Introduction

Zooplankton studies of the marine environment have long focused on the larger size zooplankton fraction, namely the mesozooplankton, ranging in size from 200 μm to 20 mm (Paffenhöfer, 1993; Calbet, 2001; Zervoudaki et al., 2007). Studies dealing with the smaller size fraction of microzooplankton focus mainly on the unicellular heterotrophic protists participating in the energy flow through the microbial loop (e.g., Calbet et al., 2003; Landry & Décima, 2017). This approach left out a big part of the zooplankton biomass largely represented by the developmental stages of copepods and small-sized copepod species including among others highly successful invasive species (Calbet, 2001; Gallienne & Robins, 2001; Zervoudaki et al., 2007; Kourkoutmani et al., 2023), all comprising the metazoan microzooplankton community. Within this community, representatives of the Phylum Rotifera are found.

Rotifera, a group of micrometazoans consisting of more than 2100 species (inclusive subspecies) are recorded in almost every type of aquatic habitats; however, they have long been considered to be mainly found in inland water bodies of various salinities (Wallace et al., 2015). Their presence in marine ecosystems is largely overlooked, although more than 200 species exclusively inhabit marine waters (Fontaneto et al., 2006; Wallace et al., 2006). The main reason for this is the large mesh size net (200 μm) used to sample marine mesozooplankton, following the ICES zooplankton methodology manual (UNESCO, 1968), that fails to capture these small-sized metazoans. As a result, the vast literature mentioning rotifers deals with inland water bodies, mainly freshwater but also brackish and saline. Few studies deal with marine rotifers (e.g., Park & Marshall, 2000; Leasi & De Smet, 2020), while there is increased literature concerning ecotoxicological studies and aquaculture use for rotifers of the Brachionus plicatilis species complex that is found in water bodies with increased salinity (Fontaine & Revera, 1980; Ferrando et al., 1993; Manfra et al., 2017; Li et al., 2020). Furthermore, rotifers can be important components of the marine coastal food web due to their life history characteristics of high reproductive rates and short developmental times (Herzig, 1983; Wallace et al., 2006). They are reported to contribute significantly to the total zooplankton production (Johansson, 1983; Ojaveer et al., 2010) reaching up to more than 80% of the zooplankton biomass. This is typical for eutrophicated coastal areas with well-established microbial loop functioning, where rotifers can play a very important role by consuming significant amount of primary production, otherwise inaccessible to mesozooplankton (Johansson, 1983; Mironova et al., 2009; Ojaveer et al., 2010).

Synchaeta is one of the most well adapted to marine environments rotifer genus, since 24 of a total of 31 taxa are truly marine (Hollowday, 2002; Fontaneto et al., 2006). However, Synchaeta is commonly missing from marine zooplankton studies and is rarely identified down to species level. These soft-bodied illoricated taxa are a great challenge for taxonomists due to difficulties in identification, especially in preserved samples (Koste, 1978; Obertegger et al., 2006; Labuce & Strake, 2017). These results in Synchaeta species are being commonly summed together as Synchaeta spp., creating a knowledge gap in their biodiversity and distribution patterns. Previous monitoring samplings (unpublished data) in the urban coastal area of Thessaloniki Bay (North Aegean Sea, Greece) have revealed the presence of Synchaeta. Therefore, we aimed to further investigate the rotifer community of the study area, along with the environmental variables related to their seasonal dynamics. More specifically, we investigated the following: (a) the rotifer’s composition and abundance of Thessaloniki Bay, (b) the principal environmental factors influencing the temporal patterns of the Synchaeta species, (c) whether i) the phytoplankton community composition and abundance, given the fact that most Synchaeta species are herbivorous, and ii) the nutrient concentrations can further influence the temporal patterns of the Synchaeta’s community, (d) the possible coexisting or competitive patterns among the Synchaeta species as well as their relationships with the other taxa of the microzooplankton community.

Material and methods

Sampling

The study was carried out in Thessaloniki Bay (40° 37′ 52.7″ N; 22° 56′ 06.1″ E) (Fig. 1) as part of a holistic monitoring of the taxonomical and functional components of the planktonic food web of the urban coastal area. Thessaloniki Bay is a semi-enclosed basin in the inner part of Thermaikos Gulf (North Aegean Sea). The Bay is characterized by restricted water circulation and shallowness (Krestenitis et al., 2012), exposed to major anthropogenic eutrophication pressure resulting in recurrent phytoplankton blooms, red tides, and mucilaginous aggregates (Genitsaris et al., 2019).

Fig. 1
figure 1

A Map of Greece, B Map of Thessaloniki Bay; Location of the sampling station is indicated with the yellow triangle. Dashed lines indicate the route of commercial ferry services

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Samplings were carried out at a shore-based fixed station on the urban coastal area (maximum depth 3.5 m) (Fig. 1), between 8:00 and 10:00 am, within two sampling periods. In 2020, the samplings were conducted on a weekly basis for a short period (September–October) due to COVID-19 restrictions. In 2021–2022, samplings were conducted weekly and monthly, based on the presence/absence of Synchaeta species. Quantitative zooplankton samples were collected by vertical hauls, while qualitative samples were collected by oblique tows, using a 50 μm mesh size plankton net in both cases. Quantitative samples were preserved in 4% formalin–seawater solution immediately after collection. During all samplings, in situ measurements of water transparency (m) using a Secchi disk, and pH, using a HI-98100 Checker Plus pH Tester, were carried out. In addition, surface water temperature (°C) and conductivity (μS/cm) were measured using the YSI Pro 1030 instrument (YSI Inc., Ohio, USA). Conductivity was transformed to salinity (PSU) based on the equation in Weyl (1964).

Water samples from the sea surface layer (1 m) using a Niskin sampler (2 l) for the analysis of phytoplankton communities; more specifically, a 0.5 l sample was collected and fixed with Lugol’s solution.

Water samples of 1 l were collected in precleaned bottles for the analysis of nutrients. Samples were kept cool during their transportation in the laboratory. Upon their arrival to the laboratory a part of the sample was filtered through 0.45 μm cellulose filters. For the sampling, preservation, handling and storage of samples the conditions described in EN ISO 5667–3 (2018) were followed.

Microscopic analysis

Immediately after collection, qualitative live zooplankton samples were transferred to the lab and examined under a microscope (Leica, Leitz Laborlux S). This procedure is recommended (e.g., Koste, 1978; Ricci & Balsamo, 2000) especially for illoricated, soft-bodied rotifer taxa like Synchaeta spp., because fixation of the sample often deforms the morphology of these rotifers resulting in their transformation into nondescript shapes (Dolan & Callegos, 1992). Synchaeta’s species were identified to species level following the identification keys by Hollowday (2002), De Smet et al. (2015), and Wilke et al. (2019) and features such as shape and size of the body, number of pedal glands, lateral antennae, the number, and shape of toes were taken into account.

Taxonomic identification of the rest of the microzooplankton community followed the keys by Conway (2012a, b) and the classification system proposed by Razouls et al. (2005–2022). Copepods (only adults) and cladocerans were identified down to species level when possible, whereas copepodites were grouped together and were not further identified. The meroplankton representatives were identified to macrotaxa level (e.g., Bivalvia, Cirripedia, etc.) since species-level identification of the meroplankton larval stages is not always possible. Quantitative samples were analyzed for abundance estimation according to Harris et al. (2000). Regarding phytoplankton, species identification was based on appropriate taxonomic keys and the enumeration was carried out following Utermöhl’s method (Utermöhl, 1958) in sedimentation chambers, using an inverted fluorescence microscope (Nikon Eclipse TE 2000-S, Melville, USA).

Analysis of nutrients

Nutrients were determined in filtrates by employing colorimetric methods, following the analytical procedure described by Hansen and Koroleff (Grasshoff et al., 1999). Briefly, the dissolved inorganic nitrogen [DIN as sum of nitrate nitrogen (N-NO3)], nitrite nitrogen (N-NO2), ammonium nitrogen (N-NH4), total nitrogen (TN), and total organic nitrogen (TON as the difference of total nitrogen minus dissolved inorganic nitrogen TN-DIN), as well as the soluble reactive phosphorus (SRP) and acid hydrolysable phosphorus, expressed as total P (TP), were determined. More analytically, nitrite was determined via formation of azo dye from the reaction with sulfanilamide and N-(1-naphthy1)-ethylenediamine. Nitrate was determined after the reduction to nitrite through cadmium column, which is then determined via the formation of an azo dye and ammonium by employing phenate method. Total nitrogen was determined in unfiltered sample after employing persulfate digestion. Orthophosphate was analyzed in filtrated sample by ascorbic method, whereas acid hydrolysable phosphorus in unfiltered sample by employing acid digestion persulfate oxidation.

Relationships between environmental variables and the Synchaeta’s community structure

The Durbin-Watson test (Durbin & Watson, 1971) was initially performed to detect temporal autocorrelation since the dataset represents a time series. In R software using the “lmtest" package (Hothorn et al., 2015), we conducted separate Durbin-Watson tests for each species testing the environmental variables individually. In all cases, the obtained non-significant results (P > 0.05) indicated the absence of autocorrelation throughout the sampling periods and consequently allowed the following interpretation of the data.

Principal Component Analysis (PCA) was then performed, based on the environmental parameters (temperature, pH, salinity, transparency and the abundance of the taxonomical groups of the phytoplankton community) measured in both sampling periods, to reveal the principal environmental factors influencing the temporal patterns of the Synchaeta’s community. The data were scaled (normalized) prior to the analysis and the variables of the phytoplankton taxonomical groups consisted only of the taxa edible for Synchaeta [size range: 5–50 μm; according to Gilbert (2022)]. The PCA was carried out in R software using the “factoextra” (Kassambara, 2016) and “FactoMineR” (Lê et al., 2008) packages and it was chosen as a first-step analysis to visualize and identify patterns from the multivariate dataset. The PCA diagram was enhanced by incorporating ellipses, which were generated by introducing an extra column in the dataset matrix that served as a grouping factor, indicating the corresponding species for each observation. The ellipses were constructed based on confidence intervals and provided insights into the dispersion and covariance patterns of the species.

After performing the PCA and visualizing the ellipses, the coordinates of the centroids for each ellipse were extracted. Our objective was to test whether the centroids of the ellipses; corresponding to the Synchaeta species, differ significantly in dimensional space (e.g., due to different traits, different response to environmental variables). Since the method requires a distance matrix as an input, the Bray–Curtis dissimilarity matrix was chosen. A permutational multivariate analysis of variance (PERMANOVA) was performed, to assess the overall differences, carried out within the “Vegan” package (Oksanen et al., 2007) in R software by the function “adonis2.” This is used for partitioning distance matrices among potential sources of variation, based on the principles of McArdle & Anderson (2001).The multi level pairwise comparison for each pair of the centroids was carried out by using the function “pairwise.adonis2” (Martinez Arbizu, 2020). The independent effect of the environmental variables on the Synchaeta’s temporal distribution patterns was assessed through a model formula using “adonis2;” as this function performs sequential, marginal and overall tests similar to anova.cca of Distance-based ReDundancy Analysis (dbrda). For the above analyses, the Bray–Curtis method was used to calculate the pairwise distances and the P-values were obtained by conducting 999 permutations. Regarding the chemical variables (nutrient concentrations), since the nutrient analysis was performed only for selected samplings of the 2021–2022 period, they were not included in the above PCA, which consisted of all sampling data. Their effect on the Synchaeta’s distribution patterns was assessed also through a model formula (“adonis2”).

The variables that contributed significantly (P < 0.05) to Synchaeta’s community differentiation over time, according to the above analyses, were then subjected to a Spearman’s correlation [“Hmisc” package (Harrell Jr & Harrell Jr, 2019) in R software] to investigate the type (positive or negative) of the relationships.

Relationships between Synchaeta species and the microzooplankton community

We performed network analysis to explore the significant relationships among Synchaeta species and other microzooplankton taxa. In order to detect the microzooplankton taxa that played a key role in the plankton food web, and also eliminate the numerous pairwise interactions for better visualization of the networks, a Hierarchical Cluster Analysis (CLUSTER) based on the Bray–Curtis similarity index was performed on the abundance matrix using group-average linking; abundance data were log(x + 1) transformed to reduce bias due to highly abundant groups. The similarity percentage analysis (SIMPER) was used to test the significance levels and sources of variance between the zooplankton assemblages of the different groups derived by the hierarchical cluster analysis. The above analyses were performed using the Plymouth Routine in Multivariate Ecological Research (PRIMER) v.6 software package (Clarke & Gorley, 2006).

The network consisted of taxa that contributed up to 50% to average dissimilarity within the groups of SIMPER analysis (Tables S6, S7). The relationships were characterized through maximal information-based non-parametric exploration (MINE) statistics by computing the maximal information coefficient (MIC) between each pair of taxa (Reshef et al., 2011), based on abundance data matrix. MIC is a non-parametric method that captures associations (linear and non-linear) between data pairs. It provides a score that represents the strength of the relationship. The matrix of MIC scores corresponding to P-values < 0.05, based on pre-computed P-values of various MIC scores at different sample sizes, was used to visualize the networks via Cytoscape 3.9.1. Positive (which indicate coexistence patterns) and negative (which indicate competitive interactions) relationships between each pair of taxa were characterized according to Hernández-Ruiz et al. (2018), who assigned strong non-linear relationships as those with a MIC-P2 > 0.5, and the connections with MIC-P2 < 0.5 as positive or negative based on the R value.

Results

Rotifers diversity and abundance

In total, 44 microzooplankton taxa were identified (Table S1), with four rotifers of the genus Synchaeta among them, i.e. Synchaeta baltica Ehrenberg, 1834; Synchaeta grimpey Remane, 1929; Synchaeta neapolitana Rousselet, 1902; Synchaeta vorax Rousselet, 1902 (Fig. S1). During the sampling period of 2020, S. baltica and S. neapolitana were the only rotifers found in the study area, reaching maximum numbers of 38 Χ 103 ind/m3 and 56 Χ 103 ind/m3, respectively. In 2021–2022 samplings, all four Synchaeta species comprised the rotifer community, but their presence varied throughout the period of investigation. More specifically, S. baltica abundance remained rather low (max: 1.5 Χ 103 ind/m3), compared to the same period in 2020 (max: 38 Χ 103 ind/m3). S. grimpei was only present in the samplings of late September-early October when it reached 4 Χ 103 ind/m3. Almost the same distribution pattern was recorded for S. vorax, which was also recorded in June 2022. Synchaeta neapolitana showed a more diverse abundance pattern within the population reaching its peak (37 Χ 103 ind/m3) in November 2021. During the first months of 2022, all Synchaeta species were totally absent, except for S. neapolitana, whose individuals were scarcely found (Fig. 2).

Fig. 2
figure 2

Temporal distribution of the Synchaeta species abundance and temperature variation in Thessaloniki Bay during the two sampling periods of 2020 and 2021–2022

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Environmental variables

Summary statistics of the environmental variables measured during the sampling periods of 2020 and 2021–2022 are given in Table 1. Temperature ranged from 8.7 oC to 31 oC, with the highest values recorded in August 2021 during the heat wave of 2021 (Androulidakis & Krestenitis, 2022). The pH values were rather stable (Stdev: 0.18), while salinity exhibited bigger fluctuations within the sampling periods (Stdev: 1.67). The concentrations of dissolved inorganic N-species exhibited the following order: NO3− > NH4+ > NO2−. The concentration of total phosphorus (TP) ranged from 20 to 90 μg P/L, while Soluble Reactive Phosphorus (SRP) values was relatively lower (< 22 μg/L) (Table 1, Fig. S2, Fig. S3).

Table 1 Summary statistics of the environmental variables measured during both sampling periods (2020 & 2021–2022) in Thessaloniki Bay
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Total phytoplankton abundance ranged from 470 to 19 × 103 cells mL−1, with the higher values recorded in the 2020 sampling period, which can be characterized as a bloom episode for the study area according to Genitsaris et al. (2019). The main contributors to phytoplankton abundance were diatoms and haptophytes, followed by dinophytes and cryptophytes. The rest of the phytoplankton groups were occasionally present but in low numbers (Table 1, Fig. S4).

Impact of the environmental variables on Synchaeta’s species community

The first two axes of the PCA biplot explained more than 50% of the Synchaeta’s species variation (Fig. 3). Although the species formed overlapping ellipses with different levels of homogeneity, the geometric centers of the clusters did not coincide, which indicates differences in environmental conditions for each species. This is supported by the pairwise comparison, which noted that all species’ clusters differed significantly (P < 0.05) within two-dimensional space, expect for S. grimpei versus S. vorax (Table S2). The environmental variables that drove the species’ temporal distribution patterns and explained the greatest amount of variance in the “adonis2” model formulas were temperature (R2 = 0.927, P = 0.005), pH (R2 = 0.636, P = 0.027) and salinity (R2 = 0.608, P = 0.040) (Table S3, Table S4).

Fig. 3
figure 3

Principal Components Analysis (PCA) biplot of the Synchaeta species based on the explanatory environmental variables (V1 temperature, V2 pH, V3 salinity, V4 transparency, V5 diatoms, V6 dinophyceae, V7 cryptophyceae, V8 haptophyceae, V9 dictyophyceae, V10 pycnococcaceae, V11  raphidiophyceae, V12 chlorophyceae), whose scores are represented by the black vectors. Colored ellipses (size determined by a 0.95-probability level) show the observations grouped by species

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Thus, from the initial multivariate dataset, the environmental variables that were extracted as statistically significant (temperature, pH, salinity) were then subjected to Spearman’s rank correlation with the Synchaeta species abundance data. More specifically, S. baltica exhibited significant positive correlation with temperature (Spearman’s rho 0.46; P < 0.01). and was also correlated negatively with salinity (Spearman’s rho -0.38; P < 0.05). Regarding pH, it was correlated positively with S. neapolitana (Spearman’s rho 0.36; P < 0.05) and negatively with S. vorax (Spearman’s rho -0.38; P < 0.05). (Table S5).

Interactions in the microzooplankton community of Thessaloniki Bay

Significant connections between species of the genus Synchaeta and the other microzooplankton taxa, according to MIC correlation coefficients (MIC values corresponding to P-values < 0.05) are visualized in the networks of Fig. 4. Overall, 36 zooplankton taxa (nodes) and 149 significant positive and negative correlations (edges) were generated from both networks. Less complex associations among zooplankton taxa, in terms of number of nodes and edges, were noted for 2020 network compared to the 2021–2022, due to the fewer samplings. More specifically, in 2020, species of Synchaeta were negatively connected with cladocerans, meroplankton larvae and cyclopoid taxa. Same interactions were also depicted in 2021–2022 network. Also, among the Synchaeta’s species community, several positive connections were noted.

Fig. 4
figure 4

Network diagrams of significant connections (P < 0.05), based on the maximal information coefficient (MIC) scores, between zooplankton taxa (nodes). The size of the nodes is analogous with the clustering coefficient of its taxa. Network comprised of taxa contributing up to 50% to average dissimilarity groups of SIMPER analysis. Different node colors represent different taxonomic groups according to the color key. The thicker edges were used to distinguish the Synchaeta’s relationships

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Discussion

Marine rotifers have rarely received researchers’ attention in terms of biodiversity and distribution patterns. Although their presence has been reported in few zooplankton studies worldwide, quantitative long-term data on rotifers are very scarce. In Greece (Aegean, Ionian and Cretan Seas) no previous records of any truly marine rotifer exist, except for the euryhaline Brachionus plicatilis reported in the Bay of Pylos (De Ridder, l967b; mentioned in De Smet et al., 2015). This is mainly due to the larger mesh size net used as well as studies focusing on areas closer to the open sea whereas marine rotifers are usually restricted to coastal areas due to high nutritional requirements (Dolan & Callegos, 1992). Thus, the present study is the first study focusing on the rotifer community of a coastal marine environment in Greece, as well as one of the very few across the Mediterranean Sea, providing ecological observations of their temporal distribution patterns and coexistence interactions along with environmental variables driving their seasonal dynamics.

Only four rotifer species were recorded in the study area, all belonging to the Synchaeta genus. The species composition is very similar to the one reported in the coastal lagoon of Etang de Thau (France) by Rougier et al. (2000) and shares some common species with the studies conducted in the Baltic Sea (Labuce & Strake, 2017; and the reference therein). However, across the published studies of marine zooplankton, identification down to species level for Synchaeta is rare. This lack of information is explained by difficulties in identification of the illoricated genus in live and preserved samples and results in summing up all the species together as Synchaeta spp. in most studies (e.g., Dippner et al., 2000; Brucet et al., 2005; Mudrak & Żmijewska 2007). However, during the last decade, few studies have been published encouraging the identification in preserved samples based on trophi morphology accompanied by observations in general morphology (Obertegger et al., 2006; Labuce & Strake, 2017).

Regarding the temporal distribution of the rotifer species in Thessaloniki Bay, temperature stood out as the determining factor. The presence of S. baltica was reported within the range of 20.7–30.3 °C and as it was positively correlated to temperature, we can assume a thermophilic distribution pattern. However, the heatwave of August 2021 (Androulidakis & Krestenitis, 2022) most probably affected S. baltica negatively, as can be inferred by the significant difference in terms of maximum abundance values reached by the species between the two periods. Intense temperature fluctuations are known to have an impact on species biological functions; metabolism and survival, affecting also their temporal occurrence and seasonal succession, as has previously been well documented for other rotifer species e.g., Brachionus spp. (Kauler & Enesco, 2011; Paraskevopoulou et al., 2020). Furthermore, in mesocosm experiments of the phytoplankton community of the study area (Stefanidou et al., 2018a) heat shock was coupled with decreased species richness and reduced phytoplankton biomass, and this adverse effect on phytoplankton can be transmitted bottom-up to rotifers affecting its population maxima. As for S. neapolitana, it was negatively correlated to temperature, and despite the lack of statistical significance, it seems to exhibit a cold stenothermal character since it was the most significant contributor to the rotifer community when the temperature started to decrease during the late autumn samplings. It was the only species recorded when water temperature dropped below 20 °C. This is in agreement with the results of Rougier et al. (2000), who report higher densities of S. neapolitana at 18 °C. Regarding S. vorax, its presence in September (2021) and June (2022) in the present study indicates a rather thermophilic character. However, Hernroth (1983) reports blooms of the species in Swedish waters in winter and spring while Rougier et al. (2000), who also record the species in Etang de Thau (France), note the presence of two forms of S. vorax, differing in total body size and foot proportion. The evidence of different temperature distribution pattern of a morphospecies, along with variations in morphometric analyses is a strong indication of cryptic speciation, which is widely reported for many rotifer species including Brachionus plicatilis species-complex (Michaloudi et al., 2017), Keratella cochlearis species-complex (Cieplinski et al., 2017), Polyarthra dolichoptera Idelson, 1925 (Obertegger et al., 2014). and Synchaeta pectinata Ehrenberg, 1832 (Obertegger et al., 2012; Kimpel et al., 2015) and which should be further examined for S. vorax. Finally, the presence of S. grimpei was quite limited and thus no clear relation with temperature can be assumed. Apart from temperature, other water parameters, like pH and salinity, are expected to be affected by climate change (Garcia-Soto et al., 2021). In the current study, the range of these parameters was within the range of occurrence for Synchaeta species (Rougier et al., 2000). However, in mesocosm experiments of the phytoplankton community of the study area (Stefanidou et al., 2018a) the double stressor effect of temperature and salinity exerted a synergistic negative effect affecting the sensitivity of communities to further physical changes (Stefanidou et al., 2018a) and can be expected to further affect the whole zooplankton community.

In order to understand coexistence or competitive patterns, food niche partitioning information is of great significance in determining food-web linkages (Yvon-Durocher et al., 2008; Gilbert, 2022). Synchaeta species are considered mainly macrophagous algivores that eat algae in the 5–50 μm size range according to Gilbert (2022), who divided rotifers into four general feeding categories defined by the ingested food types and sizes. The upper size limit of food that can be ingested is related not only to the maximal mouth opening of the rotifer but also to the food’s shape and size (Gilbert, 2022). The phytoplankton community of the eutrophic coastal area of Thessaloniki Bay often exhibits phytoplankton blooms, mainly of large-sized diatoms (Genitsaris et al., 2019; Stefanidou et al., 2018b), not within the acceptable size range for consumption by Synchaeta. Diatoms due to their elongated shape and their ability to produce mucus are inappropriate prey for marine rotifers as they often entangle on rotifers cilia, blocking the feeding procedure (Egloff, 1986). Thereby, in order to identify the possible influence of food availability, for the ordination analysis of the Synchaeta species abundance data we only considered the edible phytoplankton species (5–50 μm). Although the vectors of the taxonomical phytoplankton groups variables showed good representation in the PCA components and their contribution to the total variance was high, they did not significantly influence the temporal patterns of the Synchaeta community, thus no clear pattern regarding their relationship can be verified here. This might be explained by the high abundance of the edible phytoplankton taxa in the diverse eutrophicated marine environment of Thessaloniki Bay (Stefanidou et al., 2018b) throughout the year, which does not constitute a limiting factor for Synchaeta species growth. Probably, a targeted experimental approach could provide a better understanding of the food niche partitioning, allowing the coexistence pattern of the rotifer species recorded in the present study. Regarding the effects of nutrients, they seem to be indirect, considering their non-significant contribution to species’ variation. This is not a surprise, as nutrients have a rather direct impact on phytoplankton growth, bottom-up, affecting indirectly the zooplankton, which serve as an intermediate trophic link between primary and secondary producers.

Biotic interactions are important in shaping species’ distributions and patterns in space and time through different mechanisms; predation, competition, and mutualism (Bascompte & Stouffer, 2009; Wisz et al., 2013). Network analysis revealed several negative links among Synchaeta species and the other zooplankton taxa, which can be indicative of competitive trophic interactions and predator–prey relations among taxa. More specifically, negative links between the Synchaeta species and the filter feeding taxa of the cladocerans Penilia avirostris Dana, 1849 and Pleopis polyphemoides (Leuckart, 1859), as well as the meroplanktonic larvae of Bivalvia were noted. These can be interpreted as competitive trophic interactions since most Synchaeta species are herbivorous (e.g., S. baltica), although there are species that additionally or exclusively feed on other rotifers, and the rotifer prey can be congeneric or conspecific (e.g., S. vorax) (Wilke et al., 2020). Among the congeneric species community, coexistence patterns among all Synchaeta species were reported within the study period. These positive relations between taxa can be evidence of mutually advantageous interactions and/or the independent exploitation of resources (Kruk & Paturej, 2020). None of the reported Synchaeta predators [Acartia tonsa Dana, 1849 (Stoecker & Egloff, 1987; Egloff, 1988)] were recorded in the zooplankton community of the study. The dominant copepods of the study area were Paracalanus parvus species–complex, which is herbivorous and thus cannot be considered a predator for Synchaeta, as well as Centropages typicus Kroyer, 1849 and Acartia clausi Giesbrecht, 1889, which are reported to feed selectively on a mixture of ciliates and planktonic algae, but not on Synchaeta. Of course, competitive or coexistence interactions among the zooplankton community depend on various factors e.g., food resources, niche partitioning, and predators. In our case, Synchaeta species variations were influenced by environmental variables, mainly temperature, and interspecific trophic relationships rather than the relationships with the other components of the microzooplankton community.