Allison M. Veach

Abiotic controls and temporal variability of river metabolism: multiyear analyses of Mississippi and Chattahoochee River data

Abstract. Whole-ecosystem metabolism is an important indicator of the role of organic matter, C cycling, and trophic structure in rivers. Ecosystem metabolism is well studied in small streams, but less is known about metabolism in large rivers. We estimated daily whole-ecosystem metabolism over 2 y for 1 site each at the Mississippi and Chattahoochee Rivers in the USA to understand factors influencing temporal patterns of ecosystem metabolism. We estimated rates of gross primary production (GPP), community respiration (CR), and net ecosystem production (NEP) with a curve-fitting approach with publicly available discharge (Q), dissolved O2, temperature, and photosynthetically active radiation (PAR) data. Models were run for week-long blocks, and power analyses suggested that rates should be established at least once for each 10-wk period throughout the year to characterize annual rates of metabolism accurately in these 2 rivers. We analyzed weekly rates averaged over 10-wk periods with Spearman rank correlation to identify potential drivers and with path analyses to identify interactions among variables driving GPP, CR, and NEP. Both rivers had an overall negative NEP, and the Mississippi River had stronger seasonal trends. In the Mississippi River, CR was strongly positively correlated with Q, which suggests variation in seasonal availability of allochthonous C. In the Chattahoochee, CR was most strongly positively correlated with GPP, whereas GPP was negatively correlated with Q, which suggests that autochthonous processes and water-column light attenuation played important roles in C dynamics. Our results suggest that these large rivers were net heterotrophic at annual time scales but autotrophy can be important seasonally.

Fire and grazing influences on rates of riparian woody plant expansion along grassland streams.

Grasslands are threatened globally due to the expansion of woody plants. The few remaining headwater streams within tallgrass prairies are becoming more like typical forested streams due to rapid conversion of riparian zones from grassy to wooded. Forestation can alter stream hydrology and biogeochemistry. We estimated the rate of riparian woody plant expansion within a 30 m buffer zone surrounding the stream bed across whole watersheds at Konza Prairie Biological Station over 25 years from aerial photographs. Watersheds varied with respect to experimentally-controlled fire and bison grazing. Fire frequency, presence or absence of grazing bison, and the historical presence of woody vegetation prior to the study time period (a proxy for proximity of propagule sources) were used as independent variables to predict the rate of riparian woody plant expansion between 1985 and 2010. Water yield was estimated across these years for a subset of watersheds. Riparian woody encroachment rates increased as burning became less frequent than every two years. However, a higher fire frequency (1–2 years) did not reverse riparian woody encroachment regardless of whether woody vegetation was present or not before burning regimes were initiated. Although riparian woody vegetation cover increased over time, annual total precipitation and average annual temperature were variable. So, water yield over 4 watersheds under differing burn frequencies was quite variable and with no statistically significant detected temporal trends. Overall, burning regimes with a frequency of every 1–2 years will slow the conversion of tallgrass prairie stream ecosystems to forested ones, yet over long time periods, riparian woody plant encroachment may not be prevented by fire alone, regardless of fire frequency.

The Populus holobiont: dissecting the effects of plant niches and genotype on the microbiome

BackgroundMicroorganisms serve important functions within numerous eukaryotic host organisms. An understanding of the variation in the plant niche-level microbiome, from rhizosphere soils to plant canopies, is imperative to gain a better understanding of how both the structural and functional processes of microbiomes impact the health of the overall plant holobiome. Using Populus trees as a model ecosystem, we characterized the archaeal/bacterial and fungal microbiome across 30 different tissue-level niches within replicated Populus deltoides and hybrid Populus trichocarpa × deltoides individuals using 16S and ITS2 rRNA gene analyses.ResultsOur analyses indicate that archaeal/bacterial and fungal microbiomes varied primarily across broader plant habitat classes (leaves, stems, roots, soils) regardless of plant genotype, except for fungal communities within leaf niches, which were greatly impacted by the host genotype. Differences between tree genotypes are evident in the elevated presence of two potential fungal pathogens, Marssonina brunnea and Septoria sp., on hybrid P. trichocarpa × deltoides trees which may in turn be contributing to divergence in overall microbiome composition. Archaeal/bacterial diversity increased from leaves, to stem, to root, and to soil habitats, whereas fungal diversity was the greatest in stems and soils.ConclusionsThis study provides a holistic understanding of microbiome structure within a bioenergy relevant plant host, one of the most complete niche-level analyses of any plant. As such, it constitutes a detailed atlas or map for further hypothesis testing on the significance of individual microbial taxa within specific niches and habitats of Populus and a baseline for comparisons to other plant species.

Spatial and successional dynamics of microbial biofilm communities in a grassland stream ecosystem.

Biofilms represent a metabolically active and structurally complex component of freshwater ecosystems. Ephemeral prairie streams are hydrologically harsh and prone to frequent perturbation. Elucidating both functional and structural community changes over time within prairie streams provides a general understanding of microbial responses to environmental disturbance. We examined microbial succession of biofilm communities at three sites in a third‐order stream at Konza Prairie over a 2‐ to 64‐day period. Microbial abundance (bacterial abundance, chlorophyll a concentrations) increased and never plateaued during the experiment. Net primary productivity (net balance of oxygen consumption and production) of the developing biofilms did not differ statistically from zero until 64 days suggesting a balance of the use of autochthonous and allochthonous energy sources until late succession. Bacterial communities (MiSeq analyses of the V4 region of 16S rRNA) established quickly. Bacterial richness, diversity and evenness were high after 2 days and increased over time. Several dominant bacterial phyla (Beta‐, Alphaproteobacteria, Bacteroidetes, Gemmatimonadetes, Acidobacteria, Chloroflexi) and genera (Luteolibacter, Flavobacterium, Gemmatimonas, Hydrogenophaga) differed in relative abundance over space and time. Bacterial community composition differed across both space and successional time. Pairwise comparisons of phylogenetic turnover in bacterial community composition indicated that early‐stage succession (≤16 days) was driven by stochastic processes, whereas later stages were driven by deterministic selection regardless of site. Our data suggest that microbial biofilms predictably develop both functionally and structurally indicating distinct successional trajectories of bacterial communities in this ecosystem.

Woody plant encroachment, and its removal, impact bacterial and fungal communities across stream and terrestrial habitats in a tallgrass prairie ecosystem.

Woody plant encroachment has become a global threat to grasslands and has caused declines in aboveground richness and changes in ecosystem function; yet we have a limited understanding on the effects of these phenomena on belowground microbial communities. We completed riparian woody plant removals at Konza Prairie Biological Station, Kansas and collected soils spanning land-water interfaces in removal and woody vegetation impacted areas. We measured stream sediments and soils for edaphic variables (C and N pools, soil water content, pH) and bacterial (16S rRNA genes) and fungal (ITS2 rRNA gene repeat) communities using Illumina MiSeq metabarcoding. Bacterial richness and diversity decreased with distance from streams. Fungal richness decreased with distance from the stream in wooded areas, but was similar across landscape position while Planctomycetes and Basidiomycota relative abundance was lower in removal areas. Cyanobacteria, Ascomycota, Chytridiomycota and Glomeromycota relative abundance was greater in removal areas. Ordination analyses indicated that bacterial community composition shifted more across land-water interfaces than fungi yet both were marginally influenced by treatment. This study highlights the impacts of woody encroachment restoration on grassland bacterial and fungal communities which likely subsequently affects belowground processes and plant health in this ecosystem.

Correction: Fire and Grazing Influences on Rates of Riparian Woody Plant Expansion along Grassland Streams

The authors have found errors in this paper. These errors do not change the overall conclusions of the study. There are errors in the last two sentences of the second paragraph of the “Spatial analysis of riparian vegetation” subsection of the Methods. The correct sentences are: Due to changes in fire frequency and other management treatments over time or the confounding effect of multiple wild fires and partially burned watersheds, only data for 20 out of 54 watersheds were retained for further analyses. Of these 20 watersheds, half were grazed, but only 1 was grazed by cattle so no differences between native and cattle grazed watersheds were determined in this study. There are errors in Fig 1, “Spatial extent of woody plant species within a 30 m riparian buffer across the 4 watersheds of the Kings Creek basin monitored for stream discharge during 1985, 1991, and 2010.” The Natural Trail, N4A, and C1A are no longer included in the analysis. Please see the corrected Fig 1 and its legend here. Fig 1 Spatial extent of woody plant species within a 30 m riparian buffer across the 4 watersheds of the Kings Creek basin monitored for stream discharge during 1985, 1991, and 2010. There are errors in the last sentence of the fourth paragraph of the “Spatial analysis of riparian vegetation” subsection of the Methods. The correct sentence is: The cumulative number of burns that had taken place between 1980 and 2010 for each watershed was collected through the Konza Prairie Biological Station LTER network burn history database [34]. There are errors in the first sentence of the “Riparian vegetation spatial analysis” subsection of the Results. The correct sentence is: Analyses of 30 m riparian buffers revealed an increase in wooded vegetation over time among all watersheds except two (Watershed 2B, β = −0.06 and White Pasture β = −0.008). There are errors in the first sentence of the second paragraph of the “Riparian vegetation spatial analysis” subsection of the Results. The correct sentence is: Linear regression models indicated that the cumulative number of burns between 1980 and 2010, and the historical presence of woody vegetation, significantly predicted the rate of riparian vegetation expansion (P < 0.01, Adj. R2 = 0.51, F3,16 = 7.60; Fig 2). Fig 2 The association between the linear regression slopes calculated for each watershed’s change in riparian vegetation from 1985–2010 and the cumulative number of burns since 1980 using a multiple, linear regression model. There are errors in the second sentence of the second paragraph of the “Riparian vegetation spatial analysis” subsection of the Results. The correct sentence is: Further, the average rate of expansion of watersheds with forest present historically was significantly greater than those without forest (P = 0.06, T = −2.04, df = 17; Fig 2). There are errors in the first sentence of the third paragraph of the “Riparian vegetation spatial analysis” subsection of the Results. The correct sentence is: A breakpoint was detected between burn frequency and woody expansion rate at 13 (±9.12 S.E) burns over the 30 years or at about 2.3 years between burns (Overall model: Adj. R2 = 0.37). There are errors in the second sentence of the third paragraph of the “Riparian vegetation spatial analysis” subsection of the Results. The correct sentence is: Only the regression model fit on the side of the breakpoint with fewer than 13 burns had a significant slope, indicating that the cumulative number of burns significantly predicts the rate of woody expansion (P = 0.01, T = -4.18, Fig 3). Fig 3 The association between the linear regression slopes calculated for each watershed’s change in riparian vegetation from 1985–2010 and the cumulative number of burns since 1980 using a segmented regression model. There are errors in the third sentence of the third paragraph of the “Riparian vegetation spatial analysis” subsection of the Results. The correct sentence is: Due to the low number of watersheds which had >13 burns over the study period, the segmented regression did not indicate a significant regression slope after the break. There are errors in the last sentence of the third paragraph of the “Riparian vegetation spatial analysis” subsection of the Results. The correct sentence is: The rate of woody expansion for watersheds with a cumulative number of burns greater than 13 were significantly different from zero (Mean = 0.29, P = 0.03, T = 2.65, df = 7; Fig 3) indicating that burning regimes implemented more frequently than every 2.3 years may not necessarily prevent woody encroachment. There are errors in Fig 2, “The association between the linear regression slopes calculated for each watershed’s change in riparian vegetation from 1985–2010 and the cumulative number of burns since 1980 using a multiple, linear regression model”. Please see the corrected Fig 2 and its legend here. There are errors in Fig 3, “The association between the linear regression slopes calculated for each watershed’s change in riparian vegetation from 1985–2010 and the cumulative number of burns since 1980 using a segmented regression model”, and its legend. Please see the corrected Fig 3 and its legend here. There are errors in the third sentence of the first paragraph of the “Factors influencing riparian, woody vegetation expansion” subsection of the Discussion. The correct sentence is: The rate of riparian woody vegetation expansion was significantly predicted by the cumulative number of burns taken place between 1980 and 2010. There are errors in the second sentence of the second paragraph of the “Factors influencing riparian, woody vegetation expansion” subsection of the Discussion. The correct sentence is: Segmented regression results suggests that a threshold may be reached for woody vegetation cover at ~13 burns over the 30 year study period signifying that there is a change in the way riparian woody vegetation cover responds to fire when implemented every ~2 years (Fig 3).

Modification of plant cell wall chemistry impacts metabolome and microbiome composition in Populus PdKOR1 RNAi plants

AimsWe examined the effect of downregulating PdKOR1 gene, an endo-β-1,4-glucanase gene family member previously characterized to affect cellulose biosynthesis and cell wall composition in Populus, on the secondary metabolome and microbiome of field-grown Populus deltoides.MethodsWe revealed differences in metabolite profiles of PdKOR1 RNAi and control roots using gas chromatography-mass spectrometry, and microbiome identification via Illumina MiSeq 16S and ITS2 rRNA sequencing in root endospheres and rhizospheres.ResultsPdKOR1 RNAi root metabolites differed from control plants: free amino acids (valine, isoleucine, alanine) were reduced while caffeoyl-shikimates, salicylic-acid derivatives, and flavonoid metabolites increased in PdKOR1 RNAi roots. The Actinobacterial family Micromonosporaceae were more abundant in RNAi root endospheres, whereas Nitrospirae was reduced in PdKOR1 RNAi rhizospheres. Ascomycota were lower and Basidiomycota greater in PdKOR1 rhizospheres. Bacterial and fungal community composition, as measured by Bray-Curtis dissimilarity, differed between PdKOR1 RNAi and control rhizospheres and endospheres.ConclusionsThese results indicate that modification of plant cell walls via downregulation of PdKOR1 gene in Populus impacts carbon metabolism in roots and concomitant alterations in root-associated microbial communities. Such an understanding of functional and ecological implications of biomass chemistry improvement efforts is critical to address the goals of sustainable bioenergy crop production and management.

Use of in-field bioreactors demonstrate groundwater filtration influences planktonic bacterial community assembly, but not biofilm composition

Using in-field bioreactors, we investigated the influence of exogenous microorganisms in groundwater planktonic and biofilm microbial communities as part of the Integrated Field Research Challenge (IFRC). After an acclimation period with source groundwater, bioreactors received either filtered (0.22 μM filter) or unfiltered well groundwater in triplicate and communities were tracked routinely for 23 days after filtration was initiated. To address geochemical influences, the planktonic phase was assayed periodically for protein, organic acids, physico-/geochemical measurements and bacterial community (via 16S rRNA gene sequencing), while biofilms (i.e. microbial growth on sediment coupons) were targeted for bacterial community composition at the completion of the experiment (23 d). Based on Bray-Curtis distance, planktonic bacterial community composition varied temporally and between treatments (filtered, unfiltered bioreactors). Notably, filtration led to an increase in the dominant genus, Zoogloea relative abundance over time within the planktonic community, while remaining relatively constant when unfiltered. At day 23, biofilm communities were more taxonomically and phylogenetically diverse and substantially different from planktonic bacterial communities; however, the biofilm bacterial communities were similar regardless of filtration. These results suggest that although planktonic communities were sensitive to groundwater filtration, bacterial biofilm communities were stable and resistant to filtration. Bioreactors are useful tools in addressing questions pertaining to microbial community assembly and succession. These data provide a first step in understanding how an extrinsic factor, such as a groundwater inoculation and flux of microbial colonizers, impact how microbial communities assemble in environmental systems.

Testing the light:nutrient hypothesis: Insights into biofilm structure and function using metatranscriptomics

Aquatic biofilms are hotspots of biogeochemical activity due to concentrated microbial biomass (Battin, Kaplan, Newbold, & Hansen, ). However, biofilms are often considered a single entity when their role in biogeochemical transformations is assessed, even though these biofilms harbour functionally diverse microbial communities (Battin, Besemer, Bengtsson, Romani, & Packmann, ; Veach, Stegen, Brown, Dodds, & Jumpponen, ). Often overlooked are the biotic interactions among biofilm components that can affect ecosystem‐scale processes such as primary production and nutrient cycling. These interactions are likely to be especially important under resource limitation. Light is a primary resource mediating algal photosynthesis and both phototrophic and heterotrophic production due to bacterial reliance on C‐rich algal exudates (Cole, ). However, current understanding of function–structure linkages in streams has yet to unravel the relative degree of these microbial feedbacks under resource availability gradients. In this issue of Molecular Ecology, Bengtsson, Wagner, Schwab, Urich, and Battin ( ) studied stream biofilm responses to light availability to understand its impact across three domains of life. By integrating biogeochemical rate estimation and metatranscriptomics within a microcosm experiment, they were able to link primary production and nutrient uptake rates to algal and bacterial metabolic processes and specify what taxa contributed to gene expression. Under low light, diatoms and cyanobacteria upregulated photosynthetic machinery and diatom‐specific chloroplast rRNA suggesting heightened transcriptional activity under light limitation to maintain phototrophic energy demands. Under high light, heterotrophic bacteria upregulated mRNAs related to phosphorous (P) metabolism while biofilm P uptake increased indicating high bacterial‐specific P demand when algal biomass was high. Together, these results indicate that biogeochemical function is mediated by complex microbial interactions across trophic levels.

Nitrogen enrichment suppresses other environmental drivers and homogenizes salt marsh leaf microbiome

Microbial community assembly is affected by a combination of forces that act simultaneously, but the mechanisms underpinning their relative influences remain elusive. This gap strongly limits our ability to predict human impacts on microbial communities and the processes they regulate. Here, we experimentally demonstrate that increased salinity stress, food web alteration and nutrient loading interact to drive outcomes in salt marsh fungal leaf communities. Both salinity stress and food web alterations drove communities to deterministically diverge, resulting in distinct fungal communities. Increased nutrient loads, nevertheless, partially suppressed the influence of other factors as determinants of fungal assembly. Using a null model approach, we found that increased nutrient loads enhanced the relative importance of stochastic over deterministic divergent processes; without increased nutrient loads, samples from different treatments showed a relatively (deterministic) divergent community assembly whereas increased nutrient loads drove the system to more stochastic assemblies, suppressing the effect of other treatments. These results demonstrate that common anthropogenic modifications can interact to control fungal community assembly. Furthermore, our results suggest that when the environmental conditions are spatially heterogeneous (as in our case, caused by specific combinations of experimental treatments), increased stochasticity caused by greater nutrient inputs can reduce the importance of deterministic filters that otherwise caused divergence, thus driving to microbial community homogenization.