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1. Phosphorus availability and leaching losses in annual and perennial cropping systems in an upper US Midwest landscape

   https://doi.org/10.5061/dryad.8sf7m0cpx  

   Hussain, Mir Zaman ; Hamilton, Stephen ; Robertson, G. Philip ; Basso, Bruno ( January 2021 , Dryad)

   Excessive phosphorus (P) applications to croplands can contribute to eutrophication of surface waters through surface runoff and subsurface (leaching) losses. We analyzed leaching losses of total dissolved P (TDP) from no-till corn, hybrid poplar (Populus nigra X P. maximowiczii), switchgrass (Panicum virgatum), miscanthus (Miscanthus giganteus), native grasses, and restored prairie, all planted in 2008 on former cropland in Michigan, USA. All crops except corn (13 kg P ha−1 year−1) were grown without P fertilization. Biomass was harvested at the end of each growing season except for poplar. Soil water at 1.2 m depth was sampled weekly to biweekly for TDP determination during March–November 2009–2016 using tension lysimeters. Soil test P (0–25 cm depth) was measured every autumn. Soil water TDP concentrations were usually below levels where eutrophication of surface waters is frequently observed (> 0.02 mg L−1) but often higher than in deep groundwater or nearby streams and lakes. Rates of P leaching, estimated from measured concentrations and modeled drainage, did not differ statistically among cropping systems across years; 7-year cropping system means ranged from 0.035 to 0.072 kg P ha−1 year−1 with large interannual variation. Leached P was positively related to STP, which decreased over the 7 years in all systems. These results indicate that both P-fertilized and unfertilized cropping systems may leach legacy P from past cropland management. Experimental details The Biofuel Cropping System Experiment (BCSE) is located at the W.K. Kellogg Biological Station (KBS) (42.3956° N, 85.3749° W; elevation 288 m asl) in southwestern Michigan, USA. This site is a part of the Great Lakes Bioenergy Research Center (www.glbrc.org) and is a Long-term Ecological Research site (www.lter.kbs.msu.edu). Soils are mesic Typic Hapludalfs developed on glacial outwash54 with high sand content (76% in the upper 150 cm) intermixed with silt-rich loess in the upper 50 cm55. The water table lies approximately 12–14 m below the surface. The climate is humid temperate with a mean annual air temperature of 9.1 °C and annual precipitation of 1005 mm, 511 mm of which falls between May and September (1981–2010)56,57. The BCSE was established as a randomized complete block design in 2008 on preexisting farmland. Prior to BCSE establishment, the field was used for grain crop and alfalfa (Medicago sativa L.) production for several decades. Between 2003 and 2007, the field received a total of ~ 300 kg P ha−1 as manure, and the southern half, which contains one of four replicate plots, received an additional 206 kg P ha−1 as inorganic fertilizer. The experimental design consists of five randomized blocks each containing one replicate plot (28 by 40 m) of 10 cropping systems (treatments) (Supplementary Fig. S1; also see Sanford et al.58). Block 5 is not included in the present study. Details on experimental design and site history are provided in Robertson and Hamilton57 and Gelfand et al.59. Leaching of P is analyzed in six of the cropping systems: (i) continuous no-till corn, (ii) switchgrass, (iii) miscanthus, (iv) a mixture of five species of native grasses, (v) a restored native prairie containing 18 plant species (Supplementary Table S1), and (vi) hybrid poplar. Agronomic management Phenological cameras and field observations indicated that the perennial herbaceous crops emerged each year between mid-April and mid-May. Corn was planted each year in early May. Herbaceous crops were harvested at the end of each growing season with the timing depending on weather: between October and November for corn and between November and December for herbaceous perennial crops. Corn stover was harvested shortly after corn grain, leaving approximately 10 cm height of stubble above the ground. The poplar was harvested only once, as the culmination of a 6-year rotation, in the winter of 2013–2014. Leaf emergence and senescence based on daily phenological images indicated the beginning and end of the poplar growing season, respectively, in each year. Application of inorganic fertilizers to the different crops followed a management approach typical for the region (Table 1). Corn was fertilized with 13 kg P ha−1 year−1 as starter fertilizer (N-P-K of 19-17-0) at the time of planting and an additional 33 kg P ha−1 year−1 was added as superphosphate in spring 2015. Corn also received N fertilizer around the time of planting and in mid-June at typical rates for the region (Table 1). No P fertilizer was applied to the perennial grassland or poplar systems (Table 1). All perennial grasses (except restored prairie) were provided 56 kg N ha−1 year−1 of N fertilizer in early summer between 2010 and 2016; an additional 77 kg N ha−1 was applied to miscanthus in 2009. Poplar was fertilized once with 157 kg N ha−1 in 2010 after the canopy had closed. Sampling of subsurface soil water and soil for P determination Subsurface soil water samples were collected beneath the root zone (1.2 m depth) using samplers installed at approximately 20 cm into the unconsolidated sand of 2Bt2 and 2E/Bt horizons (soils at the site are described in Crum and Collins54). Soil water was collected from two kinds of samplers: Prenart samplers constructed of Teflon and silica (http://www.prenart.dk/soil-water-samplers/) in replicate blocks 1 and 2 and Eijkelkamp ceramic samplers (http://www.eijkelkamp.com) in blocks 3 and 4 (Supplementary Fig. S1). The samplers were installed in 2008 at an angle using a hydraulic corer, with the sampling tubes buried underground within the plots and the sampler located about 9 m from the plot edge. There were no consistent differences in TDP concentrations between the two sampler types. Beginning in the 2009 growing season, subsurface soil water was sampled at weekly to biweekly intervals during non-frozen periods (April–November) by applying 50 kPa of vacuum to each sampler for 24 h, during which the extracted water was collected in glass bottles. Samples were filtered using different filter types (all 0.45 µm pore size) depending on the volume of leachate collected: 33-mm dia. cellulose acetate membrane filters when volumes were less than 50 mL; and 47-mm dia. Supor 450 polyethersulfone membrane filters for larger volumes. Total dissolved phosphorus (TDP) in water samples was analyzed by persulfate digestion of filtered samples to convert all phosphorus forms to soluble reactive phosphorus, followed by colorimetric analysis by long-pathlength spectrophotometry (UV-1800 Shimadzu, Japan) using the molybdate blue method60, for which the method detection limit was ~ 0.005 mg P L−1. Between 2009 and 2016, soil samples (0–25 cm depth) were collected each autumn from all plots for determination of soil test P (STP) by the Bray-1 method61, using as an extractant a dilute hydrochloric acid and ammonium fluoride solution, as is recommended for neutral to slightly acidic soils. The measured STP concentration in mg P kg−1 was converted to kg P ha−1 based on soil sampling depth and soil bulk density (mean, 1.5 g cm−3). Sampling of water samples from lakes, streams and wells for P determination In addition to chemistry of soil and subsurface soil water in the BCSE, waters from lakes, streams, and residential water supply wells were also sampled during 2009–2016 for TDP analysis using Supor 450 membrane filters and the same analytical method as for soil water. These water bodies are within 15 km of the study site, within a landscape mosaic of row crops, grasslands, deciduous forest, and wetlands, with some residential development (Supplementary Fig. S2, Supplementary Table S2). Details of land use and cover change in the vicinity of KBS are given in Hamilton et al.48, and patterns in nutrient concentrations in local surface waters are further discussed in Hamilton62. Leaching estimates, modeled drainage, and data analysis Leaching was estimated at daily time steps and summarized as total leaching on a crop-year basis, defined from the date of planting or leaf emergence in a given year to the day prior to planting or emergence in the following year. TDP concentrations (mg L−1) of subsurface soil water were linearly interpolated between sampling dates during non-freezing periods (April–November) and over non-sampling periods (December–March) based on the preceding November and subsequent April samples. Daily rates of TDP leaching (kg ha−1) were calculated by multiplying concentration (mg L−1) by drainage rates (m3 ha−1 day−1) modeled by the Systems Approach for Land Use Sustainability (SALUS) model, a crop growth model that is well calibrated for KBS soil and environmental conditions. SALUS simulates yield and environmental outcomes in response to weather, soil, management (planting dates, plant population, irrigation, N fertilizer application, and tillage), and genetics63. The SALUS water balance sub-model simulates surface runoff, saturated and unsaturated water flow, drainage, root water uptake, and evapotranspiration during growing and non-growing seasons63. The SALUS model has been used in studies of evapotranspiration48,51,64 and nutrient leaching20,65,66,67 from KBS soils, and its predictions of growing-season evapotranspiration are consistent with independent measurements based on growing-season soil water drawdown53 and evapotranspiration measured by eddy covariance68. Phosphorus leaching was assumed insignificant on days when SALUS predicted no drainage. Volume-weighted mean TDP concentrations in leachate for each crop-year and for the entire 7-year study period were calculated as the total dissolved P leaching flux (kg ha−1) divided by the total drainage (m3 ha−1). One-way ANOVA with time (crop-year) as the fixed factor was conducted to compare total annual drainage rates, P leaching rates, volume-weighted mean TDP concentrations, and maximum aboveground biomass among the cropping systems over all seven crop-years as well as with TDP concentrations from local lakes, streams, and groundwater wells. When a significant (α = 0.05) difference was detected among the groups, we used the Tukey honest significant difference (HSD) post-hoc test to make pairwise comparisons among the groups. In the case of maximum aboveground biomass, we used the Tukey–Kramer method to make pairwise comparisons among the groups because the absence of poplar data after the 2013 harvest resulted in unequal sample sizes. We also used the Tukey–Kramer method to compare the frequency distributions of TDP concentrations in all of the soil leachate samples with concentrations in lakes, streams, and groundwater wells, since each sample category had very different numbers of measurements. Individual spreadsheets in “data table_leaching_dissolved organic carbon and nitrogen.xls” 1.    annual precip_drainage 2.    biomass_corn, perennial grasses 3.    biomass_poplar 4.    annual N leaching _vol-wtd conc 5.    Summary_N leached 6.    annual DOC leachin_vol-wtd conc 7.    growing season length 8.    correlation_nh4 VS no3 9.    correlations_don VS no3_doc VS don Each spreadsheet is described below along with an explanation of variates. Note that ‘nan’ indicate data are missing or not available. First row indicates header; second row indicates units 1. Spreadsheet: annual precip_drainage Description: Precipitation measured from nearby Kellogg Biological Station (KBS) Long Term Ecological Research (LTER) Weather station, over 2009-2016 study period. Data shown in Figure 1; original data source for precipitation (https://lter.kbs.msu.edu/datatables/7). Drainage estimated from SALUS crop model. Note that drainage is percolation out of the root zone (0-125 cm). Annual precipitation and drainage values shown here are calculated for growing and non-growing crop periods. Variate    Description year    year of the observation crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” precip_G    precipitation during growing period (milliMeter) precip_NG    precipitation during non-growing period (milliMeter) drainage_G    drainage during growing period (milliMeter) drainage_NG    drainage during non-growing period (milliMeter)      2. Spreadsheet: biomass_corn, perennial grasses Description: Maximum aboveground biomass measurements from corn, switchgrass, miscanthus, native grass and restored prairie plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2.   Variate    Description year    year of the observation date    day of the observation (mm/dd/yyyy) crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” replicate    each crop has four replicated plots, R1, R2, R3 and R4 station    stations (S1, S2 and S3) of samplings within the plot. For more details, refer to link (https://data.sustainability.glbrc.org/protocols/156) species    plant species that are rooted within the quadrat during the time of maximum biomass harvest. See protocol for more information, refer to link (http://lter.kbs.msu.edu/datatables/36) For maize biomass, grain and whole biomass reported in the paper (weed biomass or surface litter are excluded). Surface litter biomass not included in any crops; weed biomass not included in switchgrass and miscanthus, but included in grass mixture and prairie. fraction    Fraction of biomass biomass_plot    biomass per plot on dry-weight basis (Grams_Per_SquareMeter) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying column biomass per plot with 0.01 3. Spreadsheet: biomass_poplar Description: Maximum aboveground biomass measurements from poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2. Note that poplar biomass was estimated from crop growth curves until the poplar was harvested in the winter of 2013-14. Variate    Description year    year of the observation method    methods of poplar biomass sampling date    day of the observation (mm/dd/yyyy) replicate    each crop has four replicated plots, R1, R2, R3 and R4 diameter_at_ground    poplar diameter (milliMeter) at the ground diameter_at_15cm    poplar diameter (milliMeter) at 15 cm height biomass_tree    biomass per plot (Grams_Per_Tree) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying biomass per tree with 0.01 4. Spreadsheet: annual N leaching_vol-wtd conc Description: Annual leaching rate (kiloGrams_N_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_N_Per_Liter) of nitrate (no3) and dissolved organic nitrogen (don) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen leached and volume-wtd mean N concentration shown in Figure 3a and Figure 3b, respectively. Note that ammonium (nh4) concentration were much lower and often undetectable (<0.07 milliGrams_N_Per_Liter). Also note that in 2009 and 2010 crop-years, data from some replicates are missing.    Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) vol-wtd no3 conc.    Volume-weighted mean no3 concentration (milliGrams_N_Per_Liter) vol-wtd don conc.    Volume-weighted mean don concentration (milliGrams_N_Per_Liter) 5. Spreadsheet: summary_N leached Description: Summary of total amount and forms of N leached (kiloGrams_N_Per_Hectare) and the percent of applied N lost to leaching over the seven years for corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen amount leached shown in Figure 4a and percent of applied N lost shown in Figure 4b. Note the fraction of unleached N includes in harvest, accumulation in root biomass, soil organic matter or gaseous N emissions were not measured in the study. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) N unleached    N unleached (kiloGrams_N_Per_Hectare) in other sources are not studied % of N applied N lost to leaching    % of N applied N lost to leaching 6. Spreadsheet: annual DOC leachin_vol-wtd conc Description: Annual leaching rate (kiloGrams_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_Per_Liter) of dissolved organic carbon (DOC) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for DOC leached and volume-wtd mean DOC concentration shown in Figure 5a and Figure 5b, respectively. Note that in 2009 and 2010 crop-years, water samples were not available for DOC measurements.     Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 doc leached    annual leaching rates of nitrate (kiloGrams_Per_Hectare) vol-wtd doc conc.    volume-weighted mean doc concentration (milliGrams_Per_Liter) 7. Spreadsheet: growing season length Description: Growing season length (days) of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in the Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Date shown in Figure S2. Note that growing season is from the date of planting or emergence to the date of harvest (or leaf senescence in case of poplar).   Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year    year of the observation growing season length    growing season length (days) 8. Spreadsheet: correlation_nh4 VS no3 Description: Correlation of ammonium (nh4+) and nitrate (no3-) concentrations (milliGrams_N_Per_Liter) in the leachate samples from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data shown in Figure S3. Note that nh4+ concentration in the leachates was very low compared to no3- and don concentration and often undetectable in three crop-years (2013-2015) when measurements are available. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” date    date of the observation (mm/dd/yyyy) replicate    each crop has four replicated plots, R1, R2, R3 and R4 nh4 conc    nh4 concentration (milliGrams_N_Per_Liter) no3 conc    no3 concentration (milliGrams_N_Per_Liter)   9. Spreadsheet: correlations_don VS no3_doc VS don Description: Correlations of don and nitrate concentrations (milliGrams_N_Per_Liter); and doc (milliGrams_Per_Liter) and don concentrations (milliGrams_N_Per_Liter) in the leachate samples of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data of correlation of don and nitrate concentrations shown in Figure S4 a and doc and don concentrations shown in Figure S4 b. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year    year of the observation don    don concentration (milliGrams_N_Per_Liter) no3     no3 concentration (milliGrams_N_Per_Liter) doc    doc concentration (milliGrams_Per_Liter) 

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2. Heterogeneous landscape promotes distinct microbial communities in an imperiled scrub ecosystem

   https://doi.org/10.1080/00275514.2023.2258268  

   David, Aaron S. ; Hernandez, Damian J. ; Menges, Eric S. ; Sclater, Vivienne L. ; Afkhami, Michelle E. ; Searcy, Christopher A. ( November 2023 , Mycologia)

   Habitat heterogeneity is a key driver of biodiversity of macroorganisms, yet how heterogeneity structures belowground microbial communities is not well understood. Importantly, belowground microbial communities may respond to any number of abiotic, biotic, and spatial drivers found in heterogeneous environments. Here, we examine potential drivers of prokaryotic and fungal communities in soils across the heterogenous landscape of the imperiled Florida scrub, a pyrogenic ecosystem where slight differences in elevation lead to large changes in water and nutrient availability and vegetation composition. We employ a comprehensive, large-scale sampling design to characterize the communities of prokaryotes and fungi associated with three habitat types and two soil depths (crust and subterranean) to evaluate (i) differences in microbial communities across these heterogeneous habitats, (ii) the relative roles of abiotic, biotic, and spatial drivers in shaping community structure, and (iii) the distribution of fungal guilds across these habitats. We sequenced soils from 40 complete replicates of habitat × soil depth combinations and sequenced the prokaryotic 16S and fungal internal transcribed spacer (ITS) regions using Illumina MiSeq. Habitat heterogeneity generated distinct communities of soil prokaryotes and fungi. Spatial distance played a role in structuring crust communities, whereas subterranean microbial communities were primarily structured by the shrub community, whose roots they presumably interacted with. This result helps to explain the unexpected transition we observed between arbuscular mycorrhiza–dominated soils at low-elevation habitats to ectomycorrhiza-dominated soils at high-elevation habitats. Our results challenge previous notions of environmental determinism of microbial communities and generate new hypotheses regarding symbiotic relationships across heterogeneous environments. 

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3. The Geochemistry of Magnetite and Apatite from the El Laco Iron Oxide-Apatite Deposit, Chile: Implications for Ore Genesis

   La Cruz, N. ( November 2020 , Economic geology)

   null (Ed.)

   The textures of outcrop and near-surface exposures of the massive magnetite orebodies (>90 vol % magnetite) at the Plio-Pleistocene El Laco iron oxide-apatite (IOA) deposit in northern Chile are similar to basaltic lava flows and have compositions that overlap high- and low-temperature hydrothermal magnetite. Existing models— liquid immiscibility and complete metasomatic replacement of andesitic lava flows—attempt to explain the genesis of the orebodies by entirely igneous or entirely hydrothermal processes. Importantly, those models were developed by studying only near-surface and outcrop samples. Here, we present the results of a comprehensive study of samples from outcrop and drill core that require a new model for the evolution of the El Laco ore deposit. Backscattered electron (BSE) imaging, electron probe microanalysis (EPMA), and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) were used to investigate the textural and compositional variability of magnetite and apatite from surface and drill core samples in order to obtain a holistic understanding of textures and compositions laterally and vertically through the orebodies. Magnetite was analyzed from 39 surface samples from five orebodies (Cristales Grandes, Rodados Negros, San Vicente Alto, Laco Norte, and Laco Sur) and 47 drill core samples from three orebodies (Laco Norte, Laco Sur, and Extensión Laco Sur). The geochemistry of apatite from eight surface samples from three orebodies (Cristales Grandes, Rodados Negros, and Laco Sur) was investigated. Minor and trace element compositions of magnetite in these samples are similar to magnetite from igneous rocks and magmatic-hydrothermal systems. Magnetite grains from deeper zones of the orebodies contain >1 wt % titanium, as well as ilmenite oxyexsolution lamellae and interstitial ilmenite. The ilmenite oxyexsolution lamellae, interstitial ilmenite, and igneous-like trace element concentrations in titanomagnetite from the deeper parts of the orebodies are consistent with original crystallization of titanomagnetite from silicate melt or high-temperature magmatic-hydrothermal fluid. The systematic decrease of trace element concentrations in magnetite from intermediate to shallow depths is consistent with progressive growth of magnetite from a cooling magmatic-hydrothermal fluid. Apatite grains from surface outcrops are F rich (typically >3 wt %) and have compositions that overlap igneous and magmatic-hydrothermal apatite. Magnetite and fluorapatite grains contain mineral inclusions (e.g., monazite and thorite) that evince syn- or postmineralization metasomatic alteration. Magnetite grains commonly meet at triple junctions, which preserve evidence for reequilibration of the ore minerals with hydrothermal fluid during or after mineralization. The data presented here are consistent with genesis of the El Laco orebodies via shallow emplacement and eruption of magnetite-bearing magmatic-hydrothermal fluid suspensions that were mobilized by decompression- induced collapse of the volcanic edifice. The ore-forming magnetite-fluid suspension would have rheological properties similar to basaltic lava flows, which explains the textures and presence of cavities and gas escape tubes in surface outcrops. 

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4. Landscape characteristics shape surface soil microbiomes in the Chihuahuan Desert

   https://doi.org/10.3389/fmicb.2023.1135800  

   Hansen, Frederick A. ; James, Darren K. ; Anderson, John P. ; Meredith, Christy S. ; Dominguez, Andrew J. ; Pombubpa, Nuttapon ; Stajich, Jason E. ; Romero-Olivares, Adriana L. ; Salley, Shawn W. ; Pietrasiak, Nicole ( June 2023 , Frontiers in Microbiology)

   Introduction Soil microbial communities, including biological soil crust microbiomes, play key roles in water, carbon and nitrogen cycling, biological weathering, and other nutrient releasing processes of desert ecosystems. However, our knowledge of microbial distribution patterns and ecological drivers is still poor, especially so for the Chihuahuan Desert. Methods This project investigated the effects of trampling disturbance on surface soil microbiomes, explored community composition and structure, and related patterns to abiotic and biotic landscape characteristics within the Chihuahuan Desert biome. Composite soil samples were collected in disturbed and undisturbed areas of 15 long-term ecological research plots in the Jornada Basin, New Mexico. Microbial diversity of cross-domain microbial groups (total Bacteria, Cyanobacteria, Archaea, and Fungi) was obtained via DNA amplicon metabarcode sequencing. Sequence data were related to landscape characteristics including vegetation type, landforms, ecological site and state as well as soil properties including gravel content, soil texture, pH, and electrical conductivity. Results Filamentous Cyanobacteria dominated the photoautotrophic community while Proteobacteria and Actinobacteria dominated among the heterotrophic bacteria. Thaumarchaeota were the most abundant Archaea and drought adapted taxa in Dothideomycetes and Agaricomycetes were most abundant fungi in the soil surface microbiomes. Apart from richness within Archaea ( p  = 0.0124), disturbed samples did not differ from undisturbed samples with respect to alpha diversity and community composition ( p  ≥ 0.05), possibly due to a lack of frequent or impactful disturbance. Vegetation type and landform showed differences in richness of Bacteria, Archaea, and Cyanobacteria but not in Fungi. Richness lacked strong relationships with soil variables. Landscape features including parent material, vegetation type, landform type, and ecological sites and states, exhibited stronger influence on relative abundances and microbial community composition than on alpha diversity, especially for Cyanobacteria and Fungi. Soil texture, moisture, pH, electrical conductivity, lichen cover, and perennial plant biomass correlated strongly with microbial community gradients detected in NMDS ordinations. Discussion Our study provides first comprehensive insights into the relationships between landscape characteristics, associated soil properties, and cross-domain soil microbiomes in the Chihuahuan Desert. Our findings will inform land management and restoration efforts and aid in the understanding of processes such as desertification and state transitioning, which represent urgent ecological and economical challenges in drylands around the world. 

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5. Invasion‐induced root–fungal disruptions alter plant water and nitrogen economies

   https://doi.org/10.1111/ele.13724  

   Bialic‐Murphy, Lalasia ; Smith, Nicholas G. ; Voothuluru, Priya ; McElderry, Robert M. ; Roche, Morgan D. ; Cassidy, Steven T. ; Kivlin, Stephanie N. ; Kalisz, Susan ; Rejmanek, ed., Marcel ( March 2021 , Ecology Letters)

   Despite widespread evidence that biological invasion influences both the biotic and abiotic soil environments, the extent to which these two pathways underpin the effects of invasion on plant traits and performance remains unknown. Leveraging a long‐term (14‐year) field experiment, we show that an allelochemical‐producing invader affects plants through biotic mechanisms, altering the soil fungal community composition, with no apparent shifts in soil nutrient availability. Changes in belowground fungal communities resulted in high costs of nutrient uptake for native perennials and a shift in plant traits linked to their water and nutrient use efficiencies. Some plants in the invaded community compensate for the disruption of nutritional symbionts and reduced nutrient provisioning by sanctioning more nitrogen to photosynthesis and expending more water, which demonstrates a trade‐off in trait investment. For the first time, we show that the disruption of belowground nutritional symbionts can drive plants towards alternative regions of their trait space in order to maintain water and nutrient economics.

    

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