Understanding the community composition and diversity of bacterioplankton along physiochemical and biological gradients will help identify ecological processes driving the production and transformation of organic matter in these regions, and will provide a test for the original River Continuum Concept, which hypothesized that microbial communities change along the aquatic continuum to adapt to the inefficiencies of upstream communities by forming new communities adapted to consume resources released from upstream environments Vannote et al.
We used rRNA gene amplicon sequencing to characterize bacterial diversity in samples collected along the river-ocean continuum of the Amazon River and its plume in the western tropical North Atlantic Ocean. We collected samples in the lower Amazon River on three sampling trips in September and December , and in May , capturing the late declining September , early rising December , and maximum May river discharge periods Lentz and Limeburner, ; Ward et al.
Our results suggest that bacterial communities in the Amazon River are shaped by environmental factors controlled by seasonality in river discharge, while communities in the Amazon plume are insensitive to seasons and instead are loosely structured by salinity, which provides a proxy for the evolving inorganic and organic constituents of the plume. Amazon River samples were collected on three cruises September , December , and May from five stations: Although this channel is not typically considered to be part of the Amazon River, its discharge travels north and is integrated with the plume.
Amazon River sampling stations from September , December , and May are indicated on both maps. Surface water salinity along cruise tracks are indicated with colors.
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Three types of DNA samples were collected at each station. Cells were also partitioned into two size fractions by sequential filtration through 2. These filters were immediately submerged in RNAlater in sterile 50 mL tubes. Filtration and stabilization of all samples was completed within 30 min of water collection.
River temperature and pH were measured using a Thermo Orion A Plus meter with the probe immersed in an overflowing graduated cylinder. River conductivity and dissolved oxygen were measured using an Amber Science m and a YSI 55 m, respectively, with the probes immersed in the same graduated cylinder. Plume measurements of DOC, chlorophyll a, bacterial abundance, and bacterial production were described in Medeiros et al. DNA was extracted using methods adapted from Zhou et al. For Sterivex filters, RNAlater preservative was pushed out of the filter cartridge with a sterile syringe, and the filter was triple-rinsed with either sterile water river or sterile 0.
Each mm 0. Each mm 2. DNA was then extracted according to Crump et al. DNA from this study and from six arctic rivers previously analyzed with a different method; Crump et al. Quality control used the AmpliconNoise pipeline with recommended procedures for Titanium sequencing chemistry. Maximum sequence length was set to bp Parse. OTUs identified as internal standard T.
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Alpha-diversity was estimated using Catchall Bunge, , which computes maximum likelihood estimates of diversity based on a suite of parametric and non-parametric models. Beta-diversity was estimated using Bray—Curtis similarity Clarke, and weighted and unweighted UniFrac distance analyses Lozupone et al. OTUs that characterize each sample group were identified with Indicator Species Analysis using labdsv Roberts, and indval packages Dufrene and Legendre, in R.
Inferred bacterial associations co-occurrence and mutual exclusion within plume and river samples were computed using the CoNet v1. Two separate association networks were constructed using rarefied OTU tables for plume and river. For each network, co-occurrence and mutual exclusion associations were identified using an ensemble of correlation Spearman and Pearson coefficients and distance Bray—Curtis and Kullback—Leibler dissimilarity measures metrics. For each association metric and each edge, renormalized permutation and bootstrap scores were generated following the ReBoot procedure developed by Faust et al.
The measure-specific p -values from multiple association metrics were merged using the Simes method Sarkar and Chang, and false-discovery rate correction was performed using Benjamini—Hochberg multiple testing correction Benjamini and Hochberg, Only top- and bottom-ranking edges from each association measure were kept in the network analysis, and only edges supported by at least two of the four association metrics were retained in the final network inference of associations among taxa.
Amazon river biogeochemistry varied by season and by sampling site Supplemental Table S2. River discharge was in the late declining phase in September , and in the very early rising stage in December, reaching maximal discharge in May Temperature, conductivity, pH, and oxygen saturation were negatively related to discharge at most sites. DIN, nitrate, and ammonium concentrations were highly variable and followed no clear trends, but nitrite was positively related to river discharge. Amazon River plume biogeochemistry varied as a function of river dilution, reflected in salinity, and was similar between the high river discharge in May and low discharge in September Supplemental Table S3.
Temperature varied by only 2. Closed symbols represent unfractionated samples and free-living bacteria 0. Plume communities in the 0. Communities in deep samples collected below the plume were most similar to communities at the plume edge. This pattern held regardless of sampling dates, size fractions, and the groups identified based on community composition.
CCA analysis showed that conductivity and pH explain Variability in plume bacterial communities was better explained by environmental factors when major groupings of communities were analyzed separately Table 2. Seasonal shifts were also observed for freshwater relatives of the Alphaproteobacteria SAR11, and Cyanobacteria mainly Synechococcus and Merismopedia , both of which had highest proportions during low discharge 4. Taxonomic diversity and salinity of Amazon River and Plume samples grouped by environment following Figure 2 , and showing the most abundant taxonomic groups.
Plume samples are ordered by environmental group for the 0. Freshwater taxa were almost entirely absent from plume samples, averaging 0. The low salinity plume community was dominated by SAR11, Flavobacteriia, Gammaproteobacteria, and Synechococcus , and some samples included Sphingobacteriia and Firmicutes. High salinity plume communities were dominated by Synechococcus and SAR11, and many of the dominant low salinity taxa were rare or absent.
As sample salinity increased toward the plume edge, the proportion of Synechococcus declined and the proportion of Prochlorococcus increased Figure 3. Indicator taxa reflected this taxonomic shift between the low salinity plume and the plume edge Supplemental Table S4 and Figures S3, S4. Indicators for the low salinity plume both size fractions included a very abundant Synechococcus , and several Gammaproteobacteria, SAR11, and Bacteroidetes Flavobacteriia and Sphingobacteriia. High salinity plume had few indicator taxa other than one very abundant Synechococcus in the 0.
In the river network, indicator taxa within each indicator group showed strong co-occurrence positive edges and 0 negative edge Figure 4A , and were much more highly connected for the May and December mainstem than the September mainstem. Indicator taxa in the May and December mainstem groups were also negatively correlated to each other, while September mainstem indicator taxa showed few correlations with themselves or with other indicator groups Figure 4A. Indicator taxa assigned in the May and September-December tributary groups showed mutual exclusion between groups 13 positive edges and 59 negative edges and with mainstem groups 97 positive edges and negative edges.
This mutual exclusion from mainstem groups was stronger for indicator taxa in the September-December tributary group positive edges and 53 negative edges than the May tributary group 44 positive edges and negative edges Figure 4A. Number of indicator taxa for sample groups, and results of co-occurrence network analyses showing the number of taxa included, the number of positive and negative correlations edges , and the number of correlations per taxa for each indicator group. The number of positive blue and negative red edges i. Line thickness and numbers indicate the number of edges between node groups.
Nodes and edges associated with non-indicator taxa are not shown. Similar to the river network, indicator taxa in the plume network showed a high degree co-occurrence within the plume edge, low salinity, and, to a lesser extent, DDA groups Figure 4B and Table 3.
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Indicator taxa in the low salinity group showed strong mutual exclusion from other indicator groups 85 positive edges and negative edges , with the most mutual exclusion detected with plume edge indicator taxa 46 positive edges and negative edges , and DDA indicator taxa 11 positive edges and negative edges.
In contrast, high salinity plume indicators were not strongly correlated with themselves or with taxa in any other group 67 positive edges and 58 negative edges. Indicator taxa in the DDA and plume edge groups showed the strongest co-occurrence between any of the plume indicator groups positive edges and 8 negative edges Figure 4B. This first survey of microbial communities along the river-ocean continuum of the largest river in the world revealed that seasonal and spatial patterns were not fundamentally different than those previously observed in smaller rivers Levine and Crump, ; Sekiguchi et al.
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Seasonal shifts in phylogeny were consistent with community variability in other rivers Fortunato et al. These results suggest that the Amazon River has two seasonal microbial communities, as opposed to the three seasonal communities identified for temperate and arctic rivers Crump et al. Plume communities showed no seasonal differences and instead varied spatially, loosely tracking salinity. However, salinity only explained a small fraction of the community variability in the plume Table 2 , and plume communities were strikingly different in DDA bloom samples than in other high salinity plume samples.
These results suggest that although salinity provides physical structure to plume ecosystems, the composition of communities is driven by other environmental and biotic factors including nutrients, DOC chemistry, and phytoplankton community composition Goes et al. Co-occurrence networks identified highly interconnected assemblages associated with the low salinity near-shore region, DDA blooms, and the plume edge region, and weakly interconnected assemblages in the high salinity regions, suggesting that the high salinity plume region supports transitional communities that are influenced by mixing of oceanic communities from outside or below the plume, and by species sorting as these communities adapt to local environmental conditions, consistent with Hewson et al.
Similar results were found in long reaches of the lower Changjiang and Danube rivers Sekiguchi et al. However, the majority of DOM molecular formulae did not change along this reach, suggesting a mixture of compounds resistant to microbial consumption and compounds that are biologically labile but are continuously replenished by autochthonous production and lateral inputs of DOM Seidel et al. Amazon River bacterial communities varied seasonally, tracking changes in river discharge and associated environmental conditions. Seasonality is typical of bacterioplankton communities Crump et al.
In temperate and Arctic Rivers, seasonal variation correlated with river discharge, rainfall, temperature, nutrient concentration, and organic matter composition Crump and Hobbie, ; Crump et al. In the tropical Amazon River mainstem, we found a similar set of environmental factors correlated with microbial communities Table 1. Across all these systems, river discharge appears to be the master variable controlling seasonal changes in microbial communities.
Discharge influences the environmental conditions that drive species sorting in microbial communities by controlling the flux of materials e.
Discharge also influences turbidity and the magnitude of solar insolation, and thus water temperature and phytoplankton production, both of which are potential controls on seasonal patterns in microbial community composition. The strongest signal of seasonality in river microbial communities was a shift from a high proportion of Betaproteobacteria during high discharge May to high proportions of Actinobacteria, Cyanobacteria, and freshwater SAR11 Alphaproteobacteria during low discharge September and December.
A similar pattern was seen in the Columbia River, with more Betaproteobacteria during the spring freshet, and more Actinobacteria and freshwater SAR11 during summer and fall Fortunato et al. A study on the effects of impoundment on the Ebro River showed that damming reduced Betaproteobacteria and increased Actinobacteria and Alphaproteobacteria Ruiz-Gonzalez et al.
This is consistent with a catchment-scale study of the Thames River that linked the development of Actinobacteria communities to water residence time Read et al. In-stream water residence time is negatively related to discharge in rivers Worrall et al. Moreover, bulk respiration rates in the Amazon River are generally highest during low discharge Benner et al. Taken together, these observations provide strong evidence that river discharge drives seasonal patterns in river microbial community composition by controlling the ratio of terrestrial to algal-produced DOM, and by controlling the time available for species sorting to produce a microbial community adapted to these DOM conditions.
In the Amazon plume, microbial community composition did not vary by season and instead varied with salinity and several co-varying environmental factors. We found that communities in unfractionated samples and in the 0. This contrasts with the Columbia River plume, where seasonality in microbial communities corresponded to seasonal changes in coastal upwelling conditions and to mixing of seasonally varying river communities Fortunato et al.
Original Research ARTICLE
Also, seasonal changes in environmental conditions e. These factors limit seasonal changes in the environmental conditions experienced by tropical river plume microbial communities and accentuate spatial variability across the salinity gradient that reflects the gradual mixing of river water. A number of studies show changing community composition along salinity gradients in estuarine and coastal environments Crump et al. In the Amazon plume, these changes tracked shifts in the composition and dynamics of DOM Medeiros et al.
We found significant differences in bacterial community composition between all of these regions, and, within the high salinity plume region, communities varied depending on whether they co-occurred with a DDA bloom. These results suggest that although microbial community composition tracks salinity across the entire Amazon River plume, it is more likely that bacterial communities are controlled by the composition of phytoplankton communities and the chemistry of DOM.
This conclusion is supported by our correlation analyses Table 2 , which show that chlorophyll fluorescence and several other factors contribute significantly to models of bacterial community composition within most regions of the plume. Low salinity communities correlated most strongly with DOC concentration and phosphate PO 4 3- , but also correlated with several other factors. This community had the highest proportions of Bacteroidetes Flavobacteriia and Sphingobacteriia and Gammaproteobacteria, and a recent study demonstrated high gene expression by these taxa in several of these samples Satinsky et al.
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Avoiding History (7) (Tides of the Continuum) by Keegan Hennis
Amazon Inspire Digital Educational Resources. Amazon Rapids Fun stories for kids on the go. Amazon Restaurants Food delivery from local restaurants. ComiXology Thousands of Digital Comics. She never knew she had it in her, but the foundations to build a great leader had been laid for years in her life. With advancements come labor and maintenance, and danger. A new threat from an old enemy is detected and must be stopped. But how could an enemy be so effective against our heroes, unless they had help from a spy?
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Books by Keegan Hennis
Lists with This Book. This book is not yet featured on Listopia. Oct 21, R. James rated it liked it. A decent story, albeit a bit juvenile in the way the author allows a starship to recruit someone from a college a community college at that and allow them to write their own ticket. But this is SciFi, and as such a reader should be able to suspend disbelief.
Author Keegan Hennis did a good job in that respect, and so I can overlook the naivete of such a plotline.