Using digital PCR to predict ciliate abundance from ribosomal RNA gene copy numbers.

Ciliates play a key role in most ecosystems. Their abundance in natural samples is crucial for answering many ecological questions. Traditional methods of quantifying individual species, which rely on microscopy, are often labour-intensive, time-consuming and can be highly biassed. As a result, we investigated the potential of digital polymerase chain reaction (dPCR) for quantifying ciliates. A significant challenge in this process is the high variation in the copy number of the taxonomic marker gene (ribosomal RNA [rRNA]). We first quantified the rRNA gene copy numbers (GCN) of the model ciliate, Paramecium tetraurelia, during different stages of the cell cycle and growth phases. The per-cell rRNA GCN varied between approximately 11,000 and 130,000, averaging around 50,000 copies per cell. Despite these variations in per-cell rRNA GCN, we found a highly significant correlation between GCN and cell numbers. This is likely due to the coexistence of different cellular stages in an uncontrolled (environmental) ciliate population. Thanks to the high sensitivity of dPCR, we were able to detect the target gene in a sample that contained only a single cell. The dPCR approach presented here is a valuable addition to the molecular toolbox in protistan ecology. It may guide future studies in quantifying and monitoring the abundance of targeted (even rare) ciliates in natural samples.

Simultaneous analysis of seven 16S rRNA hypervariable gene regions increases efficiency in marine bacterial diversity detection.

Environmental DNA sequencing is the gold standard to reveal microbial community structures. In most applications, a one-fragment PCR approach is applied to amplify a taxonomic marker gene, usually a hypervariable region of the 16S rRNA gene. We used a new reverse complement (RC)-PCR-based assay that amplifies seven out of the nine hypervariable regions of the 16S rRNA gene, to interrogate bacterial communities in sediment samples collected from different coastal marine sites with an impact gradient. In parallel, we employed a traditional one-fragment analysis of the hypervariable V3-V4 region to investigate whether the RC-PCR reveals more of the 'unseen' diversity obtained by the one-fragment approach. As a benchmark for the full deck of diversity, we subjected the samples to PCR-free metagenomic sequencing. None of the two PCR-based approaches recorded the full taxonomic repertoire obtained from the metagenomics datasets. However, the RC-PCR approach detected 2.8 times more bacterial genera compared to the near-saturation sequenced V3-V4 samples. RC-PCR is an ideal compromise between the standard one-fragment approach and metagenomics sequencing and may guide future environmental sequencing studies, in which bacterial diversity is a central subject.

Comparing quantile regression spline analyses and supervised machine learning for environmental quality assessment at coastal marine aquaculture installations.

Organic enrichment associated with marine finfish aquaculture is a local stressor of marine coastal ecosystems. To maintain ecosystem services, the implementation of biomonitoring programs focusing on benthic diversity is required. Traditionally, impact-indices are determined by extracting and identifying benthic macroinvertebrates from samples. However, this is a time-consuming and expensive method with low upscaling potential. A more rapid, inexpensive, and robust method to infer the environmental quality of marine environments is eDNA metabarcoding of bacterial communities. To infer the environmental quality of coastal habitats from metabarcoding data, two taxonomy-free approaches have been successfully applied for different geographical regions and monitoring goals, namely quantile regression splines (QRS) and supervised machine learning (SML). However, their comparative performance remains untested for monitoring the impact of organic enrichment introduced by aquaculture on marine coastal environments. We compared the performance of QRS and SML using bacterial metabarcoding data to infer the environmental quality of 230 aquaculture samples collected from seven farms in Norway and seven farms in Scotland along an organic enrichment gradient. As a measure of environmental quality, we used the Infaunal Quality Index (IQI) calculated from benthic macrofauna data (reference index). The QRS analysis plotted the abundance of amplicon sequence variants (ASVs) as a function to the IQI from which the ASVs with a defined abundance peak were assigned to eco-groups and a molecular IQI was subsequently calculated. In contrast, the SML approach built a random forest model to directly predict the macrofauna-based IQI. Our results show that both QRS and SML perform well in inferring the environmental quality with 89% and 90% accuracy, respectively. For both geographic regions, there was high correspondence between the reference IQI and both the inferred molecular IQIs (p < 0.001), with the SML model showing a higher coefficient of determination compared to QRS. Among the 20 most important ASVs identified by the SML approach, 15 were congruent with the good quality spline ASV indicators identified via QRS for both Norwegian and Scottish salmon farms. More research on the response of the ASVs to organic enrichment and the co-influence of other environmental parameters is necessary to eventually select the most powerful stressor-specific indicators. Even though both approaches are promising to infer environmental quality based on metabarcoding data, SML showed to be more powerful in handling the natural variability. For the improvement of the SML model, addition of new samples is still required, as background noise introduced by high spatio-temporal variability can be reduced. Overall, we recommend the development of a powerful SML approach that will be onwards applied for monitoring the impact of aquaculture on marine ecosystems based on eDNA metabarcoding data.

Geographic characteristics and environmental variables determine the diversities and assembly of the algal communities in interconnected river-lake system.

Algal blooms in lakes are a major hazard worldwide. Although various geographical and environmental patterns affect algal communities during river-lake transit, a thorough understanding of what patterns shape the algal communities is still rarely researched, particularly in complex interconnected river-lake systems. In this study, focusing on the most typical interconnected river-lake system in China, the Dongting Lake, we collected paired water and sediment samples in summer, when algal biomass and growth rate are at high levels. Based on 23S rRNA gene sequencing, we investigated the heterogeneity and the differences in assembly mechanisms of planktonic and benthic algae in Dongting Lake. Planktonic algae contained more Cyanobacteria and Cryptophyta, while sediment harbored higher proportions of Bacillariophyta and Chlorophyta. For planktonic algae, stochastic dispersal dominated the assembly of the communities. Upstream rivers and confluences were important sources of planktonic algae in lakes. Meanwhile, for benthic algae, deterministic environmental filtering shaped the communities, and the proportion of benthic algae exploded with increasing N:P ratio and Cu concentration until reaching thresholds of 1.5 and 0.013 g/kg respectively, and then started falling, showing non-linear responses. This study revealed the variability of different aspects of algal communities in different habitats, traced the main sources of planktonic algae, and identified the thresholds for benthic algal shifts in response to environmental filters. Hence, upstream and downstream monitoring as well as thresholds of environmental factors should be considered in further aquatic ecological monitoring or regulatory programs of harmful algal blooms in these complex systems.

Maximising signal-to-noise ratios in environmental DNA-based monitoring.

Man's impacts on global ecosystems are increasing and there is a growing demand that these activities be appropriately monitored. Monitoring requires measurement of a response metric ('signal') that changes maximally and consistently in response to the monitored activity irrespective of other factors ('noise'), thus maximising the signal-to-noise ratio. Indices derived from time-consuming morphology-based taxonomic identification of organisms are a core part of many monitoring programmes. Metabarcoding is an alternative to morphology-based identification and involves the sequencing of short fragments of DNA ('markers') from multiple taxa simultaneously. DNA suitable for metabarcoding includes that extracted from environmental samples (eDNA). Metabarcoding outputs DNA sequences that can be identified (annotated) by matching them against archived annotated sequences. However, sequences from most organisms are not archived - preventing annotation and potentially limiting metabarcoding in monitoring applications. Consequently, there is growing interest in using unannotated sequences as response metrics in monitoring programmes. We compared the sequences from three commonly used markers (16S (V3/V4 regions), 18S (V1/V2 regions) and COI) and, sampling along steep impact gradients, showed that the 16S and COI sequences were associated with the largest and smallest signal-to-noise ratio respectively. We trialled four separate, intuitive, noise-reduction approaches and demonstrated that removing less frequent sequences improved the signal-to-noise ratio, partitioning an additional 25 % from noise to explanatory factors in non-parametric ANOVA (NPA) and reducing dispersion in the data. For the 16S marker, retaining only the most frequently observed sequence, per sample, resulting in nine sequences across 150 samples, generated a near-maximal signal-to-noise ratio (95 % of the variance explained in NPA). We recommend that NPA, combined with rigorous elimination of less frequent sequences, be used to pre-filter sequences/taxa being used in monitoring applications. Our approach will simplify downstream analysis, for example the identification of key taxa and functional associations.

Environmental DNA-based profiling of benthic bacterial and eukaryote communities along a crude oil spill gradient in a coral reef in the Persian Gulf.

Coral reef ecosystems in the Persian Gulf are frequently exposed to crude oil spills. We investigated benthic bacterial and eukaryote community structures at such coral reef sites subjected to different degrees of polycyclic aromatic hydrocarbon (PAH) pollution using environmental DNA (eDNA) metabarcoding. Both bacterial and eukaryote communities responded with pronounced shifts to crude oil pollution and distinguished control sites, moderately and heavily impacted sites with significant confidentiality. The observed community patterns were predominantly driven by Alphaproteobacteria and metazoans. Among these, we identified individual genera that were previously linked to oil spill stress, but also taxa, for which a link to hydrocarbon still remains to be established. Considering the lack of an early-warning system for the environmental status of coral reef ecosystems exposed to frequent crude-oil spills, our results encourage further research towards the development of an eDNA-based biomonitoring tool that exploits benthic bacterial and eukaryote communities as bioindicators.

Re-description and molecular phylogeny of the free-swimming peritrichs Hastatella radians Erlanger, 1890 and H. aesculacantha Jarocki & Jakubowska, 1927 (Ciliophora, Peritrichia) from China.

The present work investigated Hastatella radians Erlanger, 1890 (type species), and H. aesculacantha Jarocki & Jakubowska, 1927, isolated from Chongming Island, Shanghai, China, based on light microscopy observations of live individuals, silver staining and molecular phylogeny methods. Hastatella radians is characterized by its size (about 30-75 µm long, 25-70 µm wide), two girdles of ectoplasmic processes, infundibular polykinety 3 (P3) composed of two rows of unequal length and terminating beyond adstomal end of P2, 8-22 transverse silverlines between peristome and trochal band, and 5-9 between trochal band and scopula. The infraciliature of H. aesculacantha is described for the first time, and we improved its diagnosis: cell size about 25-52 × 20-41 µm in vivo; four girdles of ectoplasmic processes; P3 two-rowed, outer row much shorter than inner one; 5-7 silverlines between peristome and trochal band and four or five between trochal band and scopula. Phylogenetic analyses based on SSU rDNA sequences revealed that Hastatella is monophyletic and has a close relationship to Astylozoon enriquesi.

Ultrastructure of Diophrys appendiculata and new systematic consideration of the euplotid family Uronychiidae (Protista, Ciliophora).

The ultrastructure of ciliates carries important cytological, taxonomical, and evolutionary signals for these single-celled eukaryotic organisms. However, little ultrastructural data have been accumulated for most ciliate groups with systematic problems. In the present work, a well-known marine uronychiid, Diophrys appendiculata, was investigated using electron microscopy and a comparison with, and a discussion considering, phylogenetic analyses were made. The new findings primarily show that: (i) this species lacks the typical alveolar plate, bears cortical ampule-like extrusomes, and has microtubular triads in the dorsal pellicle, and thus exhibits some ultrastructural features in common with most of its previously studied congeners; (ii) each adoral membranelle before the level of frontal cirrus II/2 contains three rows of kinetosomes and each membranelle after the level of frontal cirrus II/2 contains four rows, which might be related with morphogenesis and could be considered as a distinctive character of Diophrys; (iii) some structural details of the buccal field, such as the extra-pellicular fibrils, pellicle, pharyngeal disks and microtubular sheet, were documented. In addition, based on the ultrastructural comparison of representatives, we discuss the differentiation between the subfamilies Diophryinae and Uronychiinae. A hypothetical systematic relationship of members in the order Euplotida based on a wide range of data is also provided.

Comparing sediment preservation methods for genomic biomonitoring of coastal marine ecosystems.

To avoid loss of genetic information in environmental DNA (eDNA) field samples, the preservation of nucleic acids during field sampling is a critical step. In the development of standard operating procedures (SOPs) for eDNA-based compliance monitoring, the effect of different routinely used sediment preservations on biological community structures serving as bioindicators has gone untested. We compared eDNA metabarcoding results of marine bacterial communities from sample aliquots that were treated with a nucleic acid preservation solution (treated samples) and aliquots that were frozen without further treatment (non-treated samples). Sediment samples were obtained from coastal locations subjected to different stressors (aquaculture, urbanization, industry). DNA extraction efficiency, bacterial community profiles, and measures of alpha- and beta-diversity were highly congruent between treated and non-treated samples. As both preservation methods provide the same relevant information to environmental managers and regulators, we recommend the inclusion of both methods into SOPs for biomonitoring in marine coastal environments.

High salinity gradients and intermediate spatial scales shaped similar biogeographical and co-occurrence patterns of microeukaryotes in a tropical freshwater-saltwater ecosystem.

Microeukaryotes play key ecological roles in the microbial web of aquatic ecosystems. However, large knowledge gaps urgently need to be filled regarding the biogeography with associated shaping mechanisms and co-occurrence patterns of microeukaryotes under freshwater-saltwater gradients, especially true in tropical regions. Here, we investigated microeukaryotes of six mixed freshwater-saltwater regions in the Pearl River Estuary and surrounding coasts in southern China, with salinity ranging 0.1-32.0% and distances spanned up to 500 km, using molecular ecological methods. Results indicate that the biogeography of abundant and rare microeukaryotic communities was similar, both their co-occurrence patterns and biogeographical patterns were driven by deterministic and stochastic processes. The environmental factors with higher selective pressure than dispersal limitation meant that the role of deterministic process in structuring communities was more significant than that of stochastic process, and salinity played important role in structuring both microeukaryotic communities and networks. The abundant communities had stronger influence on entire microeukaryotic communities and seemed to be more sensitive to environmental changes than their rare counterparts, while rare ones had stronger interspecific relationships. Finally, the geographic scale and environmental gradients of study regions should firstly be clarified in future research on the ecological processes of microeukaryotes before conclusions are drawn.

Global Trends of Benthic Bacterial Diversity and Community Composition Along Organic Enrichment Gradients of Salmon Farms.

The analysis of benthic bacterial community structure has emerged as a powerful alternative to traditional microscopy-based taxonomic approaches to monitor aquaculture disturbance in coastal environments. However, local bacterial diversity and community composition vary with season, biogeographic region, hydrology, sediment texture, and aquafarm-specific parameters. Therefore, without an understanding of the inherent variation contained within community complexes, bacterial diversity surveys conducted at individual farms, countries, or specific seasons may not be able to infer global universal pictures of bacterial community diversity and composition at different degrees of aquaculture disturbance. We have analyzed environmental DNA (eDNA) metabarcodes (V3-V4 region of the hypervariable SSU rRNA gene) of 138 samples of different farms located in different major salmon-producing countries. For these samples, we identified universal bacterial core taxa that indicate high, moderate, and low aquaculture impact, regardless of sampling season, sampled country, seafloor substrate type, or local farming and environmental conditions. We also discuss bacterial taxon groups that are specific for individual local conditions. We then link the metabolic properties of the identified bacterial taxon groups to benthic processes, which provides a better understanding of universal benthic ecosystem function(ing) of coastal aquaculture sites. Our results may further guide the continuing development of a practical and generic bacterial eDNA-based environmental monitoring approach.

Identifying the minimum amplicon sequence depth to adequately predict classes in eDNA-based marine biomonitoring using supervised machine learning.

Environmental DNA metabarcoding is a powerful approach for use in biomonitoring and impact assessments. Amplicon-based eDNA sequence data are characteristically highly divergent in sequencing depth (total reads per sample) as influenced inter alia by the number of samples simultaneously analyzed per sequencing run. The random forest (RF) machine learning algorithm has been successfully employed to accurately classify unknown samples into monitoring categories. To employ RF to eDNA data, and avoid sequencing-depth artifacts, sequence data across samples are normalized using rarefaction, a process that inherently loses information. The aim of this study was to inform future sampling designs in terms of the relationship between sampling depth and RF accuracy. We analyzed three published and one new bacterial amplicon datasets, using a RF, based initially on the maximal rarefied data available (minimum mean of > 30,000 reads across all datasets) to give our baseline performance. We then evaluated the RF classification success based on increasingly rarefied datasets. We found that extreme to moderate rarefaction (50-5000 sequences per sample) was sufficient to achieve prediction performance commensurate to the full data, depending on the classification task. We did not find that the number of classification classes, data balance across classes, or the total number of sequences or samples, were associated with predictive accuracy. We identified the ability of the training data to adequately characterize the classes being mapped as the most important criterion and discuss how this finding can inform future sampling design for eDNA based biomonitoring to reduce costs and computation time.

Lake Ecosystem Robustness and Resilience Inferred from a Climate-Stressed Protistan Plankton Network.

Network analyses of biological communities allow for identifying potential consequences of climate change on the resilience of ecosystems and their robustness to resist stressors. Using DNA metabarcoding datasets from a three-year-sampling (73 samples), we constructed the protistan plankton co-occurrence network of Lake Zurich, a model lake ecosystem subjected to climate change. Despite several documentations of dramatic lake warming in Lake Zurich, our study provides an unprecedented perspective by linking changes in biotic association patterns to climate stress. Water temperature belonged to the strongest environmental parameters splitting the data into two distinct seasonal networks (October-April; May-September). The expected ecological niche of phytoplankton, weakened through nutrient depletion because of permanent thermal stratification and through parasitic fungi, was occupied by the cyanobacterium Planktothrix rubescens and mixotrophic nanoflagellates. Instead of phytoplankton, bacteria and nanoflagellates were the main prey organisms associated with key predators (ciliates), which contrasts traditional views of biological associations in lake plankton. In a species extinction scenario, the warm season network emerged as more vulnerable than the cold season network, indicating a time-lagged effect of warmer winter temperatures on the communities. We conclude that climate stressors compromise lake ecosystem robustness and resilience through species replacement, richness differences, and succession as indicated by key network properties.

An integrative approach sheds new light onto the systematics and ecology of the widespread ciliate genus Coleps (Ciliophora, Prostomatea).

Species of the genus Coleps are one of the most common planktonic ciliates in lake ecosystems. The study aimed to identify the phenotypic plasticity and genetic variability of different Coleps isolates from various water bodies and from culture collections. We used an integrative approach to study the strains by (i) cultivation in a suitable culture medium, (ii) screening of the morphological variability including the presence/absence of algal endosymbionts of living cells by light microscopy, (iii) sequencing of the SSU and ITS rDNA including secondary structures, (iv) assessment of their seasonal and spatial occurrence in two lakes over a one-year cycle both from morphospecies counts and high-throughput sequencing (HTS), and, (v) proof of the co-occurrence of Coleps and their endosymbiotic algae from HTS-based network analyses in the two lakes. The Coleps strains showed a high phenotypic plasticity and low genetic variability. The algal endosymbiont in all studied strains was Micractinium conductrix and the mutualistic relationship turned out as facultative. Coleps is common in both lakes over the whole year in different depths and HTS has revealed that only one genotype respectively one species, C. viridis, was present in both lakes despite the different lifestyles (mixotrophic with green algal endosymbionts or heterotrophic without algae). Our results suggest a future revision of the species concept of the genus Coleps.

Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap.

A decade after environmental scientists integrated high-throughput sequencing technologies in their toolbox, the genomics-based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end-users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or "in development", hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics-based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (a) Taxonomy-based analyses focused on identification of known bioindicators or described taxa; (b) De novo bioindicator analyses; (c) Structural community metrics including inferred ecological networks; and (d) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programmes that leverage recent analytical advancements, while pointing out current limitations and future research needs.

Aquatic food webs in deep temperate lakes: Key species establish through their autecological versatility.

Microbial planktonic communities are the basis of food webs in aquatic ecosystems since they contribute substantially to primary production and nutrient recycling. Network analyses of DNA metabarcoding data sets emerged as a powerful tool to untangle the complex ecological relationships among the key players in food webs. In this study, we evaluated co-occurrence networks constructed from time-series metabarcoding data sets (12 months, biweekly sampling) of protistan plankton communities in surface layers (epilimnion) and bottom waters (hypolimnion) of two temperate deep lakes, Lake Mondsee (Austria) and Lake Zurich (Switzerland). Lake Zurich plankton communities were less tightly connected, more fragmented and had a higher susceptibility to a species extinction scenario compared to Lake Mondsee communities. We interpret these results as a lower robustness of Lake Zurich protistan plankton to environmental stressors, especially stressors resulting from climate change. In all networks, the phylum Ciliophora contributed the highest number of nodes, among them several in key positions of the networks. Associations in ciliate-specific subnetworks resembled autecological species-specific traits that indicate adaptions to specific environmental conditions. We demonstrate the strength of co-occurrence network analyses to deepen our understanding of plankton community dynamics in lakes and indicate biotic relationships, which resulted in new hypotheses that may guide future research in climate-stressed ecosystems.

Supervised machine learning is superior to indicator value inference in monitoring the environmental impacts of salmon aquaculture using eDNA metabarcodes.

Increasing anthropogenic impact and global change effects on natural ecosystems has prompted the development of less expensive and more efficient bioassessments methodologies. One promising approach is the integration of DNA metabarcoding in environmental monitoring. A critical step in this process is the inference of ecological quality (EQ) status from identified molecular bioindicator signatures that mirror environmental classification based on standard macroinvertebrate surveys. The most promising approaches to infer EQ from biotic indices (BI) are supervised machine learning (SML) and the calculation of indicator values (IndVal). In this study we compared the performance of both approaches using DNA metabarcodes of bacteria and ciliates as bioindicators obtained from 152 samples collected from seven Norwegian salmon farms. Results from standard macroinvertebrate-monitoring of the same samples were used as reference to compare the accuracy of both approaches. First, SML outperformed the IndVal approach to infer EQ from eDNA metabarcodes. The Random Forest (RF) algorithm appeared to be less sensitive to noisy data (a typical feature of massive environmental sequence data sets) and uneven data coverage across EQ classes (a typical feature of environmental compliance monitoring scheme) compared to a widely used method to infer IndVals for the calculation of a BI. Second, bacteria allowed for a more accurate EQ assessment than ciliate eDNA metabarcodes. For the implementation of DNA metabarcoding into routine monitoring programmes to assess EQ around salmon aquaculture cages, we therefore recommend bacterial DNA metabarcodes in combination with SML to classify EQ categories based on molecular signatures.

Molecular Data Reveal a Cryptic Diversity in the Genus Urotricha (Alveolata, Ciliophora, Prostomatida), a Key Player in Freshwater Lakes, With Remarks on Morphology, Food Preferences, and Distribution.

Species of the ciliate genus Urotricha are key players in freshwater plankton communities. In the pelagial of lakes, about 20 urotrich species occur throughout an annual cycle, some of which play a pivotal role in aquatic food webs. For example, during the phytoplankton spring bloom, they consume a remarkable proportion of the algal production. In ecological studies, urotrich ciliates are usually merely identified to genus rank and grouped into size classes. This is unsatisfying considering the distinct autecological properties of individual species and their specific spatial and temporal distribution patterns. As a basis for future research, we characterized in detail four common urotrich morphotypes, i.e., specimens identified as U. furcata and tentatively as U. agilis, U. pseudofurcata, and U. castalia, using state-of-the-art methods. We used an integrative polyphasic approach, in which morphological studies (in vivo observation, silver staining methods, scanning electron microscopy) were linked with a molecular approach exploiting four different gene fragments as taxonomic DNA barcodes with different resolution potential (SSU rDNA, ITS-1, ITS-2, hypervariable V4 and V9 regions of the SSU rDNA). We shed light on the diversity of urotrich ciliates as well as on their global distribution patterns, and annual cycles. Additionally, we coupled individual species occurrences and environmental parameters, and subsequently modeled the distribution and occurrence, using logistic regressions. Furthermore, for one strain putatively identified as U. castalia, we ascertained the optimal cultivation media and food preferences. Thereby, our comprehensive view on these important freshwater ciliates that frequently occur in environmental high throughput sequencing datasets worldwide will allow future studies to better exploit protistan plankton data from lakes.