aquatic insects along environmental gradients in a karst river system: a comparative analysis of ept...

RESEARCH PAPER

Aquatic insects along environmental gradientsin a karst river system: A comparative analysisof EPT larvae assemblage components

Mojca Hrovat 1, Gorazd Urbanič1 and Ignac Sivec2

1 Biotechnical Faculty, Department of Biology, University of Ljubljana, Ljubljana, Slovenia2Slovenian Museum of Natural History, Ljubljana, Slovenia

Studying the response of assemblages either to natural or human induced environmental gradientsis challenging due to spatial and temporal heterogeneity in river systems. This study identified therelationship between environmental variables, as well as sampling date, and Ephemeroptera (E),Plecoptera (P), Trichoptera (T) assemblages in a karst river system. Each assemblage component(E, P, T, EP, ET, PT, andEPT) is analyzed separately. Ten direct influential variables (water depth,water temperature, current velocity, dissolved oxygen concentration, oxygen saturation, pH, nitrateconcentration, total solids, and total suspended solids) and sampling datewere considered in orderto identify environmental gradients after removal of the effect of stream size and habitatheterogeneity; both parameters were used as covariables. Thus, the observed effects of selected10 variables were those that were not explained also by Strahler stream order and habitatheterogeneity. Examination of the EPT assemblage characteristics using the non-parametricKruskal–Wallis test showed significant spatial variations in richness/diversity metrics (number ofEPT taxa, Shannon–Wiener diversity) but not in functional metrics (Gatherers/Collectors, Grazers,and Scrapers). The temporal variation in all metrics was substantial. Assemblage variances (R2)and adjusted assemblage variances (R2

adj) of>35%and>20%, respectively, were obtained.Mainenvironmental gradients representing hydrogeology and eutrophication as well as hydraulic andoxygen gradient for EPT assemblages were recognized. It appears that eutrophication is the keyecological factor for most assemblage components in a Dinaric karst river system, althoughPlecoptera assemblages were best explained by temperature regime and oxygen conditions. Thisstudy suggests that a variation in the diversity betweenE,P, andT taxa influences anassemblage–environment relationship. Environmental variables that were best predictors for assemblages withmore than one insect group were associated with assemblages with high diversity. This studyprovides methods and approaches to recognize the main factors determining biotic assemblages’structures in Dinaric karst river systems and thus provides a basis to develop appropriatebiodiversity conservation policies.

Received: February 15, 2013Revised: December 23, 2013Accepted: February 16, 2014

Keywords:Ephemeroptera / Eutrophication / Karst river / Plecoptera / Trichoptera

1 Introduction

Understanding dynamics of river ecosystems requireknowledge on environmental features as determinants ofbiotic structure and on interactions between biotic assemb-lages and the environment. Benthic invertebrate assemb-lages are structured by a variety of ecological factors atmultiple spatial scales, ranging from geographical scale

Handling Editor: Michael Schäffer

Correspondence: Dr. Gorazd Urbanič, Department of Biology,University of Ljubljana, Biotechnical Faculty, Večna pot 111,SI-1000 Ljubljana, SloveniaE-mail: [email protected]

Abbreviations: pCCA, partial canonical correspondence analysis;TDS, total dissolved solid; TSS, total suspended solid

International Review of Hydrobiology 2014, 99, 222–235 DOI 10.1002/iroh.201301641

222 © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

(ecoregions) to a local scale (sampling site) (Sandin andJohnson, 2000). The types of river systems exhibitconsiderable natural and human induced heterogeneityin environmental conditions and biotic structure due tocatchment geomorphology, land use, flow characteristicsand climatic conditions of the region (Lorenz et al., 2004;Thorp et al., 2006; Pavlin et al., 2011). In this regard,distinguishing characteristics of river systems determineecological patterns of benthic invertebrate assemblagestructure (e.g. Allan, 2004; Lorenz et al., 2004; Urbanič andToman, 2007). River systems are open systems, whoseenvironmental variables vary dramatically not just at spatialscales (the size of the sampling unit) but also at temporalscale (the frequency of the observations) (Giller andMalmqvist, 1998; Thorp et al., 2006). However, riverecologists have often included spatial dimension in thestudies of ecological patterns of assemblages (e.g.Boyero, 2003; Sandin and Johnson, 2004; Galbraithet al., 2008), whereas the temporal dimension has usuallybeen neglected. We used karst rivers as a model system toexamine the spatio-temporal variation of variables influ-encing Ephemeroptera, Plecoptera, Trichoptera (EPT)assemblage components.

Published investigations have examined the controlof environmental variables for various EPT assemblagesacross distinct river systems at local, landscape, orregional levels, such as in lowland rivers (e.g. Haidekkerand Hering, 2008; Song et al., 2009), mountain streams(e.g. Boyero, 2003; Galbraith et al., 2008), and karstsystems with specific hydrological and thermal charac-teristics (e.g. Habdija et al., 2002; Previšić et al., 2007;Hrovat et al., 2009). Since studies are designed at manyspatial and also temporal scales, different conclusionson the role of environmental variables for the assemb-lages are given, depending on the scale and the set ofvariables included into the analysis. Nevertheless, thevariation in responses to assemblage components toenvironment variables remains poorly studied. Consid-ering assemblage component requirements shouldperceptibly extend the basis for understanding thecomplex interactions between spatio-temporal hetero-geneity and biodiversity in riverine landscapes. Heinoand Mykra (2008) provided separate analyses for E, P,and T assemblages in selected headwater streams inFinland where a degree of variation between groups wasobserved. To our knowledge, this was the first timewhere the variability of assemblage–environment rela-tionships has been compared among assemblagecomponents.

In the present study, we examine the relationshipbetween EPT assemblages and environmental variablesincluding sampling date for each assemblage component(E, P, T, EP, ET, PT, and EPT) separately using karstrivers as the model system. In particular, the focus has

been on (i) identifying explanation power of 10 seasonallyvaried variables for each assemblage component and(ii) comparing assemblage–environment relationship var-iability among assemblage components.

2 Methods

2.1 Description of sampling sites

The investigated sites were located in three karst springrivers within the Kolpa catchment in southeast Slovenia(Fig. 1). The area is part of the inland water ecoregion,Dinaric western Balkan (Illies, 1978; Urbanič, 2008a), andthe bioregion, Sub-Dinaric hills and plains (Urbanič,2008b). Specific surface and subterranean features,predominantly composed of permeable carbonate bed-rocks are characteristic of the area. Sampling sites werelocated in the lowlands, with an altitude and slope rangingfrom 131 to 148m a.s.l. and 0.58 to 1.29 ‰, respectively,but at different distances to karst spring, stream order, andstream width (Table 1). Substrate categories at eachsampling sites were defined based on AQEM riversampling guidelines (AQEMConsortium, 2002). Mesolithaland microlithal were the dominant substrate types at mostsampling sites, while argyllal was dominant at LaML.These substrate types represented between 30 and 50%of the substrate composition. Substrate heterogeneity,calculated as weighted average of seven substratecategories for a given sampling site, was the highest atthe site KoKr and lowest at site LaML (Table 1).Weightingswere defined based on the particle size of the substratecategory in the ascending order from one (argyllal) toseven (megalithal) (Table 2). More detailed information onthe characteristics of sampling sites is given in Hrovat et al.(2009).

Figure 1. Location of sampling sites (KrKr, Krupa Krupa;KoKr, Kolpa Krasinec; LaML, Lahinja Mala Lahinja; LaBu,Lahinja Butoraj; LaPr, Lahinja Primostek).

International Review of Hydrobiology 2014, 99, 222–235 Aquatic insects along environmental gradients in a karst

© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 223

2.2 Field and laboratory procedures

Macroinvertebrates, as well as environmental variableswere sampled from January to December 2005. Benthossampling was performed at approximate monthly intervals,except in April and July in all three rivers and in Augustin Kolpa River due to high water levels. Altogether49 samples were collected. Standardized multihabitatsampling approach (AQEM Consortium, 2002) accordingto substrate composition was used to collect the benthicfauna. In the field, samples were preserved in 96%ethanol. In the laboratory, EPT larvae were sorted, countedand identified to lowest possible taxonomic level (mostlyspecies). Each time a benthos sample was taken, fieldmeasurements of water temperature, water depth, dis-solved oxygen concentration, oxygen saturation, conduc-tivity, pH and current velocity were measured using WTW340i Multimeter, staff gauge and current meter. Watersamples were collected at the same time and taken to thelaboratory for analysis of nitrate, ortho-phosphate, totalsuspended solids (TSS), and total dissolved solids (TDS),according to ISO International Standards (ISO 6878,ISO 7890-3, ISO 11923).

2.3 Statistical analyses

Quantitative data on EPT taxa were used to calculatebiological metrics based on Asterics 3.01 taxa list (AQEMconsortium, 2002). Four metrics (the number of EPT taxa,Shannon–Wiener diversity, and the percentage of themostcommon functional feeding groups, i.e. % of Grazers andScrapers and % of Gatherers/Collectors) were selected inorder to compare EPT assemblage compositions amongsampling sites. We used the non-parametric Kruskal–Wallis test with Mann–Whitney post hoc tests to analyzemetrics for significant differences (p< 0.05) among sites;the Bonferroni correction (a¼ 0.05/n, where n is thenumber of tests) accounted for multiple testing. A non-parametric Kruskal–Wallis test with Mann–Whitney posthoc tests and the Bonferroni correction was also appliedto evaluate significant differences in all measuredenvironmental variables (water depth, water temperature,current velocity, dissolved oxygen concentration, oxygensaturation, pH, nitrates, total solids, and TSSs) amongsites. Ortho-phosphates concentrations were below de-tection level and thus were not included in the analysis.The correlation between environmental variables including

Table 1. Main characteristics of sampling sites

River Krupa Lahinja Lahinja Lahinja Kolpa

Location Krupa Mala Lahinja Butoraj Primostek KrasinecSampling site code KrKr LaML LaBu LaPr KoKrGauss–Krüger Y-coordinate 5517304 5516212 5516392 5523705 5522502Gauss–Krüger X-coordinate 5054461 5040354 5044158 5053864 5050181Stream order (Strahler) 1 1 3 4 5Distance to karst spring (km) 1.05 0.97 8.69 34.57 101.80Stream width (m) 20.2 4.5 14.0 40.6 90.0Substrate heterogeinity 4.75 2.85 4.70 4.70 4.90

Table 2. Inorganic substrate categories (sensu AQEM consortium, 2002) and used weightings in the Canonicalcorrespondence analyses

Category Description Particle size Weighting

Megalithal Large cobbles, boulders and blocks, bedrock >40 cm 7Macrolithal Coarse blocks, cobbles 20–40 cm 6Mesolithal Fist to hand-sized cobbles 6–20 cm 5Microlithal Coarse gravel 2–6 cm 4Akal Fine to medium-sized gravel 0.2–2 cm 3Psammal Sand 6mm–2mm 2Psammopelal Sand and mud <0.2mm �

Pelal Mud and sludge (organic) <0.006mm �

Agryllal Silt, loam, clay <0.006mm 1

�, substrate was not recorded.

M. Hrovat et al. International Review of Hydrobiology 2014, 99, 222–235


sampling date was tested using Sperman’s rank correla-tion. Kruskal–Wallis test and Sperman’s rank correlationwere performed using SPSS 19.0 and Past 2.17.

To investigate the relationships between environmen-tal, as well as time variables and different EPT assemblagecomponents (E, P, T, EP, ET, PT, and EPT) partialcanonical correspondence analysis (pCCA) (Ter Braak,1986) was performed using CANOCO 4.5 software (TerBraak and Šmilauer, 2002). For the analyses, environ-mental and biotic data were log x and log (xþ 1)transformed, respectively. We considered 10 directinfluential variables (sensu Urbanič and Toman, 2007)recorded at all benthos sampling occasions (water depth,water temperature, current velocity, dissolved oxygenconcentration, oxygen saturation, pH, nitrate concentra-tion, total solids, and TSSs) and two covariables (Strahlerstream order, habitat heterogeneity) in order to identifyenvironmental gradients with the removed effects ofstream size and microhabitat conditions. Thus, observedeffects of the 10 selected variables were those that werenot explained by Strahler stream order and habitatheterogeneity. Sampling dates were sin (2px/365) trans-formed prior to analysis (Hrovat and Urbanič, 2012). Thesignificance of selected environmental variables forassemblages was defined by using Monte-Carlo permuta-tion test (999 permutations). For each variable thecontribution to the explained variance of the assemblagecomponent before (l) and after (la) forward selection wascalculated. In addition, an EPT assemblage component (E,P, T, EP, ET, PT, and EPT) specific relative portion offorward selected variables defined as RS¼ l/lmax wascalculated. The comparison was also made for the totalexplained variation in assemblage components associatedwith environmental variables before and after forwardselection. As the sample size and the number ofindependent variables in the model influence the resultof the pCCA (Kromrey and Hines, 1995) Ezekiel’sadjustment of fractions was calculated using the followingequation (Peres-Neto et al., 2006):

R2ðY=X Þadj ¼ 1� n � 1

n � p � 1ð1� R2

Y=X Þ

where n is the sample size, p is the number of predictors,and R2

Y=X is the sample estimation of the assemblagevariance r2Y=X .

3 Results

3.1 Environmental characteristics

Statistically significant differences (Kruskal–Wallis test,H¼ 11.9–34.8, p< 0.05) were observed among sampling

sites for 6 of 10 seasonally varied variables: water depth,dissolved oxygen concentration, oxygen saturation, con-ductivity, nitrate concentration, and current velocity(Fig. 2). All measured values of oxygen saturation wererelatively high (>75%), with the small variation over studyperiod between the five sites. Dissolved oxygen concen-tration showed the same pattern as the oxygen saturationwith high values (�7.5mg/L) during sampling periods.Strong positive correlation between dissolved oxygenconcentration and oxygen saturation was found (Spear-man’s rank correlation, r¼ 0.86, p<0.001). The highestfluctuations in water depth (almost 1m) at the time ofsampling were observed at the sampling site in the KolpaRiver (KoKr), whereas at other sites fluctuations did notreach 0.5m. Sites differed significantly in water depth andwater depth showed statistically significant although weakpositive correlation with conductivity (Spearman’s rankcorrelation, r¼0.40, p< 0.001) and nitrates (Spearman’srank correlation, r¼0.29, p<0.05). The range in nitrateconcentration was lowest at the sampling site KoKr, wherealso statistically significant lower values were observed(Mann–Whitney U test, p<0.01). In contrast, the highestnitrate concentrations were recorded at the upstreamsampling sites of the Krupa (KrKr) and Lahinja River(LaML) although values did not exceed 6.5mg/L. Conduc-tivity showed a similar pattern to nitrate with the lowestvalues in KoKr and highest values in KrKr. Spearman’sanalysis showed significant positive correlation betweennitrates and conductivity (Spearman’s rank correlation,r¼0.54, p<0.001). Current velocity varied substantiallyamong sampling occasions at each site but less amongsites although at sampling site LaBu significantly lowervalues were recorded. Four environmental variables(water temperature, pH, TSSs, and total solids) showedno statistically significant difference (Kruskal–Wallis test,H¼ 1.3–8.7, p> 0.05) among sampling sites (Fig. 2).However, sampling sites downstream of the springexperienced much greater variability in water temperatureover the study period compared to headwater sites KrKrand LaML, with highest and lowest temperature recordedat site KoKr. Values of total solids and TSSs variedsubstantially among sampling occasions but not amongsites. For pH values little variation with sampling periodwas observed, and values were comparable for allsampling sites. However, according to Spearman’stest pH values decreased significantly with decreasingconductivity and nitrates (Appendix A).

3.2 EPT taxa

Altogether, 108 EPT taxa were identified, 26 Ephemer-optera (E), 17 Plecoptera (P), and 65 Trichoptera (T).Mean abundances (individuals/m2) of different taxa andthe total mean EPT abundance varied greatly across



Figure 2. The box plots of environmentalvariables used in pCCA and the results ofKruskal–Wallis nonparametric test for fivesampling sites (see Fig. 1 for sampling sitecodes). The box plots represent median(solid line), interquartile range (box), rangeof the data (whiskers), and outliers(circles). The number of samples per site(n) is 10 but 9 for KoKr. For a givensampling site, box plots with differentletters are significantly different (Mann–Whitney post hoc test, p< 0.05, using theBonferroni correction).



sampling sites (Appendix B). Among biological metrics, thenumber of EPT taxa, Shannon–Wiener diversity and thepercentage of the most represented feeding groups(Gatherers/Collectors, Grazers, and Scrapers) for eachof five sampling sites are presented in Fig. 3. Statisticallysignificant differences (Kruskal–Wallis test, H¼ 18.8 and13.8, p< 0.05) among sampling sites were observed in thenumber of EPT taxa and Shannon–Wiener diversity. Thenumber of EPT taxa at the headwater sampling sites LaMLand KrKr was lower than at other sampling sites. Samplingsite LaML also had the lowest variation in the number ofEPT taxa. The highest Shannon–Wiener diversity valueswere recorded at sampling sites KrKr and LaBu, while atsite LaBu much greater variation of diversity values wasobserved. However, there were no statistically significantdifferences (Kruskal–Wallis test, H¼ 2.2–7.5, p> 0.05)among sampling sites in the percentage of the Gatherers/Collectors and Grazers and Scrapers. The medianpercentage for both feeding groups was above 30% ateach site, except at site KoKr. At latter site the lowestpercentage of Gatherers/Collectors was 8%) although thevalues were highly variable (Fig. 3).

3.3 EPT assemblage components versusenvironmental variables

The results of the pCCA were used to define and comparethe explanatory power of environmental variables, as well

as sampling date, for the variation of assemblagecomponents with removal of the effect of stream sizeand habitat conditions. The values of marginal andconditional effects of environmental variables variedamong assemblage components (Table 3). Assemblagecomponents exhibited some variability when combined,but less variability in the number of variables thatwere significantly related to assemblage components.Five variables (four for P assemblage) were found to besignificant for each assemblage component. Their level ofexplanatory power varied between assemblage compo-nents, whereas some variables showed same significancelevel for different assemblage components. The bestpredictors of assemblage compositions with highestsignificance level of explanation (p<0.001) were conduc-tivity and water depth, together with dissolved oxygenconcentration and current velocity. Conductivity explainedthe highest share (11–16%) of variability for mostassemblage components, except for P and EPT, wherewater temperature and dissolved oxygen attributed 30 and14% of variation, respectively. Lower, but significantexplanatory power (p< 0.01) was observed for samplingdate but also for dissolved oxygen concentration andwater temperature. For some assemblage components(e.g. E, P, EP, ET, EPT) conductivity, oxygen saturation,water temperature, current velocity, or sampling date werealso less important but still significant (p< 0.05). Thesame level of significance was observed for pH, but only

Figure 3. The box plotsand the results of Kruskal–Wallis nonparametric testfor the number of EPT taxa,Shannon-Wiener diversity,and for the percentage ofthe most represented feedinggroups (Gatherers/Collectors,Grazers and Scrapers) forfive sampling sites (see Fig. 1for sampling site codes). Thebox plots represent median(solid line), interquartilerange (box), range of thedata (whiskers) and outliers(circles). The number of sam-ples per site (n) is 10 but 9 forKoKr. For a given samplingsite, box plots with differentletters are significantly dif-ferent (Mann–Whitney posthoc test, p< 0.05, using theBonferroni correction).



for the T assemblage, for other components the variablewas less important. Other variables (nitrates, total solids,and TSSs) were not significant for any of assemblagecomponents, explaining <10% of variability.

Examination of the EPT taxa ordination diagram clearlyindicated two gradients explaining the distribution of taxa(Fig. 4). Variables correlated with the first pCCA axisdescribed a gradient associated with oxygen conditions

and hydraulic characteristics. On the second pCCA axis,EPT taxa were strongly associated with hydrogeologicalconditions and eutrophication gradient, primarily based onthe conductivity, but also on nitrate concentration. Onthe CCA diagram, three main ecological groups of EPTtaxa were recognized. The first group of taxa (e.g.Rhyacophila sp. sensu stricto, Rhyacophila aurata, Hydro-ptila angulata, Orthotrichia sp., Cheumatopsyche lepida,

Table 3. Marginal (l) and conditional (la) effects of environmental variables for each assemblage component

Variable Code

Assemblage

E P T EP ET PT EPT

l la l la l la l la l la l la l la

Conductivity(mS/cm)

Conductivity 0.11 0.11�� 0.19 0.2� 0.14 0.14�� 0.13 0.13�� 0.14 0.14�� 0.16 0.16�� 0.13 0.13��

Sampling date Date 0.04 0.05� 0.14 0.07 0.1 0.10�� 0.07 0.05� 0.07 0.07�� 0.11 0.08� 0.09 0.06��

Water depth (m) Depth 0.08 0.09�� 0.22 0.07 0.12 0.07� 0.11 0.10�� 0.1 0.1�� 0.14 0.07� 0.12 0.12��

Dissolved oxygen(mg/L)

Dissolved O2 0.07 0.06�� 0.28 0.11 0.08 0.06 0.11 0.12�� 0.08 0.07�� 0.13 0.12�� 0.14 0.14��

NO3� (mg/L) NO3 0.06 0.03 0.07 0.02 0.12 0.07 0.07 0.03 0.09 0.04 0.12 0.07 0.08 0.05

Oxygen saturation(%)

O2 saturation 0.04 0.03� 0.06 0.13� 0.05 0.05 0.05 0.05� 0.04 0.04 0.05 0.07 0.1 0.05

pH pH 0.04 0.01 0.03 0.05 0.09 0.08� 0.04 0.03 0.06 0.04 0.08 0.07 0.06 0.03Water temperature(°C)

Temperature 0.05 0.03 0.3 0.3�� 0.07 0.07 0.1 0.05� 0.06 0.05� 0.11 0.07 0.07 0.04

TSSs (mg/L) TSS 0.02 0.02 0.03 0.02 0.05 0.05 0.02 0.02 0.03 0.02 0.05 0.04 0.04 0.02Total solids (mg/L) TSSþTDS 0.02 0.02 0.07 0.04 0.07 0.05 0.03 0.02 0.04 0.03 0.07 0.05 0.04 0.03Current velocity(m/s)

Velocity 0.05 0.03 0.19 0.17� 0.14 0.14�� 0.07 0.04 0.08 0.05 0.15 0.15�� 0.13 0.06�

Significance level based on 999 Monte-Carlo permutations: �p<0.05, ��p<0.01, ��p<0.001.

Figure 4. pCCA diagram of the first two ordination axes with (A) EPT taxa (D), (B) samples and selected environmentalvariables (arrows). Strahler stream order and substrate heterogeneity were used as covariables. Taxa names are givenin Appendix 2.



Heptagenia sulphurea) occurred at low values of thehydrogeological conditions/eutrophication gradient. Onthe other hand, taxa belonging to the second group (e.g.Polycentropus irroratus, Sericostoma sp., Phryganeagrandis, Hydropsyche sp. – juv., Hydropsyche angusti-pennis, Ecdyonurus sp. (helveticus group) prefered highvalues of hydrogeological conditions/eutrophication gradi-ent. Taxa belonging to the third group (e.g. Adicellareducta, Leuctra fusca, Leuctra prima, Leuctra nigra,Brachyptera risi, Nemoura avicularis) were associated withlow values of hydraulic and oxygen conditions. The fourthgroup (e.g. Ephemera danica, Seratella ignita, Baetisrhodani, Centroptilum luteolum, Leuctra albida/fusca,Nemoura cinerea, Lype reducta, Odontocerum albicorne)was concentrated in the center of the diagram and showedno preference to the two main environmental gradients.

The environmental variables that were related toassemblage components exhibited some between assem-blage components variation (Table 4). The number ofvariables with high relative importance (>0.8) variedbetween assemblage components. Conductivity wassignificantly related to most assemblage components.The exception was the P assemblage where watertemperature and dissolved oxygen were highly importantvariables. Different combinations of variables were alsoobserved for the EPT assemblage, including dissolvedoxygen concentration, conductivity, current velocity andwater depth as highly important variables. There were alsodifferences in the number of variables with low relativeimportance (<0.3) for assemblage components. For mostassemblage components TSSs and total solids showedthe lowest relative importance. Nitrates and pH were alsoless important for P assemblage while oxygen saturationwere less important for P and ET assemblages.

Between assemblage differences were not observedin the percentage of the total variability of assemblagecomponents explained either by variables before forward

selection (FS) or by forward selected variables. Thepercentage of the total variability (R2) before forwardselection ranged from 35.2% (T) to 42.1% (EP). Afterforward selection the percentage of the total variability (R2)was lower than before FS, ranging from 21.3% (T)to 33.1% (EP). Adjusted variance values were lower(R2

adj < 25%), but still little diversity across assemblageswas observed. A similar pattern, but lower R2 and R2

adj

values were observed for forward selected variables.

4 Discussion

4.1 Environmental variables and EPTassemblage components

Although this study focuses on the importance ofseasonally varied environmental variables, the importanceof spatial variation on EPT fauna has been found in manystudies (e.g. Helešic, 2001; Svitok, 2006; Urbanič andToman, 2007; Pastuchová et al., 2008). Strahler streamorder and substrate heterogeneity are associated withstream size and habitat complexity and thus, we consid-ered both as covariables in the pCCA analysis. Somemeasured variables in our study (e.g. current velocity, totalsolids, and TSSs) varied substantially among samplingoccasions. Nevertheless, six of ten measured variables inthis study exhibited significant spatial variation. Townsendet al. (1997) considered temporal variation equally or moreimportant in the determination of assemblage compositionthan the more traditionally measured spatial variation. Inour study, significant spatial variation between sites wasonly observed in richness/diversity metrics, whereastemporal variation in richness/diversity and functionalmetrics was substantial at most sites. Substrate character-istics were not substantially different and therefore theavailability of food resources throughout the year may

Table 4. Relative importance of forward selected environmental variables for each assemblage composition

Variable Code

Assemblage

E P T EP ET PT EPT

Conductivity (mS/cm) Conductivity 1.00 0.63 1.00 1.00 1.00 1.00 0.93Sampling date Date 0.36 0.47 0.71 0.54 0.50 0.69 0.64Water depth (m) Depth 0.73 0.73 0.86 0.85 0.71 0.88 0.86Dissolved oxygen (mg/L) Dissolved O2 0.64 0.93 0.57 0.85 0.57 0.81 1.00NO3

� (mg/L) NO3 0.55 0.23 0.86 0.54 0.64 0.75 0.57Oxygen saturation (%) O2 saturation 0.36 0.20 0.36 0.38 0.29 0.31 0.71pH pH 0.36 0.10 0.64 0.31 0.43 0.50 0.43Water temperature (°C) Temperature 0.45 1.00 0.50 0.77 0.43 0.69 0.50TSSs (mg/L) TSS 0.18 0.10 0.36 0.15 0.21 0.31 0.29Total solids (mg/L) TSSþTDS 0.18 0.23 0.50 0.23 0.29 0.44 0.29Current velocity (m/s) Velocity 0.45 0.63 1.00 0.54 0.57 0.94 0.93



influence variation of assemblage composition and trophicstructure more than habitat characteristics (Previšićet al., 2007). The lowest number and variation in EPTtaxa was recorded at upstream sites, which is inaccordance with the River Continuum concept, RCC(Vannote et al., 1980). According to the RCC, biodiversityin headwaters is likely to be determined by low thermalheterogeneity and low nutrients, whereas spring sites inour study mainly exhibited low thermal variation. However,Shannon–Wiener diversity index did not show the samelongitudinal pattern as the number of EPT taxa, possiblydue to limited elevation gradient of our sampling sites,when considering environmental conditions as a functionof the altitude (Ward, 1986). Nevertheless, the RCCsuggests that not only structural but also functionalcharacteristics of assemblages to change consistentlywith environmental gradients along rivers. Conversely, inour study almost no spatial variation but temporal changesin feeding group composition was observed. Temporalvariation in environmental variables could account forthese patterns, possibly by affecting the distribution andavailability of food resources.

Dominant environmental variables influencing EPTassemblage components in karst rivers in Slovenia wereconductivity, dissolved oxygen, current velocity and waterdepth. These variables were rather weakly correlated andtheir values varied significantly among sites. Conversely,the variation in the conductivity and oxygen concentrationwas small, whereas substantial variation in the currentvelocity was recorded at all sites. Hydrological conditionsof karst streams are specific and may vary also due tosubterranean flow to karst springs. Thus, high dischargeevents in karst springs may occur (Barquin andDeath, 2009), influencing groundwater and karst springchemistry conditions (Vesper et al., 2001). Conductivityhas been cited as one of the most apparent variablesrelated to eutrophication gradient (e.g. Soldán et al., 1998;Hrovat et al., 2009). There are also natural causesaffecting conductivity that depend on geology (Soldánet al., 1998). In Dinaric western Balkan ecoregion, bothgeological characteristics and inorganic pollution areimportant factors affecting T taxa (Urbanič and Toman,2007). In our study, parameters addressing geologicalcharacteristics were not measured, whereas conductivityshowed a statistically significant positive correlation tonitrate, indicating nitrate as one of the important ionsaffecting conductivity values. Observed concentrationsof nitrate were highest in both Lahinja in Krupa karstspring sections, underlying the vulnerability of karst riversdue to rapid surface infiltration and anthropogeniccontamination through the migration of water via sinkholesand groundwater dominated river networks (Bicalhoet al., 2012). Therefore, nutrients and particularly nitratesare often elevated in karst spring river systems (Peterson

et al., 2002). Thus, eutrophication is potentially importantstressor on benthic invertebrate assemblages in SlovenianDinaric karst area due to its unique geomorphologicfeatures as well as high substrate permeability. Weassume that in-stream nutrient loading from intensivefertilization in agricultural land in Slovenia is enhancedthrough permeable surface to subterranean and surfacewater causing eutrophication of karst river systems.

Our results also revealed the major importance of watertemperature and dissolved oxygen for Plecoptera (P)assemblage components compared to other assemblagecomponents, therefore, reflecting greater dependence ofthese assemblages on oxygen conditions. Similarly, Krno(2003, 2007) found water temperature and oxygen regime,but also conductivity to be strong predictor of P assem-blage in Carpathian Rivers. Nevertheless, our observa-tions suggest that hydraulics and nutrients, besidesoxygen regime are highly important for EPT assemblagesin karst rivers. Species such as N. avicularis, Leuctrafusca, L. nigra, and B. risi, found at the lowest oxygenconditions in our study, possess a wide ecological range,also occurring in slightly polluted rivers (Soldánet al., 1998). In our study, hydraulics seems to be moreimportant than oxygen regime for some P species (e.g.Taeniopteryx nebulosa, L. prima) often found in shallowwater (Hrovat et al., 2009). Recently, hydraulics wasdetermined as the main gradient responsible for thevariation in EPT assemblage in Carpathian Rivers(Pastuchová et al., 2008). In fact, current velocity wasfound as the only variable with relation to EPT faunaamong directly measured variables. These predictions,like those from our study, seem to indicate that specieshave specific preferences for environmental conditionstypical of various types of river systems.

4.2 Between-assemblage componentsvariation in response to environmentalvariables

In our study, assemblage components exhibited similarpercentages of the total variability explained by environ-mental variables. Nevertheless, different combinationsand the number of forward selected variables significantlyexplaining EPT assemblage components were recog-nized. Heino and Mykra (2008) compared the relationshipbetween E, P, and T assemblages and environmentalvariables and observed some variation in the mostinfluential environmental variables among different insectgroups. However, they studied boreal streams with uniquecharacteristics, and not comparable to karst river systemsin our study. Moreover, they used a different set ofvariables previously known to structure macroinvertebrateassemblages, although some environmental variables(water depth, conductivity, velocity, and pH) were the



same as in our study. Nevertheless, it was suggestedthat various insect assemblages may respond differentlyto the same set of environmental variables and thisobservation was apparent also from our study. Heino andMykra (2008) observed that environmental variablesstrongly associated to E and T assemblages are of minorimportance for the P assemblage. On the other hand,weobserveddifferent patterns amongE,PandTassemblagecomponents. Nevertheless, the best explanatory variablefor the P assemblage showed low explanatory power forE and T assemblages and vice versa.

Furthermore, for E and ET assemblages in this studyall environmental variables were similar in importance,whereas the T assemblage response was most compara-ble to PT and EPT assemblages with three mainexplanatory variables. The best explanatory variable forthe P assemblage was not highly important for all otherassemblage structures indicating the P assemblageresponse was specific. We assume that this could berelated to the variation in the diversity between E, P, and Ttaxa, given that highly important variables for assemblageswith more than one insect group were associated withassemblage components with high diversity. Thus, in twocomponent assemblages in our study it was shown thatenvironmental variable with highest importance for themost abundant insect group, i.e. for E in EP assemblages,overrides the importance of most influential variable forother insect component in the same assemblage. Thisassumption was supported by the fact that conductivity, asthe most important variable for the E and T assemblagecomponents, was also highly influential for the EPTassemblage. Apparently, current velocity was of majorimportance for EPT assemblage for its importance for themost diverse T assemblage. Since the P assemblage wasless diverse than E and T assemblages in our study, themost important variable for the P assemblage, watertemperature, was less influential for the EPT assemblage.

We thank Prof. Bill P. Stark and Prof. John E. Brittain,for correcting the English text and anonymous referees forvaluable comments.

The authors have declared no conflict of interest.

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Appendix A. Spearman rank correlation coefficients (r) for correlations between environmental variables (��p<0.001,�p< 0.05). The number of samples (n) is 49.

VariableNO3

�

(mg/L)Temperature(°C)

DissolvedO2 (mg/L)

O2 saturation(%)

Currentvelocity(m/s) pH

Totalsolids(mg/L)

TSS(mg/L)

Waterdepth(m)

Samplingdate

Conductivity (mS/cm) 0.541�� 0.100 �0.200 �0.276 �0.091 �0.304� 0.244 0.107 0.395�� 0.171NO3

� (mg/L) 0.164 �0.183 �0.183 �0.092 �0.315� 0.232 0.008 0.293� �0.026Temperature (°C) �0.667�� 0.304� 0.110 �0.079 �0.215 0.299� 0.266 �0.638��

Dissolved O2 (mg/L) 0.859�� 0.223 0.129 0.266 �0.272 �0.282� 0.429��

O2 saturation (%) 0.392�� 0.081 0.069 �0.163 �0.192 0.152Current velocity (m/s) �0.243 �0.036 �0.249 0.163 0.137pH 0.015 0.298� �0.614�� 0.156Total solids (mg/L) �0.279� �0.146 0.426��

TSS (mg/L) �0.125 �0.541��

Water depth (m) �0.076



Appendix B. The list of 108 taxa of Ephemeroptera (E), Plecoptera (P), and Trichoptera (T) larvae. Numberscorrespond to the mean abundance (individuals/m2)�SE found at each sampling site for each taxa andfor E, P, T, and EPT taxa altogether. Sampling site codes are indicated in Fig. 1.

Code Taxon name

Sampling site

KrKr KoKr LaML LaBu LaPr

Ephemeroptera1 Baetis fuscatus 49.2�33.42 Baetis scambus/fuscatus 4.2� 3.7 1.6� 1.03 Baetis liebenauae 1.4� 0.94 Baetis lutheri 12.6�4.1 0.1� 0.1 1.7� 1.3 3.5� 2.35 Baetis rhodani 239.7� 137.8 40.9�14.8 586.6� 218.7 31.8� 8.5 215.8� 99.86 Baetis sp. – juv. 1.5� 1.57 Baetis vernus 54.3� 40.1 19.3� 6.1 0.2� 0.28 Nigrobaetis niger 22.7� 9.9 4.4� 2.19 Centroptilum luteolum 16.1� 4.5 3.2�2.1 20.8� 8.7 18.0� 5.7 10.4� 2.710 Centroptilum pennulatum 1.5� 0.7 0.1� 0.111 Caenis luctuosa 0.1� 0.1 1.1� 0.5 13.1� 6.012 Caenis rivulorum 1.4� 0.7 8.2� 5.913 Serratella ignita 92.3� 58.5 138.4�88.8 5.7� 3.4 57.7� 41.6 271.5� 199.914 Ephemerella notata 0.1�0.115 Torleya major 0.2� 0.1 0.2�0.116 Ephemera danica 2.8� 0.7 3.7�1.6 40.8� 5.1 3.8� 1.7 12.8� 3.817 Ephemera vulgata 0.9� 0.5 0.1� 0.118 Ecdyonurus sp. (helveticus group) 0.2� 0.1 0.8� 0.419 Ecdyonurus sp. (venosus group) 12.3� 4.2 7.0�2.720 Electrogena sp. 14.0� 3.2 38.8� 4.5 91.1� 17.1 1.0� 0.321 Heptagenia sulphurea 7.3�3.922 Rhithrogena sp. 3.2�1.123 Habrophlebia fusca 5.3� 2.3 0.1�0.1 51.9� 32.0 21.7� 13.1 1.7� 1.224 Habrophlebia lauta 0.1�0.1 0.1� 0.125 Paraleptophlebia submarginata 8.2� 2.1 6.8�2.8 15.8� 5.7 4.9� 1.7 1.0� 0.726 Siphlonurus aestivalis 3.1� 2.1

Plecoptera27 Siphonoperla sp. 0.1�0.128 Capnia bifrons 0.1�0.1 0.7� 0.529 Leuctra albida/fusca 76.9� 41.3 77.7�47.8 0.1� 0.1 6.5� 4.030 Leuctra fusca 20.1� 11.131 Leuctra nigra 0.1�0.132 Leuctra prima 0.9� 0.833 Nemoura avicularis 0.2� 0.134 Nemoura cinerea 9.9� 8.7 0.1�0.1 76.6� 24.7 15.3� 7.5 21.1� 9.335 Nemurella pictetii 9.2� 4.0 9.5� 4.236 Isoperla inermis 29.0� 8.037 Isoperla lugens 6.4� 2.838 Perlodes sp. 0.1�0.139 Brachyptera tristis 1.0� 0.5 26.0�12.7 0.1� 0.1 1.5� 1.0 0.2� 0.240 Brachyptera risi 0.1� 0.141 Taeniopteryx nebulosa 0.4�0.3 1.1� 0.5 0.2� 0.242 Taeniopteryx schoenemundi 1.1�0.5 0.1� 0.1 0.1� 0.143 Taeniopteryx sp. – juv. 0.1�0.1 0.2� 0.1

Trichoptera44 Beraea pullata 0.2� 0.145 Beraeodes minuta 1.5� 0.7 0.7�0.7 0.5� 0.2 0.2� 0.2 0.2� 0.146 Micrasema setiferum 442.3�229.6 7.1� 3.5 0.1� 0.1

(continued)



Appendix B. (Continued)

Code Taxon name

Sampling site


47 Agapetus laniger 0.7�0.448 Agapetus ochripes 0.1�0.149 Goera pilosa 0.1� 0.1 1.0� 0.551 Silo piceus 0.6�0.352 Cheumatopsyche lepida 0.6�0.253 Hydropsyche angustipennis 0.7� 0.454 Hydropsyche contubernalis 0.2�0.155 Hydropsyche incognita 8.7�5.1 0.1� 0.156 Hydropsyche pellucidula 8.2�5.2 2.8� 1.7 11.4� 4.657 Hydropsyche pellucidula/incognita – juv. 16.6�7.858 Hydropsyche saxonica 5.8� 1.6 37.7� 12.4 14.8� 6.2 5.2� 1.459 Hydropsyche sp – juv. 1.9� 1.960 Hydroptila angulata 0.4�0.461 Hydroptila forcipata 0.2�0.1 0.1� 0.162 Hydroptila martini 0.6� 0.4 1.5� 1.563 Hydroptila sparsa 0.2�0.2 0.1� 0.164 Hydroptila sp. 5.3� 2.4 50.1�26.7 2.9� 2.8 10.1� 4.0 1.6� 0.865 Orthotrichia sp. 1.1�0.5 0.1� 0.166 Lepidostoma hirtum 17.7�8.2 2.1� 0.9 0.2� 0.167 Adicella reducta 0.1� 0.168 Athripsodes albifrons 0.3� 0.369 Athripsodes aterrimus 0.1� 0.1 0.1� 0.170 Athripsodes cinereus 4.1�1.6 0.8� 0.6 1.0� 0.771 Ceraclea dissimilis 1.4�1.172 Leptocerus interruptus 4.2� 2.473 Mystacides azurea 17.7�11.7 1.6� 0.8 1.3� 0.774 Mystacides nigra 0.1� 0.1 11.1�9.8 0.2� 0.2 0.4� 0.275 Oecetis notata 0.3�0.276 Oecetis testacea 0.3�0.2 0.1� 0.177 Setodes bulgaricus 0.6�0.678 Setodes punctatus 8.3�2.279 Anabolia furcata 1.1� 0.3 0.2� 0.1 1.9� 0.9 1.0� 0.680 Chaetopteryx fusca 4.0� 2.0 8.5� 4.7 0.5� 0.481 Glyphotaelius pellucidus 0.1� 0.1 0.3� 0.2 0.5� 0.5 0.1� 0.183 Halesus digitatus/tesselatus 0.5� 0.3 2.8� 1.3 2.2� 1.184 Hydatophylax infumatus 0.2� 0.185 Limnephilus lunatus 0.1� 0.1 1.4� 0.886 Limnephilus rhombicus 0.7�0.3 0.1� 0.1 1.5� 1.1 0.1� 0.187 Limnephilinae sp.-juv. 14.0� 5.5 0.2�0.2 13.9� 9.6 6.6� 2.7 4.9� 2.688 Potamophylax cingulatus 0.5� 0.2 0.2� 0.289 Potamophylax rotundipenis 0.2� 0.290 Odontocerum albicorne 1.6� 0.6 33.5� 9.1 0.2� 0.191 Wormaldia subnigra 0.1�0.1 2.4� 1.3 0.6� 0.3 2.5� 1.992 Phryganea grandis 0.1� 0.193 Cyrnus trimaculatus 2.0� 0.6 0.2� 0.294 Plectrocnemia conspersa 1.3� 0.4 1.7� 0.595 Polycentropus flavomaculatus 0.8� 0.4 0.2� 0.296 Polycentropus irroratus 0.9� 0.8 0.8� 0.497 Lype phaeopa 0.1� 0.198 Lype reducta 1.3� 0.7 0.2�0.1 6.5� 1.3 0.7� 0.3 0.9� 0.399 Psychomyia pusilla 0.6�0.2 0.1� 0.1 0.4� 0.2

(continued)



Appendix B. (Continued)

Code Taxon name

Sampling site


100 Tinodes dives 2.6� 0.7101 Tinodes pallidulus 0.3� 0.2102 Tinodes sp. 0.5� 0.3103 Rhyacophila aurata 0.1�0.1104 Rhyacophila dorsalis 11.8� 3.0 0.2� 0.2105 Rhyacophila dorsalis/palmeni 5.1�2.3106 Rhyacophila fasciata 16.5� 4.9 0.2�0.2 3.5� 1.0107 Rhyacophila fasciata/aurata 0.6�0.4108 Rhyacophila sp. sensu stricto 0.3�0.3 0.1� 0.1109 Notidobia ciliaris 0.3� 0.2 2.2� 1.2 1.9� 1.6110 Sericostoma sp. 0.2� 0.1 0.4�0.2 3.5� 1.3

E 392.4� 185.0 272.9�111.2 836.8� 251.1 268.3� 60.9 541.8� 252.4P 126.3� 38.8 105.9�43.7 86.5� 27.4 32.5� 9.7 41.7� 11.0T 57.2� 9.5 600.8�263.9 110.9� 17.3 79.5� 11.6 48.8� 10.7EPT 575.9� 221.1 979.6�240.4 1034.2� 237.1 380.3� 58.4 632.2� 254.3



aquatic insects along environmental gradients in a karst river system: a comparative analysis of ept...

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