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Primary Research Paper

Local and regional factors determining aquatic and semi-aquatic bug

(Heteroptera) assemblages in rivers and streams of Greece

Ioannis Karaouzas* & Konstantinos C. GritzalisHellenic Centre for Marine Research, Institute of Inland Waters, 46.7 km Athens-Sounion Av., P.O. BOX 712, 190 13,

Anavissos, Attica, Greece

(*Author for correspondence: Tel.: +30-22910-76391; Fax: +30-22910-76323; E-mail: ikarz@ath.hcmr.gr)

Received 21 December 2005; in revised form 15 June 2006; accepted 17 June 2006; Published online 31 August 2006

Key words: Heteroptera, assemblages, distribution, variation partitioning, multivariate analysis, environmental fac-

tors, Greece

Abstract

Heteroptera species were collected from 48 sites distributed throughout the mainland and island complexes of

Greece during 19992004. The aims of this study were to investigate Heteroptera distribution and abundance

in Greek streams, identify the environmental factors that are linked to variation in their assemblages and to

partition the influence of environmental and spatial components, alone and in combination, on Heteroptera

community composition. Canonical ordination techniques (CCA) were used to determine the relationship

between environmental variables and species abundance, while variation partitioning was performed using

partial CCA to understand the importance of different explanatory variables in Heteroptera variation.

Heteroptera variation was decomposed into independent and joint effects of local (physicochemical variables,

microhabitat composition, stream width and depth), regional (land use/cover) and geographic variables(longitude, latitude, altitude and distance to source). Land use/cover, aquatic and riparian vegetation, stream

size and water chemistry were the most important factors structuring Heteroptera assemblages. At regional

scale, bug assemblages were mainly divided into those found in forested and agricultural landscapes,

following water quality and microhabitat composition at local scale. Local variables accounted for 48% of the

total explained variation, regional variables for 20% whereas geographical position appeared to be the least

influencing factor (8.5%). The results of partial constraint analyses suggested that local variables play a major

role in Heteroptera variation followed by regional variables.

Introduction

Aquatic Heteroptera, commonly known as true

bugs, are an important group of aquatic insects that

possess unique characteristics and adaptations.

The most distinguished characteristic of this order

is the beak, a piercing mouthpart, which has a

suctorial function. Each family of this order differs

considerably in morphology and ecological pref-

erences while many species display specific habitat

preferences (i.e., Corixidae) (Macan, 1954; Savage,

1990, 1994a). In rivers, they are found along themargins of shallow water (Corixidae), on the water

surface of lentic (pool) stream zones (Gerridae

and Veliidae) and lotic (riffle) stream zones

(some Veliidae), and among aquatic vegetation

(Notonectidae, Nepidae and Naucoridae). They

may also be found under rocks in fast waters (some

Naucoridae). Aquatic Heteroptera have significant

ecological effects (McCafferty, 1981; Hutchinson,

1993) and economic importance, which has

probably been underestimated (Papacek, 2001).

Hydrobiologia (2006) 573:199212 Springer 2006DOI 10.1007/s10750-006-0274-1

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Ecologists studying Heteroptera have long been

seeking theuse of freshwater bugs for biomonitoring

and classification purposes. Prior to the develop-

ment of monitoring models, many studies have

been conducted in the past to ascertain bug speciesassociation with environmental parameters. Bug

species associations were first examined by Macan

(1938) who reported the relationship of Corixid

species with marginal vegetation and water

chemistry. Macan (1939) also provided general

ecological notes on each species of the Corixidae

family in his identification key. Members of the

Society of British Entomology and other

researchers later on provided much information on

the ecological and geographical distribution of

Heteroptera species (Brown, 1948; Popham, 1949,1950) that have been summarised in Savages key

(1989). Savage (1982, 1990) found close correlation

of bug species composition with water chemistry

followed by Eyre & Foster (1989) who found that

water acidity affects species composition. Savage

(1994b) used Corixid species as indicators of

organic pollution that was supplemented by the

saprobic values of Sla decek and Sla deckova (1994).

Jansson (1977, 1987) used Micronecta species as

indicators of water quality in Finish Lakes.

Recently, Hufnagel et al. (1999) proposed a new

approach for habitat characterisation obtained bynew indices and cenological values of bug species.

Distributional patterns and assemblages of this

insect group are well established in several coun-

tries around the globe (Savage, 1990; Moreno

et al., 1997; Biro, 2003), while their importance as

biological indicators for classifying water bodies,

especially in the United Kingdom, has been

established more than two decades (Savage, 1982).

Unfortunately, Heteroptera, among other insect

groups, have been rarely studied in Greece and

most of the few studies that exist have been

conducted by foreign scientists that collected

species from various geographic parts during short

periods of the year (Drosopoulos, 1980). These

studies however, involved only species records and

their geographical area of collection (e.g., Josifov,

1959; Illies, 1978; Magnien, 2000). Drosopoulos

(1980) was the first to assemble records from

museums and literature, including his personal

collections, to complete a check list of both

terrestrial and aquatic bugs of Greece. Recently,

Petrakis & Roussis (2001) have examined the

bioindication value of Hellenic aquatic Heterop-

tera using an algorithmic approach. In addition,

they have also provided a check list of Greek

Heteroptera species with their distribution in seven

areas that they examined.This work contributes to the general knowledge

of Greek river Heteroptera with the provision of

species records and their localities that will

supplement the Hellenic check lists provided by the

aforementioned authors. The objectives of this

work are (a) to investigate Heteroptera distribution

patterns in running waters, (b) to assess the envi-

ronmental factors that might explain their assem-

blage structure and (c) to partition the influence of

environmental and spatial components, alone and

in combination, on Heteroptera variation.

Materials and methods

Site description

Greece is a small Mediterranean country with

an area of approximately 132,000 km2 and is

divided into the mainland and surrounding island

complexes. Its recent geological morphology has

formed a multitude of basins, drained mainly by

small and medium sized rivers (Skoulikidis et al.,2006). Running waters in the entire country range

from small streams in the highlands and in semi-

arid grasslands and islands, to medium and large

rivers and from ephemeral streams to perennial

rivers.

Heteroptera sampling

Heteroptera species were collected from 1999 to

2004 within the framework of two EU R&D

programs, AQEM (The Development and Testing

of an Integrated Assessment System for the Eco-

logical Quality of Streams and Rivers throughout

Europe using Benthic Macroinvertebrates) and

STAR (Standardisation of River Classifications):

Framework method for calibrating different

biological survey results against ecological quality

classifications to be developed for the Water

Framework Directive (2000/60/EC). The

AQEM-STAR (AQEM Consortium, 2002)

sampling method was used for the collection of

Heteroptera species within the two projects, while

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within the STAR project the RIVPACS (Armitage

et al., 1983) method was additionally used.

The AQEM-STAR methodology is based on a

multi-habitat scheme designed for sampling major

habitats proportionally according to their presencewithin a sampling reach. A sample consists of 20

replicates taken from all microhabitat types at the

sampling site with a share of at least 5% coverage,

which must be distributed according to the share of

microhabitats. Benthic macroinvertebrates were

collected using a rectangular hand net of

0.25 m 0.25 m with a mesh size of 500 lm nytex

screen. Each of the 20 replicates was taken by

positioning the net and disturbing the substrate in

an area that equals the square of the frame width

upstream of the net (0.25

0.25 m). Thus, a totalof 1.25 m2 (0.25 0.25 20 replicates) was sam-

pled for each sampling site. Sampling started at the

downstream end of the reach and proceeded up-

stream. Benthic samples were preserved with eth-

anol concentration of ca. 70%. Heteroptera species

were collected with soft tweezers from the samples

and identified with Savages (1989) and Tamaninis

(1979) keys.

The RIVPACS sampling method involves a

3-min kick sample and a 1-min manual search

which involves the collection of individual speci-

mens from the water surface, submerged rocks, logsand vegetation. Each macroinvertebrate habitat in

the sampling area was sampled proportionally to its

cover with a pond net with a mesh size of 1 mm and

with a frame of 0.25 m 0.25 m. Storage, preser-

vation as well as identification procedures were the

same to those of the AQEM-STAR method.

Environmental data

Prior to the collection of Heteroptera, the hydro-

morphology, land use/cover, geology of the site and

its catchment area, in-stream habitat composition

and physicochemical variables were examined and

recorded. Geographical, geological, morphological

and land use/cover data have been used in ArcView

GIS software to derive the respective information.

Particularly, land use data have been acquired by

using the CORINE Land Cover database of

Greece. The extent of the land cover classes at each

sampling catchment has been estimated with the

contribution of the ArcGIS Spatial Analyst, while

the surface areas have been rounded up to a 10%

spatial step. For the geological information a

preexisting geological map from the Institute of

Geologic and Mineralogic Exploration (IGME)

has been used in combination with the relevant

geologic formations database, which provided thetype of rocks and sediments in the area of interest.

Additionally, the altitude of each sampling station

has been estimated by utilizing the digital contour

map of Greece with an altitude step of 20 m

in ArcView. Physical parameters (estimated

discharge, water temperature, pH, conductivity

and dissolved oxygen) were measuredin situ. Water

samples were collected, preserved at 4 C, filtered

upon arrival in the laboratory and analysed for

alkalinity, chloride, total hardness and nutrients

(nitrite, nitrate, ammonia, phosphate and totalphosphorous). In total, 40 environmental variables

were recorded.

Data analysis

Eighty stream sites were sampled in total, some

during three seasonal periods (summer, winter and

spring), during two (the vast majority of the sites)

or during one. Thus, for multiple seasonally

assessed sites the mean number of species abun-

dance was calculated. The same was conducted for

environmental data recorded more than oneseason. Species collected with RIVPACS method

and were not found with the AQEM-STAR

method within a site, were included into the data

set in order to gain the highest possible number of

different bug species.

To find groups of Heteroptera species based on

their occurrences, a hierarchical cluster analysis

was performed. The BrayCurtis similarity (pres-

enceabsence) and group average linkage were

used as similarity distance and linkage rule,

respectively. Canonical ordination techniques were

used to examine the relationship between the

environmental variables and species occurrence.

Prior to Canonical Correspondence Analysis

(CCA), variation in Heteroptera data was

examined by running a Detrended Correspondence

Analysis (DCA; Hill, 1979) to ensure a unimodal

rather than linear distribution. Gradient of varia-

tion is provided by the first DCA axis in which

taxon compositional turnover is measured in

standard deviation units (SD). Along each axis a

full turnover in taxon composition between

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samples occurs after 4.0 SD. The unimodal

assumption of DCA is accepted if the gradient

length of the first axis is greater than 3.0 SD (Ter

Braak & Prentice, 1988). The first DCA axis (SD:

4.576) confirmed the unimodal assumption andthus the CCA application while the first four DCA

axes accounted for 38.5% of the variation in Het-

eroptera data. To avoid multicollinearity between

environmental variables, a Pearson Product

Moment correlation analysis was run and those

variables highly associated with any other were

removed from the analysis, as they would have no

unique contribution to the regression equation (Ter

Braak, 1986). Forward selection of environmental

variables was used to ascertain the minimal set of

variables that explain species data. Significance ofenvironmental variables was determined by means

of a Monte Carlo permutation test.

Partial canonical correspondence analysis

(CCA) (Borcard et al., 1992) was performed to

partition the variation of Heteroptera species into

independent components following the procedure

carried out by Qinghong (1997). Qinghong (1997)

used partial redundancy analysis (RDA) to parti-

tion the variation of species in his study. As bug

species in this study showed a unimodal rather than

a linear distribution, partial CCA was used instead.

Environmental variables were separated into threegroups of environmental data; geographic

(latitude, longitude, altitude and distance from

source), local (physicochemical variables, micro-

habitat composition, stream depth and width) and

regional (land use and cover) to partition the

influence of each environmental group on the total

variance of bug species. Variation partitioning was

performed in two steps: (1) by running a partial

CCA of Heteroptera abundance (response)

variables and all three groups of environmental

variables (explanatory variables) and (2) by

running a partial CCA using one of the three

environmental groups (explanatory variables) and

the remainder two groups together (covariables)

and the other way around (Table 3, combinations

1, 2, 3), with or without the covariables (see

Table 3). Partial CCA was applied four times

within each combination of the three environ-

mental variable groups (see Table 3). A total of 12

runs were accomplished, in which the association

between species and an explanatory variable is

examined after the influence of a covariable has

been separated. In this way, the pure influence from

each environmental group and of the joint effects

was partitioned from the total explained variation.

Canonical ordination techniques were carried out

using the package CANOCO for Windows 4.5(Ter Braak & Smilauer, 2002), correlation analysis

with STATISTICA version 6 and cluster analysis

with PRIMER 5.

Results

Heteroptera assemblages

From the 80 sampling sites, Heteroptera species

were found at 48 stream sites (Fig. 1). A total of 23species (Table 1) were collected and identified

accounting for 424 individuals. It should be

mentioned that four species (Hebrussp.,Notonecta

sp., Velia sp., two individuals) at four sites, poor

specimen condition prevented identification of

Heteropterans and hence were removed from the

analysis.

Aquarius najas was the most abundant and

common (in terms of the number of sites which it

was found) species accounting for 28% of the total

species abundance and was found in 28 sites. Velia

caprai was the next most abundant speciesaccounting for 13% followed by Gerris lacustris

(9%), Notonecta maculata (8%) and Micronecta

poweri (6.5%). Following Aquarius najas, Velia

caprai, was the most widely distributed species

being found in 15 sites.Notonecta maculata,Gerris

lacustrisand Hydrometra stagnorumwere found in

14, 11 and 10 sites, respectively. At family level,

Gerridae, followed by Veliidae, were the most

abundant and common taxa, thus showing a

tolerance to a wide range of environmental fea-

tures. Notonectidae was the next most frequently

occurring family whereas Corixidae was the next

most abundant family. The highest species richness

was found in moderate to highly nutrient enriched

sites with pool (non-visible flow) waters and close

to the sea, in which 10 different species were found

in Kalipeuki and seven in Messini.

Cluster analysis revealed six groups of

Heteroptera assemblages (Fig. 2) based on

the strongest rankings of the dendrogram (less

than 20%). Starting from the right end of the

dedrogram towards the left, Plea minutissima and

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the Corixids Sigara striata, Sigara falleni and

Sigara dorsalis formed group 1. Notonecta glaucaand Ilyocoris cimicoides formed a distinctive

group (group 2) while Velia caprai, Gerris

lacustris, Hydrometra stagnorum and Aquarius

najas formed group 3. Micronecta poweri and

Micronecta scholtzi comprised group 4. Velia

pelagonensis and Microvelia pygmaea created

group 5, which cannot however be considered as

valid, since a single individual of each species was

found and were commonly present in the same

stream site. The same also applies for Sigara

nigrolineataand Microvelia reticulata that formed

group 6.

Ordination analysis

Pearson ProductMoment Correlation analysis

revealed strong associations between various

environmental variables. For example, altitude

and slope (r = 0.90, p 0.01), conductivity and

total hardness (r = 0.80, p 0.01), alkalinity and

conductivity (0.83, p 0.01) and finally, nitrite

with ammonium, phosphate and nitrate (0.72, 0.71

Figure 1. Map showing the stream sites where Heteroptera species were found. Numbered below are the sampling sites followed by

their stream names. The small map on the upper right corner indicates the eco-region numbers (Illies, 1967) and the three hydro-

chemical zones of Greece (Skoulikidis et al., 2004). 1. Perasmata, Fonias; 2. Gria Vathra, Tsivdogianni; 3. Mesohori, Bospos; 4.

Symvola, Bospos; 5. Gorgona, Xanthia; 6. Ag.Barbara, Arkoudorema; 7. Dipotama, Arkoudorema; 8. Prasinada, Tributary of

Arkoudorema; 9. Thermia, Diavolorema; 10. Ano Poroia, Poroia; 11. Tripotamon, Lygkos; 12. Pidoderi, Aliakmon; 13. Adartiko,

Aliakmon; 14. Melas, Aliakmon; 15. Kotas, Aliakmon; 16. Paliouris, Thyamis; 17. Milea, Aoos; 18. Olosson, Mavrorema; 19.

Kalipeuki, Skamnias; 20. Smokovo, Onohonos; 21. Kaitsa, Onohonos; 22. Gorgopotamos Bridge, Gorgopotamos; 23. Gorgopotamos

Village, Gorgopotamos; 24. Dimosaris, Dimosaris; 25. Adias, Adias; 26. Platanistos, Platanistos; 27. Reumata, Aspropotamos; 28.

Piros, Prevedos; 29. Karytena, Alfeios; 30. Tsouraki, Tsouraki; 31. SL98, Tsouraki; 32. Methydrio, Stenon; 33. Gortys, Lousios;

34.Marina, Neda; 35.Elea, Neda; 36.Kalonero, Peristeria; 37. Artiki, Peristeria; 38.Vrachopanagitsa, Pamisos; 39.Ag.Floros, Pamisos;

40.Aris, Pamisos; 41.Messini, Pamisos; 42. Apolakkia, Sianitis; 43. Gadouras, Gadouras; 44. Egkares, Egkares; 45. Amphilissos,

Amfilissos; 46. Pyrgos, Amphilissos; 47. Manolates, Kokorrema; 48. Ampeliko, Vourkou.

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Table1.HeteropterachecklistofGreekrunningwaterswiththeirgeographicaldistributionandselectedecologicalp

references

Heteropterataxalist

Geographical

distribution

Altitude(m)

Distance

tosource

(km)

Slopeof

valleyfloor

(%)

Conductivity

(lS/cm)

N.E

S.E

N.W

S.W

800

20

020

2040

40+

600

Nepomorpha

Nepidae

Nepacinerea(Linnaeus,1758)

Ranatralinearis(Linnaeus,1758)

Corixidae

Micronectapoweripoweri

(Douglas&Scott,

1869)

Micronectasc

holtzi(Fieber,

1860)

Hesperocorixasa

hlbergi

(Fieber,

1848)

Sigara

dorsalis(Leach,

1817)

Sigaranigrolineatanigrolineata

(Fieber,

1848)

Sigarastriata(Linnaeus,1758)

Sigara

falleni(Fieber,

1848)

Naucoridae

Ilyocoriscimicoi

descimicoi

des

(Linnaeus,1758)

Notonectidae

Notonectaglaucaglauca

(Linnaeus,1758)

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Notonectamaculata

(Fabricious,1794)

Pleidae

Pleaminutissimaminutissima

(Leach,

1817)

Gerromorpha

Hydrometridae

Hydrometrastagnorum

(Linnaeus,1758)

Veliidae

Microveliapygmaea(Dufour,1833)

Microveliareticulata

(Burmeister,

1835)

Veliapelagonensis(Hoberlandt,1941)

Veliacapraicaprai(Tamanini,1947)

Veliacurrens(Fabricius,1794)

Gerridae

Aquariuspaludum(Fabricius,1794)

Aquariusnajas(DeGeer,1773)

Gerrislacustris(Linnaeus,1758)

Limnoporusru

foscutellatus

(Latreille,

1807)

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and 0.71, respectively). Therefore, slope, total

hardness, alkalinity, and nitrite were removed

prior to CCA. Hence, from the 40 environmental

variables, 35 were retained. Eleven of the 35 envi-

ronmental variables were significant (p 0.05) in

explaining species variation as derived from CCA.

Total variance in species abundance data was 4.214

and the sum of all canonical eigenvalues 3.519

(Table 2). The percentage of the total variation of

bug species explained by the environmental vari-

ables accounted thus for 83.5% (3.519 100/

4.214).

The relationship of Heteroptera species with the

11 environmental variables is presented in Figure 3

(see Electronic Supplementary Material1). The first

ordination axis (horizontal axis) reflected a gradi-

ent mostly related to forests, aquatic macrophytes

(emergent and submerged), cropland and chloride.

Forest abundance decreased from the left towards

to right end of the axis. The second axis (vertical

axis) indicated that phosphate and stream width

had the next largest effect on Heteroptera occur-

rence. Chloride ions, aquatic macrophytes and

cropland decreased from the right toward the left

end of the axis.

On the upper right of the ordination diagram,

Sigara dorsalis, Nepa cinerea, Sigara striata,

Hesperocorixa sahlbergi, Micronecta poweri,

Micronecta scholtzi, Sigara falleni, Plea minutiss-

ima and Ranatra linearis were associated with

phosphate, open grassland/bushlands, stream

width, chloride and cropland. The first four species

exhibited very strong associations with phosphate

and stream width, while they were less influenced

by the rest of the variables. The remaining species

showed closer associations with cropland and

chloride ions. On the bottom right quadrant,

Notonecta glauca and Ilyocoris cimicoides were

associated with aquatic macrophytes and reeds.

Table 2. Results of the CCA analyses between environmental variables and Heteroptera species. Total inertia is the total variance in

species abundance data

CCA axes 1 2 3 4 Total inertia

Eigenvalues 0.578 0.465 0.425 0.383 4.214

Speciesenvironment correlations 0.968 0.97 0.959 0.945

Cumulative percentage variance

Of species data 13.7 24.7 34.8 43.9

Of speciesenvironment relation 16.4 29.6 41.7 52.6

Sum of all eigenvalues 4.214

Sum of all canonical eigenvalues 3.519

1 Electronic supplementary material is available for

this article at http://dx.doi.org/10.1007/s10750-006-

0274-1 and accessible for authorised users

Figure 2. Hierarchical cluster analysis presenting groups of bug species with similar assemblages.

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Forests (deciduous, coniferous and mixed

native), altitude and water temperature presentedclose relationships with Sigara nigrolineata,

Microvelia reticulata, Microvelia pygmaea, Velia

pelagonensis, Velia caprai, Gerris lacustris, Aquarius

najas, Velia currens, Hydrometra stagnorum and

Limnoporous rufoscutellatus. Forests however,

displayed the highest association with the bug

species of the respective quadrant. All forested sites,

except three, were accompanied with significant

proportion of boulders and gravel and with low

proportion of aquatic vegetation. Finally, the upper

left quadrant reflected the relationship of latitudewithNotonecta maculataand Aquarius paludum.

Variance partitioning

Twelve runs of partial CCA were performed to

explain variation in bug species data by the three

groups of environmental data and their combina-

tions (Table 3). The pure (independent) effect of

local (40.1%), regional (16.8%) and geographic

(7.2%) variables was acquired. Moreover, the pure

effect of joint regional and geographic (25.1%),

geographic and local (48.6%) and local and re-gional (71.4%) variables were obtained. Using the

results of Table 3 and the hypothetical model of

Qinghong (1997), total explained variation was

partitioned into seven parts (Table 4). Thus, the

final equation, in terms of percentage of variation

(total inertia 100/4.214), can be written as shown

in Table 5.

Common variation (the joint fraction of the

three environmental groups) accounted for only

3% (total inertia 100/3.519, same equation

applies for the following components) of the totalexplained variation. The joint effect of local and

regional variables accounted for 17%, whereas

for local and geographic 1.5%. The joint effect of

regional and geographic accounted also approxi-

mately 1.5%. Pure local variables were the

main source of variation accounting for 48%

of the total explained variation followed by

regional (20%) and geographic (8.5%) variables.

Unexplained variation accounted for 16.5%

(10083.5) (see Fig. 4).

Figure 3. CCA plotshowing the relationship of the 23 bug species (+) withthe significant environmentalvariables. Firstaxis is horizontal

and second axis vertical. Codes for both species and environmental variables are shown in Electronic Supplementary Material.

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Discussion

It is well documented that some bug species

display distinct habitat preferences (Macan, 1954;

Savage 1982, 1994a, b) while others are capable to

inhabit a wide range of habitats, from rock pools

to more complex habitats such as lakes (Savage,

1989; Kurzatkowska, 1993; Svensson et al., 2000).

Some bugs can tolerate environmental conditions

that would be lethal to other invertebrate species,

Table 3. Variation partitioning by partial canonical correspondence analysis (CCA) of Heteroptera distribution explained by three

groups of environmental variables, geographical (Geo), local (Loc) and regional (Reg). The total inertia used here is the sum of all

canonical eigenvalues. The sum of all eigenvalues in a correspondence analysis of the species matrix is 4.214. Thus, the total percentage

of the total variation of bug species matrix for each step is: total inertia 100/4.214

Run Responder Environmental variables Covariable Total inertia % Variation

Total effect: all environmental variables

Species All groups 3.519 83.5

Partial effect 1 Combination: Loc and Reg & Geo

1 Species Loc Reg & Geo 1.691 3.519 40.1

2 Species Reg & Geo 1.828 43.4

3 Species Reg & Geo Loc 1.059 3.519 25.1

4 Species Loc 2.460 58.4

Joint effect: Loc M Reg & Geo = 2.460) 1.691 = 1.828) 1.059 = 0.769 18.2

Partial effect 2 Combination: Reg and Geo & Loc

1 Species Reg Geo & Loc 0.708 3.519 16.8

2 Species Geo & Loc 2.811 66.7

3 Species Geo & Loc Reg 2.047 3.519 48.6

4 Species Reg 1.472 34.9

Joint effect: Reg M Geo & Loc = 2.811) 1.472 = 2.047) 0.708 = 1.339 31.8

Partial effect 3 Combination: Geo and Loc & Reg

1 Species Geo Loc & Reg 0.302 3.519 7.2

2 Species Loc & Reg 3.217 76.3

3 Species Loc & Reg Geo 3.011 3.52 71.4

4 Species Geo 0.509 12.1

Joint effect: Geo M Loc & Reg = 3.217) 0.509 = 3.011) 0.302 = 2.708 64.3

Table 4. Total explained variance of Heteroptera distribution calculated by using the results of Table 3 and the hypothetical model ofQinghong (1997)

Part Environmental variables & covariables Equations Results

(1) Joint effect of Loc and [Geo and Reg] A + B + C = 0.769

(2) Joint effect of Reg and [Loc and Geo] A + C + D = 1.339

(3) Joint effect of Geo and [Loc and Reg] B + C + D = 2.708

(4) Pure Loc + pure Reg + joint effect

of Loc and Reg

1.691 + 0.708 + A = 3.011 Joint effect of Loc and Reg = A = 0.612

(5) Pure Loc + pure Geo + joint effect

of Loc and Geo

1.691 + 0.302 + B = 2.047 Joint effect of Loc and Geo = B = 0.054

(6) Pure Reg + pure Geo + joint effect

of Reg and Geo

0.708 + 0.302 + C = 1.059 Joint effect of Reg and Geo = C = 0.049

(7) Joint effect of Geo, Loc and Reg (D)

Total explained variance

(TEV) = D + 0.612 + 0.054 + 0.049 + 0.708 + 0.302 + 1.691 = 3.519

D = 0.103

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while others show less tolerance. Several Corixid

species were found in acidic mining lakes of

Lusatia, Germany with a pH < 3 (Wollmann,

2000) whereas the quality of the environment

(i.e., water pollution and hydromorphological

degradation) influences the successful colonisationand populations of the water strider A. najas

(Ahlroth et al, 2003). Heteroptera are most diverse

in warm, heavily vegetated, lentic or slow lotic

waters. A study carried out in Lake Balaton,

Hungary and its adjacent streams, showed greater

species richness and abundance in streams with

environmental heterogeneity (Biro, 2003). In fact,

greatest bug species richness in Greek running

waters was found in lentic streams with rich aquatic

vegetation and with increased nutrient levels.

Streams with restricted microhabitat diversity

(i.e., abiotic substrates such as boulders, gravel,

etc.) supported the least number of different spe-

cies, usually those of the Gerridae and Veliidae

families.

Gerridae and Veliidae species were widely

distributed withA. najas and V. caprai inhabitingmost streams of this study. A. najas, as shown

from cluster analysis, occurred frequently with

V. caprai, H. stagnorum and G. lacustris in for-

ested sites at medium altitudes (Fig. 3, Table 1).

Regarding Corixids, S. dorsalis, S.striata and

S. falleni seemed to be closely associated with

each other and were found in sites with simi-

lar environmental characteristics. Similarly,

M. poweri and M. scholtzi also appeared to

inhabit sites with the same characteristics (e.g.,

microhabitat composition). Common eco-groups

Figure 4. Variance partitioning of Heteroptera species data. (A) Represents the bulk variation in original Heteroptera data explained

by the three environmental groups (Bu) and the unexplained (Uv) variation; (B) Represents the pure effect of local (a), regional (b),

geographic (c) and unexplained variables (d) (d = {a + b + c})Bu); (C) Represents the joint effects of regional and geographic (bc),

geographic and local (ca) and local and regional (ab) variables; (D) Represents the joint effects of the total explained variance using

data from Table 3 and the hypothetical model of Qinghong (1997), (ab) Local and regional, (ac) local and geographic and (cb) regional

and geographic variables and finally (E) Represents the pure effects of the total explained variance using of local (a), regional (b) and

geographic (c) variables.

Table 5. Table presenting the final equation of the total explained variance (TEV) of Heteroptera distribution. TEV = Joint

variation + partial joint variation + unique variation

Joint variation Partial joint variation Unique variation TEV

Reg, Geo, Loc Loc and Reg Reg, Geo, LocLoc and Geo

Reg and Geo

2.4 14.5 + 1.2 + 1.2 16.8 + 7.16 + 40.1 83.50%

Loc, Local; Geo, Geographic; Reg, Regional.

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have been established in Hungary by Hufnagel

et al. (1999), which are not very distinct from

those of this study.

The results of the ordination analysis

revealed the significant environmental variablesthat were the major explanatory variables struc-

turing Heteroptera assemblages. Land use/cover,

microhabitat composition, stream size, water

chemistry and geographic position were the most

important variables along the four CCA axes in

explaining Heteroptera variation. The significant

association of water chemistry with bug species is

well documented (Savage, 1982, 1989, 1994a;

Jansson, 1987). Some Corixids have been shown to

be associated with organic pollution (Biesiadka &

Tabaka, 1990) but also with natural eutrophicconditions (Savage, 1982, 1994a). Water body-size

has been perceived as a significant factor deter-

mining Heteroptera assemblages and distribution,

especially for Corixidae (Macan, 1954; Savage,

1994a). Relationships between water body-size

and bug species have been detected by Hufnagel

et al. (1999) who distinguished Heteroptera pref-

erences for habitat types ranging from small

shallow waters to large deep waters. Bug preferences

are not constrained to habitat types. Microhabitat

composition (e.g., aquatic vegetation, sand, gravel,

etc.) is of major importance to Heteroptera assem-blages and distribution (Macan, 1938, 1939; Savage,

1989; Garcia-Aviles, 1996). Distribution patterns of

this insect group are well defined in some countries

(Savage, 1990; Popham, 1949; Macan, 1939), how-

ever limited information is available regarding

Heteroptera associations directly with larger scale

variables such as land use/cover.

The upper and bottom right of the ordination

reflected the distribution of species in relatively

large and deep running or standing waters with

rich aquatic vegetation and nutrients. Most

streams on the right of the ordination flow

through agricultural basins, which consequently

lead in most cases to the increase of nutrient and

organic (municipal and farm waste) concentra-

tions. Corixidae and Pleidae species were mainly

found in pool waters with the aforementioned

characteristics. The bottom left of the ordination

diagram reflected species found in smaller sized

(width and depth) streams, with abundant forests

at relatively higher altitudes and with colder

waters. Forests however, were the most important

variable and seemed to be mostly associated with

the species of the respective quadrant. Veliidae and

Gerridae species of this quadrant were collected

either at the pool zones of the sampling reach or at

faster currents. A. najas, G. lacustris and V. capraiwere generalists since they were found almost in all

stream habitat types, however their abundances

were greater in forested streams. Finally,

geographic position (latitude) was associated with

species of the upper left quadrant, especially

N. maculata.

Partial CCA indicated that local variables play

a major role in Heteroptera variation while

geographical position appears to be the least

influencing factor. Land use/cover was the second

most important environmental factor determiningspecies variation. It is well known that regional

and local variables are interrelated since stream

hydromorphology and quality are influenced by

land use/cover (Vannote et al., 1980; Allan, 2004;

Sandin & Johnson, 2004). In fact, the joint effect of

local and regional variables accounted for 17% of

the total explained variation. In contrast, when

geographical variables were combined either with

local or regional variables, variation loading

accounted for 1.5%, respectively. This suggests

that geographic location is perhaps less important

to Heteroptera species variation, possibly due tothe migration and dispersal abilities of this insect

group (Popham, 1964).

Concluding, it should be taken into account

that some sites were sampled during three seasons

while others during two or only one. This could

have influenced to some extent the results of this

study, as some species were probably missed if

they occurred in another season than the one

sampled. In addition, those sites sampled more

than one season and with an extra sampling

method (RIVPACS) were likely to comprise more

different species (individuals) than those sites

sampled once and with only one method simply

due to the higher number of individuals being

sampled. Nevertheless, this study provided a first

clue of the influence of spatial and environmental

components on Heteroptera species and the key

abiotic (environmental) variables structuring

Heteroptera assemblages in Greek running waters.

Moreover, distribution patterns of Heteroptera

species were described along with several selected

ecological preferences (Table 1).

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Conclusions

At regionalscale, assemblages were mainly dividedin

forested and agricultural landscapes, which conse-

quently influence the quality and hydromorpholog-ical condition of the receiving waters (Allan et al.,

1997; Allan, 2004). At local scale, species distribution

was separated according to water quality and

microhabitat composition, except common bugsthat

were widely distributed. Local, followed by regional

variables, were the main environmental factors

determining Heteroptera assemblages while geo-

graphic position exhibited the least influence. These

findings denote that some bug species could possibly

be used for biomonitoring purposes, as changes to

local and/or regional parameters are likely to affecttheir assemblage structure. Understanding bothlocal

and regional-scale parameters is essential for

explaining the factors that structure aquatic and

semi-aquatic bug assemblages.

Acknowledgements

The data of this work were collected within the

framework of the AQEM [EVK 1-CT1999-00027]

and STAR [EVK-CT-2001-00089] projects funded

by the European Commission, 5th Framework

Program, Energy, Environment and SustainableDevelopment, Key Action 1: Sustainable Manage-

ment and Water Quality and by the General

Secretariat for Research and Technology, Ministry

of Development, Greece. We would also like to

thank Dr Anthony Polwart and Mrs Hazel Hulme

from Keele University for providing us some

important articles as well as Dr Steven Declerck

and an anonymous referee for their valuable

comments and suggestions on this manuscript.

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