NORTH-WESTERN JOURNAL OF ZOOLOGY 15 (2): 117-126 ©NWJZ, Oradea, Romania, 2019 Article No.: e181301 http://biozoojournals.ro/nwjz/index.html

Species-area relationships for aquatic biota in several shallow lakes from the Fizeș Valley (Transylvania, Romania)

Karina Paula BATTES, Mirela CÎMPEAN*, Laura MOMEU, Anca Mihaela ȘUTEU, Giulia PAULIUC, Alexandru Nicolae STERMIN and Alin DAVID

Babeș-Bolyai University, Faculty of Biology and Geology, Department of Taxonomy and Ecology, 5-7 Clinicilor Str. 400006, Cluj-Napoca, Romania. *Corresponding author, M. Cîmpean, E-mail: [email protected]

Received: 18. November 2017 / Accepted: 03. July 2018 / Available online: 09. January 2018 / Printed: December 2019

Abstract. Species-area relationships (SAR) for aquatic invertebrates and phytoplankton were investigated in several shallow lakes from the Fizeș River catchment area. The lakes differed in their morphometric, physical-chemical, biotic and habitat characteristics, with two constant distinctive clusters: the lakes from the main river course, highly exploited for fish farming and generally charac- terized by higher organic and nutrient loads, and the lakes from the river tributaries, with better environmental conditions, in- creased depth and lower exploitation intensities. Lake area was a poor predictor of taxon richness for lacustrine invertebrates. Low- intensity exploitation regime, the presence of submerged vegetation, lake depth and natural land use in the catchment area repre- sented the most important independent variables explaining the number of taxa for both planktonic and benthic invertebrates. In case of phytoplankton, however, lake area was an important predictor for taxon richness. Our results represent the first holistic ap- proach to investigating SAR patterns of aquatic biota in natural and man-made lakes from the region.

Key words: explanatory variables, taxon richness, zooplankton, zoobenthos, phytoplankton.

Introduction germeier & Schlosser 1989, Hoagstrom & Berry 2006) or even aquatic pathogens (Lyons et al. 2010). Taxon richness represents an important dimension of biodi- Shallow lakes, natural or artificial, represent habitats versity in all ecosystems (Chapin et al. 2000, Gotelli & with important roles in biodiversity conservation due to Colwell 2001, Begon et al. 2006). Larger areas tend to support their ability to harbour various biotic communities (Down- a higher number of species, a relation well documented in ing & Leibold 2002, Angélibert et al. 2004, Williams et al. ecology for almost a century (Drakare et al. 2006). Numerous 2004, Scheffer et al. 2006, Davies et al. 2008). Limnetic com- factors affect this species-area relationship (SAR), from those munities are extremely diverse, including autotrophs, con- related to geography (like latitude, altitude, depth, climatic sumers of different orders and decomposers (Reynolds variation, isolation) to physical-chemical parameters, distur- 2004). The diversity variation patterns of lentic algae, bances and biological processes (like competition, predation, macrophytes, planktonic and benthic invertebrates or fish community succession) (Begon et al. 2006). tend to be complex and not straightforward, due to the mul- There are several SAR hypotheses (Jenkins & Buikema titude of environmental factors related to them (Dodson et 1998, Connor & McCoy 2001, Turner & Tjørve 2005). One of al. 2000, Declerck et al. 2005, Scheffer et al. 2006). the most influential is the equilibrium theory of island biogeog- According to the SAR, larger lakes were hypothesized to raphy (MacArthur & Wilson 1967), stipulating that smaller, sustain higher taxon richness. This was found to be true for isolated islands have lower species richness, which in turn phytoplankton, but the opposite applied in some other cases. depends on immigration and extinction rates. Other factors For zooplankton and zoobenthos, a higher number of taxa causing SAR include: habitat heterogeneity (larger areas sup- was found in deeper, moderately exploited lakes, with sub- port a higher diversity of habitats) (Williams 1943), interme- merged vegetation and a more natural land use in the water- diate disturbance conditions (higher diversity is common to re- shed. In this context, the aim of the present paper was to in- gions with moderate disturbances) (Connell 1978) or random vestigate the relationship between the number of taxa and placement (larger regions are simply larger ”nets”) (Coleman lacustrine characteristics for several shallow lakes from the 1981). Fizeș River Valley (Transylvania, Romania). Planktonic and SAR was analysed at different spatial scales, differing in benthic invertebrates represented the main focus of the re- latitude or altitude, for different species and habitats, aquatic search, but algal communities were also considered. or terrestrial (see review from Drakare et al. 2006). Among The Fizeș River catchment area represented a suitable aquatic habitats, shallow lakes represent a good research study area, because it harbours more than 450 ha of shallow topic with respect to SAR, due to their insular character, as lakes differing in size, depth, origin, exploitation intensity or isolated water bodies surrounded by dry land. Several stud- other physical-chemical characteristics (Sorocovschi 2008). ies revealed the taxon richness of various aquatic communi- Various biotic communities from the Fizeș Valley were pre- ties, connected to lacustrine characteristics (Declerck et al. viously described: algae (Gudasz et al. 2000, Momeu et al. 2005, Scheffer et al. 2006, Nolby et al. 2015). SAR was also 2006), microcrustaceans (Battes & Momeu 2014), zoobenthos considered for separate lentic communities, like phytoplank- (Coman et al. 2016), fish (Nădăşanu & Bud 2012), amphibi- ton (Borics et al. 2016, Naselli-Flores & Padisák 2016, Várbíró ans and birds (David 2008, Hartel et al. 2009). As far as we et al. 2017), zooplankton (Browne 1981, Dodson 1992, Hobæk know, no SAR analyses were conducted for lake planktonic et al. 2002, Karatayev et al. 2005, Dodson et al. 2009), benthic and benthic communities in the region, hence the novelty of invertebrates (Hall et al. 2004, Harris et al. 2011), fish (An- the present paper.

118 K.P. Battes et al.

Material and methods to genera for Ephemeroptera and Heteroptera, to families for Dip- tera, Odonata and Trichoptera and to various taxonomic levels for Study area the other groups (see the Appendix). The Fizeș catchment area (part of the Someș -Tisa system) is a hilly Relative abundance, frequency and the Shannon-Wiener diver- region of an average altitude of 383 m, located in the Transylvanian sity index were calculated for lacustrine animal communities. Multi- Plain, central Romania (Schreiber 2003). Most of the fish ponds from variate data analysis methods were used to observe aggregation the Fizeș River and its tributaries are man-made; Lake Știucii repre- trends in the sampling lakes based on abiotic characteristics (Multi- sents the only natural lake from the catchment (Sorocovschi 2005, ple Correspondence Analysis - MCA) and biotic data (Principal 2008) (Table 1). However, fish stocking is common in all water bod- Component Analysis - PCA). Partial Least Squares Regression (PLS- ies from the area, with lower exploitation intensities in the lakes lo- R) was used to obtain a simultaneous map of multiple dependent cated on the tributaries. The sampling lakes are included in two variables (Y, the number of taxa for lake invertebrates and phyto- Natura 2000 areas: ROSPA0104 Bazinul Fizeșului and ROSCI0099 plankton) and explanatory variables (X, lake characteristics, both Lacul Știucilor-Sic-Puini-Bonțida. Table 2 presents a list of abbrevia- quantitative and qualitative). PLS-R model, recommended when ex- tions used in the present paper. planatory variables are correlated (Tenenhaus et al. 2005), employed a set of 2 components to best explain the dependent variables from Sampling and analyses the present study. The Q²cum index, showing the global contribution Qualitative samples were collected in October 2012, May, August of the components to the predictive quality of the model, recorded and November 2013, using mesh nets of 55 µm for microcrustaceans, low values (0.2-0.3). However, R²Y and R²X cum indices (that corre- 250 µm for zoobenthos and 40 µm for phytoplankton. Samples were spond to the correlations between the explanatory and dependent preserved in 4% formaldehyde in the field. Water temperature, pH variables with the components) were closer to 1, indicating that the 2 and dissolved oxygen were measured using portable meters. Plank- components generated by PLS-R summarized well both the Xs and tonic microcrustaceans and algae were identified to the species level the Ys. Statistical analyses were performed using Xlstat software, using standard keys, while zoobenthic invertebrates were identified version 19.5.47467.

Table 1 Lakes from the Fizeș River catchment area considered for the present study and their basic characteristics (lake type, surface area and maximum depth according to Baciu (2006); Sorocovschi (2005) and Schreiber (2003), respectively).

Sampling lake Lake code GPS coordinates Location Type Surface area (ha) Maximum depth (m) Lake Cătina CA 46.841609° the Fizeș anthropic, fishpond 20-50 <2 N 24.129140°E Lake Florian FL 46.850625°N the Fizeș anthropic, fishpond <20 <2 24.094469°E Lake Geaca 2 GE2 46.865417°N the Fizeș anthropic, fishpond <20 <2 24.082666°E Lake Geaca 3 GE3 46.879268°N the Fizeș anthropic, fishpond <20 <2 24.085320°E Lake Sucutard 1 SU1 46.886678°N the Fizeș anthropic, fishpond 20-50 <2 24.077033°E Lake Sucutard 2 SU2 46.900800°N the Fizeș anthropic, fishpond 20-50 2-5 24.066993°E Lake Țaga Mare TM 46.932032°N the Fizeș anthropic, fishpond >50 <2 24.079264°E Lake Roșieni RO 46.844981°N the Ciortuș anthropic, reservoir <20 <2 24.085108°E Lake Năsal NA 46.931711°N the Suciuaș anthropic, reservoir <20 2-5 24.111816°E Lake Știucii LS 46.967030°N the Bonț natural 20-50 >5 23.900877°E

Table 2 Abbreviations used for lake characteristics and lacustrine biotic communities.

Lake characteristics Biotic communities DO - the average dissolved oxygen values measured in %C,O - percentage of Chironomidae and Oligochaeta; 2012 and 2013; %E,T,O - percentage of Ephemeroptera, Trichoptera and Odonata; EV - emergent vegetation around the lake (%) a - algae; EXPL - exploitation intensity (fish farming; low / high); as - number of β-α-mesosaprobic, α-mesosaprobic, α-mesosaprobic- LA - lake area (ha); polysaprobic and polysaprobic taxa; LOC - location of the lake (the main river course / the bs - number of β-mesosaprobic taxa; tributaries) et - number of eutrophic taxa LU - dominant land use around the lake (AGR - agricul- H - Shannon-Wiener index of diversity; tural; NAT - natural; RUR -rural) mc - microcrustaceans; MD - maximum depth (m); mt - number of mesotrophic taxa; pH - the average pH values measured in 2012 and 2013; no.tax - number of taxa; PVA - paludous vegetation area (ha); os - number of oligosaprobic and oligosaprobic-β-mesosaprobic taxa; SV - submerged vegetation (present at sampling). ot - number of oligotrophic taxa; zb - zoobenthos.

Aquatic biota SAR in the Fizeș Valley, Romania 119

Results preferences, they respond differently to habitat pressures, thus they better characterize the ecological status of shallow Lake characteristics lakes. Oligochaeta, Mollusca and Diptera (Chironomidae) The association between the sampling lakes was depicted us- were present in all zoobenthic communities, other groups ing MCA (Fig. 1). Two groups were clearly separated, based recorded high frequencies, given the lentic character of the on the main physical, chemical and geographical characteris- sampling sites: Odonata, Heteroptera or Ephemeroptera. tics. The first cluster included the lakes located on the Fizeș Zoobenthic communities of the lakes located on the main River tributaries (Lakes Roșieni - RO, Năsal - NA, and Știucii Fizeș River course recorded high percentages of Oligochaeta - LS), characterized by lower exploitation pressure, higher and Chironomidae, ranging from 70 to 97%, while lacustrine depth and natural pastures dominating the surrounding communities from the tributaries were characterized by high landscape. The second group contained the lakes from the percentages of Ephemeroptera, Trichoptera and Odonata main river course, located in agricultural landscape (Lakes (exceeding 50% for lakes Năsal and Știucii). Green algae Cătina - CA and Sucutard 1 - SU1) or rural regions (Lakes (Chlorophyta) and diatoms (Bacillariophyta) dominated Florian - FL, Geaca 2 - GE2, Geaca 3 - GE3, Sucutard 2 - SU2 phytoplankton communities in all sampling lakes, with per- and Țaga Mare - TM), characterized by intensive fish farm- centages exceeding 50%. The vast majority of algal taxa indi- ing, lower depth and more alkaline waters. Lake area was cated high quantities of organic matter (β-mesosaprobic to usually lower in the first group. Paludous vegetation areas, polysaprobic conditions) and eutrophic waters. however, varied among the lakes, similar to the percentage Biotic parameters determined similar aggregation of the of emergent vegetation encircling the lakes. sampling lakes, when visualized in a PCA biplot (Fig. 2): the lakes located on the tributaries remained as a different Lake biota group. TM was also included in this cluster, but its location High numbers of taxa were recorded in the shallow lakes was not well linked with the two axes. Zooplankton and analysed for the present paper: 42 microcrustacean species, zoobenthos parameters referred to the number of taxa, 38 zoobenthos taxa and 305 phytoplankton species. Pooled Shannon-Wiener index of diversity and the percentage of data were used, including all sampling seasons (see the Ap- different zoobenthic groups. Since algal communities repre- pendix). sent a reliable tool in lacustrine ecological status assessment The most frequent microcrustacean species (Frequency > (Dokulil 2003), the sampling lakes were also classified de- 70%) were ubiquitous for freshwater pelagic habitats: Alona pending on the number of algal taxa indicating different rectangula, Bosmina longirostris, Chydorus sphaericus, Cyclops saprobic conditions (a direct measure of water organic pollu- vicinus, Eucyclops serrulatus proximus and Thermocyclops tion) and/or different trophic conditions (a measure of water oithonoides. On the other hand, microcrustacean communities nutrient loads). from the lakes located on the Fizeș tributaries included high The number of animal taxa and the Shannon-Wiener percentages of species with low frequencies: 45% in Lake values were significantly positively correlated to each other Năsal or 31% in Lake Roșieni. and to the percentage of benthic Ephemeroptera, Trichoptera For zoobenthic fauna, biodiversity assessment should and Odonata. On the other hand, microcrustacean parame- consider all taxa at low taxonomic resolution, rather than ters were poorly discriminated. one group identified to the species level (Labat 2017).Various Better ecological conditions were depicted in the lakes benthic groups include different guilds or environmental from the Fizeș tributaries (LS, NA and RO): a higher number

Figure 1 Multiple Correspondence Analysis (MCA) plot (axes F1 and F2: 57.66 %) of the shallow lakes consid- ered for the present study and the most important lake characteristics; abbreviations as in Tables 1 and 2. 120 K.P. Battes et al.

Figure 2 Principal Component Analysis (PCA) biplot (axes F1 and F2: 69.23%) for the sampling lakes, and their aggregation based on different biotic parameters; abbreviations as in Tables 1 and 2.

of animal taxa, higher diversity index values, higher per- the explanatory variables in the regression model: the centages of benthic non-tolerant taxa (Ephemeroptera, greater the absolute value of a coefficient, the greater the Trichoptera and Odonata), lower loads of nutrients and de- weight of the variable. Low-intensity exploitation regime, composing organic matter, shown by numerous algal species the presence of submerged vegetation, lake depth and natu- indicating oligosaprobic to β-mesosaprobic conditions and ral land use near the lake represented the most important oligotrophic to mesotrophic conditions. independent variables explaining the number of taxa for both planktonic and benthic invertebrates (Fig. 4 B, C). On SAR the other hand, the number of phytoplankton taxa was best SAR refers to the dependence of the number of taxa in a re- predicted by different variables: rural land use near the lake, gion (S) on the area (A). It follows a power function depicted high intensity exploitation regime, emergent vegetation en- as: S = cAz, where c and z are constants. The slope parameter circling the lake and lake area (Fig. 4 D). (z) estimates the rate of increase in species number as area increases, while the intercept parameter (c) depends on envi- ronmental factors and sampled taxa (Connor & McCoy Discussions 2001). The relationship between the number of lacustrine inver- Our results contradicted SAR in case of aquatic inverte- tebrate taxa and the lake area did not follow the expected brates, both planktonic and benthic. If lakes were to be con- trend. The slope parameter was negative for microcrusta- sidered isolated islands, the number of lacustrine taxa ceans and zoobenthic invertebrates, showing a higher num- should increase with the lake area: many studies support ber of taxa in smaller lakes (Fig. 3 A, B). Maximum depth this observation for zooplankton (Browne 1981, Dodson better described the relationship between invertebrates and 1992, Shaw & Kelso 1992, O'Brien et al. 2004, Karatayev et al. their environment, since deeper lakes supported higher 2005) and for zoobenthos (Hall et al. 2004, Moraes et al. numbers of taxa (Fig. 3 D, E). The number of phytoplankton 2014). In our data set, however, exploitation intensity, the taxa, on the other hand, tended to increase with the lake presence of submerged vegetation, lake depth and natural area, and to decrease with the lake depth (Fig. 3 C, F). How- land use around the lake were better predictors than surface ever, the coefficient of determination (R2) was low in all area. Similar alternative explanations for various taxa rich- cases. ness were described in the literature (Hall et al. 2004, O'Brien Higher numbers of lacustrine invertebrate taxa were re- et al. 2004, Declerck et al. 2005, Karatayev et al. 2005, Am- corded in the lakes located on the Fizeș tributaries exposed sinck et al. 2006, Hassall et al. 2011, Merrix-Jones et al. 2013). to minimum exploitation intensities, as shown by the corre- Other drivers of aquatic invertebrate richness in shallow lation diagram produced in the Partial Least Squares Regres- lakes were found to be water chemistry: pH, nutrient con- sion model (Fig. 4 A). Four explanatory variables were centration or conductivity (Declerck et al. 2005, Dodson et al. highly influential for building the PLS-R model (variable 2009, Tavernini et al. 2009, Harris et al. 2011), hydroperiod importance for the projection >1): exploitation intensity, pH, (Moraes et al. 2014) and primary production (Dodson 1992, the presence of submerged vegetation and maximum depth. Dodson et al. 2000, 2009). Indeed, pH represented a highly The goodness of fit of the statistical model was appropri- influential variable for our regression model, since lakes in- ate, with R2 values reaching 0.729, 0.651 and 0.747 for the tensely exploited for fish farming (CA, SU1) tended to record taxon number of microcrustaceans, zoobenthic invertebrates a more alkaline pH. and phytoplankton, respectively. Goodness of fit standard- So, lake area alone was a poor indicator of taxon richness ized coefficients were used to compare the relative weight of in aquatic invertebrates from the Fizeș River Valley, since

Aquatic biota SAR in the Fizeș Valley, Romania 121

Figure 3 Species-area relationships (SAR) between biotic communities (microcrustaceans, zoobenthos and phytoplankton) and lake characteristics (lake area: A, B, C and maximum depth: D, E, F)

small lakes recorded high faunal diversities (proven by the was explained in the literature by the presence of non- negative slope values z of the species-area relationship isolated (mainland) areas (Connor & McCoy 2001), equiva- model). These findings agree with the literature. Declerck et lent to the metapopulation model (Hanski & Gyllenberg al. (2005) related species richness to submerged vegetation 1997), with colonists from inside the sampling area. Indeed, and total phosphorus, rather than lake area. Nolby et al. aquatic invertebrates from the study lakes could be consid- (2015) indicated lake state and fish biomass as the major ered as part of a metacommunity, consisting of local com- drivers of macrophyte and invertebrate richness. Scheffer et munities that exchange colonists (Wilson 1992, Leibold et al. al. (2006) proved that smaller lakes, with lower fish densities 2004). Higher connectivity should lead to higher species but higher macrophyte abundances could support remark- richness, in accordance with the classic island biogeography able diversity of invertebrates, amphibians and birds. model (MacArthur & Wilson 1967). This was also described In our study, lake depth represented a better predictor for aquatic invertebrates (Dodson 1992, Merrix-Jones et al. for invertebrate taxon richness than lake area. The shallow 2013). On the other hand, other studies found no direct cau- character of the study water bodies (<7m) led to a clear in- sality between the two parameters (Scheffer et al. 2006, Har- crease of zooplankton and zoobenthic taxa in deeper lakes, ris et al. 2011, Moraes et al. 2014, Nolby et al. 2015). Our as previously described in the literature (O'Brien et al. 2004, data showed that moderate isolation increased taxon rich- Amsinck et al. 2006, Begon et al. 2006). ness, since the lakes located on the tributaries sustained Species-area relationship model obtained for the Fizeș higher microcrustacean and zoobenthic number of taxa. Valley lakes recorded low slope values (z), showing low Similar results were reported for zooplankton (Hobæk et al. taxon accumulation with increasing lake depth. This trend 2002, Cottenie et al. 2003).

122 K.P. Battes et al.

Figure 4 Results of the Partial Least Squares Regression (PLS-R) model: the correlation between dependent variables (in blue, Y), inde- pendent variables (in red, X) and the sampling lakes on axes t1 and t2 of the first PLS-R component (A); standardized coefficients of the explanatory variables (95% confidence interval) for the number of taxa (as dependent variable) of microcrustaceans (B), zooben- thic invertebrates (C) and algae (D) (abbreviations as in Tables 1 and 2)

The patterns of phytoplankton taxon richness differed for aquatic invertebrates in various natural lakes, since the from the aquatic invertebrate ones in the Fizeș Valley shal- present study relied mostly on data characteristic to man- low lakes, as described in other studies dealing with various made habitats. taxonomic groups (Declerck et al. 2005). Larger lakes sup- Our findings are crucial to the implementation of man- ported higher numbers of phytoplankton taxa, as reported agement plans in the Fizeș Valley area (ROSPA0104 and RO- previously in the literature (Borics et al. 2016, Naselli-Flores SCI0099), since most of the lakes considered for the present & Padisák 2016, Zębek & Szymańska 2017). However, other study are exploited for fish farming. Keeping human inter- variables explained the number of algal taxa in the study ference to moderate intensities, by controlling recreational lakes: the rural land-use near the lakes and high exploitation fishing, reed harvesting, siltation and nutrient inputs from intensity, probably related to higher inputs of nutrients in the catchment is a key factor in biodiversity conservation at a the lakes. Phytoplankton taxon richness decreased in deeper regional scale. lakes (unlike for aquatic invertebrates), probably because deeper, darker water layers are not favourable for auto- trophs. Our results showed similar patterns for zooplankton and References zoobenthos communities in the shallow lakes from the Fizeș Amsinck, S.L., Strzelczak, A., Bjerring, R., Landkildehus, F., Lauridsen, T.L., Valley: more taxa were found not in larger lakes (as SAR Christoffersen, K., Jeppesen, E. (2006): Lake depth rather than fish predicts), but deeper ones, with submerged vegetation and planktivory determines cladoceran community structure in Faroese lakes– with an intermediate disturbance regime due to lower ex- evidence from contemporary data and sediments. Freshwater Biology 51(11): 2124-2142. ploitation intensity and natural land use in the catchment Angélibert, S., Marty, P., Céréghino, R., Giani, N. (2004): Seasonal variations in area. However, subsequent studies should investigate SAR the physical and chemical characteristics of ponds: implications for Aquatic biota SAR in the Fizeș Valley, Romania 123

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+ Appendix 1 and 2 - 2 pages Appendix 1 supplementary Microcrustacean species identified in the shallow lakes from the Fizeș River Valley during 2012 and 2013 Appendix 2. Zoobenthic taxa identified in the shallow lakes from the Fizeș River Valley during 2012 and 2013

Aquatic biota SAR in the Fizeș Valley, Romania 125

Appendix 1 supplementary Microcrustacean species identified in the shallow lakes from the Fizeș River Valley during 2012 and 2013 (abbre- viations: CA - Lake Cătina; FL - Lake Florian; GE2 - Lake Geaca 2; GE3 - Lake Geaca 3; SU1 - Lake Sucutard 1; SU2 - Lake Sucutard 2; TM - Lake Țaga Mare; RO -Lake Roșieni; NA - Lake Năsal; LS - Lake Știucii)

Microcrustacean taxa / Sites CA FL GE2 GE3 SU+ SU2 TM RO NA LS Branchiopoda, Cladocera Alona guttata + + + + Alona rectangula + + + + + + + + Alonella excisa + Bosmina longirostris + + + + + + + + Bosmina (Eubosmina) longispina + Ceriodaphnia dubia + Ceriodaphnia pulchella + Chydorus sphaericus + + + + + + + + Daphnia cucullata + + Daphnia longispina + Daphnia magna + Daphnia pulex + Diaphanosoma orghidani + + Disparalona rostrata + Dunhevedia crassa + Graptoleberis testudinaria + Ilyocryptus agilis + Ilyocryptus sordidus + + Leydigia acanthocercoides + Leydigia leydigi + Macrothrix laticornis + + Moina micrura + + + + + + Moina cf.brachiata + Pleuroxus aduncus + Scapholeberis aurita + + Scapholeberis kingii + Scapholeberis mucronata + + + + + Simocephalus vetulus + + + + + + Maxillopoda, Copepoda Acanthocyclops robustus + + + Arctodiaptomus pectinicornis + Canthocamptus staphylinus + Cryptocyclops bicolor + Cyclops vicinus + + + + + + + + + Ectocyclops phaleratus + + Eucyclops serrulatus proximus + + + + + + + Eudiaptomus zachariasi + + Megacyclops viridis + + Mesocyclops leuckarti + + + + Metacyclops gracilis + Microcyclops varicans + Thermocyclops crassus + Thermocyclops oithonoides + + + + + + + +

126 K.P. Battes et al.

Appendix 2. Zoobenthic taxa identified in the shallow lakes from the Fizeș River Valley during 2012 and 2013 (abbreviations: CA - Lake Cătina; FL - Lake Florian; GE2 - Lake Geaca 2; GE3 - Lake Geaca 3; SU1 - Lake Sucutard 1; SU2 - Lake Sucutard 2; TM - Lake Țaga Mare; RO -Lake Roșieni; NA - Lake Năsal; LS - Lake Știucii)

Zoobenthos taxa / Sites CA FL GE2 GE3 SU1 SU2 TM RO NA LS Nematoda + + + + + + + + + Tardigrada + Mollusca: Gastropoda + + + + + + + + + + Bivalvia + Hirudinea + + + Oligochaeta + + + + + + + + + + Hydrachnidia + + Crustacea: Amphipoda + + Isopoda + + + + + + Ostracoda + + + + + Mysidacea + + + + Insecta: Coleoptera + + + + + + + + Diptera: Ceratopogonidae + + + + Chaoboridae + + Chironomidae + + + + + + + + + + Culicidae + + + + + + Dixidae + + + Limoniidae + + + Psychodidae + + + + Stratiomydae + + Tipulidae + + Ephemeroptera: Caenis + + + + + Cloeon + + + + + + + + + Heteroptera: Corixa + + + + + + + + Micronecta + Naucoris + + + + + + Plea + + + + Megaloptera + + Odonata: Aeshnidae + + Coenagrionidae + + + + + + + + + Corduliidae + + Lestidae + Libellulidae + + + + + Trichoptera: Ecnomidae + + Hydroptilidae + + Polycentropodidae + Leptoceridae + Limnephilidae + +