PRIMARY RESEARCH PAPER

Spatial distribution of subfossil Chironomidae in surfacesediments of a large, shallow and hypertrophic lake(Taihu, SE China)

Yanmin Cao • Enlou Zhang • Xu Chen •

N. John Anderson • Ji Shen

Received: 30 November 2011 / Revised: 18 January 2012 / Accepted: 5 February 2012 / Published online: 11 March 2012

� Springer Science+Business Media B.V. 2012

Abstract Spatial heterogeneity of benthic commu-

nities has clear implications for estimating lake

production, biodiversity as well as identifying repre-

sentative sites for palaeolimnological studies. This

study investigates chironomid variability and the

controlling factors (i.e., environmental and spatial

variables) in surface sediments from Taihu Lake

(2,338 km2), a hypertrophic lake in the Yangtze delta

in eastern China. The spatial distribution of chirono-

mids shows distinct heterogeneity. Microchironomus

tabarui-type and Tanypus dominate the midge com-

munities around the estuaries, while Cricotopus

sylvestris-type and Polypedilum nubifer-type are the

predominant taxa in the East Bays and the East Taihu

Lake. Redundancy analysis was used for exploring

the relationships between chironomid variability and

environmental and spatial stressors. Four variables

were identified as significant factors that influence

chironomid community structures. The high nutrient

concentrations around the estuarial areas favor the

development of nutrient-tolerant taxa. Water depth-

related oxygen depletion in the open lake during algae

blooms prohibits the survival of many organisms,

except for a few hypoxic-resistant species. High

transparency in the East Bays and the East Taihu

Lake indirectly creates a favorite microhabitat for

macrophyte-associated chironomid species through

aquatic plants. Space per se is a significant forcing

factor for organism community and distribution at

scales of [1,000 km2. It might be important to

consider spatial variables more explicitly in future

studies of chironomids in large lakes where multiple

stressors make the interactions within the ecosystem

more complicated. This study aims to illustrate the

ecological characteristics of specific chironomid taxa

related to a ‘‘microecosystem’’ which is contributed

by the multiple environmental gradients within a large

lake, and to provide empirical support for interpreta-

tion of palaeochironomid data.

Keywords Chironomid assemblages � Trophic

state �Water depth �Macrophytes � Spatial variables �Taihu Lake

Handling editor: Jasmine Saros

Y. Cao � E. Zhang (&) � J. Shen

State Key Laboratory of Lake Science and Environment,

Nanjing Institute of Geography and Limnology,

Chinese Academy of Sciences, Nanjing 210008,

People’s Republic of China

e-mail: elzhang@niglas.ac.cn

Y. Cao

Graduate School of the Chinese Academy of Sciences,

Beijing 100049, People’s Republic of China

X. Chen

Faculty of Earth Sciences, China University of

Geosciences, Wuhan 430074, People’s Republic of China

N. John Anderson

Department of Geography, Loughborough University,

Loughborough LE11 3TU, UK

123

Hydrobiologia (2012) 691:59–70

DOI 10.1007/s10750-012-1030-3

Introduction

Lakes are inherently variable at a range of spatial and

temporal scales (Mehner et al., 2005). As well as the

overt distinction between the littoral and pelagial

zones, variability is particularly enhanced within the

littoral zone and the benthos where habitat heteroge-

neity (macrophytes, substrate, etc.) can result in

diverse communities, making systematic sampling

problematic. Despite this, studies in both limnology

and palaeolimology of small lakes tend to focus on a

single sample site, which tends to be from a deep-

water location in the middle of a lake (Korhola, 1999;

Sweetman & Smol, 2006). However, ignoring or at

least down-playing environmental heterogeneity in

large lakes has considerable influences on estimating

biodiversity and productivity because of the more

complicated and variable interactions of stressors

(Walker et al., 1984). Chironomids are an important

aspect of secondary production in lakes, and colonize

in a range of habitats both within the littoral zone and

profundal sediments; as such, they are model organ-

isms for assessing spatial heterogeneity in lakes.

The distribution of chironomids in surface sedi-

ments has been widely used as a means of studying

spatial variability of living chironomid fauna (Walker

et al., 1984; Frey, 1988). Since the remains of midges

deposited over the past few years accumulate in the

uppermost centimeter of sediment, using fossil head

capsules from a single sample can generate chirono-

mids of sufficient quantity and varieties. Moreover,

the taxonomic resolution of chironomid fossils is

equivalent to remains preserved in sediment cores.

The revealed relationships between chironomids and

environmental variables could be directly applied to

the interpretation of palaeochironomid data (Langdon

et al., 2010). Large ([100 km2) shallow lakes possess

a wide range of environmental gradients (e.g., trophic

status and wind conditions) as well as diverse micro-

habitat conditions (e.g., physical substrate), support-

ing the colonization of diverse chironomid fauna. For

example, due to the influence of wind stress, sediment

transport would be enhanced by sediment winnowing

and focusing in shallow lakes (Eggermont et al.,

2007). In such systems, regular resuspension can be

seen as pulsed disturbance events that may influence

chironomid community structures (cf. Broderson

et al., 2001). Therefore, the relationships between

chironomid communities and environmental variables

may be more complicated. Large and shallow lakes

have already attracted more attention recently (Eg-

germont et al., 2007).

The middle and lower reaches of the Yangtze River

contain the largest freshwater lake group in China,

most of which are large and shallow (Yang et al.,

2008). Taihu Lake is the third largest lake (2338 km2)

in the Yangtze floodplain, and it is a shallow, heavily

wind-stressed lake characterized by its distinct wind,

pollution, macrophyte, and algal zones. For instance,

wind speed is much faster (by 0.5–0.6 m s-1) on the

open lake than in the embayments; western tributaries

contribute most of the pollutants to the lake; algal

blooms occur every summer in northern bays in recent

years, while the East Taihu Lake is in a macrophyte-

dominated state all the year round. In addition,

environmental variables show specific spatial struc-

ture, and this exerts an influence on organism distri-

bution. The marked environmental and spatial

gradients within Taihu Lake suggest that it is a

valuable study site to study the interactions of

environmental gradients with spatial parameters at

km scales. This study aims to (a) detect the variability

among chironomid communities in surficial sediments

from distinct lake areas, (b) discuss the potential

environmental and spatial variables controlling the

chironomid distribution.

Materials and methods

Study site

Taihu Lake (30�5504000–31�3205800N, 119�5203200–120�3601000E) has an average depth of 1.9 m, a mean

volume of 4.43 9 109 m3, and a retention time of

309 days. The basin is located in the subtropical

monsoon climate zone with an annual average

temperature of 15.3–16.0�C. The prevailing wind

direction is ESE and the mean monthly maximum

wind speed ranges between 3.8 and 4.3 m s-1 (Sun &

Huang, 1993). Western and southwestern inflows

account for about 80–90% volume of runoff, and

two main outflows (i.e., Taipu River and Wusongjiang

River) permit a hydraulic connection between the lake

and Huangpu River, which flushes into the East China

Sea. The Taihu catchment is one of the most densely

populated regions (total 36 million; 2.9% of the whole

nation) in China (Sun & Huang, 1993). Taihu Lake

60 Hydrobiologia (2012) 691:59–70

123

provides comprehensive services for regional eco-

nomic and social development, including flood

storage, aquaculture, tourism, agricultural irrigation,

and drinking water for municipalities such as Wuxi,

Shanghai, Suzhou, and Huzhou.

Unfortunately, Taihu Lake has experienced eutro-

phication and pollution from industrial development

and domestic sewage over recent decades. The nutrient

enrichment before the 1980s was due to the increase of

total nitrogen and CODMn related to the agricultural

fertilization. After the 1980s, however, the increased

input of phosphorus and nitrogen resulted from

domestic and industrial wastewater lead to the further

deterioration in water quality. The mean concentration

of total phosphorus reached 85 lg l-1, which is more

than 2.6 times higher than that in 1988. Since 2000,

pervasive cyanobacterial blooms occur each summer

and expand from local embayments initially to nearly

the entire lake. The lake ecosystem has subsequently

deteriorated. For instance, the submerged macrophytes

in the northern bays disappeared, fish species and some

zoobenthos have tended toward miniaturization, and

biodiversity decreased (Qin et al., 2004).

Field and laboratory methods

We obtained 27 surface sediments from Taihu Lake in

2008 with a Kajak gravity corer (Fig. 1). The sampling

sites were located with a GPS. The surface 1 cm of

sediments was extruded for fossil chironomid head

capsules analysis.

Water samples were collected quarterly at the

sediment sampling sites (in February, May, August,

and November) to represent the annual average

limnological conditions. Water temperature, Secchi

depth (SD), water depth, and pH were measured in

field using a multi-parameter underwater sensor (YSI

6600, Yellow Springs Instruments Inc.), while the

presence of macrophytes at each sampling site was

recorded (without the information of the type of

macrophytes). Suspended solids (SS), conductivity,

chemical oxygen demand (CODMn), dissolved oxygen

(DO), total phosphorus (TP), total nitrogen (TN), and

chlorophyll a (Chl a) in the water column were

measured in the laboratory using standard techniques

(Jin & Tu, 1990).

Sediment samples for chironomid analysis were

processed according to the standard techniques

(Brooks et al., 2007). Wet sediment samples were

deflocculated with 10% KOH in a water bath at 75�C

for 15 min, and then sieved on 212- and 90-lm

meshes. The residue was transferred to a grooved

perspex sorting tray and examined manually under a

stereo-zoom microscope at 925 magnification with

fine forceps. The hard substrate in Taihu Lake yield

low chironomids, so a minimum of 40 identifiable

Fig. 1 Sampling sites of surface sediments and the isobath in Taihu Lake. Dotted line enclosed area has no sampling site

Hydrobiologia (2012) 691:59–70 61

123

whole head capsules from each sample is expected to

be representative (Wiederholm & Eriksson, 1979)

although higher numbers (e.g., 50 or 100) were

recommended by several currently available studies

(Larocque, 2001; Quinlan & Smol, 2001). Head

capsules were permanently mounted on slides using

Hydromatrix�, ventral side uppermost, and subse-

quently identified at 9100–9400 magnification using

the taxonomy of Brooks et al. (2007), with reference to

Wiederholm (1983), Oliver & Roussel (1983), and

Rieradevall & Brooks (2001).

Numerical and statistical analyses

All taxa recovered from the 27 samples were catego-

rized into three units. If a taxon failed to exceed three

occurrences, it was defined as rare; taxa with more

than three occurrences were identified as either

common or uncommon when their mean frequency

exceeded or remained below one specimen per

sample, respectively (cf. Eggermont et al., 2007).

Species richness was calculated as the total number of

taxa present in each sample.

GPS coordinates of 27 sampling sites were recorded

in the field. The original coordinate values were z-score

transformed and these standard coordinates were used

to create the dataset of spatial variables derived from

PCNM using the program Spacemaker2 (Borcard &

Legendre, 2004, http://www.bio.umontreal.ca/legendre/).

A matrix of Euclidean distances between samples was

computed and subsequently truncated based on trun-

cation distance, which was equal to or larger than the

largest distance between neighbors. Subsequently, a

principal coordinate analysis (PCoA) was performed

on the truncated distance matrix. Thereafter, eigen-

vectors associated with positive eigenvalues were kept

and used in the subsequent ordination analysis.

Gradient analysis was calculated on a dataset that

only contains the taxa that occurred twice at least with a

percentage exceeding 2%. Environmental variables

were log10 (x ? 1) or square root transformed, and the

presence/absence of macrophytes was coded as a 1/0

dummy variable prior to ordination analysis. Species

gradient length of the first axis of a detrended

correspondence analysis (DCA) (ter Braak & Prentice,

1988) was 1.979 standard deviation (\2 SD) units,

indicating that most taxa would exhibit linear distri-

butions, and that examination of chironomid–environ-

ment relationships using redundancy analysis (RDA)

would be appropriate (ter Braak & Smilauer, 2002).

Automatic selection was used to identify a minimum

subset of significant explanatory variables. Monte

Carlo permutation tests (n = 999 unrestricted permu-

tations) were used to test the significance of each

variable. Then, a series of RDAs on all the significant

variables were performed, sequentially eliminating the

explanatory variable with the highest variable inflation

factor (VIF) until all VIFs were\20 (Hall et al., 1999).

A series of partial RDAs were also performed to

calculate the variance in the chironomid data that is

explained by the unique effects of the individual

variables selected in the forward-selection. All the

ordination analyses were based on square root trans-

formed percentage data with down-weighting of rare

taxa. The analyses were performed using CANOCO

version 4.5 (ter Braak & Smilauer, 2002). The response

patterns of major taxa to gradients of significant

variables were modeled with a generalized linear

model (GLM, McCullagh & Nelder, 1989) using the

Gaussian species distribution. The GLM modeling and

curves were also performed with CANOCO 4.5 and

CanoDraw (ter Braak & Smilauer, 2002).

Results

Faunal composition

2,767 chironomid head capsules which belong to 4

subfamilies, 36 genera, 47 taxa were recovered from

the 27 surface-sediment samples (Fig. 2). Of the total

taxa, 20 common species account for 95.6% of the

fauna. The most abundant taxa with abundances

exceeding 5% are Microchironomus tabarui-type

(17.3%), Tanypus (10.8%), Chironomus plumosus-

type (8.5%), Microchironomus (7.4%), Tanytarsus

(6.1%), Dicrotendipes nervosus-type (6.0%), Crico-

topus sylvestris-type (5.4%), and Polypedilum nubif-

er-type (5.1%). 14 taxa were classified as uncommon,

together making up 3.7% of the total number of

chironomid head capsules. The last group was com-

posed of the remaining 13 taxa with less than 1% of the

total occurrences.

Several taxa occurred in most samples all over the

lake (Fig. 3). For example, Tanytarsus and Micro-

chironomus taxa (non-M. tabarui-type) were found in

the majority of the samples, albeit with relatively low

abundances in the northern basin. In contrast, several

62 Hydrobiologia (2012) 691:59–70

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other taxa showed marked preferences for specific

environmental conditions. C. sylvestris-type, Parata-

nytarsus taxa and P. nubifer-type were dominant

species in the East Bays and East Taihu Lake;

Meiliang Bay, Gonghu Bay, and the western part of

Taihu Lake were characterized by C. plumosus-type,

Harnischia, and M. tabarui-type; the estuaries were

dominated by M. tabarui-type and Tanypus.

The samples showed low densities of head capsules

of chironomid larvae, averaging only three head

capsules per gram of dry sediment (Fig. 4). Taxon

richness in each sample ranged from 11 to 24 that is

Fig. 2 Species-frequency data for the total chironomid species assemblages in Taihu Lake

Fig. 3 Diagram of main chironomid assemblages in Taihu Lake

Hydrobiologia (2012) 691:59–70 63

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represented as the raw number of taxa presence

(Fig. 4). Both the number of taxa and concentration

of head capsules declined with the gradient of water

depth. The average taxon number changed from 18 in

samples with water depth less than 2 m to 14 in[2-m

depth, while the mean concentrations of head capsules

decreased from 4.5 to merely 1.9 with the boundary of

2-m depth.

Environmental variables

Statistical summary for selected physical and chemical

parameters was provided in Table 1. TP ranged from

38 to 384 lg l-1, and TN from 1,187 to 9,534 lg l-1.

High concentrations of TP and TN were present in the

northern part of the lake, whereas low values appeared

in samples from East Bays and East Taihu Lake. The

sample (T10) near the estuary of the Dapu River

possessed the highest nutrient concentrations. The

fluctuations of water depth and SD covered relatively

narrow spectrums among the sites. Samples in the East

Bays and East Taihu Lake possessed the water depth

less than 2 m and showed relatively high transparency

and low abundance of SS, with the development of

macrophytes presence throughout the year.

Ordination analysis

In the RDA analysis, 73.3% of the variation in the

chironomid assemblages was explained by the first

two RDA axes (Fig. 5). Water depth, TP, TN, SD, and

PCNM2 comprised the minimum subset of significant

environmental factors (P \ 0.05) explaining 43.0% of

the cumulative chironomid variance.

In order to test the significance of the selected

variables derived from the forward selection, another

RDA was performed with TP and TN manually selected

as the first two variables. Table 2 shows that the

importance of the selected variables somewhat fluctu-

ates in the first and second runs. The variables identified

in the first run, however, retained their significance,

which ascertained the reliability of the selection.

Partial RDA denoted that 6.9, 5.6, 3.6, and 5.2%

variance of chironomid fauna was captured solely by

water depth, TN, SD, and TP, respectively. The

proportion of all the significant environmental vari-

ables was 34.6%, while spatial variable PCNM2

accounted for 4.3% of all the explained variance of

chironomid assemblages.

Fig. 4 Scatters of species richness (crosses) and chironomid

concentration (gray triangles) along with water depth

Table 1 Summary of selected physical and chemical parameters for 27 sediment samples from Taihu Lake

Parameters Minimum Maximum Mean Median Standard Deviation

Water depth (m) 1.30 2.73 2.05 2.10 0.40

Temperature (�C) 16.83 20.00 18.93 18.98 0.54

Secchi depth (m) 0.17 0.84 0.36 0.32 0.16

Suspended solids (mg l-1) 15.23 98.02 54.40 55.40 21.91

Total phosphorus (lg l-1) 38.23 384.38 121.55 104.61 78.95

Total nitrogen (lg l-1) 1187.20 9534.59 3371.96 2774.68 2124.11

Chlorophyll a (lg l-1) 3.59 59.97 16.05 10.50 14.23

Conductivity (lS cm-1) 480.00 786.25 610.65 589.38 78.48

Dissolved oxygen (mg l-1) 5.40 9.43 8.53 8.82 0.95

pH 7.76 8.47 8.18 8.21 0.16

Chemical oxygen demand (mg l-1) 3.53 17.41 5.68 5.02 2.81

64 Hydrobiologia (2012) 691:59–70

123

Figure 5a shows the relationships between domi-

nant species and environmental and spatial variables.

For example, M. tabarui-type and Tanypus showed

close relationships with TP and TN. C. plumosus-type

is positively correlated with water depth and PCNM2,

while C. sylvestris- and P. nubifer-type is plotted

along the transparency gradient. A monotonically

increasing or unimodal species pattern is responded to

significant variable gradients (Fig. 6). Within the

current gradients of parameters, C. plumosus-type

shows a gradual increase when the water depth

exceeded 1.5 m, while a nearly linear correlation is

found between C. sylvestris-type and water transpar-

ency. The response of M. tabarui-type to TP concen-

tration is described by a unimodal model with an

optimum of *250 lg l-1 TP. Figure 5b demonstrates

that the 27 samples are distributed as three groups

along different environmental gradients. Samples

around the estuaries (including T10, T00, T16, T17,

T06, T01) are present in the ordination space charac-

terized by effects of high nutrient concentrations. The

ordination district with effects of high transparency is

occupied by samples from the East Bays and East

Taihu Lake (i.e., T12, T30, T25, T27, T24, T26, T28).

The remaining samples from Meiliang Bay, the open

lake and Gonghu Bay are present in the ordination

space characterized by effects of deeper water and

higher PCNM2.

Discussion

Many driving forces govern the diversity and abun-

dance of chironomid assemblages. Several inter-lake

Fig. 5 Biplot of RDA

ordination for 27 surface-

sediment samples.

a Ordination of main species

([5%) and environment

variables; b ordination of

samples and environment

variables: circles estuaried

samples, triangles samples

in the open lake, Meiliang

Bay and Gonghu Bay,

squares samples in East

Taihu lake and East Bays

Table 2 Selected significant variables by manual forward selection in the two runs of RDA and the corresponding explanatory

variance

First run Second run

Variable P value

estimates

Added

explanation

F value Variable P value

estimates

Added

explanation

F value

Water depth 0.001 0.153 4.521 TP 0.001 0.127 3.629

TP 0.001 0.123 4.058 TN 0.001 0.122 3.916

TN 0.001 0.068 2.378 Water depth 0.001 0.095 3.302

SD 0.035 0.044 1.607 SD 0.031 0.044 1.607

PCNM 2 0.037 0.042 1.544 PCNM 2 0.035 0.042 1.544

Total variance explained 0.43 Total variance explained 0.43

Total variance 0.669 Total variance 0.669

Hydrobiologia (2012) 691:59–70 65

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and within lake studies have discussed the important

effects of environmental variables on chironomid

variability. Langdon et al. (2006) assessed the rela-

tionships between chironomids and water quality in 57

different lakes, and concluded that the biological

communities responded to nutrient change although

not always directly. Seven shallow lakes and one deep

lake were investigated by Engels & Cwynar (2011) to

determine the sensitivity of chironomids to water

depth. Different taxa showed their specific water depth

in these USA lakes. Quinlan et al. (2003) surveyed the

spatially structured variations in algae and chironomid

communities in 86 lakes in south-central Ontario,

Canada, and explored the combined influences of

environmental and spatial variables. Due to the diverse

habitats, large and shallow lakes possess more com-

plicated interactions among various parameters. RDA

results (Fig. 5) showed that the chironomid fauna

compositions in Taihu Lake were mainly influenced

by nutrient, water depth, transparency, and spatial

factors.

Nutrient status

Nutrient enrichment is generally a key factor affecting

the variation of aquatic organisms in freshwater

ecosystems (Langdon et al., 2006; Zhang et al.,

2006). In Taihu Lake, chironomid distribution was

significantly controlled by trophic condition. Samples

around the estuaries showed high positive correlations

with TN and TP, even when the influences of other

environmental variables were partialled out. M. taba-

rui-type and Tanypus were the dominant taxa in these

samples. They are nutrition-resistant and pollution-

tolerant species, and always prefer severe eutrophic or

polluted conditions (Grodhaus, 1963; Gong et al.,

2001; Zhang et al., 2006). The littoral regions,

especially estuaries, are in general more vulnerable

than pelagic zones with respect to nutrient inputs. In

Taihu Lake, the western tributaries contribute 60%

volume of runoff and carry a great deal of pollutants

into the lake (Xu & Qin, 2005). For example, the

concentrations of TP, TN, and CODMn in Dapu River

(flows through Yixing City) exceeded 200 lg l-1,

4500 lg l-1, and 7.5 mg l-1 in July 2000, respec-

tively (Qin, 2008). In this study, the highest nutrient

concentrations (384 lg l-1 TP and 9,534 lg l-1 TN)

occurred in the sample near the Dapu estuary in our

dataset (i.e., T10).

In contrast, samples from the Meiliang Bay and the

open lake were characterized by C. plumosus-type. C.

plumosus-type is a eutrophic species and abundant in

many nutrient-rich lakes all over the world. For

instance, the TP optimum of C. plumosus-type is

269 lg l-1 in the combined chironomid–TP calibra-

tion dataset for English Midlands and Wales (Brooks

et al., 2001). From the biological perspective, Meili-

ang Bay and the open lake have already been in the

eutrophic state. However, their nutrient and pollutant

concentrations are expected to be slightly lower than

that in samples near estuaries due to the larger distance

to the river outlets. Moreover, wind-driven hydraulic

exchange with the open area allows a short residence

time of pollutions, and relieves the nutrient enrich-

ment in Meiliang Bay. Correspondingly, Tanypus

nearly disappeared in these regions.

Fig. 6 Species response curves to related significant environmental variables

66 Hydrobiologia (2012) 691:59–70

123

Water depth

No matter what in previous studies is focused on

modern aquatic fauna (Xiong et al., 2007) or on

subfossil assemblages (Olander et al., 1997; Laroc-

que et al., 2006), water depth has always been

identified as a prominent environmental factor

explaining the variability of the interested fauna.

In deep lakes, water depth usually exerts an indirect

impact on the distribution and abundance of chiron-

omids through the availability of DO and food

(Korhola et al., 2000). Despite a depth range within

the samples of only *2 m, water depth is the major

factor controlling the composition of chironomid

taxa in Meiliang Bay, Gonghu Bay, and the open

lake. The predominant chironomid taxon in these

regions is C. plumosus-type.

At Taihu Lake, strong wind-driven mixing provides

the open lake with adequate DO in general (Sun &

Huang, 1993). However, cyanobacterial blooms have

occurred almost every year during the period of May

to October from the late 1990s, and the center of Taihu

Lake has begun to suffer blue-green algae blooms

since 2000 (Qin, 2008). The southeastern wind

brought by the subtropical monsoon results in a

massive accumulation of algae in the northwest part of

the lake, leading to hypoxic or even anoxic conditions

in the bottom of the lake (Qin et al., 2010). For

instance, the DO could generate a vertical difference

of 8.67 mg l-1 by cyanobacterial blooms scum in the

northern bays (mainly Meiliang Bay) as ‘‘diurnal

stratification’’ or in short-term of 1–2 days (Zhao

et al., 2011), but the DO values in the mixed water

column ranged between 5 and 9 mg l-1 among our 27

samples. The frequent occurrence of hypoxic/anoxic

result from the vertical difference would be sufficient

to prohibit the development of chironomids. Few

aquatic organisms can survive in these extreme

habitats except for some hypoxic-resistant species.

Chironomus and certain taxa of the Tanytarsus genus

are able to survive hypoxic conditions because of their

possession of hemoglobin (Little & Smol, 2000). C.

plumosus-type showed its preference to deeper water

although the maximum of depth is less than 3 m

(Fig. 6). Moreover, C. plumosus-type is known to

characterize the eutrophic lakes with the soft, unstable,

and organic-rich substrata (Broderson et al., 2001).

Meiliang Bay (except for the zones around estuaries)

is characterized by fine silt and low oxygen (Cai et al.,

2010), which favors the development of C. plumosus-

type (Quinlan & Smol, 2001).

The important impact of water depth on midge

communities was also displayed by the variations of

species richness and chironomids’ concentrations

(Fig. 4). Both the number of taxa and concentration

of head capsules showed a significant negative corre-

lation with water depth (R = -0.66, -0.49, respec-

tively; P \ 0.05). The depth induced changes of food

in quality and quantity and the intermittent hypoxic/

anoxic in the bottom of water might allow for the

variations of chironomid diversity and production.

Transparency

Water transparency always influences chironomid

distribution indirectly by way of mediating the devel-

opment of macrophytes (Weatherhead & James,

2001). On one hand, the development of submerged

macrophytes is mostly dependent on light climate

condition (Middelboe & Markager, 1997; Vestergaard

& Sand-Jensen, 2000). On the other hand, the aquatic

vegetation would improve the light condition because

it can reduce the turbulence. Furthermore, aquatic

plants provide available food and habitat in quality and

quantity for chironomid community. In addition,

bottom-dwelling invertebrates can also protect them-

selves from predation under the shelter of plants.

In Taihu Lake, samples in the East Bays and East

Taihu Lake are positively correlated with water

transparency, and they were dominated by Polypedi-

lum, Cricotopus, and Paratanytarsus taxa. These

genera are typical of clear water (high transparency)

and are macrophyte-inhabiting chironomid taxa (Bro-

dersen et al., 2001; Ruiz et al., 2006) in standing water.

Midge components (e.g., C. sylvestris-type) also

respond positively to the flourishing macrophytes-

dominated microhabitat in Taihu Lake (Fig. 6). The

East Bays and East Taihu Lake have high coverage of

macrophytes, which are dominated by Potamogeton

malaianus Miq. during the past 20 years (Gu et al.,

2005). It is reported that Potamogeton were the favorite

plants for Polypedilum and Cricotopus (Berg, 1950;

Brodersen et al., 2001). In addition, Paratarytarsus

taxa also mainly benefit a lot from high transparency

effects of aquatic plants. Remarkably, Paratarytarsus

show the highest abundance in the eutrophic sam-

ple T14 (with 115 lg l-1 TP and 2,833 lg l-1 TN).

Gonghu Bay used to be a macrophyte dominated zone

Hydrobiologia (2012) 691:59–70 67

123

with high coverage of aquatic plants. Unfortunately,

the macrophytes have declined or even disappeared

due to a gradual increase in the nutrient concentrations

in this region during the recent years. However,

massive plant macrofossils were found in the sedi-

ments. Available studies note that chironomids prefer

the senescent, dead, or decomposed plants as a food

source and substratum (Smock & Stoneburner, 1980;

Oertli & Lachavanne, 1995). Therefore, it is evident

that chironomids associated with macrophytes often

grow well in spite of the poor development of

macrophytes in the nutrient-rich Gonghu Bay.

In this study, water transparency rather than

macrophytes acted as the controlling factor in chiron-

omid composition in the East Taihu Lake and East

Bays. The incomplete data of aquatic plants in our

database may be the most appropriate explanation for

this situation. The presence or absence of macrophytes

is just summarized using the 1/0 dummy variable in

the ordination analyses. This study neglected the

measurements of the plants biomass leading to a

selection of indirect explanatory variables.

Spatial variables

Spatial configurations of biotic communities mainly

derive from two decisive aspects. Activities among

neighboring individuals compose ecological processes

and generate autocorrelation of communities (Tilman

& Kareiva, 1997) which is the so-called ‘‘autogenous

structure.’’ There is no relationship between this

spatial structure and any environmental factor (Yang

et al., 2009). On the other hand, environmental

variables themselves alter spatially. The response of

organism species to specific environmental factors at

specific scales leads to the spatial heterogeneity in

species communities (Legendre, 1993). Only if the

scale is broad enough, the different responses of

species to specific environmental conditions would be

shown. Spatial structures in the distribution of organ-

isms are inherent properties of ecological systems

(Fortin & Dale, 2005). As decomposition vectors of

spatial relationship among sampling sites, significant

PCNM variables can explain all the spatial scales

directly. Partial ordination analysis revealed that

spatial variables solely explained merely 4.3% of the

chironomid distributions. The interaction between

spatial and environmental variables accounted for

4.1% (=43.0% - 34.6% - 4.3%) of the total vari-

ance in fauna compositions. It means that 48.8%

(=4.1/(4.3 ? 4.1)) of the influence of the spatial

structures exerted on organisms through its interac-

tions with the environmental factors. Although the

contribution of autogenous structure (4.3%), which is

independent of environmental differences, was not

very conspicuous, it is a significant variable in

explaining chironomid variance. In a large aquatic

system like Taihu Lake ([1,000 km2), the spatial

structure itself is a necessary factor to consider in the

assessment of forcing variables in species distribution

and composition.

Conclusions

The chironomid communities in surface sediments

from Taihu Lake display strong heterogeneity. In

particular, significant indicators such as M. tabarui-

type, Tanypus, C. plumosus-type, C. sylvestris-type

are distributed unevenly across the lake.

RDA indicated that nutrient status, water depth,

transparency, and spatial variables were significant

factors determining the chironomid distributions in the

large, shallow, and wind-driven lake. This study

shows that water depth acting as a significant param-

eter is not restricted to deep lakes. It may exert its

impact on biocommunities through oxygen depletion

even in shallow lakes such as Taihu Lake. The spatial

variables alone explained merely 4.3% of the chiron-

omid variability. More than 48% of the influence by

spatial structure was contributed by means of the

potential interactions with environmental variables. It

suggests that the spatial variable must be considered at

scales of[1,000 km2 in future studies. This study also

show that the specific multiple environmental factors

can be identified by unique chironomid assemblages

configured in particular spatiotemporal scales, and

remind us that information recorded by one core from

a huge lake might be insufficient when trying to

understand the environmental succession throughout

the whole basin in paleolimnological studies. This

study reveals the characteristics of some ‘‘microeco-

system’’-related chironomid taxa, and would provide

support for the interpretation of chironomid data in

palaeolimnological studies.

68 Hydrobiologia (2012) 691:59–70

123

Acknowledgments This study was supported by the National

Basic Research Program of China (No: 2008CB418103,

2012CB956100), the Knowledge Innovation Program of the

Chinese Academy of Sciences (kzcx2-yw-319) and National

Natural Science Foundation of China (41072267). We thank

Dr. Yunlin Zhang and Dr. Xuhui Dong for their help on the

preparation of this manuscript, and two anonymous referees for

comments which helped improve the manuscript.

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