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Estuarine, Coastal and Shelf Science 68 (2006) 462e472www.elsevier.com/locate/ecss
Unravelling the complex structure of a benthic community:A multiscale-multianalytical approach to an estuarine sandflat
Luis Gimenez a,b,*, Caterina Dimitriadis a, Alvar Carranza a,Ana Ines Borthagaray a, Marcel Rodrıguez a
a Seccion Oceanologıa, Facultad de Ciencias, Igua 4225, 11400 Montevideo, Uruguayb Biologische Anstalt Helgoland, Foundation Alfred Wegener Institute for Polar and Marine Research, 27498 Helgoland, Germany
Received 11 January 2006; accepted 6 March 2006
Available online 2 May 2006
Abstract
We evaluated scale-dependent patterns of distribution of sandflat macroinfauna of a coastal lagoon, using different analytical approaches. Thesampled area was divided into three sectors (outer, inner-north, inner-south) containing small promontories. At each side of the promontories wedefined stations in a line transect across the sandflat. Thus, we evaluated sediment characteristics and macroinfaunal responses to sectors,orientations and intertidal levels; animalesediment relationships were also studied. At a large scale, there was a clear pattern of sedimentcomposition and macroinfaunal abundance. While the outer sector had medium to coarse sands, reflecting the high hydrodynamic conditions ex-isting near the lagoon entrance, the inner sectors showed sandy and muddy sediments. Most species were in low abundances at the outer sector. Atsmall scale, macroinfaunal abundance and species richness decreased sharply towards the upper sandflat level. Also at small scale, sediment com-position limited the maximal densities reached by all species, with exception of the deposit-feeding polychaetes. Thus, the macroinfaunal com-munity at muddy sediments was dominated by burrowing deposit-feeders, while all species peaked in sandy sediments. Our results suggestthat physical factors shaping macroinfaunal communities operate at different scales and are better detected using several analytical approaches.Large scale patterns, associated with along-shore variations in disturbance by currents, were detected as changes in the mean abundance of macro-infauna. Small scale patterns, related to sediment characteristics, were observed as changes in maximal densities of macroinfauna. Small scalepatterns, associated with the level of inundation of the sandflats, were detected through changes in the abundance and presence of macroinfauna.The evaluation of the role of the physical conditions on communities must involve the use of several sampling and analytical approaches.� 2006 Elsevier Ltd. All rights reserved.
Keywords: community structure; macroinfauna; spatial scales; Uruguay
1. Introduction
A growing body of literature suggests that ecologicalsystems in general (Wiens, 1989; Levin, 1992) and intertidalbenthic communities in particular (Thrush et al., 1997; Gimenezand Yannicelli, 2000; Fraschetti et al., 2005) vary at variousspatial scales. This is the consequence of a combination ofphysical and biological factors operating in a scale-dependent
* Corresponding author. Biologische Anstalt Helgoland, Foundation Alfred
Wegener Institute for Polar and Marine Research, Kurpromenade, 27498 Hel-
goland, Germany.
E-mail address: [email protected] (L. Gimenez).
0272-7714/$ - see front matter � 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.ecss.2006.03.001
fashion. Recent work on soft-sediment communities has fur-ther shown that patterns can be detected using a combinationof different analytical approaches (Thrush et al., 2003, 2005).Ecological factors may shape patterns of distribution in differ-ent ways: for instance a factor may produce sharp boundariesin species distribution, detected through changes in presence/absence of macroinfauna; a further factor may only reducethe mean abundance of macroinfauna. Alternatively, the waypatterns are observed depends merely on the detailed combi-nation of observation methods used in a particular study.Whatever the cause, it is clear that the combination of scalesof observation and analytical tools is an advance towards un-ravelling complex patterns of macroinfaunal abundance.
463L. Gimenez et al. / Estuarine, Coastal and Shelf Science 68 (2006) 462e472
Complex patterns of macroinfaunal distribution character-ize sandflats and other soft bottom habitats (Reise, 1985,1991; Thrush et al., 1997). Across- and along-shore environ-mental gradients produce patchiness of macroinfauna at differ-ent spatial scales (e.g. sand ripples, pits produced by fishpredators, burrows or mounds produced by infaunal activity:Thrush et al., 1997; Reise, 2002). Topographic structures af-fect macroinfauna through changes in hydrological character-istics and sediment composition. Hydrological conditions mayaffect settlement, resuspension and transport of macroinfauna(Whitlatch et al., 1998) or change sediment characteristics orfood supply (Ford et al., 2001). Sediment characteristicsmay affect macroinfauna in various ways (Gray, 1981;Snelgrove et al., 1994). In some coastal lagoons sedimentheterogeneity may even obscure patterns related to salinity(Teske and Wooldridge, 2001, 2003). In the intertidal zones,topographic structures may combine with intertidal gradientsto produce patchiness of macroinfauna.
Narrow microtidal sandflats (<50 m wide, tidal range<0.50 m: Anonymous, 1997; O’Connor, 1991) characterizethe streams and coastal lagoons of the Uruguayan coast. Ata scale of hundreds of Kilometres, the community structurevaries mainly in relation to coastal salinity gradients and var-iability among streams and lagoons (Gimenez et al., 2005). Inthis paper, we examined community patterns within one ofthese sites, the open/closed Rocha lagoon (Fig. 1a). We useda multiscale approach to evaluate the spatial and temporal var-iability in species diversity, macroinfaunal composition andspecies abundance in response to: (a) lagoon sectors in thesouthern region, (b) promontories within each sector, and(c) the intertidal gradient at each side of each promontory.We used different analytical approaches to evaluate potentialeffects of physical factors on presence/absence, mean andmaximal abundance of macroinfaunal species.
2. Methods
2.1. Sampling design and laboratory analysis
Our sampling sites were located on the southern region ofRocha lagoon (Fig. 1b). This portion is formed by the lagooninlet, opening temporarily to the sea, and a lagoon arm (‘‘canalde la barra vieja’’) that runs north-eastward, parallel to thecoast. Water temperature varies seasonally (9e25 �C).Salinity (range 5e25) varies in relation to the patterns of waterexchange between the lagoon and the sea. Salinity gradients areformed after the opening of the sand bar (25 at the lagoon en-trance, 10e15 towards NE). We divided the sampling area intothree sectors, namely the outer sector, at the lagoon entrance,and two sectors located, respectively, at the south and northborders of the lagoon arm (inner-south and inner-north sec-tors). Each sector contained a variable number of sites consist-ing in promontories limiting a 50-m wide portion of sandflaton each side. We assigned them numbers and randomlyselected sites for subsequent sampling. The number of sitesselected depended on the extension of each sector: in the outerand the inner-north sectors we sampled two sites, in the
inner-south sector we sampled three sites as this was largerthan the other two sectors. The sides of most studied promon-tories were oriented either towards the lagoon entrance (W di-rection) or to the opposite direction (E), respectively; theexception was the site O1 (Fig. 1b) where the lagoon entrancewas to the south. For simplicity, all orientations are thereaftercategorized as ‘‘W’’ (towards the lagoon entrance) and ‘‘E’’(opposite direction). At each side of a promontory we defineda line transect, with 10 stations (Stn.) separated by 2 m withStn. 1 located at the upper border of the sandflats, demarcated
59° 56° 53° 50°W
Brazil
Uruguay33°S
Atlantic Ocean
a
Rocha
lagoon
5 km
O1O2 IS2
IS1
IS3IN2
IN1
Site
Transect
Stations
Sites within sectors
PromontoryMacrofauna and sediment
Macrofauna
Southern region
50m
Channel
Inlet
b
Fig. 1. Geographic location of Rocha lagoon in Uruguay, at the Atlantic coast
of South America (a) and summary of sampling design at the southern, estu-
arine region of the lagoon (b). The estuarine region of the lagoon was divided
into three sectors: an outer sector near the lagoon entrance, inner-south and
inner-north at each side of the lagoon shore. Sites in each sector are abbrevi-
ated as O1, O2 (outer sector), IS1, IS2, IS3 (inner-south) and IN1, IN2 (inner-
north). There were two transects per site; each transect contained 10 stations.
464 L. Gimenez et al. / Estuarine, Coastal and Shelf Science 68 (2006) 462e472
by sand dunes (inlet sector) or with marshes (channel sectors).Transects covered the sandflats up to a depth of 0.30e0.80 m.This sampling procedure was repeated three times, in April,July and September of 2002.
We took samples of macroinfauna and beach profile at allstations; sediment for granulometric analysis was taken atStns. 1, 5 and 10. Macroinfauna was sampled with a PVCcorer (diameter¼ 16 cm) to 20 cm depth. The samples werepassed through a sieve of 1-mm mesh size; organisms werecollected and preserved in 4% formalin for eventual speciesdetermination in the laboratory. The sandflat profile was deter-mined following Emery (1961) using Stn. 1 as reference. Sed-iment samples for granulometric and organic matter analyseswere taken with a small corer (5 cm wide). In the laboratory,sediment samples for granulometric analysis were dried for24 h at 60 �C, weighed, and wet-sieved in a mesh of0.063 mm to separate the sand and mud (silt/clay) fractions.The sand fraction was dried again, weighed and mechanicallysieved through a series of meshes from 4 mm up to 0.063 mm(interval 1 B, Buchanan, 1984). The mud fraction was not fur-ther analysed. The resulting fractions were weighed again.Samples for organic matter were wrapped in aluminium foiland immediately frozen; in the laboratory they were dried,weighed, burnt for 6 h at 450 �C and weighed again. Organiccontent is expressed as the percentage of the sediment dryweight.
2.2. Statistical analyses
For the sediment fractions we first run a principal compo-nent analysis (PCA: Legendre and Legendre, 1998); thereafterwe run a four-way ANOVA (Quinn and Keough, 2002) withthe variables that contributed most to the formation of the firsttwo principal components. The factors in the ANOVA wereOrientation, Level, and Time as within-subject factors andSector as between-subject factor. This design was also usedto analyse sandflat profile and organic matter. We used arcsinetransformed data of granulometric fractions and logarithmic-transformed data of the proportion of organic matter of thesediment, in order to reach variance homogeneity andnormality of residuals. When the variances were not homoge-neous after transformation, we followed Underwood (1997)and adjusted the critical levels to a value equal to the p-valuefor variance heterogeneity.
Three main analytical approaches were used to analysebenthic community data: (a) ANOVA, multivariate analysesand regression methods using abundance and species richness,(b) maximal density analysis, and (c) c2-test using proportionsof presence/absence of macroinfauna. Analyses of abundancewere done using total abundance per transect and abundanceper station (¼density) as variables. Total abundance per tran-sect was calculated by pooling abundance of all 10 stationsof each transect. This permitted us to run tests for specieswith very patchy distribution within each transect: in a previ-ous study Gimenez and Yannicelli (2000) found that in specieswith patchy distribution across levels abundance per transect,but not density, changed with longshore topographic features.
For density, we used data from Stns. 1, 5 and 10 (upper, mid-dle and lower levels), since in these stations we also had dataof sedimentary characteristics. Thus, macroinfaunal densitydata allowed us to test for associations between sediment char-acteristics and macroinfauna.
For abundance per transect, we used a three-way ANOVAto evaluate the role of Orientation, Time (both factors aswithin-subject, repeated measures) and Sector (between-subject factor) on total abundance, species richness and abun-dance of all species collected. For density, we used four-wayANOVA as in the case of granulometric data. This designwas used to analyse total abundance, species richness, andalso abundance of the species selected from a principal coor-dinate analysis (PCO: Legendre and Legendre, 1998; Ander-son, 2003). The PCO was made using BrayeCurtis ontransformed data of abundance per transect. Abundance pertransect, density and species richness were always logarith-mic-transformed [ln (Xþ 1)]: transformed data, but not rawdata, were homogeneous and normally distributed.
Multiple-forward stepwise regression was used to explorerelationships between total density, species richness per stationand sediment fractions. For this analysis we followed a hierar-chical approach, analysing: (a) the data set for all regions andlevels, (b) data sets within each region, and (c) within eachlevel. Potential relationships between the macroinfaunalassemblage and sediment characteristics were first evaluatedusing permutational multivariate multiple regression (Anderson,2001, 2004; McArdle and Anderson, 2001; Anderson et al.,2004), thereafter called PERMMREG. Multiple-forwardstepwise regression was used as a second step to explorerelationships between density of selected species as done fortotal density and species richness.
Maximal density analysis was used to evaluate the potentialrole of sediment characteristics as limiting factors. Maximaldensity analysis explores the possibility that environmentalfactors constrain the maximal density (Thomson et al., 1996;Thrush et al., 2005). For this approach, we pooled sedimentaryfractions in three groups, mud, very fine to medium sand, andcoarse sand to gravel. The following procedure was made foreach sedimentary fraction separately: (a) the proportion ofa sedimentary fraction (e.g. mud) was calculated; (b) 10 cate-gories were defined 0e0.099, 0.1e0.199,.,0.9e1.0; (c) foreach species, data of density were binned into these categories,according to the proportion of the sediment fraction (e.g. mud)present in each station; and (d) for each category, the maximaldensity of each species was recorded. Polynomial regressionanalysis was used to explore the potential relationships amongmaximal density of each species and sediment category. Thiswas done for each sediment fraction separately, using datasets of all regions. We did not run an analysis within regionsand levels for two reasons: (a) most maximal densities oc-curred within particular combination of levels and regions;and (b) the range of variability in sediment fractions waslow at particular intertidal levels and regions (see Section 3).
Tests on presence/absence data were used to evaluate ifthere was an association between the water level and the pres-ence of macroinfauna at the upper sandflat level (Stn. 1).
465L. Gimenez et al. / Estuarine, Coastal and Shelf Science 68 (2006) 462e472
Natural variation in the water level did not allow for a regres-sion approach (see Section 3). Instead, we used c2-test withYates correction to evaluate the association between the num-ber of samples with/without macroinfauna and the number ofsamples covered/uncovered by water. Observations of waterlevel were pooled in two categories according to whether thewater level was: (1) at or beyond the upper level or (2) belowthat level. The low variability in water level precluded the useof a larger number of categories.
3. Results
3.1. Physical characteristics of the sandflats
The sandflat profiles varied among sites: at O1 the sandflatshowed the steepest slope [tg (a)¼ 0.055e0.097] while thoseat O2 showed the shallowest one (0.014e0.022). At othersites the profile varied from shallow to steep. The slope variedsignificantly with Level (F2,8¼ 8.23, p< 0.01) andOrientation� Time interaction (F2,4¼ 5.54, p< 0.04). Thesandflats were steeper at the upper level (0.049) than at thelower one (0.034). The slope was higher at the E side(0.048) of the promontories than at the W side (0.047) inJune and September.
In most cases, water level covered one half to three quartersof the flats, usually up to Stns. 2e4; in September most ofsampling sites were completely underwater. A considerablevariation in the water level was found at O2 (range Stns.4e8) while in other sites variations were smaller (range:Stns. 1e4).
Granulometric fractions ranged from gravel to mud (clay/silt); they were dominated by mud, medium and coarse sandfractions (Fig. 2a). Mud and coarse sand fractions were themain contributors to the formation of the first component ofPCA (arcsine transformed proportions of the granulometricfractions: Fig. 2b): these fractions were negatively correlated.Mud fraction (arcsine transformed data) was significantly af-fected by the interactions Level�Orientation (F2,8¼ 9.00,p< 0.01) and Orientation� Sector� Time (F4,8¼ 13.39,p< 0.001). Mud content increased from less than 5% at theupper level, to 10e60% at the lower level, but this incrementwas higher at the E sides of the promontories. The effect of theorientation on the mud fraction depended on the sector andtime: in the outer sector the difference in the mud fractionbetween sides was small. At the inner-south sector this differ-ence was consistent through time.
The very coarse sand fraction followed by gravels contrib-uted to the second principal component. Very coarse sand didnot significantly vary with any factor. The fraction of gravels(<5%) varied significantly with sector (F2,4¼ 15.92, p<0.01) and with the Orientation� Level interaction (F2,8¼4.89, p< 0.04). The percentage of organic matter (range 0.01e10.5%) varied significantly with Time (1.4e2.2%; F2,8¼ 10.12,p< 0.01), Level (F2,8¼ 15.92, p< 0.01) and Orientation(F1,4¼ 20.36, p< 0.01). Organic matter increased from thehigher intertidal level (mean¼ 0.3%) to the lower one (3.3%);it was significantly higher at the E sides of the promontories
(2.6%) as compared to the W sides (1.0%). Organic matterwas positively correlated with mud and very fine sand fractions(multiple R2¼ 0.80).
3.2. Macroinfauna
Total abundance per transect (ln-transformed data) was sig-nificantly affected by the interaction Orientation� Time(F2,4¼ 12.88, p< 0.001): for instance in April, abundancewas significantly higher in the E side than in the W (meansW and E: 47 and 56 ind. transect�1, respectively), but the pat-tern was reversed in September (W and E: 235 and 147 ind.transect�1). Abundance was also affected by Sector�Time (F4,8¼ 4.14, p< 0.05), varying more at the outer sector(from 6 to 141 ind. transect�1) than in the inner sectors (e.g.south-inner region: from 110 to 250 ind. transect�1).
For total macroinfaunal density there was a significantinteraction effect (F4,8¼ 3.9, p< 0.05), whereby density var-ied differently among sectors (Fig. 3a). Total macroinfaunaldensity also increased from the upper to the lower intertidallevel, but this increase varied with time (significant Level�Time interaction: F4,16¼ 3.56, p< 0.05): in April the incre-ment was not as strong as in other times (Fig. 3b).
Species richness per transect (range 0e11) was affected bythe interaction Time� Sector (F4,8¼ 23.4, p< 0.0002). InApril the outer sector had a low species richness (1e3 speciestransect�1) as compared to the inner sectors (3e9 species pertransect�1). In June and September species richness did notdiffer among sectors (3e11 species). Species richness persample (range 0e7) was significantly lower at the upper levelthan the other levels irrespective of time (F2,8¼ 9.78,p< 0.01): in the upper level, 43% of the samples where devoidof macroinfauna while in the other levels this occurred in9e12% of the samples. The effect of sector depended ontime (F4,8¼ 4.11, p< 0.05). However, species richness wasconsistently lower at the outer sector (>90% devoid of macro-infauna) than at the inner sectors.
The two principal axes obtained by PCO on macroinfaunalabundance per transect explained 63% of the total variance.This ordination tended to separate transects from the outervs. inner sectors (Fig. 4). The polychaete Nephtys fluviatilisand Heteromastus similis correlated with the first axis, charac-terizing the inner sectors; these sectors were also characterizedby the gastropod Heleobia australis, the bivalve Erodona mac-troides, the isopod Pseudosphaeroma platensis and the amphi-pod Jassa falcata. The isopod Dies fluminensis showed a highcorrelation with the second axis, characterizing the outer sec-tor (Fig. 4). Other species such as the polychaetes Laeonereisacuta and the tanaid Sinelobus stanfordi showed low correla-tion with the principal axes.
Species abundance and density changed mainly with time,sector and level; effects of orientation depended on other fac-tors (Table 1); here we focused on spatial variations. The mostcomplex pattern was found in Nephtys fluviatilis, as shown bythe significant interactions. Abundance per transect was signif-icantly lower in the outer sector (15 ind. transect�1) than in theinner sectors (40 ind. transect�1). The patterns of density were
466 L. Gimenez et al. / Estuarine, Coastal and Shelf Science 68 (2006) 462e472
Mud
Coarse sand
Very coarse sand
Very fine sand
Medium sand
Fine gravel
Medium gravel
Mud Very fine sand Fine sand
Medium sand Coarse sand Very coarse sand Gravels
O1
O2IS1
IS2 IS3
IN1 IN2
W E W E W E
W EW EW EW E
a
b
0
20
40
60
80
100
0
20
40
60
80
100
0
20
40
60
80
100
0
20
40
60
80
100
0
20
40
60
80
100
0
20
40
60
80
100
0
20
40
60
80
100
U M L U M L
U M L U M L U M L U M L U M L U M L U M L U M L
-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4Factor 1 (58.9%)
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
Fact
or 2
(23.
6%)
Fig. 2. Variability in the granulometric fractions. (a) Time-averaged distribution of sediment fractions along the southern region of Rocha lagoon, depicted as
a contour line. Each panel refers to a site (O1 and O2: outer-sector; IN1eIN2: inner-north sector; IS1eIS3: inner-south sector). Within each site, each bar refers
to a combination of orientation (W, E) and sandflat level (U: upper, M: middle, L: lower). (b) Principal component plot with the contribution of each grain fraction
to the formation of the first and second axes; the percentage of variation explained by each axis is given in parenthesis.
467L. Gimenez et al. / Estuarine, Coastal and Shelf Science 68 (2006) 462e472
complex: density tended to be low in the outer sector (Fig. 5a),and in the upper level (Fig. 5b); an increased abundance at thewest sides of the promontories was observed only in Septem-ber (Fig. 5c).
Abundance of Heteromastus similis was significantly lowerat the outer sector (mean¼ 10 ind. transect�1) than at the innersector (80 ind. transect�1). Density varied among times andlevels (Table 1), increasing from the upper to the lower level.No significant variations were found for the polychaete Laeo-nereis acuta (mean density: 1e8 ind. 0.02 m�2) and the bi-valve Erodona mactroides (mean density: 1e9 ind. 0.02 m�2).
For other species, such as Heleobia australis, Dies flumi-nensis and Jassa falcata, density was very patchy: resultshere are given as abundance per transect only (Table 1).For H. australis there was a significant interaction Sec-tor�Orientation (Fig. 6a): abundance was low at the outersector; effects of side were restricted to the inner-north sector.Dies fluminensis was significantly affected by the interactionTime� Sector: temporal variability was high in the outer sec-tor (Fig. 6b). Abundance of the tanaid Sinelobus stanfordi
0
5
10
15
20
25
30AprilJuneSeptember
Tota
l den
sity
(ind
0.0
2 m
-2)
Sector
Transect level
0
5
10
15
20
25
30 AprilJune September
b
a
Tota
l den
sity
(ind
0.0
2 m
-2)
Upper Middle Lower
Outer Inner S Inner N
Fig. 3. Changes in mean total macroinfaunal density depending on: (a) sam-
pling time and lagoon sectors, and (b) sampling time and sandflat levels. Error
bars are standard errors of the mean.
significantly varied among sectors depending on the orienta-tion or sampling times. In the outer sector, S. stanfordi wasnearly absent in all sites irrespective to sampling time and ori-entation, while in the inner sectors this species was more abun-dant in the W side of the promontories (Fig. 6c).
3.3. Relationships between macroinfauna andenvironmental variables
At the scale of the whole sampled area, density and speciesrichness per station were positively correlated with the fractionof very fine sands (Table 2). Within the inner sectors, middleand lower levels or combination of these levels and sectorstotal density and richness were also explained by the mudcontent or the organic matter. In all cases the proportion of
-50 -25 0 25 50
-0.5 0.0 0.5
-50
-25
0
25
50
-0.5
0.0
0.5
Outer Inner-south Inner-north
Factor 1 (42.6%)
Fact
or 2
(20.
9%)
D. fluminensis
N. fluviatilis
H. similis 12
3 4
Fig. 4. Biplot of principal coordinate analysis on macroinfaunal assemblage at
the southern region of Rocha lagoon: symbols are transects according to dif-
ferent lagoon sectors; arrows show correlation among macroinfaunal species
and the first two coordinate axes. The numbers in plot indicates species as fol-
lows: 1: Heleobia australis, 2: Erodona mactroides, 3: Pseudosphaeroma pla-tensis, 4: Jassa falcata.
Table 1
Summary of significant p-values after four-way ANOVA to test for effects of
sector, sampling times, orientation and level on abundance per transect and
density of the main macroinfaunal species. Abbreviations: Nefl: Nephtys flu-viatilis, Hetsi: Heteromastus similis, Heau: Heleobia australis; Difl: Dies flumi-
nensis; Sist: Sinelobus stanfordi; Jafa: Jassa falcata
Factors Nefl Hetsi Heau Difl Sist Jafa
Abundance
Sector (S ) <0.03 <0.05 <0.02
Time (T ) <0.001 <0.001 <0.001 <0005 <0.005 <0.02
Orientation (O) <0.001
S�O <0.04 <0.005
S� T <0.001 <0.03
Density na na na
S <0.01
L <0.001 <0.005
T <0.001 <0.005
S� T <0.02
L� S <0.001
T�O <0.04
468 L. Gimenez et al. / Estuarine, Coastal and Shelf Science 68 (2006) 462e472
variance explained by sediment characteristics was rather low(<50%).
The PERMMREG found significant relationships betweenmacroinfaunal assemblages and sediment parameters, in par-ticular with very fine sand, organic content and mud (Table3). However, when correlations among independent variableswere considered (forward stepwise procedure), the varianceexplained was low (ca. 15%). Correlations among species den-sity and sediment fractions were, in most cases, very low ifsignificant (R2< 0.20). The highest correlation was foundwhen the analysis was restricted to the data of the middle
Den
sity
(ind
0.0
2 m
-2)
Sector
Orientation
0
1
2
3
4
5
6
7AprilJuneSeptember
0
2
4
6
8AprilJuneSeptember
0
0
2
4
6
8OuterInner-southInner-North
b
Level
Den
sity
(ind
0.0
2 m
-2)
Den
sity
(ind
0.0
2 m
-2)
a
c
Upper Middle Lower
Outer Inner-south Inner-north
EastWest
Fig. 5. Nephtys fluviatilis. Changes in mean density depending on (a) sectors
and sampling time, (b) levels and sectors and (c) orientation and sampling
time. Error bars show standard errors of the mean.
plus lower levels within the inner regions: Laeonereis acutawas positively correlated with gravel, fine and very fine sandfractions (R2¼ 0.32); Erodona mactroides was positively cor-related with fine sand (R2¼ 0.25).
Maximal density analysis gave better fits (Table 4). Signif-icant correlations were found for Nephtys fluviatilis, Erodonamactroides, Heleobia australis and Dies fluminensis vs. mud
0
20
40
60
80
100AprilJuneSeptember
0
10
20
30
40WestEast
0
4
8
12WestEast
Sector
Sector
Sector
a
b
c
Abun
danc
e (in
d tra
nsec
t-1)
Abun
danc
e (in
d tra
nsec
t-1)
Abun
danc
e (in
d tra
nsec
t-1)
Outer Inner-south Inner-north
Outer Inner-south Inner-north
Outer Inner-south Inner-north
Fig. 6. Changes in mean abundance per transect in three macroinfaunal spe-
cies: (a) Heleobia australis in relation to sector and orientation (west, east),
(b) Dies fluminensis in relation to sector and sampling time, (c) Sinelobus stan-
fordi in relation to sector and orientation. Error bars show standard errors of
the mean.
469L. Gimenez et al. / Estuarine, Coastal and Shelf Science 68 (2006) 462e472
fraction categories. In all these species maximum density de-creased from low to high mud fractions (Fig. 7; D. fluminensisnot shown: absent when the proportion of mud was >0.2). Sig-nificant quadratic coefficients indicated non-linear trends inE. mactroides and H. australis, as they peaked in sedimentswith 50e70% of very fine to medium sands; D. fluminensispreferred sediments with 70e80% of coarse sand and gravelfractions.
Maxima of Heteromastus similis and Laeonereis acutashowed two peaks, at the lowest and intermediate to high
Table 3
Results of PERMMREG species data on environmental variables. Abbrevia-
tions: %Var: percentage variance explained in the species data by each vari-
able; %Cum: cumulative variance explained after a forward-stepwise
procedure
Variable %Var %Cum F P
(a) Variables fitted individually
Medium gravel 2.03 2.56 0.038
Fine gravel 0.90 1.13 0.348
Very coarse sand 0.76 0.94 0.456
Coarse sand 4.70 6.11 0.004
Medium sand 0.97 1.22 0.282
Fine sand 0.55 0.69 0.604
Very fine sand 6.01 7.93 0.002
Mud 4.79 6.24 0.004
Organic matter 5.48 7.18 0.002
Depth 3.13 4.01 0.008
(b) Variables fitted sequentially
Very fine sand 6.01 6.01 7.93 0.002
Organic matter 2.26 8.27 3.03 0.020
Mud 1.61 9.88 2.18 0.088
Medium sand 1.26 11.14 1.71 0.152
Depth 0.95 12.09 1.29 0.262
Fine gravel 0.89 12.98 1.22 0.256
Coarse sand 0.63 13.61 0.86 0.464
Fine sand 0.59 14.20 0.80 0.510
Medium gravel 0.61 14.80 0.82 0.468
Very coarse sand 0.66 15.47 0.90 0.440
Table 2
Multiple-forward stepwise correlations between sediment fractions and total
density or species richness using the whole data set, or subsets from different
regions or levels as indicated in the first column. Only significant correlations
( p< 0.05 are shown). Abbreviations: Vf-sand: very fine sand; OM: organic
matter
Vf-sand Mud OM
Total density
All data 0.23
Middleþ lower level 0.33
Inner sectors 0.21 �0.22
Upper level �0.3
Middleþ lower level �0.62
Richness
All data 0.23
Upper level 0.38
Middleþ lower level �0.22
Outer sector 0.39
Inner sectors �0.22
Upper level 0.44 �0.45
Lower level �0.51
mud fractions (Fig. 7): neither the linear nor the quadratic co-efficients were significant. These two species showed a singlemaximum at intermediate levels (30e50%) of very fine to me-dium sand fractions (Fig. 7): L. acuta showed a significantnon-linear relationship, dominated by the quadratic coefficient(Table 4); H. similis showed a flatter response that it was sig-nificantly explained by a cubic polynomial ( y¼ 111.5x�160.5x3; R2¼ 0.67, p< 0.05) dominated by the non-linearterm.
The upper level of the sandflats contained macroinfauna invery low density. There was a significant association betweenpresence of macroinfauna and water level being at or aboveStn. 1 ( p< 0.01): of 34 cases with the water level at Stn. 1we found 24 cases of presence of macroinfauna; in eight caseswhen the water level was below that station we found only onecase of presence.
4. Discussion
Here, we show that variation in benthic communities is scale-dependent and that different analytical approaches are necessaryto unravel complex spatial and temporal patterns. The capacityto detect patterns and understand community function is limitedby the diversity of reference frames (scales of observation) andanalytical tools used by ecologists. Using only one scale, anda single analytical tool we will get only the ‘‘shadow’’ of whatthe real community structure is. In our case, scale-dependentfactors were associated with the topography and sediment char-acteristics of the sandflats. The effects were detected as changesin the mean abundance or density, as limitations of maximaldensities or as changes in patterns of presence/absence of organ-isms. The combination of sampling design and analytical toolsallowed us to disentangle relationships among macroinfauna,topography and sediment characteristics.
4.1. Large scale patterns
We found lower total abundance, density and species rich-ness at the outer sector as compared to the inner sectors. Thefactor operating at this scale acted by reducing the mean abun-dance or density, but they did not preclude colonization of thesediments. This is why most species showed temporal variabil-ity, but peaked in the inner sectors. The only exception was theisopod Dies fluminensis, found in the outer sector especially inSeptember.
Table 4
Forward stepwise polynomial regression coefficients and determination
coefficient (R2) among sediment fraction categories and species maximum
abundance. Only significant coefficients ( p< 0.05) are shown
Species/coefficients Mud Mud2 R2 Sand Sand2 R2
Laeonereis acuta 40.4 �48.8 0.56
Nephtys fluviatilis �66.7 0.44
Heteromastus similis
Erodona mactroides �84.7 68.4 0.61
Heleobia australis �79.0 66.6 0.65
Dies fluminensis �188.5 0.46
470 L. Gimenez et al. / Estuarine, Coastal and Shelf Science 68 (2006) 462e472
0.0 0.2 0.4 0.6 0.8 1.00
5
10
15
20
25MudSand
0.0 0.4 0.6 0.8 1.00
10
20
30MudSand
0
10
20
30
40MudSand
0
5
10
15
20
25MudSand
0
2
4
6
8
10MudSand
Nephtys fluviatilis
Den
sity
(ind
0.0
2 m
-2)
0.2
Laeonereis acuta
Erodona mactroides
Proportions of sediment fractions
Heteromastus similis Heleobia australis
Proportions of sediment fractions
Fig. 7. Maximal density of the main macroinfaunal species of the estuarine sandflats of Rocha lagoon in relation to the proportion of mud or very fine sand, given at
intervals of 0.1.
The factor operating at large scale is most likely related tovariations in hydrodynamic conditions. High hydrodynamicconditions of the outer sector may disturb organisms by erod-ing or unstabilizing sediments. Changes in macroinfaunalcommunities associated with hydrological changes havebeen observed in other estuarine systems (Blanchet et al.,2005), in particular in estuarine inlets (Holland and Dean,1977; Jorcın, 1999; Passadore et al., in press). The hydrody-namic conditions of the outer sector of Rocha lagoon may re-semble that of streams studied by Gimenez et al. (2005) whereabundance and species richness is also low.
4.2. Small scale patterns: sandflat orientation andsediment type
In Rocha lagoon, we did not observe ridges, channels or rip-ples as in other flats (e.g. Whitehouse et al., 2000). The main fea-tures were related to promontories that affected the sandflatprofile and sediment composition: the bottoms at the deeper Esides of the promontories were dominated by muddy sediments.This pattern may reflect differences in hydrodynamic conditionsdue to depth and water circulation within the lagoon (Barnes,
1980). The role of sandflat orientation on macroinfaunal abun-dance or density of any particular species was not as importantfor other factors. If any, sandflat orientation affected macroin-fauna by indirectly affecting the sediment characteristics.
Sediments, in particular mud content, acted as a limitingfactor (Thrush et al., 2003, 2005), constraining maximalabundances. The effect of sediments was thus detected in adifferent way as the effect of large scale topography: therewas only a small portion of variability in density explainedby the sediments in other regression analyses. Sedimentsmay play a strong role in structuring benthic infaunal commu-nities (Gray, 1981; Mannino and Montagna, 1997; Teske andWooldridge, 2001; Ysebaert and Herman, 2002) but some-times may only affect the relative abundances (Holland,1985) or limit the development of maximal densities (Thrushet al., 2003, 2005).
The limitation produced by sediments was related to thelife history of the species. For the suspensivore Erodona mac-troides and the surface deposit-feeder Heleobia australis, max-imal densities were restricted to sediments with low mudcontent (<20%). Epibenthic and surface deposit-feeding crus-taceans reached also minimal densities in muddy sediment.
471L. Gimenez et al. / Estuarine, Coastal and Shelf Science 68 (2006) 462e472
For all these species, muddy sediments may either directly orindirectly affect survival, e.g. by limiting suspension feeding(Rhoads and Young, 1970), surface activity, or oxygen avail-ability due to high levels of organic matter (Holland, 1985;Magni et al., 2005), or by indirectly affecting species interac-tions. Whatever the cause, these species developed high den-sity patches only in sandy sediments. In contrast, theburrowing deposit-feeder polychaetes Heteromastus similisand Laeonereis acuta peaked in sediments with low or moder-ately high (60%) mud content, with intermediate levels of fineto medium sand. Another polychaete, the predatoredeposit-feeder Nephtys fluviatilis, showed a mixed pattern, peakingat low mud content but common at muddy sites.
4.3. Small scale patterns: sandflat level
The stronger small scale pattern of distribution was associ-ated with the sandflat level: very low density and species rich-ness were recorded at the upper level, especially at the outerregion. Thus, the factor operating at this level had a strong ef-fect, as compared with factors limiting the mean or the maxi-mal density, as it limited the overall colonization of thesediments by macroinfauna.
The pattern of macroinfauna must be associated with thevariation in the position of the water level as suggested bythe significant association between water level and macroin-fauna both at the upper levels. The upper levels of the sandflatswere more frequently inundated by water and colonized bymacroinfauna at the inner sectors. The position and variationin the water level may control the colonization rate or thesurvival of macroinfauna. Unlike most known sandflats (LeHiret al., 2000; Dyer et al., 2000) those of Rocha lagoon aremicrotidal. Thus, water level does not vary significantly atthe scale of tides: the most important variations should occurat longer time scales as the consequence of wind-driven cur-rents or existing cycles of filling and water discharge (Condeet al., 2002).
In summary, benthic macroinfauna may be affected by a setof interacting factors, varying in space and time and operatingat different spatial scales. Effects were detected using manyanalytical tools. The effect of a factor operating at large scale,possibly associated with variations in hydrodynamicconditions, was detected as changes in the mean abundance ofmacroinfauna. At a scale of metres, effects of mud contentwere detected as limitations in the maximal densities of allbut burrowing deposit-feeding polychaetes. Also at scales ofmetres, a factor associated with the water level had a stronginfluence on macroinfauna, reducing mean abundance andproducing sharp distribution boundaries. To detect of complexcommunity patterns, it is necessary to combine multiscale andmultianalytical approaches.
Acknowledgements
Financial support was given to L.G., by Comision Sectorialde Investigacion Cientıfica, Universidad de la Republica, Uru-guay. We thank Tito Olivera and Erika Meerhoff for field
assistance. The paper was improved by comments of threeanonymous reviewers.
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