A C T A O E C O L O G I C A 3 0 ( 2 0 0 6 ) 3 1 2 – 3 2 1

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Original article

Patterning of impoundment impact on chironomidassemblages and their environment with useof the self-organizing map (SOM)

Tadeusz Penczak*, Andrzej Kruk, Maria Grzybkowska, Małgorzata Dukowska

Department of Ecology and Vertebrate Zoology, Institute of Ecology and Environmental Protection, University of Łódź, 12/16 Banacha Str.,90237 Łódź, Poland

A R T I C L E I N F O

Article history:

Received 9 June 2005

Accepted 18 May 2006

Available online 30 June 2006

Keywords:

Lowland river

Dam reservoir

Discharge fluctuations

Environmental factors

Chironomidae

Long-term study

Kohonen artificial neural network

* Corresponding author. Tel.: +48 42 665 5817;E-mail address: penczakt@biol.uni.lodz.pl (T.

1146-609X/$ - see front matter © 2006 Elsdoi:10.1016/j.actao.2006.05.007

A B S T R A C T

The paper assesses the impact of the Jeziorsko dam reservoir on chironomid assemblages

and selected environmental factors in the Warta River, Poland, by means of patternsrecognized with the self-organizing map (SOM, Kohonen unsupervised artificial neuralnetwork). Over 1988–1996, in four annual cycles, a total of 233 monthly samples were col-

lected in a seven order section of the river at two sites: WAA (backwater) located about2 km upstream from the Jeziorsko Reservoir, and WAB (tailwater) located about 1.5 kmdownstream from the reservoir’s dam. At each site three habitats were selected: H1, H2

and H3 at WAA, and H11, H12 and H13 at WAB. H1 and H11 were located in the depositional

area close to the banks, H2 and H12 about 6–7 m towards the mid-river and H3 and H13 inthe mid-river. SOM effectively vertically separated H1 and H11 (bank habitats) from H3 andH13 (the mid-river zone of both sites) and H2 (the transition zone of the upstream site).

The H12 samples were scattered all over SOM but still exhibited a slight temporal gradient.At the end of the study the water discharge, especially in summers, stabilized at WAB at alevel lower than natural and as a result submerged macrophytes appeared at H12 making

the abundance of macroinvertebrates increase very quickly. Moreover, a weaker horizontalgrouping of samples by season and by site of collection (upstream or downstream fromthe reservoir) was observed over SOM: 1) bank upstream habitat H1, with hydrological

regime resembling natural, was separated from the downstream H11, which enlarged andcontracted in response to dam operation, 2) deeper habitats were less dependent on waterlevel and this is why they underwent seasonal fluctuations. To sum up, the deepest habi-tats were most resistant to water level fluctuations, while the formerly most productive

habitat at the tailwater WAB site, H11, became the most negatively impacted. Neverthe-less, the reservoir has not negatively influenced chironomid density, because the latterincreased closer the mid-river, at H12, where large patches of macrophytes developed.

© 2006 Elsevier Masson SAS. All rights reserved.

1. Introduction

The construction of a dam appears to have a great impact

upon whole aquatic life in the tailwater (Ward and Stanford,

fax: +48 42 665 5817.Penczak).

evier Masson SAS. All ri

1980; Armitage, 1984, 1987; Petts, 1984; Penczak et al., 1998).Impoundment studies are typically conducted by comparingthe communities (1) in the affected downstream area beforeand after impounding, (2) between sites located upstreamand downstream from an impoundment, or (3) between thedownstream area and a nearby reference stream (Weisberg etal., 1990). Our studies represent the second type and concernthe response of macrobenthic community to the functioning

ghts reserved.

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of one of the largest lowland reservoirs in Poland (in terms of

its area – 42.3 km2), which was built in the seven order sec-

tion of the Warta River. Special emphasis was put on chiro-

nomid communities (Diptera) because as numerical domi-

nants in the studied river they seem to play a key role in

energy transformation and elements cycling (Grzybkowska et

al., 1990; Grzybkowska and Dukowska, 2001, 2002; Penczak et

al., 2001), and like most macroinvertebrates are convenient

indicators of the state of the ecosystem (Park et al., 2005; Ver-

donschot, 2005). Previously, these biota and environmental

factors were examined with use of PCA and cluster analysis

(Grzybkowska and Dukowska, 2001, 2002). Recently, artificial

neural networks have been implemented for recognizing pat-

terns in complex spatio-temporal data. These analytical

tools are robust to strongly skewed distributions and easily

deal with non-linear relations between variables (Chon et

al., 2001) often recorded for counts of organisms (Quinn and

Keough, 2002). Because it is desirable to examine the results

of several techniques (Matthews, 1998; Compin et al., 2005)

the aim of this study is to pattern the impoundment impact

on chironomid assemblages and selected environmental fac-

tors in the Warta River, Poland, with the Kohonen self-

organizing map (SOM).

2. Study area

The 808 km long Warta River arises at 380 m a.s.l., and emp-

ties into the Odra River at 13 m a.s.l. Its catchment area is ca.

53710 km2. The study area was established in its upper

course in a seven order section and comprised two sites:

WAA (backwater) located about 2 km upstream from the

Jeziorsko Reservoir, and WAB (tailwater) located about

1.5 km downstream from the dam (Fig. 1).

Fig. 1 – Study area with marked sampling sites and habitats: upH13) from the Jeziorsko Reservoir.

In the backwater (WAA) the river was about 56 m wide,with a maximum depth of 2.0 m, whereas in the tailwater(WAB) these parameters were 70 and 2.0 m, respectively.The river slope did not exceed 0.5‰. Based on a previousproject (Grzybkowska et al., 1990) three habitats wereselected at each site: H1, H2 and H3 at WAA, and H11, H12 andH13 at WAB. H1 and H11 were located in the depositional areaclose to the banks, H2 and H12 about 6–7 m towards the mid-river, and H3 and H13 in the mid-river with the maximumdepth of 1.3 m (Fig. 1).

The reservoir was constructed in 1986. The sampling wasstarted 2 years later and was conducted over four annualcycles: from March 1988 to February 1989 (cycle 1), fromMarch 1991 to February 1992 (cycle 2), from March 1992 toFebruary 1993 (cycle 3) and from March 1995 to February1996 (cycle 4), i.e. before (cycles 1, 2, 3) and after (cycle 4) ahydroelectric plant began operating in the dam in September1994.

At the beginning of our investigations, due to some faultsin the dam construction, the sluices stayed open, and theresulting hydrological regime did not markedly differedfrom the natural (Fig. 2). In the next sampling cycles the dif-ferences in water discharge between the downstream andupstream sites were much bigger.

The inorganic substratum was sand at all habitats exceptH12, where gravel and pebbles were also scattered. Along thebank of the upstream site Phalaris arundinacea L. dominatedwith scattered Sparganium erectum L. em Rchb., Rorippa amphi-

bia (L.) Besser, Glyceria fluitans L. and Rannunculus repens L.The riparian plants of both sites were mostly willows (Salixsp.) and rarely Alnus sp. At the tailwater banks small patchesof Poa palustris L. were present. Habitat H11 may be called the“varial zone” (Stanford and Hauer, 1992) because it enlargedand contracted in response to dam operation, especially inthe summers of the two last sampling cycles. At the bank of

stream (WAA: H1, H2, H3) and downstream (WAB: H11, H12,

Fig. 2 – Discharge for four annual sampling cycles at the two sites of the Warta River located upstream (WAA) anddownstream (WAB) from the Jeziorsko Reservoir.

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this habitat Agrostis stolonifera L. developed. In turn at H12

small patches of Potamogeton pectinatus L. appeared duringthe summer of 1992 while large patches of P. pectinatus andsmall patches of P. lucens L. overgrew the river bed duringthe last two investigated summers. These macrophytes,especially in June, were spread over with filaments of Clado-phora glomerata (L.) Kutz. Both H11 and H12, especially in thesummers of the two last sampling cycles were covered bylarge amounts of fine particulate organic matter (FPOM); atthe latter habitat due to the presence of macrophytes,which trap POM.

Further details of both sites are given by Grzybkowska etal. (1990, 2003) and by Grzybkowska and Dukowska (2001,2002).

3. Materials and methods

Altogether 233 monthly samples of invertebrates and theirenvironment were collected at the two sites: 109 samples atWAA, and 124 samples at WAB (Figs. 1 and 3 and Table 1).The lower number of samples at WAA resulted from frozein winters and from spring floods, which prevented accessto the sites.

In each habitat (Hn), its area, temperature, depth and cur-rent speed were measured. The composition of particulateinorganic matter was analyzed according to Cummins(1962). His method divides the dry sediments into sizeclasses that have been shown to be of ecological significance.The analysis of size fractions was made on a weight basis. Toaid statistical analysis of the substrate, field data on particlesize distribution were transformed into the single substrateindex (SI) by summing the mid point values of size classesweighted by their percentage cover (Quinn and Hickey,1990). These samples were also used to determine theorganic matter content in the bottom sediment. For this pur-pose a 1 mm sieve was used to separate benthic particulateorganic matter (BPOM): > 1 mm (coarse – BCPOM) and < 1 mm(fine – BFPOM) (Petersen et al., 1989). Next, the benthic

organic matter was dried at 60 °C for 2 days, weighed, ashedat 600 °C for 2 hours and reweighed.

In order to determine the amounts of transported organicmatter (TPOM) triplicate water samples of 0.01 m3 weretaken. In the laboratory these samples were filtered throughWhatman GF/C glass-fiber filters (1.2 μm), which were pre-weighed. All seston samples were analyzed as described forbenthic POM.

At all habitats, benthic samples of 50 cm2 each were alsocollected to estimate chlorophyll α concentrations. Thebenthic material was rinsed. The obtained solution was cen-trifuged and the supernatant was used to determine chloro-phyll α concentration by spectrophotometry (Golterman etal., 1978).

Out of environmental variables the final dataset com-prised the following 10 variables: water depth and discharge,current velocity, water temperature, substrate index (SI),amounts of BCPOM, BFPOM, TPOM, concentration of chloro-phyll α and percentage of bottom covered by macrophytes.

Each sample of invertebrates consisted of 10 subsamplesof 10 cm2 each (100 cm2 of stream-bed area) taken with atubular sampler at a given habitat (Hn). Invertebrates weresorted from the detritus by hand and preserved in 10% for-malin. All macroinvertebrates from quantitative sampleswere counted; these data were used to estimate density ingiven sampling habitats (Hn). Chironomids were identifiedto the species level when possible. Because exact identifica-tion of chironomids on the basis of their larvae was oftenimpossible, we reared their immature stages in the labora-tory from the additional qualitative samples taken eachtime and from each habitat in order to obtain larval andpupal skins and imagines. For statistical analyses 33 chirono-mid taxa, each accounting for at least 1% of the abundancein any sample, were included. Out of them the genera Nano-cladius, Thienemannia, Corynoneura and Thienemaniella were notpresented in figures because conclusions formulated forthem might be doubtful due to their sporadic occurrence.

The patterns in the data relating to the chironomidassemblage (33 variables) and its environment (10 variables)were recognized with use of the Kohonen unsupervised arti-

Table 1 – Number of samples from studied habitatsassigned to each SOM cluster. Habitats arranged accord-ing to their occurrence in clusters

H1 H11 H12 H2 H3 H13 TotalAB 3 0 9 28 30 28 98C 2 1 15 9 0 4 31DE 35 46 21 2 0 0 104TOTAL 40 47 45 39 30 32 233

Fig. 3 – The mean annual densities of Chironomidae andother macroinvertebrates at each habitat of the two sites ofthe Warta River located upstream (H1, H2, H3 – WAA) anddownstream (H11, H12, H13 – WAB) from the JeziorskoReservoir over four annual sampling cycles: 1 – 1988/89,2 – 1991/92, 3 – 1992/93, 4 – 1995/96; *) presence ofmacrophytes, ?) lack of data.

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ficial neural network (ANN), also referred to as a self-

organizing map (SOM) (Kohonen, 1982). This type of analysis

has already been successfully applied in ecological studies

(Chon et al., 1996, 2000; Lek and Guegan, 2000; Lek et al.,

2005), including those of invertebrates (Céréghino et al.,

2003; Park et al., 2003; Verdonschot, 2005). ANN is a simplefunctional and structural model of a human brain, in which

processing units (neurons) are ordered into the input and

output layers (Fig. 4). The data are presented onto the input

neurons. Their number is equal to the number of variables

(in this study 43). The output neurons are arranged on atwo-dimensional lattice (in this study 5 × 4, selected from

among other tried options) [Fig. 4]. During the learning pro-

cess, a model of a sample (in terms of abiotic and biotic para-

meters) is created in each output neuron. Models in neigh-boring neurons are similar, while models in distant neuronsare much different. Models from all the neurons cover all thevariability observed in the dataset. Each neuron reacts withsome activation to the presented real sample. The level ofthe activation is dependent on how much the samplematches the model. The neurons differ in the level of activa-tion and a given real sample is assigned to the neuron that ismost activated. Relations between models of any two neu-rons relate also to the real samples assigned to them, i.e.samples in distant neurons are dissimilar while samples innearby neurons are similar, unless the nearby neurons arein different clusters. The clusters can be identified on thebasis of a unified distance matrix (U-matrix; Ultsch, 1993)showing Euclidian distances along the SOM. On theU-matrix the darker shading indicates higher dissimilarity,i.e. boundaries between clusters (Fig. 4).

The grouping performed with SOM was compared withresults of the conventional cluster analysis (Ward’s linkage)obtained with use of the same dataset (Chon et al., 1996).

4. Results

4.1. Macroinvertebrate community

At the beginning of the dam functioning the pattern of zoo-benthos distribution was similar at both sites of the WartaRiver—the highest density was observed close to the bankand sharply decreased toward the mid-river (Fig. 3). At theend of the study, however, the discharge downstream fromthe reservoir stabilized (particularly in summer) at a levellower than natural (Fig. 2). As a result submerged macro-phytes appeared at H12, and the abundance of macroinverte-brates very quickly increased at this habitat (Fig. 3), whileH11, formerly richest in chironomids, became the most nega-tively impacted because of being often totally exposed.

The contribution of Chironomidae to the total macroin-vertebrate abundance was high at each habitat bothupstream and downstream from the Jeziorsko Reservoir.The highest dominance of chironomids (about 70%) wasrecorded in the first sampling cycle at each site, at habitatsH2, H3 and H11, H12 (Fig. 3). It is worth noting that the pre-sence of submerged macrophytes at H12 caused an increasein some taxa of macrobenthos, especially in dipterans: Chir-onomidae and Simuliidae. In turn the lowest percentage ofmidges was noted at H2, during the last sampling cycle(< 5%), due to an extraordinarily large number of small indi-viduals of Oligochaeta from May to September (Fig. 3).

4.2. Self-organizing map

In the output layer of SOM three clusters have been distin-guished: AB (neurons A1-B4), C (neurons C1-C4) and DE (neu-rons D1-E4) (Fig. 4). The most distant clusters (AB and DE)exhibited considerable differences in the spatial origin ofthe samples assigned to them (Fig. 5 and Table 1). The mosthomogenous cluster by the criterion of spatial origin was DEcontaining samples almost exclusively from the bank habi-tats of both sites (H1, H11) and from the transition zone of a

Fig. 4 – The structure of the self-organizing map applied. The 20 output neurons create a topological map (5 × 4). Theunified-distance matrix (U-matrix) shows dark boundaries between clusters AB, C and DE.

316 A C T A O E C O L O G I C A 3 0 ( 2 0 0 6 ) 3 1 2 – 3 2 1

downstream site (H12). The only two exceptions were sam-ples from H2 collected in winter in cycle 1 (H2Jan1 andH2Feb1) (Fig. 5). In cluster AB almost 90% (86 out of 98) sam-ples came from habitats H3 and H13 (the mid-river zone ofboth sites) and from H2 (the transition zone of the upstreamsite). The remaining samples were collected at H12 and H1.Middle-located cluster C was of intermediate character andcontained samples from all the sites with clear dominanceof samples from H12 (Fig. 5 and Table 1).

Thus, the vertical grouping (assignation to clusters) ofsamples was dependent on the spatial criterion, with excep-tion of the H12 samples. The latter were numerous in all theclusters but still their number exhibited a clear vertical gra-dient (Table 1). Moreover, samples from H12 collected in cycle4 were not present in cluster AB (Fig. 5) and thus the verticalgradient exhibited by the samples from H12 was of a slighttemporal character.

Except for the very strong vertical gradient, also horizon-tal gradients can be seen. They result from grouping samplesby season and by site of collection (upstream or downstreamfrom the reservoir). On the left side of SOM most sampleswere collected in warmest months including summer, whileon the right side the predominant samples were taken incoldest months including winter (Fig. 5). Samples from theupstream habitats (H1, H2 and H3) were more frequent onthe left side, and samples from habitats located downstreamfrom the dam (H11, H12 and H13) were more common on theright side (Fig. 5). In AB the seasonal gradient was stronger(there were fewer exceptions), while in DE the spatial gradi-ent was more evident. In E1 on the left side the samples fromH1 predominated, while in D4 and E4 on the right side mostsamples came from H11 (Fig. 5).

4.3. Cluster analysis

The cluster analysis of environmental parameters and den-sity of species revealed two clusters: 1) the bank zone ofboth sites (H1, H11), with the intermediate zone of WAB(H12), and 2) the mid-river zone of both sites (H3, H13), withthe intermediate zone of WAA (H2) and WAB (H12) (Fig. 6).The only habitat present in both clusters 1 and 2 was theintermediate zone of WAB (H12), which resembles the classi-fication obtained with SOM. H12 was assigned to cluster 2during the first sampling cycle and then shifted to the clus-ter 1 because of the appearance of submerged macrophytes.Thus macrophyte habitat H12 was located on the dendrogramvery closely to the “varial zone” (H11) during the three lastsampling cycles. Consequently, cluster 1 corresponded withcluster DE of SOM, while cluster 2 was similar to cluster ABof SOM (Fig. 5).

4.4. Environmental parameters and chironomidabundance

Most studied environmental parameters showed a clear ver-tical gradient over SOM. Cluster AB grouped samples col-lected at deep habitats without submerged macrophytes,with higher current velocity and little amounts of two frac-tions of benthic organic matter: CPOM and FPOM (Fig. 7).Contrary to this, cluster DE was characterized by shallowhabitats with slow water current and rich in periphyton,CPOM and FPOM.

Also the horizontal grouping of samples is reflected in theenvironmental variables. The seasonal gradient is bestexhibited by higher water temperature recorded for the left

Fig. 5 – Samples assigned to the output neurons. Each sample code consists of the habitat symbol (H1, H2, H3, H11, H12, H13),then month and symbol for the sampling cycle: 1 – 1988/89, 2 – 1991/92, 3 – 1992/93, 4 – 1995/96.

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side of SOM, where the samples collected in warmestmonths were assigned to. Also macrophytes and periphytonwere recorded to a greater extent on the left side of SOM. Thehorizontal spatial gradient in cluster DE of SOM seemed toresult from the presence or absence of water. It is bestshown by low depth and current velocity at the right side ofDE, that is for the bank habitat downstream from the dam(Figs. 5 and 7). In cluster AB of SOM the horizontal spatialgradient was weaker and resulted from differences in lessdecisive factors including water depth (but not its presenceor lack), water discharge and amounts of POM (Fig. 7).

Also most species considerably differed between the SOMclusters, thus showing strong habitat preferences. For exam-ple rather psammophilous, small-sized species Monodiamesabathyphila (Kieff.), Paratendipes connectens sensu Lipina, Polype-dilum convictum Walk. and Robackia demeijerei Krus were pre-

sent in the samples assigned to cluster AB, i.e. the deep habi-tats with fast water current (Figs. 7 and 8). Ablabesmyiamonilis L., Procladius Skuse (two predatory taxa), Prodiamesaolivacea Mg., Chironomus riparius Mg., Endochironomus albipennis(Mg.), Glyptotendipes gripekoveni Mg., Microtendipes chloris (Mg.)and Polypedilum pedestre Mg. were common in cluster DE, i.e.at shallow habitats with slowly flowing water, rich in organicmatter (Figs. 7 and 8). Note that mainly large size species ofChironomini, such as C. riparius and G. gripekoveni, were ableto exist in the “varial zone” (H11) with a large amount of fineparticulate organic matter. This zone was the effect of thereduction of stream flows below their natural levels in thetailwater during the last two sampled summers (Figs. 5 and8). Certain species were similarly distributed in all the clus-ters: Cricotopus bicinctus (F.), Eukiefferiella gracei (Edw.) (thesetwo Orthocladiinae taxa were recorded at all habitats except

Fig. 7 – Environmental factors over the SOM. Darker shading (scaled independently for each component) visualizes highervalues. For each variable a range of variability is presented.

Fig. 6 – Dendrogram of habitats based on environmental parameters and chironomid density at the upstream (WAA: H1, H2,

H3) and downstream site (WAB: H11, H12, H13) of the Warta River over four annual sampling cycles: 1 – 1988/89, 2 – 1991/92,3 – 1992/93, 4 – 1995/96 (given in brackets).

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mid river in winter/spring), Cryptochironomus defectus Kieff.,Paracladopelma camptolabis (Kieff.), Paratendipes albimanus(Mg.) and Cladotanytarsus mancus (Walk.) (Fig. 8). Most of allthe studied taxa were more abundant on the left side ofSOM, that is in summer (Figs. 5 and 8).

The highest temporal differences between the samplingcycles in the pattern of chironomid distribution at the tail-water site (WAB) were recorded at the bank (H11) and transi-tion (H12) habitats. Because H12 was covered by patches ofmacrophytes from late May till August or September duringthe two last sampling cycles (Figs. 5 and 7), it provided anexcellent substratum for epiphytes and macroinvertebrates,mainly small-sized Orthocladiinae larvae, such as Cricotopus

sylvestris (F.) (scrapers), and Parachironomus arcuatus Goetgh.(Chironomini predator) (Fig. 8). In turn the retention ofFPOM in the dense bed of macrophytes (Fig. 7) created favor-able conditions for pelophilous: C. riparius and G. gripekoveni;these taxa, besides at H11 also existed at H1 (Fig. 8).

Potthastia longimana Kieff., Dicrotendipes nervosus (Staeger),Polypedilum nubeculosum Mg., Polypedilum scalaenum Schrank,Stictochironomus histrio (F.), Paratanytarsus confusus Palmenand Tanytarsus gregarius (Kieff.) avoided deep habitats(Figs. 7 and 8), while the requirements of Odontomesa fulva(Kieff), Cryptochironomus borysthenicus Tshern. and Paraten-dipes albimanus (Mg.) are difficult to define on the basis of col-lected data.

Fig. 8 – Density of chironomid taxa over SOM. Darker shading (scaled independently for each component) visualizes highervalues. For each taxon the observed density varied from zero to the number of specimens per m2 presented on the left sideof SOM.

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5. Discussion

The SOM method effectively grouped samples into clusters

depending on the character of habitats they came from.

Samples from deeper habitats were separated from samples

from shallow habitats. SOM showed very well even the spe-

cific character of habitat H12. At first sight the presence of

samples from H12 in all the clusters might have suggested

an ineffective grouping. However, a more detailed analysis

indicated temporal changes in the habitat resulting from

the stabilization of water discharge at a level lower than

natural and occurrence of submerged macrophytes

(Grzybkowska and Dukowska, 2001, 2002; Grzybkowska, et

al. 2003). The presence of samples collected at H12 in cycle 4

only in clusters C and DE (Fig. 5) clearly indicates a change in

environmental factors towards reduced depth and water cur-

rent, and higher amounts of macrophytes and particulate

organic matter (Fig. 7). Similarly as in this temporal compar-

ison, also in previous studies it has been proved that samples

from the same site grouped in few neurons usually indicatelittle changes while scattered all over the SOM may be a signof profound human-induced modifications includingimpoundment (Penczak and Kruk, 2005).

Explanation can be found even if few samples from agiven habitat were assigned to a different cluster ratherthan their majority. Such an exception to a very effectivegrouping were samples H1Feb1, H1Feb2 and H1Apr3, assignedto cluster AB along with samples from deeper habitats. Theyall were collected during the spring thaw, when water levelconsiderably increased.

Changes in water level after impoundment also allowexplaining the differences in horizontal gradients betweenclusters AB and DE. This factor was more decisive in thecase of shallower than deeper habitats. High daily fluctua-tions in water level, frequently observed after impoundmentdownstream from the dam, often made the shallower habi-tats completely exposed (Penczak et al., 1998). This is why incluster DE the bank zone habitat H11 located downstreamfrom the reservoir was separated from located upstream H1

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where the hydrological regime still resembled natural. Thesamples from deeper habitats assigned to cluster AB wereless dependent on water level and this is why they under-went seasonal fluctuations and the spatial horizontal gradi-ent over cluster AB of SOM was weaker than over DE.

The results obtained with SOM are supported by conven-tional cluster analysis, which clearly showed, similarly toSOM, both 1) the spatial separation of habitats H1 and H11

from H2, H3 and H13, and 2) transition of previously deeperH12 towards a shallow habitat (due to the appearance of sub-mergent macrophytes). SOM, however, is regarded a tool ableto effectively distinguish clusters even when conventionalmethods fail (Cho, 1997). It additionally provides a very com-pressed graphical visualization of data and thus allows mul-tiaspect comparisons (Horrigan et al., 2005; Kwak et al., 2005;Park et al., 2005). On the basis of SOM in this study the parti-cular environmental factors characteristic for a given habitatwere correctly identified (e.g. H11 is present in DE, whichgroups shallow habitats with slow water current). Similarly,SOM allows to follow changes in qualitative composition ofa community and relate them to proper environmental fac-tors, e.g. epiphytic fauna such as C. sylvestris and P. arcuatusincreased at H12 as an effect of the appearance of submergedmacrophytes. SOM also identified species preferring thesame or completely different habitats (via the same or oppo-site pattern over SOM, respectively). For example A. monilisand Procladius have the same environmental requirements,which much differ from R. demeijerei. The method is thusvery useful in exploring data, especially when there mayexist certain effects which are not expected by theresearcher. Conventional analysis is useful in further steps,when the significance of differences or strength of correla-tions is to be determined (Lek et al., 2005). Such details onthe dependences between the studied chironomid speciesand environmental parameters, assessed with conventionalmethods in the Warta River, were given by Grzybkowskaand Dukowska (2002).

There are some similarities between the patterns ofchanges in macroinvertebrates and in fish in river fragmentsadjacent to the Jeziorsko Reservoir. The impact of the damon fish assemblages has been studied since 1985 (Penczak etal., 1998, 2005; Głowacki and Penczak, 2000; Penczak andKruk, 2000; Kruk and Penczak, 2003). The greatest modifica-tions in the ichthyofauna—similarly as in invertebrates—were recorded at the downstream site and partly resultedfrom short-term fluctuations in water level influencing fishfood base and reproduction. The other crucial factors were1) the existence of dam precluding migratory fish from theirupstream reproductive migrations and thus leading to theirextirpation, and 2) mass downstream migration of theyoung-of-the-year of eurytopic (i.e. tolerant) species fromthe reservoir leading to an increase in fish abundance butalso drastically changing the dominance structure. As aresult in the recent years at the downstream site the fishassemblage stabilized but at a completely different levelthan in the pre-impoundment period: the importance ofrheophilic and migratory species was drastically reduced,while the eurytopic species became new dominants (Penczaket al., 1998, 2005; Penczak and Kruk, 2000; Kruk and Penczak,2003; Kruk, 2004, 2006).

Acknowledgements

This work was partly supported with grants of the Univer-sity of Łódź No 505/537, 505/649, 505/692 and the State Com-mittee for Scientific Research No 645/P04/96/10. We wish toacknowledge our teachers of ANN, Professors Ryszard Tadeu-siewicz (AGH, Poland), Sovan Lek (CESAC, France) and Young-Seuk Park (Kyung Hee University, Korea). We thank ŁukaszGłowacki for English correction, and our colleagues and stu-dents for their help in fieldwork.

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