig. 1 Map showing location of study site, vegetation zones, and sampling transects

330 Wetlands (2011) 31:329–341

and 4) “Emergent Marsh” (EM), a permanently floodedemergent/floating-leaved/submergent community visuallydominated by bulrush (Schoenoplectus) and water lilies(Nuphar). Although zones overlapped somewhat, forming acoenocline coinciding with the elevation gradient, Gathmanet al. (2005) used cluster analysis to show that the zones inthis site and other nearby wetlands were true sub-communities, not just artifacts of human visual bias.

From 1996 through 1998, Lake Huron's water levelrose and then fell, creating a high-water year (1997)during which the mid-summer water-level peak wasapproximately 30 cm higher than it was in the previousand subsequent years (Figs. 2 and 3). In Year 1 of ourstudy (1996), the WM was only flooded during thesummer months, and had not been flooded at all in theprevious year. In Year 2, the WM and part of the HZ wereflooded year-round. Water level peaked in late July of thatyear and then began a multi-year general decline, so Year3 was the opposite of Year 1, with the WM flooded

initially, but gradually draining through summer as thelake level receded. During this three-year period, changesin the distributions of some plant species occurred, but thegeneral pattern and locations of plant zones remained thesame (Gathman et al. 2005).

Sample Collection

We established five transects running from the WM downthrough the TZ to the EM, placing wooden stakes to markone sampling station in each zone on each transect, for atotal of 15 stations. We used a laser level to ensure that allfive WM stations were at the same elevation, and that allTZ stations were 20 cm lower in elevation, and all EMstations 20 cm lower than the TZ stations (Fig. 2). Uponseeing the higher water level in Year 2, we established a HZstation on each transect, 20 cm higher than the WMstations. The average transect length was 120 m, so theaverage distance between sampling stations was ~40 m,although inter-station distances varied somewhat becausethe wetland slope was not uniform. We also installed a staffgauge at the low end of the middle transect, checked thewater level twice daily during each several-day samplingvisit, and calculated the average of all water-level readingsfor each visit. We later compared our water-level data toNOAA data from the nearest gauging stations (at Macki-naw City ~20 mi to the southwest and Detour Village~25 mi to the east) to ensure that our monthly and yearlyestimates of water-level change were accurate.

During each of three months (June, July, August) of eachyear, we collected 15 samples (5 transects×3 zones). Highwater allowed us to collect samples from the HZ duringYear 2 (and June of Year 3, but these five samples wereexcluded from statistical analysis). Sample locations werebetween 5 and 15 m (randomly selected, but avoidingpreviously sampled locations) from each sampling station,on a line perpendicular to the transect. A sample consistedof three collections at 1-m intervals along the line, with thethree combined to reduce the effects of small-scale spatialvariation in community composition. Each compositedsample contained invertebrates collected from 495.3 cm2

of substrate surface area and overlying water and vegeta-tion. We forced a stovepipe sampler (metal tube, 14.5 cmdiam., 165.1 cm2 cross-section) vertically into the substrateto enclose soil, detritus, and overlying water. We removedlarge detritus and plant material by hand and placed it in asieve-bottom bucket (250-μm mesh), stirred water in thesampler, and pumped water and suspended material fromthe sampler into the bucket using a commercially availablemanual siphon pump consisting of a plastic hand-squeezedbulb atop a one-way valve assembly with incurrent andexcurrent tubes. Each sample consisted of material pumpedby 100 bulb squeezes (~3.5 L). In the laboratory, we

1980 1985 1990 1995 2000

Year

176.0

176.5

177.0

177.5

Project start

Lake

Lev

el (

m)

Fig. 3 Lake Huron monthly mean water levels (meters aboveInternational Great Lakes Datum), DeTour Village, Michigan, 1980through 1998 (Data source: U.S. Department of Commerce, NOAA/NOS, Silver Spring, MD)

WM

TZEM

HZ

July Year 2

July Year 1

July Year 3

Mean Distance: 120 m

SampleStations:

Water Levels

WM

TZEM

HZSampleStations:

Fig. 2 Schematic cross-section along representative transect, withapproximate July water levels from each year. Sampling stationsindicated by tick marks above each vegetation zone designator(HZ=Hummock Zone, WM=Wet Meadow, TZ=Transition Zone,EM=Emergent Marsh). Vertical scale greatly exaggerated

Wetlands (2011) 31:329–341 331

randomly subsampled 33% of each sample using a standard250-μm sieve divided into six equal-sized "pie pieces”. Wepicked invertebrates from each subsample under 10×magnification and identified to the lowest practical taxo-nomic level according to Pennak (1989) and Merritt andCummins (1996).

Statistical Analysis

We used multivariate analyses to determine whethercommunities varied continuously along the elevationgradient from zone to zone, and whether communitieschanged from year to year. We converted taxa counts torelative abundance (RA, proportion of each taxon withinthe sample), then used arcsine-squareroot transformation tonormalize distributions. Following the procedures describedby McCune and Grace (2002), we used PC-ORD, v. 5.10(MjM Software Design, Gleneden Beach, OR) to performNon-Metric Multidimensional Scaling (NMDS), with Sor-ensen (Bray-Curtis) distances, excluding taxa occurring infewer than three samples. We then performed a MonteCarlo test using 250 runs with randomized data to assesslevels of stress. Initially, we performed NMDS ordinationon all samples (150 points), and then ordinated each yearand month separately (e.g., all June samples overthree years). Single-year analysis only revealed subtlemonthly variations reflecting differences in life cycles ofabundant taxa, so we aggregated monthly samples toproduce an ordination plot of 45 points (5 transects×3zones×3 years), which was simpler but retained the samebasic pattern as the other ordinations.

To directly assess variation among zones and years, weused PC-ORD to conduct Multi-Response PermutationProcedures (MRPP, Bray-Curtis distance) with post-hocpairwise comparisons (with sequential Bonferroni correc-tion). First, to assess invertebrate variation along theelevation gradient, we used separate MRPP for each year.Next, we assessed year-to-year changes by using separateMRPP for each zone over the three-year period.

We next examined the spatial and temporal variations inthose taxa that appeared to be most influential in theordination. We selected a subset of taxa that: 1) had astrongly positive or negative Pearson correlation (|r|>0.5)of RA with ordination axes, and 2) comprised ≥1% of allindividuals collected during the study. We analyzed thesetaxa using two-way fully factorial repeated-measuresanalysis of variance (ANOVA; SPSS v.17.0), with RA(arcsine-squareroot transformed) as the dependent variable,Year and Zone as main factors, and a significance level ofα=0.05 for all effects. When the Year×Zone interactionwas not significant, we examined any significant maineffects using sequential-Bonferroni-corrected post-hoc pair-wise comparisons. When the Year×Zone interaction was

significant, we used one-way ANOVAs to examine tempo-ral differences in each zone and spatial differences duringeach year, followed by pairwise comparisons adjusted foran overall α=0.05 significance for all 18 tests (three peryear, three per zone). We also compared total invertebratedensity (square-root transformed), taxon richness, Shannondiversity, and Pielou’s evenness across zones and yearsusing the same ANOVA procedure. We excluded HZsamples from all repeated-measures ANOVAs because theywere only collected during one year.

To examine individual-taxon distribution changes overthe sampling period, we calculated mean gradient positions(GP) during each month as weighted-averages:

GP ¼P4

i¼0i� Ni

P4

i¼0Ni

;

where GP=gradient position, i=vegetation zone code (HZ=0,WM=1, TZ=2, EM=3), and Ni=total abundance of thetaxon in zone i. This produced five replicate mean gradientpositions (one per transect) for each taxon in each year. Weused repeated-measures ANOVAwith sequential-Bonferroni-corrected post-hoc tests to examine GP differences amongyears. While this measure was only a proxy for the “center”of a taxon’s distribution along the gradient, it had theadvantage of condensing information from an entire transectinto a single measure, and provided an efficient way toexamine each taxon’s shifts along the wetland elevationgradient.

Results

Spatial and Temporal Variation in Community Composition

Ordination, based on >24,000 invertebrates in 65 taxa,clearly separated samples from each of the vegetationzones, both spatially and temporally (final stress: 14.3, p=0.004). These separations were statistically significantaccording to MRPP: all zones differed from each otherduring each year (p<0.001 overall; p≤0.013 for all pairwisecomparisons), and each zone changed significantly duringboth time intervals (p<0.001 overall; p<0.01 for allpairwise comparisons). Dimension 1 of the ordination (x-axis, Fig. 4), representing 73.9% of variation, indicated aninvertebrate-community gradient coinciding with the vege-tation zone sequence during each year. It also indicatedreduced heterogeneity along the gradient over time becauseWM and TZ communities were “pulled” to the right towardthe EM communities in Years 2 (1997) and 3 (1998),shortening the x-axis distance along the gradient. This

332 Wetlands (2011) 31:329–341

homogenization of communities reflected increasing dom-inance of a subset of taxa that spread upslope and increasedsubstantially in density throughout the study period. Ofthese taxa, four (Caecidotea, Chironomidae, Caenidae,Amphipoda) collectively comprised 60% of invertebratescollected in Year 1, and increased to 80% of all inverte-brates in Year 3. Meanwhile, Dimension 2 (y-axis, Fig. 4),representing 16.3% of the variation, reflected year-to-yearchanges among taxa (described below) that shifted upslopein Year 2, and then downslope again in Year 3, causingchanges in dominance structures in the first time intervalthat were partly reversed in the second.

Twelve taxa met our criteria for highest importance inthe ordination. These and most other taxa could be sortedinto four response patterns (Table 1) based on assessment oftheir spatiotemporal changes indicated by univariate anal-yses (described below) and qualitative judgement: 1) high-elevation specialists typically had highest RA in the WM inYear 1, in the HZ in Year 2, and in the WM again in Year 3,although generally at considerably lower RA than earlier;2) rapid, reversing taxa rapidly occupied the WM underhigh-water conditions, becoming some of the most domi-nant taxa there, but then retreated back to lower positions inYear 3; 3) time-lagged responders expanded upslope more

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

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on 2

WM

EM

TZ

HZ

WMEM

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TZ

EM

TZ

WM

EM

TZ

HZ

WMEM

WM

TZ

EM

TZ

Fig. 4 NMDS ordination of sam-ples, Years 1–3. Dimensions 1and 2 of the ordination are repre-sented by the x and y axes,respectively. Polygon vertices in-dicate locations of each replicatesample from each zone-yearcombination (HZ=HummockZone, WM=Wet Meadow,TZ=Transition Zone, EM=Emergent Marsh). Lines connectzones for each year. Dotted:Year 1; Solid: Year 2; Dashed:Year 3

Table 1 Invertebrate taxa sorted into four response groups based on quantitative and qualitative assessment of results. Taxa in bold met criteriafor being most important in the NMDS ordination

Group 1: High-elevation Specialists Group 2: Rapid Reversers Group 3: Time-lagged Responders Group 4: Low-elevation Specialists

Ceratopogonidae Chironomidae Amphipoda Lirceus

Chydoridae Ostracoda Caecidotea Ancylidae

Cyclopoida Daphniidae Caenidae Chrysomelidae

Gyraulus Coenagrionidae Aeshnidae Corduliidae

Planorbula Hydracarina Phryganeidae Hirudinea

Dytiscidae Hydroptilidae Sididae

Halacarida Lumbriculidae

Harpacticoida Nematoda

Isotomidae Ostracoda

Limnephilidae Physidae

Oribatei Sphaeriidae

Succineidae

Wetlands (2011) 31:329–341 333

gradually over both time intervals, attaining high RA inYear 3 at higher elevations than where they started in Year1; and 4) low-elevation specialists always remained mostcommon in the EM, showing little indication of substantialupslope spread.

The 12 most-important taxa varied along the gradient,but each also changed differently in different zones, asindicated by the fact that the Year×Zone interaction wassignificant (p<0.05) in most of the 12 ANOVAs (Table 2).Of these taxa, high-elevation specialists (Ceratopogonidae,Chydoridae, Cyclopoida, Gyraulus, Planorbula) werestrongly associated with the negative end of the x-axis(RA negatively correlated with Dimension 1), and hadsignificantly higher RA in the WM than in lower zones inone or more years (testwide α=0.05, Bonferroni-correctedpairwise comparisons), and/or declined significantly in theWM during one or both of the time intervals (Tables 2 and3). Each of these taxa also had relatively high RA in the HZin Year 2 (Fig. 5).

Five of the other important taxa were strongly associatedwith the opposite, positive end of the x-axis, but showedvaried response patterns. Amphipoda, Caecidotea (Aselli-

dae), and Caenidae were time-lagged responders, withsignificantly lowest RA in the Year 1 WM (Table 2), butlater shifting their distributions upslope and becoming veryabundant in the WM and/or TZ by Year 3 (Fig. 5). Thesethree were probably among the most important in homog-enizing communities because of their increasing dominanceover time in all zones (Table 3). While the greatestcommunity change occurred in the WM, as indicated bythe substantial rightward “shift” of WM points along the x-axis from Year 1 to Year 2, the EM changed the least, inpart because of taxa like Lirceus (a second Asellidae) whichwas a low-elevation specialist. We classified Ostracoda as arapid, reversing taxon because of its distribution changesalong the gradient, although it was consistently positivelycorrelated with Dimension 2 because its RA was highest inthe EM (Table 2, Fig. 5).

Two of the most-important taxa were rapid reversers thatwere negatively correlated with Dimension 2. Chironomi-dae RA was always highest in the TZ, but increased in allzones in Year 2, and decreased significantly in Year 3(Table 2; Fig. 5), contributing substantially to the down-ward “pulling” of the Year 2 points along the y-axis in the

Table 2 Results of two-way factorial repeated-measures ANOVA(with post-hoc pairwise comparisons) on transformed relativeabundances of taxa meeting criteria for highest influence in theNMDS ordination. If interaction effect was significant, pairwise tests

were t-test comparisons of zones within each year and years withineach zone. Pairwise differences only reported if significant at test-wideα=0.05 for each taxon, after sequential Bonferroni correction.WM=wet meadow; TZ=transition zone; EM=emergent marsh

Dependent variable Effects p-values Pairwise significant differences

Zone Year Interaction among zones among years

Amphipoda <0.001 0.057 <0.001 Year 1: WM < TZ & EM WM: Year 1 & 2<Year 3Year 2: WM & TZ < EM

Caecidotea 0.291 <0.001 <0.001 Year 1: WM < TZ & EM WM: Year 1 & 2<Year 3Year 2: WM < EM

Year 3: WM > EM

Caenidae 0.004 <0.001 0.001 Year 1: WM < TZ & EM

Ceratopogonidae 0.026 <0.001 0.262 All Years: WM > EM All Zones: Year 1>Year 2 & 3

WM: Year 1>Year 2

TZ: Year 1>Year 2 & 3

Chironomidae 0.011 <0.001 0.636 All years: TZ > WM & EM All zones: Year 2>Year 3

Chydoridae 0.097 <0.001 0.659 All Zones: Year 1>Year 2 & 3

WM: Year 1>Year 3

Cyclopoida <0.001 0.003 0.047 Year 1: WM > EM

Daphniidae 0.337 0.249 <0.001 Year 2: WM > EM WM: Year 1<Year 2>Year 3

EM: Year 1>Year 2

Gyraulus 0.000 0.394 0.016 Year 1: WM > TZ & EM WM: Year 2>Year 3Year 2: WM > TZ & EM

Lirceus <0.001 <0.001 <0.001 Year 2: WM < EM TZ: Year 1<Year 2

Year 3: WM < TZ < EM EM: Year 1 & 2<Year 3

Ostracoda <0.001 0.015 0.024 Year 1: WM < TZ < EMYear 3: WM & TZ < EM

Planorbula 0.000 0.029 0.144 All Years: WM > TZ > EM All Zones: Year 1>Year 3Year 1: WM > EM

334 Wetlands (2011) 31:329–341

Table 3 Ten most abundant taxa, ranked by relative abundance (standard error in parentheses) in each zone during each year based on stovepipesamples (months combined; June-only in hummock zone, Year 3)

Year 1 Year 2 Year 3

Hummock Zone (HZ)

n.a. Chironomidae 28.1% (3.2) Asellidae 26.4% (10.0)

Asellidae 14.4% (2.5) Chironomidae 17.7% (3.1)

Cyclopoida 8.3% (1.8) Caenidae 8.8% (3.1)

Daphniidae 8.1% (2.4) Cyclopoida 6.0% (2.3)

Planorbidae 7.5% (1.8) Isotomidae 5.8% (1.8)

Macrothricidae 4.8% (2.8) Planorbidae 4.2% (1.4)

Sphaeriidae 4.0% (0.9) Ceratopogonidae 3.9% (1.9)

Physidae 3.6% (1.5) Harpacticoida 3.8% (1.4)

Caenidae 3.2% (1.5) Sphaeriidae 3.2% (1.4)

Chydoridae 2.5% (0.9) Libellulidae 2.4% (0.8)

Wet Meadow (WM)

Chironomidae 18.6% (2.8) Chironomidae 32.2% (4.5) Asellidae 36.9% (4.5)

Cyclopoida 10.2% (2.2) Caenidae 17.7% (3.9) Caenidae 16.5% (5.1)

Oribatei 8.0% (2.7) Asellidae 15.9% (3.6) Chironomidae 15.9% (3.4)

Planorbidae 6.8% (2.4) Planorbidae 5.2% (0.9) Amphipoda 7.8% (1.7)

Sphaeriidae 6.5% (3.2) Cyclopoida 4.2% (0.8) Cyclopoida 6.1% (1.0)

Asellidae 5.1% (1.5) Daphniidae 4.1% (1.2) Isotomidae 1.9% (0.6)

Ceratopogonidae 4.6% (1.1) Physidae 3.3% (1.2) Planorbidae 1.7% (0.5)

Brachycera 4.4% (0.9) Naididae 1.8% (0.6) Ceratopogonidae 1.3% (0.4)

Harpacticoida 3.8% (3.9) Ostracoda 1.8% (0.7) Ostracoda 0.9% (0.9)

Naididae 3.7% (1.6) Libellulidae 1.5% (0.4) Physidae 0.9% (0.2)

Transition Zone (TZ)

Chironomidae 28.7% (3.0) Chironomidae 32.6% (3.7) Caenidae 29.8% (6.1)

Asellidae 12.5% (1.6) Asellidae 24.4% (3.8) Asellid 24.2% (3.8)

Caenidae 12.3% (2.4) Caenidae 17.0% (2.3) Chironomidae 21.6% (2.4)

Amphipoda 8.2% (1.5) Amphipoda 4.1% (1.1) Amphipoda 5.4% (1.5)

Cyclopoida 4.2% (0.7) Planorbidae 2.2% (1.1) Cyclopoida 4.1% (1.0)

Daphniidae 3.0% (1.1) Naididae 2.4% (1.0) Ostracoda 2.0% (0.3)

Ceratopogonidae 3.0% (0.7) Daphniidae 2.1% (0.7) Daphniidae 1.7% (0.5)

Chydoridae 2.6% (0.9) Physidae 1.9% (0.5) Macrothricidae 1.5% (0.8)

Macrothricidae 2.4% (1.0) Ostracoda 1.9% (0.7) Naididae 1.3% (0.3)

Ostracoda 2.1% (0.8) Cyclopoida 1.8% (0.5) Planorbidae 1.0% (0.3)

Emergent Marsh (EM)

Chironomidae 22.2% (3.2) Asellidae 32.8% (3.9) Asellidae 26.4% (3.9)

Asellidae 17.9% (2.5) Chironomidae 23.8% (3.2) Caenidae 16.3% (2.4)

Caenidae 16.6% (3.9) Caenidae 13.3% (2.0) Chironomidae 14.8% (2.2)

Amphipoda 10.5% (2.3) Amphipoda 10.1% (1.4) Ostracoda 12.3% (4.6)

Ostracoda 6.3% (2.0) Cyclopoida 3.5% (1.2) Amphipoda 9.6% (1.5)

Cyclopoida 2.9% (0.7) Ostracoda 3.4% (1.0) Cyclopoida 2.2% (0.4)

Naididae 2.7% (1.0) Naididae 1.9% (0.6) Naididae 2.2% (0.5)

Macrothricidae 2.6% (1.2) Sphaeriidae 1.2% (0.6) Coenagrionidae 1.9% (0.5)

Daphniidae 1.8% (0.5) Sididae 0.8% (0.2) Lymnaidae 1.5% (1.0)

Ceratopogonidae 1.8% (1.0) Planorbidae 0.8% (0.3) Ancylidae 1.4% (0.3)

Wetlands (2011) 31:329–341 335

ordination (Fig. 4). Daphniidae RAwas highest in the TZ inYear 1, but it rapidly expanded its distribution far upslope,with a significant WM increase in Year 2 and a peak in theHZ (Fig. 5), followed by a significant WM decrease

(Table 2). Meanwhile, at the other end of the y-axis, theonly most-important taxon with high influence was Caeci-dotea because of its strong increase in WM dominance inYear 3 (Table 3). Several less-abundant wet-meadow

Caecidotea

0.1

0.2

0.3

0.4

0.5

0.1

0.2

0.3

0.4

0.5

0.1

0.2

0.3

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Rel

ativ

e A

bund

ance

Caenidae Chironomidae

Cyclopoida Daphniidae

Ostracoda

0.02

0.04

0.06

0.08Ceratopogonidae

HZ WM TZ EM

Planorbula

0.00

0.01

0.02

0.03

0.04

0.05

0.06

ZoneHZ WM TZ EM

Zone

Chydoridae

0.00

0.01

0.02

0.03

0.04

0.05Gyraulus

0.01

0.02

0.03

0.04

0.05

0.06

ZoneHZ WM TZ EM

Rel

ativ

e A

bund

ance

0.05

0.10

0.15

0.05

0.10

0.15Lirceus

Rel

ativ

e A

bund

ance

Amphipoda

0.05

0.10

0.15

0.05

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0.05

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CaecidoteaCaecidotea CaenidaeCaenidae ChironomidaeChironomidae

CyclopoidaCyclopoida DaphniidaeDaphniidae

OstracodaOstracoda Ceratopogonidae

PlanorbulaPlanorbula ChydoridaeChydoridaeGyraulusGyraulus

LirceusLirceus

AmphipodaAmphipoda

Fig. 5 Temporal and spatial variation in mean relative abundances oftwelve taxa meeting criteria for highest importance in the NMDSordination (error bars: +/− 1 standard error). Lines connect zones for

each year. Dotted: Year 1; Solid: Year 2; Dashed: Year 3. Notedifferent y-axis scales. HZ=Hummock Zone, WM=Wet Meadow, TZ=Transition Zone, EM=Emergent Marsh

336 Wetlands (2011) 31:329–341

specialists were also highly positively correlated withDimension 2 because they were relatively more abundantin the WM in Years 1 and 3 than in Year 2. The Year 3increasing RAs of all these taxa, as well as the decreases inChironomidae and Daphniidae, were probably most re-sponsible for the “pulling” the Year 3 WM and TZ upwardin the ordination plot (Fig. 4).

Variation in Invertebrate Density and Diversity

Total invertebrate density, Shannon diversity, taxon rich-ness, and evenness changed differentially along the gradientduring the study period, as indicated by significant (p<0.05) Year×Zone interactions in ANOVAs. For example,invertebrate density increased dramatically in all zones overthe three-year period (Fig. 6), but rates of change variedalong the gradient. Density was highest in the EM initially,but from Year 1 to Year 2 the greatest increase occurred inthe WM (~176%). In the second time interval, the TZdensity increased ~220% to far exceed the other zones’densities (much of that increase was Caenidae, whichconcentrated in the Year 3 TZ, while Caecidotea dominatedthe WM community). Shannon diversity in Year 1 showeda decreasing trend from WM to EM. After WM and TZdeclines in Year 2 (Fig. 6), diversity increased modestly inthe EM samples in Year 3, but remained low in other zones.This resulted in an apparent reversal of the spatial trend:from highest diversity in the WM in Year 1 to highestdiversity in the EM in Year 3.

Diversity differences reflected spatial and temporaldifferences in richness and evenness. Overall, the greatestdiversity loss occurred in the WM, but initial richness, andsubsequent losses in Year 2, were higher in the other zones(Fig. 6). Taxa richness decreased overall from Year 1 toYear 2, but almost fully rebounded in Year 3 in all zones.Evenness was initially highest in the WM, but decreasedsignificantly throughout the period to become lower thanEM evenness. Lower evenness coincided with substantiallyhigher dominance of higher-elevation communities byCaecidotea, Chironomidae, Caenidae, and, to a lesserextent, Amphipoda.

Temporal Changes in Individual Taxa and DominanceAlong the Gradient

Changes in community composition and evenness in eachzone were a result of 1) differential changes in taxadistributions along the gradient, and 2) differential changesin abundances among taxa in each zone. Of the 33 taxatested, 24 had statistically significant (ANOVA, p<0.05after Bonferroni correction) changes in GP during one orboth of the time intervals (Table 4). In most cases, GPsshifted upslope and then downslope, and for nine taxa bothof these shifts were statistically significant (Dytiscidaelarvae, Ceratopogonidae, Chydoridae, Cyclopoida, Physi-dae, Lumbriculidae, Chironomidae, Daphniidae, Coena-grionidae). Year 1 taxonomic composition of communitiesalong the gradient can be roughly inferred from Table 4

HZ WM TZ EM

Zone

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HZ WM TZ EMHZ WM TZ EM HZ WM TZ EMHZ WM TZ EM

Fig. 6 Temporal and spatialvariation in mean per-sampleinvertebrate abundance, Shan-non diversity, taxa richness, andevenness (error bars: +/− 1standard error). Lines connectzones for each year. Dotted:Year 1; Solid: Year 2; Dashed:Year 3. HZ=Hummock Zone,WM=Wet Meadow, TZ=Transi-tion Zone, EM=EmergentMarsh. Points with same lettersindicate significant differences(ANOVA w/pairwise compari-sons of zones within years andyears within zones, α=0.05)

Wetlands (2011) 31:329–341 337

Table 4 Comparison of annual mean gradient positions (GP)invertebrate taxa. Lower GP indicates higher-elevation. n=total speci-mens collected from five transects in each year, p-values fromANOVA. GPs with same letters across rows differed significantly in

Bonferroni pairwise comparisons (α=0.05 test-wide). ResponseGroups: 1=High-elevation Specialists; 2=Rapid Reversers; 3=Time-lagged Responders; 4=Low-elevation Specialists (See Table 1)

Response group Year 1 Year 2 Year 3

Taxon GP n GP n GP n p

Bithyniidae 1.00 1 1.00 2 1.71 40 n.a.

Isotomidae 1 1.13 25 0.66 2 1.12 85 n.a.

Diptera-Brachycera 1.29 36 1.99 24 1.99 33 0.1

Limnephilidae 1 1.35 27 0.83 3 2.00 2 n.a.

Oribatei 1 1.36 75 0.69a 22 1.57a 37 0.007

Dytiscidae larvae 1 1.40a 5 0.00a,b 7 1.38b 18 <0.001

Succineidae 1 1.41 16 0.50 4 1.32 22 n.a.

Harpacticoida 1 1.42a 29 0.32a 52 1.27 53 0.001

Ceratopogonidae 1 1.71a 89 0.73a,b 64 1.77b 110 <0.001

Libellulidae 1.78 26 1.18 60 1.63 40 0.09

Planorbidae 1 1.78a 80 0.92a 287 1.78 157 0.022

Nematoda 2 1.83 28 1.09 12 2.05 27 0.011

Sphaeridae 2 1.88 77 1.00a 146 2.43a 77 0.004

Chydoridae 1 1.89a 91 0.93a,b 91 1.92b 72 0.008

Cyclopoda 1 1.96a 175 1.10a,b 333 1.74b 557 0.003

Corduliidae 4 2.00 1 2.16 9 2.54 22 n.a.

Aeshnidae 3 2.10a 7 2.06b 3 1.42a,b 20 0.001

Naididae 2.11 79 1.83 133 2.38 174 0.048

Lymnaeidae 2.12 8 1.20 9 1.92 27 0.3

Physidae 2 2.15a 40 0.88a,b 153 1.63b 54 0.001

Lumbriculidae 2 2.20a 29 1.26a,b 50 1.89b 61 <0.001

Polycentropodidae 2.26 16 1.61 17 2.01 60 0.2

Chironomidae 2 2.26a 981 1.50a,b 2257 2.02b 2293 <0.001

Macrothricidae 2.31 125 1.34 115 2.39 159 0.07

Hydroptilidae 2 2.32 20 1.24a 11 2.38a 28 0.008

Hydracarina 2 2.33 18 1.67 40 2.51 68 0.023

Daphniidae 2 2.36a 74 0.76a,b 282 2.14b 166 <0.001

Leptoceridae 2.41 22 1.98 35 2.45 42 0.1

Chrysomelidae larv. 4 2.42 6 2.93 10 2.91 6 0.6

Caenidae 3 2.48a 356 1.78 1115 1.97a 3342 0.002

Caecidotea 3 2.48a 530 1.81 1464 1.82a 3091 0.027

Amphipoda 3 2.60a 333 2.40 297 2.14a 908 0.029

Phryganeidae 3 2.60a 20 1.58 22 2.16a 20 0.015

Coenagrionidae 2 2.74a 40 1.78a,b 54 2.63b 98 <0.001

Ostracoda 2 2.75a 139 1.89a 179 2.49 415 0.009

Corixidae 2.75 6 1.34 10 2.50 2 n.a.

Sidididae 4 2.77 11 2.70 19 3.00 11 0.5

Hirudinea 2 2.80a,b 17 1.84a 28 2.19b 51 0.003

Lirceus 4 3.00 7 2.69 75 2.62 556 0.08

Ancylidae 4 3.00 2 2.75 6 2.68 72 n.a.

Baetidae – 0 3.00 5 3.00 1 n.a.

Calanoida – 0 0.50 4 3.00 1 n.a.

Hydrobiidae – 0 2.91 7 3.00 3 n.a.

338 Wetlands (2011) 31:329–341

because taxa are sorted according to Year 1 GP. The top ofthe table includes the high-elevation specialists, most ofwhich had upslope GP shifts and were collected in lowernumbers in Year 2, and then reversed these trends in Year 3.Rapid reversers occur further down in the table, but showsimilar GP changes. The time-lagged taxa initially had low-elevation GPs, but showed upslope GP shifts in both timeintervals, and low-elevation specialists typically showedlittle or no indication of upslope GP movement.

Differential density changes caused taxon-dominances tochange. For example, although Chironomidae abundanceincreased greatly during both periods, by Year 3 they wereno longer the most dominant taxon in any zone because ofdramatic increases in Asellidae and Caenidae (Table 3). The10 most-abundant taxa in the Year 1 WM were mostlyhigh-elevation specialists that no longer appeared in the top10 list for the next one or two years (e.g., Oribatei,Sphaeriidae, Ceratopogonidae, Chydoridae, Brachycera,Harpacticoida). In the TZ and EM, Daphniidae andMacrothricidae were initially among the 10 most abundanttaxa (Table 3), but in Year 2 they were not because theyshifted distributions upslope, and were among the 10 mostabundant in the WM (Daphniidae) and HZ (both). By Year3 they were again in the top 10 in the TZ. Physidae snailssuddenly appeared in the top 10 of the HZ, WM, and TZ inYear 2, even though they were not among the 10 mostabundant in any zone during Year 1 or Year 3.

Discussion

Our study indicated that the invertebrate community variedcontinuously along the sequence of wetland plant zones andthat most taxa shifted or expanded their distributionsupslope during the high-water period, decreasing the faunalheterogeneity along the gradient. The spatial shifts indistribution of so many invertebrate taxa suggested thatthe factors associated with changing hydrologic conditionswere the main drivers of faunal gradient positions duringour study, and that vegetation zonation was relatively lessimportant, although some taxa (e.g., Ancylidae, Cordulii-dae, Chrysomelidae larvae, Lirceus, Sididae) appeared toprefer the emergent marsh, showing little indication ofupslope shifts throughout the study.

It is unlikely that any single factor was responsible forthe spatiotemporal variation in communities that weobserved. Various factors associated with hydrologic con-ditions probably varied along the elevation gradient andchanged over the study period, such as flooding regime,depth, temperature, dissolved oxygen, water chemistry,wave energy, and detritus conditioning. Flooding regimeis a very important influence on wetland invertebratecommunity composition (Wiggins et al. 1980; Batzer and

Wissinger 1996), and the Year 1 (1996) wet meadow in ourstudy was mostly inhabited by invertebrates commonlyfound in temporary wetlands (e.g., Cyclopoida, pulmonateGastropoda, Dytiscidae, Harpacticoida) while most taxathat are usually excluded from such habitats were absent orrare (e.g., most Odonata and Trichoptera). This is notsurprising because the wet meadow had not been floodedfor two years prior to Year 1 (Fig. 3). Most of the wet-meadow fauna were taxa that had adaptations for survivalin low-oxygen conditions, and in this site and severalneighboring wetlands, dissolved oxygen was found todecrease along the elevation gradient from lower elevationsup into densely vegetated zones (Gathman and Keas 1999).However, temperature also decreased along this gradient,while conductivity strongly increased (but nitrate, ammo-nium, and phosphorus did not vary). Cardinale et al. (1997)found that invertebrates in Saginaw Bay wetlands variedalong chemical gradients caused by wave-driven pelagic-littoral mixing. With similar results, Burton et al. (2002)proposed a conceptual model involving gradual vegetation-mediated attenuation of wave energy as a key driver ofinvertebrate variation from outer (lakeward) to inner(shoreward) wetland zones. It is possible that invertebratesin our study also responded to chemical gradients, andperhaps these gradients shifted up and down the slope aswater level changed. However, our site was a fairly smallbay that was very well protected from open-lake waves byits strongly curved shoreline and a complex archipelagoshielding the bay from the open lake, so wave energy wasprobably less influential than in other coastal wetlands. Onthe other hand, seiches were common in Mackinac Bay, andstrong storm surges were observed on occasion. These mayhave been responsible for creating chemical gradients, andmay also have caused some passive invertebrate movement.

Biotic factors were another possible driver of spatiotem-poral variation in invertebrate communities. Some taxa thatseem unlikely to be able to move quickly, either actively orpassively (e.g., Lumbriculidae, Nematoda), appeared toshift along the gradient, and it is possible that these taxaand others were subjected to varying predation pressuresand other causes of differential mortality and/or reproduc-tion. Many studies have shown significant effects of fishpredators on invertebrates, and Wellborn et al. (1996)suggested that a lack of fish predation is a key determinantof invertebrate community composition in temporarilyflooded habitats. We found that invertebrate predatorassemblages varied spatially and temporally at our studysite, and many fish species invaded higher-elevation zonesas water rose, including small invertebrate-feeders at thehighest elevation (Gathman 2000) and large piscivores insomewhat deeper areas (Gathman and Keas 1999). Ifpredation on wet-meadow specialists increased in high-water years, then it is probably especially important that

Wetlands (2011) 31:329–341 339

higher-elevation wetlands remain as refuges for theseinvertebrates. Loss of high-elevation wetland zones throughland development, such as the highway that truncates theupper end of the Mackinac Bay wetland (Fig. 1), may posesubstantial threats to biotic diversity in coastal wetlands.

The most important factors contributing to the homog-enization of invertebrate communities in our study wereprobably: 1) the large increases in the four most abundanttaxa in all vegetation zones, 2) the declines of many Year 1wet-meadow taxa, and 3) upslope spread of many taxa.Rather than entire assemblages shifting en masse up ordown the slope, individual taxa responded differently tohydrological changes. The fastest responders appeared to bethose that could actively or passively move quickly alongthe slope with water-level changes. Aerial dispersal can bean important mode of insect colonization of temporarilyflooded wetland habitats (Jeffries 1994; Batzer andWissinger 1996), and many insects in our study appearedto quickly take advantage of the newly flooded wetmeadow and hummock zone in Year 2 (and then shiftedback downslope again as the water level fell in Year 3).Cladocera also rapidly exploited the newly flooded areas(and reversed direction downslope just as quickly as waterreceded). As planktonic animals, they were likely carriedupslope by rising water in Year 2 or hatched from ephippiadeposited in the wet meadow during the previous year.

In contrast, Caecidotea and Amphipoda, despite beingrelatively mobile animals, exhibited time-lagged responsesto water movements. One possible explanation for this isthat, as detritivores, they would only move upslope afternewly flooded detritus had been sufficiently conditioned bymicrobes. Libellulidae (and, to a lesser extent, Aeshnidae)dragonfly nymphs became conspicuously abundant in theYear 3 wet meadow (and June hummock zone; see Table 3),even though water levels were dropping (Gathman 2000).Many of these nymphs were found dead in shallow, warmpools of water in depressions on the recently exposed wetmeadow substrate. Stranded aquatic invertebrates are easyprey for birds and terrestrial animals, and may nutritionallysupplement terrestrial communities (see Batzer 2004).

If subsequent studies in other coastal wetlands supportour findings that invertebrate communities can changewithin vegetation zones when water level changes, it willbe important to consider the implications for long-termcoastal wetland monitoring. Results could depend on theyear in which data are collected. Burton et al. (1999)developed an invertebrate-based Index of Biotic Integrity(IBI) for Lake Huron coastal wetlands, using different setsof metrics for each vegetation zone. Uzarski et al. (2004)found that this IBI was robust to changes in water level.However, Wilcox et al. (2002) felt that IBIs would beineffective in many Great Lakes coastal wetlands becauseof the strong effects of water-level changes on habitats,

noting that it is not only the current water level that couldaffect biotic communities at a given time, but also theprevious several-year hydrologic history of each site.Clearly further examination is needed to determine howbest to incorporate water-level data into long-term coastal-wetland monitoring plans, and to generate a betterunderstanding of coastal-wetland invertebrate ecology.

Acknowledgments This research was supported by The NatureConservancy, Michigan Chapter, the Michigan Coastal ManagementProgram of the Michigan Department of Environmental Quality(MDEQ), the Les Cheneaux Economic Forum, the Land and WaterManagement Division of MDEQ, and the Nongame Wildlife SmallGrants Program of the Michigan Department of Natural Resources. Wethank Brian Keas and Sam Riffell for field-sampling assistance, and JohnWheeler and Becca Jacobson for reviewing a draft of this paper.

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