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Ecological Indicators

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ynthesising the trait information of European ChironomidaeInsecta: Diptera): Towards a new database

ónia R.Q. Serraa,∗, Fernando Cobob,c, Manuel A.S. Graç aa, Sylvain Dolédecd,aria João Feioa

MARE – Marine and Environmental Sciences Centre, Department of Life Sciences, University of Coimbra, Largo Marquês de Pombal,004-517 Coimbra, PortugalDepartment of Zoology and Physical Anthropology, Faculty of Biology, University of Santiago de Compostela,5782 Santiago de Compostela A Coruña, SpainEstación de Hidrobioloxia ‘Encoro do con’, Castroagudín, 36617 Vilagarcía de Arousa, Pontevedra, SpainUniversity Lyon 1, UMR 5023 LEHNA, Biodiversité des Ecosystèmes Lotiques, Bât Forel, 69622 Villeurbanne Cedex, France

r t i c l e i n f o

rticle history:eceived 30 April 2015eceived in revised form 28 July 2015ccepted 17 September 2015vailable online 6 November 2015

eywords:hironomidaeuzzy codingioassessmentltonian traitsrinnelian traits

a b s t r a c t

Chironomidae are among the most conspicuous and ecological diverse group of freshwater invertebrates.They may dominate unimpacted communities in abundance and biomass, accounting for more than 50%of macroinvertebrate species in standing and flowing waters. In deep zones of eutrophic lakes and highlyhuman-impacted streams, they are often the only family of aquatic insects remaining. In bioassessmentprogrammes, Chironomids are often identified at the family and subfamily levels, due to difficulties inthe taxonomic identification of larvae resulting from a high intrinsic morphological similarity. This maypotentially result in bias as, similarly to Ephemeroptera, Trichoptera or Plecoptera, Chironomidae species,which are replaced along natural and human-impacted gradients due to differences in their ecologicalrequirements. Recently, multiple trait-based approaches have been proposed to complement taxonomic-based assessment of streams and rivers using macroinvertebrates. However, the lack of specific traitinformation for Chironomidae prevents their use in the functional assessment of communities. Therefore,here, we aimed to: (1) develop a trait database for European Chironomidae genera that can be used infuture bioassessment and ecological studies; (2) evaluate, by multivariate analyses, whether our newdatabase provides additional information on Chironomidae compared to the trait information providedin the commonly used European trait database (Tachet et al., 2010); and (3) determine whether thenew information on Eltonian traits (proxy to biological traits) translates the most accepted phylogeneticrelationships among Chironomidae subfamilies. We gathered information on 744 species and 178 genera,for 37 traits covering 186 trait categories, and found substantial differences between our database and thecommonly used European trait database. In addition, available information on traits was not always inagreement with phylogenetic relationships among subfamilies. Orthocladiinae and Chironominae whichare considered sister groups in evolutionary terms actually showed confident trait relatedness based on

Eltonian traits tree while the remaining relationships between subfamilies are questionable. In addition,different traits can occur in closely related taxa depending on the environmental drivers operating ontheir habitats. Our study reveals that the usually accepted redundancy within the Chironomidae familyand subfamilies must be a product of averaging the information from finer taxonomic resolution addedto the substantial lack of information for this insect group.

© 2015 Elsevier Ltd. All rights reserved.

∗ Corresponding author. Tel.: +351 916635553.E-mail addresses: [email protected] (S.R.Q. Serra), [email protected] (F. Cobo)

[email protected] (M.J. Feio).

ttp://dx.doi.org/10.1016/j.ecolind.2015.09.028470-160X/© 2015 Elsevier Ltd. All rights reserved.

, [email protected] (M.A.S. Graç a), [email protected] (S. Dolédec),

dx.doi.org/10.1016/j.ecolind.2015.09.028http://www.sciencedirect.com/science/journal/1470160Xhttp://www.elsevier.com/locate/ecolindhttp://crossmark.crossref.org/dialog/?doi=10.1016/j.ecolind.2015.09.028&domain=pdfmailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]/10.1016/j.ecolind.2015.09.028

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. Introduction

Chironomidae is the most widely distributed dipteran family; itsarvae have colonised terrestrial habitats, as well as marine habi-ats, and fresh waters. The family can tolerate a wide range ofnvironmental conditions, and some taxa can be found in extremenvironments including ice-cold glacial trickles, hot springs, andather unusual environments such as sub-desert steppes, aquaticygropetric habitats and leaf axis of plants or rot-hole of treesArmitage et al., 1995; Cobo and Blasco-Zumeta, 2001; Vallenduuknd Pillot, 2007; Pillot, 2009, 2013).

Chironomidae richness worldwide is estimated at 20,000pecies, but the lack of adequate description and identification dif-culties at finer taxonomic resolution such as genus or speciesuggest that this number is underestimated (Armitage et al., 1995;offman and Ferrington, 1996).

In fresh waters, the Chironomidae family can account for50% of the macroinvertebrate community (Armitage et al., 1995;offman and Ferrington, 1996), it is particularly abundant in reser-oirs, lakes and in lowland rivers and urban streams, and may behe only insect remaining in highly human-impacted water bodiesCoffman and Ferrington, 1996; Raunio et al., 2011; Andersen et al.,013). Chironomidae play a key role in organic matter processingy consuming fine particles of organic matter and transferringnergy and nutrients to upper trophic levels since they repre-ent prey for an array of organisms, including other invertebrates,sh and birds. They thus have a great influence over productivitynd population dynamics of top consumers. Finally, Chironomidaessemblages change along the river continuum similarly to EPT taxaEphemeroptera, Plecoptera and Trichoptera) (e.g. Prat et al., 1983;obo and González, 1990, 1991; Lindegaard and Brodersen, 1995;untí et al., 2009) and according the lake typology (Saether, 1979;rodersen and Lindegaard, 1999; Mousavi, 2002).

Historically, Chironomidae family played an important role inake and running water classification based on its trophic level andaprobity, which reflected the production and decomposition ofrganic material (Saether, 1979). Fossil chironomid assemblageslso provided insights on past environmental conditions (Walker,001; Brooks, 2006) whereas abnormalities in body parts, mostlyouthpart deformities, have been used as indicator of contaminant

ffects in both water and sediments (Rosenberg, 1992). There-ore, the bioassessment potential of Chironomidae is great, beingf particular importance in environments where other inverte-rate groups are not present. This family includes taxa tolerant toifferent water salinity, pH, depth, temperature, organic carbon,utrients and oxygen concentration (e.g. Laville and Vinç on, 1991;chmidt et al., 2010; Servia et al., 2004) among other environmen-al variables. Some Chironomidae occur in good quality waters (e.g.heopelopia spp., Conchapelopia pallidula, Orthocladius thienemannind Zavrelimyia melanura; Vallenduuk and Pillot, 2007; Marzialit al., 2010; Pillot, 2013), whereas others are rather tolerant to highrganic contamination and high trophic degrees (e.g. Chironomusiparius, Rheocricotopus fuscipes and Rheocricotopus chalybeatus;rodersen and Quinlan, 2006; Marziali et al., 2010; Prat et al., 2013)r low levels of dissolved oxygen (e.g. Procladius sp. and Eukief-eriella claripennis; Bazzanti and Seminara, 1987; Marziali et al.,010). Despite the wide range of responses to the environmen-al gradients, the bioassessment of running waters generally use aoarse taxonomic resolution for depicting Chironomidae assem-lages (Rosenberg, 1992; Coffman, 1995; Hawkins and Norris,000) because of the difficulties associated with the morphological

dentification of larvae beyond family and subfamily.

Besides the usual taxonomy-based approaches, trait-based

pproaches are being increasingly used as an alternative to assesstream biological integrity (Dolédec and Statzner, 2010). Traits mayelp to reveal the cause of impairment and give an indirect insight

cators 61 (2016) 282–292 283

into which ecosystem functions may be affected by human dis-turbance (Archaimbault et al., 2005; Culp et al., 2011; Feio andDolédec, 2012). Since traits are indicators of function, communitytrait composition allows a better understanding of stream function-ing (Vieira et al., 2006). However, few researchers have attemptedto quantify trait information for Chironomidae, with some of themachieving a trait database at the subfamily and tribe levels (seeTachet et al., 2010 for Europe and Poff et al., 2006 for North Amer-ica). Few works gathered information at the species or genus levelconsidering a reduced number of traits and/or taxa (see Franquet,1996 for France; and Vieira et al., 2006 for North America).

Here, we had three objectives. First, we aimed to categorisethe European Chironomidae genus characteristics into a set of21 traits and 110 categories used in Tachet et al. (2010) for allaquatic macroinvertebrates and a set of 16 additional traits spe-cific to Chironomidae. Secondly, we investigated the distributionand variability of trait patterns within Chironomidae subfamilies,using the new trait database. Given the great variability reflectedin trait heterogeneity within each Chironomidae subfamily, weexpected that trait information gathered at higher or lower levelof taxonomic resolution would determine differences in traitspatterns gathered within each subfamily. To determine whetherour database was actually providing additional assessment infor-mation, we contrasted trait patterns given by our database atthe genus level with that obtained at the subfamily-level in thetrait database of Tachet et al. (2010), which is commonly usedin bioassessment studies. Finally, assuming that heritable traits(Eltonian) of organisms could disclose evolutionary processes oper-ating among taxa, Chironomidae subfamily traits relatedness wasexpected to reflect their phylogenetic distances across subfamilies.Therefore, we compared the subfamily Eltonian trait relatednesswith the most accepted Chironomidae phylogeny found in litera-ture (Saether, 2000; Cranston et al., 2010, 2012).

2. Methods

2.1. European freshwater Chironomidae traits

We defined an a priori list of European species and genera. Dueto a lack of consensus among European Chironomidae fauna taxalists and guides (e.g. Illies, 1978; Andersen et al., 2013; Sorianoet al., 1997; Cobo et al., 2001; Vallenduuk and Pillot, 2007; Pillot,2009, 2013) we followed the Fauna Europaea database (http://www.faunaeur.org/see, Saether and Spies, 2013) with 194 and1261 genera and species entries distributed among eight subfami-lies: Buchonomyiinae, Chironominae, Diamesinae, Orthocladiinae,Podonominae, Prodiamesinae, Tanypodinae, and Telmatogetoni-nae.

Our database includes the most widespread European Chirono-midae species, covering a wide geographic area, different categoriesof water bodies at different altitudes and latitudes. Lotic and lenticfreshwater systems were given equal importance, being mentionedin at least 20% and 19% of total references used, respectively. Ref-erences covering temporary freshwater systems and hygropetrichabitats were also included. Brackish habitat references were alsoused to support the trait salinity preferences. Other references useddid not focus on a specific type of aquatic habitat but addressedecological, physiological, morphological and/or life history char-acteristics of specific taxa. Whenever possible, the references forwhich species were first described in Europe were exploited. Infor-mation gathered from publications between 1931 and 2013 (ca.

150), including articles, books and a few PhD theses, were used todescribe the species traits.

The initial list was composed of 21 traits and 110 categories ofbiological, physiological traits and ecological requirements, as used

http://www.faunaeur.org/seehttp://www.faunaeur.org/seehttp://www.faunaeur.org/seehttp://www.faunaeur.org/seehttp://www.faunaeur.org/see

2 al Indicators 61 (2016) 282–292

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Table 1Traits and their categories and codes used in the European Chironomidae database.Eltonian and Grinnellian traits are ordered with (1) traits coded by the first authorof this paper, (2) traits adapted from the European database of Tachet et al. (2010)and (3) traits similar to those in Tachet et al. (2010).

Traits Categories Code

Eltonian

Emergencesea-son

Winter EMWINTSpring EMSPRISummer EMSUMMAutumn EMAUTU

Flight perioda

Winter FLYWINTSpring FLYSPRISummer FLYSUMMAutumn FLYAUTU

Emergence durationShort period (some hours to fewdays; 15 d.) EDWIDE

Number of eggs per eggmass

1000 EGGMAS4

Length of larvaldevelopment (months)

≤1 DEVLARV12 DEVLARV23 DEVLARV34 DEVLARV45 DEVLARV56 DEVLARV67 DEVLARV78 DEVLARV8≥9 DEVLARV9

Hibernationphase/instar(overwinter diapause)

Egg HIBEGG1st instar HIBINST12nd instar HIBINST23rd instar HIBINST34th instar HIBINST4

Distance travelled inaquatic habitat (m)

1000 DISAQU4

Distance travelled inaerial habitat (m)

1000 DISAER4

Tube constructionTube absent TUBNONTube without shape, unorganised TUBUNOTube rigid TUBRIG

HaemoglobinPresent HBPRESAbsent HBNONE

Eltonian adapted from Tachet et al. (2010)

Respiration (#tracheas)12 tracheas TRACH16 tracheas TRACH23 tracheas TRACH3

Substrate relation

Free living FREELVBurrower BURROWMiner MINERFixed (substrate or plants) FIXED

Potential number ofgenerations peryear/Voltinisma

1 GENY12 GENY23 GENY3>3 GENYM

Resistanceforms/habits

Eggs, gemmule, statoblast, shell RFEGGCocoons RFCOCResistant stages to desiccation RFSTADiapause or quiescence RFDIAPNone RFNON

84 S.R.Q. Serra et al. / Ecologic

n Tachet et al. (2010); some traits and categories were adaptediven the type of information available for Chironomidae (Table 1).

set of 16 additional traits specific to Chironomidae larvae includedespiration (number of tracheas), tube construction, number of eggser egg mass, flight period, duration of emergence, distance ofquatic and/or aerial dispersion, hibernation stages, length of larvalevelopment, oxygen saturation preferences, presence/absence ofaemoglobin, migration type, depth preferences, optimal temper-ture interval for emergence, chlorinity, and general/gross habitatTable 1). Traits that differed among Chironomidae life stages wereathered for the fourth larval instar (except for number of eggser egg mass, flight period, and others) and categorised into Grin-ellian or Eltonian traits according to the terminology of Devictort al. (2010) and Mondy and Usseglio-Polatera (2014). Grinnellianraits are related to taxon requirements and performance over aange of environmental conditions considering biotic and/or abioticesources (e.g. pH, temperature, and food preferences), whereasltonian traits focus on the impact of the species on its environ-ent, emphasising their functional role in the ecosystem rather

han their response to particular resources (e.g. body size, voltin-sm, feeding habits).

Following Franquet (1996), the affinity of species or genera torait category was quantified using the number of references citinghis category for a given taxon. The higher the number of referencesssociating a taxon to a trait category, the greater the affinity ofhat taxon to that particular trait category. Taxa with no availablenformation on a trait were scored ‘zero’ for all categories, and werereated as missing values, being replaced by the mean of all taxaaving information for a given trait category. Trait-affinity scoresere further treated as frequency distributions and standardised to

um 1 for a given taxon-trait combination, to give the same weighto each taxon and to each trait in further analyses. This procedures known as fuzzy coding (Chevenet et al., 1994).

Total number of genera described per trait was estimated toefine the best described traits, i.e., with information gathered forore than 50% of the European genera. The genus trait database is

rovided as Supplementary data with the list of the references usedo extract trait information and the list of species used to describeach genus.

.2. Comparison between the two databases

To determine whether our trait database built at the speciesnd genus levels involved different distributions of taxa (subfamily,ribes) compared to the database of Tachet et al. (2010), we useduzzy Correspondence Analysis (FCA) that enables the joint ordi-ation of taxa and trait categories (Chevenet et al., 1994). FCA uses

matrix (n × p) to interpret the relationships between trait cate-ories (p) and resemblances among individual taxa (n). The affinityrofile of each trait category among taxa enables the positioning ofach trait category at the weighted average of taxa that uses thisategory. The variance of these positions corresponds to a corre-ation ratio (i.e. the highest the correlation ratio the highest theeparation of taxa across trait categories) and FCA maximises theverage correlation ratio across traits when FCA was performedeparately on Grinnellian and Eltonian traits. For comparison withhe European trait database of Tachet et al. (2010) (hereafter TDB

Tachet DataBase), the fuzzy information of our database at theenus level (hereafter GDB – Genus DataBase) was averaged at theubfamily and tribe levels. Afterwards, these average affinity scoresere rescaled so that their sum, for each of these coarser taxonomic

roups for a given trait, equals one. Thereby, traits were described at

he same scale for all different taxonomic levels of resolution. Whilehe biological information in Tachet et al. (2010) describes onlyhe Podonominae, Tanypodinae, and Orthocladiinae subfamiliesnd the Chironomini and Tanytarsini tribes, our database included

Deeper penetration in substrateduring dryness

RFSUB

S.R.Q. Serra et al. / Ecological Indicators 61 (2016) 282–292 285

Table 1 (Continued)


Eltonian taken from Tachet et al. (2010)

Dispersal

Passive aquatic AQUPASActive aquatic AQUACTPassive aerial AERPASActive aerial AERACT

Feeding habits

Fine sediment eater DEFEEShredder SHRScraper, grazer SCRFilter FFEEDTPredator (piercer, cutting orswallowing)

PRED

Parasite PARAS

Life cycle duration≤1 year LCEQ1>1 year LCMO1

Maximal body size ofthe 4th instara (mm)

2.5–5 SIZE2>5–10 SIZE3>10–20 SIZE4>20–40 SIZE5

Reproduction type

Free isolated eggs FREEGGAttached isolated eggs CEMEGGClutches (cemented or attached) CEMCLUFree clutches FRECLUEndophytic clutches CLUVEGTerrestrial clutches CLUTERAsexual reproduction ASEXU

Type of aquatic stagesa

Egg EGGLarva LARVAPupa PUPAAdult (imago) IMAGO

Grinnellian

Chlorinity (g Cl−1)

0.3–1 CHLOR2>1–3 CHLOR3>3–10 CHLOR4>10 CHLOR5

Oxygen saturationpreferencesa

Stable always > 50% OXSTABUnstable 10–50% OXUNST10–12 10OPTEM12>13–15 13OPTEM15≥16 OPTEM16

Type of migrationHorizontal MIGHORVertical MIGVER

Grinnellian adapted from Tachet et al. (2010)

Food typea

Fine sediment + microorganisms SEDMICDebris < 1 mm DEBRI1Plant debris > 1 mm DEBRI2Living microphytes MICPHYLiving macrophytes MACPHYDead animals DEADANLiving microinvertebrates MICINVLiving macroinvertebrates MACINVLiving vertebrates VERTEBWood WOOD

Table 1 (Continued)


Bacteria BACTER

Temperaturepreferences

Psychrophilic 15 ◦C TTHERMEurythermic TEURYTHemistenothermic THEMIS

pH preferencesa

4–5 4PH5>5–6 5PH6>6–7 6PH7>7–8 7PH8≥8 PHM8

Substrate preferencesa

Stone, boulder, cobble, pebble STONESGravel GRAVELSand SANDSilt SILTMacrophytes and filamentousalgae

MAPFAL

Microphytes MIPHYTTwigs, roots BRANCHLitter, finer organic matter LITTERMud ORGMUDInvertebrates MINVERWood microhabitat WOODMMosses MOSSES

Trophic statuspreferences

Oligotrophic OLIGTRMesotrophic MESOTREutrophic EUTRHypertrophic (Mesohumic) HYPTR1Hypertrophic (Polyhumic) HYPTR2

Longitudinaldistribution alongstream channela

Crenon CRENOEpirhithron EPIRITMetarhithron METRITHyporhithron HYPRITEpipotamon EPIPOTMetapotamon METPOTEstuary ESTUAROutside river system OUTFLUKryon (glacial fead habitats) KRYON

Transversaldistribution alongstream channela

River channel CHANNELBanks, connected side-arms BANKSDPonds, pools, disconnectedside-arms

POOLPN

Marshes, peat-bog MARSHBTemporary waters TEMPORLakes LAKESGroundwaters UNDERGHygropetric HYGROPArtificial water medium(Impoundment reservoirs, ditch,canal, pipeline, sewage filter bed),

ARTIF

Water surface WATSURBottom BOTTOM

Grinnellian taken from Tachet et al. (2010)

Saprobity

Xenosaprobic XENOSAPOligosaprobic OLIGSAP�-Mesosaprobic BMESSAP�-Mesosaprobic AMESSAPPolysaprobic POLYSAP

Salinity preferencesaFresh water FRESHWBrackish water BRACKI

Altitudinalpreferencesa (m)

1000–2000 (piedmont) ALTI2>2000 (Alpine) ALTI3

Current velocitypreferencesa (cm s−1)

None VELO125–50 VELO3>50 VELO4

a Traits that were described for more than 50% of European genera.b Refers to the preferential habitat: lentic or lotic, small or large running water

bodies, or semi-terrestrial habitats.

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dditional tribes: Pseudochironomini (Chironominae), Diamesinae,elmatogetoninae, Buchonomyiinae, and Prodiamesinae.

Finally, to assess the variability in community trait compositionxplained by the difference between GDB and TDB, we computedetween-class variance (with class as type of database; see Dolédecnd Chessel, 1987; ter Braak, 1988) and tested its significancegainst simulated values obtained after 999 permutations of theows of the trait-composition arrays.

.3. Chironomidae subfamily trait relatedness

FAC was performed on Eltonian traits of genera averaged at theubfamily level. The resulting FCA coordinates of the 8 subfami-ies along the 7 axes (n − 1; in which n is the smallest rank of therait matrix; here, the number of subfamilies) was used to yield theuclidean distance matrix among subfamilies. Finally, neighbour-oining (Saitou and Nei, 1987; Studier and Keppler, 1988) allowedstimated a tree among subfamilies. Bootstrap procedure was usedo assess tree’s accuracy and the ‘confidence’ of each tree biparti-ion (Efron et al., 1996). This representation was visually comparedith the most accepted evolutionary relationships of Chironomi-ae subfamilies derived from cladistics analysis (Saether, 2000) andolecular analysis (Cranston et al., 2010, 2012).Statistics and graphical outputs were computed with the ade4

Thioulouse et al., 1997; Chessel et al., 2004; Dray et al., 2007) andpe libraries (Paradis et al., 2004; Paradis, 2012) implemented in Rreeware (R Development Core Team, 2013).

. Results

.1. European freshwater Chironomidae trait database

Our final list contained 178 Chironomidae genera and 744pecies distributed among 8 subfamilies. Biological informationn species and genera was found in the literature for ∼59% ofhe most widespread European species, and 92% of the Europeanenera for 37 traits (Table 2 and see in the information supplieds Supplementary data). From all of the gathered trait informa-ion, 11 Grinnellian and 4 Eltonian traits had information for morehan 50% of the European Chironomidae genera present in theatabase (indicated in Table 1). The best described Grinnellian traitsere: transversal distribution in streams and general/gross habitatreferences (>90% of genera present in the database). Food types,H tolerance, salinity preferences, longitudinal distribution alongtreams, altitude, substrate preferences, current velocity prefer-nces, oxygen and depth preferences were described for 53–74%f the genera. The best described Eltonian traits (for more than 95%f genera) were: maximal size of the fourth larval instar and type ofquatic stages. Potential number of generations per year (voltinism)

nd flight period were described for 50–53% of genera.

Chironomidae subfamilies with less information were theuchonomyiinae, Podonominae and Telmatogetoninae. Tanypo-inae, Orthocladiinae and Chironominae subfamilies had also

able 2enera and species diversity and percentage of genera and species described in the litera

Genus diversity Genus described Genus described

Buchonomyiinae 1 1 100 Chironominae 64 59 92 Diamesinae 11 9 82 Orthocladiinae 78 69 88 Podonominae 5 5 100 Prodiamesinae 3 3 100 Tanypodinae 30 30 100 Telmatogetoninae 2 2 100

Total 194 178

cators 61 (2016) 282–292

genera with less information such as Meropelopia sp. (Tany-podinae), Lappodiamesa sp. (Diamesinae), Stackelbergina sp.(Orthocladiinae), Gillotia sp. and Neostempellina sp. (Chironom-inae). Prodiamesinae were relatively well characterised; onlyMonodiamesa sp. had less information than the other 2 generaincluded in the subfamily (Prodiamesa and Odontomesa). A totalof 16 European genera of the Diamesinae, Orthocladiinae andChironominae subfamilies lacked trait information resulting inthe absence of entries in the database: Arctodiamesa, Pagastia,Arctosmittia, Bavarismittia, Boreosmittia, Corynoneurella, Lappoki-efferiella, Molleriella, Neobrillia, Prosmittia, Tavastia, Baeotendipes,Carbochironomus, Nilomyia, Olecryptotendipes, Synendotendipes.

The distribution of trait categories varied greatly across subfam-ilies for some traits (food types, salinity preferences, longitudinaldistribution, Fig. 1a–c), whereas for other traits, all Chironomidaebehaved in a very similar way (e.g. altitudinal preferences, pH tol-erance, number of generation per year, Fig. 1d–f). Tanypodinae,Orthocladiinae and Chironominae that contained the highest diver-sity of genera described (ca. 89% of all Chironomidae) covered wideecological amplitude. Other less diversified subfamilies were asso-ciated with specific environments: Buchonomyiinae (Buchonomyiathienemanni) were recorded at low altitudes and in lotic habi-tats, whereas Diamesinae had a higher affinity for upper reaches(e.g. kryon, reaches fed by ice-melt) with higher current veloci-ties and water temperature

S.R.Q. Serra et al. / Ecological Indicators 61 (2016) 282–292 287

Fig. 1. Proportions of subfamilies for selected traits and respective categories (see Table 1 for acronyms) with (a) food type, (b) salinity preferences, (c) longitudinal distribution,( .

sav2(sP

d) altitudinal preferences (e) pH tolerance, and (f) number of generations per year

pecies richness in comparison to other subfamilies, affinities couldlso vary within the same trait. For instance, the Diamesa sp. lar-ae showed affinities from small to large size categories (SIZE

to SIZE 4; Fig. 2), whereas Protanypus sp. showed larger sizesSIZE 4; Fig. 2). Considering food types, Diamesa sp. generally con-ume living microphytes such as diatoms (MICPHY; Fig. 2) whereasotthastia sp. feed on detrital particles (DEBRI1 and 2; Fig. 2).

Diamesinae subfamily also includes genera with a wider spectrumof food preferences (Protanypus sp., Boreoheptagyia sp.). Similarly,Chironominae subfamily includes opportunistic genera able to feed

on almost any food item (e.g. Chironomus sp. and Glyptotendipes sp.)and genera that live in woody microhabitats and introduce wood intheir diets, being true wood miners with the ability to digest woodfibres (e.g. Stenochironomus sp.; WOOD).

288 S.R.Q. Serra et al. / Ecological Indicators 61 (2016) 282–292

Fig. 2. Representation of two traits (maximal body size of the 4th larval instar and food type) and their categories (see Table 1 for acronyms) for two subfamilies (Chironominaeand Diamesinae). The information is presented against a taxonomic tree with two taxonomic levels: l1 (tribe) and l2 (subfamily). Chironominae (I2 Chrn) are represented int Tnyt).B porti(

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dsamSm

TCtt

he top leaf of the cluster by two tribes (Chironominii, I1 Chrn; and Tanytarsinii, I1rhp; Diamesini l1 Dmsn; and Protanypodini, l1 Prtn). The size of each square is proat right).

.2. Comparison between the two databases

FCA performed on Grinnellian traits showed low but significantifferences between the two databases (15% of variance explained;imulated p-value = 0.001; Fig. 3a, Table 3). Transversal distributionlong the stream channel, pH, and to a lesser extent food type, were

ore important contributors for the difference among databases.

ubstrate preferences and transversal distribution each explainedore than 30% of variance over first axis (32% and 40% respectively),

able 3orrelation ratios of Grinnellian traits for Chironomidae subfamilies/tribes (fromwo databases, the new developed in this work and existing in Tachet et al., 2010) onhe first-two axes of the fuzzy correspondence analysis and respective eigenvalues.

Variables (Grinellian traits) Axis

F1 F2

Temperature 0.079 0.131pH 0.050 0.112Trophic degree 0.009 0.068Saprobity 0.091 0.014Salinity 0.043 0.030Altitude 0.134 0.007Longitudinal distribution 0.183 0.052Transversal distribution 0.398 0.271Substrate preferences 0.319 0.081Current velocity 0.059 0.050Food type 0.135 0.086Eigenvalues 0.137 0.082

Diamesinae are represented in the below leaf by three tribes (Boreoheptagyiini, l1onal to the frequency of the corresponding trait category (on top) for a given genus

whereas altitude preferences, food type and longitudinal distri-bution explained 13%, 14% and 18% of the variance, respectively.Transversal distribution also had a high contribution to explaindistribution over the second axis (explaining 27% of the variance),whereas temperature and pH preferences were also relevant (13%and 11% variance explained, respectively).

FCA performed on Eltonian traits likewise revealed significantdifferences between databases (32.7%, simulated p-value = 0.002;Fig. 3b, Table 4) to which reproduction type, resistance form and,

Table 4Correlation ratios of Eltonian traits for Chironomidae subfamilies/tribes (from twodatabases, the newly developed in this work and the existing one from Tachet et al.,2010), on the first-two axes of the fuzzy correspondence analysis and respectiveeigenvalues.

Variables (Eltonian traits) Axis

F1 F2

Size (4th instar larva) 0.043 0.066Life cycle duration 0.334 0.035Voltinism 0.164 0.428Aquatic stage 0.019 0.043Reproduction 0.756 0.084Dispersal 0.066 0.024Resistance 0.792 0.017Feeding habits 0.139 0.099Respiration 0.004 0.000Substrate relation 0.158 0.102Eigenvalues 0.248 0.090

S.R.Q. Serra et al. / Ecological Indi

Fig. 3. Fuzzy correspondence analysis (FCA) plot performed on (a) Grinnellian traitsand (b) Eltonian traits of the two databases, considering the information existingat the subfamily level and few tribe levels for Chironominae. The information isgrouped by database with GDB, the European database developed using the genusinformation compiled at the subfamilies and tribe levels; and TDB, the database fromTachet et al. (2010) at the subfamily/tribe level. Subfamily or subfamily/tribe levelis identified by their 5 first letters (Bucho – Buchonomyiinae; Chiro – Chironomi-nae; ChirC – Chironomini; Diame – Diamesinae; Ortho – Orthocladiinae; Podon –Podonominae; Prodi – Prodiamesinae; Tanyp – Tanypodinae; ChirT – Tanytarsini;Telma – Telmatogetoninae); followed by a G for GDB plot, or a T for the TDB plot.Ellipses include 80% of the points for readability.

Fig. 4. Trait relatedness tree estimated among Chironomidae subfamilies using neighbouto each node represent the percentage of partitions present in bootstrap trees.

cators 61 (2016) 282–292 289

to a lesser extent, life cycle duration had the highest contributions.Resistance forms and reproduction type explained 79% and 76% ofthe variance, respectively, considering the first axis, followed by lifecycle duration, which explained 33% of the variation. The variancealong axis 2 was explained by voltinism (43% variance explained)and to a lesser extent by the substrate relation, explaining 10% ofthe variance.

3.3. Chironomidae subfamily trait relatedness

The neighbour-joining analysis performed on the 20 Eltoniantraits revealed the subfamilial trait similarity among Orthocladi-inae and Chironominae on one hand, and Podonominae withTanypodinae on the other hand (Fig. 4). The analysis also showedtrait similarity between Diamesinae and Prodiamesinae with Tany-podinae and Podonominae segregating them from Orthocladiinaeand Chironominae.

The accuracy of the tree assessed through the bootstrap anal-ysis give confidence to the group formed by Orthocladiinae andChironominae, with 100% of trees showing the same combination.All other nodes and bipartitions do not reveal a strong confidence,with confidence values below 0.44 revealing no trait relatednesssignal.

4. Discussion

Most studies in which Chironomidae were used at higher taxo-nomic resolution than subfamily or tribe are historically associatedto lakes, either considering subfossil Chironomidae assemblagesfor paleolimnological studies or the analysis of communities indeeper zones (Raunio et al., 2011). In running waters, the exten-sive use of Chironomidae in bioassessment is still a matter ofdebate. Some authors have suggested that assessments may bemore efficient by eliminating Chironomidae from the protocolsand by using resources for analysing additional sites (Hawkins andNorris, 2000; Rabeni and Wang, 2001). Some authors fully agreedwith the family-level and its ability to detect impairment (Móraet al., 2008), while others have even strongly recommended the useof a finer level of taxonomic resolution for Chironomidae, showingthat family level yielded much weaker assemblage-environmentrelationships, which emphasised the risk of reducing accuracy inbioassessment (King and Richardson, 2002).

Here, we defend the hypothesis that Chironomidae could beappropriate indicators of environmental conditions, as the same

taxonomic group includes tolerant (e.g. Chironomus) and sensi-tive (e.g. Diamesa spp.) taxa to human impacts (Armitage et al.,1995; King and Richardson, 2002; Lencioni et al., 2012). One mainproblem for bioassessment purposes arises from the difficulties

r-joining, given the Euclidean distances of their FCA coordinates. Values associated

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f taxonomic identification and the poor knowledge on traits athe genus or species level, contrary to other groups of freshwa-er invertebrates (e.g. Poff et al., 2006; Tachet et al., 2010). Aimingo fill this gap, information on 37 Chironomidae traits and 184rait categories was compiled in this study. The number of speciesnd genera covered by our database (59% and 92% respectively)ighlights the great effort that is still needed to understand theehaviour, physiology and ecological tolerances of Chironomidaepecies. Considering the Eltonian traits only, the development ofhe database clearly showed the poor information available in theiterature as only 4 of this biological type of traits were charac-erised for more than 50% of the European genera. Our databasehus identifies the genera and species to which more attentionhould be given in future studies due to reduced available informa-ion. One of the reasons for the reduced and uneven informationn Chironomidae traits is the fact that morphological and physio-ogical studies typically focus on Chironomus species (e.g. C. tentansnd C. riparius), because they are easy to keep in the laboratory andse in routine ecotoxicological tests (Ankley et al., 1994; Armitaget al., 1995; Penttinen and Holopainen, 1995).

A total of 16 European genera belonging to Diamesinae,rthocladiinae and Chironominae subfamilies lacked information

Supplementary data). The inability to characterise these generaay be due to their limited distribution so far (e.g. Molleriella,eobrillia) and their small number of species in Europe (e.g. Baeo-

endipes noctivagus, Nilomyia aculeata) or by their relatively recentr very recent discover (e.g. Olecryptotendipes Zorina 2007; Arc-osmittia Zelentsov 2006). One advantage of our database is thatny additional information available on references not used in theriginal dataset can be simply added to the information providedn Supplementary data.

Compared to pre-existing information (i.e., from Tachet et al.,010), the data that we compiled at the genus level resulted in sig-ificant differences in the separation of Chironomidae subfamilies.his suggests that differences in specialisation among Chironomi-ae occur primarily at higher levels of taxonomic resolution (genusnd species). Even at the genus level, generalisation should bearefully considered since environmental requirements, life historyraits, and sensitivity to anthropogenic pressure may vary consid-rably within a genus (Rossaro et al., 2006; Lencioni et al., 2007). Aigh number of species per family in many aquatic environmentssually suggests an extensive adaptive radiation by diversification

f ancestral species into several ecologically different species bydaptive morphological, physiological and/or behavioural diver-ence in those environments limiting the utility of the family level

ig. 5. Subfamily relationships among Chironomidae subfamilies given by: (a) cladistic Saether, 2000) and (b) and (c) molecular phylogenies by Cranston et al. (2010, 2012), res

cators 61 (2016) 282–292

in bioassessment (Hawkins and Norris, 2000; King and Richardson,2002).

It is common to find autoecological information at the subfamilylevel mentioning faunistic patterns along environmental gradients(e.g. longitudinal, altitudinal) such as the greater abundance ofDiamesinae and Orthocladiinae upstream, giving place to Tany-podinae and Chironominae downstream (Prat et al., 1983; Bitušíket al., 2006; Lencioni et al., 2007). Averaging trait affinities of Chi-ronomidae subfamilies showed that they were distinct from eachother considering some traits (e.g. maximal body size, food type);although the great trait diversity within each subfamily suggeststhat the subfamily level operating in the database of Tachet et al.(2010) is not appropriate. The latter database points out the eco-logical redundancy in the Chironomidae family and subfamilies,which may be simply due to the averaging operation, that masksthe real trait diversity of Chironomids. Such false redundancy washighlighted by others (e.g. Lenat and Resh, 2001) and may compro-mise the results of studies that attempt to recognise Chironomidaefaunistic patterns using a lower taxonomic level.

The differences between the subfamily/tribe trait patterns gath-ered at low and high levels of taxonomic resolution (TDB and GDBrespectively) were clear for pH tolerance, transversal distributionin the river channel, reproduction types, resistance forms, and, to alesser extent, food types and life-cycle duration. Food type is oftenconsidered a key factor in the distribution of Chironomidae speciesalong with temperature. Additionally, flow regime and pH also haveindirect influence on their distribution by regulating food avail-ability, quantity and quality (Lencioni et al., 2007; Vallenduuk andPillot, 2007). Consequently, differences in these traits can compro-mise multiple trait-based assessments.

A given set of traits in the organisms of the same species resultfrom the process of evolution and adaptation to specific environ-mental conditions. Thus, it is generally accepted that there is a linkbetween taxa phylogenetic relatedness and the traits they possess(Kraft et al., 2007). Usseglio-Polatera et al. (2000) recognised thattraits related to morphology, physiology and life history (Eltoniantraits) appeared to be more constrained by phylogeny than traitsrelated to behaviour and habitat preferences (Grinnellian traits).Therefore, we expected that the most recently diverged subfam-ilies would tend to share more Eltonian trait categories amongtheir taxa than subfamilies that diverged a long time ago from theChironomidae common ancestor. The tree estimated by neighbour-

joining revealed a small trait distance between Chironominae andOrthocladiinae subfamilies, which is in agreement with cladistics(Saether, 2000;Fig. 5a) and molecular phylogeny studies (Cranston

analyses using parsimony of morphological characters of adults, pupae and larvaepectively.

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t al., 2010, 2012; Fig. 5b and c). In fact, Orthocladiinae retain somencestral traits (e.g. respiration type given by the number of tra-heas), which slightly differentiates the two subfamilies, whereashe presence of other much more recent traits (e.g. presence ofaemoglobin) bring together the two subfamilies.

The fact that Eltonian trait patterns do not necessarily reflecthylogenetic relationships of the subfamilies means that envi-onmental drivers are operating differently in species leading toigher functional diversity, exposing the labile nature of traits.everal authors argued that more closely related taxa may note ecologically similar since the same ecological function canvolve through different pathways depending on the environ-ental drivers operating in the habitats, often called trait lability

hrough evolutionary time (Webb et al., 2002; Poff et al., 2006;avender-Bares et al., 2009). According to Poff et al. (2006), mul-iple trait-based approaches should precisely take advantage ofhe selection of traits relatively unconstrained by phylogeny (i.e.

ore evolutionary labile), with low statistical and phylogeneticorrelations, and more responsive to local selection, such as vol-inism, which tell more about the drivers and environmentallters that operate in the systems than about the history of theaxa.

. Conclusions

Among lacustrine macroinvertebrates, Chironomidae have beenell studied and pointed as a powerful paleo-environmental

ndicator when using preserved subfossil assemblages collectedrom lake sediments. The value of Chironomidae as an indica-or is not only associated to its wide distribution or communityomposition, but also to potential morphological responses tohanges in environmental conditions such as exposure to contam-nants. Despite its demonstrated importance and ecological role,n many freshwater studies (e.g. springs, streams, littoral of lakes)hironomidae are still disregarded or neglected with their iden-ification kept at family/subfamily levels. This has been limiting

more extensive use of Chironomidae in biomonitoring and thenowledge about autecology of taxa therein. Our study showshat Chironomids are indeed a quite diverse group with differ-nt ecological requirements and characteristics, and if used athe genus or species level, they have the potential to improvehe signals provided by ecological assessment tools, either inaxonomic-based structural assessments or in indirect functionalssessments using multiple-traits-based approaches. To furtherrove these insights, tests comparing both types of assessmentsased on sub-family level and genus/species level are needed.ur database, which is the first comprehensive European database

or Chironomidae traits at the genus level to the best of ournowledge, can be used for that purpose, as well as in ecologicaltudies on functional patterns of freshwater systems, especiallyhose including habitats that are traditionally considered lessiverse.

cknowledgments

This work was made possible by funding of the Portuguese Foun-ation for Science and Technology (FCT) through a PhD scholarshipSFRH/BD/80188/2011), the cotutelage between the University ofoimbra and the University of Lyon 1, and the cooperation betweenhe MARE, University of Coimbra, Portugal, and the LEHNA – Lab-

ratoire d’Ecologie des Hydrosystèmes Naturels et Anthropisés,niversity of Lyon, France. The research partly benefited from

he European Community’s Seventh Framework Programme, Grantgreement No. 603629-ENV-2013-6.2.1-Globaqua.

cators 61 (2016) 282–292 291

Appendix A. Supplementary data

Supplementary data associated with this article can be found,in the online version, at http://dx.doi.org/10.1016/j.ecolind.2015.09.028.

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