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Groundwater-surface water mixing shifts ecological assembly processes and stimulates organic carbon turnover.

Stegen JC, Fredrickson JK, Wilkins MJ, Konopka AE, Nelson WC, Arntzen EV, Chrisler WB, Chu RK, Danczak RE, Fansler SJ, Kennedy DW, Resch CT, Tfaily M - Nat Commun (2016)

Bottom Line: Environmental transitions often result in resource mixtures that overcome limitations to microbial metabolism, resulting in biogeochemical hotspots and moments.Riverine systems, where groundwater mixes with surface water (the hyporheic zone), are spatially complex and temporally dynamic, making development of predictive models challenging.Our results indicate that groundwater-surface water mixing in the hyporheic zone stimulates heterotrophic respiration, alters organic carbon composition, causes ecological processes to shift from stochastic to deterministic and is associated with elevated abundances of microbial taxa that may degrade a broad suite of organic compounds.

View Article: PubMed Central - PubMed

Affiliation: Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, Richland, Washington 99352, USA.

ABSTRACT
Environmental transitions often result in resource mixtures that overcome limitations to microbial metabolism, resulting in biogeochemical hotspots and moments. Riverine systems, where groundwater mixes with surface water (the hyporheic zone), are spatially complex and temporally dynamic, making development of predictive models challenging. Spatial and temporal variations in hyporheic zone microbial communities are a key, but understudied, component of riverine biogeochemical function. Here, to investigate the coupling among groundwater-surface water mixing, microbial communities and biogeochemistry, we apply ecological theory, aqueous biogeochemistry, DNA sequencing and ultra-high-resolution organic carbon profiling to field samples collected across times and locations representing a broad range of mixing conditions. Our results indicate that groundwater-surface water mixing in the hyporheic zone stimulates heterotrophic respiration, alters organic carbon composition, causes ecological processes to shift from stochastic to deterministic and is associated with elevated abundances of microbial taxa that may degrade a broad suite of organic compounds.

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Relationships between βNTI and mixing model deviations.(a) βNTI estimate of each community as a function of the deviation between observed and expected NPOC; the solid line indicates the regression model and statistics are provided; open, closed black and closed grey symbols indicate hyporheic, river and ground water samples, respectively. (b) Box plots summarizing distributions of median βNTI values—from a—that are or are not associated with a negative NPOC deviation; βNTI values included in the ‘negative deviation' distribution are to the left of the dashed grey line in a and all other βNTI values are included in the ‘positive or no deviation' distribution; the displayed P-value indicates a significant difference between distributions by a Kruskal–Wallis test.
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f6: Relationships between βNTI and mixing model deviations.(a) βNTI estimate of each community as a function of the deviation between observed and expected NPOC; the solid line indicates the regression model and statistics are provided; open, closed black and closed grey symbols indicate hyporheic, river and ground water samples, respectively. (b) Box plots summarizing distributions of median βNTI values—from a—that are or are not associated with a negative NPOC deviation; βNTI values included in the ‘negative deviation' distribution are to the left of the dashed grey line in a and all other βNTI values are included in the ‘positive or no deviation' distribution; the displayed P-value indicates a significant difference between distributions by a Kruskal–Wallis test.

Mentions: Coupling NPOC deviations with βNTI further revealed that homogeneous ecological selection565859 emerged in places and times of mixing. More specifically, median βNTI values for a given sample were significantly (P=0.0009, R2=0.19) and positively related to the deviation from the NPOC mixing model (Fig. 6a); this relationship was stronger when considering only hyporheic zone samples (P=0.0001, R2=0.37). Median βNTI values associated with negative NPOC deviations were also significantly (P<<0.0001) more negative than βNTI values associated with effectively no deviation (Fig. 6b). Negative NPOC deviations—which we equate with groundwater–surface water mixing-induced increases in microbial activity—were therefore associated with βNTI values centred near −2, which indicates a shift to a selective environment dominated by a small number of consistently imposed selective pressures (that is, homogeneous ecological selection). This aligns with experimental results of Findlay et al.31 but contrasts sharply with microbial communities from the current study associated with no effective deviation from the NPOC mixing model; these communities were associated with βNTI values (centred near −1) consistent with stochastic ecological processes58.


Groundwater-surface water mixing shifts ecological assembly processes and stimulates organic carbon turnover.

Stegen JC, Fredrickson JK, Wilkins MJ, Konopka AE, Nelson WC, Arntzen EV, Chrisler WB, Chu RK, Danczak RE, Fansler SJ, Kennedy DW, Resch CT, Tfaily M - Nat Commun (2016)

Relationships between βNTI and mixing model deviations.(a) βNTI estimate of each community as a function of the deviation between observed and expected NPOC; the solid line indicates the regression model and statistics are provided; open, closed black and closed grey symbols indicate hyporheic, river and ground water samples, respectively. (b) Box plots summarizing distributions of median βNTI values—from a—that are or are not associated with a negative NPOC deviation; βNTI values included in the ‘negative deviation' distribution are to the left of the dashed grey line in a and all other βNTI values are included in the ‘positive or no deviation' distribution; the displayed P-value indicates a significant difference between distributions by a Kruskal–Wallis test.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4829693&req=5

f6: Relationships between βNTI and mixing model deviations.(a) βNTI estimate of each community as a function of the deviation between observed and expected NPOC; the solid line indicates the regression model and statistics are provided; open, closed black and closed grey symbols indicate hyporheic, river and ground water samples, respectively. (b) Box plots summarizing distributions of median βNTI values—from a—that are or are not associated with a negative NPOC deviation; βNTI values included in the ‘negative deviation' distribution are to the left of the dashed grey line in a and all other βNTI values are included in the ‘positive or no deviation' distribution; the displayed P-value indicates a significant difference between distributions by a Kruskal–Wallis test.
Mentions: Coupling NPOC deviations with βNTI further revealed that homogeneous ecological selection565859 emerged in places and times of mixing. More specifically, median βNTI values for a given sample were significantly (P=0.0009, R2=0.19) and positively related to the deviation from the NPOC mixing model (Fig. 6a); this relationship was stronger when considering only hyporheic zone samples (P=0.0001, R2=0.37). Median βNTI values associated with negative NPOC deviations were also significantly (P<<0.0001) more negative than βNTI values associated with effectively no deviation (Fig. 6b). Negative NPOC deviations—which we equate with groundwater–surface water mixing-induced increases in microbial activity—were therefore associated with βNTI values centred near −2, which indicates a shift to a selective environment dominated by a small number of consistently imposed selective pressures (that is, homogeneous ecological selection). This aligns with experimental results of Findlay et al.31 but contrasts sharply with microbial communities from the current study associated with no effective deviation from the NPOC mixing model; these communities were associated with βNTI values (centred near −1) consistent with stochastic ecological processes58.

Bottom Line: Environmental transitions often result in resource mixtures that overcome limitations to microbial metabolism, resulting in biogeochemical hotspots and moments.Riverine systems, where groundwater mixes with surface water (the hyporheic zone), are spatially complex and temporally dynamic, making development of predictive models challenging.Our results indicate that groundwater-surface water mixing in the hyporheic zone stimulates heterotrophic respiration, alters organic carbon composition, causes ecological processes to shift from stochastic to deterministic and is associated with elevated abundances of microbial taxa that may degrade a broad suite of organic compounds.

View Article: PubMed Central - PubMed

Affiliation: Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, Richland, Washington 99352, USA.

ABSTRACT
Environmental transitions often result in resource mixtures that overcome limitations to microbial metabolism, resulting in biogeochemical hotspots and moments. Riverine systems, where groundwater mixes with surface water (the hyporheic zone), are spatially complex and temporally dynamic, making development of predictive models challenging. Spatial and temporal variations in hyporheic zone microbial communities are a key, but understudied, component of riverine biogeochemical function. Here, to investigate the coupling among groundwater-surface water mixing, microbial communities and biogeochemistry, we apply ecological theory, aqueous biogeochemistry, DNA sequencing and ultra-high-resolution organic carbon profiling to field samples collected across times and locations representing a broad range of mixing conditions. Our results indicate that groundwater-surface water mixing in the hyporheic zone stimulates heterotrophic respiration, alters organic carbon composition, causes ecological processes to shift from stochastic to deterministic and is associated with elevated abundances of microbial taxa that may degrade a broad suite of organic compounds.

Show MeSH
Related in: MedlinePlus