WORLD’S LARGE RIVERS CONFERENCE

Hydromorphological degradation impact on benthicinvertebrates in large rivers in Slovenia

Gorazd Urbanic

Received: 31 January 2012 / Accepted: 8 December 2012

� Springer Science+Business Media Dordrecht 2012

Abstract Large rivers are amongst the most

degraded ecosystems. We studied a relationship

between hydromorphological degradation and benthic

invertebrates in large rivers in Slovenia. Five indices

of the Slovenian hydromorphological assessment

methodology were used to develop a HM stressor

gradient. Natural type-specific habitat diversity was

considered in the hydromorphological stressor gradi-

ent building and thus two hydromorphological types of

large rivers were defined. CCA ordination with five

HM indices and 315 benthic invertebrate taxa revealed

variations in taxa response along the HM stressor

gradient. First CCA axis species values were used to

develop a taxon-specific river fauna value (Rfi),

whereas tolerance values (biplot scaling) were used

to determine a hydromorphological indicative weight

(HWi). Rfi, HWi, and log5 abundance classes were

combined using weighted average approach to con-

struct a River fauna index for large rivers (RFIVR).

Several additional benthic invertebrate-based metrics

were also tested against the HQM. A Slovenian

multimetric index for assessing the hydromorpholog-

ical impact on benthic invertebrates in large rivers

(SMEIHVR) was constructed from the RFIVR and a

functional metric %akal ? lithal ? psammal taxa

(scored taxa = 100%). The strong relationship

between hydromorphological stressor gradient and

SMEIHVR index provides us with an effective assess-

ment system and river management tool.

Keywords Hydromorphology � SIHM � Benthic

invertebrates � Large rivers � Bioassessment �Ecological status � Boundary setting

Introduction

Most large rivers have a long history of human use.

They have served as transportation routes, sources of

food, water and power, objects of artistic and meta-

physical interest, and as sinks for waste products

(Johnson et al., 1995). In Europe, river works, although

primitive, started several centuries ago and today, most

European large rivers are channelized and highly

fragmented by dams and only few catchments have

free-flowing large rivers (Tockner et al., 2008). River

hydromorphological degradation is one of the main

threats to the ecological integrity of large rivers in

Electronic supplementary material The online version ofthis article (doi:10.1007/s10750-012-1430-4) containssupplementary material, which is available to authorized users.

Guest editors: H. Habersack, S. Muhar & H. Waidbacher /

Impact of human activities on biodiversity of large rivers

G. Urbanic (&)

Institute for Water of the Republic of Slovenia,

1000 Ljubljana, Slovenia

e-mail: gorazd.urbanic@izvrs.si

G. Urbanic

Department of Biology, University of Ljubljana,

Biotechnical Faculty, 1000 Ljubljana, Slovenia

123

Hydrobiologia

DOI 10.1007/s10750-012-1430-4

Europe and several methods for evaluating the hydro-

morphological characteristics and quality of rivers

have been developed (Muhar et al., 1996, 1998; Raven

et al., 1998; LAWA, 2000; Fleischhacker & Kern,

2002; Pedersen & Baattrup-Pedersen, 2003; Tavzes &

Urbanic, 2009).The RHS method was developed in the

UK (Raven et al., 1998) and tested in several European

countries (Erba et al., 2006; Szoszkiewicz et al., 2006;

Tavzes et al., 2006). Buffagni & Kemp (2002) used an

extended version of this method, to provide them with a

better description of the hydro-morphological com-

plexity of southern European rivers. Tavzes & Urbanic

(2009) modified the RHS method by giving weights to

recorded features and developed a Slovenian hydro-

morphological (SIHM) assessment methodology with

five hydromorphological indices based on the RHS

survey and/or hydrological modifications caused by

impoundments. Accordingly, in the Hydromorpholog-

ical quality and modification (HQM) index a combi-

nation of morphological and hydrological parameters

was applied.

In 2000, the European Union established its

framework for the protection and more integrative

river basin management of all water bodies in Europe

by launching the Water Framework Directive (WFD)

(EU, 2000). A key objective was to achieve at least a

‘‘good’’ ecological status/potential by 2015 for all

surface water bodies. Assessment of the ecological

quality status of water bodies is primarily based on

biological quality elements with supporting physico-

chemical and hydromorphological elements. One such

biological quality element in rivers is the benthic

invertebrates. Benthic invertebrates have been used to

monitor environmental conditions in streams and

rivers for over 100 years (Cairns & Pratt, 1993). Their

ubiquity, long aquatic phases and taxonomic diversity

and sensitivity to different environmental stress

remain a key component of bio-assessment pro-

grammes (Hellawell, 1977; Furse et al., 2006). In

Europe, several benthic invertebrate-based systems

exist (Birk et al., 2010) but most have been developed

using a ‘‘general approach’’ to address the effects of

stressors (Hering et al., 2006a). Especially for large

rivers, the focus has been on detecting the impact of

physico-chemical pressure (pollution), while hydro-

morphological impact is rarely addressed. In some

cases, due to a limited pressure gradient pressure–

impact relationship was not possible to test and in

some cases assumptions were made based on smaller

rivers (Scholl et al., 2005; Sporka et al., 2009; Gabriels

et al., 2010).

There are two main multimetric index development

approaches: a general approach and a stressor-specific

approach. Deciding between them depends on the aim,

pressure data availability and sensitivity of the biological

component to different stressors. It is known that benthic

invertebrates differ in sensitivity to different stressors

(Chessman & McEvoy, 1998; Yuan, 2004; Hering et al.,

2006b), so it is possible to have stressor-specific taxa

indicative values (Yuan, 2004). The utility of a stressor-

specific assessment system with a good pressure–impact

relationship will greatly enhance the river management.

However, any action that aims to improve the ecosystem,

e.g., restoration, mitigation, pollution enforcement, is not

inherently determined by the index value, but may be

deduced from the metrics (Barbour et al., 1999).

Hydromorphological alteration is one of the most

significant stressors affecting stream biota (Raven

et al., 2002; Feld, 2004; Lorenz et al., 2004; Ofenbock

et al., 2004; Tavzes & Urbanic, 2009) and a need exists

to assess the impact of this pressure in order to develop

the responsible and integrated management of water

bodies based on sound assessment methods. Albeit

benthic invertebrates can be used to monitor the

hydromorphological stressor in large rivers (Hering

et al., 2006b; Doledec & Statzner, 2008) the relation-

ships existing between the biota and hydromorpho-

logical alterations are poorly defined.

In this study, we aim to (i) to define hydromorpho-

logical types of large rivers in Slovenia, (ii) investigate

large river benthic invertebrate assemblages in rela-

tion to hydromorphological stressor variables, (iii)

assess the relationship between hydromorphological

alterations and benthic invertebrate metrics, and (iv) to

develop an ecological status assessment and classifi-

cation method for large Slovenian rivers.

Methods

Study area

Slovenia covers a total area of 20,273 km2 and features

4,573 km of rivers with catchments larger than 10 km2.

The rivers extend over four ecoregions: Po lowland

(ER3), Alps (ER4), Dinaric Western Balkan (ER5), and

Pannonian lowland (ER11) (Illies, 1978; Urbanic,

2008a) (Fig. 1). According to the WFD typology (EU,

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2000) there are eight large and very large rivers with a

catchment area[1,000 and[10,000 km2, respectively,

known as the Drava, Mura, Sava, Kolpa, Krka, Savinja,

Ljubljanica, and the Soca (Urbanic, 2008b, 2011;

OGRS, 2009). With the exception of the Soca, which

belongs to the Adriatic Sea catchment, the rivers belong

to the Danube River catchment. At sampled river

stretches mean annual discharges ranged from approx-

imately 40–300 m3/s (Uhan & Bat, 2003). Samples

were collected from sites with a catchment area between

1,000 and 15,000 km2 at an altitude between 52 and

364 m a.s.l. (Table 1). Most sampled sites are in high

and good ecological status and few in moderate status

according to the module organic pollution and eutro-

phication according to the Slovenian ecological assess-

ment system (Urbanic, 2011, Cvitanic et al., 2012).

Benthic invertebrates

Biological data were obtained as part of the monitoring

and assessment system development programmes in

Slovenia (Urbanic et al., 2008; Cvitanic et al., 2008).

Benthic invertebrates were collected in the period

between 2005 and 2009 during low to medium

discharge; between June and September. An exception

was made for the Drava and Mura rivers, which on

account of their natural hydrological regime were

sampled in the winter (December–February). The

sampling procedure followed the standardized Slove-

nian river bioassessment protocol and is given in detail

elsewhere (Urbanic et al., 2006; OGRS, 2009; Pavlin

et al., 2011). Samples were collected in the wadeable

part of the main channel or in the littoral zone of the

impoundments to the depth of 1 m using hand net. On

each occasion at every site twenty sub-sampling units

with a total sampling area of 1.25 m2 were taken along

a 100–250 m river stretch in proportion to the coverage

of the microhabitat types (Urbanic et al., 2005).

Microhabitat types were defined as a combination of

substrate and flow type with at least 5% coverage

(Appendix 1 in supplementary material). The channel

substrate of each sampling site were classified

Fig. 1 Study area with ecoregions (numbers) and large rivers with sampling locations (dots)

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according to AQEM Consortium (2002), whereas flow

characteristics according to Environmental Agency

(2003) and Urbanic et al. (2005) (Appendix 1 in

supplementary material). Subsamples were combined

prior to processing. Each sample was sub-sampled, and

the benthic organisms from a quarter of the whole field

sample were identified and enumerated (Petkovska &

Urbanic, 2009). Benthic invertebrates were identified

to the taxonomic level used for the assessment of

ecological river status in Slovenia (OGRS, 2009),

usually species and genus (Appendix 2 in supplemen-

tary material). In total, 337 samples were collected at

82 sites (Fig. 1). Most sites were sampled several times

during one period but not more than once per year.

Hydromorphological pressure gradient

The hydromorphological (HM) pressure gradient was

defined according to the Slovenian hydromorpholog-

ical (SIHM) assessment methodology (Tavzes &

Urbanic, 2009) with some updates in the calculation

of the Hydrological modification index (HLM). First, a

RHS survey (Raven et al., 1998) was performed and

the position of impoundments and sampling sites was

defined and finally the data were used to calculate five

hydromorphological indices following the slightly

modified procedure laid down by Tavzes & Urbanic

(2009) (Appendices 3–6 in supplementary material).

River habitat quality (RHQ) index and River habitat

modification (RHM) index were calculated using RHS

survey data (Fig. 2). An RHS survey was performed at

the same 82 sampling sites for the benthic inverte-

brates (Fig. 1). Surveys were conducted only once in

the study period. The selected sampling sites covered

slightly to highly distorted conditions that reflect the

various levels of perturbance caused by hydromor-

phological alteration (Table 2). However, no site was

pristine. Calculation of the hydrological modification

index (HLM) depends on the position of the sampling

site relative to the impoundment. Two main types of

sites are recognised (i) sites downstream of the

impoundment and (ii) sites within an impoundment

(Appendix 3 in supplementary material). Sites outside

the impoundment and with no upstream impoundment

have HLM = 1. Tavzes & Urbanic (2009) considered

only large impoundments, whereas in our study small

and medium-sized impoundments were considered as

well (Appendix 4 in supplementary material). At each

sampling site downstream the barrier a HLM index is

calculated considering the distance between the

upstream barrier and the sampling site (HLMmc) and

all tributaries between the upstream barrier and the

sampling site (HLMt) (Fig. 3). The influence of each

tributary depends on the catchment size class relative

to the main channel (Appendix 3 in supplementary

material). Five catchment size classes are considered

in the calculation of the HLM index, \10, 10–100,

100–1000, 1000–2500, [2500 km2 or with a mean

annual discharge [50 m3/s. According to Tavzes &

Urbanic (2009) sites within a large impoundment have

Table 1 Main characteristics of sampled rivers with distribution of sampling sites and samples

River HM type Eco-HM type Catchment size range (km2) Altitude range (m a.s.l.) No. sites (samples)

Savinja Simple channel Inter-mountain 1,037–1,842 231–190 5 (20)

Krka Simple channel Lowland-deep 1,110–2,346 164–141 12 (50)

Kolpa Simple channel Lowland-deep 1,170–2,002 175–127 5 (18)

Ljubljanica Simple channel Lowland-deep 1,300–1,544 283–282 2 (6)

Ljubljanica Complex channel Lowland-braided 1,704–1,842 281–262 6 (22)

Soca Complex channel Alpine 1,347–1,573 112–52 5 (20)

Sava Complex channel Alpine 1,198–2,287 364–265 7 (26)

Sava Simple channel Inter-mountain 4,774–7,655 247–152 8 (40)

Sava Complex channel Lowland-braided 7,781–12,786 140–130 4 (16)

Mura Complex channel Lowland-braided 9,774–10,833 243–158 10 (33)

Drava Simple channel Inter-mountain 11,720–13,090 338–252 8 (42)

Drava Complex channel Lowland-braided 13,181–15,079 236–178 10 (44)

Total 1,037–15,079 364–52 82 (337)

HM hydromorphological

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HLM = 0. In order to assess the influence within all

impoundments we developed new criteria and also

take into account the upstream distance from the

barrier and calculated HLM index within an impound-

ment (HLMimp) (Appendix 5 in supplementary mate-

rial). Hydromorphological modification (HMM) index

and Hydromorphological quality and modification

(HQM) index were calculated combining RHM,

HLM, and/or RHQ index (Fig. 2). In order to calculate

HMM and HQM indices values of RHQ and RHM

were converted to a common scale of 0–1 inclusive

(normalized), where 0 indicates the lowest (most

degraded) and 1 the highest value (pristine). Normal-

isation was done according to the Eq. 1:

Value ¼ Indexvalue� Loweranchor

Reference value� Lower anchorð1Þ

The reference value of the RHM index equals 0

indicating no morphological modifications (Tavzes &

Urbanic, 2009), whereas the lower anchor is defined as

the 95th percentile of all RHM values. The RHQ

values show high variability at the least impacted sites

(RHM B 5). Accordingly, type-specific reference

RHQ values were defined and used. This value is

defined as the maximum observed value within a

defined hydromorphological type. For both RHQ types

the lower anchor is defined using the 5th percentile of

all the RHQ values.

Fig. 2 Flowchart of the analytical procedure. HM hydromor-

phological, RHS river habitat survey, RHQ river habitat quality

index, RHM river habitat modification index, HLM hydrological

modification index, HMM hydromorphological modification

index, HQM hydromorphological quality and modification

index, CCA canonical correspondence analysis, RFI river fauna

index, Rs Spearman’s rank correlation coefficient, SMEIHslovenian multimetric index for assessing the hydromorpholog-

ical impact on benthic invertebrates, VR large rivers

Fig. 3 Shematic view with the necessary information to

calculate the hydrological modification index (HLM) at the

sampling site marked as 1. di distance from the impoundment, lilength of the impoundment. HLMt = HLM value of the

tributary at the confluence with the main channel.

HLMmc = HLM value of the main channel at the sampling site

marked as 1

Table 2 Median, minimum, and maximum values of hydro-

morphological (HM) indices of the Slovenian hydromorpho-

logical (SIHM) system

HM index RHQ RHM HLM nRHQ nRHM HMM HQM

Median 210 30 0.68 0.67 0.73 0.65 0.66

Minimum 46 0 0.00 0.00 0.00 0.00 0.00

Maximum 327 141 1.00 1.00 1.00 0.99 0.95

n normalised

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Development of an benthic invertebrate-based

ecological classification system for large rivers

in Slovenia

Definition of the large river hydromorphological types

In Slovenia, ecological types of large rivers are

defined by a combination of ecoregion, catchment

size, summer water temperature, and river channel

characteristics evaluated using different biological

quality elements (Urbanic, 2008b, 2011). However,

this study uses only four key eco-hydromorphological

types of large rivers; alpine, inter-mountain, lowland-

braided (including anabranching) and lowland-deep

(Table 1). Differences in RHQ index values among

types were tested using the Mann–Whitney U-test;

based on these results the RHQ-based hydromorpho-

logical types were defined.

Development of the river fauna index for large rivers

(RFIVR)

A canonical correspondence analysis (CCA) was

performed using all benthic invertebrate taxa from

each development dataset (170 samples) and five

hydromorphological (HM_SI) indices (Fig. 3). CCA

was applied using the CANOCO 4.5 software package

(Ter Braak & Smilauer, 2002); log (y ? 1) transfor-

mation of benthic invertebrate abundance data was

selected. The CCA is useful because it explains taxa

variability and shows important patterns of variation

in the benthic invertebrate assemblage composition as

accounted for by the hydromorphological variables.

Furthermore, the overall importance of each SIHM

index in explaining the species–habitat relationship

was tested. To determine the relative importance of the

variables, the ‘‘forward step-wise selection’’ during

the CCA (Ter Braak, 1986) was used. This process

tested the individual effects of each of the environ-

mental variables (marginal effects) and the effect that

each variable has in addition to other selected

variables (conditional effects) (Leps & Smilauer,

2003). To assess deviation from a randomly generated

distribution, a Monte Carlo estimation (999 unre-

stricted permutations) was applied to the ‘‘forward

selection’’ procedure (Leps & Smilauer, 2003). The

RFIVR was developed using hydromorphological

preferences (river fauna (Rfi) values) and tolerance

(hydromorphological indicative weights (HWi)) of

taxa along the first CCA axes. River fauna (Rfi) values

were then determined using CCA ordination axis 1

species scores (biplot scaling):

Rfi ¼SC CCA1i

SC CCA1max

ð2Þ

where SC_CCA1i is the CCA ordination axis 1 species

score (biplot scaling) of the ith taxon and SC_CCA1-

max is the absolute maximum value of the CCA

ordination axis 1 species score (biplot scaling).

Hydromorphological indicative weights (HWi) were

determined using the CCA ordination axis 1 species

tolerance (root mean squared deviation for species)

according to Table 3.

The River fauna index for large rivers (RFIVR) was

calculated according to the following equation:

RFIVRj¼Pn

i¼1 aci� Rfi � HWiPn

i¼1 aci � HMið3Þ

where aci is the log5 abundance class of the ith taxon,

Rfi is the river fauna value of the ith taxon, HWi is the

hydromorphological indicative weight of the ith taxon

and n is the number of indicative taxa. Abundance

classes were defined according to the Table 4.

Creating the Slovenian multimetric index

for assessment of hydromorphological alteration

impact on benthic invertebrates in large rivers

(SMEIHVR)

We assigned approximately half of the dataset (170

samples) to a development (training) dataset and the

rest to a validation (test) dataset (167 samples).

Splitting the data in half was a conservative approach

as we did not want to reduce the power of our

validation tests. We calculated more than 200 biolog-

ical metrics using the ASTERICS 3.1.1 software

package (ASTERICS, 2006) (Fig. 3). Sensitivity/

Table 3 Determination of the hydromorphological indicative

weight (HMi) from the CCA axis 1 species tolerance (root

mean-squared deviation for species)

Tolerance (ti) HMi

ti \ 0.2 5

0.2 \ ti \ 0.4 4

0.4 \ ti \ 0.6 3

0.6 \ ti \ 0.8 2

ti [ 0.8 1

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tolerance metrics (Hering et al., 2004) were eliminated

immediately since the newly developed RFIVR already

belongs to that group. In addition, also eliminated

were inappropriate composition/abundance, richness/

diversity, and functional metrics for example in cases

where no indicator taxa are present and metrics

regarded as irrelevant for use in large river assess-

ments. The association between each remaining

benthic invertebrate metric and the hydromorpholog-

ical pressure gradient expressed as the HQM index

was examined using Spearman rank correlation coef-

ficients (RSp). Correlations were calculated using a

development dataset. At first, only one metric per

metric group was to be included in the multimetric

index, but we found that using development dataset

only sensitivity/tolerance and functional metrics

respond adequately to the hydromorphological pres-

sure gradient (RSp [ 0.5). Therefore, a final expres-

sion of the Slovenian multimetric index for assessment

of the hydromorphological alteration impact on ben-

thic invertebrates in large rivers (SMEIHVR) is as

follows:

SMEIHVRi¼ 2 � RFIVRi

þ%ALPð100%Þ3

ð4Þ

where RFIVR is the river fauna index of large rivers

and %ALP (100%) is percentage of akal, lithal, and

psammal preferences (scored taxa = 100%). Both

metrics were normalised prior to calculating the

SMEIHVR. The lower anchor of %ALP (100%) and

RFIVR was defined as the 95th and 5th percentile,

respectively, of the metric value of the fifth HQM

class. The reference values of both metrics were

calculated as best observed values increased by 5%.

We also examined the strength of the relationship

between SMEIHVR and the hydromorphological

pressure gradient (HQM index) using a Pearson

correlation coefficient. Calculations were made

separately for the development and the validation data

set. Correlation coefficients and regression analyses

were performed using SPSS 17 (SPSS Inc, 2008).

Boundary setting of five ecological status classes

The SMEIHVR boundary values corresponding to the

five classes of the ecological status were determined

(EU, 2000). The reference value (ecological quality

ratio = 1) and the lower boundary are based on the

reference value and the lower boundary of both

metrics used for the SMEIHVR and were not redefined

for the multimetric index. Other boundary values were

defined based on the response of sensitive and tolerant

taxa along the ecological status gradient using a paired

metrics approach: (1) for each sampling site we

calculated the portion of the ‘‘sensitive’’ taxa (Rfi \ 0)

and the portion of the ‘‘tolerant’’ taxa (Rfi [ 0) of

RFIVR, (2) we then established the relation between

both ‘‘sensitive’’ taxa and ‘‘tolerant’’ taxa portions and

the SMEIHVR, and (3) the boundary values between

five ecological status classes were defined based on the

changes in the ratio between the ‘‘sensitive’’ taxa and

‘‘tolerant’’ taxa. Four boundary values were set where

characteristic shifts in the community were observed

along the gradient:

(a) High/good boundary was defined where the

portion of tolerant taxa begins to increase

(tolerant \ sensitive).

(b) Good/moderate boundary was defined where the

portion of tolerant taxa reach the portion of

sensitive taxa (tolerant & sensitive).

(c) Moderate/poor boundary was defined where the

portion of tolerant taxa exceeds the portion of

sensitive taxa (tolerant [ sensitive).

(d) Poor/bad boundary was defined where portion of

tolerant taxa start to dominate (tolerant �sensitive).

Results

Typology

Differences are observable in the river habitat qual-

ity (RHQ) index values between the four eco-

hydromorphological large river types (Kruskal–Wallis

v2 test = 68.1, P \ 0.0001, Fig. 4). However, there

Table 4 Abundance transformation of benthic invertebrates

used in the River fauna index calculation

Abundance Abundance

class (aci)

1–5 1

6–25 2

26–125 3

126–625 4

[625 5

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are no differences between the RHQ values of alpine

and lowland-braided (Mann–Whitney U = 2208,

P = 0.108) and inter-mountain and lowland-deep

(Mann–Whitney U = 3258, P = 0.105). Two hydro-

morphological types can be defined on the basis of

RHQ index values: simple channel rivers and complex

channel rivers (Mann–Whitney U test, U = 7444,

P \ 0.001, Fig. 5). A higher RHQ reference value was

obtained for a complex channel (327) than for a simple

channel (237), whereas the lower anchor was equiv-

alent (116) (Table 5). The same reference value (0)

and lower anchor (112) for both hydromorphological

river types were defined for the river habitat modifi-

cation (RHM) index.

River fauna index

The CCA of the five hydromorphological (HM)

indices and 170 benthic invertebrate samples with

315 taxa explains 8.2% of the taxa variability (Fig. 6).

This relatively low explained variability is in part a

result of the diverse natural river conditions (several

river types) a fact reflected in the assemblages

included in the analyses. Nevertheless, HM indices

show good explanatory power (Table 6). The mar-

ginal effects of the HM indices vary between 0.18

(HQM index and HLM index) and 0.10 (RHM index).

In general, indices that combine morphological and

hydrological modifications and/or habitat characteris-

tics (HQM index, HMM index) and an index that

reflects hydrological alterations (HLM index) show a

higher marginal effect (0.16–0.18) than those indices

reflecting either morphological alteration or habitat

characteristics (RHM and RHQ index) (marginal

effect = 0.10–0.11). Forward selection procedure

reveals that conditional effects of the HM indices

varies between 0.07 and 0.18, and each statistically

significantly (P \ 0.001) variable explains the indi-

vidual share of the taxa variability. Most HM indices

have good explanatory power along the first CCA axis

which represents changes in the hydromorphological

stressor gradient (Fig. 6). River fauna values (Rfi) and

hydromorphological indicative weights (HWi) were

defined for 315 benthic invertebrate taxa (Appendix 2

in supplementary material). Rfi values theoretically

can range from ?1 (heavily altered conditions) to -1

(pristine conditions), but the lowest observed Rfi value

was -0.61 (Fig. 7). The highest number of benthic

taxa indicates moderately to slightly altered conditions

with Rfi values between ?0.2 and -0.2. More than a

quarter of the taxa have a very good indicator power

(HWi = 5) (Fig. 7), while half have either a low

(HWi = 2) or very low (HWi = 1) indicative power,

suggesting slightly to heavily altered sites. Although

several taxa have low indication power, many do show

changes in their abundance along the HM stressor

gradient and abundance also contributes to the RFIVR

index. Low indication power taxa that show changes in

the abundance along the pressure gradient are also

good indicators.

A combination of all three parameters in the RFIVR

reveals a statistically significant response of the newly

Fig. 4 Boxplots of river habitat quality (RHQ) index values of

four eco-hydromorphological river types with the results of the

Kruskal–Wallis v2 test

Fig. 5 Boxplots of river habitat quality (RHQ) index values of

two hydromorphological river types with the results of the

Mann–Whitney U test

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123

developed index to the HQM index, with a high

coefficient of determination (Fig. 8). Such a response

was expected using the development dataset

(R2 = 0.70), since Rfi and HWi values were derived

from the taxa distribution along the first CCA axes,

which correlates highly (Pearson r [ 0.9) with the

HQM index. The validation data set also confirms this

good relationship (R2 = 0.67). Theoretically, the

range of RFIVR values is the same as that of the river

fauna indicative values, i.e., -1 to ?1. However, in

our study RFIVR values for the least disturbed sites

were around -0.2, whereas for heavily degraded sites

they were between 0.2 and 0.4.

Slovenian multimetric index for assessment

of the hydromorphological impact on benthic

invertebrates in large rivers (SMEIHVR)

Richness/diversity (r/d) and composition/abundance

(c/a) metrics show at most a modest correlation

(Spearman rho \ 0.50) to the HQM index (Table 7).

The number of EPTCBO taxa and the percentage of

EPT taxa using abundance classes are the best metrics

of the r/d and c/a group, respectively. Generally,

richness metrics performed better than diversity met-

rics since the best diversity metric (Margalef diversity

index, Spearman rho = 0.32, P \ 0.01) was not

present amongst the best five r/d metrics. The Margalef

diversity index was also the only tested diversity metric

that shows a statistically significant (P \ 0.05)

response to the hydromorphological pressure gradient.

Several functional metrics representing different char-

acteristics for example substrate, habitat, locomotion

type and feeding type, show at least a good relationship

(Spearman rho [ 0.5) to the HQM index (Table 7).

Besides the newly developed RFIVR for the sensitive/

tolerance group only one functional metric was

selected to construct a Slovenian multimetric index

for assessment of the hydromorphological impact on

benthic invertebrates in large rivers (SMEIHVR). The

percentage of passive filter feeders was a best func-

tional metric (Spearman rho = 0.64, P \ 0.001) but

was inappropriate because of the narrow range of

values that are reflected in the relatively low coefficient

of determination (R2 \ 0.15). Thus, we chose the

second best functional metric, i.e., % Akal ? Li-

thal ? Psammal (scored taxa = 100%) (Spearman

Fig. 6 CCA ordination diagram with 315 benthic invertebrate

taxa (open triangles) and five SIHM hydromorphological

indices (arrows)

Table 6 Marginal (Lambda 1) and conditional (Lambda A)

effects of the hydromorphological indices, P value and F value

Variable Lambda 1 Lambda A P F

HQM 0.18 0.18 0.001 5.14

HLM 0.18 0.08 0.001 2.30

HMM 0.16 0.08 0.001 2.24

nRHQ 0.11 0.09 0.001 2.60

nRHM 0.10 0.07 0.001 2.11

n normalised

Table 5 Hydromorphological river type-specific reference values and lower anchors of river habitat quality (RHQ) index and river

habitat modification (RHM) index

Hydromorphological river type RHQ index RHM index

Reference value Lower anchor Reference value Lower anchor

Simple channel rivers 237 116 0 112

Complex channel rivers 327 116 0 112

Hydrobiologia

123

rho = 0.60, P \ 0.001), which reveals a wide range of

data along the hydromorphological stressor gradient

and good coefficient of determination (R2 = 0.38).

Table 8 gives the reference value and the lower anchor

of both metrics used in the SMEIHVR. The SMEIHVR

show a statistically significant response to the HQM

index, with a high coefficient of determination using

development (R2 = 0.72) and validation data set

(R2 = 0.68) (Fig. 9).

Boundary values of five ecological status classes

were defined based on the changes in the portion of

sensitive and tolerant taxa (Fig. 10; Table 9). The

portion of sensitive taxa decreases from high to bad

ecological status, whereas the portion of tolerant taxa

increases. The portion of tolerant taxa begins to

increase (high/good boundary) at EQR = 0.86,

whereas at EQR = 0.64 the portion of tolerant taxa

reach the portion of sensitive taxa (good/moderate

boundary). The intersection of regression curves

representing portion of tolerant and portion of sensi-

tive taxa occurred approximately at the EQR = 0.5

and at the EQR = 0.38 the portion of tolerant taxa

exceeds the portion of sensitive taxa (moderate/poor

boundary). The portion of tolerant taxa start to

dominate at EQR = 0.10 (poor/bad boundary). To

adjust the SMEIHVR boundary values to boundary

Fig. 7 Frequency distribution of river fauna values (Rfi) (a) and hydromorphological indicative weights (HWi) (b) of large river

benthic invertebrate taxa

Fig. 8 Regression plots of HQM index versus river fauna index (RFI_VR) using a development data set (a) and validation data set (b)

Hydrobiologia

123

values of other benthic invertebrate-based indices,

already developed in Slovenia, they were transformed

using following equations:

SMEIHVR SMEIHVR_transformed

C0.86 0.8 ? 0.2*(SMEIHVR-0.86)/(1.00–0.86)

0.64–0.85 0.6 ? 0.2*(SMEIHVR-0.64)/(0.86–0.64)

0.38–0.63 0.4 ? 0.2*(SMEIHVR-0.38)/(0.64–0.38)

0.10–0.37 0.2 ? 0.2*(SMEIHVR-0.10)/(0.38–0.10)

\0.10 0.2*(SMEIHVR)/(0.10)

Table 9 shows the five ecological status classes with

equal distances between upper and lower class

boundary value were obtained.

Discussion

Stressor gradient

River habitat alterations associated with upstream and

downstream barriers and catchment land use influence

river conditions and consequently impact aquatic com-

munities (Giller & Malmqvist, 1998; Allan & Castillo,

2007). Certain authors emphasize that the stressor

gradient applied in pressure-impact studies is a simpli-

fication of the true stressor gradient to which biota are

exposed (Angradi et al., 2009; Orlando-Bonaca et al.,

2012). Our hydromorphological stressor gradient

encompasses the diversity and integrity of habitat

structure (RHQ index), artificial features (RHM index)

and upstream barriers (HLM index). The habitat

structure and its diversity is also dependent on natural

conditions. Tavzes & Urbanic (2009) found differences

in the RHQ values of the reference sites between certain

ecological types of the Ecoregion Alps and defined two

RHQ-based hydromorphological types. In this study,

using a RHQ data from at the least disturbed large river

sites, significant differences were observed between

river types with a simple channel (non-braided rivers;

straight and meandering) and a complex channel

(braided rivers; including anabranching). Thus, natural

habitat structure and diversity need to be incorporated

when constructing a hydromorphological pressure gra-

dient. Wang et al., (2008) criticises the fact that stressors

are often treated as additive and equal in importance. In

the HQM index, importance of the morphological and

hydrological parameter depends on the difference in the

assessment class, and the parameter that assesses the

worse class is given higher weight. Suitability of the HM

indices of the SIHM system for large rivers was verified

Table 7 Spearman’s rank correlation coefficient for each pair

of the selected metrics and the habitat quality and modification

(HQM) index

Metric Metric

group

HQM

index

% Passive filter feeders f 0.64**

% Type Akal ? Lithal ? Psammal

(scored taxa = 100%)

f 0.60**

% Type RP (scored taxa = 100%) f 0.56**

% Type RP f 0.55**

% Type RP (abundance classes)

(scored taxa = 100%)

f 0.55**

% Littoral (scored taxa = 100%) f -0.55**

Active filter feeders/passive filter

feeders (all taxa)

f -0.53**

% Type Akal (scored taxa = 100%) f 0.52**

EPT % (abundance classes) c/a 0.45**

Trichoptera abundance c/a 0.43**

EPT taxa % (Austria) c/a 0.40**

Ephemeroptera abundance c/a 0.40**

Coleoptera abundance c/a 0.40**

Number of EPTCBO (Ephemeroptera,

Plecoptera, Trichoptera, Coleoptera,

Bivalvia, Odonata) taxa

r/d 0.45**

Number of EPT (Ephemeroptera,

Plecoptera, Trichoptera) taxa

r/d 0.43**

Number of Elmidae taxa r/d 0.43**

Number of Trichoptera taxa r/d 0.43**

Number of Ephemeroptera taxa r/d 0.42**

Number of taxa r/d 0.36**

Margalef diversity index r/d 0.32**

Shannon–Wiener diversity index r/d 0.13

Simpson diversity index r/d 0.07

Evenness r/d -0.04

c/a compositional/abundance, f functional, r/d richness/

diversity, RP locomotion type rheophil taxa

** P \ 0.01

Table 8 Reference values and lower boundaries of metrics

building SMEIHVR

Metric Metric type Reference

value

Lower

boundary

RFIVR Sensitive/tolerance -0.27 0.28

%Akal ? Lithal ?

Psammal (sum 100%)

Functional 100 9

Hydrobiologia

123

with the CCA. All five indices explain taxa variability.

The HQM index, which represents a combination of

different hydromorphological conditions, explains the

highest portion of taxa variability. Thus, in our opinion,

the selected stressor gradient represents a reliable

reflection of human-induced hydromorphological alter-

ations on large rivers in Slovenia.

Relation of taxa to HM gradient and RFIVR

Several developed benthic invertebrate indices are

based on tolerances or preferences for specific,

measurable stressor (Armitage et al., 1983; Hilsenhoff,

1987; Davy-Bowker et al., 2005; Smith et al., 2007)

but rarely for hydromorphological stressor (Chessman

& McEvoy, 1998; Lorenz et al., 2004). The SIGNAL-

DAM index (Chessman & McEvoy, 1998) assesses the

effect of an upstream dam, but the authors complain

that the index performs poorly. The lack of diagnostic

power was explained by the differences in the flow

regimes downstream of the dams. In our study, the

RFIVR index derives from the distribution of taxa

along the first CCA axes, which has a strong corre-

lation to the HLM index that represents a stressor

Fig. 9 Regression plots of HQM index versus Slovenian multimetric index for assessing the hydromorphological impact on benthic

invertebrates in large rivers (SMEIHVR) using a benthic invertebrate development data set (a) and validation data set (b)

Fig. 10 Boundary setting

between ecological status

classes using changes in

portion of sensitive and

tolerant taxa along the

ecological quality ratio of

the SMEIHVR index

Hydrobiologia

123

gradient due to upstream barriers. The HLM index was

also one of the hydromorphological indices explaining

the highest portion of benthic invertebrate taxa

distribution (Table 6). Besides sites below the barriers

we also included other sites within the impounding

area upstream of the barrier. Sites within the large

impoundments show clear deviation in the benthic

invertebrate assemblages from the least disturbed

sites. Lorenz et al. (2004) developed the German

Fauna index (GFI) by assessing the impact of alter-

ations in stream morphology. Several type-specific

GFIs were developed for small and medium-sized

rivers with a catchment area of up to 1,000 km2,

whereas we developed a RFIVR index based on large

river data with a catchment area between 1,000 and

15,000 km2 (Table 1).

Taxa tolerance-based indices or indices with taxa

preferences for a specific stressor usually use one to

three taxon characteristics; indicative value, indicative

weight, and abundance. Indices using all three

parameters include the most complete taxon informa-

tion (Saprobien index, Zelinka & Marvan, 1961).

However, indices that address hydromorphological

impact on the benthic invertebrates often use only an

indicative value; e.g., the SIGNAL-DAM index,

which is based on the BMWP (Chessman & McEvoy,

1998) or indicative value and abundance; e.g., the

GFIs (Lorenz et al., 2004). We include in the RFIVR all

three characteristics, but found that several taxa occur

along the whole hydromorphological stressor gradient

and have therefore a low hydromorphological indic-

ative weight (HWi). Our findings are in accordance

with Orlando-Bonaca et al. (2012), who studied the

relation of hydromorphological variables to the taxo-

nomic structure of the littoral benthic invertebrates in

coastal waters. They also found that several taxa with

low HM indicative weight but observe differences in

the abundance along the HM stressor gradient. Feld &

Hering (2007) report that sensitive taxa, e.g., Epheme-

roptera, Plecoptera, and Trichoptera, decreased with

environmental (hydromorphological and land use)

stress, whereas many tolerant taxa like Oligochaeta,

Chironomidae, and Gastropoda do not and are present

even under natural conditions from which they

conclude that degradation mainly causes a loss of

sensitive taxa rather than a community shift from

sensitive to tolerant organisms. Our results only

partially confirm this conclusion. First of all, amongst

the more sensitive groups we find several tolerant taxa,

for instance Trichoptera and Ephemeroptera (Appen-

dix 2 in supplementary material). Second, several taxa

were only found at degraded sites and we cannot

confirm that there is no shift in the community along

the hydromorphological stressor gradient.

Relation of metrics to HM gradient

Several metrics from all metric groups show a

statistical significant correlation to the HM stressor

gradient. However, besides the newly developed

RFIVR that confirms the very good relationship not

only with the development dataset but also with the

validation dataset, only certain functional metrics

correlate well (RSp [ 0.5) with the HQM index. It

seems that functional and sensitivity/tolerance metrics

are less sensitive to natural and bio-geographical

factors affecting communities. Statzner et al. (2005)

find that in large European rivers functional commu-

nity attributes may be more consistent than taxonomic

composition, a fact confirmed for medium-sized

Central and West-European lowland rivers (Feld &

Hering, 2007). In terms of typology, our data do not

provide a perfectly comparable set of large rivers

(Urbanic, 2008b, 2011) and from a habitat diversity

point of view large rivers can be divided into two

groups. Thus, relatively poor correlations between

richness/diversity metrics and composition/abundance

metrics with HM gradient were expected. Townsend

& Hildrew (1994) suggest that the first response to

habitat degradation is a reduction in species richness,

whereas Kerans & Karr (1994) conclude that total

taxon richness declines with increasing variety of

anthropogenic stresses. Taxa richness shows a statis-

tical significant (P \ 0.01) negative response to

increased HM degradation as expected but the number

Table 9 Boundary values and transformed boundary values of

the SMEIHVR for the five classes of the ecological status

Boundary SMEIHVR SMEIHVR_

transformed

Reference value 1 1

Boundary high/good status 0.86 0.8

Boundary good/moderate status 0.64 0.6

Boundary moderate/poor status 0.38 0.4

Boundary poor/bad status 0.10 0.2

Lower anchor 0 0

Hydrobiologia

123

of sensitive taxa groups (e.g., number of EPTCBO and

EPT taxa,) and their abundance (e.g., percentage of

EPT, Trichoptera, Ephemeroptera) were better pre-

dictors of HM alterations. However, contradictory

information is available at least regarding the response

of the EPT abundance to the HM stressor. Tavzes et al.

(2006) who conducted a study of a small urban stream

reported that the percentage of EPT increases along

the HM pressure gradient. Metrics that respond

differently amongst reaches were observed along the

great rivers (Angradi et al., 2009). The conclusion was

that metrics that varied in their response along the

river are not necessarily based on the same taxa. Thus,

we expect that splitting a data set in perfectly

comparable sets of large rivers correlation coefficients

of richness/diversity metrics and composition/abun-

dance metrics with the HM stressor gradient would be

higher. However, due to the relatively low number of

large rivers in Slovenia it can represent a river reach-

specific assessment system. Such approach has

already been applied to the great rivers in the USA

but longer river reaches were considered (Angradi

et al., 2009).

SMEIHVR

WFD (EC, 2000) requires that multiple attributes of

river communities (taxonomic composition and abun-

dance, ratio of disturbance sensitive to insensitive

taxa, and the level of diversity) are integrated into an

assessment of river conditions. Multimetric indices are

a common solution. Some authors for example Karr &

Chu (1999) think that these multimetric indices should

ensure that each metric type is represented by a similar

number of metrics. However, we think that at least in

stressor-specific systems a lower number of metrics

that respond well to the stressor gradient is a better

solution than using several metrics from all the metric

groups that show only a modest response. A combi-

nation of the HM stressor-specific RFIVR of the

sensitivity/tolerance group and a functional metric

related to microhabitats (%ALP100%) is in our

opinion a good compromise between fulfilling the

requirements of the WFD and the quality of the

information gained by the index. Knowing the cause of

the possible degradation is crucial to mitigating stress

and to rehabilitate and maintain the biological and

functional integrity of a river. In order to fulfil the

requirements of the WFD, it is necessary to have

several stressor-specific systems that enable the

assessment of the overall ecological quality and all

systems together need to cover all required specific

aspects of the benthic invertebrate community and not

each of them.

Rivers are often affected by multiple stressors, e.g.,

organic pollution, acidification, eutrophication, and

hydromorphological alterations. Any stressor-specific

bioassessment system must have a good pressure–

impact relationship, but even more important is that it

shows a stronger response to the addressed stressor

than to other stressors. Chessman & McEvoy (1998)

found that their SIGNAL-DAM index performed

poorly on both criteria, whereas Lorenz et al. (2004)

found their performance was poorer in respect to the

second criteria since the GFI was sensitive to organic

pollution—Saprobien index (R2 = 0.55). We also find

a statistically significant relationship between

SMEIHVR and the Slovenian Saprobien index (SIG3)

(Urbanic et al., 2006; Urbanic, 2011) but the correla-

tion is much lower (R2 = 0.26) (Fig. 11). Moreover,

almost 90% of our samples have a good status

according to the module ‘‘organic pollution’’ of the

Slovenian national assessment system (Urbanic, 2011)

and several of those less than good according to the

SMEIHVR. We can conclude that SMEIHVR is an

effective stressor-specific tool for assessing the hy-

dromorphological impact on benthic invertebrates in

large rivers in Slovenia.

The ecological status class boundary setting proce-

dure is one of the most crucial steps in the develop-

ment of the WFD compliant assessment systems.

Achieving good ecological status is one of the main

objectives of the WFD (EU, 2000) and national river

basin management plans and therefore setting good

status class boundary values is essential. Different

approaches have been taken in Europe (Birk et al.,

2010), and rarely ecologically based boundary setting

unlike in this study (Lyche Solheim et al., 2008; Kuhar

et al., 2011). In our opinion, changes in the portion of

sensitive and tolerant taxa along the multimetric index

gradient has proven to be a useful boundary setting

approach. It can be used in any system where indices

are based on tolerances or preferences for specific,

measurable stressor, which enables a comparable

boundary setting independent of the water category,

type, and biological community and also when no

reference sites are available as was the case in this

study of large rivers.

Hydrobiologia

123

Conclusions

A hydromorphological stressor gradient was con-

structed using morphological diversity and alterations

together with hydrological alterations. This proved to

be the best possible combination as the highest

percentage of benthic invertebrate taxa variability

was explained using the HQM index. Natural type-

specific habitat diversity was considered in the

hydromorphological stressor gradient building and

thus two hydromorphological types of large rivers

were defined: simple channel and complex channel.

We also find that benthic invertebrate taxa are well

distributed along the hydromorphological alteration

gradient in large rivers, which allows the development

of an RFIVR index. Several authors suggest that along

the HM gradient only sensitive taxa are disappearing.

In our study shifts in the assemblages were observed.

In contrast to taxa distribution and the developed

sensitivity/tolerance RFIVR index, richness/diversity,

and composition/abundance metrics were less strongly

related to the hydromorphological stressor gradient.

This is an indicator of the natural variability in large

rivers. Functional metrics did perform well and show a

good relationship to the hydromorphological stressor

gradient, which allows a multimetric index to be built

based on reliable metrics. Boundary setting is a crucial

step in the development of any classification systems.

To set boundaries where shifts in communities occur,

an ecological approach is preferable. In our opinion,

setting boundaries according to changes in the ratio

between sensitive and tolerant taxa that occur along

the multimetric response is one such approach,

especially when indices are based on tolerances or

preferences for a specific stressor. A stressor-specific

approach is commonly used in river quality assess-

ment systems to accurately assess the effect of a

specific stressor. However, assessment systems

addressing a hydromorphological stressor often show

a significant and very good response to other stressors

like organic pollution (Lorenz et al., 2004). We

observed only a minor response to organic pollution

using SMEIHVR. Taking both stressor-specific

approach and a stronger response to the addressed

stressor than to other stressors is crucial to developing

an effective assessment system and decision support

tool for environmental managers.

Acknowledgments The author gratefully acknowledges the

members of the project team for their assistance both in the field

and in the laboratory, Vesna Petkovska who performed the RHS

surveys, David. Heath for his valuable comments and for

correcting the English and two anonymous reviewers for

providing comments that improved the paper. This study was

supported by the Ministry of the Environment and Spatial

Planning of the Republic of Slovenia as a part of the national

program for the implementation of the EU Water Framework

Directive.

References

Allan, J. D. & M. M. Castillo, 2007. Stream Ecology: Structure

and Function of Running Waters, 2nd ed. Springer, New

York: 436 pp.

Angradi, T. R., M. S. Pearson, T. M. Jicha, D. L. Taylor, D.

W. Bolgrien, M. F. Moffett, K. A. Blocksom & B. H. Hill,

2009. Using stressor gradients to determine reference

expectations for great river fish assemblages. Ecological

Indicators 9: 748–764.

Armitage, P. D., D. Moss, J. F. Wright & M. T. Furse, 1983. The

performance of a new biological water quality score system

based on macroinvertebrates over a wide range of unpol-

luted running-water sites. Water Research 17: 333–347.

AQEM Consortium, 2002. Manual for the application of the

AQEM system. A comprehensive method to assess Euro-

pean streams using benthic macroinvertebrates, developed

for the purpose of the Water Framework Directive. Version

1, February 2002: 198 pp.

ASTERICS, Version 3.01., 2006. Deutsches Bewertungssyste-

mauf Grundlage des Makrozoobenthos.Software-Handbuch

fur die deutsche Version. http://www.fliessgewaesser-

bewertung.de/downloads/asterics_handbuch_3_0.pdf.

Barbour, M. T., J. Gerritsen, B. D. Snyder & J. B. Stribling,

1999. Rapid Bioassessment Protocols for Use in Streams

Fig. 11 Regression plots Slovenian multimetric index for

assessing the hydromorphological impact on benthic inverte-

brates in large rivers (SMEIH_VR) against Slovenian saprobic

index (SIG3) using a benthic invertebrate development data set

Hydrobiologia

123

and Wadeable Rivers: Periphyton, Benthic Macroinverte-

brates and Fish, 2nd edn. U.S. EPA. Office of Water,

Washington, DC. EPA/841-B-98-010.

Birk, S., J. Strackbein & D. Hering, 2010. WISER methods

database. Version: March 2011. http://www.wiser.eu/

programme-and-results/data-and-guidelines/method-database/.

Buffagni, A. & J. L. Kemp, 2002. Looking beyond the shores of

the United Kingdom: addenda for the application of river

habitat survey in South European Rivers. Journal of Lim-

nology 61(2): 199–214.

Cairns, J. & J. R. Pratt, 1993. A history of biological monitoring

using benthic macroinvertebrates. In Rosenberg, D. M. &

V. H. Resh (eds), Freshwater Biomonitoring and Benthic

Macroinvertebrates. Chapman and Hall, New York: 10–27.

Chessman, B. C. & P. K. McEvoy, 1998. Towards diagnostic

biotic indices for river macroinvertebrates. Hydrobiologia

364: 169–182.

Cvitanic, I., E. Sodja, M. Dobnikar Tehovnik, S. Ambrozic, J.

Grbovic, B. Jesenovec, A. Kolenc, et al., 2008. River water

quality monitoring in Slovenia report 2006 Ljubljana:

Environmental Agency of the Republic of Slovenia,

Ljubljana, Slovenia: 121 pp (in Slovene). [http://www.

arso.gov.si/vode/reke/publikacije%20in%20poro%C4%8Dila/

Porocilo_reke_2006.pdf].

Cvitanic, I., B. Jesenovec, B. Rotar, & M. Sever, 2012. Quality

status assessment of the rivers in Slovenia in years 2009

and 2010. Environmental Agency of the Republic of

Slovenia, Ljubljana, Slovenia: 40 pp (in Slovene). [http://

http://www.arso.gov.si/vode/reke/

publikacije%20in%20poro%C4%8Dila/

REKE%20porocilo%202009-2010.pdf].

Davy-Bowker, J., J. F. Murphy, G. R. Rutt, J. E. C. Steel & M.

T. Furse, 2005. The development and testing of a macro-

invertebrate biotic index for detecting the impact of acidity

on streams. Archiv fur Hydrobiologie 163: 383–403.

Doledec, S. & B. Statzner, 2008. Invertebrate traits for the

biomonitoring of large European rivers: an assessment of

specific types of human impact. Freshwater Biology 55:

617–634.

Erba, S., A. Buffagni, N. Holmes, M. O’Hare, S. Scarlett & A.

Stenico, 2006. Preliminary testing of river habitat survey

features for the aims of the WFD hydro-morphological

assessment: an overview from the STAR Project. Hydro-

biologia 566: 281–296.

EU, 2000. Directive 2000/60/EC of the European Parliament

and of the Council establishing a framework for the

Community action in the field of water policy—EU Water

Framework Directive (WFD).

Feld, C. K., 2004. Identification and measure of hydromorpho-

logical degradation in Central European lowland streams.

Hydrobiologia 516: 69–90.

Feld, C. K. & D. Hering, 2007. Community structure or func-

tion: effects of environmental stress on benthic macroin-

vertebrates at different spatial scales. Freshwater Biology

52: 1380–1399.

Fleischhacker, T. & K. Kern, 2002. Ecomorphological survey of

large rivers. German Federal Institute of Hydrology,

Postfach, Koblenz, pp. 1–41.

Furse, M., D. Hering, O. Moog, P. Verdonschot, R. K. Johnson, K.

Brabec, K. Gritzalis, A. Buffagni, P. Pinto, N. Friberg, J.

Murray-Bligh, J. Kokes, R. Alber, P. Usseglio-Polatera, P.

Haase, R. Sweeting, B. Bis, K. Szoszkiewicz, H. Soszka, G.

Springe, F. Sporka & I. Krno, 2006. The STAR project: con-

text, objectives and approaches. Hydrobiologia 566: 3–29.

Gabriels, W., K. Lock, N. De Pauw & P. L. M. Goethals, 2010.

Multimetric macroinvertebrate index flanders (MMIF) for

biological assessment of rivers and lakes in Flanders

(Belgium). Limnologica: Ecology and Management of

Inland Waters 40: 199–207.

Giller, P. S. & B. Malmqvist, 1998. The Biology of Streams and

Rivers. Biology of Habitats. Oxford University Press, New

York.

Hellawell, J. M., 1977. Change in natural and managed eco-

systems: detection, measurement and assessment. Pro-

ceedings of the Royal Society of London. Series B:

Biological Sciences 197: 31–35.

Hering, D., O. Moog, L. Sandin & P. F. M. Verdonschot, 2004.

Overview and application of the AQEM assessment sys-

tem. Hydrobiologia 516: 1–20.

Hering, D., C. K. Feld, O. Moog & T. Ofenbock, 2006a. Cook

book for the development of a multimetric index for bio-

logical condition of aquatic ecosystems: experiences from

the European AQEM and STAR projects and related ini-

tiatives. Hydrobiologia 566: 311–324.

Hering, D., R. K. Johnson & A. Buffagni, 2006b. Linking

organism groups—major results and conclusions from the

STAR project. Hydrobiologia 566: 109–113.

Hilsenhoff, W. L., 1987. An improved biotic index of organic

stream pollution. Great Lakes Entomologist 20: 31–39.

Illies, J., 1978. Limnofauna Europaea, 2nd ed. Gustav Fischer

Verlag, Stuttgart, New York.Johnson, B. L., W. B. Richardson & T. J. Naimo, 1995. Past,

present, and future concepts in large river ecology. Bio-

Science 45: 134–141.

Karr, J. R. & E. W. Chu, 1999. Restoring Life in Running

Waters: Better Biological Monitoring. Island Press,

Washington, DC.

Kerans, B. L. & J. R. Karr, 1994. A benthic index of biotic

integrity (B-IBI) for rivers of the Tennessee Valley. Eco-

logical Application 4: 768–785.

Kuhar, U., M. Germ, A. Gaberseik & G. Urbanie, 2011.

Development of a river macrophyte index (RMI) for

assessing river ecological status. Limnologica: Ecology

and Management of Inland Waters 41: 235–243.

LAWA, 2000. Gewasserstrukturgutekartierung in der Bundes-

republik Deutschland—Verfahren fur kleine und mit-

telgroße Fließgewasser. Kulturbuch-Verlag, Berlin.

Leps, J. & P. Smilauer, 2003. Multivariate Analysis of Eco-

logical Data using CANOCO. Cambridge University Press,

Cambridge.

Lorenz, A., C. K. Feld & D. Hering, 2004. Typology of streams in

Germany based on benthic invertebrates: ecoregions, zona-

tion, geology and substrate. Limnologica 389: 379–389.

Lyche Solheim, A. L., S. Rekolainen, S. J. Moe, L. Carvalho, G.

Phillips, R. Ptacnik, W. E. Penning, L. G. Toth, C. O’Toole,

A. K. L. Schartau & T. Hesthagen, 2008. Ecological

threshold responses in European lakes and their applica-

bility for the Water Framework Directive (WFD) imple-

mentation: synthesis of lakes results from the REBECCA

project. Aquatic Ecology 42: 317–334.

Muhar, S., M. Kainz, M. Kaufmann & M. Schwarz, 1996. Aus-

weisungflusstypspezifisch erhaltener Fliessgewasserabschnitte

Hydrobiologia

123

in Osterreich. Osterreichische Bundesgewasser, BMLF, Was-

serwirtschaftskataster, Wien.

Muhar, S., M. Kainz & M. Schwarz, 1998. Ausweisung-

flusstypspezifisch erhaltener Fliessgewasserabschnitte in

Osterreich Fliessgewasser mit einem Einzugsgebiet

[500 km2 ohne Bundesflusse. BMLF, BMUJF, Wasser-

wirtschaftskataster, Wien.

Ofenbock, T., O. Moog, J. Gerritsen & M. Barbour, 2004. A

stressor specific multimetric approach for monitoring

running waters in Austria using benthic macro-inverte-

brates. Hydrobiologia 516: 251–268.

OGRS, 2009. Rules on Monitoring of Surface Water. Pravilnik o

monitoringu stanja povrsinskih voda. Official Gazette of

the Republic of Slovenia 10: 832-839 (in Slovene). [

http://www.uradni-list.si/_pdf/2009/Ur/u2009010.pdf].

Orlando-Bonaca, M., B. Mavric & G. Urbanic, 2012. Devel-

opment of a new index for the assessment of hydromor-

phological alterations of the Mediterranean rocky shore.

Ecological Indicators 12: 26–36.

Pavlin, M., S. Birk, D. Hering & G. Urbanic, 2011. The role of

land use, nutrients, and other stressors in shaping benthic

invertebrate assemblages in Slovenian rivers. Hydrobio-

logia 678: 137–153.

Pedersen, M. L. & A. Baattrup-Pedersen, 2003. Økologisk

overvagning i vandløb og pa vandløbsnære arealer under

NOVANA 2004–2009. Teknisk Anvisning fra DMU nr.

21. National Environmental Research Institute (in Danish).

Petkovska, V. & G. Urbanic, 2009. Effect of fixed-fraction

subsampling on macroinvertebrate bioassessment of rivers.

Environmental Monitoring and Assessment 169: 179–201.

Raven, P. J., N. T. H. Holmes, F. H. Dawson, P. J. A. Fox, M.

Everard, I. R. Fozzard & K. J. Rouen, 1998. River habitat

quality the physical character of rivers and streams in the

UK and Isle of Man. (River Habitat survey Report No. 2.).

Environment Agency.

Raven, P. J., N. T. H. Holmes, P. Charrier, F. H. Dawson, M. Naura

& P. J. Boon, 2002. Towards a harmonised approach for hy-

dromorphological assessment of rivers in Europe: a qualitative

comparison of three survey methods. Aquatic Conservation:

Marine and Freshwater Ecosystems 12: 405–424.

Scholl, F., A. Haybach & B. Konig, 2005. Das erweiterte Pot-

amontypieverfahren zur okologischen Bewertung von

Bundeswasserstraßen (Fließgewassertypen 10 und 20:

kies- und sandgepragte Strome, Qualitatskomponente

Makrozoobenthos) nach Maßgabe der EU-Wasserrah-

menrichtlinie. Hydrologie & Wasserbewirtschaftung 49:

234–247.

Smith, A. J., R. W. Bode & G. S. Kleppel, 2007. A nutrient biotic

index (NBI) for use with benthic macroinvertebrate com-

munities. Ecological Indicators 7: 371–386.

Sporka, F., Z. Pastuchova, L. Hamerlık, M. Dobiasova & P.

Beracko, 2009. Assessment of running waters (Slovakia)

using benthic macroinvertebrates—derivation of ecologi-

cal quality classes with respect to altitudinal gradients.

Biologia 64: 1196–1205.

SPSS Inc, 2008. SPSS Base 17.0 for Windows User’s Guide.

Chicago, IL, USA.

Statzner, B., B. Bis, S. Doledec & F. Scholl, 2005. Invertebrate

traits for the biomonitoring of large European rivers: an

initial assessment of trait patterns in least impacted river

reaches. Freshwater Biology 50: 2136–2161.

Szoszkiewicz, K., A. Buffagni, J. Davy-Bowker, J. Lesny, B.

H. Chojnicki, J. Zbierska, R. Staniszewski & T. Zgola,

2006. Occurrence and variability of river habitat survey

features across Europe and the consequences for data col-

lection and evaluation. Hydrobiologia 566: 267–280.

Tavzes, B. & G. Urbanic, 2009. New indices for assessment of

hydromorphological alteration of rivers and their evalua-

tion with benthic invertebrate communities: Alpine case

study. Review of hydrobiology 2: 133–161.

Tavzes, B., G. Urbanic & M. J. Toman, 2006. Biological and

hydromorphological integrity of the small urban stream.

Physics and Chemistry of the Earth 31: 1062–1074.

Ter Braak, C. J. F., 1986. Canonical correspondence analysis: a

new eigenvector technique for multivariate direct gradient

analysis. Ecology 67: 1176–1179.

Ter Braak, C. J. F. & P. Smilauer, 2002. CANOCO Reference

Manual and CanoDraw for Windows User’s Guide: Soft-

ware for Canonical Community Ordination (Version 4.5).

Microcomputer Power, Ithaca, NY.

Tockner, K., U. Uehlinger, C. T. Robinson, D. Tonolla, R. Siber

& F. D. Peter, 2008. Introduction to European rivers. In

Tockner, K., C. T. Robinson & U. Uehlinger (eds), Rivers

of Europe. Academic Press, London: 1–21.

Townsend, C. R. & A. G. Hildrew, 1994. Species traits in

relation to a habitat templet for river systems. Freshwater

Biology 31: 265–275.

Uhan, J. & M. Bat, (eds.) 2003. Vodno bogastvo Slovenije. MOP-

Agencija Republike Slovenije za okolje, Ljubljana, p 131.

Urbanic, G., 2008a. Redelineation of European inland water

ecoregions in Slovenia. Review of Hydrobiology 1: 17–25.

Urbanic, G., 2008b. Inland water subecoregions and bioregions

of Slovenia. Natura Sloveniae 10: 5–19.

Urbanic, G., 2011. Ecological status assessment of rivers in

Slovenia—an overview. Natura Sloveniae 13: 5–16.

Urbanic, G., S. Ambrozic, M. J. Toman, B. Rotar & J. Grbovic,

2006. Adaptation of the Saprobic index in accordance with

the Water Framework Directive for assessment of eco-

logical status of rivers in Slovenia based on Benthic

invertebrates. Ljubljana: University of Ljubljana, Bio-

technical Faculty, Department of Biology (in Slovene).

Urbanic, G., S. Podgornik, M. Germ, G. Kosi, A. Gaberscik, S.

Remec-Rekar, K. Plibersek, et al., 2008. Sampling and

analyses of new biological data (phytobenthos, benthic

invertebrates, macrophytes) for the completing and

development of ecological status assessment methodolo-

gies. Report 2008. Institute for Water of the Republic of

Slovenia, Ljubljana (in Slovene).

Urbanic, G., M. J. Toman & C. Krusnik, 2005. Microhabitat

type selection of caddisfly larvae (Insecta: Trichoptera) in a

shallow lowland stream. Hydrobiologia 541: 1–12.

Wang, L., T. Brenden, P. Seelbach, A. Cooper, D. Allan, J.

R. Clark & M. Wiley, 2008. Landscape based identification

of human disturbance gradients and reference conditions

for Michigan streams. Environental Monitoring and

Assessment 141: 1–17.

Yuan, L. L., 2004. Assigning macroinvertebrate tolerance

classifications using generalised additive models. Fresh-

water Biology 49: 662–677.

Zelinka, M. & P. Marvan, 1961. Zur Prazisierung der biolog-

ischen Klassifikation der Reinheit fließender Gewasser.

Archiv fur Hydrobiologie 57: 389–407.

Hydrobiologia

123