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Title: Short‐term plant–soil feedback experiment fails to predict outcome of competition observed in long‐term field experiment
Abstract

Mounting evidence suggests that plant–soil feedbacks (PSF) may determine plant community structure. However, we still have a poor understanding of how predictions from short‐term PSF experiments compare with outcomes of long‐term field experiments involving competing plants. We conducted a reciprocal greenhouse experiment to examine how the growth of prairie grass species depended on the soil communities cultured by conspecific or heterospecific plant species in the field. The source soil came from monocultures in a long‐term competition experiment (LTCE; Cedar Creek Ecosystem Science Reserve, MN, USA). Within the LTCE, six species of perennial prairie grasses were grown in monocultures or in eight pairwise competition plots for 12 years under conditions of low or high soil nitrogen availability. In six cases, one species clearly excluded the other; in two cases, the pair appeared to coexist. In year 15, we gathered soil from all 12 soil types (monocultures of six species by two nitrogen levels) and grew seedlings of all six species in each soil type for 7 weeks. Using biomass estimates from this greenhouse experiment, we predicted coexistence or competitive exclusion using pairwise PSFs, as derived by Bever and colleagues, and compared model predictions to observed outcomes within the LTCE. Pairwise PSFs among the species pairs ranged from negative, which is predicted to promote coexistence, to positive, which is predicted to promote competitive exclusion. However, these short‐term PSF predictions bore no systematic resemblance to the actual outcomes of competition observed in the LTCE. Other forces may have more strongly influenced the competitive interactions or critical assumptions that underlie the PSF predictions may not have been met. Importantly, the pairwise PSF score derived by Bever et al. is only valid when the two species exhibit an internal equilibrium, corresponding to the Lotka–Volterra competition outcomes of stable coexistence and founder control. Predicting the other two scenarios, competitive exclusion by either species irrespective of initial conditions, requires measuring biomass in uncultured soil, which is methodologically challenging. Subject to several caveats that we discuss, our results call into question whether long‐term competitive outcomes in the field can be predicted from the results of short‐term PSF experiments.

 
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Award ID(s):
1831944
NSF-PAR ID:
10400333
Author(s) / Creator(s):
 ;  ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Ecology
Volume:
104
Issue:
2
ISSN:
0012-9658
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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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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  3. null (Ed.)
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  4. Abstract Questions

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    Methods

    In two consecutive greenhouse experiments, we tested whether two functions — nitrogen assimilation (Experiment 1) and biomass regrowth after disturbance (Experiment 2) — co‐varied, and how each function corresponded to leaf and root functional traits.

    Results

    In Experiment 1, four co‐occurring shrubs differed in their temporal patterns of nitrogen uptake, and nitrogen uptake was positively correlated with resource‐acquisitive leaf traits (leaf percent nitrogen). In Experiment 2, the biomass regrowth of a resource acquisitive and a resource conservative species was the same regardless of competitive environment (i.e., when grown in pots of mixed‐species or same‐species pairs). Rather than being associated with the capture of new nitrogen, biomass resprouting of both species was associated with the size of below‐ground resource stores and specific root length.

    Conclusions

    Our work suggests that resource acquisition and processing may be decoupled from each other after disturbance, and also highlights the need for explicit tests of the relationships between root traits and above‐ground plant function.

     
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  5. Abstract

    Recent studies have shown the potential for negative plant–soil feedbacks (PSFs) to promote stable coexistence, but have not quantified the stabilizing effect relative to other coexistence mechanisms. We conducted a field experiment to test the role of PSFs in stabilizing coexistence among four dominant sagebrush steppe species that appear to coexist stably, based on previous work with observational data and models. We then integrated the effects of PSF treatments on focal species across germination, survival, and first‐year growth. To contribute to stable coexistence, soil microbes should have host‐specific effects that result in negative feedbacks. Over two replicated growing seasons, our experiments consistently showed that soil microbes have negative effects on plant growth, but these effects were rarely host‐specific. The uncommon host‐specific effects were mostly positive at the germination stage, and negative for growth. Integrated effects of PSF across early life‐stage vital rates showed that PSF‐mediated self‐limitation occasionally had large effects on projected plant biomass, but occurred inconsistently between years. Our results suggest that while microbially‐mediated PSF may not be a common mechanism of coexistence in this community, it may still affect the relative abundance of dominant plant species via changes in host fitness. Our work also serves as a blueprint for future investigations that aim to identify underlying processes and test alternative mechanisms to explain important patterns in community ecology.

     
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