Outer membrane vesicles can contribute to cellulose degradation in Teredinibacter turnerae, a cultivable intracellular endosymbiont of shipworms.

Teredinibacter turnerae is a cultivable cellulolytic Gammaproeteobacterium (Cellvibrionaceae) that commonly occurs as an intracellular endosymbiont in the gills of wood-eating bivalves of the family Teredinidae (shipworms). The genome of T. turnerae encodes a broad range of enzymes that deconstruct cellulose, hemicellulose, and pectin and contribute to lignocellulose digestion in the shipworm gut. However, the mechanism by which symbiont-made enzymes are secreted by T. turnerae and subsequently transported to the site of lignocellulose digestion in the shipworm gut is incompletely understood. Here, we show that T. turnerae cultures grown on carboxymethyl cellulose (CMC) produce outer membrane vesicles (OMVs) that contain a variety of proteins identified by LC-MS/MS as carbohydrate-active enzymes with predicted activities against cellulose, hemicellulose, and pectin. Reducing sugar assays and zymography confirm that these OMVs retain cellulolytic activity, as evidenced by hydrolysis of CMC. Additionally, these OMVs were enriched with TonB-dependent receptors, which are essential to carbohydrate and iron acquisition by free-living bacteria. These observations suggest potential roles for OMVs in lignocellulose utilization by T. turnerae in the free-living state, in enzyme transport and host interaction during symbiotic association, and in commercial applications such as lignocellulosic biomass conversion.

Bacteria exposed to antiviral drugs develop antibiotic cross-resistance and unique resistance profiles.

Antiviral drugs are used globally as treatment and prophylaxis for long-term and acute viral infections. Even though antivirals also have been shown to have off-target effects on bacterial growth, the potential contributions of antivirals to antimicrobial resistance remains unknown. Herein we explored the ability of different classes of antiviral drugs to induce antimicrobial resistance. Our results establish the previously unrecognized capacity of antivirals to broadly alter the phenotypic antimicrobial resistance profiles of both gram-negative and gram-positive bacteria Escherichia coli and Bacillus cereus. Bacteria exposed to antivirals including zidovudine, dolutegravir and raltegravir developed cross-resistance to commonly used antibiotics including trimethoprim, tetracycline, clarithromycin, erythromycin, and amoxicillin. Whole genome sequencing of antiviral-resistant E. coli isolates revealed numerous unique single base pair mutations, as well as multi-base pair insertions and deletions, in genes with known and suspected roles in antimicrobial resistance including those coding for multidrug efflux pumps, carbohydrate transport, and cellular metabolism. The observed phenotypic changes coupled with genotypic results indicate that bacteria exposed to antiviral drugs with antibacterial properties in vitro can develop multiple resistance mutations that confer cross-resistance to antibiotics. Our findings underscore the potential contribution of wide scale usage of antiviral drugs to the development and spread of antimicrobial resistance in humans and the environment.

Abundant and persistent sulfur-oxidizing microbial populations are responsive to hypoxia in the Chesapeake Bay.

The number, size and severity of aquatic low-oxygen dead zones are increasing worldwide. Microbial processes in low-oxygen environments have important ecosystem-level consequences, such as denitrification, greenhouse gas production and acidification. To identify key microbial processes occurring in low-oxygen bottom waters of the Chesapeake Bay, we sequenced both 16S rRNA genes and shotgun metagenomic libraries to determine the identity, functional potential and spatiotemporal distribution of microbial populations in the water column. Unsupervised clustering algorithms grouped samples into three clusters using water chemistry or microbial communities, with extensive overlap of cluster composition between methods. Clusters were strongly differentiated by temperature, salinity and oxygen. Sulfur-oxidizing microorganisms were found to be enriched in the low-oxygen bottom water and predictive of hypoxic conditions. Metagenome-assembled genomes demonstrate that some of these sulfur-oxidizing populations are capable of partial denitrification and transcriptionally active in a prior study. These results suggest that microorganisms capable of oxidizing reduced sulfur compounds are a previously unidentified microbial indicator of low oxygen in the Chesapeake Bay and reveal ties between the sulfur, nitrogen and oxygen cycles that could be important to capture when predicting the ecosystem response to remediation efforts or climate change.

Influence of Simplified Microbial Community Biofilms on Bacterial Retention in Porous Media under Conditions of Stormwater Biofiltration.

Porous media filters are used widely to remove bacteria from contaminated water, such as stormwater runoff. Biofilms that colonize filter media during normal function can significantly alter performance, but it is not clear how characteristics of individual populations colonizing porous media combine to affect bacterial retention. We assess how four bacterial strains isolated from stormwater and a laboratory strain, Pseudomonas aeruginosa PAO1, alter Escherichia coli retention in experimental sand columns under conditions of stormwater filtration relative to a clean-bed control. Our results demonstrate that these strains differentially affect E. coli retention, as was previously shown for a model colloid. To determine whether E. coli retention could be influenced by changes in relative abundance of strains within a microbial community, we selected two pairs of biofilm strains with the largest observed differences in E. coli retention and tested how changes in relative abundance of strain pairs in the biofilm affected E. coli retention. The results demonstrate that E. coli retention efficiency is influenced by the retention characteristics of the strains within biofilm microbial community, but individual strain characteristics influence retention in a manner that cannot be determined from changes in their relative abundance alone. This study demonstrates that changes in the relative abundance of specific members of a biofilm community can significantly alter filter performance, but these changes are not a simple function of strain-specific retention and the relative abundance. Our results suggest that the microbial community composition of biofilms should be considered when evaluating factors that influence filter performance. IMPORTANCE The retention efficiency of bacterial contaminants in biofilm-colonized biofilters is highly variable. Despite the increasing number of studies on the impact of biofilms in filters on bacterial retention, how individual bacterial strains within a biofilm community combine to influence bacterial retention is unknown. Here, we studied the retention of an E. coli K-12 strain, as a model bacterium, in columns colonized by four bacterial strains isolated from stormwater and P. aeruginosa, a model biofilm-forming strain. Simplified two-strain biofilm communities composed of combinations of the strains were used to determine how relative abundance of biofilm strains affects filter performance. Our results provide insight into how biofilm microbial composition influences bacterial retention in filters and whether it is possible to predict bacterial retention efficiency in biofilm-colonized filters from the relative abundance of individual members and the retention characteristics of cultured isolates.

Interaction dynamics and virus-host range for estuarine actinophages captured by epicPCR.

Viruses impact microbial diversity, gene flow and function through virus-host interactions. Although metagenomics surveys are rapidly cataloguing viral diversity, methods are needed to capture specific virus-host interactions in situ. Here, we leveraged metagenomics and repurposed emulsion paired isolation-concatenation PCR (epicPCR) to investigate viral diversity and virus-host interactions in situ over time in an estuarine environment. The method fuses a phage marker, the ribonucleotide reductase gene, with the host 16S rRNA gene of infected bacterial cells within emulsion droplets providing single-cell resolution for dozens of samples. EpicPCR captured in situ virus-host interactions for viral clades with no closely related database representatives. Abundant freshwater Actinobacteria lineages, in particular Rhodoluna sp., were the most common hosts for these poorly characterized viruses, with interactions correlated with environmental factors. Multiple methods used to identify virus-host interactions, including epicPCR, identified different and largely non-overlapping interactions within the vast virus-host interaction space. Tracking virus-host interaction dynamics also revealed that multi-host viruses had significantly longer periods with observed virus-host interactions, whereas single-host viruses were observed interacting with hosts at lower minimum abundances, suggesting more efficient interactions. Capturing in situ interactions with epicPCR revealed environmental and ecological factors shaping virus-host interactions, highlighting epicPCR as a valuable technique in viral ecology.

Effect of Strain-Specific Biofilm Properties on the Retention of Colloids in Saturated Porous Media under Conditions of Stormwater Biofiltration.

Filter performance can be affected by bacterial colonization of the filtration media, yet little is known about how naturally occurring bacteria modify the surface properties of filtration media to affect colloidal removal. We used sand columns and simulated stormwater conditions to study the retention of model colloidal particles, carboxyl-modified-latex (CML) beads, in porous media colonized by naturally occurring bacterial strains. Colloid retention varied substantially across identical columns colonized by different, in some cases closely related, bacterial strains in a cell density independent manner. Atomic force microscopy was applied to quantify the interaction energy between CML beads and each bacterial strain's biofilm surface. We found interaction energy between CML and each strain was significantly different, with adhesive energies between the biofilm and CML, presumed to be associated with polymer-surface bonding, a better predictor of CML retention than other strain characteristics. Overall, the findings suggest that interactions with biopolymers in naturally occurring bacterial biofilms strongly influence colloid retention in porous media. This work highlights the need for more investigation into the role of biofilm microbial community composition on colloid removal in porous media to improve biofilter design and operation.

Contribution of Time, Taxonomy, and Selective Antimicrobials to Antibiotic and Multidrug Resistance in Wastewater Bacteria.

The use of nontherapeutic broad-spectrum antimicrobial agents triclosan (TCS) and benzalkonium chloride (BC) can contribute to bacterial resistance to clinically relevant antibiotics. Antimicrobial-resistant bacteria within wastewater may reflect the resistance burden within the human microbiome, as antibiotics and pathogens in wastewater can track with clinically relevant parameters during perturbations to the community. In this study, we monitored culturable and resistant wastewater bacteria and cross-resistance to clinically relevant antibiotics to gauge the impact of each antimicrobial and identify factors influencing cross-resistance profiles. Bacteria resistant to TCS and BC were isolated from wastewater influent over 21 months, and cross-resistance, taxonomy, and monthly changes were characterized under both antimicrobial selection regimes. Cross-resistance profiles from each antimicrobial differed within and between taxa. BC-isolated bacteria had a significantly higher prevalence of resistance to "last-resort antibiotic" colistin, while isolates resistant to TCS exhibited higher rates of multidrug resistance. Prevalence of culturable TCS-resistant bacteria decreased over time following Food and Drug Administration (FDA) TCS bans. Cross-resistance patterns varied according to sampling date, including among the most clinically important antibiotics. Correlations between strain-specific resistance profiles were largely influenced by taxonomy, with some variations associated with sampling date. The results reveal that time, taxonomy, and selection by TCS and BC impact features of cross-resistance patterns among diverse wastewater microorganisms, which could reflect the variety of factors influencing resistance patterns relevant to a community microbiome.

Vertical transport of sediment-associated metals and cyanobacteria by ebullition in a stratified lake.

Bubbles adsorb and transport particulate matter both in industrial and marine systems. While methane-containing bubbles emitted from anoxic sediments are found extensively in aquatic ecosystems, relatively little attention has been paid to the possibility that such bubbles transport particle-associated chemical or biological material from sediments to surface waters of freshwater lakes. We quantified transport of particulate material from sediments to the surface by bubbles in Upper Mystic Lake, MA and in a 15 m tall experimental column. Vertical particle transport was positively correlated with the volume of gas bubbles released from the sediment. Particles transported by bubbles originated almost entirely in the sediment, rather than being scavenged from the water column. Concentrations of arsenic, chromium, lead, and cyanobacterial cells in bubble-transported particulate material were similar to those of bulk sediment, and particles were transported from depths exceeding 15 m, resulting in daily fluxes as large as 0.18 mg of arsenic m-2 and 2 × 104 cyanobacterial cells m-2 in the strongly stratified Upper Mystic Lake. While bubble-facilitated arsenic transport currently appears to be a modest component of total arsenic cycling in this lake, bubble-facilitated cyanobacterial transport could comprise as much as 17% of recruitment in this lake and may thus be of particular importance in large, deep, stratified lakes.

Current State of and Future Opportunities for Prediction in Microbiome Research: Report from the Mid-Atlantic Microbiome Meet-up in Baltimore on 9 January 2019.

Accurate predictions across multiple fields of microbiome research have far-reaching benefits to society, but there are few widely accepted quantitative tools to make accurate predictions about microbial communities and their functions. More discussion is needed about the current state of microbiome analysis and the tools required to overcome the hurdles preventing development and implementation of predictive analyses. We summarize the ideas generated by participants of the Mid-Atlantic Microbiome Meet-up in January 2019. While it was clear from the presentations that most fields have advanced beyond simple associative and descriptive analyses, most fields lack essential elements needed for the development and application of accurate microbiome predictions. Participants stressed the need for standardization, reproducibility, and accessibility of quantitative tools as key to advancing predictions in microbiome analysis. We highlight hurdles that participants identified and propose directions for future efforts that will advance the use of prediction in microbiome research.

Dynamics of microbial populations mediating biogeochemical cycling in a freshwater lake.

BACKGROUND: Microbial processes are intricately linked to the depletion of oxygen in in-land and coastal water bodies, with devastating economic and ecological consequences. Microorganisms deplete oxygen during biomass decomposition, degrading the habitat of many economically important aquatic animals. Microbes then turn to alternative electron acceptors, which alter nutrient cycling and generate potent greenhouse gases. As oxygen depletion is expected to worsen with altered land use and climate change, understanding how chemical and microbial dynamics impact dead zones will aid modeling efforts to guide remediation strategies. More work is needed to understand the complex interplay between microbial genes, populations, and biogeochemistry during oxygen depletion. RESULTS: Here, we used 16S rRNA gene surveys, shotgun metagenomic sequencing, and a previously developed biogeochemical model to identify genes and microbial populations implicated in major biogeochemical transformations in a model lake ecosystem. Shotgun metagenomic sequencing was done for one time point in Aug., 2013, and 16S rRNA gene sequencing was done for a 5-month time series (Mar.-Aug., 2013) to capture the spatiotemporal dynamics of genes and microorganisms mediating the modeled processes. Metagenomic binning analysis resulted in many metagenome-assembled genomes (MAGs) that are implicated in the modeled processes through gene content similarity to cultured organism and the presence of key genes involved in these pathways. The MAGs suggested some populations are capable of methane and sulfide oxidation coupled to nitrate reduction. Using the model, we observe that modulating these processes has a substantial impact on overall lake biogeochemistry. Additionally, 16S rRNA gene sequences from the metagenomic and amplicon libraries were linked to processes through the MAGs. We compared the dynamics of microbial populations in the water column to the model predictions. Many microbial populations involved in primary carbon oxidation had dynamics similar to the model, while those associated with secondary oxidation processes deviated substantially. CONCLUSIONS: This work demonstrates that the unique capabilities of resident microbial populations will substantially impact the concentration and speciation of chemicals in the water column, unless other microbial processes adjust to compensate for these differences. It further highlights the importance of the biological aspects of biogeochemical processes, such as fluctuations in microbial population dynamics. Integrating gene and population dynamics into biogeochemical models has the potential to improve predictions of the community response under altered scenarios to guide remediation efforts.

Evolution of a Vegetarian Vibrio: Metabolic Specialization of Vibrio breoganii to Macroalgal Substrates.

While most Vibrionaceae are considered generalists that thrive on diverse substrates, including animal-derived material, we show that Vibrio breoganii has specialized for the consumption of marine macroalga-derived substrates. Genomic and physiological comparisons of V. breoganii with other Vibrionaceae isolates revealed the ability to degrade alginate, laminarin, and additional glycans present in algal cell walls. Moreover, the widely conserved ability to hydrolyze animal-derived polymers, including chitin and glycogen, was lost, along with the ability to efficiently grow on a variety of amino acids. Ecological data showing associations with particulate algal material but not zooplankton further support this shift in niche preference, and the loss of motility appears to reflect a sessile macroalga-associated lifestyle. Together, these findings indicate that algal polysaccharides have become a major source of carbon and energy in V. breoganii, and these ecophysiological adaptations may facilitate transient commensal associations with marine invertebrates that feed on algae.IMPORTANCE Vibrios are often considered animal specialists or generalists. Here, we show that Vibrio breoganii has undergone massive genomic changes to become specialized on algal carbohydrates. Accompanying genomic changes include massive gene import and loss. These vibrios may help us better understand how algal biomass is degraded in the environment and may serve as a blueprint on how to optimize the conversion of algae to biofuels.

High resolution time series reveals cohesive but short-lived communities in coastal plankton.

Because microbial plankton in the ocean comprise diverse bacteria, algae, and protists that are subject to environmental forcing on multiple spatial and temporal scales, a fundamental open question is to what extent these organisms form ecologically cohesive communities. Here we show that although all taxa undergo large, near daily fluctuations in abundance, microbial plankton are organized into clearly defined communities whose turnover is rapid and sharp. We analyze a time series of 93 consecutive days of coastal plankton using a technique that allows inference of communities as modular units of interacting taxa by determining positive and negative correlations at different temporal frequencies. This approach shows both coordinated population expansions that demarcate community boundaries and high frequency of positive and negative associations among populations within communities. Our analysis thus highlights that the environmental variability of the coastal ocean is mirrored in sharp transitions of defined but ephemeral communities of organisms.

Temporal Dynamics of In-Field Bioreactor Populations Reflect the Groundwater System and Respond Predictably to Perturbation.

Temporal variability complicates testing the influences of environmental variability on microbial community structure and thus function. An in-field bioreactor system was developed to assess oxic versus anoxic manipulations on in situ groundwater communities. Each sample was sequenced (16S SSU rRNA genes, average 10,000 reads), and biogeochemical parameters are monitored by quantifying 53 metals, 12 organic acids, 14 anions, and 3 sugars. Changes in dissolved oxygen (DO), pH, and other variables were similar across bioreactors. Sequencing revealed a complex community that fluctuated in-step with the groundwater community and responded to DO. This also directly influenced the pH, and so the biotic impacts of DO and pH shifts are correlated. A null model demonstrated that bioreactor communities were driven in part not only by experimental conditions but also by stochastic variability and did not accurately capture alterations in diversity during perturbations. We identified two groups of abundant OTUs important to this system; one was abundant in high DO and pH and contained heterotrophs and oxidizers of iron, nitrite, and ammonium, whereas the other was abundant in low DO with the capability to reduce nitrate. In-field bioreactors are a powerful tool for capturing natural microbial community responses to alterations in geochemical factors beyond the bulk phase.

Adaptive radiation by waves of gene transfer leads to fine-scale resource partitioning in marine microbes.

Adaptive radiations are important drivers of niche filling, since they rapidly adapt a single clade of organisms to ecological opportunities. Although thought to be common for animals and plants, adaptive radiations have remained difficult to document for microbes in the wild. Here we describe a recent adaptive radiation leading to fine-scale ecophysiological differentiation in the degradation of an algal glycan in a clade of closely related marine bacteria. Horizontal gene transfer is the primary driver in the diversification of the pathway leading to several ecophysiologically differentiated Vibrionaceae populations adapted to different physical forms of alginate. Pathway architecture is predictive of function and ecology, underscoring that horizontal gene transfer without extensive regulatory changes can rapidly assemble fully functional pathways in microbes.

Surveys, simulation and single-cell assays relate function and phylogeny in a lake ecosystem.

Much remains unknown about what drives microbial community structure and diversity. Highly structured environments might offer clues. For example, it may be possible to identify metabolically similar species as groups of organisms that correlate spatially with the geochemical processes they carry out. Here, we use a 16S ribosomal RNA gene survey in a lake that has chemical gradients across its depth to identify groups of spatially correlated but phylogenetically diverse organisms. Some groups had distributions across depth that aligned with the distributions of metabolic processes predicted by a biogeochemical model, suggesting that these groups performed biogeochemical functions. A single-cell genetic assay showed, however, that the groups associated with one biogeochemical process, sulfate reduction, contained only a few organisms that have the genes required to reduce sulfate. These results raise the possibility that some of these spatially correlated groups are consortia of phylogenetically diverse and metabolically different microbes that cooperate to carry out geochemical functions.

Massively parallel sequencing of single cells by epicPCR links functional genes with phylogenetic markers.

Many microbial communities are characterized by high genetic diversity. 16S ribosomal RNA sequencing can determine community members, and metagenomics can determine the functional diversity, but resolving the functional role of individual cells in high throughput remains an unsolved challenge. Here, we describe epicPCR (Emulsion, Paired Isolation and Concatenation PCR), a new technique that links functional genes and phylogenetic markers in uncultured single cells, providing a throughput of hundreds of thousands of cells with costs comparable to one genomic library preparation. We demonstrate the utility of our technique in a natural environment by profiling a sulfate-reducing community in a freshwater lake, revealing both known sulfate reducers and discovering new putative sulfate reducers. Our method is adaptable to any conserved genetic trait and translates genetic associations from diverse microbial samples into a sequencing library that answers targeted ecological questions. Potential applications include identifying functional community members, tracing horizontal gene transfer networks and mapping ecological interactions between microbial cells.

Natural bacterial communities serve as quantitative geochemical biosensors.

UNLABELLED: Biological sensors can be engineered to measure a wide range of environmental conditions. Here we show that statistical analysis of DNA from natural microbial communities can be used to accurately identify environmental contaminants, including uranium and nitrate at a nuclear waste site. In addition to contamination, sequence data from the 16S rRNA gene alone can quantitatively predict a rich catalogue of 26 geochemical features collected from 93 wells with highly differing geochemistry characteristics. We extend this approach to identify sites contaminated with hydrocarbons from the Deepwater Horizon oil spill, finding that altered bacterial communities encode a memory of prior contamination, even after the contaminants themselves have been fully degraded. We show that the bacterial strains that are most useful for detecting oil and uranium are known to interact with these substrates, indicating that this statistical approach uncovers ecologically meaningful interactions consistent with previous experimental observations. Future efforts should focus on evaluating the geographical generalizability of these associations. Taken as a whole, these results indicate that ubiquitous, natural bacterial communities can be used as in situ environmental sensors that respond to and capture perturbations caused by human impacts. These in situ biosensors rely on environmental selection rather than directed engineering, and so this approach could be rapidly deployed and scaled as sequencing technology continues to become faster, simpler, and less expensive. IMPORTANCE: Here we show that DNA from natural bacterial communities can be used as a quantitative biosensor to accurately distinguish unpolluted sites from those contaminated with uranium, nitrate, or oil. These results indicate that bacterial communities can be used as environmental sensors that respond to and capture perturbations caused by human impacts.

Computational methods for high-throughput comparative analyses of natural microbial communities.

One of the most widely employed methods in metagenomics is the amplification and sequencing of the highly conserved ribosomal RNA (rRNA) genes from organisms in complex microbial communities. rRNA surveys, typically using the 16S rRNA gene for prokaryotic identification, provide information about the total diversity and taxonomic affiliation of organisms present in a sample. Greatly enhanced by high-throughput sequencing, these surveys have uncovered the remarkable diversity of uncultured organisms and revealed unappreciated ecological roles ranging from nutrient cycling to human health. This chapter outlines the best practices for comparative analyses of microbial community surveys. We explain how to transform raw data into meaningful units for further analysis and discuss how to calculate sample diversity and community distance metrics. Finally, we outline how to find associations of species with specific metadata and true correlations between species from compositional data. We focus on data generated by next-generation sequencing platforms, using the Illumina platform as a test case, because of its widespread use especially among researchers just entering the field.

Distribution-based clustering: using ecology to refine the operational taxonomic unit.

16S rRNA sequencing, commonly used to survey microbial communities, begins by grouping individual reads into operational taxonomic units (OTUs). There are two major challenges in calling OTUs: identifying bacterial population boundaries and differentiating true diversity from sequencing errors. Current approaches to identifying taxonomic groups or eliminating sequencing errors rely on sequence data alone, but both of these activities could be informed by the distribution of sequences across samples. Here, we show that using the distribution of sequences across samples can help identify population boundaries even in noisy sequence data. The logic underlying our approach is that bacteria in different populations will often be highly correlated in their abundance across different samples. Conversely, 16S rRNA sequences derived from the same population, whether slightly different copies in the same organism, variation of the 16S rRNA gene within a population, or sequences generated randomly in error, will have the same underlying distribution across sampled environments. We present a simple OTU-calling algorithm (distribution-based clustering) that uses both genetic distance and the distribution of sequences across samples and demonstrate that it is more accurate than other methods at grouping reads into OTUs in a mock community. Distribution-based clustering also performs well on environmental samples: it is sensitive enough to differentiate between OTUs that differ by a single base pair yet predicts fewer overall OTUs than most other methods. The program can decrease the total number of OTUs with redundant information and improve the power of many downstream analyses to describe biologically relevant trends.

Reproducibility of Vibrionaceae population structure in coastal bacterioplankton.

How reproducibly microbial populations assemble in the wild remains poorly understood. Here, we assess evidence for ecological specialization and predictability of fine-scale population structure and habitat association in coastal ocean Vibrionaceae across years. We compare Vibrionaceae lifestyles in the bacterioplankton (combinations of free-living, particle, or zooplankton associations) measured using the same sampling scheme in 2006 and 2009 to assess whether the same groups show the same environmental association year after year. This reveals complex dynamics with populations falling primarily into two categories: (i) nearly equally represented in each of the two samplings and (ii) highly skewed, often to an extent that they appear exclusive to one or the other sampling times. Importantly, populations recovered at the same abundance in both samplings occupied highly similar habitats suggesting predictable and robust environmental association while skewed abundances of some populations may be triggered by shifts in ecological conditions. The latter is supported by difference in the composition of large eukaryotic plankton between years, with samples in 2006 being dominated by copepods, and those in 2009 by diatoms. Overall, the comparison supports highly predictable population-habitat linkage but highlights the fact that complex, and often unmeasured, environmental dynamics in habitat occurrence may have strong effects on population dynamics.