PRIMARY RESEARCH PAPER

The role of land use, nutrients, and other stressorsin shaping benthic invertebrate assemblages in Slovenianrivers

Maja Pavlin • Sebastian Birk • Daniel Hering •

Gorazd Urbanic

Received: 8 April 2011 / Revised: 14 July 2011 / Accepted: 23 July 2011 / Published online: 7 August 2011

� Springer Science+Business Media B.V. 2011

Abstract Studying human impacts on riverine sys-

tems is challenging due to the natural diversity and

multiple stressors that are co-occurring. The term

stressor(s) refers to variable(s) of anthropogenic

landscape changes and local abiotic stream conditions

that reflect human activities. We compared the effects

of different stressors and natural (typology) factors on

benthic invertebrate assemblages in Slovenian rivers.

A total of 270 river sites located in four European

ecoregions were sampled, covering the gradient of

human perturbation ranging from (near)natural to

highly degraded/polluted. Stressors commonly affect-

ing running waters were grouped into management-

relevant stressor-groups: land use (percentage of

urban, intensive and non-intensive agricultural, and

natural land use in the catchment and sub-catchment),

eutrophication (in-stream concentrations of phospho-

rus and nitrogen compounds) and other stressors (e.g.,

organic pollution, hydromorphological alteration). We

compared the effects of the stressor-groups on inver-

tebrate assemblages using partial Canonical Corre-

spondence Analysis and tested the explanatory power

of each stressor-group using typology variables as

covariates. Stressors and typology variables showed

high independent effects (40–65%) on invertebrate

assemblages despite considerable joint effects from

land use and typology. Stressors’ pure effects

accounted for 70%, and interactions among the

stressor-groups made up the remaining 30% of the

total explained variability. Altogether, this study gives

a good perspective on the ability to separate the effects

of anthropogenic stressors from natural causes, as well

as to separate effects among groups of stressors.

Against this background, we advocate stressor-specific

analyses to provide or improve guidelines for selecting

appropriate measures to enhance the river status.

Keywords Benthic invertebrates � Variance

partitioning � Multiple stressors � Organic pollution �Eutrophication � Land use

Introduction

Running waters are some of the most degraded

ecosystems on earth (Giller & Malmqvist, 1998).

Handling editor: P. Noges

Electronic supplementary material The online version ofthis article (doi:10.1007/s10750-011-0836-8) containssupplementary material, which is available to authorized users.

M. Pavlin (&) � G. Urbanic

Institute for Water of the Republic of Slovenia,

1000 Ljubljana, Slovenia

e-mail: maja.pavlin@izvrs.si

S. Birk � D. Hering

Department of Applied Zoology/Hydrobiology,

University of Duisburg-Essen, 45117 Essen, Germany

G. Urbanic

Biotechnical Faculty, Department of Biology, University

of Ljubljana, 1000 Ljubljana, Slovenia

123

Hydrobiologia (2011) 678:137–153

DOI 10.1007/s10750-011-0836-8

This is a consequence of streams being strongly

influenced not just by local factors but also by the

landscape through which they flow (Hynes, 1975;

Vannote et al., 1980). The extent of landscape

transformation is staggering, causing a global transi-

tion from undisturbed to human-dominated land-

scapes (Meyer & Turner, 1994). Aquatic ecosystem

conditions are, thus generally, a result of a blend of

natural and anthropogenic factors. In river manage-

ment, the impact of human factors is usually evalu-

ated by bioassessment systems that take into account

natural variability by using stream typologies (e.g.

Lorenz et al., 2004). Typologies often incorporate a

conceptual framework of spatially nested controlling

factors in which large scale physical factors like

climate, geology and topography influence the geo-

morphic processes that shape channels at intermedi-

ate scales and thereby create and maintain habitats

important to the biota at smaller scales (Allen &

Starr, 1982; Frissell et al., 1986; Snelder & Biggs,

2002).

Research focus in river ecology has recently

shifted from identifying those forces that determine

spatial patterns in community composition (e.g.

Townsend et al., 2003; Sandin & Johnson, 2004;

Begon et al., 2006; Johnson et al., 2007) towards

management-oriented studies of multiple stressor

effects (e.g. Ormerod et al., 2010; Fausch et al.,

2010; Bunn et al., 2010). In most cases, running

waters are simultaneously affected by multiple

stressors that interact with each other and are closely

related across spatial scales (Roth et al., 1996; Snyder

et al., 2003; Townsend et al., 2003; Buck et al., 2004;

Sandin & Johnson, 2004; Johnson et al., 2007;

Johnson et al., 2009). The effects of various degra-

dation including nutrient enrichment, organic pollu-

tion, hydromorphological alteration, and the influence

of anthropogenic land use on river condition are

extensively discussed in the literature (e.g. Vollenwe-

ider, 1968; Sherwood, 1989; Newson & Newson,

2000; Barendregt & Bio, 2003; Nijboer & Verdons-

chot, 2004; Allan, 2004; Yates & Bailey, 2006;

Hilton et al., 2006; European Commission, 2009;

Aldridge et al., 2009; Tavzes & Urbanic, 2009).

Despite this, the spatio-temporal relevance of indi-

vidual stressor-groups for determining river status is

poorly understood but it is of major importance in

effective ecosystem management (Bunn et al., 2010).

Our understanding of the nature of multiple-stressor

effects on species populations, communities and

ecosystems is limited and the investigation of these

effects remains a major challenge in river manage-

ment (Ormerod et al., 2010).

To determine the actions necessary for improving

the status of water ecosystems both natural and the

effects of stressors on biological communities will

need to be identified. Trying to distinguish the effects

of stressors and natural factors, authors often restrain

their dose–response studies to ‘‘stream types’’ (e.g.

Sliva & Williams, 2001; see Yates & Bailey, 2010).

The restriction of a study to a stream type, however,

strongly limits the selection of sampling points and

thus the reliability of the stated relationship. Further-

more, no general patterns can be derived by type-

specific studies. Alternatively, natural variables can

be used as covariables when studying the effects of

anthropogenic stress on the biota. Variance partition-

ing by (partially) constrained ordination represents a

common approach in which community composition

is analysed in response to several sets of explanatory

variables (Borcard et al., 1992; Økland, 2003). Using

this approach, authors have studied the effects either

at different spatial scales (e.g. Sandin & Johnson,

2004) or using the impact of various stressors (land

use, e.g., Niyogi et al., 2007; hydromorphological

alteration and land use, e.g., Feld & Hering, 2007).

Surprisingly, few studies in running waters have

focused on variance partitioning among stressor-

groups (e.g. agriculture at different spatial scales: Pan

et al., 2004). To our knowledge, this is the first time

that the variability in benthic invertebrate assem-

blages has been partitioned among different groups of

anthropogenic stressors in such a naturally diverse

region like Slovenia using partial Canonical Corre-

spondence Analysis (pCCA).

Herein, we analyse a dataset of 270 river sites

covering four European inland water ecoregions

(Urbanic, 2008a) and the main stressor gradients

present in Slovenia (Urbanic & Toman, 2007). The

term stressor(s) refers to variable(s) of anthropogenic

landscape changes and local abiotic stream condi-

tions that reflect human activities and is used in this

sense throughout this article. We subsume natural

factors not influenced by man-made disturbance

using the term typology. Individual and joint effects

of typology and stressors are identified by pCCA. The

aims of our study are to (i) compare how individual

environmental variables of typology and stressors are

138 Hydrobiologia (2011) 678:137–153

123

related to each other, (ii) test the explanatory power

of each environmental variable for the benthic

invertebrate assemblages, (iii) quantify the distinctive

effects of typology and different stressor-groups, for

example eutrophication and land use, in structuring

stream benthic macroinvertebrate assemblages, and

(iv) test the possibility to discern among the

responses of benthic invertebrate assemblages to

land use, eutrophication, and other stressors.

Materials and methods

Data

Slovenia covers a total area of 20,273 km2. It is

dominated by forest (over 60% coverage), which

ranks it among the most forested countries in Europe.

However, land categories are not evenly distributed

throughout Slovenia (Petek, 2004). Carbonate bed-

rock geology prevails and accounts for over 50% of

the area, while *40% is siliceous and less than 10%

flysch, but high local variability is present. Slovenia

has 4,573 km of rivers within catchments larger than

10 km2. The rivers extend over three main ecoregions

of the country (Alps: ER4, Dinaric Western Balkan:

ER5 and Pannonian lowland: ER11) with an almost

equal share of river length: 35, 30, and 34%,

respectively (Fig. 1). The Po lowland (ER3) accounts

for less than 1% of the area and contributes 1% to the

entire river network (Urbanic, 2008a). In the official

river typology used for bioassessment (OGRS, 2009)

the four ecoregions are subdivided into 16 bioregions

based on the predominant geology, altitude and slope

of the catchment area (Urbanic, 2008b). Within each

bioregion, river types are described by river-size and

additional typological descriptors ‘‘e.g. karst spring,

periodical flooding, intermittency and lake influence

(OGRS, 2009; Urbanic, 2011)’’. River-size is classi-

fied into four categories, determined by catchment

area and mean annual discharge (Table 1).

We selected 270 river sites across all four ecore-

gions: 71 sites in ER4, 106 sites in ER5, 83 sites in

ER11, and 10 sites in ER3 (Fig. 1).The sampling sites

cover (near) natural to highly distorted conditions

reflecting the various levels of perturbance caused by

hydromorphological alteration, pollution and/or

catchment land-use. None of the sites were located

at impounded river sections. A list of sampling sites

is provided in Online Resource 1.

Biological and physico-chemical data were

obtained as part of the monitoring and assessment

system development programmes in Slovenia (e.g.

Urbanic et al., 2008; Cvitanic et al., 2008). Benthic

invertebrates were sampled between 2005 and 2008

during low to medium discharge at each site on a

single occasion. The sampling procedure followed the

standardised Slovenian river bioassessment protocol

(OGRS, 2009). Rivers were generally sampled

between May and October except for the 14 large

rivers that were sampled in winter due to their natural

hydrological regime. On each occasion, twenty sub-

sampling units with a total sampling area of 1.25 m2

were sampled in proportion to the coverage of

microhabitat types. Microhabitat types were defined

as a combination of substrate and flow type with

coverage of at least 5%. Samples then underwent

laboratory sub-sampling procedures, and benthic

organisms from a quarter of the whole field sample

were identified and enumerated (Petkovska & Urba-

nic, 2009). Benthic invertebrates were determined to

the taxonomic level used for the assessment of

ecological river status in Slovenia (OGRS, 2009),

i.e. mostly to species level (Online Resource 2). All

physico-chemical variables were measured between

2005 and 2008 on a single occasion following standard

protocols (OGRS, 2002). This monitoring was carried

out in the same month of the invertebrate sampling and

during similar discharge conditions. Sampling com-

prised on-site measurements of conductivity, pH,

oxygen concentration and saturation, water tempera-

ture and laboratory analysis of nutrient concentrations,

magnesium, potassium, BOD5, chloride, and sulphate

among others. Furthermore, hydromorphological

(HM) alteration in the riverbed was evaluated, and

land use characteristics at the catchment and sub-

catchment scale were obtained. The sub-catchment

areas were defined as parts of the catchment area

upstream of a sampling site to the confluence of an

important tributary. In small rivers, the sub-catchment

could thus account for up to 100% of the total

catchment area, whereas the sub-catchment accounted

for a maximum of 16% in large rivers (see Table 2).

Eight land use variables were calculated on a catch-

ment and sub-catchment scale using Corine Land

Cover (CLC) data (CLC, 2007). We distinguished four

categories of land use: natural and semi-natural areas

Hydrobiologia (2011) 678:137–153 139

123

(CLC classes 3, 4, and 5), urban areas (CLC class 1),

areas characterised by extensive agricultural practices

(CLC categories 2.3.1, 2.4.3, 2.4.4) and areas charac-

terised by intensive agricultural practices (CLC cate-

gories 2.1, 2.2, 2.4.1 and 2.4.2). Altitude and slope

data were calculated from a Digital Elevation Model

with 5 m accuracy. Sampling sites were classified into

five HM alteration classes according to the Slovenian

national classification of hydromorphological modifi-

cations (Table 3, after WMI, 2002) which is based on

longitudinal and lateral riverbed heterogeneity, ero-

sion control and the connectivity with floodplain and

hyporheic zones.

Variable selection

Natural variables were selected from literature data of

the Slovenian river typology (i.e. typology variables;

Table 2). The ecoregion, intermittency and karst

spring influence were coded as dummy variables.

Alkalinity was used as a proxy for the geology (see

Urbanic, 2008a, b). We grouped the stressor variables

into three groups relevant in river management: land

use upstream of a sampling site, eutrophication, and

other possible anthropogenic influences (Table 2).

The intention was to form groups of explanatory

variables that represent the main anthropogenic

stressors affecting river ecosystems (e.g. Leuven &

Poundevigne, 2002; Snyder et al., 2003; Allan, 2004,

Dudgeon et al., 2006; Heathwaite, 2010). The land

use group consisted of four main land use categories

at two spatial scales, with intensive and non-intensive

agricultural practices distinguished. A group of

potential eutrophication variables comprised all

available data on inorganic phosphorus and nitrogen

compounds in the water. The group of other stressors

included parameters of organic pollution (BOD5,

TOC and oxygen saturation), HM alteration class

Fig. 1 Location of the study sites (3 Ecoregion Po lowland, 4 Ecoregion Alps, 5 Ecoregion Dinaric Western Balkan, 11 Ecoregion

Pannonian lowland)

Table 1 Catchment size

classesCatchment size class Catchment size (km2)

1 Small rivers 10–100

2 Medium-sized rivers 100–1,000

3 Medium-sized to large rivers 1,000–2,500 and mean annual discharge\50 m3/s

4 Large rivers [2,500 or mean annual discharge [50 m3/s

140 Hydrobiologia (2011) 678:137–153

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(Table 3) and other indicators of human pollution that

included water concentrations of chloride, sulphate

ions and total suspended solids (Table 2). We chose

chloride and sulphate since their concentrations in

running waters are dominated by human waste water

(Barendregt & Bio, 2003).

Statistical analysis

All environmental variables were checked for outliers.

Two of the initial 272 sampling sites were removed

due to extreme values in environmental variables (e.g.

BOD5 value of 120 mg O2/l; Ptot value of 4,290 lg P/

Table 2 Groups of environmental variables (typology—natu-

ral environmental factors; land use—percentages of land use in

the catchment and sub-catchment; eutrophication—

eutrophication variables, and other—other stressors) with their

Min—minimum, Max—maximum and Average or Median*

(for Size_cl and HM_cl) values

Environmental variable Unit Code Variable group Average/median*

(Min–Max)

Transformation

Slope (%) Slope Typology 7.9 (0.0–89.7) ln(x ? 1)

Ecoregion: Alps As dummy variable ER4 Typology 71

Ecoregion: Pannonian lowland As dummy variable ER11 Typology 83

Catchment size class Classified 1–4 Size_cl Typology 2 (1–4)

Altitude (m.a.s.l.) Alt Typology 290 (1–896) ln(x ? 1)

Ecoregion: Dinaric Western Balkan As dummy variable ER5 Typology 106

Karst spring influence As dummy variable Kspring Typology 47

Ecoregion: Po lowland As dummy variable ER3 Typology 10

Alkalinity (leqv/) Alk Typology 3,616 (400–6,529) ln(x ? 1)

Intermittency As dummy variable Inter Typology 14

Land use-subatchment

Natural and semi-natural (%) NAT_sc Land use 58 (0–100) arcsin(sqrtx)

Intensive agriculture (%) I-AG_sc Land use 22 (0–100) arcsin(sqrtx)

Urban (%) URB_sc Land use 6 (0–90) arcsin(sqrtx)

Non-intensive agriculture (%) NI-AG_sc Land use 13 (0–74) arcsin(sqrtx)

Land use-catchment

Intensive agriculture (%) I-AG_c Land use 15 (0–57) arcsin(sqrtx)

Natural and semi-natural (%) NAT_c Land use 69 (24-100) arcsin(sqrtx)

Urban (%) URB_c Land use 2 (0–41) arcsin(sqrtx)

Non-intensive agriculture (%) NI-AG_c Land use 14 (0–42) arcsin(sqrtx)

Chloride ion (mg/l) Cl Other 8.7 (0.4–130.0) ln(x ? 1)

Total organic carbon (mg/l) TOC Other 2.3 (0.3–23.0) ln(x ? 1)

Sulphate ion (mg SO42-/l) SO4 Other 18.2 (1.5–750.0) ln(x ? 1)

BOD5 (mg O2/l) BOD Other 1.7 (0.2–7.6) ln(x ? 1)

Oxygen saturation (%) DO_Sat Other 97 (8–170) ln(x ? 1)

Hydromorphological alteration class Classified 1–5 HM_cl Other 2 (1–5)

Total suspended solids (mg/l) TSS Other 19.5 (0.4–140.0) ln(x ? 1)

Total phoshorus (lg P/l) Ptot Eutrophication 94 (2–4569) ln(x ? 1)

Orthophosphate (lg PO43-/l) PO4 Eutrophication 154 (2–13169) ln(x ? 1)

Total nitrogen (mg N/l) Ntot Eutrophication 1.6 (0.4–9.1) ln(x ? 1)

Nitrate (mg NO3-/l) NO3 Eutrophication 4.8 (0.1–33.7) ln(x ? 1)

Nitrite (mg NO2-/l) NO2 Eutrophication 0.062 (0.001–2.376) ln(x ? 1)

Ammonium (mg NH4?/l) NH4 Eutrophication 0.124 (0.003–4.693) ln(x ? 1)

For dummy variables number of sites coded as ‘‘1’’ is given

Hydrobiologia (2011) 678:137–153 141

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l) and species composition. An appropriate transfor-

mation was individually selected for each variable to

bring explanatory variables to a uniform scale (Leps

& Smilauer, 2003; Table 2). Environmental variables

that change in a multiplicative way were ln(x ? 1)

transformed; for proportional data an arcsin(sqrt

x) transformation was used (Legendre & Legendre,

1998). Benthic invertebrate data were ln(x ? 1)

transformed. The environmental data structure was

evaluated using Spearman rank correlation coeffi-

cients (RSp), calculated for each pair of environmental

variables using SPSS Statistics version 17.0 (SPSS

Inc., 2008).

Ordination techniques were applied using the

software package CANOCO 4.5 (ter Braak &

Smilauer, 2002); downweighting of rare species

(taxa) was used in all cases. To determine the

compositional gradient length the invertebrate data

were analysed using Detrended Correspondence

Analysis (DCA; Hill & Gauch, 1980) with detrend-

ing by segments and non-linear rescaling. Since

these gradient lengths were greater than two stan-

dard deviations, we assumed unimodal species

responses and, thus, Canonical Correspondence

Analysis (CCA; ter Braak & Prentice, 1988) and

partial Canonical Correspondence Analysis were

applied (Borcard et al., 1992). pCCA is based on

CCA and allows partitioning of the variation in a

species-sample data matrix into different sets of

explanatory variables and their combined effects. As

pCCA allows statistical testing of the quantified

variation, it is widely used in ecological studies

(Økland & Eilertsen, 1994; e.g. Sandin & Johnson,

2004). To gain an overview of the environment-

assemblage relations, a CCA analysis with an

automatic forward selection procedure on all envi-

ronmental variables was applied first (Fig. 2). Prior

to pCCA, we selected variables within each vari-

able-group (typology, land use, eutrophication, and

other stressors). With the intention to avoid over-

estimation of the explained variance (Borcard et al.,

1992; Økland & Eilertsen, 1994), a comparable

procedure of automatic forward selection was

applied over all explanatory groups. All variables

were selected and tested using a Monte Carlo

permutation test (999 unrestricted permutations);

the Bonferroni correction (a = 0.05/n, where n is

the number of tests) accounted for multiple testing.

To evaluate each variable’s exploratory power

before and after forward selection, each variable’s

independent and conditional effects, (k1 and ka,

respectively) were divided by the total assemblage

variability. pCCA was then used to compare the

explanatory power of the different variable groups

driving the faunal assemblages. Faunal variance

components were partitioned into the following

Table 3 Hydromorphological alteration classes (after WMI, 2002)

Hydromorphological alteration class

1 Natural and

semi-natural

Natural and semi- natural streams and rivers with a heterogene riverbed. Riverbed and riverbank erosion

possible. Connection to floodplain not disturbed. Channel-bars can be formed, natural bed load not disturbed

2 Slightly altered River bed heterogeneity in a near-to natural condition, although it can be locally desreased. Riverbed and

riverbank erosion is possible, although it can be locally disturbed due to smaller alterations of only local

influence. Connection with the hyporheic zone is not disturbed. The river-floodplain connectivity is locally

disturbed, but the periodicity of flood events remains unchanged. The river bed erosion is limited, but

channel bars can still be formed

3 Moderately

altered

Riverbed heterogeneity is absent. Objects or mechanisms for riverbed- or riverbank-erosion controlle are

present, however natural materials are used, thus the connection between the river and it’s hyporheic zone is

still possible. The river-floodplain connectivity is locally disturbed, periodicity and duration of flood events

is reduced. River bed and river bank erosion is controlled, and channel bars are rarely formed

4 Severely

altered

No riverbed heterogeneity. Riverbed and riverbank erosion is controlled by rigid objects or mechanisms.

Despite the use of natural materials the rigidity of these structures, disturbes the connection between the river

and it’s hyporheic zone. The river- floodplain connection is disturbed due to urban areas on one or both river-

banks. Flood events are obstructed. River bed and river bank erosion is controlled, although transportation of

material is possible, deposition of the material is controlled or obstructed

5 Extensively

altered

No riverbed heterogeneity. Riverbed and riverbank erosion is controlled by rigid objects or mechanisms, thus

the connection to the hyporheic zone is completely obstructed. The river- floodplain connection is disturbed

due to urban areas on one or both river-banks. Flood events are obstructed. Erosion and deposition process

are obstructed

142 Hydrobiologia (2011) 678:137–153

123

variable groups: (a) each stressor-group and typol-

ogy group; (b) stressor-groups: land use, eutrophi-

cation and other (Fig. 2).

Since the sample size and the number of indepen-

dent variables in the model influence the result of the

pCCA (Kromrey & Hines, 1995), Ezekiel’s adjust-

ment of fractions was calculated using the following

equation (Peres-Neto et al., 2006):

R2ðY=XÞadj ¼ 1� n� 1

n� p� 11� R2

Y=X

� �;

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

and R2Y=X is the sample estimation of the assemblage

variance q2Y=X . However, due to the same number of

samples and a similar number of predictors (between 3

and 7) in all the models, the ratio of explained variance

among the variable groups remained very similar

(regression curve; y = 0.839x - 0.005, r2 = 0.999,

P \ 0.01).

Results

Relationships among the stressor variables

Spearman rank correlation (RSp) resulted in several

statistically significant relationships (P \ 0.05)

between pairs of environmental variables (Table 3).

Fig. 2 Flowchart of the

analytical procedure (i–ivaims of the paper, see

‘‘Introduction’’ section). RSp

Pearson rank correlation

coefficient, CCA canonical

correspondence analysis, FSforward selection, pCCApartial canonical

correspondence analysis

Hydrobiologia (2011) 678:137–153 143

123

Strong correlations (RSp [ 0.70) were observed

mostly between variables within the stressor-groups;

land use variables (intensive agriculture and natural

land use in the catchment), and eutrophication vari-

ables (total nitrogen and nitrate). Strong correlations

were rare among the variables of different stressor-

groups, except for a high negative correlation of

chloride ion concentration and the percentage of

natural areas in the catchment (RSp = -0.71). Several

variable pairs of different stressor-groups showed

moderate correlations (0.50 \ RSp \ 0.70). The per-

centage of intensive agriculture in the catchment was

negatively correlated with altitude, whereas it posi-

tively related to chloride ion concentration and TOC.

Alternatively, the percentage of natural areas in the

catchment was negatively correlated with TOC, total

phosphorous concentration, nitrite concentration and

total nitrogen concentration. The percentage of urban

land use in the catchment was positively correlated

with size class, chloride concentration and nitrite

concentration. All other pairs of variables of different

explanatory groups were weak (RSp \ 0.50) (Table 4).

Benthic macroinvertebrate responses

The total amount of variance (inertia) in the species

data was 3.674, including the 270 sites and 448

benthic macroinvertebrate taxa. The total explained

variance in the dataset, including all 30 environmen-

tal variables, was 0.969 (26%). When tested individ-

ually, the slope, ecoregion Alps, and the intensive

agriculture in the catchment were the most explan-

atory environmental variables, each explaining [4%

of the benthic invertebrate variability (Table 5).

Catchment size and altitude each explained between

2 and 3% of the benthic invertebrate variability.

Comparable to the latter was the explanatory power

of chloride, total organic carbon, and total phospho-

rus concentration. Percentage of (semi) natural areas

was the most explanatory among the sub-catchment

land-use variables, indicating the presence of a blend

of anthropogenic land uses in the sub-catchments

(Table 5). The common explanatory model of the 15

variables resulting from forward-selection of all

environmental variables (P \ 0.05, using the Bon-

ferroni correction), explained 0.730 (20%) of the

dataset variance (Fig. 3). The automatically built

model comprises variables from each of the explan-

atory groups (Table 6) including seven typology

variables (ecoregion level to river type descriptors),

the in-stream measures of general and organic

pollution (other-stressor group), as well as nitrogen

and phosphorus enrichment variables (eutrophication

group) and three variables of anthropogenic land use

on the catchment level. Each of the 15 environmental

variables was also chosen when running the forward

selection for each explanatory variable group sepa-

rately (Table 5).

Explanatory variables in the response models

Testing each explanatory group individually, 21 of

the 30 environmental variables significantly contrib-

uted to the explained variance (Table 5). Each

variable group comprised three to seven forward

selected variables, used in the variance partitioning.

The highest number of selected variables was in the

typology group (7 out of 9), followed by the other

stressors group (5 out of 6), land use group (5 out of

8) and eutrophication group (3 out of 6). In the

typology group a combination of large-scale variables

(e.g. all ecoregions) and local conditions (e.g. karst

spring influence) was observed. For other stressors

the selected variables reflected organic pollution as

well as other human activities. In the land use group

all catchment scale and one sub-catchment scale

variable were included. The eutrophication group

comprised the in-stream nitrate concentration

beside the total phosphorus and the total nitrogen

concentrations.

Variance partitioning among typology

and stressor-groups

The sum of all constrained eigenvalues using the 21

explanatory variables selected from the individual

variable groups was 0.766 (Table 5). Twenty-eight

percent of the variance in the species data could be

attributed to these explanatory variables. Individual

variable groups explained between 4 and 14% of the

total assemblage variability. The highest percentage

was explained by the typology group, followed by the

land use group and the other group, whereas the

eutrophication group explained the lowest percentage

(Fig. 4).

Variance partitioning between the typology group

and an individual stressor-group revealed the unique

effects of the stressor-groups, ranging from 16%

144 Hydrobiologia (2011) 678:137–153

123

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Hydrobiologia (2011) 678:137–153 145

123

(eutrophication) up to 22% (other stressors) of the

explained variability (Fig. 5a). The joint effects (% of

the total explained variance) of each stressor-group

and the typology group varied considerably. The

relative joint effects were highest for the land use

group (31%) and lowest for the eutrophication group

(9%). The ratio calculated for each stressor-group,

revealed pure effects being higher (eutrophication),

approximately equal (other stressors) or lower (land

use) compared to the size of the joint effects

(Fig. 5b).

Variance partitioning among the stressor-groups

Three stressor-groups collectively explain 15% of the

total variance in benthic invertebrate assemblages.

Land use was clearly the most important among the

stressor-groups by uniquely determining 37% of the

total explained variability in the assemblage compo-

sition. Pure effects from other stressors and eutro-

phication explained an additional 22 and 11% share,

respectively. Interactions among the tested stressor-

groups were less important, altogether contributing

30% of the explained variability. The highest value

(13%) was observed for joint effects of land use and

other stressors. Joint effects of all three stressor-

groups accounted for 9%, whereas each of the

remaining two joint effects explained B5% (Fig. 6).

Discussion

Stressor-typology interactions

To identify and tackle the important issues of river

management, human impacts need to be isolated from

the natural background. The degree of human activ-

ities often covaries with the dominant natural gradi-

ents in an area (Allan, 2004; Petek, 2004), therefore

studying human impacts can be challenging. In our

study weak to moderate correlations (RSp \ 0.70) are

observed between pairs of natural (typology) and

stressor variables. Intensive agriculture in the catch-

ment decreases with altitude, whereas urban land use

in the catchment increases with river-size. This is

expected since natural gradients influence the suit-

ability of locations for agricultural and urban devel-

opment (Petek, 2004). Covariations among several

natural and anthropogenic factors that shape benthicTa

ble

4co

nti

nu

ed

RS

pC

lT

OC

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4B

OD

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2

PO

40

.36

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0.2

7*

*0

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**

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3*

*0

.14

*0

.60

**

Nto

t0

.53

**

0.2

6*

*0

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**

0.2

7*

*2

0.1

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*0

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0.1

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0.5

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NO

30

.43

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*0

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0.0

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*2

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*0

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**

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*0

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0.7

8*

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NO

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0.5

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*0

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9*

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0.3

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0.6

8*

*0

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NH

40

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0.3

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0.5

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Fo

ren

vir

on

men

tal

var

iab

leco

des

see

Tab

le2

Fo

rv

aria

ble

-co

de

des

crip

tio

ns

see

Tab

le2

146 Hydrobiologia (2011) 678:137–153

123

invertebrate communities (see Ward et al., 2001;

Malmqvist, 2002; Allan, 2004 for a review) often

mask the independent effects of human activities on

aquatic communities (Sliva & Williams, 2001).

However, in our dataset relations among all but the

aforementioned variable pairs were relatively weak

(RSp \ 0.50).

Our common response model yields several typol-

ogy and stressor variables and not only confirms the

importance of natural and anthropogenic factors in

shaping benthic invertebrate communities but also

indicates their (partial) independent effects. In

reviewing the literature, no records on the amount of

joint effects between typology and individual

Table 5 Percentage of the assemblage variance explained by each environmental variable’s independent (before FS before forward

selection) and conditional effects (after FS after forward selection within each variable-group)

Environmental variable Variable group Explained variance (%) P

Before FS After FS

Slope Typology 4.63 4.63 0.001

Ecoregion: Alps Typology 4.08 2.72 0.001

Ecoregion: Pannonian lowland Typology 2.72 – n.s.

Catchment size class Typology 2.45 1.36 0.001

Altitude Typology 2.18 – n.s.

Ecoregion: Dinaric Western Balkan Typology 1.91 1.91 0.001

Karst spring influence Typology 1.63 1.09 0.001

Ecoregion: Po lowland Typology 1.09 1.09 0.001

Alkalinity Typology 1.09 _ n.s.

Intermittency Typology 0.82 0.82 0.001

Land use-subcatchment

Natural and semi-natural Land use 2.45 0.54 0.008

Intensive agriculture Land use 2.45 – n.s.

Urban Land use 1.63 – n.s.

Non-intensive agriculture Land use 0.54 – n.s.

Land use-catchment

Intensive agriculture Land use 4.35 4.35 0.001

Natural and semi-natural Land use 3.54 0.82 0.001

Urban Land use 2.99 1.91 0.001

Non-intensive agriculture Land use 1.09 1.36 0.001

Chloride ion Other 2.99 2.99 0.001

Total organic carbon Other 2.72 1.09 0.001

Sulphate ion Other 1.91 1.63 0.001

BOD5 Other 1.36 0.54 0.007

Oxygen saturation Other 1.09 – n.s.

Hydromorphological alteration Other 0.82 – n.s.

Total suspended solids Other 0.82 0.82 0.001

Total phoshorus Eutrophication 2.18 2.18 0.001

Orthophosphate Eutrophication 1.91 – n.s.

Total nitrogen Eutrophication 1.63 0.82 0.001

Nitrate Eutrophication 1.36 0.54 0.001

Nitrite Eutrophication 1.36 – n.s.

Ammonium Eutrophication 1.09 – n.s.

P the significance of the Monte Carlo permutation test in variable FS, n.s. not significant. The variables with significant conditional

effect (P \ 0.05, using the Bonferroni correction) were later used in variance partitioning

Hydrobiologia (2011) 678:137–153 147

123

stressor-groups were found. In our study joint effects

vary from 35% (eutrophication) but do not exceed

60% (land use). Land use is anticipated to have the

highest joint effects with typology variables among all

stressor-groups, due to the correlated environmental

variables of the two groups, as discussed earlier. An

ecoregion-specific analysis may yield considerably

lower joint effects between the typology and land use

variable groups but the length of the land use gradients

would differ among ecoregions. Yet, still a relatively

good separation between typology variables and the

stressor-groups is discernable. The Slovenian land-

scape yields high natural diversity as well as diverse

loads of different stressors that are not necessarily

related to natural conditions.

Multiple stressors

In degraded rivers stressors often co-occur (Ormerod

et al., 2010) but the response of the biotic commu-

nities cannot be attributed to individual stressors or

specific combinations of them, if these are highly

correlated. In particular nutrient enrichment and

organic pollution are often closely related (Friberg

et al., 2009). Authors that report the response of

benthic invertebrates to nutrient concentrations

(Skoulikidis et al., 2004; Camargo et al., 2004; Smith

et al., 2007) either tend not to consider parameters of

organic pollution at the same time, or handle effects

of the two stressors together (e.g. Yuan, 2004). Our

dataset with only weak correlations (RSp B 0.31)

between BOD5 and nutrient concentrations meets the

prerequisite for testing the assemblage variability for

the common and unique contributions of nutrient

enrichment and organic pollution.

A number of studies also indicate strong correla-

tions between land use and the abiotic (as well as

Fig. 3 CCA ordination diagram of the 270 sites and the 15

forward-selected environmental variables. See Table 2 for the

codes of environmental variables

Table 6 Percentage of the

assemblage variance,

additionally explained by

each of the 15 selected

environmental variables

(P \ 0.05, using the

Bonferroni correction)

After FS after forward

selection from all 30

environmental variables

No. Environmental variable Variable group Explained variance (%)

After FS

1 Slope Typology 4.63

2 Ecoregion: Alps Typology 2.72

3 Sulphate ion Other 1.91

4 Ecoregion: Pannonian lowland Typology 1.36

5 Catchment size class Typology 1.36

6 Ecoregion: Po lowland Typology 1.36

7 Karst spring influence Typology 1.09

8 Intermittency Typology 0.82

9 Total phoshorus Eutrophication 0.82

10 Nitrate Eutrophication 0.82

11 Total suspended solids Other 0.82

12 Intensive agriculture-catchment Land use 0.82

13 BOD5 Other 0.54

14 Urban-catchment Land use 0.54

15 Non-intensive agriculture-Catchment Land use 0.54

148 Hydrobiologia (2011) 678:137–153

123

biotic) in-stream conditions (Johnson et al., 1997;

Quinn, 2000; Sponseller et al., 2001; Snyder et al.,

2003; Arvola et al., 2004; Donohue et al., 2006,

Kuhar et al., 2010). Against all expectations, only two

in-stream stressor variables are at least moderately

correlated with land use variables in our dataset

(chloride and TOC concentration). Elevated concen-

trations of these variables usually result from a wide

range of human activities (Sherwood, 1989; Arvola

et al., 2004) that explain high correlations with

anthropogenic land use parameters. Authors reported

that land use influences stream ecosystems by

mechanisms related to sedimentation (e.g. Quinn,

2000), hydromorphological alteration (Osborne &

Kovacic, 1993) and nutrient enrichment (Niyogi

et al., 2007). Boyer et al. (2002) found land use to

be a strong predictor of the total nutrient loading. Our

results, however, do not confirm their findings. It

might be that local characteristics driven by land use

changes are not (sufficiently) described by the

parameters in the dataset. The large-scale variables

may be associated with features such as rainfall run-

off relationships that control the possible impacts of

flow and the addition of sediments and nutrients

(Townsend et al., 2003). Another possible explana-

tion is that the independent variations among the

parameters of land use (being a driver) and the

remaining stressor-groups in Slovenian rivers are a

consequence of a combined point source and diffuse

pollution. Due to the mosaic structure of the Slove-

nian landscape being colonised by small farms and

settlements there are no large areas with a single use.

An exceptionally high percentage of (near)-natural

areas compared to other European countries provides

combinations of various magnitudes of different

stressors, i.e., locally altered or polluted rivers having

an entire span of the catchment land-use alterations.

Therefore, the correlation structure of our data

provides a good basis to test the possibilities of

discriminating between the impacts of nutrient

enrichment (eutrophication), other stressors (organic

pollution, other pollution and hydromorphological

Fig. 4 The absolute shares of the variability in the benthic

invertebrate assemblages explained by each of the four

explanatory groups individually

Fig. 5 Variation partitioning of the benthic invertebrate

assemblages between the typology explanatory group and each

of the stressor-groups (land land use, other other stressors, eutro

eutrophication); given as a percentage (%) of the common and

both unique contributions, and b percentage (%) of the common

and the anthropogenic stressor-group contribution

Fig. 6 Common and unique contributions of the stressor-

groups (land land use, eutro eutrophication, other other

stressors) to the total explained assemblage variance

Hydrobiologia (2011) 678:137–153 149

123

alterations) and land use on benthic invertebrate

assemblages.

In a multiple stressor environment, where the

stressors are not highly correlated, their specific

effects on the biotic communities can be evaluated.

Nevertheless, various interactions among these stress-

ors might affect the aquatic communities even more

than each individual stressor alone (Folt et al., 1999;

Feld, 2004; Allan, 2004; Ormerod et al., 2010). We

found that pure effects of stressor-groups (70%) are

more than twice as high as the sum of their combined

effects (30%) (see Fig. 6). In our study, effects of the

stressor-groups are compared. However, most of the

aforementioned researchers compared, in a different

context, the effects of two or more individual

environmental variables that we might have assigned

to the same stressor-group (e.g. BOD5 and hydro-

morphological alteration level, or urban and agricul-

tural land use). Pure effects of eutrophication and

other stressors (including organic pollution) are

considerably higher compared to their joint effects.

In contrast to other studies (e.g. Camargo et al., 2004;

Friberg et al., 2009) we were able to differentiate the

impacts of nutrient enrichment and organic pollution

on benthic invertebrates. Since local river-conditions

are also driven by catchment-land use, this might

result in high joint effects between land use and the

local stressors. In fact, among all combinations of the

tested stressor-groups the joint effects of land use and

other stressors are the highest, but the common

effects of land use and the eutrophication group are

the lowest. In our study land use is the best predictor

group, explaining 60% of the assemblage variance

partitioned among the tested stressor-groups. Almost

40% of the assemblages’ variability explained by

land use is independent from the influences of

eutrophication or other on-site stressors. As dis-

cussed earlier, land use can affect stream biota

through changes in the local environment. By the

assemblage response in our study, an important

additional impact of land use is implied. In general,

it seems that the inseparable effects of different

stressors on benthic invertebrates reported in the

literature (e.g. Friberg et al., 2009) might be due to

the correlated stressors. Our results indicate low joint

effects of the stressor-groups, especially between

eutrophication and land use variables. It is likely that

benthic invertebrates react, for instance, to fluxes of

nutrients or other (toxic) substances (Johnson et al.,

2007) that were not measured in our study, but

originate from urban or agricultural surfaces in the

catchment. Elevated in-stream concentrations of, for

example, nutrients resulting from anthropogenic land

use could only be measured by extensive short-step

measurements that would be expensive and not easy

to accomplish.

Conclusions

Typology and stressor-groups separately explained

considerable shares of the invertebrate variability in

Slovenian rivers. This indicates that changes from

land use, as well as eutrophication and other stressors

can be distinguished from the influences of typology

variables on invertebrate assemblages. Although

aquatic communities in running waters are in most

cases simultaneously affected by multiple stressors,

the individual impacts of nutrient enrichment, other

stressors (water pollution, sedimentation and hydro-

morphological alteration) and land use on benthic

invertebrate assemblages can be discerned. An impor-

tant share of catchment land-use influences are not

explained by the benthic invertebrate responses to in-

stream stressor variables normally provided by the

chemical monitoring survey. Against this background,

we advocate stressor-specific analyses to provide or

improve guidelines for selecting appropriate measures

to enhance river status.

Acknowledgments This study was supported by the

Slovenian Research Agency (Contract No. 1000-06-310036)

and Ministry of the Environment and Spatial Planning of the

Republic of Slovenia. The paper was written with financial

support of the Slovene Human Resources Development and

Scholarship Fund (Contract No. 11012-14/2010). We would

also like to thank Maja Kregar for the cartographical support

and Matej Cunder for the land use data processing. We would

also like to thank the anonymous reviewers for their useful

comments to a previous version of the manuscript.

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