Johannes M. H. Knops's research while affiliated with Xi'an Jiaotong-Liverpool University and other places

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Publications (210)


Fig 1. Effects of nutrient addition on beta diversity and an integrated framework to understand plant diversity change across spatial scales. Nutrient addition may cause vegetation homogenization (decrease β diversity), little change, or vegetation differentiation among local communities due to different responses of plant diversity at local and larger spatial scales (i.e. alpha and gamma diversity). This, in turn, is a result of different responses of localized and widespread species to nutrient addition. Species occurring in all local communities within sites are referred to as widespread species, otherwise, localized species.
Fig. 5. Predicted effects of nutrient addition on alpha, gamma, and beta diversity (∆α, ∆γ, and ∆β) at individual sites and the overall effects for forb, graminoid, legume, and woody species. Diversity refers to the Hill number with q of 1. Colors and dots are the same as Fig. 3. See Table S6 for estimated overall means and 95% confidence intervals for ∆α, ∆γ, and ∆β for forb, graminoid, legume, and woody species.
Nutrient addition in grasslands worldwide reveals proportional plant diversity decline across spatial scales but little change in beta diversity
  • Preprint
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March 2024

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416 Reads

Qingqing Chen

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Shane A. Blowes

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Emma Ladouceur

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[...]

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Plant diversity decline under nutrient addition in local grassland communities is typically ascribed to the loss of rare species, species with particular traits ill-suited for high nutrient levels, and displacement of many localized species with a few widespread species. Whether these changes result in stronger diversity decline and vegetation homogenization at larger spatial scales (aggregated local communities) remains largely unknown. Using a standardized replicated fertilization experiment in 70 grasslands on six continents, we found proportional species loss at local and larger spatial scales but no vegetation homogenization under nutrient addition. Moreover, nutrient addition drove proportional species loss across spatial scales irrespective of species abundance, provenance, life form, or distribution. These results demonstrate that nutrient addition poses a potential threat to all plant functional groups including widespread dominant species that may be critical for ecosystem functions and services. One-Sentence Summary Nutrient addition did not lead to biotic homogenization in experimental grasslands across the world.

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The percentage of seeds germinated for the species recorded in the Petri dish experiment. The four species on the left are crop species, while the four species on the right are weed species. The treatments are control (green), goldenrod extract (yellow), walnut extract (brown), and a combination of goldenrod and walnut extract (red). For all species, the same order of treatments is given, even if the box was too small to be colored. The boxes show the range of 50% of mean data with the horizontal line and the whiskers representing the median, and upper, and lower data limits without the outliers (circles), respectively.
Germination time (a), plant size after two (b) and after 4 weeks (c) of experimental treatment, and the number of leaves (d) at the end of the experiment. The treatments are control (green), goldenrod extract (yellow), walnut extract (brown), and a combination of goldenrod and walnut extract (red). For all species, the same order of treatments is given, even if the box was too small to be colored. Note that no treatment with the combined extracts was available for Cichorium and that the plant size after 4 weeks was not measured for Cichorium, while the number of leaves was not determined for Matricaria. The boxes show the range of 50% of mean data with the horizontal line giving the median, and the whiskers representing the median, and upper, and lower data limits without the outliers (circles), respectively.
The measurements of biomass, i.e. total biomass (a), above-ground biomass (b), root biomass (c), and the relationship between root and total biomass (d). The treatments are control (green), goldenrod extract (yellow), walnut extract (brown), and a combination of goldenrod and walnut extract (red). For all species, the same order of treatments is given, even if the box was too small to be colored. The boxes show the range of 50% of mean data with the horizontal line giving the median, and the whiskers representing the median, and upper, and lower data limits without the outliers (circles), respectively.
The measurements of photosynthesis-related factors, i.e. maximum efficiency of PS II (Fv/Fm) (a), photochemical quenching (qP) (b), greenness index (c), and non-photochemical quenching (NPQ) (d). The treatments are control (green), goldenrod extract (yellow), walnut extract (brown), and a combination of goldenrod and walnut extract (red). For all species, the same order of treatments is given, even if the box was too small to be colored. The boxes show the range of 50% of mean data with the horizontal line giving the median, and the whiskers representing the median, and upper, and lower data limits without the outliers (circles), respectively.
Multiple invasive species affect germination, growth, and photosynthesis of native weeds and crops in experiments

December 2023

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114 Reads

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2 Citations

Scientific Reports

Alien plant species regularly and simultaneously invade agricultural landscapes and ecosystems; however, the effects of co-invasion on crop production and native biodiversity have rarely been studied. Secondary metabolites produced by alien plants may be allelopathic; if they enter the soil, they may be transported by agricultural activities, negatively affecting crop yield and biodiversity. It is unknown whether substances from different alien species in combination have a greater impact on crops and wild plants than if they are from only one of the alien species. In this study, we used a set of common garden experiments to test the hypothesis that mixed extracts from two common invasive species have synergistic effects on crops and weeds (defined as all non-crop plants) in European agricultural fields compared to single-species extracts. We found that both the combined and individual extracts had detrimental effects on the seed germination, seedling growth, biomass, and photosynthetic performance of both crops and weeds. We found that the negative effect of mixed extracts was not additive and that crop plants were more strongly affected by invasive species extracts than the weeds. Our results are important for managing invasive species in unique ecosystems on agricultural land and preventing economic losses in yield production.


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

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485 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

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1,190 Reads

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13 Citations

Nature Communications

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.


Graphical illustration of five stability facets in three community aspects investigated in this study
Methods used for quantifying stability facets are shown. We investigate the effects of nutrient addition (NPK) on (a) each of the five stability facets within each community aspect, (b) pairwise correlations among stability facets within each community aspect, and (c) pairwise correlations of stability among community aspects for a given stability facet. Resistance_Dry: resistance during dry growing seasons; Resistence_Wet: resistance during wet growing seasons; Recovery_Dry: recovery after dry growing seasons; Recovery_Wet: recovery after wet growing seasons.
Effects of nutrient addition (NPK) on each of the five stability facets in each of the three community aspects
Resistance_Dry: resistance during dry growing seasons; Resistence_Wet: resistance during wet growing seasons; Recovery_Dry: recovery after dry growing seasons; Recovery_Wet: recovery after wet growing seasons. Saturated line colors represent significant treatment effects at p ≤ 0.05 and faded line colors represent non-significant treatment effects. The significance of treatment effects was assessed using t test. See Supplementary Table 3 for test statistics, effect sizes, standard errors of the effect size, degrees of freedom, and p values for the two-tailed test.
Pairwise correlations among the five stability facets in each of the three community aspects under ambient (control) and nutrient addition (NPK) conditions
Resistance_Dry: resistance during dry growing seasons; Resistence_Wet: resistance during wet growing seasons; Recovery_Dry: recovery after dry growing seasons; Recovery_Wet: recovery after wet growing seasons. Saturated line colors represent significant correlations, corresponding to 95% confidence intervals of the correlation coefficients that do not overlap with 0. Faded line colors represent non-significant correlations. See Supplementary Table 5 for test statistics and 95% confidence intervals for each correlation coefficient.
Pairwise correlations among stability of the three community aspects for a given stability facet under ambient (control) and nutrient addition (NPK) conditions
Resistance_Dry: resistance during dry growing seasons; Resistence_Wet: resistance during wet growing seasons; Recovery_Dry: recovery after dry growing seasons; Recovery_Wet: recovery after wet growing seasons. Saturated line colors represent significant correlations, corresponding to 95% confidence intervals of the correlation coefficients that do not overlap with 0. Faded line colors represent non-significant correlations. See Supplementary Table 6 for test statistics and 95% confidence intervals for each correlation coefficient.
Multidimensional responses of grassland stability to eutrophication

October 2023

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712 Reads

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2 Citations

Nature Communications

Eutrophication usually impacts grassland biodiversity, community composition, and biomass production, but its impact on the stability of these community aspects is unclear. One challenge is that stability has many facets that can be tightly correlated (low dimensionality) or highly disparate (high dimensionality). Using standardized experiments in 55 grassland sites from a globally distributed experiment (NutNet), we quantify the effects of nutrient addition on five facets of stability (temporal invariability, resistance during dry and wet growing seasons, recovery after dry and wet growing seasons), measured on three community aspects (aboveground biomass, community composition, and species richness). Nutrient addition reduces the temporal invariability and resistance of species richness and community composition during dry and wet growing seasons, but does not affect those of biomass. Different stability measures are largely uncorrelated under both ambient and eutrophic conditions, indicating consistently high dimensionality. Harnessing the dimensionality of ecological stability provides insights for predicting grassland responses to global environmental change.




Masting is uncommon in trees that depend on mutualist dispersers in the context of global climate and fertility gradients

June 2023

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969 Reads

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7 Citations

Nature Plants

The benefits of masting (volatile, quasi-synchronous seed production at lagged intervals) include satiation of seed predators, but these benefits come with a cost to mutualist pollen and seed dispersers. If the evolution of masting represents a balance between these benefits and costs, we expect mast avoidance in species that are heavily reliant on mutualist dispersers. These effects play out in the context of variable climate and site fertility among species that vary widely in nutrient demand. Meta-analyses of published data have focused on variation at the population scale, thus omitting periodicity within trees and synchronicity between trees. From raw data on 12 million tree-years worldwide, we quantified three components of masting that have not previously been analysed together: (i) volatility, defined as the frequency-weighted year-to-year variation; (ii) periodicity, representing the lag between high-seed years; and (iii) synchronicity, indicating the tree-to-tree correlation. Results show that mast avoidance (low volatility and low synchronicity) by species dependent on mutualist dispersers explains more variation than any other effect. Nutrient-demanding species have low volatility, and species that are most common on nutrient-rich and warm/wet sites exhibit short periods. The prevalence of masting in cold/dry sites coincides with climatic conditions where dependence on vertebrate dispersers is less common than in the wet tropics. Mutualist dispersers neutralize the benefits of masting for predator satiation, further balancing the effects of climate, site fertility and nutrient demands.


Population- and leaf-scale fungal and prokaryotic diversity are positively correlated with plant biomass (n = 22) and prokaryotic diversity is negatively correlated with soil carbon:nitrogen ratio (n = 18) based on multi-model inference from a suite of mixed effects models
Leaf-scale diversity is only from control plots. All tests are two-tailed.
Leaf-scale fungal diversity is constrained by population-scale fungal diversity, whereas population-scale and leaf-scale bacterial diversity are similar across sites (n = 23)
Leaf-scale diversity is only from control plots. Dotted line shows 1:1 relationship. Gray shading indicates 95% confidence envelope. All tests are two-tailed.
Fertilization increases leaf-scale fungal and prokaryote diversity, and fencing increases leaf-scale fungal diversity
“Nutrient” is the effect of fertilization, “Fence” is the effect of fencing, and “Nut*Fnc” is the interaction between Fertilization and Fencing with zero indicating additivity. Error bars represent the standard error of the effect estimate based on 515 observations nested by site, block, and plot in a mixed effects model (Table S4).
Fertilization increases biomass and shading and reduces plant diversity
Fencing reduced plant diversity and increased effects of fertilization on shading (positive interaction). “Nutrient” is the effect of fertilization, “Fence” is the effect of fencing, and “Nut*Fnc” is the interaction between Fertilization and Fencing with zero indicating additivity. Error bars represent the standard error of the effect estimate based on 515 observations nested by site, block, and plot in a mixed effects model (Table S5). All tests are two-tailed.
Nutrient and fencing effects on leaf-scale endophyte diversity are mediated by biomass effects on shade
Arrow width represents magnitude of standardized coefficients (Table S6). Double-headed, dashed arrows indicate relationships modeled as correlated errors. Black arrows represent positive coefficients and orange arrows represent negative coefficients. All tests are two-tailed.
Globally consistent response of plant microbiome diversity across hosts and continents to soil nutrients and herbivores

June 2023

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496 Reads

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2 Citations

Nature Communications

All multicellular organisms host a diverse microbiome composed of microbial pathogens, mutualists, and commensals, and changes in microbiome diversity or composition can alter host fitness and function. Nonetheless, we lack a general understanding of the drivers of microbiome diversity, in part because it is regulated by concurrent processes spanning scales from global to local. Global-scale environmental gradients can determine variation in microbiome diversity among sites, however an individual host’s microbiome also may reflect its local micro-environment. We fill this knowledge gap by experimentally manipulating two potential mediators of plant microbiome diversity (soil nutrient supply and herbivore density) at 23 grassland sites spanning global-scale gradients in soil nutrients, climate, and plant biomass. Here we show that leaf-scale microbiome diversity in unmanipulated plots depended on the total microbiome diversity at each site, which was highest at sites with high soil nutrients and plant biomass. We also found that experimentally adding soil nutrients and excluding herbivores produced concordant results across sites, increasing microbiome diversity by increasing plant biomass, which created a shaded microclimate. This demonstration of consistent responses of microbiome diversity across a wide range of host species and environmental conditions suggests the possibility of a general, predictive understanding of microbiome diversity.


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

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453 Reads

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1 Citation

Ecosphere

Ecosphere

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.


Citations (84)


... In our study, we observed www.nature.com/scientificreports/ that the lowest concentration (5 g/L) of T. kanedae aqueous extract acted as a root length promoter for the four examined plants. However, higher concentrations of the extract significantly impeded root elongation, aligning with findings by Magdalena Lenda et al. 34 . The allelochemicals present in the aqueous extract disrupted cellular membrane integrity, inhibited apical cell division, and curtailed the growth of embryonic roots and axes [35][36][37] , which collectively obstructed root system elongation and led to a reduction in root length. ...

Reference:

Allelopathic effects of Thuidium kanedae on four urban spontaneous plants
Multiple invasive species affect germination, growth, and photosynthesis of native weeds and crops in experiments

Scientific Reports

... Several investigations have shown that fluctuations in SOC content are mostly caused by environmental factors such as precipitation and temperature [49,62,68]. This study's redundancy analysis revealed that precipitation was the primary cause of the fluctuation in SOC content. ...

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

Nature Communications

... Disturbances are changing in frequency, intensity, and cause worldwide (e.g., in forests: Weed et al., 2013grasslands: Joyce et al., 2016;Chen et al., 2023;drylands: Maestre et al., 2022;coral: Vercelloni et al., 2020;Chen et al., 2023). In addition to advancing fundamental knowledge in disturbance ecology (Wohlgemuth et al., 2022), updated and more integrative theories relevant to ecosystem functioning are needed to guide disturbance management, and better anticipate and simulate ecosystems' responses to disturbance in this era of rapid global change. ...

Multidimensional responses of grassland stability to eutrophication

Nature Communications

... In Central Europe, this effect arose from the collapse of the socialist system in the early 1990s, which led to changes in agricultural production (Valkó et al., 2017). As a result, many grasslands have either been converted into arable land or abandoned, which has supported the succession and the emergence of trees and shrubs and geographically alien species, many of which are considered invasive (Csecserits et al., 2011;Czarniecka-Wiera et al., 2019;Daskalova and Kamp, 2023;Lenda et al., 2023;Young et al., 2005). ...

Abandoned land: Linked to biological invasions
  • Citing Article
  • July 2023

Science

... Moreover, in some species tree and leaf growth is reduced in years of high seed production, creating large-scale fluctuations in carbon sequestration [14][15][16] . The spatial scale of synchrony is a key aspect that amplifies the ecological importance of year-to-year variation in seed production [17][18][19] . However, several key questions on how plants synchronize masting over such extensive spatial scales remain unanswered. ...

Masting is uncommon in trees that depend on mutualist dispersers in the context of global climate and fertility gradients

Nature Plants

... Confirming this, our results highlight the role of water availability for both measures of soil biological activity, with higher activity levels at sites with higher MAP and soil water content. Previous studies have found herbivores to reduce soil water content 59 and to have negative effects on soil organisms, especially in unproductive ecosystems 17,19,53,60,61 . Our results suggest that a reduction in soil activity with herbivory is the dominant pattern in grasslands. ...

Globally consistent response of plant microbiome diversity across hosts and continents to soil nutrients and herbivores

Nature Communications

... However, the influence of climate warming and human activities, particularly overgrazing, which has led to grassland degradation, has created an optimal Glires species habitat. 37 Their reproductive capacity and lifespan have increased significantly, resulting in a surge in the Glires population. In this context, the Chinese government and relevant departments have prioritized controlling the Glires population. ...

Anchoring grassland sustainability with a nature‐based small burrowing mammal control strategy

... We found the second assumption-that the reason for anomalies did not vary by subregion-reasonable, as biotic and abiotic processes were unlikely to vary dramatically between subregions. For a more thorough explanation of this modeling approach, see the references above and the appendices of Dee et al. (2023). In our causal model, the lack of interaction terms was purposeful. ...

Clarifying the effect of biodiversity on productivity in natural ecosystems with longitudinal data and methods for causal inference

Nature Communications

... Specifically, we examined changes in the microbial metabolic quotient, which is the microbial respiration C per unit of microbial biomass C. This metric is negatively associated with microbial C use efficiency (Luo et al. 2023;Risch et al. 2023;Xu et al. 2017). We found that Mg addition increased microbial biomass C, but had no effect on microbial respiration. ...

Drivers of the microbial metabolic quotient across global grasslands
  • Citing Article
  • April 2023

Global Ecology and Biogeography

... Species coexistence is jointly determined by intrinsic growth rate, interspecific competition strength and carrying capacity (Chesson, 2000;Song et al., 2020;Vandermeer, 1975). Because of a broader elevation range in Minya Konka of southwest China, compared to that in south-central Vietnam, we originally anticipated that this mountain could have higher environmental heterogeneity (and therefore high habitat carrying capacity) to support amphibian survival and coexistence (Agra et al., 2023;Daleo et al., 2023;Stein et al., 2014;Tang et al., 2018). However, the reality was quite different, as the local inertial effect emerged as the primary driver shaping the structure of the amphibian community in this mountainous range (see Figure 3). ...

Environmental heterogeneity modulates the effect of plant diversity on the spatial variability of grassland biomass

Nature Communications