An index of human alteration of lake shore morphology

OLIVER MILERa,*, GWENDOLIN PORSTa, ELAINE MCGOFFb, FRANCESCA PILOTTOc, LOUISE DONOHUEd,TAMARA JURCAd, ANGELO SOLIMINIc, LEONARD SANDINe, KENNETH IRVINEd, JUKKA AROVIITAf,

RALPH CLARKEg and MARTIN T. PUSCHa

aLeibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, GermanybDepartment of Aquatic Sciences & Assessment, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden

cDepartment of Public Health and Infectious Diseases, Sapienza University of Rome, Roma, ItalydSchool of Natural Sciences, Trinity College Dublin, Dublin, IrelandeDepartment of Bioscience, Aarhus University, Silkeborg, Denmark

fFreshwater Centre, Finnish Environment Institute (SYKE), University of Oulu, Oulu, FinlandgSchool of Applied Sciences, Christchurch House, Bournemouth University, Poole, United Kingdom

ABSTRACT

1. Morphological degradation constitutes one of the most severe threats to the ecological integrity of lakes. Thedevelopment of biotic assessment methods for human lake shore alterations using littoral macroinvertebratesrequires quantification of the degree of degradation by a stressor index and is complicated through simultaneousphysical pressures that alter natural habitat structure.

2. The Lake Habitat Survey (LHS) method and macroinvertebrate sampling were used to produce apan-European dataset of morphological lake shore degradation and macroinvertebrate densities covering51 lakes in seven countries and across four geographical regions – northern, western, southern andcentral Europe.

3. Lake Habitat Survey parameters that differed significantly among three categories of morphological pressurewere combined to develop the stressor index components ‘Number of habitats’, ‘Habitat diversity’, ‘Totalpercentage volume inhabited by macrophytes’, ‘Sum of macrophyte types’, ‘Sum of vegetation cover types’,‘Sum of coarse woody debris/roots/overhanging vegetation’, ‘Pressure index’ (number of human disturbancesources) and ‘Natural/artificial dominant land cover type’.

4. Stressor index components were tested for cross-correlations and for differences among pressure levels. Thefinal composition of the stressor index was optimized for the four studied geographical regions in Europe. Theresulting stressor index correlated more strongly with macroinvertebrate metrics than simpler site-specific LHSparameters or the HabQA index developed previously in one lake in north-western Europe.

5. The stressor index developed provides deeper insight into the morphological pressures that affect littoralinvertebrate communities. The results also support the use of LHS to quantify morphological stressors at samplingsite level, which can ease developing other multimetric bioassessment methods.

6. The stressor index offers the possibility for wide and regional specific application to assess hydromorphologicalpressures on lakes to assist conservation planning and management and further global efforts to develop and testbiotic assessment methods for lakes.Copyright # 2014 John Wiley & Sons, Ltd.

*Correspondence to: O. Miler, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany. Email: oliver.miler@web.de

Copyright # 2014 John Wiley & Sons, Ltd.

AQUATIC CONSERVATION: MARINE AND FRESHWATER ECOSYSTEMS

Aquatic Conserv: Mar. Freshw. Ecosyst. (2014)

Published online in Wiley Online Library(wileyonlinelibrary.com). DOI: 10.1002/aqc.2534

Received 20 December 2013; Revised 08 September 2014; Accepted 28 September 2014

KEY WORDS: riparian; lake; littoral; Water Framework Directive; hydromorphology; environmental impact assessment;invertebrates; benthos; urban development; recreation

INTRODUCTION

The hydromorphology (sensu Council of theEuropean Communities, 2000) of littoral zonesstrongly governs their riparian and aquaticprimary production (Schindler and Scheuerell,2002; Brauns et al., 2011). Morphologically intactlittoral zones support good water quality throughpollutant retention and self-purification (Mickle,1993; Sollie and Verhoeven, 2008) and dissipatewave energy reaching the shoreline (Wetzel, 2001;Schmieder, 2004; Gabel et al., 2008, 2012; Strayerand Findlay, 2010). High physical complexity andhabitat diversity typified by many morphologicallyundisturbed littoral zones support complex foodwebs and high taxonomic diversity (Strayer andFindlay, 2010; Brauns et al., 2011). Alteration ofthese habitats by shoreline development is a severethreat to ecosystem integrity (Engel and Pederson,1998; Elias and Meyer, 2003; Schmieder, 2004;Donohue et al., 2009), well illustrated by changesin diversity, functioning, and taxonomic structureof littoral macroinvertebrate communities (Braunset al., 2007a, b, 2011; de Sousa et al., 2008; Porstet al., 2012). Impacts include the establishment ofrecreational beaches or physical bank protection(Strayer and Findlay, 2010; Brauns et al., 2011);loss of coarse woody debris (CWD) (Christensen et al.,1996; Francis and Schindler, 2006; Marburg et al.,2006); reduction of macrophyte stands, e.g. littoralreed belts (Radomski and Goeman, 2001; Elias andMeyer, 2003; Jennings et al., 2003); and alterations ofsediment particle size composition (Jennings et al.,2003). The potential of littoral macroinvertebrates asindicators of hydromorphological degradation, drivenby policy demands for better ecological assessment ofwater bodies (Council of the European Communities,2000) has been tested through detailed research inrecent years (Brauns et al., 2007a, b, 2011; Aroviitaand Hämäläinen, 2008; Jurca et al., 2012; Porstet al., 2012; Sandin and Solimini, 2012; McGoffet al., 2013a, b).

The complexity of littoral habitats makes themsuitable for application of multimetric bioticindices to describe and quantify characteristics ofbiotic communities and their response to humanpressures (Karr and Chu, 1999; Hering et al.,2006; Birk et al., 2012). While effects of chemicalpollutants such as nutrients and acidity are oftencalibrated against a single stressor variable such astotal phosphorus (TP) (Phillips et al., 2011;Mischke et al., 2012) or acidity (McFarland et al.,2010) many lakes are subject to multiple andsimultaneous pressures (Ormerod et al., 2010). Inthe littoral zones of lakes multiple physical pressureson hydromorphology can become manifest acrossdifferent scales and linked to land use, complexityand diversity of littoral habitats and riparianvegetation structure (Strayer and Findlay, 2010;McGoff et al., 2013a, b).

Attempts to apply consistent methods to lakehabitat assessment include the US EnvironmentalProtection Agency (EPA) Environmental MonitoringAssessment Program (EMAP) Field OperationsManual for Lakes (Kaufmann and Whittier, 1997;US EPA, 2007, 2009, 2010; Kaufmann et al.,2014a, c), further developed by the Lake HabitatSurvey (LHS) (Rowan et al., 2006, 2008) thatincorporated aspects of both the EMAP approachand that of the River Habitat Survey (Raven et al.,1998, 2000). Attempts to link descriptors of littorallake habitat structure to littoral invertebratecommunities have been limited to only a fewstudies. Applying LHS, McGoff and Irvine (2009)developed the stressor index HabQA to describethe effects of morphological alterations onmacroinvertebrates. Peterlin and Urbanič (2012)developed the Lakeshore Modification Index(LMI) to assess hydromorphological alterationusing macroinvertebrate indicators (Soliminiet al., 2012). Kaufmann et al. (2014b) developedtechniques to link biotic responses of fish andbirds to lake shore disturbance.

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The need for regional habitat assessment andassociated biotic indicators as required forimplementation of the EC Water FrameworkDirective (WFD; Council of the EuropeanCommunities, 2000) led to the current study todevelop a pan-European morphological stressorindex for lake shores. This was based onenvironmental parameters recorded by LHS andwas used to calibrate two previously developedbiotic indices that assess the ecological quality oflake shores: LIMCO (Littoral InvertebrateMultimetric Index based on Composite Sampling)and LIMHA (Littoral Invertebrate MultimetricIndex based on Habitat Sampling) (Lyche Solheimet al., 2013; Miler et al., 2013).

MATERIALS AND METHODS

Lake Habitat Survey (LHS) and LHS indices

Littoral, lake shore and riparian conditions wereassessed using the LHS method. The basic surveyunit of LHS is the ‘habplot’, equivalent to the‘sampling station’ of the EMAP Field OperationsManual for Lakes (US EPA, 2007). The LHS LakeHabitat Quality Assessment Score (LHQA) assesseslittoral and riparian habitat diversity, physicalcomplexity, naturalness of the shore, and theoccurrence of specific, ecologically significant habitatfeatures and is calculated from an extensive set ofvariables collected in the field. The habplot is a 15marea along the shoreline that extends 10m lakewardsinto the littoral zone, comprises the beach and bankface, and extends 15m landwards into theriparian zone. Rowan et al. (2006) recommendedsummarizing data from a survey of 10 equallyspaced habplots around the lake-shore perimeter,together with a less detailed assessment ofintervening sections of the lake. This study usednine habplots in three categories of disturbance.The Habitat Quality Assessment Score (HabQA)adapted the LHQA to describe habitat diversity,naturalness, and occurrence of specific habitatfeatures within single habplots to derive an assessmenttechnique for macroinvertebrates (McGoff andIrvine, 2009).

Macroinvertebrate sampling schedule and collectionof LHS data

In spring/autumn 2009 and spring 2010macroinvertebrate sampling and LHS surveys werecarried out across 51 lakes located in eightEuropean geographical regions and within sevencountries (northern Germany (nine lakes), Denmark(two lakes), Sweden (nine lakes), Finland (fourlakes), Ireland (nine lakes) and Great Britain (threelakes), see Miler et al., 2013). In Italy, 15 lakes weresampled to accommodate expected geographicaldifferences between the subalpine (northern Italy;eight lakes) and Mediterranean region (centralItaly, seven lakes) (Miler et al., 2013). Within eachhabplot composite samples of macroinvertebrateswere collected, with sampling effort in proportionto substrata and vegetation cover.

Within each lake, three sites were selected a priorito represent categories of ‘undisturbed’, ‘softalterations’ and ‘hard alterations’ based on visualexamination of the littoral, shore, and riparianzones. Soft alterations were characterized byriparian clear-cutting and/or recreational beaches.Hard alterations were characterized by retainingwalls and/or rip-rap. For some lakes a modifieddesign was needed since not all pressure categorieswere present, resulting in only two instead of threemorphological pressure categories. An analysis ofvariance (see Results) showed that most stressorindex components indicated better morphologicalconditions at ‘soft alteration’ than at ‘hardalteration’ sites, so for clarity this paper refers torespective pressure categories of ‘no’, ‘medium’

and ‘high alteration’.

Stressor index development

AsMiler et al. (2013) showed that macroinvertebratecommunities could be grouped into country/regionpairs of Germany/Denmark, Ireland/Great Britain,Sweden/Finland and Central Italy/Northern Italy,stressor index development was done separately foreach of these four regions. The LHS parametersused for the stressor index development were eitherbased on presence (=1)/absence (=0) values, orclassified by areal cover or volume occupied suchthat 0 (= 0%), 1 (>1–10%), 2 (>10–40%), 3(>40–75%) and 4 (>75%) (Rowan et al., 2006).

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In addition, in the LHS protocol an arealcover/volume, e.g. of littoral macrophytes or riparianvegetation cover types, was marked with a tick, whenit was present, but only in low percentage values,i.e. >0–1% (Rowan et al., 2006). A tick was classifiedas 0.5, falling between 0 (=0%) and 1 (>1–10%).

To test the usefulness of the LHS stressorvariables (i.e. stressor index components) formacroinvertebrate-based biotic assessment indexconstruction, the pressure driver ‘morphologicaldegradation’, indicated as no, medium and highalteration, was tested against response variables ofthe biotic lake shore assessment index LIMCO(Miler et al., 2013), and the ‘Margalef diversity’ ofmacroinvertebrates and ‘No. EPTCBO taxa’ (thenumber of Ephemeroptera, Plecoptera, Trichoptera,Coleoptera, Bivalvia and Odonata taxa).

For each geographical region, LHS parameterswere tested with an analysis of variance (ANOVA)for significant differences among pressure categories(Table S1, Supplementary material). LHS parametersdifferentiating well among the three pressure levelswere combined into new composite parameters: 1.

‘Number of habitats’ comprising the number ofLHS parameters bedrock/boulder, cobbles/pebbles,sand/silt/clay, coarse woody debris, overhangingvegetation, roots and macrophytes (macrophyteswere described with the LHS parameter ‘TotalPVI’, i.e. ‘Total percentage volume inhabited bymacrophytes’); 2. ‘Sum of vegetation cover types’,calculated as the sum of the classified areal cover ofthe six vegetation cover types in the riparian zone(Table 1) and the ‘Sum of macrophyte types’ as thesum of the classified areal cover of the 12macrophyte types in the littoral zone (Table 1); 3.‘Habitat diversity’ calculated as the Shannon–Wienerdiversity of the littoral habitats used in the ‘Numberof habitats’; 4. ‘Sum of coarse woody debris(CWD)/roots/overhanging vegetation’ calculated asthe sum of the classified areal cover of CWD, rootsand overhanging vegetation; 5. ‘Dominant landcover’, assessed visually in the field and codified byexpert judgement into two categories, natural andartificial, to which values of 1 (high morphologicalcondition) and 5 (poor morphological condition)were assigned, respectively (Tables 1 and S2);

Table 1. Description of the components of the morphological stressor index

Stressor index component Explanation

Number of habitats Presence/absence of the following habitat types in the littoral zone:bedrock/boulder, cobbles/pebbles, sand/silt/clay, coarse woodydebris, overhanging vegetation, roots, macrophytes

Habitat diversity Shannon–Wiener diversity of the classified areal cover of the following habitattypes in the littoral zone: bedrock/boulder, cobbles/pebbles, sand/silt/clay,coarse woody debris, overhanging vegetation, roots, macrophytes

Total PVI Classified total percentage volume inhabited by macrophytes in the littoral zone

Sum of macrophyte types Sum of the classified areal cover of the 12 macrophyte types (according toRowan et al., 2008) in the littoral zone: ‘Liverworts/mosses/lichens’,‘Emergent broad-leaved herbs’, ‘Emergent reeds/sedges/rushes’,‘Floating-leaved (rooted)’, ‘Free-floating’, ‘Submerged broad-leaved’,‘Submerged short, stiff-leaved’, ‘Submerged linear-leaved’, ‘Submergedfine- and dissected-leaved’, ‘Filamentous algae’, ‘Phytobenthos’, ‘Seaweeds’

Sum of vegetation cover types Sum of the classified areal cover of the six vegetation cover types (accordingto Rowan et al., 2008) in the riparian zone: tall vegetation (> 5m height): treeswith diameter > 0.3m and < 0.3m; medium vegetation (< 5m and > 0.5m height):‘woody shrubs and saplings’, ‘tall herbs and grasses’; shortvegetation (< 0.5m height): ‘woody shrubs and seedlings’, ‘herbs, grasses and bryophytes’

Sum of CWD/roots/ overhanging vegetation Sum of the classified areal cover of coarse woody debris (CWD), roots andoverhanging vegetation in the littoral zone

Pressure index Number of human pressures (assessed over the entire site, see Table S3). Pressureindex =Number of Category 1 pressures (within a 50m buffer zone around thehabplot) + 2 Number of Category 2 pressures (within a 50m buffer zone aroundthe habplot) + 2 Number of Category 1 pressures (within a habplot) + 4 Numberof Category 2 pressures (within a habplot).

Natural/artificial dominant land cover type Presence/absence of natural/artificial dominant land cover type (according toRowan et al., 2008; Table S2) in the riparian zone

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and 6. ‘Pressure index’ constructed from thenumber of human pressures within a habplot andwithin a 50m buffer zone around a habplot andcodified by expert judgement into impact categoriesof low (category 1) and high (category 2) (Tables 1and S3). High impact human pressures wereweighted four times within a habplot and doublewithin a 50m buffer zone around a habplot(Table 1). Low impact human pressures wereweighted double within a habplot and were notweighted within a 50m buffer zone around ahabplot (Table 1). Only the LHS parameter ‘TotalPVI’ was not used as a composite parameter.

Separately for each geographical region, thecomposite parameters were normalized from 0 to1, with the 5% percentile value rescaled to 0 andthe 95% percentile value rescaled to 1. Valuessmaller than 0 or larger than 1 were reset to 0 and1, respectively. In a second step these values werereclassified into a scale from 1 (best condition) to 5(worst condition), analogous to ecological statusclasses (1=high to 5=bad) according to the ECWFD (Council of the European Communities,2000). The rescaling for all composite parameterswas performed after Hering et al. (2006) andVlek et al. (2004) as: 0–0.2→ 5, 0.21–0.4→ 4,0.41–0.6→3, 0.61–0.8→2, 0.81–1→ 1. For thedefinition of the ‘Pressure index’ an inverse scalingwas applied so that an increase in number reflectsincreasing degradation, in contrast with the othercomposite parameters. The composite parameters‘Number of habitats’, ‘Habitat diversity’, ‘TotalPVI’, ‘Sum of macrophyte types’, ‘Sum of vegetationcover types’, ‘Sum of CWD/roots/overhangingvegetation’, ’Pressure index’ and ‘Natural/artificialdominant land cover type’ (Tables 1, S2 and S3)were tested with an ANOVA and displayedsignificantly lower values (after rescaling to valuesfrom 1 to 5) at natural sites compared with highalteration sites and/or at natural sites comparedwith medium alteration sites (Figures 1–3, Table 2).These composite parameters were then chosen asstressor index components.

Subsequently, 24 stressor index combinationswere calculated as the unweighted means of fourto five stressor index components. Analogous tothe process for composite parameters (see above),these were tested with an ANOVA for how well

Figure 1. One-way ANOVA of the stressor index component ‘Habitatdiversity’ (for details see Tables 1 and 2) among the three pressurelevels, with an ANOVA (H= high alteration, M=medium alteration,N= natural), performed separately for the four geographical regions(D=Germany, DK=Denmark, IRL= Ireland, GB=Great Britain,S = Sweden, FIN=Finland, IC = central Italy, IN= northern Italy).A and B indicate significantly different mean values. ‘Habitatdiversity’ is normalized from 1 to 5, 1 indicating the maximum and 5

the minimum value of ‘Habitat diversity’.

Figure 2. One-way ANOVA of the stressor index component ‘TotalPVI’ (for details see Tables 1 and 2) among the three pressure levels,with an ANOVA (H= high alteration, M=medium alteration,N= natural) performed separately for the four geographical regions(D=Germany, DK=Denmark, IRL= Ireland, GB=Great Britain,S = Sweden, FIN=Finland, IC = central Italy, IN= northern Italy). Aand B indicate significantly different mean values. ‘Total PVI’ isnormalized from 1 to 5, 1 indicating the maximum and 5 the

minimum value of ‘Total PVI’.

Figure 3. One-way ANOVA of the stressor index component ‘Pressureindex’ (for details see Tables 1 and 2) among the three pressure levels,with an ANOVA (H= high alteration, M=medium alteration,N= natural) performed separately for the four geographical regions(D=Germany, DK=Denmark, IRL= Ireland, GB=Great Britain,S = Sweden, FIN=Finland, IC = central Italy, IN= northern Italy).A, B and C indicate significantly different mean values. The ‘Pressureindex’ is normalized from 1 to 5, 1 indicating the minimum and 5 the

maximum value of the ‘Pressure index’.

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they distinguished among no, medium and highalteration sites. The stressor index combination thatreflected the differences among the pressure levelsbest, and especially between high/medium shoremodification and natural sites, was chosen for eachgeographical region (Figure 4 and Table 2). Sincethe values of the stressor index components wereclassified on a scale from 1 (best condition) to 5(worst condition), the values of the stressor indexcombinations were also in this range (Figure 4).The variables ‘Number of habitats’/‘Habitat

diversity’ and ‘Total PVI’/‘Sum of macrophytetypes’ showed a substantial cross-correlation(Spearman-rank correlations) with Rho>0.8, andhence were not used together in the same stressorindex combination.

The strengths of the morphological stressor indexand the HabQA, as an alternative environmentalstressor index (McGoff and Irvine, 2009), to indicatemorphological alterations were compared bycorrelating both indices with the multimetric indexLIMCO and the metrics ‘Margalef diversity’ and‘No. EPTCBO taxa’. Spearman-rank correlationswere performed owing to heteroscedasticity andnon-normal distribution of the data. Cohen (1988)classified correlations with Rho±0.1 to ±0.29 asweak, ±0.30 to ±0.49 as of intermediate strengthand ±0.5 to ±1.0 as of high strength. All statisticalanalyses were performed using SAS version 9.1.3(SAS Institute Inc., Cary, NC).

RESULTS

Stressor index development

ManyLHSparameters revealed significant differencesamong pressure categories (Table S1). Modeldevelopment of grouping LHS parameters intorelevant composite parameters provided better

Table 2. Description of the components of the morphological stressor index developed for the four geographical regions (D=Germany/DK=Denmark, IRL= Ireland/GB=Great Britain, S = Sweden/FIN=Finland, IC = central Italy/IN= northern Italy)

F Duncan test F Duncan test F Duncan test F Duncan test

Stressor index component D/DK IRL/GB S/FIN IC/IN

Number of habitats 20.58 *** Na Hb Mb 25.44 *** Na Hb Mb 28.15 *** Na Hb Mb 10.89 *** Na Hb Mb

Habitat diversity 14.34 *** Na Hb Mb 14.74 *** Na Hb Mb 23.51 *** Na Hb Mb 6.93 ** Na Hb Mb

Total PVI 26.05 *** Na Mb Hb 2.05 ns Na Ma Ha 4.86 ** Ma Na Hb 29.91 *** Na Mb Hb

Sum of macrophyte types 19.56 *** Na Mb Hb 4.02 * Na Mb Hb 4.03 * Na Mab Hc 14.88 *** Na Ha Mb

Sum of vegetationcover types

60.81 *** Na Hb Mb 26.54 *** Na Mb Hc 53.05 *** Na Mb Hc 14.44 *** Na Mb Hb

Sum of CWD / roots /overhanging vegetation

51.48 *** Na Mb Hb 25.66 *** Na Hb Mb 24.36 *** Na Mb Hb 9.35 *** Na Mb Hb

Pressure index 163.69 *** Ha Mb Nc 47.30 *** Ha Mb Nb 10.17 *** Ma Ha Nb 99.23 *** Ha Mb Nc

Natural/artificialdominant landcover type

226.68 *** Na Mb Hb 6.06 ** Na Hb Mb 57.22 *** Na Mb Hb 65.68 *** Na Mb Hb

Bold font indicates the stressor index components chosen for each geographical region. Differences of the stressor index components amongthe three pressure levels N (natural), M (medium alteration) and H (high alteration) were tested with an analysis of variance (ANOVA,with subsequent Duncan post hoc test). Significance levels of the ANOVA results are indicated as follows: ns = not significant, *= P< 0.05,**= P< 0.01, ***= P< 0.001. Duncan test results at the three pressure levels N, M and H are sorted in decreasing order with respect tothe mean values of the respective stressor index component. Significant differences among alteration levels are indicated by the superscriptletters a, b and c

Figure 4. One-way ANOVA of the chosen stressor index variants (fordetails see Tables 1 and 2) among the three pressure levels, with anANOVA (H= high alteration, M=medium alteration, N= natural)performed separately for the four geographical regions(D=Germany, DK=Denmark, IRL= Ireland, GB=Great Britain,S = Sweden, FIN=Finland, IC = central Italy, IN= northern Italy).

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discrimination of morphological degradation thansingle LHS parameters (Tables 2 and S1). Theseintegrated patterns of morphological degradationgenerally provided effective stressor index componentsacross the geographical range of this study(Figures 1–3, Table 2, S1).

Natural sites had a higher habitat diversity, highernumber of habitats, higher macrophyte volume(i.e. ‘Total PVI’), higher share of CWD/roots/overhanging vegetation, a higher number ofvegetation cover types and predominantly naturalland use (Figures 1–3, Table 2). Significantdifferences in the ‘Pressure index’ indicated thatnatural sites had a lower number of pressureswithin a habplot and/or within a 50m buffer zonearound a habplot, compared with medium or highalteration sites, or both. The variables ‘Pressureindex’, ‘Number of habitats’/‘Habitat diversity’and ‘Total PVI’/‘Sum of macrophyte types’ wereselected for all four final regional stressor indices(Table 2). The ‘Sum of CWD/roots/overhangingvegetation’ was only used in the stressor indices forGermany/Denmark and central Italy/northernItaly and the ‘Natural/artificial dominant landcover type’ only in the stressor index for centralItaly/northern Italy (Table 2). The ‘Sum ofvegetation cover types’ was identified as a suitablestressor index component in the three regionsGermany/Denmark, Ireland/Great Britain andSweden/Finland, but not in central Italy/northernItaly.

Correlations of the stressor index and HabQA withLIMCO and selected metrics

Spearman-rank correlations of the multimetric lakeshore assessment index LIMCO with the stressorindex and the HabQA showed better results forthe former. For all four regions the absolute Rhovalues were larger in LIMCO–stressor index thanin LIMCO–HabQA correlations (Table 3).Spearman-rank correlations of the ‘Margalefdiversity’ and the ‘No. EPTCBO taxa’ with thestressor index and the HabQA yielded similarresults, with larger absolute Rho values in‘Margalef diversity’/‘No. EPTCBO taxa’–stressorindex than in ‘Margalef diversity’/‘No. EPTCBOtaxa’–HabQA correlations (Table 3).

DISCUSSION

Comparison with existing lake shore morphology indices

Overall the stressor indices provided effectivesummaries of the LHS parameters in a regionallyspecific multimetric assessment of the categories ofhydromorphological impact on littoral invertebrates.This is an important finding for the assessment ofshoreline modifications in lakes, and in directsupport of the policy needs of the WFD (Council ofthe European Communities, 2000) and HabitatsDirective (Council of the European Communities,1992). Common metrics of quality assessment canhelp to align the goals and implementation of bothDirectives. The results also allow wider applicabilityand development for planning and restoration oflakes generally in support of evidence-basedconservation and management (Nel et al., 2009;

Table 3. Rho values from Spearman-rank correlations of the biotic lakeshore assessment index LIMCO, ‘Margalef diversity’ and ‘No.EPTCBO taxa’ (i.e. the number of Ephemeroptera, Plecoptera,Trichoptera, Coleoptera, Bivalvia and Odonata taxa) with the stressorindex and the HabQA for the four geographical regions(D=Germany / DK=Denmark, IRL= Ireland / GB=GreatBritain, S = Sweden / FIN=Finland, IC = central Italy /IN= northern Italy)

Region Index type Metric Rho

DE/DK Stressorindex LIMCO �0.69 ***IRL/GB Stressorindex LIMCO �0.47***S/FIN Stressorindex LIMCO �0.39 ***IC/IN Stressorindex LIMCO �0.49 ***DE/DK HabQA LIMCO 0.62 ***IRL/GB HabQA LIMCO 0.46 ***S/FIN HabQA LIMCO 0.19 *IC/IN HabQA LIMCO 0.39 ***DE/DK Stressorindex Margalef diversity �0.61 ***IRL/GB Stressorindex Margalef diversity �0.27 **S/FIN Stressorindex Margalef diversity �0.16 nsIC/IN Stressorindex Margalef diversity �0.38 ***DE/DK HabQA Margalef diversity 0.58 ***IRL/GB HabQA Margalef diversity 0.16 nsS/FIN HabQA Margalef diversity �0.016 nsIC/IN HabQA Margalef diversity 0.26 **DE/DK Stressorindex No. EPTCBO Taxa �0.51 ***IRL/GB Stressorindex No. EPTCBO Taxa �0.33 ***S/FIN Stressorindex No. EPTCBO Taxa �0.20 *IC/IN Stressorindex No. EPTCBO Taxa �0.38 ***DE/DK HabQA No. EPTCBO Taxa 0.46 ***IRL/GB HabQA No. EPTCBO Taxa 0.18 nsS/FIN HabQA No. EPTCBO Taxa 0.017 nsIC/IN HabQA No. EPTCBO Taxa 0.22 *

Significance of the Spearman-rank correlations is indicated as follows:ns = not significant, *= P< 0.05, **= P< 0.01, ***= P< 0.001

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Linke et al., 2011) and for the development ofbiotic indicators of ecosystem health (Reice andWohlenberg, 1993).

There are only a few studies on the developmentof site-specific stressor indices for lakes (US EPA,2007, 2009; McGoff and Irvine, 2009; Peterlin andUrbanič, 2012; Kaufmann et al., 2014a, b, c). The‘Riparian vegetation cover complexity index’ iscalculated as the whole-lake mean of thepercentages of areal cover of six vegetation covertypes at 10 sampling stations/habplots analogousto the ‘Sum of vegetation cover types’ (US EPA,2009; Kaufmann et al., 2014c). The ‘Littoral covercomplexity index’ consists of the whole-lake meanof the areal cover percentages of 10 littoral featuretypes at 10 sampling stations/habplots, e.g. the ‘%CWD’, the ‘% boulders’ and the ‘% emergedvegetation’ (US EPA, 2009; Kaufmann et al.,2014c). Hence, the ‘Littoral cover complexity index’contains features that have been used in this studyto compose the ‘Sum of CWD/roots/overhangingvegetation’, the ‘Habitat diversity’/‘Number ofhabitats’ and the ‘Total PVI’/‘Sum of macrophytetypes’.

A common characteristic of both indicesdeveloped by the US EPA (2009) and Kaufmannet al. (2014a, c) is the use of the geometric meanbetween the upper and lower diameter bounds ofeach areal cover class (i.e. a mean percentagevalue), rather than the cover class itself as used inthis study. For example, for the cover class 3 (outof the cover classes 0.5, 1, 2, 3 and 4, seeMethods) the mean of 40% and 75%, i.e. 57.5%,was used by Kaufmann et al. (2014a, c) instead ofthe value 3 of the respective cover class. Althoughvalues of both indices of the 10 samplingstations/habplots are averaged to assess the wholeshoreline, site-specific values for the single samplingstations/habplots can be calculated. Hence, ‘Riparianvegetation cover complexity index’ and ‘Littoral covercomplexity index’ can be used also as site-specificdescriptors of habitat diversity and thus as potentialstressor indices for macroinvertebrate assessmentmethods.

The ‘Lakeshore anthropogenic disturbance index(intensity and extent)’ assesses the extent of humanshoreline alterations at the whole lake level(Kaufmann et al., 2014a, b, c). Altogether the

occurrences of 12 types of human disturbancealong the shore are calculated for the ‘Lakeshoreanthropogenic disturbance index (intensity and extent)’and agriculture-related activities are weighted with thefactor 5 compared with non-agriculture-relatedactivities (Kaufmann et al., 2014a, c). The ‘Lakeshoreanthropogenic disturbance index (intensity and extent)’bears strong similarities to the ‘Pressure index’developed in this study: both indices take into accountthe distance of human disturbances from a samplingstation/habplot by weighting those present within asampling station/habplot double compared with thosepresent within a 50m buffer zone around the samplingstation/habplot (Kaufmann et al., 2014a, c). However,it is questionable if the influence of agriculture-relatedactivities should be emphasized as strongly as in the‘Lakeshore anthropogenic disturbance index (intensityand extent)’ with respect to macroinvertebratecommunities. In contrast to the ‘Pressure index’ in thisstudy, the ‘Lakeshore anthropogenic disturbance index(intensity and extent)’ by Kaufmann et al. (2014a, b, c)contains a proportional measure, i.e. the ‘proportionof the 10 sampling stations/habplots with at least onetype of anthropogenic activity present’. It is a wholelake, not a site-specific index, and thus cannot be usedas such at site level and to potentially calibratemacroinvertebrate assessment methods.

Similar to this study, the composition of all threeUS EPA indices differed among geographicalregions (Kaufmann et al., 2014a, b, c). Morespecifically, three geoclimatic regions of the USA,the Eastern Highlands, the Plains and Lowlands,and the West were distinguished, and within eachgeoclimatic region different lake types accordingto geology, regional climate and lake morphology(Kaufmann et al., 2014a, b, c). This supports theneed to account for regional specificities during thedevelopment of lake shore stressor indices.

Correlations of the stressor index with macroinvertebratemetrics

The biotic assessment index LIMCO and themetrics ‘Margalef diversity’ and ‘No. EPTCBOtaxa’ correlated better with the stressor indexdeveloped in this study than with the HabQA(McGoff and Irvine, 2009). LIMCO wasdeveloped with the same dataset used in the EU

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WISER project (Lyche Solheim et al., 2013; Mileret al., 2013). The HabQA correlated well withtaxon richness in the study of McGoff and Irvine(2009) (Pearson correlation, r=0.62), but wasdeveloped using data from only one lake (LoughCarra in the Republic of Ireland). Furthermore,several HabQA components are not applicable toother types of lakes. Lough Carra is surroundedby diverse wetland habitats whereas most lakes ofthe WISER project rarely contained more than oneadjacent wetland habitat, even at sampling sitesclassified as natural. In McGoff and Irvine (2009)the ‘Number of wetland habitats’ contributed up to5 out of a 16 maximum attainable HabQA score,which leads to a high sensitivity of the HabQAscore for the impact of the number of encounteredwetland habitat types. As another example, theHabQA parameter ‘Presence of a trash line’ whichmay be present frequently at medium alterationsites (i.e. recreational beaches), is often missing athighly altered sites (i.e. hard bank engineering sites)because the trash does not gather so well nearriprap or stone walls. Further preliminary workstudying the relationship between severalmacroinvertebrate metrics with the HabQA in thegeographical region Germany/Denmark (Gräwe,Porst and Miler, unpublished results) suggested theneed for development of a stressor index applicableto a wider geographical scale.

The applicability of LHS variables to indicatemorphological effects on macroinvertebratecommunities has already been shown by Jurcaet al. (2012) and McGoff et al. (2013a). The LHSfeatures ’Lakewards extension of macrophytes’,‘Macrophyte PVI’, ‘Diversity of macrophytetypes’, ‘Diversity of littoral features’, ’Vegetationstructural complexity’ and ‘Diversity and extent ofnatural land-cover types’ identified as relevant formacroinvertebrate diversity and communitystructure (Jurca et al., 2012; McGoff et al., 2013a)were also included as components of the stressorindex developed in this study. More specifically,‘Vegetation structural complexity’ and ‘Diversityof macrophyte types’ in Ireland/Great Britainand ‘Macrophyte PVI’ in central Italy/northernItaly (McGoff et al., 2013a) correspond in thisstudy to the stressor index components ‘Sum ofvegetation cover types’, ‘Sum of macrophyte types’

and ‘Total PVI’ in the respective geographicalregions.

The stressor index developed in the present studyshows strong similarities to the Lake ShoreModification Index (LMI) developed by Peterlinand Urbanič (2012) for two Slovenian alpine lakes.Components of the LMI include the ‘Presence oflittoral wooden structures’, ‘Potential alterations ofwater depth and natural substratum’, ‘Land use’and the ‘Presence and extent of buildings, roads andinfrastructure for recreational and commercialpurposes’. In contrast the variables ‘Habitatdiversity’, the ‘Number of habitats’ and a measureof the complexity of riparian vegetation are notincluded in the LMI despite their known importancefor macroinvertebrate communities (Erman, 1984;Brauns et al., 2007a, 2011; Remsburg and Turner,2009; Strayer and Findlay, 2010). However, thelimited domain of the study to the two alpine lakes(Solimini et al., 2012) provides high uncertainty ofwider application without further regional testingacross other lake types.

Use of the stressor index for conservation andrestoration planning

The stressor index developed here includesmorphological shoreline parameters that showedsignificant statistical relationships with compositionalmetrics of benthic macroinvertebrate communities.More specifically, the stressor index can be used toquantify human morphological alteration ofshorelines in order to calibrate biotic responses.Given the continued global pressures on lakes,including impacts from shoreline developments,further development and adaptation of simple andeffective multimetric monitoring provides animportant component of lake management andregional conservation planning. Lake littoral andriparian zones provide essential areas for theecological dynamics and productivity of many lakes(Vadeboncoeur et al., 2002; Solimini et al., 2006) andassessing those functions is a component ofunderstanding not only the conservation value, butalso the wider range of ecosystem services theyprovide. It is increasingly recognized that monitoringlittoral macroinvertebrates can provide useful andcost effective assessment methods (Solimini et al.,

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2012; Miler et al., 2013). The morphological stressorindex developed in this study correlated well withthe multimetric macroinvertebrate-based assessmentindex LIMCO and furthermore with two metricsindicating diversity (‘Margalef diversity’) and thepresence of disturbance-sensitive taxa (‘No.EPTCBO ‘taxa’). The stressor index, therefore, hasthe necessary qualities to act well as a surrogatemeasure (see also Nel et al., 2009, 2011; Linkeet al., 2011) for the conservation value of lakeshores and for assessing impacts from shorelinedevelopment. The study furthermore adds evidenceof the general relevance of rapid assessment ofthe habitat structure of lakes as developed by theLHS and the US EMAP procedures for lakemanagement and policy support.

ACKNOWLEDGEMENTS

This study was supported by the European Union7th Framework Programme (FP7) project WISER(Water bodies in Europe: Integrative Systems toassess Ecological status and Recovery, contractno. 226273), and supplemented by a number ofnational funding sources. Leonard Sandin wasfunded by the Marie Curie Actions of theEuropean Commission (FP7-2010-PEOPLE-IEF)through the FRESHCLIM project (project nr273215). We thank Steffen Bader, MarcelloBazzanti, Valentina Di Vito, Davide Frosali,Daniel Gräber, Marlies Gräwe, Iain Gunn,Francesco Livretti, Marlen Mährlein, LucianaMastrantuono, Elise Münch, Jürgen Schreiber,Sonja Stendera, Pau Torrents and Regina Wilkesfor substantial help in successfully overcoming themany challenges associated with extensive lakehabitat survey, field sampling, and sampleprocessing tasks. We are very grateful to Philip R.Kaufmann and one anonymous reviewer for theirconstructive suggestions and proposed correctionsthat greatly improved the manuscript.

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