a littoral fauna index for assessing the impact of lakeshore alterations in alpine lakes

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This article is protected by copyright. All rights reserved.

A Littoral Fauna Index for assessing the impact of lakeshore alterations in Alpine lakes

Gorazd Urbanič

University of Ljubljana, Biotechnical Faculty, Department of Biology, Večna pot 111, 1000

Ljubljana, Slovenia, e-mail address: [email protected]

Institute for Water of the Republic of Slovenia, Hajdrihova 28c, 1000 Ljubljana, Slovenia,

phone: +386 1 4775300, e-mail address: [email protected]

Corresponding author: Gorazd Urbanič, Institute for Water of the Republic of Slovenia,

Hajdrihova 28c, Ljubljana, Slovenia

E-mail: [email protected]

Fax number: +386 1 4775 343

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/eco.1392

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Abstract

Lakeshore deterioration is a major threat to the ecological integrity of lakes worldwide. In this

study the relationship between littoral benthic invertebrates and the Lakeshore Modification

Index (LMI) was examined. The influence of the taxonomic resolution on littoral benthic

invertebrate community response to the Lakeshore Modification Index was assessed using the

results of the canonical correspondence analysis (CCA) and the relationship between the taxa

richness, Shannon-Wiener diversity index and the LMI. Benthic invertebrates were sampled

in summer between 2006 and 2011 in two natural and two artificial Alpine lakes using a

littoral microhabitat type sampling scheme. Ordination analyses show a similar explanation

power when the lowest available taxonomic level was used in comparison to the family-level.

Taxa richness and the Shannon-Wiener diversity index indicate that lakeshore modifications

impair littoral biological conditions with a loss of richness and diversity independently of lake

type and used taxonomic level. For 64 families a lakeshore modification indicative value

(LIV) between 1 and 9 were set based on the distribution among five lakeshore modification

classes. A Littoral Fauna Index (LFI) was then derived by summation of the LIV values

recorded at each site. A validation dataset from natural and artificial lakes confirmed the good

relationships between LMI and LFI but revealed some differences in the relationships. The

results show that the impacts of lakeshore modifications can be assessed using littoral benthic

invertebrate assemblages with a family-level based LFI, which makes this method cost-

effective and appropriate for routine monitoring.

Keywords: bioassessment, benthic invertebrates, hydromorphology, LMT sampling,

reference conditions, taxonomic resolution

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Introduction

Measuring the quality of water ecosystems using biota is a common approach in water

monitoring. Moreover, the European Water Framework Directive (EU, 2000) requires an

assessment of the ecological status of surface water bodies using specific biological elements

including phytoplankton, phytobenthos and macrophytes, benthic invertebrates, and fish. For

lakes, benthic invertebrates are one of the key biological elements used for assessing their

ecological status and several lake biotic indices based on benthic invertebrates have been

developed (for an overview see Solimini et al., 2006; Birk et al., 2010; Rossaro et al., 2011;

Solimini and Sandin, 2012).

Authors are often looking for a balance between the operational and economic requirements

against the level of information required to identify environmental gradients or the potential

impact of stressors (Bowman and Bailey, 1997, Verdonshot, 2006, Monk et al., 2012).

Thus, the minimum level of taxonomic resolution or “taxonomic sufficiency” (Ellis, 1985) is

often tested (e.g. Bowman and Bailey, 1997; Lenat and Resh, 2001; Schmidt-Kloiber and

Nijboer, 2004). It was concluded that decision on taxonomic resolution depends on the

objectives of the study (Reynoldson and Wright, 2000).

While most of the lake benthic invertebrate-based indices evaluate either eutrophication or

acidification pressures, few takes into account the hydromorphological pressure on lake

ecosystems (e.g. Aroviita and Hämäläinen, 2007; Brauns et al., 2007a; White et al., 2010).

Studies investigating hydromorphological pressures that affect littoral benthic invertebrates,

however, have focused on the impact of water level fluctuations, whereas the impact of

morphological lakeshore modifications has rarely been tested (see Bänzinger, 1995; Brauns et

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al., 2007b, McGoff & Sandin, 2012; Porst et al., 2012; Urbanič et al., 2012). The need to

include this pressure is essential given that lakeshore deterioration is a major threat to the

ecological integrity of lakes worldwide (Ostendorp et al., 1995; Engel and Pederson, 1998;

Elias and Meyer, 2003). Ostendorp et al. (2004) argue that knowledge about the links between

pressures and specific impacts is poor, albeit some biological impacts are quantified; e.g., for

birds (Traut and Hostetler, 2004), amphibians (Woodford and Meyer, 2003), fish (Jennings et

al., 1999; Scheuerell and Schindler, 2004), macrophytes (Radomski and Goeman, 2001; Elias

and Meyer, 2003) and benthic invertebrates (Bänzinger, 1995; Brauns et al., 2007b).

Bänzinger (1995) compares the benthic invertebrate communities found in three natural and

five artificial types of land-water interfaces in a pre-alpine lake, while Brauns et al. (2007b)

examine the impact that three types of shoreline development and recreational use have on the

eulittoral (0-0.2 m water depth) and infralittoral (0.2 – 1.2 m water depth) communities of

lowland lakes. In neither study was an assessment method developed. More recently,

methodologies for lakeshore habitat assessment have been designed to support the Water

Framework Directive (e.g., Rowan et al., 2006; Peterlin and Urbanič, 2007, 2012) but the

actual relationship between habitat quality and benthic invertebrate taxa is yet to be

investigated.

For this reasons this study has the following aims: i) to assess the influence of the taxonomic

resolution on littoral benthic invertebrate community response to the lakeshore modification

variables and Lakeshore Modification Index (LMI) using the lowest available taxonomic level

family-level data, ii) to investigate the relationship between the LMI and two

richness/diversity metrics (Number of taxa and Shannon-Wiener diversity index), iii) to

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define lakeshore modification indicative values for benthic invertebrate families and iv) to

develop a biotic index (metric) for assessing lakeshore quality.

Study lakes

Four lakes of the Ecoregion Alps (Illies, 1978, Urbanič, 2008a) were investigated (Table I).

Lake Bohinj and Lake Bled are natural glacial lakes located in the North-western part of

Slovenia and belong to two different national lake types: deep alpine and deep pre-alpine,

respectively (Urbanič et al., 2007). Both natural lakes are small, deep and have steep slopes

with a comparable reference trophic status, but differ in their residence time and their mean

annual temperature (Remec-Rekar and Bat, 2003; Wolfram et al., 2009) (Table I). Lakes also

differ in the present trophic status: Lake Bohinj is oligotrophic and Lake Bled is oligo-

mesotrophic (Urbanič et al., 2007). On the other hand Lake Velenje and Lake Družmirje are

artificial lakes which were formatted as a consequence of sub-surface coal mining after the

Second World War. Both artificial lakes are meso-eutrophic (Remec-Rekar, 2010).

Sixty-one sampling sites were selected, twenty six in each natural lake, six in lake Velenje

and three sites in lake Družmirje (Figure 1). All sites were classified into lakeshore

modification classes according to the WFD compliant assessment system used in Slovenia

(Peterlin and Urbanič, 2007, 2012). They were chosen through evaluation of lakeshore

modifications due to the abiotic stressor gradient. The sampling sites range from natural i.e.

morphologically pristine (19 sites), slightly altered (16 sites), moderately altered (15 sites), to

extensively altered (11 sites) (Tables II-III). Further information on lakeshore modification

classes can be found elsewhere (Peterlin and Urbanič, 2012; Urbanič et al., 2012).

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Methodology

Benthic invertebrate sampling

Benthic invertebrates in the littoral zones of lakes were sampled during the summer (mid July

– end of August) of 2006, 2007, 2008, 2009, 2010 or 2011 at each site on a single occasion.

Sampling sites were selected in the middle of each 20 m wide lakeshore modification

assessment section used in the assessment of the stressor gradient. A proportional stratified

sampling approach was used and the sampling regime followed the littoral microhabitat type

(LMT) sampling protocol (Urbanič et al., 2012). In the LMT the strata are defined as a

combination of a depth class and a substrate category (inorganic and organic), proportionate

to their coverage in the sampling site. A sampling site covers 10 m of lakeshore-length to a

distance of 10 m toward the open water – or alternatively, to the point at which the water

depth exceeds 1 m. A sampling site (area) can thus cover up to 100 m2 of the nearshore littoral

bottom, however, it is much smaller when the slopes are steep. Four water-depth classes were

defined: 0-0.25 m, 0.25-0.5 m, 0.5-0.75 m and 0.75-1 m. The positions at which the depth-

class changes were determined at the borders of the sampling site. Based on these change-

positions, the water depths were then mapped at the sampling site and used to define the depth

strata. The substrate-categories in line with those in the AQEM river sampling guidelines

(AQEM consortium, 2002) but with slight modifications defined the substrate strata (Table

IV). All “substrate x depth class” combinations defined microhabitat types. Microhabitat

types with at least 10 % coverage for site were sampled for benthic invertebrates. A sample

consisted of ten sampling units, each of them collected from an area of 0.0625 m2, which is

equivalent to a total area of 0.625 m2 of the lake littoral bottom. Samples were collected using

a 500 µm mesh-size hand net and preserved in the field using 4% formaldehyde solution. All

benthic invertebrates were, identified to their lowest possible taxonomic level, usually to

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species or genus, rarely to (sub)family level (Chironomidae, Tubificidae) and stored in 70%

ethyl alcohol.

The stressor gradient

Lakeshore Modification Index (LMI) was used as a stressor gradient (Peterlin and Urbanič,

2007, 2012). Assessment score of the LMI includes physical alterations and human use in

four zones; the littoral, shoreline and riparian zones and in the lakeshore region up to 100 m

from the riparian zone. For each zone, the authors classify, on a level of 1-5, distinct physical

alterations and human uses (score 1: no additional activities, score 2: occasional activities

score 3: intense seasonal activities, score 4: intense seasonal and/or moderate year-round

activities and score 5: intense year-round activities). These are then combined in a zone

specific alteration score. Four zone specific alteration scores were calculated. The alteration

score for the littoral zone (Ci1) was obtained using equation (1):

Ci1 = Ci1-water + Ci1-wood + Ci1-sub + Ci1-build + Ci1-use (1)

where:

Ci1-water - change in water level in the littoral zone in the section 'i' Ci1-wood - presence of wooden structures in the littoral zone in the section 'i'

Ci1-sub - change in substratum in the littoral zone in the section 'i'

Ci1-build - buildings and infrastructure covered areas in the littoral zone in the section 'i'

Ci1-use - zone use intensity in the littoral zone in the section 'i'

The alteration score for the shoreline zone (Ci2) was calculated according to equation (2):

Ci2 = Ci2-sbi+Ci2-use (2)

where:

C i2-sbi - substratum alteration and extent of area covered by buildings in shoreline in the

section 'i'

Ci2-use - zone use intensity in shoreline zone in the section 'i'

Alteration score for the riparian zone (Ci3) was calculated according to equation (3):

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Ci3 = Ci3-sbi+Ci3-use (3)

where:

C i3-sbi - substratum alteration and extent of area covered by buildings in riparian zone in the

section 'i'

Ci3-use - zone use intensity in riparian zone in the section 'i'

Alteration score for lakeshore region (Ci4) is calculated according to equation (4):

Ci4 = Ci4-land+Ci4-use (4)

where:

C i4-land - prevailing land use in lakeshore region in the section 'i'

Ci4-use - zone use intensity in lakeshore region in the section 'i'

The authors then combine these scores into an Lakeshore Modification Index (LMI) for the

evaluated lakeshore section:

∑=

=4

1jiji CLMI (5)

where Cij – alteration score of the j-th zone variable (j = 1 – littoral zone, j = 2 – shoreline

zone, j = 3 – riparian zone, j = 4 – lakeshore region).

The result is that the LMI score increases with the level of degradation found at the lakeshore.

In the LMI, site-specific and lake-specific LMI scores can be calculated (Peterlin and

Urbanič, 2012). In our study a site-specific LMI scores were used, which are based on the

assessment of the 20 m long sections of the lakeshore.

Dataset, ordination and metric testing

The dataset was divided into two subsets; development and validation dataset. The first

(development) dataset represents 28 samples collected in 2006 and 2007 at Lake Bohinj and

Lake Bled, 14 from each lake. This dataset was used to study the relationship between the

pressure gradient (Lakeshore Modification Index - LMI) and the benthic invertebrate

community including the development of the new index. The validation dataset represents 33

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samples collected between 2008 and 2011, twelve samples were collected at lake Bled, twelve

at Lake Bohinj, six at lake Velenje and three at lake Družmirje. This dataset was used to

validate the relationship between the LMI and the index developed in this study.

Initially, a canonical correspondence analysis (CCA) was used to study the relationships

between the development benthic invertebrate dataset and the Lakeshore Modification Index

(LMI) variables (Table V). In order to consider the spatial dependence structure, latitude and

longitude of the sampling sites being in the Slovenian national grid were used as covariables

(Zuur et al., 2010). Temporal dependence structure was not considered as it was found not

important (Urbanič et al., 2012). To assess the influence of the taxonomic resolution on the

benthic invertebrate community response, two benthic invertebrate data matrices were

prepared, one including the lowest available taxonomic level (130 taxa, Table VI) and the

other including the family-level (64 families, Table VI). Prior the analyses biological data

were log (x+1) transformed. The explained variance calculated as the quotient between the

sum of the canonical eigenvalues and total inertia, and the correlation between the first

canonical axis and the Lakeshore Modification Index (LMI) was used to assess the influence

of both taxonomic levels on the benthic invertebrate community response. For both datasets

contour lines for the LMI were drawn using a local regression smoothing method to better

visualize the results. In addition, the relationship between the LMI and the taxa richness and

the Shannon-Wiener diversity index, was performed for pooled data and for each lake and

each taxa matrice separately. We calculated a one-way analysis of covariance (ANCOVA) (1)

to test differences in sensitiveness of richness and the Shannon-Wiener diversity index to

changes in LMI values for lowest available taxonomic level and family level and (2) to test

the influence of the determination level on the sensitiveness of the richness and the Shannon-

Wiener diversity index. ANCOVA was conducted using PAST 2.08 (Hammer et al., 2001).

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The CANOCO program (Ter Braak and Šmilauer, 2002) was used to perform ordinations and

smoothing method, whereas SPSS Statistics version 17.0 (SPSS Inc., 2008) for relationships.

Littoral fauna index (LFI) development and validation

A new index based on the family-level was developed and the procedure is described below.

1. Analyse the 28 samples in the database to determine the number of occurrences (nji) of

family i in each lakeshore modification (LM) class j. For this, the five lakeshore

modification classes were used as defined by Peterlin and Urbanič (2007, 2012), but

classes 4 and 5 were merged. In the preceding steps, only those families occurring at

least twice in the development dataset were included.

2. Derive the probabilities of occurrence (pji) of family i in LM class j:

pji = nji /Nj (1)

where Nj is the number of samples taken from sites in the LM class j.

3. Estimate the valences (vji) of the family i:

∑=

= 4

1

ji

10 v

jji

ji

p

p (2)

4. Apply the following sequences of rules to derive the lakeshore modification indicative

value (LIV) of the family i. Indicative values are based on two main taxa

characteristics, preferences of taxa for LM classes and tolerances of taxa along the LM

pressure gradient. Thus, two main rules were applied; i) simplified weighted average

rule was used to derive preferences of sensitive taxa (indicative values decrease with

the increase of the pressure) and ii) tolerant taxa occurring along the whole lakeshore

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modification gradient have low indicative values. Lakeshore modification indicative

values between 1 and 9 were then derived.

LM class valences Description LIV v1 v2 v3 v4-5

9 ≥5 <5 - - Dominating in LM class 1 or equally distributed in LM class 1 and 2 and absent from LM casses >2

8 <5 >5 - - Dominating in LM class 2 and absent from LM casses >2 7 ≥5 >0 >0 - Dominating in LM class 1, present also in LM class 3

and absent from LM casses >3 7 4 ≥4 >0 - Dominating in LM class 2, present also in LM class 3

and absent from LM casses >3 6 ≤4 <4 ≤3 - Equaly present in LM classes 1-3 and absent from LM

casses >3 6 ≤3 ≥5 ≤3 AND >0 - Dominating in LM class 2 and almost equally present in

LM classe 1and 3 and absent from LM casses >3 5 4 >0 ≥3 - Dominating in LM classes 1 and 3, present in class 2 and

absent from LM casses >3 5 ≤3 ≤4 ≥3 - Dominating in LM classes 2 and 3, present in class 1 and

absent from LM casses >3 4 4 >0 >0 1 Dominating in LM classes 1 and present in all remaining

classes, but with low frequency in LM classes 4-5 3 <4 >0 >0 1 Dominating in LM classes 1-3 and present in LM

classes 4-5 3 ≥4 >0 >0 >1 Dominating in LM classes 1 and almost equally present

in all remaining LM classes 2 <4 <4 <4 >1 Almost equally present in LM classes 1-3 and present in

LM classes 4-5 1 2,5 2,5 2,5 2,5 Equally present in all LM classes

For those families present in only one sample, the indicative values were defined using a

combination of a statistical approach and expert opinion.

5. Define an equation to calculate the Littoral fauna index (LFI).

∑=

=n

iiLIVLFI

1

where LIVi is the lakeshore modification indicative value of the family i

6. Examine the relationship between the pressure gradient (Lakeshore Modification

Index) and the LFI.

The relationships were examined using the development and validation dataset. Validation

dataset was split in two datasets; data from natural lakes and data from artificial lakes. To

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determine whether exist differences between the natural and artificial lakes in observed

relationships, we calculated a one-way analysis of covariance (ANCOVA).

Since lakes Bled and Bohinj belong to distinct lake types, the lake-specific reference LFI

values, calculated as the median of the reference sites (LM class = 1), were compared using a

non-parametric Mann-Whitney U-test.

Results

Pressure-impact relationship

The CCA ordination analyses showed a similar explanation power for both family-level (total

inertia: 1.575, explained variance = 20%, test of significance of all canonical axes P = 0.003)

and the lowest available taxonomic level data (total inertia: 1.893, explained variance = 21%,

test of significance of all canonical axes P = 0.001). The fit of the loess model (R2) to the LMI

was 0.86 for lowest available-level and 0.83 for family-level (Figure 2). Similar relationships

for both tested taxonomic levels were observed also for richness and Shannon-Wiener

diversity index (Figure 3). Relationships between the LMI and richness and the Shannon-

Wiener diversity index were statistically significant (P <0.01) in both lakes. Taxa richness (R2

= 0.72–0.81) and family richness (R2 = 0.71–0.73) showed higher coefficient of determination

(R2) than for the Shannon-Wiener diversity index based on family-level (R2 = 0.47–0.52) and

lowest taxonomic-level (R2 = 0.33–0.53). Moreover, richness was also more sensitive to

changes in LMI values than Shannon-Wiener diversity index having higher absolute slope (k

factor) of the regression line (One-way ANCOVA, F = 65.36 – 68.83, p < 0.0001). Richness

calculated using lowest available level showed slightly higher sensitivity to changes in LMI

than with family level (One-way ANCOVA, F = 4.97, p = 0.03). There was no difference

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between the taxonomic levels in sensitivity of the Shannon-Wiener diversity index (One-way

ANCOVA, F = 8.98E-09, p = 0.99).

Index development and validation

Due to similar results in the explanation power of the CCA ordination and tested relationships

for both determination levels (family-level and the lowest available taxonomic level) we

decided to develop LFI using family-level data. Lakeshore modification indicative values

(LIV) were derived for 64 families, for 56 families using the described statistical approach

and for 8 families, using a combination of a statistical approach and expert opinion

(Table VI). High LIV values (>7) were obtained for those families found at the reference and

at slightly altered sites, whereas at heavily altered sites and for tolerant families low LIV

values (<3) were obtained. In terms of frequency distribution, the most outstanding is a LIV

of 7 with highest number of families (17) whereas other LIV values are represented with <10

families (Figure 4). This means that specimens of many families were mostly found at the

reference and slightly altered sites but occurred also at moderately altered lakeshores. The

second peak, but much smaller, observed in the frequency distribution occurred at a LIV = 3

indicating that many families were present along the whole lakeshore modification gradient

and were not specific to any site alteration. Only a few families were present at the heavily

altered sites. With a LIV of 1, a typical example of a tolerant family is Chironomidae.

At pristine sites (low LMI), high LFI values were noted, whereas at heavily altered sites (high

LMI), low LFI values were observed (Figure 5).There was no statistically significant

difference between the reference LFI values of the Lake Bled and Lake Bohinj (Mann-

Whitney U = 9.5, P > 0.05). LFI showed a good response to the lakeshore modification

gradient (LMI) with a coefficient of determination (R2) of 0.79 (p < 0.001) (Figure 5a). A

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validation datasets from natural and artificial lakes confirmed good relationship (R2 = 0.70-

0.78, p < 0.001) (Figure 5b). However, observed relationships between LMI and LFI of the

natural and artificial lakes showed statistically significant differences in adjusted means (One-

way ANCOVA, F = 14.82, p < 0.001) but not in slopes (One-way ANCOVA, F = 2.30, p =

0.14).

Discussion

Taxonomic level

In routine water monitoring, managing time and costs are a major challenge, so any savings

are welcome. One way to optimize the cost-effectiveness of a biological monitoring

programme is by reducing the taxonomic resolution, a strategy that has been often debated

(Hawkins et al., 2000; Reynoldson and Wright, 2000; Bailey et al., 2001). Certain authors

suggest that the macroinvertebrate response will be more precise using species-level (Lenat

and Resh, 2001; Schmidt-Kloiber and Nijboer, 2004; O’Toole et al., 2008), whereas others

find no difference in the response of species and/or genus and family-level (Bowman and

Bailey, 1997; Feio et al., 2006; Pond et al., 2008) or that family-level actually performs even

better (Reynoldson et al., 2001). Hawkins et al. (2000) formulates the hypothesis that for

regions with few genera and species per family, species- and family-level assessments will

perform similarly; however, for regions with many genera and species per family, important

information on species-level could be lost with a coarser taxonomic resolution. Reynoldson

and Wright (2000) similarly concluded that family-level identifications are appropriate for

sampling areas that have low taxa richness. This study did not find a major difference in

community response to lakeshore modification pressure between the lowest available-level

(mostly species and genus) and the family-level. Also both metrics (taxa richness and

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Shannon-Wiener diversity index) showed similar response to the LMI pressure independent

of the used taxonomic level, although lowest-available level proved to be slightly more

sensitive to the LMI pressure. One reason might be that the lakes are oligotrophic and oligo-

mesotrophic usually with few taxa (species and genera) per family (Figure 6), although most

diverse lake families like the Chironomidae were not determined to the species level. Another

reason may be a result of the addressed pressure type that was lakeshore modifications in our

study. Concerning eutrophication pressure, O’Toole et al. (2008) concludes that the response

of a lake’s macroinvertebrate community is more precise when using the species-level than a

coarser taxonomic resolution, supporting the theory that sensitivity to pollution may vary

considerably within genera (Wiggins and Mackay, 1978; Moog et al., 1995; Lenat and Resh,

2001; Schmidt-Kloiber and Nijboer, 2004). Hence, small observed differences between tested

taxonomic levels indicate that, the required taxonomic level of identification used in a lake

monitoring programme depends also on the pressure type.

Indicative values

Macroinvertebrate data were collected over six successive years, and from four lakes, whereas

indicative values were defined using data from two lakes. O’Toole et al. (2008) argues that

regarding trophic status the associations of benthic invertebrates reported in the literature are

often based on either a single or only a small numbers of lakes. In contrast, they used a large

dataset, but found that simple metrics based on a large collated dataset are of limited value for

classifying lakes, since the R2 of the regression generally explains a low amount of variation

in the relationships. Metrics with a low predictive capacity to relate pressure to impact are of

limited value for lake management (Håkanson, 2001) as reliable results are necessary when

budgeting for specific implementation measures. Thus, decision of the used dataset in

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development of the index should depend on the expected similarity in responses to

perturbations.

In this analysis, the indicative values for 64 benthic invertebrate families were distributed

along the lakeshore modification stressor gradient (LMI) (Table VI). Not many data are

available about the sensitivity of benthic invertebrate families to lakeshore modifications.

Generally, it is accepted that any environmental degradation will reduce species diversity and

number of sensitive taxa (Hughes and Noss, 1992). In line with this several family-level

indices were developed in the past (e.g. Armitage et al., 1983; Camargo, 1993: Chessman,

1995) but according to our knowledge, no similar family-level biotic index was developed for

lakes. Some family-level biotic indices developed for river assessment; e.g. Biological

Monitoring Working Party (BMWP) with its average score per taxon (ASPT) (Armitage et

al., 1983), are used also in the lake assessment (Birk et al., 2010). Chessman and McEvoy

(1998) stressed that aquatic macroinvertebrates differ in their sensitivity to different stressors

even at the family level. Our comparison of LIV and BMWP family scores showed a slight

positive correlation but was not statistically significant (Pearson’s r = 0.17, p>0.05). Thus, it

seems that for littoral benthic invertebrate families it is necessary to define stressor specific

indicative values.

Some families like Astacidae, Branchiobdellidae, Limnephilidae, Ancylidae, Goeridae,

Aphelocheiridae and Bithyniidae only occur at the reference and slightly altered sites.

Consistent with this, Brauns et al. (2007b) found that Bithynia tentaculata (Linnaeus, 1758) is

one of the species with a strong preference for root habitats, which in our lakes occur at

natural lakeshores. Further, Brauns et al. (2007b) observed Lype phaeopa only in natural

shorelines, but in this study a LIV = 2 is derived for the family Psychomyiidae, which

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indicates that specimens of this family were present along the whole lakeshore modification

gradient. It would be possible that levels of tolerances among members of the family

Psychomyiidae vary. In both studied lakes three species of the family Psychomyiidae were

present: Tinodes waeneri, Tinodes sp. and Lype reducta. Lype species are known to be

xylobionts (Waringer and Graf 1997), but are not related to just natural habitats, since – at

least in this study – they were found at heavily altered sites where walls made of brushwood

were present. Presence of wood is not necessary only related to natural habitats (Peterlin and

Urbanič, 2012) and thus, xylobionts are expected to occur along the whole lakeshore

modification gradient. The Chironomidae, the larvae of which are tolerant to lakeshore

modifications, have the lowest LIV of 1. Several other families like the Baetidae, Caenidae,

Naididae, and Elmidae are also tolerant with LIVs of 2 and 3, but the probability of finding

them is higher at moderately and heavily altered sites than at slightly altered and reference

sites. However, the LIVs still have to be tested with more and new data and therefore, some

shifting of families on the indicative gradient is expected, especially since for certain families

a statistical approach combined with expert opinion is used to establish indicative values.

Bioassessment

In calculating the Littoral Fauna Index (LFI), family richness is also accounted for since the

LFI is the sum of the LIV. Such an approach is commonly used but recognised as problematic

as being largely influenced by number of taxa in the sample and thus affected by sample size

and sample processing efficiency (e.g. Armitage et al., 1983; Chessman, 1995; Hawkes,

1997). To overcome this inherent weakness several authors proposed that the result should be

divided by the number of contributing taxa, thus providing an average score (e.g. Balloch et

al., 1976; Armitage et al., 1983). In our study, we did not recommend to calculate an average

value since a sampling approach was standardized by area (Urbanič et al., 2012). In support,

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we observed a good relationship between the lakeshore modification pressure (LMI) and the

number of families (Figure 3), which finally takes the richness moment in the LFI into

account. Moreover, in each lake a similar response to family richness exists along the pressure

gradient, despite difference in community compositions and in the trophic status of the lakes

(Urbanič et al., 2007). High LFI values, however, are only observed if many families with

high LIVs are present.

The WFD (EU, 2000) requires that the taxonomic composition, abundance, diversity, and

ratio sensitive to insensitive taxa are to be considered in any ecological status assessment

system. Albeit the LFI does not take abundance into account, the Shannon-Wiener diversity

index (for which abundances are used), was tested, but revealed a weaker relationship with

the pressure gradient than the family richness (Figure 3). Therefore, any future work has to

include the testing of additional metrics and the combination of these with the LFI to fulfil the

WFD requirements. In the present study lakes Bled and Bohinj belong to two different types

and trophic status, nevertheless, no statistically significant differences were observed between

the reference LFI values and similar relationships between the LFI and the pressure gradient

(LMI) exist in both lakes. On the other hand, data from two artificial lakes revealed different

relationship between LMI and LFI but not in the slope of the regression curve. This indicates

that the littoral benthic invertebrate community responds similarly to lakeshore modifications

in tested lakes meaning that the newly developed index unlikely to be alpine lake-type

dependent. The observed strong relationship between the LMI and LFI was expected since the

same macroinvertebrate data were used for index development as well as allocation of the

indicative values (LIV). Nevertheless, the validation data set confirmed the strong correlation

between LFI and pressure gradient also for two artificial lakes which were not included in the

index development (Figure 5).

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Conclusions

Due to increased deterioration of the lakeshores (Ostendorp et al., 1995; Engel and Pederson,

1998; Elias and Meyer, 2003) the impact of the lakeshore modifications need to be addressed.

It appears from this study that littoral benthic invertebrates can be used as indicators of

lakeshore alterations. In routine monitoring cost-effective methods are appropriate and thus

often family based methods are used (e.g. Armitage et al., 1983, Chessman, 1995). In our

study no major differences were observed in the explanation power of the family level and

lowest available level and thus, family-based LFI was developed. Although the index still

needs to be tested on other lake types, the LFI is appropriate for making an ecological

assessment of the impact of lakeshore modifications. This is confirmed by the good

relationship obtained between the LFI and the pressure gradient (LMI) using validation

dataset. However, to be effective a stressor-specific biotic index needs two key properties, to

show a strong relationship to the addressed pressure and to assume weaker responses to other

pressures (Chessman and McEvoy, 1998). The second key property still needs to be tested.

Acknowledgements

The author gratefully acknowledges the members of the project team for their assistance both

in the field and in the laboratory and David Heath for his valuable comments and correcting

the English and anonymous reviewers for providing comments that improved the paper. This

work was supported by the Ministry of the Environment and Spatial Planning of the Republic

of Slovenia as a part of the national programme for the implementation of the EU Water

Framework Directive.

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Table I. Main characteristics of the studied lakes.

Parameter/Lake Bohinj Bled Velenje Družmirje Bioregion (Urbanič 2008b)

Carbonate Alps-Danube river basin

Subalpine hills-Danube river basin



Latitude 46° 17' 02'' 46° 21' 53'' 46o22'33' 46o22'40'' Longitude 13° 51' 35'' 14° 05' 40'' 15o05'16'' 15o03'47'' Catchment area (km2) >100 8.1 20.5 31.7 Elevation (m a.s.l.) 526 475 367 360 Surface area (km2) 3.28 1.44 1.35 0.7 Volume (Mio m3) 92.4 26.6 25.0 25.0 Depth - maximum (m) 45 31 55 87 Average depth (m) 28 19 19 24 Residence time (year) 0.4 1.5 >2 <0.5 Trophic state Oligotrophic Oligo-Mesotrophic Meso-Eutrophic Meso-Eutrophic

Table II. Description of the four lakeshore modification (LM) classes.

LM class Description Lakeshore alterations 1 Natural Natural riparian vegetation (trees and scrubs) is present. Littoral

area with none or minor alterations (presence of tree trunks and branches).

2 Slightly altered Natural riparian vegetation is generally present, but partially altered (e.g. some trees are removed). Littoral area is slightly altered (e.g. fewer wooden substrates are present).

3 Moderately altered Riparian vegetation without trees. Transposition of substrates (e.g. rocks and stones) in the littoral zone; an influence often present is bathing areas.

4 Extensively and severely altered

Riparian vegetation without trees and scrubs or completely absent. In the littoral zone wooden sheet piling and wooden shore fortification are present or fortified. Riparian zone covered with asphalt or concrete, part of urban or industrial areas.

Table III. Number of sampling sites (development dataset) for each lake regarding the

Lakeshore modification (LM) class.

LM class/Lake Bohinj Bled Velenje Družmirje Sum

1 12 (5) 6 (3) 1 (0) 0 19 (8) 2 6 (3) 7 (4) 2 (0) 1 (0) 16 (7) 3 7 (5) 6 (4) 1 (0) 1 (0) 15 (9) 4 1 (1) 7 (3) 2 (0) 1 (0) 11 (4)

Sum 26 (14) 26 (14) 6 (0) 3 (0) 61 (28)

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Table IV. Inorganic and organic substrate categories used for benthic invertebrate sampling. Inorganic substrate Description Megalithal (>40 cm) large cobbles, bolders and blocks, bedrock Macrolithal (>20 cm to 40 cm) coarse blocks, head-sized cobbles Mesolithal (>6 cm to 20 cm) fist to hand-sized cobbles Microlithal (>2 cm to 6 cm) coarse gravel Akal (>0.2 cm to 2 cm) fine to medium-sized gravel Psammal (>6 µm to 2 mm) sand Psammopelal (<2 mm) mixture of sand with mud Pelal (<6 µm) mud (including organic mud and sludge) Argyllal silt, loam, clay (inorganic) Organic substrate Macro-algae filamentous algae, algal tufts Floating-leaved macrophytes e.g. Nuphar, Nymphea, Potamogeton natans Submersed macrophytes macrophytes, including moss and Characeae Emergent macrophytes e.g. Thypha, Carex, Phragmites Living parts of terrestrial plants fine roots, floating riparian vegetation Xylal (wood) tree trunks (dead wood), branches, roots CPOM deposits of coarse particulate organic matter (e.g. fallen leaves) FPOM deposits of fine particulate organic matter Sewage bacteria and -fungi e.g. Sphaerotilus, Leptomitus, sulfur bacteria (e.g. Beggiatoa,

Thiothrix), sludge Debris organic and inorganic matter deposited within the splash zone area (e.g.

mussel shells, snail shells)

Table V. Number of sampling sites, reference sampling sites, minimum and maximum values

of hydromorphological variables used in the Canonical Correspondence Analyses. LMI–

Lakeshore Modification Index.

Lake Lake Bled Lake Bohinj No. sampling sites 14 14 No. reference sampling sites 5 6 LMI range 9.75-38 11.25-32 LMI class range 1-4 1-4 Littoral zone score range 6-24 6-23 Shoreline zone score range 1.5-7.5 1.5-6.0 Riparian zone score range 1.5-5.0 1.0-3.5 Lakeshore region score range 0.5-2.5 0.75-1.5

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Table VI. Taxa list with family Lakeshore modification Indicative Values (LIV), distribution

of valences over lakeshore modification classes (vi). Families marked with an asterisk (*)

have indicative values defined using a combination of a statistical approach and expert

opinion.

LM class valences Taxon Family LIV v1 v2 v3 v4-5 Astacus astacus Astacidae 9 10 0 0 0 Branchiobdella sp. Branchiobdellidae 9 10 0 0 0 Chaetopteryx fusca Limnephilidae 9 6 4 0 0 Halesus digitatus/tesselatus Limnephilinae - juv Acroloxus lacustris Ancylidae 9 6 4 0 0 Ancylus fluviatilis Goera pilosa Goeridae 9 5 5 0 0 Ochthebius sp. Hydraenidae - adult 8 3 7 0 0 Bythinia tentaculata Bithyniidae 8 0 10 0 0 Leuctra sp. Leuctridae 8 0 10 0 0 Aphelocheirus aestivalis Aphelocheiridae 8 0 10 0 0 Haliplus sp. - larvae Haliplidae - larvae 8 0 10 0 0 Orectochilus villosus Gyrinidae - adult* 8 - - - - Ochthebius sp. - larvae Hydraenidae - larvae* 8 - - - - Cordulia aenea Corduliidae 7 7 0 3 0 Somatochlora metallica Planaria torva Planariidae 7 6 0 4 0 Polycelis nigra /tenuis Gyraulus albus Planorbidae 7 6 2 2 0 Gyraulus crista Hippeutis complanatus Planorbis planorbis Habrophlebia fusca Leptophlebiidae 7 6 0 4 0 Habrophlebia lauta Paraleptophlebia submarginata Micronecta sp. Corixidae 7 5 3 2 0 Eiseniella tetraedra Lumbricidae 7 5 3 2 0 Nemoura sp. Nemouridae 7 5 3 2 0 Notidobia ciliaris Sericostomatidae 7 5 4 1 0 Sericostoma sp. Dendrocoelum album Dendrocoelidae 7 5 2 3 0 Dendrocoelum lacteum Dugesia lugubris/polychroa Dugesiidae 7 4 4 2 0 Dugesia tigrina Bythinella schmidti Hydrobiidae 7 4 5 1 0 Sadleriana fluminensis Physa fontinalis Physidae 7 4 4 2 0 Physella acuta Ephemera danica Ephemeridae 7 4 4 2 0 Ephemera sp. Anax imperator Aeshnidae* 7 - - - - Pyrrhosoma nymphula Coenagrionidae* 7 - - - - Platycnemis pennipes Platycnemididae* 7 - - - - Bidessus sp. Dytiscidae - adult* 7 - - - - Graptodytes sp. Nebrioporus sp. Platambus maculatus Valvata piscinalis Valvatidae 6 4 3 3 0

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Table VI. continued

LM class valences Taxon Family LIV v1 v2 v3 v4-5 Sialis fuliginosa Sialidae 6 4 3 3 0 Sialis lutaria Sialis nigripes Graptodytes sp. - larvae Dytiscidae - larvae 6 4 3 3 0 Nebrioporus sp. - larvae Platambus maculates - larvae Asellus aquaticus Asellidae 6 4 3 3 0 Enchytraeidae Gen. sp. Enchytraeidae 6 3 5 2 0 Tipula sp. Tipulidae 6 2 5 3 0 Ephemerella ignita Ephemerellidae* 6 - - - - Chrysops sp. Tabanidae* 6 - - - - Tabanus sp. Alboglossiphonia hyalina Glossiphoniidae 5 4 2 4 0 Glossiphonia complanata Helobdella stagnalis Hemiclepsis marginata Erpobdella octoculata Erpobdellidae 5 3 4 3 0 Erpobdella sp. Erpobdella testacea Aulodrilus pluriseta Tubificidae 5 3 4 3 0 Branchiura sowerbyi Peloscolex sp. Peloscolex ferrox Tubificidae - without chaetae Tubificidae - with chaetae Gomphus vulgatissimus Gomphidae 5 4 0 6 0 Siphlonurus lacustris Siphlonuridae 5 3 0 7 0 Siphlonurus sp. Coelostoma orbiculare Hydrophilidae - adult 5 0 0 10 0 Chaoborus flavicans Chaoboridae 5 0 0 10 0 Beris sp. Stratiomyiidae 5 0 0 10 0 Cyrnus trimaculatus Polycentropodidae 4 4 2 3 1 Polycentropus irroratus Hydrachnidia (Hydracarina) Gen. sp. Hydrachnidia 4 4 2 3 1 Lumbriculus variegatus Lumbriculidae 3 3 3 3 1 Stylodrilus heringianus Athripsodes aterrimus Leptoceridae 3 3 3 3 1 Athripsodes bilineatus Athripsodes cinereus Athripsodes sp. (cf. dalmatinus) Mystacides azurea Mystacides nigra Oecetis lacustris Oecetis ochracea Oecetis testacea Radix auricularia Lymnaeidae 3 3 4 2 1 Radix balthica Radix labiata Radix sp. - juv Caenis horaria Caenidae 3 3 3 3 1 Caenis macrura Nais sp. Naididae 3 3 4 3 1 Pristina longiseta Stylaria lacustris Uncinais uncinata

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Table VI. continued

LM class valences Taxon Family LIV v1 v2 v3 v4-5 Pisidium sp. Sphaeriidae 3 2 3 4 1 Gammarus lacustris Gammaridae 3 5 0 2,5 2,5 Ceratopogoninae Gen. sp. Ceratopogonidae 3 2 4 1 3 Dasyhelea sp. Elmis sp. Elmidae - adult 3 2 3 1 4 Limnius sp. Riolus sp. Ecnomus tenellus Ecnomidae 3 1 0 3 6 Potamanthus luteus Potamanthidae 2 0 0 0 10 Pericomini Gen. sp. Psychodidae 2 0 0 0 10 Hydroptila sp. Hydroptilidae 2 2 2 2 4 Limnius sp. - larvae Elmidae - larvae 2 2 3 3 2 Elmis sp. - larvae Riolus sp. - larvae Baetis fuscatus/scambus Baetidae 2 3 3 2 2 Centroptilum luteolum Lype reducta Psychomyiidae 2 3 2 2 3 Psychomyia pusilla Tinodes sp. Tinodes waeneri Brillia bifida Chironomidae 1 2,5 2,5 2,5 2,5 Chironomini Gen. sp. Corynoneura sp. Orthocladiinae Gen. sp. Tanypodinae Gen. sp. Tanytarsini Gen. sp.

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Figure 1. Study area and sampling sites (development sites - dots; validation sites - open dots;

a – Lake Bled; b – Lake Bohinj; c – Lake Velenje, d – Lake Družmirje).

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Figure 2. Lakeshore Modification Index (LMI) gradient projected onto the development

sampling sites along the first 2 axes of the CCA. The fit of the loess model is reported for

each dataset; a) lowest available taxonomic level, b) family-level. Values represent LMI

scores.

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Figure 3. Relationship between the Lake Modification Index (LMI) and a) family richness, b)

Shannon-Wiener diversity (family level); triangles (▲) – Lake Bohinj, open dots (○) – Lake

Bled.

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Figure 4. Frequency distribution of the lakeshore modification indicative values (LIV).

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Figure 5. Relationship between Lakeshore Modification Index (LMI) and Littoral Fauna

Index (LFI); a – development dataset, b – validation dataset of the Lake Bled and Lake Bohinj

(□ and solid line) and of the Lake Velenje and Družmirje (▲ and dashed line)

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Figure 6. Frequency distribution of the number of taxa per family.

a littoral fauna index for assessing the impact of lakeshore alterations in alpine lakes

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