Synchrony of Eukaryotic and Prokaryotic Planktonic Communities in Three Seasonally Sampled Austrian Lakes

Böck, Christina; Salcher, Michaela M.; Jensen, Manfred; Pandey, Ram Vinay; Boenigk, Jens

doi:10.3389/fmicb.2018.01290

Cited by 27 publications

(24 citation statements)

References 116 publications

(151 reference statements)

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“…Even though restricted dispersal and distribution pattern at least for some taxa are unquestioned [9][10][11][12] and large-scale variation between protist communities have been shown [13][14][15] the resulting pattern as well as areas of high and low protist diversity are largely unresolved. Distinct habitats may differ considerably in taxon richness 14,[16][17][18] , which is often related to environmental factors such as pH, altitude and trophy. Examples for regional differences in taxon richness of animals and plants as well as of protists are mountain ranges [18][19][20][21][22] which, however, basically reflect effects of one of the formerly mentioned abiotic factors, i.e.…”

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confidence: 99%

Centers of endemism of freshwater protists deviate from pattern of taxon richness on a continental scale

Olefeld

Böck

Jensen

et al. 2020

Sci Rep

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Here, we analyzed patterns of taxon richness and endemism of freshwater protists in europe. even though the significance of physicochemical parameters but also of geographic constraints for protist distribution is documented, it remains unclear where regional areas of high protist diversity are located and whether areas of high taxon richness harbor a high proportion of endemics. further, patterns may be universal for protists or deviate between taxonomic groups. Based on amplicon sequencing campaigns targeting the SSU and itS region of the rDnA we address these patterns at two different levels of phylogenetic resolution. Our analyses demonstrate that protists have restricted geographical distribution areas. for many taxonomic groups the regions of high taxon richness deviate from those having a high proportion of putative endemics. in particular, the diversity of high mountain lakes as azonal habitats deviated from surrounding lowlands, i.e. many taxa were found exclusively in high mountain lakes and several putatively endemic taxa occurred in mountain regions like the Alps, the pyrenees or the Massif central. Beyond that, taxonomic groups showed a pronounced accumulation of putative endemics in distinct regions, e.g. Dinophyceae along the Baltic Sea coastline, and chrysophyceae in Scandinavia. Many other groups did not have pronounced areas of increased endemism but geographically restricted taxa were found across europe.

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confidence: 99%

Centers of endemism of freshwater protists deviate from pattern of taxon richness on a continental scale

Olefeld

Böck

Jensen

et al. 2020

Sci Rep

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“…In fact, the seasonal patterns of microbes were mostly in synchrony. While several studies (Crump & Hobbie, ; Kent et al, ) indicate seasonally repetitive patterns across several years in prokaryotes, single‐year studies find contrasting patterns, from directional changes in phytoplankton and stochastic patterns in prokaryotes (Su et al, ) to coherent temporal patterns between prokaryotes and protists (Bock et al, ; Kent et al, ). Here, we showed that synchrony in the seasonal signal between prokaryotes and protists was consistent across years despite an ecosystem change.…”

Section: Discussionmentioning

confidence: 99%

“…Microbial communities change over multiple timescales (i.e., from hours, days, weeks, seasons) in response to different abiotic and biotic forces (Fuhrman, Cram, & Needham, ). Prokaryotes (Crump & Hobbie, ; Kent et al, ; Rusak, Jones, Kent, Shade, & McMahon, ) and protists (Simon et al, ) show repetitive seasonal cycles and can vary in synchrony with both groups showing coherent patterns in some alpine lakes (Bock, Salcher, Jensen, Pandey, & Boenigk, ) and across a large landscape (Kent, Yannarell, Rusak, Triplett, & McMahon, ). However, prokaryotes and protists communities can also develop different patterns with the extent of dissimilarities depending on taxonomical resolution as shown in a subtropical lake (Su et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Few studies (Bock et al, ; Su et al, ) contemporarily assess the temporal patterns of freshwater prokaryote and protist communities by high‐throughput sequencing and thus embrace a high resolution of community diversity. Most freshwater studies are based on comparisons of genetic‐based prokaryotes and microscopy‐based phytoplankton data (e.g., Eiler et al, ; Kent et al, ; Liu et al, ), with the latter often grouping small morphologically difficult‐to‐distinguish algae into nontaxonomical categories.…”

Section: Introductionmentioning

confidence: 99%

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Multifaceted aspects of synchrony between freshwater prokaryotes and protists

2019

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Community composition of freshwater prokaryotes and protists varies through time. Few studies contemporarily investigate temporal variation of these freshwater communities for more than 1 year. We compared the temporal patterns of prokaryotes and protists in three distinct habitats for 4 years (2014–2017) in Lake Tovel, a cold‐water lake. This lake showed a marked temperature increase in 2017 linked to altered precipitation patterns. We investigated whether microbial communities reflected this change across habitats and whether changes occurred at the same time and to the same extent. Furthermore, we tested the concept of hydrological year emphasizing the ecological effect of water renewal on communities for its explanatory power of community changes. Microbe diversity was assessed by Illumina sequencing of the V3–V4 hypervariable region of the 16S rRNA gene and 18S rRNA gene, and we applied co‐inertia analysis and asymmetric eigenvector maps modelling to infer synchrony and temporal patterns of prokaryotes and protists. When considering community composition, microbes were invariable in synchrony across habitats and indicated a temporal gradient linked to decreasing precipitation; however, when looking at temporal patterns, the extent of synchrony was reduced. Small‐scale patterns were similar across habitats and microbes and linked to seasonally varying environmental variables, while large‐scale patterns were different and partially linked to an ecosystem change as indicated by increasing water transparency and temperature and decreasing dissolved oxygen. Our advanced statistical approach outlined the multifaceted aspect of synchrony when linked to community composition and temporal patterns.

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“…We applied this framework to a dataset from a large-scale survey of European lakes [40][41][42]. Lakes are considered as sentinels of ecosystem change at different temporal and geographical scales [43,44].…”

Section: Introductionmentioning

confidence: 99%

Quantifying the information content of lake microbiomes using a machine learning-based framework

Sperlea¹,

Kreuder²,

Beißer³

et al. 2020

Preprint

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Background: Bacteria and microbial eukaryotes occupy a wide range of ecological niches and are essential for the functioning of ecosystems. The advent of next-generation sequencing methods enabled the study of environmental microbial community compositions. Yet, many questions regarding the stability and functioning of environmental microbiomes remain open. Results: In the current study, we present a methodological framework to quantify the information shared between the microbial community of a habitat and the abiotic parameters of this habitat. It is built on theoretical considerations of systems ecology and makes use of state-of-the-art machine learning techniques. It can also be used to identify bioindicators. We apply the framework to a dataset containing operational taxonomic units (OTUs) as well as more than twenty physico-chemical and geographic parameters measured in a large-scale survey of European lakes. While a large part of variation (up to 61\%) in many physico-chemical parameters can be explained by microbial community composition, some of the examined parameters only share little information with the microbiome. Moreover, we have identified OTUs that act as `multi-task’ bioindicators that could be potential candidates for lake water monitoring schemes. Conclusions: This study demonstrates the benefits of machine learning approaches in microbial ecology. Our results represent, for the first time, a quantification of information shared between the lake microbiome and a wide array of ecosystem parameters. Building on the results and methodology presented here, it will be possible to identify microbial taxa and processes central for the functioning and stability of lake ecosystems.

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Synchrony of Eukaryotic and Prokaryotic Planktonic Communities in Three Seasonally Sampled Austrian Lakes

Cited by 27 publications

References 116 publications

Centers of endemism of freshwater protists deviate from pattern of taxon richness on a continental scale

Centers of endemism of freshwater protists deviate from pattern of taxon richness on a continental scale

Multifaceted aspects of synchrony between freshwater prokaryotes and protists

Quantifying the information content of lake microbiomes using a machine learning-based framework

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