Lori A. Biederman’s research while affiliated with Iowa State University and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (55)


Fig. 1 | Temporal change in mean peak NDVI. Fitted trend lines for 84 grasslands, with the red dashed line indicating no temporal change. The open circles are the peak NDVI measures for each grassland over time (n = 2,856). In total, 56% of sites have significant positive increases while 5% have significant declines, resulting in a fourfold difference in mean peak NDVI change. Given the
Fig. 4 | Relationship between annual remotely sensed maximum NDVI and annual live aboveground biomass. The best-fit curvilinear regression line (F 2,410 = 72.7; P < 0.0001) derives from sites with ≥3 years of live biomass, with the red shaded area (confidence curves for the fitted line) showing how estimation bias begins to widen as the annual maximum NDVI gets higher (especially >0.6). Analysis of the residuals indicates that this bias is strongly affected by higher latitude and lower elevation, with a contribution also from species richness (Methods and Supplementary Table 4).
Major factors associated with changing mean in peak NDVI in 84 grasslands, 1986-2020
Author Correction: Widening global variability in grassland biomass since the 1980s
  • Article
  • Full-text available

August 2024

·

341 Reads

Nature Ecology & Evolution

Andrew S. MacDougall

·

·

Qingqing Chen

·

[...]

·

Download

Temporal change in mean peak NDVI
Fitted trend lines for 84 grasslands, with the red dashed line indicating no temporal change. The open circles are the peak NDVI measures for each grassland over time (n = 2,856). In total, 56% of sites have significant positive increases while 5% have significant declines, resulting in a fourfold difference in mean peak NDVI change. Given the wide spread of points and the risk of outlier bias, our fitted trends derive from a linear model using a Theil–Sen median regressor. Plotted trend lines were obtained from this model and filtered based on P values from a Mann–Kendall trend test (Methods).
Relationship between changes in major explanatory factors and maximum NDVI
a, The figure emphasizes the significant effect of changes in growing season—shortening (left) or lengthening (right)—on shifts in grassland biomass declines (bottom) or increases (top) estimated with remote-sensed NDVI (F1,83 = 31.8; P < 0.0001). The units for the change in biomass (NDVI) are the regression slopes. b,c, There are also significant relationships between increasing NDVI and the slope of temperature increase by site (F1,83 = 6.6; P = 0.012; note: all sites warmed) (b) and increasing NDVI and the slope of changes in annual precipitation (F1,83 = 5.3; P = 0.023) (c). d, For species richness, there was no univariate effect on changing NDVI (F1,83 = 0.34; P = 0.56), although it interacted significantly with growing season length and warming (Table 1 and Supplementary Fig. 9). The inserted dashed lines in a–c indicate areas of no net change. All tests are linear regressions among the 84 sites of this study.
Global maps showing variation among sites in various conditions
a, Shifting average annual temperature. Data for temperature and precipitation were obtained from the Climatic Research Unit (CRU)³⁵. The largest temperature increases are mostly in the Northern hemisphere—Southern hemisphere sites have higher MATs but lower levels of warming since the 1980s. b, Average annual precipitation. c, Growing season length for 1986–2020, derived by site and over time based on differences between remotely sensed spectral greening and spectral browning each year. d, Levels of atmospheric nitrogen deposition modelled for 2014–2016, estimated using the GEOS-Chem Chemical Transport Model³⁶, which estimates wet and dry deposition of inorganic nitrogen using models of atmospheric chemistry together with meteorological data and emission data. These nitrogen data have a 2° × 2.5° resolution.
Relationship between annual remotely sensed maximum NDVI and annual live aboveground biomass
The best-fit curvilinear regression line (F2,410 = 72.7; P < 0.0001) derives from sites with ≥3 years of live biomass, with the red shaded area (confidence curves for the fitted line) showing how estimation bias begins to widen as the annual maximum NDVI gets higher (especially >0.6). Analysis of the residuals indicates that this bias is strongly affected by higher latitude and lower elevation, with a contribution also from species richness (Methods and Supplementary Table 4).
Widening global variability in grassland biomass since the 1980s

August 2024

·

576 Reads

·

4 Citations

Nature Ecology & Evolution

Global change is associated with variable shifts in the annual production of aboveground plant biomass, suggesting localized sensitivities with unclear causal origins. Combining remotely sensed normalized difference vegetation index data since the 1980s with contemporary field data from 84 grasslands on 6 continents, we show a widening divergence in site-level biomass ranging from +51% to −34% globally. Biomass generally increased in warmer, wetter and species-rich sites with longer growing seasons and declined in species-poor arid areas. Phenological changes were widespread, revealing substantive transitions in grassland seasonal cycling. Grazing, nitrogen deposition and plant invasion were prevalent in some regions but did not predict overall trends. Grasslands are undergoing sizable changes in production, with implications for food security, biodiversity and carbon storage especially in arid regions where declines are accelerating.


Forb diversity globally is harmed by nutrient enrichment but can be rescued by large mammalian herbivory

July 2024

·

350 Reads

Forbs (“wildflowers”) are important contributors to grassland biodiversity and services, but they are vulnerable to environmental changes that affect their coexistence with grasses. In a factorial experiment at 94 sites on 6 continents, we tested the global generality of several broad predictions arising from previous studies: (1) Forb cover and richness decline under nutrient enrichment, particularly nitrogen enrichment, which benefits grasses at the expense of forbs. (2) Forb cover and richness increase under herbivory by large mammals, especially when nutrients are enriched. (3) Forb richness and cover are less affected by nutrient enrichment and herbivory in more arid climates, because water limitation reduces the impacts of competition with grasses. We found strong evidence for the first, partial support for the second, and no support for the third prediction. Forb richness and cover are reduced by nutrient addition, with nitrogen having the greatest effect; forb cover is enhanced by large mammal herbivory, although only under conditions of nutrient enrichment and high herbivore intensity; and forb richness is lower in more arid sites, but is not affected by consistent climate-nutrient or climate-herbivory interactions. We also found that nitrogen enrichment disproportionately affects forbs in certain families (Asteraceae, Fabaceae). Our results underscore that anthropogenic nitrogen addition is a major threat to grassland forbs and the ecosystem services they support, but grazing under high herbivore intensity can offset these nutrient effects.


Global distribution and treatment effects
a Global map of all participating sites in the study. Red dot = data on soil microbial and detritivore activity (n = 18 sites); blue dot = data on soil microbial activity only (n = 26 sites). b, c Show two figures where we tested the effect of NPK fertilization, herbivore reduction, and the interactive effect of NPK fertilization and herbivore reduction on soil detritivore activity (log-scaled) and soil microbial activity (log-scaled). Points are raw observations; error bars indicate 95% confidence intervals. Significance levels: (*) p-value = 0.06, ns not significant.
Structural equation model of soil detritivore activity
a Soil detritivore activity as a best-fit Structural Equation Model showing the effects of NPK fertilization and herbivore reduction (Fisher’s C = 1.88; P = 0.758; d.f. = 4; 18 sites). Black arrows indicate significant positive and red arrows indicate significant negative effects in the model (P < 0.05). Dashed gray arrows indicate non-significant effects (P > 0.05) that remain in the model based on AIC. Dark gray double-headed arrows indicate paths that were treated as correlated errors in the model. Arrow widths are proportional to their effect sizes. Numbers along the arrows are standardized path coefficients. Marginal R²m: model variation explained by fixed effects; conditional R²c: model variation explained by both fixed and random effects. Significance levels: *p < 0.05; **p < 0.01; ***p < 0.001. b Direct, indirect, and net effect of MAP on soil detritivore activity, and c direct, indirect, and net effect herbivore reduction on soil detritivore activity.
Structural equation model of soil microbial activity
aSoil microbial activity as a best-fit Structural Equation Model showing the effects of NPK fertilization, herbivore reduction (A/C = 77.9, Fisher’s C = 1.932; P = 0.381; d.f. = 2; 26 sites). Black arrows indicate significant positive and red arrows indicate significant negative effects in the model (P < 0.05). Dashed gray arrows indicate non-significant effects (P > 0.05) that remain in the model based on AIC. Dark gray double-headed arrows indicate paths that were treated as correlated errors in the model. Arrow widths are proportional to their effect sizes. Numbers along the arrows are standardized path coefficients. Marginal R²m: model variation explained by fixed effects; conditional R²c: model variation explained by both fixed and random effects. Significance levels: *p < 0.05; **p < 0.01; ***p < 0.001. b Direct, indirect, and net effect of MAP on soil microbial activity, and c direct, indirect, and net effect herbivore reduction on soil microbial activity, and d direct, indirect, and net effect of NPK fertilization on soil microbial activity (scale of b) differs from c and d.
Correlation between soil microbial and detritivore activity
Correlation of soil microbial activity and detritivore activity (both log-scaled, data from 18 sites included; F = 9.15, p = 0.003). Color of data points (blue) indicates soil moisture level of the sample.
Drivers of soil microbial and detritivore activity across global grasslands

December 2023

·

574 Reads

Communications Biology

Covering approximately 40% of land surfaces, grasslands provide critical ecosystem services that rely on soil organisms. However, the global determinants of soil biodiversity and functioning remain underexplored. In this study, we investigate the drivers of soil microbial and detritivore activity in grasslands across a wide range of climatic conditions on five continents. We apply standardized treatments of nutrient addition and herbivore reduction, allowing us to disentangle the regional and local drivers of soil organism activity. We use structural equation modeling to assess the direct and indirect effects of local and regional drivers on soil biological activities. Microbial and detritivore activities are positively correlated across global grasslands. These correlations are shaped more by global climatic factors than by local treatments, with annual precipitation and soil water content explaining the majority of the variation. Nutrient addition tends to reduce microbial activity by enhancing plant growth, while herbivore reduction typically increases microbial and detritivore activity through increased soil moisture. Our findings emphasize soil moisture as a key driver of soil biological activity, highlighting the potential impacts of climate change, altered grazing pressure, and eutrophication on nutrient cycling and decomposition within grassland ecosystems.


Two structural equation models depicting the original hypothesis and the new, optimized model
The initial hypothesis (a) states that plant diversity affects SOC through the quantity of organic matter (plant biomass) inputs to soil, whereas the new model (b) states that plant diversity affects SOC though the quality of organic matter (C:N ratio). Quality of plant organic matter is depicted in the drawing of the grassland in panel b by different colors. Gray boxes show interactions. Black arrows indicate significant regressions. Asterisks indicate the level of significance of the regressions (*P < 0.05, **P < 0.01, ***P < 0.001), and (+) and (-) indicate whether the slope of the linear regression model is positive or negative. Blue arrows indicate non-significant regressions. The green boxes display the coefficient of determination (R²) for the endogeneous variables. The orange box displays the Akaike Information Criterion (AIC). The models were fitted to the site-level data. The new, optimized model (panel b)was obtained by increasing the model fit of the initial version of the new model (Fig. S6) by removing non-significant regressions. Plant diversity refers to the Shannon index. SOC stands for soil organic carbon. Plant drawings courtesy of Per-Marten Schleuss, used with permission.
Relationship between Shannon diversity index and soil organic carbon content
The relationship is shown across all 84 grassland sites (a) as well as across sites with mean annual temperature (MAT) > 15.58 °C (b), sites with mean annual precipitation (MAP) < 523 mm (c), and arid and semi-arid sites, i.e., sites with an aridity index (AI) < 0.50 (d). The linear models were plotted to the site-level data (and not to the plot data, which is shown to give insight into the within-site variability). The subsets of sites shown in panels b, c, and d are the quartiles of sites for which significant correlations were found between Shannon index and soil organic carbon content (see Table 2). For further information on the relationship between Shannon index and soil organic carbon content depending on climate see Figure S2a, c, and e.
Relationship between Shannon diversity index and soil C:N ratio
The relationship is shown across all 84 grassland sites (a) as well as across sites with mean annual temperature (MAT) > 15.58 °C (b), sites with mean annual precipitation (MAP) < 523 mm (c), and arid and semi-arid sites, i.e., sites with an aridity index (AI) < 0.50 (d). Note that by definition the aridity index increases with decreasing aridity. The linear models were plotted to the site-level data (and not to the plot data, which is shown to give insight into the within-site variability). The subsets of sites shown in panels b, c, and d are the quartiles of sites for which significant correlations were found between Shannon index and soil C:N ratio (see Table 3). For further information on the relationship between Shannon index and soil C:N ratio depending on climate see Figure S2b, d, and f.
Soil organic carbon content as a function of climate
Soil organic carbon content as a function of mean annual temperature (a), mean annual precipitation (b), and aridity index (c) across 84 grasslands. Note that by definition the aridity index increases with decreasing aridity. The linear models were plotted to the site-level data (and not to the plot data, which is shown to give insight into the within-site variability).
The positive effect of plant diversity on soil carbon depends on climate

October 2023

·

1,495 Reads

·

54 Citations

Little is currently known about how climate modulates the relationship between plant diversity and soil organic carbon and the mechanisms involved. Yet, this knowledge is of crucial importance in times of climate change and biodiversity loss. Here, we show that plant diversity is positively correlated with soil carbon content and soil carbon-to-nitrogen ratio across 84 grasslands on six continents that span wide climate gradients. The relationships between plant diversity and soil carbon as well as plant diversity and soil organic matter quality (carbon-to-nitrogen ratio) are particularly strong in warm and arid climates. While plant biomass is positively correlated with soil carbon, plant biomass is not significantly correlated with plant diversity. Our results indicate that plant diversity influences soil carbon storage not via the quantity of organic matter (plant biomass) inputs to soil, but through the quality of organic matter. The study implies that ecosystem management that restores plant diversity likely enhances soil carbon sequestration, particularly in warm and arid climates.



Conceptual diagram. Initial dominants' abundances (orange bar, top) are converted to a rank percentile which allows a standardized response range (0–1) across plots through time. Defining initial dominants in this manner means rank percentile can only remain the same or decay through time. Species fated to lose dominance may do so at different rates following the onset of press perturbations. The hypothetical red and blue species in the bottom graph both move towards extirpation, but the red species is able to maintain dominance for some time before beginning to decline. Alternatively, species may not lose their competitive advantage and simply maintain dominance (purple line).
Interactions between initial relative cover and treatments through time on rank percentile (Table S2). Coloured lines are extracted estimates from a model with initial relative cover as a continuous variable; high (dark blue) is the 95% quantile of all observed initial relative cover values and low (light blue) is the 5% quantile. Ribbons are 95% confidence intervals at the specified initial cover level. Figure S1 shows alternative graphing with treatments as coloured lines and initial cover as facets for additional comparison.
Interactions between species lifespan and treatments through time on rank percentile (Table S2). Ribbons are 95% confidence intervals. Figure S2 shows alternative graphing with treatments as coloured lines and lifespan as facets for additional comparison.
Nothing lasts forever: Dominant species decline under rapid environmental change in global grasslands

September 2023

·

421 Reads

·

4 Citations

Dominance often indicates one or a few species being best suited for resource capture and retention in a given environment. Press perturbations that change availability of limiting resources can restructure competitive hierarchies, allowing new species to capture or retain resources and leaving once dominant species fated to decline. However, dominant species may maintain high abundances even when their new environments no longer favour them due to stochastic processes associated with their high abundance, impeding deterministic processes that would otherwise diminish them. Here, we quantify the persistence of dominance by tracking the rate of decline in dominant species at 90 globally distributed grassland sites under experimentally elevated soil nutrient supply and reduced vertebrate consumer pressure. We found that chronic experimental nutrient addition and vertebrate exclusion caused certain subsets of species to lose dominance more quickly than in control plots. In control plots, perennial species and species with high initial cover maintained dominance for longer than annual species and those with low initial cover respectively. In fertilized plots, species with high initial cover maintained dominance at similar rates to control plots, while those with lower initial cover lost dominance even faster than similar species in controls. High initial cover increased the estimated time to dominance loss more strongly in plots with vertebrate exclosures than in controls. Vertebrate exclosures caused a slight decrease in the persistence of dominance for perennials, while fertilization brought perennials' rate of dominance loss in line with those of annuals. Annual species lost dominance at similar rates regardless of treatments. Synthesis. Collectively, these results point to a strong role of a species' historical abundance in maintaining dominance following environmental perturbations. Because dominant species play an outsized role in driving ecosystem processes, their ability to remain dominant—regardless of environmental conditions—is critical to anticipating expected rates of change in the structure and function of grasslands. Species that maintain dominance while no longer competitively favoured following press perturbations due to their historical abundances may result in community compositions that do not maximize resource capture, a key process of system responses to global change.


Four compositional metrics for three subsets: among all plot‐year combinations (All), among plots in the pretreatment year only (Spatial), and among years in untreated plots (Temporal). The top row focuses on the magnitude of dissimilarity: (A) abundance‐based (Bray–Curtis) dissimilarity and (B) incidence‐based (Sorensen) dissimilarity. The bottom row focuses on the relative importance of replacement: (C) the percentage of Bray–Curtis dissimilarity due to balanced variation in abundance among species and (D) the percentage of Sorensen dissimilarity due to species turnover. Each site (n = 60) is a point within each subset. Vertical lines denote quartiles within the density plots. Within each graph, different lowercase letters indicate statistically significant differences among subsets (α = 0.05).
Dot plot of standardized regression coefficients for predictors retained following multimodel inference based on a global model with 10 potential predictors. Metrics are arrayed along the x‐axis with their overall R² in parentheses; predictors are on the y‐axis. Symbols are colored by sign (positive or negative), scaled according to effect size (magnitude of standardized coefficient), and open or filled based on whether or not they were statistically significant in the final subset of models for a metric. Numerical summaries for all predictors are provided in Appendix S1: Tables S4 and S5. Simple linear models for each combination of predictor and metric are shown in Appendix S1: Figure S2. Ann., annual; Precip., precipitation; Season., seasonality; Temp., temperature; Wet. Q., wettest quarter.
Scatterplot matrix showing patterns among six metrics (four compositional metrics and two richness metrics). Compositional metrics are averaged across all plot‐year combinations (also summarized in the “All” subset in Figure 1). The diagonal values show the distribution of each metric across sites (n = 60). Metrics are graphed against one another below the diagonal, and Pearson correlations (Corr) are shown above the diagonal.
Scatterplot showing the range of differences among sites when characterized by multiple compositional metrics. The horizontal axis is the magnitude of dissimilarity (Bray–Curtis dissimilarity and Sorensen dissimilarity) and the vertical axis is the relative importance of replacement (balanced variation and species turnover). Color, symbol shape, and line type distinguish abundance‐based metrics (Bray–Curtis dissimilarity and balanced variation) from incidence‐based metrics (Sorensen dissimilarity and species turnover). The horizontal and vertical lines show the median for each compositional metric, defining four quadrants for each type of dissimilarity. Panels delineate quadrants based on incidence‐based metrics. Each black line connects the incidence‐based metrics (no symbol) and abundance‐based metrics (brown symbol) for a site (n = 60; also reported in Appendix S1: Table S6). Line length reflects the difference between abundance‐ and incidence‐based metrics at a site. Line angle reflects the importance of changes in magnitude of dissimilarity compared to relative importance of replacement at a site: a line is horizontal if the incorporation of abundance information greatly alters the amount of dissimilarity at the site but not the relative importance of replacement, vertical if this information strongly alters the relative importance of replacement but not the magnitude of dissimilarity, and at a 45° angle if it equally alters both aspects.
A R T I C L E Compositional variation in grassland plant communities

June 2023

·

493 Reads

·

5 Citations

Human activities are altering ecological communities around the globe. Understanding the implications of these changes requires that we consider the composition of those communities. However, composition can be summarized by many metrics which in turn are influenced by different ecological processes. For example, incidence-based metrics strongly reflect species gains or losses, while abundance-based metrics are minimally affected by changes in the abundance of small or uncommon species. Furthermore, metrics might be correlated with different predictors. We used a globally distributed experiment to examine variation in species composition within 60 grasslands on six continents. Each site had an identical experimental and sampling design: 24 plots × 4 years. We For affiliations refer to page 13. expressed compositional variation within each site-not across sites-using abundance-and incidence-based metrics of the magnitude of dissimilarity (Bray-Curtis and Sorensen, respectively), abundance-and incidence-based measures of the relative importance of replacement (balanced variation and species turnover, respectively), and species richness at two scales (per plot-year [alpha] and per site [gamma]). Average compositional variation among all plot-years at a site was high and similar to spatial variation among plots in the pretreatment year, but lower among years in untreated plots. For both types of metrics, most variation was due to replacement rather than nestedness. Differences among sites in overall within-site compositional variation were related to several predictors. Environmental heterogeneity (expressed as the CV of total aboveground plant biomass in unfertilized plots of the site) was an important predictor for most met-rics. Biomass production was a predictor of species turnover and of alpha diversity but not of other metrics. Continentality (measured as annual temperature range) was a strong predictor of Sorensen dissimilarity. Metrics of compositional variation are moderately correlated: knowing the magnitude of dissimilarity at a site provides little insight into whether the variation is driven by replacement processes. Overall, our understanding of compositional variation at a site is enhanced by considering multiple metrics simultaneously. Monitoring programs that explicitly incorporate these implications, both when designing sampling strategies and analyzing data, will have a stronger ability to understand the com-positional variation of systems and to quantify the impacts of human activities.


Conceptual figure describing how a change in total richness can arise from different alterations to turnover dynamics. Arrows indicate a change from control conditions. (a) Total richness declines relative to the control with nutrient addition. This phenomenon could result from a reduced rate of gain of new species through time (b), or from an enhanced rate of loss of species originally present pre‐treatment through time (c), or some combination of these two processes. Purple lines are the change in total richness, blue lines are the gain of species not already present in treatment year 0, and orange lines are the loss of species from treatment year 0. Dotted lines represent rates in control treatments, and solid lines rates under nutrient addition.
The change in absolute (top) and proportional (bottom) richness from baseline during the first 10 years of measurement in the control (left) versus NPK addition treatments (right), aggregated across 30 NutNet sites. Lines are fit from generalized additive models with site, block and plot as nested random effects. Proportional richness is the change in richness relative to plot richness in year 0. Confidence intervals are one standard error from the estimate. Purple lines are the change in total richness, orange lines are the loss of species present in treatment year 0 and 1, and blue lines are the gain of species after treatment year 1.
The change in mean annual richness of each nutrient treatment relative to the control (dashed vertical lines) calculated for each treatment year and averaged across all years for (a) absolute and (b) proportional richness. Error bars are one standard error of the mean. Purple points are the change in total richness relative to the control, blue points are the partition of total richness change associated with new species gain, and orange points are the partition associated with loss of original species.
The difference between modelled ‘control’ change in richness through time and ‘NPK’ change in richness through time (NPK − Control), partitioned by species gained (solid line) or species lost (dotted line) and by functional subgroup: (a) lifeform, (b) lifespan and (c) local provenance. This difference represents the ‘effect size’ of NPK treatment on the control treatment gains or losses through time, aggregated across 30 NutNet sites. Negative values indicate fewer species gained or more species lost in NPK compared to control treatments respectively.
Nutrient addition drives declines in grassland species richness primarily via enhanced species loss

November 2022

·

494 Reads

·

9 Citations

Declines in grassland diversity in response to nutrient addition are a general consequence of global change. This decline in species richness may be driven by multiple underlying processes operating at different time‐scales. Nutrient addition can reduce diversity by enhancing the rate of local extinction via competitive exclusion, or by reducing the rate of colonization by constraining the pool of species able to colonize under new conditions. Partitioning net change into extinction and colonization rates will better delineate the long‐term effect of global change in grasslands. We synthesized changes in richness in response to experimental fertilization with nitrogen, phosphorus and potassium with micronutrients across 30 grasslands. We quantified changes in local richness, colonization, and extinction over 8–10 years of nutrient addition, and compared these rates against control conditions to isolate the effect of nutrient addition from background dynamics. Total richness at steady state in the control plots was the sum of equal, relatively high rates of local colonization and extinction. On aggregate, 30%–35% of initial species were lost and the same proportion of new species were gained at least once over a decade. Absolute turnover increased with site‐level richness but was proportionately greater at lower‐richness sites relative to starting richness. Loss of total richness with nutrient addition, especially N in combination with P or K, was driven by enhanced rates of extinction with a smaller contribution from reduced colonization. Enhanced extinction and reduced colonization were disproportionately among native species, perennials, and forbs. Reduced colonization plateaued after the first few (<5) years after nutrient addition, while enhanced extinction continued throughout the first decade. Synthesis. Our results indicate a high rate of colonizations and extinctions underlying the richness of ambient communities and that nutrient enhancement drives overall declines in diversity primarily by exclusion of previously established species. Moreover, enhanced extinction continues over long time‐scales, suggesting continuous, long‐term community responses and a need for long‐term study to fully realize the extinction impact of increased nutrients on grassland composition.


Long-term N-addition alters the community structure of functionally important N-cycling soil microorganisms across global grasslands

November 2022

·

286 Reads

·

23 Citations

Soil Biology and Biochemistry

Anthropogenic nitrogen (N) input is known to alter the soil microbiome, but how N enrichment influences the abundance, alpha-diversity and community structure of N-cycling functional microbial communities in grasslands remains poorly understood. Here, we collected soils from plant communities subjected to up to 9 years of annual N-addition (10 g N m⁻² per year using urea as a N-source) and from unfertilized plots (control) in 30 grasslands worldwide spanning a large range of climatic and soil conditions. We focused on three key microbial groups responsible for two essential processes of the global N cycle: N2 fixation (soil diazotrophs) and nitrification (AOA: ammonia-oxidizing archaea and AOB: ammonia-oxidizing bacteria). We targeted soil diazotrophs, AOA and AOB using Illumina MiSeq sequencing and measured the abundance (gene copy numbers) using quantitative PCR. N-addition shifted the structure of the diazotrophic communities, although their alpha-diversity and abundance were not affected. AOA and AOB responded differently to N-addition. The abundance and alpha-diversity of AOB increased, and their community structure shifted with N-addition. In contrast, AOA were not affected by N-addition. AOA abundance outnumbered AOB in control plots under conditions of low N availability, whereas N-addition favoured copiotrophic AOB. Overall, N-addition showed a low impact on soil diazotrophs and AOA while effects for AOB communities were considerable. These results reveal that long-term N-addition has important ecological implications for key microbial groups involved in two critical soil N-cycling processes. Increased AOB abundance and community shifts following N-addition may change soil N-cycling, as larger population sizes may promote higher rates of ammonia oxidation and subsequently increase N loss via gaseous and soil N-leaching. These findings bring us a step closer to predicting the responses and feedbacks of microbial-mediated N-cycling processes to long-term anthropogenic N-addition in grasslands.


Citations (44)


... We first constructed phenological indicators based on the Google Earth Engine (GEE) platform, including Normalized Difference Vegetation Index (NDVI)、 Normalized Difference Water Index (NDWI). NDVI is often used to reflect surface vegetation characteristics 23 , evaluate crop growth status, and is one of the important indicators for identifying crops 24 . NDWI is a key indicator for distinguishing between water and land, often used to identify rice planting areas 25,26 . ...

Reference:

The dataset of main grain land changes in China over 1985–2020
Widening global variability in grassland biomass since the 1980s

Nature Ecology & Evolution

... Meanwhile, microorganisms had high C utilization efficiency (low qCO 2 ), resulting in less SOC loss during microbial decomposition. This is beneficial for increasing soil C storage (Spohn et al. 2023), contributing to the high SOC content in NS (Table 1). Indeed, we found that qCO 2 was significantly negatively correlated with SOC content (Table S1). ...

The positive effect of plant diversity on soil carbon depends on climate

... However, since ecosystem functioning depends heavily on the traits of the most abundant species (Grime 1998;Smith et al. 2020), if dominant species change in a way that is different from the responses of other species with lower abundance in the system (Collins et al. 2022), changes in community weighted means can diverge from how traits influence species-specific responses to global change (Lepš and de Bello 2023). For example, it is possible that dominant species may maintain dominance even if they do not have the functional trait values that are favoured by a particular global change factor if other competing species are kept in check by biotic interactions such as herbivory or pathogens (Wilfahrt et al. 2023). In this case, community-weighted trait shifts would be limited despite changes in the responses of some individual species. ...

Nothing lasts forever: Dominant species decline under rapid environmental change in global grasslands

... No protected species were sampled. Those specimens were later identified at the University of Washington Herbarium in Seattle, WA using the contemporary regional flora manual [27]. With herbarium specimens available for comparison and experts in the regional flora on the field teams, taxa were identified with unique accuracy both in the field and in the herbarium. ...

A R T I C L E Compositional variation in grassland plant communities

... several species of Vochysiaceae) (Haridasan 1982), while others release carboxylates to exclude Al (de Castro et al. 2022). However, nutrient imbalances or increased nutrient availability can favor fast-growing or invasive species, negatively impacting community assembly and diversity (Lannes et al. 2016;Muehleisen et al. 2023). ...

Nutrient addition drives declines in grassland species richness primarily via enhanced species loss

... Moreover, a large amount of N input disrupted the original nutrient distribution pattern and caused a priming effect, leading to a lower C/N ratio in the soil, resulting in changes in life history strategies [68][69][70]. Furthermore, the N addition could enhance the function of microbial taxa that specifically decompose organic compounds, facilitating material transformation and nutrient availability [71,72]. Therefore, further exploration of the effects of key functional species on nutrient turnover under different fertilization treatments is necessary. ...

Long-term N-addition alters the community structure of functionally important N-cycling soil microorganisms across global grasslands
  • Citing Article
  • November 2022

Soil Biology and Biochemistry

... Furthermore, nutrients addition seems to be intrinsically associated with reduced temporal stability of primary production ( Zhang et al. 2016 ;Zhou et al. 2020 ;Seabloom et al. 2021 ). More broadly, deleterious effects of nutrient enrichment on species diversity have been associated with increased instability of (agro) ecosystem functions ( Zhang et al. 2018 ;Carroll et al. 2021 ;Su et al. 2022 ;. Temporal stability of primary productivity is the key to stable provisioning of ecosystem services to human beings ( Su et al. 2022 ). ...

Nutrient identity modifies the destabilising effects of eutrophication in grasslands

Ecology Letters

... This research enhances our understanding of the complex tripartite interactions among plants, pathogens and AMF, highlighting the importance of these biotic factors in sustaining healthy plant populations, agricultural productivity and overall ecosystem functions. Given the increasing prevalence of pathogen damage and the cumulative loss of species, our findings underscore the critical role of these interactions in ecological resilience [34,69,70]. ...

Nutrient enrichment increases invertebrate herbivory and pathogen damage in grasslands

... After 20 years of land use, when the amounts of available phosphorus and potassium decreased to 60-80 mg kg −1 , the biomass yield in the MGunf plot decreased significantly (p < 0.05) compared to MGf and did not differ from the biomass yield at the AL site (p > 0.05). According to [46], nitrogen and phosphorus deficiencies limit grassland biomass production, as well as the efficiency of micronutrients. Mineral fertilizers have a more positive effect on biomass yield than combined nitrogen, phosphorus, and potassium fertilizers, while the pH indicator has a weakly pronounced positive effect [47]. ...

Soil properties as key predictors of global grassland production: Have we overlooked micronutrients?

Ecology Letters

... For example, livestock at stocking rates much higher than natural densities (Fløjgaard et al., 2022) or high (selective) mesoherbivore densities in simplified faunal assemblages or islands (Chollet et al., 2016) can have negative effects on diversity (Coggan et al., 2018). Recent longer term studies have improved our understanding of temporal diversity dynamics, providing important insights into the understanding of local species extirpations and the effect of dominance shifts on species richness (Koerner et al., 2018;Wilfahrt et al., 2021). Understanding the relative influence of such complexities and how different parameters result in more positive, neutral or negative responses of diversity (including trait-based functional diversity) is a major outstanding challenge. ...

Temporal rarity is a better predictor of local extinction risk than spatial rarity