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. 2018 Sep 5;8(20):10094–10112. doi: 10.1002/ece3.4483

Biodiversity explains maximum variation in productivity under experimental warming, nitrogen addition, and grazing in mountain grasslands

Jiajia Liu 1, Detuan Liu 2, Kun Xu 3, Lian‐ming Gao 4, Xue‐jun Ge 1,✉, Kevin S Burgess 5, Marc W Cadotte 6,7
PMCID: PMC6206177  PMID: 30397450

Abstract

Anthropogenic global warming, nitrogen addition, and overgrazing alter plant communities and threaten plant biodiversity, potentially impacting community productivity, especially in sensitive mountain grassland ecosystems. However, it still remains unknown whether the relationship between plant biodiversity and community productivity varies across different anthropogenic influences, and especially how changes in multiple biodiversity facets drive these impacts on productivity. Here, we measured different facets of biodiversity including functional and phylogenetic richness and evenness in mountain grasslands along an environmental gradient of elevation in Yulong Mountain, Yunnan, China. We combined biodiversity metrics in a series of linear mixed‐effect models to determine the most parsimonious predictors for productivity, which was estimated by aboveground biomass in community. We examined how biodiversity–productivity relationships were affected by experimental warming, nitrogen addition, and livestock‐grazing. Species richness, phylogenetic diversity, and single functional traits (leaf nitrogen content, mg/g) represented the most parsimonious combination in these scenarios, supporting a consensus that single‐biodiversity metrics alone cannot fully explain ecosystem function. The biodiversity–productivity relationships were positive and strong, but the effects of treatment on biodiversity–productivity relationship were negligible. Our findings indicate that the strong biodiversity–productivity relationships are consistent in various anthropogenic drivers of environmental change.

Keywords: experimental warming, functional diversity, grazing, multimodel inference, nitrogen addition, phylogenetic diversity

1. INTRODUCTION

Anthropogenic impacts such as increasing temperature, higher nitrogen addition, and overgrazing all conspire to cause rapid declines in plant biodiversity worldwide, especially in mountain grassland ecosystems, which naturally elicits concern about the consequences for the maintenance of ecosystem functioning (Chapin et al., 2000; Cingolani, Noy‐Meir, & Diaz, 2005; Roth, Kohli, Rihm, & Achermann, 2013; Urban, 2015). The relationship between plant biodiversity and ecosystem function has been a major research topic in ecology for several decades, and while there is general empirical support for a positive effect of biodiversity on function from manipulative experiments (Balvanera et al., 2006; Cardinale et al., 2006; Tilman, Isbell, & Cowles, 2014), there is a lack of clarity about how anthropogenic changes in plant biodiversity might affect biomass production in more natural systems (Zavaleta & Hulvey, 2007). Inconsistent biodiversity effects on productivity could result from how biodiversity is measured, the confounding effect of environmental heterogeneity, and the nature of the anthropogenic impacts.

One reason might be that traditional biodiversity measures, like species richness, do not sufficiently capture the critical processes such as resource complementarity and interspecific interactions that are responsible for ecosystem function, which might be better reflected in relevant traits or evolutionary histories of species in a community (Lavorel & Garnier, 2002; Partel, Laanisto, & Zobel, 2007). Recently, a number of studies have shown that measures based on phylogenetic or single or multiple functional traits appear to be superior to species richness in explaining variation in productivity of plant communities (Cadotte, 2013; Cadotte, Cavender‐Bares, Tilman, & Oakley, 2009; Flynn, Mirotchnick, Jain, Palmer, & Naeem, 2011; Liu, Zhang et al., 2015), and further supply direct links to the mechanisms controlling productivity (Cadotte, 2017). In addition to this, some studies (Liu, Zhang et al., 2015) found that statistical models that combined different biodiversity facets maximally explained the effects of biodiversity loss on ecosystem functioning or services. For example, Liu, Zhang et al. (2015) found that multivariate functional diversity was the single predictor that consistently outperformed other single‐biodiversity measures in explaining variation in productivity, but phylogenetic diversity and community‐level plant height combined to explain maximum variation. However, beyond biodiversity facets that represent species‐level differences, intraspecific variation is critically important to fully capture the diversity of plant communities (Albert et al., 2012). Ali and Mattsson (2017) evaluated the relative power of intraspecific and interspecific tree size variation and found that intraspecific variation better explained variation in aboveground biomass.

Although biodiversity is a major determinant of ecosystem productivity, the estimation of the biodiversity effect might be confounded by environmental factors and potential drivers of environmental change such as elevated temperature, nitrogen addition, and herbivory (Fridley, 2002; Hooper et al., 2005; Seabloom et al., 2017; Steudel et al., 2012; Tilman, Reich, & Isbell, 2012; Tilman et al., 2014). Thus, it is important to disentangle the relative importance of biodiversity relative to other drivers along an environmental gradient for inferring the consistent effects of biodiversity on the primary productivity of ecosystems. The majority of research on biodiversity effects on ecosystem function has been in experimentally assembled communities, and these studies generally support a positive relationship between biodiversity and ecosystem functioning (Hector et al., 1999; Liu, Zhang et al., 2015; Tilman et al., 2001). In contrast, biodiversity levels produced by an environmental gradient such as elevation might reveal different response of ecosystem productivity (Gough, Grace, & Taylor, 1994). Hence, the direct relevance of these experiments for estimating the impacts of realistic biodiversity loss due to environmental changes on ecosystem functioning remains controversial (Hector et al., 2007; Jiang, Wan, & Li, 2009).

To address the biodiversity–productivity relationships of natural communities under different anthropogenic impacts, we developed a fenced warming‐fertilizing experiment in mountain wetlands along an elevation gradient on Yulong Mountain, Yunnan, China. We employed open‐topped, passive warming chambers and urea fertilizer to simulate the projected global warming and nitrogen addition, respectively. We used a multimodel comparative approach to assess the relative contribution of single and various combinations of multivariate biodiversity indices, both with and without intraspecific variation, to predict the variance in biomass production after accounting for potential confounding factors including local environmental heterogeneity, warming, fertilizing, and grazing. We aimed to answer the following questions: (a) Does phylogenetic and functional diversity outperform traditional richness and evenness regardless of environmental heterogeneity and anthropogenic impacts? (b) Does incorporating intraspecific trait variability enhance the explanatory power of functional diversity? (c) Are biodiversity–productivity relationships comparable in experimental warming, nitrogen addition, and grazing along environmental gradient of elevation in mountain grasslands?

2. MATERIALS AND METHODS

2.1. Study sites and experimental design

We established eighteen study sites in south‐facing wetlands of regular topology of Yulong Mountain (100°10′E, 27°00′N) along an environmental gradient of elevation (2,700, 3,200, and 3,400m) within the Lijiang Alpine Botanical Garden of the Kunming Institute of Botany, Chinese Academy of Sciences in Lijiang, Yunnan Province, China. Yulong Mountain has the mean annual temperature of 12.8°C and the annual rainfall is 935 mm, which is mainly distributed from July to October with distinct dry and rainy seasons (Luo et al., 2016). Plant communities of wetlands have obvious species turnover along the elevation gradient with the dominance of the genera Isachne, Juncus at the lowest elevation, the genera Ligularia, Agrostis at the middle elevation, and the genus Agrostis at the highest elevation. All three wetlands have long livestock‐grazing histories, and each supports different types of livestock where sheep and horses graze at the lowest elevation, scalpers, and yaks graze at the middle and highest elevation, respectively.

We established six 12 × 12 m permanent fenced sites randomly distributed in wetlands within each elevation in May 2015 (Figure 1). Within each permanent site, we conducted a complete randomized block factorial experiment with each block of size 5 × 5 m. There were two factors of both experimental warming and nitrogen addition in each block and two levels for each factor. In both fertilized blocks, we applied urea fertilizer annually at the beginning of the rainy season approximately the end of May at a rate of 5 g m−2 year−1. In both warmed blocks, we applied open top chambers (OTCs), commonly employed devices to study the effects of climate warming on ecosystems (Marion et al., 1997). Here, our open top chambers were octahedral frames made of angle iron, 1.5 m maximum diameter, and 45 cm height. Six sides of each open top chamber were fastened to transparent 1.5‐mm‐thick hard plastic with adjacent edges of two plastic pieces attached with adhesive. We regularly arranged two open top chambers and two corresponding plots of the similar area in four blocks with at least 3 m between the nearest edges of adjacent plots. Furthermore, we randomly positioned 3–4 plots around each permanent site with total of 20 grazed plots. Hence, there were five treatments (T C = control, T W = warming, T N = nitrogen addition, T WN = combination of warming and nitrogen addition, and T G = livestock‐grazing; Figure 1).

Figure 1.

Figure 1

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Map of the study sites on Yulong Mountain, Lijiang, Yunnan Province, China, and the plot design. Shown are treatments: T C = control, T W = warming, T N = nitrogen addition, T WN = warming and nitrogen addition, and T G = livestock‐grazing

We recorded species richness and their abundance in a rectangular subplot of 0.5 × 0.5 m from the center of each plot at the peak of the growing season in August 2016 (Figure 1). We then harvested all the stems of each species in each subplot at ground level, dried, and weighed them to 0.1 mg to estimate biomass production (productivity).

2.2. Environmental data

After cutting the stems to ground level, we collected soil core samples from three random locations in each subplot with a cylindrical soil auger (5 cm inner diameter, 15 cm length). We combined the three replicates from the same depth for each subplot as a single composite sample, dried it in the shade, and filtered it using a 2‐mm sieve for stoichiometric analysis. We measured soil pH, concentration of nitrogen (N), phosphorus (P), and carbon (C) following the standard protocols (Sparks et al., 1996). Besides soil resources, we also collected climatic data for rainfall, air temperature, and air moisture using HOBO RG3‐M, HOBO Pro v2, respectively (Onset Computer Corporation, Bourne, MA, USA) from July to October in 2016. For each elevation, we placed one HOBO RG3‐M and two HOBO Pro v2, of which one was positioned inside an open top chamber and the other one was positioned in a control plot. We showed the detailed distributions of temperature and moisture during the experimental interval in Figure A1.

2.3. Plant traits and community phylogeny

We measured five plant traits including plant height (H, cm), leaf carbon content (LC, mg/g), leaf nitrogen content (LN, mg/g), leaf phosphorus content (LP, mg/g), and specific leaf area (SLA, cm2/g). These plant traits might reflect fundamental resource complementarity and interactions among co‐occurring species (Weiher et al., 1999; Wright et al., 2004). We recorded plant height of maximum five randomly selected individuals from each species in each subplot. We calculated the maximum of plant height for each species per plot for intraspecific variability among plots. We scanned at least 1 mature leaf of randomly selected five individuals per species in each subplot using an Epson‐V200 scanner. We then measured leaf area with image analysis software (ImageJ; http://rsb.info.nih.gov/ij). We weighed the leaves after dried to a constant weight at 60°C to 0.1 mg and calculated the specific leaf area as the ratio of leaf dry mass to leaf area. We pooled the leaves from different individuals of the same species and measured leaf carbon, nitrogen, and phosphorus content. For the missing traits data due to rare species, we substitute the average of the same traits of the same species or same genus within the same treatment.

We constructed the phylogeny for the 105 species recorded in our study using rbcL + matK regions of the chloroplast genome. The detailed descriptions of DNA extraction, amplification, and sequencing are provided in Liu, Yan et al. (2015). Here, we briefly described the inference method of phylogenetic reconstruction. We aligned the rbcL and matK sequences using MAFFT (Katoh & Standley, 2013) and concatenated matK to the rbcL to form a super matrix. We used the sequences from the same genus in BOLD as the substitutes for the missing sequences in 27 of the species. For each gene, we selected top‐ranked maximum‐likelihood model of nucleotide substitution using Akaike's information criterion, as implemented in the function modelTest in the phangorn library (Schliep, 2011) in R (R Core Team, 2016). Then, we estimated a maximum‐likelihood phylogeny using PhyML 3.0 with the starting‐tree estimated from the BioNJ (Guindon et al., 2010). We chose one representative of early diverging angiosperm lineage Amborella trichopoda as the root of phylogeny and then used a semiparametric rate‐smoothing method to transform the phylogeny to an ultrametric tree using the chronopl function with parameter value 1,000 in the R ape library (Paradis, Claude, & Strimmer, 2004).

2.4. Measures of biodiversity

Using species composition and number of individuals, we calculated traditional species richness (S) and Shannon's evenness index (H′) for each subplot. We also calculated a suite of single and multivariate functional diversity metrics based on plant traits, as well as phylogenetic diversity metrics using the maximum‐likelihood phylogeny. We listed the detailed descriptions of the measures of biodiversity in Table A1. Here, we give a brief description of important functional and phylogenetic metrics. To assess the potential effect of intraspecific trait variability, we averaged the traits for each species across all subplots in the study as its “fixed” traits and averaged the traits for each species in a given subplot as its “specific” traits. We then calculated a number of functional diversity metrics including single community‐level plant traits and multivariate functional diversity metrics for each subplot using both “fixed” and “specific” traits (Leps, de Bello, Smilauer, & Dolezal, 2011). Here, multivariate functional diversity metrics included Rao's quadratic entropy (RaoQ), which measures abundance‐weighted distances based on multiple traits (Botta‐Dukat, 2005) and functional richness (FRic), which measures the volume of the functional space occupied by the community (Villeger, Mason, & Mouillot, 2008). For the measures of phylogenetic diversity, we calculated the imbalance of abundances at higher clades (IAC), which encapsulates the distribution of individuals across the nodes in the phylogeny (Cadotte et al., 2010) and the abundance‐weighted mean nearest taxon distance in an assemblage (MNND; Cadotte et al., 2010).

2.5. General linear mixed‐effect models

We constructed a series of general linear mixed‐effect models to determine the most parsimonious relationships between productivity and the various measures of biodiversity, treatment, and local environmental factors including soil resources. We assumed that various measures of biodiversity, experimental treatments, and soil resources as fixed factors, whereas elevation, treatment, and plot were treated as hierarchical random factors. Here, the use of a normal distribution of model residuals was validated based on the normalized scores of standardized residual deviance (Q–Q plots). To evaluate model support, we used Akaike's information criterion corrected for small sample sizes (AICc; Burnham & Anderson, 2002, 2004). We also used the marginal R 2 values of the models (R m 2) as a measure of the model's goodness of fit (Nakagawa & Schielzeth, 2013).

To search for the most parsimonious models explaining patterns of biomass production, we firstly removed redundant predictors associated with phylogenetic, functional diversity metrics. We selected the relatively better‐ranked single‐biodiversity metric models in both phylogenetic and functional diversity metrics. Meanwhile, to testify whether experimental treatments affect biodiversity–productivity relationships, we regressed biomass production against each biodiversity metric with the addition or multiplication of treatment and compared the explanatory ability of these models using Akaike's information criterion weights. The detailed single‐biodiversity model ranking is listed in Table A2 and the biodiversity metrics we used in the following model construction are listed in Table 1.

Table 1.

Measures of biodiversity for general multivariate linear mixed‐effect models

Biodiversity measure Description References
IAC Imbalance of abundances among clades: measures the deviation in abundance distribution among internal splits from a null Cadotte et al. (2010)
H max Community‐level mean of plot‐specific maximum plant height values Leps et al. (2011)
LN Community‐level mean of mean leaf nitrogen content value for individual species used for all plots where the species is found Leps et al. (2011)
S Realized species richness of plot Tilman, Wedin, and Knops (1996)
MNND Mean nearest neighbor distance (the mean of the shortest distances connecting each species to any other species in the assemblage) Webb, Ackerly, McPeek, and Donoghue (2002)
RaoQ Quadratic entropy using plot‐specific trait values Botta‐Dukat (2005)
FDis Functional dispersion: weighted distances from a weighted centroid in multitrait space using plot‐specific trait values Villeger et al. (2008)
H′ Shannon's diversity index Tilman et al. (1996)
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The order from top to bottom for the measures of biodiversity represents their relative ranking using Akaike's information criterion weights.

Because of the strong correlation between most biodiversity indices (Spearman's ρ > 0.3; Table A3) and because multivariate functional indices are derived from the same trait data, we avoided including more than one of these like indices in any one model. Then, we constructed models with all remaining combinations of selected biodiversity metrics. At the meanwhile, we incorporated the interaction term between selected biodiversity metrics and experimental treatment into the model if multipliable model outperformed additive model considering treatment effects for particular selected biodiversity metrics. Finally, we also incorporated soil resources into above constructed models following the same constraint of correlation among soil resources and between selected biodiversity metrics and soil resources.

3. RESULTS

3.1. Comparisons between biodiversity metrics

As expected, phylogenetic and functional diversity indices alone outperformed traditional species richness and Shannon's evenness to explain the variation of biomass production when simultaneously considering elevation and treatment (Table 1, Table 2, Tables A2 and A4). Although only several functional diversity indices (H max, RaoQ, FDis, FDiv, detailed information see in Table A1) considering intraspecific variability attained greater model support than corresponding indices using species mean traits (Table 1, Table A2), most of these indices were selected as relatively better‐ranked single‐biodiversity metrics (Table 1). Of all functional diversity indices, the community‐level mean of “specific” maximum plant height (H max) on average accounted for the most explained variation in biomass production (R m 2 > 50%; Table 1). Phylogenetic diversity (IAC) was the top‐ranked single‐biodiversity metric of all considered biodiversity metrics here and explained the most variation in biomass production (R m 2 > 66%; Table A2).

Table 2.

General linear mixed‐effect model (GLMM) results for biomass production as a function of several fixed factors and a hierarchical random factor

Model LL k AICc ΔAICc wAICc R m 2 R c 2
S + IAC + LN + C + T 57.115 13 −85.145 0.000 0.291 91.8 99.9
S + IAC + LN + T 55.664 12 −84.706 0.439 0.234 91.6 99.9
S + IAC + LN + N + T 56.336 13 −83.588 1.557 0.134 91.7 99.9
S + IAC + C + T 54.529 12 −82.437 2.708 0.075 91.4 99.9
S + IAC + T 53.195 11 −82.189 2.955 0.066 91.3 99.9
S + IAC + FDis + C + T 55.010 13 −80.934 4.210 0.035 91.5 99.9
S + IAC + N + T 53.732 12 −80.843 4.302 0.034 91.3 99.9
S + IAC + FDis + T 53.725 12 −80.829 4.316 0.034 91.3 99.9
S + IAC + RaoQ + C + T 54.906 13 −80.726 4.418 0.032 91.5 99.9
S + IAC + RaoQ + T 53.642 12 −80.661 4.483 0.031 91.3 99.9
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Fixed factors are number of species (S), Shannon's evenness (H’), and phylogenetic diversity (IAC, imbalance of abundance at the clade; MNND, mean nearest‐neighbor distance), and community‐level mean of single functional traits (H max, plot‐specific maximum plant height; LN, mean leaf nitrogen content value for individual species used for all plots where the species is found) or multivariate functional trait indices (RaoQ, Quadratic entropy; FDis, Functional dispersion: weighted distances from a weighted centroid in multitrait space), and experimental treatments (T: T C = control, T W = warming, T N = nitrogen addition, T WN = warming and nitrogen addition and T G = livestock‐grazing), and soil resources (C, soil carbon content; N, soil total nitrogen content; P, soil total phosphorus content). Hierarchical random factor is elevation (2,700, 3,200, and 3,400 m), treatment, and plot. Values are shown for the estimated number of model parameters (k), maximum log‐likelihood (LL), and the information‐theoretic Akaike's information criterion corrected for small samples (AICc), change in AICc relative to the top‐ranked model (ΔAICc), AICc weight (wAICc, model probability), and the marginal and total variance explained (R m 2, R c 2) as a measure of the model's goodness of fit. The top 10 models are listed; the full table is shown in Appendix: Table A3.

3.2. Biodiversity effects

Of the 166 multivariate linear mixed‐effect models, the most parsimonious model included species richness (S), phylogenetic diversity (IAC), the community‐level mean of “fixed” leaf nitrogen content (LNf), soil carbon content (C), and treatment (T) accounting for >91% of the deviance explained in productivity (Table 2). After accounting for confounding effects of environmental factors and experimental treatment, biomass production generally increased with increasing species richness, phylogenetic diversity, and the community‐level mean of “fixed” leaf nitrogen content (Figure 2a–c).

Figure 2.

Figure 2

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Scatter plots of the best‐supported variables combined in the general linear mixed‐effect models to predict variation in biomass production: (a) species richness (S), (b) imbalance of abundance at the clade (IAC) based on a maximum‐likelihood phylogeny, (c) community‐level mean of mean leaf nitrogen content value for individual species used for all plots where the species is found (LN), (d) soil carbon content (C), and (e) experimental treatments (T C = control, T W = warming, T N = nitrogen addition, T WN = warming and nitrogen addition, and T G = livestock‐grazing). Dashed lines are linear regression lines, gray ribbon are their confidence intervals, and points and error bar in (e) are predicted values and their confidence intervals using general linear mixed‐effect model

3.3. Environmental and treatment effects

We found relatively weaker environment and treatment effects on biomass production compared to those of selected biodiversity metrics, but few treatment effects on biodiversity–production relationship (Table 2, Table A2). The top‐ranked model showed that grazing strongly reduced the biomass production compared with nitrogen addition; however, nitrogen addition and experimental warming showed no impact on biomass production (Figure 2e). We also found evidence for a weak negative relationship between biomass production and soil carbon content (Figure 2d).

4. DISCUSSION

Our results show that phylogenetic and functional diversity alone outperformed traditional biodiversity measures, species richness, and Shannon's evenness, for explaining variation in productivity. This corroborates observational and experimental evidence that phylogenetic and functional measures better align with the mechanisms controlling community assembly and ecosystem function than taxonomic measures (Cadotte et al., 2009; Flynn et al., 2011; Liu, Zhang et al., 2015). Of all considered functional biodiversity indices, a single functional trait was the single best predictor of productivity patterns. This is not surprising since single functional trait might explain a larger amount of variation in productivity than multivariate functional indices likely due to functional trade‐offs and coordinated variation of functional traits (Cingolani, Cabido, Gurvich, Renison, & Diaz, 2007; Roscher et al., 2012).

Meanwhile, our study revealed that transitioning from using species mean (e.g., “fixed”) traits to plot level (e.g., “specific”) traits enhanced the explanatory power of functional diversity irrespective of plant traits in isolation or combination. Including specific traits allows us to detect subtle differences in functional diversity that respond to environmental variation that does not involve species turnover (Luo et al., 2016). Indeed, Jung et al. (2014) reported that the response of subalpine grassland communities to short extreme drought events was more mediated by intraspecific trait variability than species turnover. Intraspecific trait variability, through phenotypic plasticity, can promote species coexistence through providing fitness advantages and acting as a buffer against rapid climate change (Aspinwall et al., 2015; Nicotra et al., 2010; Valladares, Gianoli, & Gomez, 2007). This might lead to the shift in plant strategies in association with resource capture and use efficiencies at the local scale, which in turn are more related to plot‐specific aboveground biomass production. Furthermore, phenotypic plasticity, especially associating with maximum plant height, might ameliorate light competition, which is assumed to be an important mechanism explaining species loss and biodiversity effects (Borer et al., 2014; Cadotte, 2017; Fridley, 2003; Hautier, Niklaus, & Hector, 2009; Zhou et al., 2017). Our results generally supported these assumptions and highlighted the critical role of intraspecific trait variability in more precisely predicting the ecosystem functioning in the face of global climate change.

Although functional diversity could explain a substantial proportion of variation in productivity, the combination of phylogenetic diversity and a functional trait (leaf nitrogen) attained more model support and greater explanatory power. This implies that functional diversity and phylogenetic diversity could complement each other in the perspective of ecosystem functioning because of their own limitations. Functional diversity was limited by the absence of potential key functional traits, for example, belowground root traits in our study (Cadotte et al., 2009). Linkage between phylogenetic diversity and real ecological differences remains unclear (Cadotte, Davies, & Peres‐Neto, 2017). Thus, the influence of unmeasured plant traits might be compensated by metrics that capture phylogenetic information, such as the distribution of abundances at the clades or the equitability of abundance‐weighted entropic measure of the distribution of evolutionary distinctiveness in an assemblage (Cadotte et al., 2010). Such a combination of functional and phylogenetic information for explaining biodiversity–productivity relationships has received support from both biodiversity manipulation experiments and natural ecosystems (Liu, Zhang et al., 2015; Zhou et al., 2017). For example, Liu, Zhang et al. (2015) found that phylogenetic diversity and plant height represented the most parsimonious combination to predict aboveground biomass production in a removal experiment where species richness and functional diversity were manipulated in alpine meadows of the Tibetan Plateau.

In this study, we found strong and positive effects of species richness on productivity in natural ecosystems after accounting for potential confounding factors. This was consistent with a review by Tilman et al. (2014), in which the diversity effect is as great as, or greater than, the effects of herbivory, nitrogen addition, and other drivers of environmental change. Although our experiment is limited in the short term by the drivers of environmental change, our results still supported a strong positive species richness–productivity relationship in natural ecosystems even after quantifying the effects of intraspecific trait variability and evolutionary history. Despite our findings, the role of biodiversity in the productivity of natural ecosystem remains controversial (Adler et al., 2011), and our results emphasize the fact that we underestimate the importance of biodiversity for ecosystem function when we use species richness only.

Our results revealed that the drivers of environmental change had negligible effects on the relationship between biodiversity and aboveground biomass production. Our finding showed that the relationship between IAC and biomass production was consistently strongest for all considered biodiversity metrics in various treatments. Cadotte (2013) showed that biomass production was strongly predicted by phylogenetic diversity and that this finding might result from species complementarity, and ultimately species coexistence mechanisms (Chesson & Warner, 1981; Hodapp, Hillebrand, Blasius, & Ryabov, 2016; Horn & Macarthur, 1972; Levins & Culver, 1971). IAC that quantifies the relative deviation in the abundance distribution of a local community from a null distribution where individuals are evenly partitioned between clade splits can be used to infer the relative importance of competition and environmental filtering for local assembly. IAC would tend toward 0 if the strength of competition was proportional to phylogenetic relatedness, while IAC would be far greater than 0 if environmental filtering was key to community structure (Cadotte et al., 2010). Meanwhile, Cadotte (2017) showed that multidimensional trait measures might drive complementarity effect through niche complementarity, while few, singular traits (mainly height) might drive selection effect through interspecific competition. Our results were generally in line with these studies, because on the one hand, maximum plant height outperformed the multivariate functional indices alone in the perspective of ecosystem productivity, implying the importance of selection effect in biomass production in natural mountain grassland ecosystems; on the other hand, we observed IAC values far greater than 0, implying the dominance of environmental filtering in local community assembly, which might contribute to the role of selection effect in our system. Our results point to the importance of both complementarity effects and selection effects for aboveground biomass production in natural mountain grassland ecosystems.

CONFLICT OF INTEREST

None declared.

AUTHOR CONTRIBUTIONS

JJL conceived the idea and designed the experiment, JJL collected the data, JJL and MWC analyzed the data, JJL and MWC led the writing of the manuscript, and all authors contributed critically to the drafts and gave final approval for publication.

DATA ACCESSIBILITY

Data available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.5b02c11

ACKNOWLEDGEMENTS

This research was funded by the National Natural Science Foundation of China (31500335, 31570210) and Ministry of Science and Technology of the People’s Republic of China (2014CB954100).

APPENDIX 1.

1.1.

Table A1.

Measures of biodiversity considered in the analysis

Diversity measure Description References
S Realized species richness of plot Tilman et al. (1996)
H′ Shannon's diversity index Tilman et al. (1996)
PD Phylogenetic diversity (the sum of all phylogenetic branch lengths connecting species together) Faith (1992)
MNND Mean nearest neighbor distance (the mean of the shortest distances connecting each species to any other species in the assemblage) Webb et al. (2002)
MPD Mean pairwise distance (the mean of all the distances connecting species in an assemblage) Webb et al. (2002)
MNNDed Abundance‐weighted MNND Webb et al. (2002)
MPDed Abundance‐weighted MPD Webb et al. (2002)
EAED Equitability of abundance‐weighted entropic measure of the distribution of evolutionary distinctiveness in an assemblage Cadotte et al. (2010)
IAC Imbalance of abundances among clades: measures the deviation in abundance distribution among internal splits from a null Cadotte et al. (2010)
FEve Functional Evenness: abundance‐weighted pairwise functional distances Villeger et al. (2008)
FDis Functional dispersion: weighted distances from a weighted centroid in multitrait space Villeger et al. (2008)
FDiv Functional divergence: mean abundance‐weighted deviance from an absolute abundance‐weighted deviance Villeger et al. (2008)
FRic Functional richness: convex hull volume of the trait space Cornwell, Schwilk, & Ackerly, 2006; Villeger et al. (2008)
RaoQ Quadratic entropy Botta‐Dukat (2005)
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Figure A1.

Figure A1

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(a) Cumulative rainfall, (b) mean air temperature within the open top chamber (OTC) and control subplots, and (c) mean moisture within the open top chamber (OTC) and control subplots at the field sites (elevation: 2,700, 3,200, and 3,400) in the growing season (July–October) in 2016

Table A2.

General linear mixed‐effect model (GLMM) results for biomass production as a function of single‐biodiversity metric, experimental treatment, and their interaction as fixed factor and hierarchical random factor

Model LL k AICc ΔAICc wAICc R m 2 R c 2
IAC + T −8.457 10 38.732 0.000 0.948 66.7 97.9
IAC × T −6.473 14 44.535 5.803 0.052 67.7 99.9
H maxs + T −44.775 10 111.367 72.635 <0.001 50.2 98.2
H maxs × T −44.220 14 120.029 81.297 <0.001 50.4 99.9
LNf + T −63.666 10 149.151 110.419 <0.001 38.3 99.9
S + T −63.748 10 149.315 110.583 <0.001 46.8 99.9
S × T −58.939 14 149.468 110.735 <0.001 49.3 99.9
LNs + T −64.135 10 150.088 111.356 <0.001 37.8 99.9
NND + T −65.273 10 152.364 113.632 <0.001 41.1 99.9
SLAf + T −65.969 10 153.755 115.023 <0.001 37.3 99.9
PD + T −66.131 10 154.081 115.349 <0.001 43.4 99.9
PD × T −61.507 14 154.603 115.871 <0.001 46.1 99.9
HEED + T −67.131 10 156.080 117.348 <0.001 36.4 99.9
LNf × T −62.699 14 156.988 118.256 <0.001 38.9 99.9
LNs × T −62.708 14 157.006 118.274 <0.001 38.7 99.9
NRIed + T −67.609 10 157.037 118.305 <0.001 35.2 99.9
RaoQs + T −67.614 10 157.047 118.315 <0.001 36.3 99.9
SLAs × T −62.735 14 157.059 118.327 <0.001 42.1 99.9
FDiss + T −67.792 10 157.403 118.670 <0.001 36.5 99.9
LPs + T −67.829 10 157.477 118.744 <0.001 33.3 99.9
LPf + T −67.870 10 157.557 118.825 <0.001 33.3 99.9
H′ × T −63.169 14 157.927 119.195 <0.001 40.1 99.9
MPD + T −68.332 10 158.483 119.751 <0.001 35.1 99.9
NNDed + T −68.340 10 158.498 119.765 <0.001 36.5 99.9
SLAf × T −63.507 14 158.604 119.872 <0.001 39.1 99.9
NND × T −63.713 14 159.016 120.284 <0.001 41.9 99.9
H′ + T −68.852 10 159.523 120.79 <0.001 36.1 99.9
LCf + T −68.908 10 159.635 120.903 <0.001 35.1 99.9
MPDed × T −64.030 14 159.650 120.918 <0.001 39.0 99.9
SLAs + T −69.078 10 159.975 121.243 <0.001 35.2 99.9
FDisf + T −69.096 10 160.010 121.278 <0.001 35.7 99.9
RaoQf + T −69.164 10 160.146 121.414 <0.001 35.5 99.9
FRicf + T −69.264 10 160.347 121.615 <0.001 38.5 99.9
NRI + T −69.288 10 160.393 121.661 <0.001 34.3 99.9
NTI + T −69.311 10 160.441 121.709 <0.001 35.2 99.9
NTIed + T −69.648 10 161.115 122.383 <0.001 34.7 99.9
FDivs + T −69.710 10 161.238 122.506 <0.001 35.2 99.9
LPs × T −64.965 14 161.520 122.788 <0.001 35.1 99.9
LCs + T −69.879 10 161.576 122.844 <0.001 34.7 99.9
LPf × T −65.036 14 161.662 122.930 <0.001 35.1 99.9
H maxf + T −70.006 10 161.830 123.098 <0.001 33.9 99.9
FRics × T −65.158 14 161.906 123.173 <0.001 39.5 99.9
FDiss × T −65.229 14 162.047 123.315 <0.001 38.7 99.9
LCf × T −65.234 14 162.058 123.326 <0.001 38.5 99.9
NRIed × T −65.310 14 162.210 123.478 <0.001 37.1 99.9
FRics + T −70.254 10 162.327 123.595 <0.001 35.5 99.9
FEvef + T −70.337 10 162.492 123.760 <0.001 33.9 99.9
FDivf + T −70.423 10 162.665 123.933 <0.001 34.4 99.9
HAED + T −70.445 10 162.707 123.975 <0.001 33.9 99.9
FEves + T −70.476 10 162.770 124.038 <0.001 33.9 99.9
MPDed + T −70.476 10 162.771 124.039 <0.001 33.9 99.9
FRicf×T −65.664 14 162.917 124.185 <0.001 41.1 99.9
NNDed×T −65.853 14 163.296 124.564 <0.001 38.8 99.9
RaoQs×T −65.916 14 163.421 124.689 <0.001 37.8 99.9
MPD × T −66.237 14 164.065 125.332 <0.001 36.9 99.9
HEED × T −66.671 14 164.932 126.199 <0.001 36.5 99.9
NRI × T −66.679 14 164.948 126.216 <0.001 36.5 99.9
NTI × T −67.022 14 165.634 126.902 <0.001 37.4 99.9
FDivs × T −67.470 14 166.530 127.798 <0.001 36.9 99.9
NTIed × T −68.012 14 167.615 128.882 <0.001 35.8 99.9
FDisf × T −68.143 14 167.876 129.144 <0.001 36.5 99.9
RaoQf × T −68.432 14 168.454 129.722 <0.001 36.0 99.9
H maxf × T −68.722 14 169.034 130.302 <0.001 34.8 99.9
FEvef × T −69.058 14 169.706 130.974 <0.001 34.6 99.9
FEves × T −69.170 14 169.930 131.198 <0.001 34.9 99.9
LCs × T −69.377 14 170.344 131.612 <0.001 35.0 99.9
HAED × T −69.433 14 170.455 131.723 <0.001 34.4 99.9
FDivf × T −69.783 14 171.155 132.423 <0.001 34.8 99.9
1 −80.940 5 172.356 133.624 <0.001 0.0 91.1
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Fixed factors are number of species (S), Shannon's evenness (H’), and phylogenetic diversity (PD, sum of branch lengths; IAC, imbalance of abundance at the clade; EAED, equitability of abundance‐weighted entropic measure of the distribution of evolutionary distinctiveness; MPD, mean pairwise distance; MNND, mean nearest‐neighbor distance; MPDed, weighted mean pairwise distance; MNNDed, weighted mean nearest neighbor distance); and community‐level mean of single functional traits using interspecific and intraspecific functional traits indicated by subscript “f” and “s,” respectively (H max , maximum plant height; LC, leaf carbon content; LN, leaf nitrogen content; LP, leaf phosphorus content; SLA, specific leaf area) or multivariate functional trait indices using interspecific and intraspecific functional traits indicated by subscript “f” and “s,” respectively (FDis, functional distribution; FRic, functional richness; FEve, functional evenness; FDiv, functional divergence; RaoQ, quadratic entropy), and experimental treatments (T: T C = control, T W = warming, T N = nitrogen addition, T WN = warming and nitrogen addition, and T G = livestock‐grazing). Hierarchical random factor is elevation (2,700, 3,200, and 3,400 m), treatment, and plot. Values are shown for the estimated number of model parameters (k), maximum log‐likelihood (LL), and the information‐theoretic Akaike's information criterion corrected for small samples (AICc), change in AICc relative to the top‐ranked model (ΔAICc), AICc weight (wAICc, model probability), and the marginal and total variance explained (R m 2, R c 2) as a measure of the model's goodness of fit.

Table A3.

Spearman's ρ correlation matrix for raw input variables

S H’ PD IAC HEED HAED MPD MPDed MNND MNNDed FRicf FEvef FDivf FDisf RaoQf H maxf LC f LN f LP f SLAf FRics FEves FDivs FDiss RaoQs Hmaxs LCs LNs LPs SLAs pH P N
H’ 0.65
PD 0.94 0.60
IAC −0.22 −0.41 −0.18
HEED 0.16 0.31 0.09 −0.42
HAED 0.27 0.35 0.20 −0.23 0.96
MPD −0.03 −0.18 0.15 0.13 −0.20 −0.17
MPDed 0.41 0.72 0.44 −0.37 0.20 0.23 0.18
MNND −0.62 −0.45 −0.43 0.18 −0.26 −0.33 0.39 −0.22
MNNDed −0.33 −0.11 −0.24 −0.05 −0.04 −0.07 0.23 0.23 0.54
FRicf 0.85 0.44 0.81 −0.12 0.09 0.19 0.12 0.33 −0.49 −0.27
FEvef −0.27 −0.14 −0.26 0.02 0.08 0.03 −0.15 −0.25 0.10 0.06 −0.29
FDivf 0.26 0.02 0.27 0.12 0.00 0.08 0.16 0.06 −0.15 −0.11 0.37 0.05
FDisf 0.27 0.20 0.21 0.07 0.04 0.11 −0.08 0.00 −0.25 −0.16 0.31 0.22 0.64
RaoQf 0.24 0.21 0.18 0.06 0.05 0.11 −0.12 −0.03 −0.24 −0.13 0.24 0.27 0.59 0.96
H maxf −0.44 −0.31 −0.40 0.45 −0.16 −0.13 −0.01 −0.22 0.33 0.17 −0.34 0.28 0.07 0.06 0.05
LC f −0.06 −0.07 −0.04 −0.12 −0.05 −0.07 0.22 0.13 0.02 0.10 0.00 −0.20 0.01 −0.15 −0.16 −0.27
LN f −0.07 0.11 −0.10 0.18 0.05 0.08 −0.36 −0.16 0.05 0.17 −0.22 0.35 −0.04 0.21 0.28 0.34 −0.27
LP f −0.44 −0.11 −0.45 0.07 −0.06 −0.13 −0.35 −0.27 0.31 0.25 −0.54 0.37 −0.30 −0.02 0.10 0.40 −0.29 0.54
SLA f −0.30 −0.33 −0.22 0.14 −0.10 −0.12 0.49 −0.07 0.29 −0.04 −0.12 −0.12 −0.06 −0.26 −0.33 0.23 0.29 −0.50 −0.37
FRics 0.86 0.46 0.81 −0.17 0.08 0.18 0.12 0.35 −0.48 −0.27 0.96 −0.28 0.35 0.31 0.25 −0.38 0.00 −0.25 −0.52 −0.15
FEves −0.29 −0.16 −0.26 0.02 0.04 −0.04 −0.17 −0.24 0.18 0.10 −0.36 0.77 0.03 0.17 0.22 0.27 −0.15 0.33 0.44 −0.07 −0.34
FDivs 0.25 0.09 0.25 0.08 −0.02 0.06 0.07 0.11 −0.18 0.00 0.32 0.13 0.80 0.62 0.59 0.12 0.00 0.06 −0.10 −0.19 0.30 0.10
FDiss 0.24 0.23 0.21 0.05 0.02 0.06 −0.10 0.01 −0.24 −0.07 0.22 0.33 0.45 0.80 0.78 0.08 −0.18 0.28 0.11 −0.24 0.22 0.30 0.64
RaoQs 0.20 0.23 0.17 0.06 0.02 0.07 −0.13 −0.03 −0.20 −0.05 0.15 0.37 0.39 0.73 0.77 0.10 −0.20 0.34 0.22 −0.29 0.15 0.35 0.57 0.96
H maxs −0.18 −0.11 −0.14 0.70 −0.19 −0.03 0.03 −0.11 0.15 0.08 −0.09 0.11 0.07 0.08 0.06 0.68 −0.19 0.28 0.22 0.08 −0.15 0.11 0.12 0.13 0.16
LC s −0.09 −0.02 −0.08 −0.12 0.05 0.00 0.05 0.07 0.02 0.02 −0.11 0.05 −0.17 −0.22 −0.23 −0.21 0.69 −0.12 −0.07 0.16 −0.11 0.05 −0.15 −0.05 −0.02 −0.12
LN s −0.08 0.12 −0.11 0.16 0.08 0.10 −0.36 −0.15 0.05 0.14 −0.24 0.39 −0.09 0.16 0.23 0.31 −0.25 0.97 0.55 −0.48 −0.27 0.36 0.01 0.29 0.37 0.27 0.06
LP s −0.44 −0.11 −0.45 0.07 −0.06 −0.13 −0.35 −0.27 0.30 0.24 −0.54 0.37 −0.31 −0.03 0.08 0.40 −0.29 0.54 1.00 −0.37 −0.52 0.45 −0.11 0.11 0.23 0.22 −0.03 0.56
SLA s −0.14 0.03 −0.07 −0.05 −0.01 −0.03 0.33 0.18 0.15 −0.02 −0.02 −0.21 −0.07 −0.17 −0.22 −0.09 0.25 −0.46 −0.31 0.64 −0.03 −0.24 −0.09 −0.08 −0.11 −0.09 0.10 −0.46 −0.32
pH 0.70 0.36 0.71 −0.06 0.11 0.22 0.16 0.30 −0.38 −0.15 0.71 −0.31 0.32 0.19 0.14 −0.41 0.08 −0.17 −0.63 −0.06 0.70 −0.39 0.28 0.15 0.07 −0.12 −0.08 −0.20 −0.64 0.05
P −0.58 −0.17 −0.59 0.05 −0.07 −0.16 −0.21 −0.26 0.36 0.21 −0.64 0.29 −0.39 −0.16 −0.07 0.28 −0.13 0.22 0.72 −0.07 −0.62 0.31 −0.25 −0.02 0.08 0.14 0.12 0.27 0.73 −0.05 −0.75
N −0.24 −0.15 −0.25 0.21 −0.22 −0.20 0.06 −0.15 0.17 −0.09 −0.14 0.09 −0.01 0.01 0.01 0.18 0.00 0.05 0.02 0.17 −0.12 0.06 −0.05 0.00 0.01 0.22 0.05 0.06 0.03 0.07 −0.35 0.27
C −0.23 −0.20 −0.24 0.17 −0.18 −0.18 0.13 −0.10 0.17 −0.05 −0.10 0.06 −0.08 −0.06 −0.09 0.17 −0.02 0.03 0.01 0.14 −0.09 0.05 −0.08 −0.08 −0.08 0.18 0.03 0.04 0.01 0.00 −0.31 0.15 0.74
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Shown are species richness (S), Shannon's evenness (H’), phylogenetic diversity (PD, sum of branch lengths; IAC, imbalance of abundance at the clade; EAED, equitability of abundance‐weighted entropic measure of the distribution of evolutionary distinctiveness; MPD, mean pairwise distance; MNND, mean nearest neighbor distance; MPDed, weighted mean pairwise distance; MNNDed, weighted mean nearest neighbor distance); multivariate functional trait indices based on interspecific and intraspecific functional traits indicated by subscript “f” and “s,” respectively (FDis, functional distribution; FRic, functional richness; FEve, functional evenness; FDiv, functional divergence; RaoQ, quadratic entropy), community‐level mean of single functional traits also using interspecific and intraspecific functional traits indicated by subscript “f” and “s,” respectively (H max, maximum plant height; LC, leaf carbon content; LN, leaf nitrogen content; LP, leaf phosphorus content; SLA, specific leaf area).

Table A4.

General linear mixed‐effect model (GLMM) results for biomass production as a function of several fixed factors and a hierarchical random factor

Model LL k AICc ΔAICc wAICc R m 2 R c 2
S + IAC + LN + C + T 57.115 13 −85.145 0.000 0.291 91.8 99.9
S + IAC + LN + T 55.664 12 −84.706 0.439 0.234 91.6 99.9
S + IAC + LN + N + T 56.336 13 −83.588 1.557 0.134 91.7 99.9
S + IAC + C + T 54.529 12 −82.437 2.708 0.075 91.4 99.9
S + IAC + T 53.195 11 −82.189 2.955 0.066 91.3 99.9
S + IAC + FDis + C + T 55.010 13 −80.934 4.210 0.035 91.5 99.9
S + IAC + N + T 53.732 12 −80.843 4.302 0.034 91.3 99.9
S + IAC + FDis + T 53.725 12 −80.829 4.316 0.034 91.3 99.9
S + IAC + RaoQ + C + T 54.906 13 −80.726 4.418 0.032 91.5 99.9
S + IAC + RaoQ + T 53.642 12 −80.661 4.483 0.031 91.3 99.9
S + IAC + FDis + N + T 54.336 13 −79.588 5.556 0.018 91.4 99.9
S + IAC + RaoQ + N + T 54.255 13 −79.425 5.719 0.017 91.4 99.9
IAC + NND + LN + C + T 20.838 13 −12.592 72.553 <0.001 78.2 98.3
IAC + NND + LN + N + T 19.234 13 −9.383 75.762 <0.001 77.9 98.2
IAC + NND + LN + T 17.491 12 −8.360 76.785 <0.001 77.1 98.2
IAC + NND + RaoQ + C + T 17.236 13 −5.386 79.758 <0.001 78.5 98.4
IAC + NND + FDis + C + T 16.871 13 −4.657 80.487 <0.001 78.8 98.3
IAC + NND + RaoQ + N + T 15.996 13 −2.907 82.238 <0.001 78.5 98.3
IAC + NND + C + T 14.646 12 −2.670 82.475 <0.001 78.3 98.1
IAC + NND + FDis + N + T 15.593 13 −2.101 83.043 <0.001 78.7 98.2
IAC + NND + RaoQ + T 14.180 12 −1.737 83.407 <0.001 77.6 98.1
IAC + NND + FDis + T 13.832 12 −1.042 84.102 <0.001 77.9 98.1
IAC + NND + N + T 13.113 12 0.396 85.541 <0.001 78.0 98.1
IAC + NND + T 11.641 11 0.918 86.062 <0.001 77.2 98.0
IAC + LN + pH + T 6.846 12 12.929 98.074 <0.001 82.3 99.9
IAC + RaoQ + pH + T 6.242 12 14.139 99.283 <0.001 81.8 99.9
IAC + FDis + pH + T 5.845 12 14.932 99.9.077 <0.001 82.1 99.9
IAC + LN + C + T 2.756 12 21.109 106.254 <0.001 69.0 99.9
IAC + pH + T 1.462 11 21.276 106.420 <0.001 80.8 99.9
IAC + RaoQ + C + T 2.402 12 21.818 106.962 <0.001 70.3 99.9
IAC + FDis + C + T 1.676 12 23.270 108.414 <0.001 70.6 99.9
IAC + LN + P + C + T 2.859 13 23.367 108.511 <0.001 67.5 99.9
IAC + RaoQ + P + C + T 2.428 13 24.229 109.373 <0.001 69.5 99.9
IAC + RaoQ + N + T 0.789 12 25.045 110.189 <0.001 70.2 99.9
IAC + FDis + P + C + T 1.745 13 25.594 110.739 <0.001 69.3 99.9
IAC + LN + N + T 0.252 12 26.118 111.262 <0.001 68.7 99.1
IAC + FDis + N + T −0.088 12 26.797 111.942 <0.001 70.6 99.9
IAC + RaoQ + P + N + T 0.794 13 27.497 112.642 <0.001 69.9 99.9
IAC + RaoQ + T −1.812 11 27.824 112.968 <0.001 68.9 99.9
IAC + LN + P + N + T 0.308 13 28.468 113.613 <0.001 67.3 98.6
IAC + LN + T −2.241 11 28.682 113.827 <0.001 67.1 98.1
IAC + FDis + P + N + T −0.058 13 29.200 114.345 <0.001 69.7 99.9
IAC + FDis + T −2.728 11 29.656 114.800 <0.001 69.2 99.9
IAC + RaoQ + P + T −1.670 12 29.961 115.106 <0.001 70.8 99.9
IAC + LN + P + T −2.219 12 31.059 116.204 <0.001 68.0 98.3
IAC + C + T −3.686 11 31.572 116.716 <0.001 68.6 99.9
IAC + FDis + P + T −2.648 12 31.919 117.063 <0.001 70.7 99.9
IAC + P + C + T −3.352 12 33.327 118.471 <0.001 65.7 99.9
IAC + N + T −6.157 11 36.515 121.659 <0.001 68.2 99.9
IAC + P + N + T −5.938 12 38.497 123.642 <0.001 65.7 98.6
IAC + T −8.457 10 38.732 123.877 <0.001 66.7 97.9
IAC + P + T −8.445 11 41.091 126.235 <0.001 66.1 97.9
H max + LN + pH + T −36.620 12 99.861 185.006 <0.001 65.3 99.9
H max + pH + T −38.365 11 99.9.930 186.075 <0.001 64.4 99.9
S + H max + T −38.574 11 101.347 186.492 <0.001 61.4 95.9
S + H max + LN + T −37.695 12 102.011 187.156 <0.001 60.4 96.4
S + H max + C + T −37.828 12 102.278 187.422 <0.001 61.2 96.0
S + H max + LN + C + T −36.857 13 102.798 187.943 <0.001 60.3 96.3
S + H max + N + T −38.205 12 103.032 188.177 <0.001 61.5 95.9
S + H max + LN + N + T −37.276 13 103.637 188.782 <0.001 60.4 96.5
NND + H max + LN + C + T −37.453 13 103.991 189.136 <0.001 56.3 96.3
NND + H max + LN + T −38.868 12 104.358 189.502 <0.001 55.6 96.3
NND + H max + C + T −39.270 12 105.162 190.307 <0.001 56.4 96.0
NND + H max + LN + N + T −38.097 13 105.278 190.422 <0.001 56.2 96.4
NND + H max + T −40.642 11 105.484 190.629 <0.001 55.7 95.9
NND + H max + N + T −39.902 12 106.425 191.570 <0.001 56.2 96.0
H max + LN + C + T −40.844 12 108.309 193.454 <0.001 51.6 99.9
H max + LN + T −42.684 11 109.567 194.712 <0.001 50.7 99.9
H max + LN + N + T −41.609 12 109.840 194.985 <0.001 51.5 99.9
H max + C + T −42.933 11 110.066 195.211 <0.001 51.3 97.2
H’ + H max + LN + C + T −40.551 13 110.187 195.332 <0.001 51.3 99.9
H max + LN + P + C + T −40.686 13 110.458 195.602 <0.001 53.8 99.9
H max + LN + P + T −42.179 12 110.980 196.125 <0.001 54.6 99.9
H max + T −44.775 10 111.367 196.512 <0.001 50.2 98.2
H max + N + T −43.679 11 111.558 196.702 <0.001 51.1 99.9
H + H max + LN + T −42.557 12 111.736 196.881 <0.001 50.4 99.9
H’ + H max + LN + N + T −41.403 13 111.891 197.036 <0.001 51.3 99.9
H max + LN + P + N + T −41.410 13 111.905 197.050 <0.001 54.0 99.9
H max + P + C + T −42.856 12 112.334 197.478 <0.001 52.9 96.9
H’ + H max + C + T −42.865 12 112.352 197.497 <0.001 51.1 97.5
H’ + H max + LN + P + C + T −40.477 14 112.544 197.688 <0.001 52.8 99.9
H max + P + T −44.432 11 113.065 198.209 <0.001 53.6 97.1
H’ + H max + LN + P + T −42.136 13 113.357 198.501 <0.001 54.1 99.9
H’ + H max + T −44.766 11 113.733 198.877 <0.001 50.1 98.3
H max + P + N + T −43.586 12 113.793 198.938 <0.001 52.8 98.4
H’ + H max + N + T −43.643 12 113.907 199.052 <0.001 50.9 99.9
H’ + H max + LN + P + N + T −41.283 14 114.157 199.301 <0.001 53.2 99.9
H’ + H max + P + C + T −42.821 13 114.727 199.871 <0.001 52.4 97.2
H’ + H max + P + T −44.429 12 115.480 200.624 <0.001 53.8 97.1
H’ + H max + P + N + T −43.573 13 116.231 201.375 <0.001 52.5 99.9
LN + pH + T −57.526 11 139.252 224.396 <0.001 52.2 99.9
NND + LN + T −58.903 11 142.006 227.150 <0.001 44.3 99.9
S + LN + T −58.913 11 142.027 227.171 <0.001 48.2 99.9
NND + LN + C + T −57.908 12 142.439 227.583 <0.001 44.9 97.7
S + LN + C + T −58.424 12 143.471 228.615 <0.001 47.9 99.9
NND + LN + N + T −58.753 12 144.128 229.273 <0.001 44.5 99.9
S + LN + N + T −58.896 12 144.414 229.559 <0.001 48.2 99.9
LN + C + T −62.205 11 148.611 233.755 <0.001 39.4 99.9
LN + T −63.666 10 149.151 234.296 <0.001 38.3 99.9
RaoQ + pH + T −62.497 11 149.193 234.338 <0.001 48.4 99.9
S + T −63.748 10 149.315 234.459 <0.001 46.8 99.9
FDis + pH + T −62.610 11 149.420 234.565 <0.001 48.5 99.9
H’ + LN + P + T −61.491 12 149.605 234.749 <0.001 50.1 99.9
S + FDis + T −62.862 11 149.923 235.068 <0.001 46.9 99.9
LN + P + T −62.889 11 149.979 235.123 <0.001 49.1 99.9
S + RaoQ + T −62.967 11 150.134 235.278 <0.001 46.6 99.9
H’ + LN + C + T −61.896 12 150.414 235.558 <0.001 40.0 99.9
H’ + LN + T −63.172 11 150.544 235.689 <0.001 39.1 99.9
LN + P + C + T −62.024 12 150.671 235.815 <0.001 41.8 99.9
pH + T −64.576 10 150.970 236.114 <0.001 47.1 99.9
LN + N + T −63.394 11 150.988 236.133 <0.001 38.7 99.9
S + C + T −63.454 11 151.108 236.253 <0.001 46.6 99.9
H’ + LN + P + C + T −61.099 13 151.283 236.427 <0.001 50.4 99.9
S + N + T −63.748 11 151.696 236.84 <0.001 46.8 99.9
S + FDis + C + T −62.554 12 151.730 236.875 <0.001 46.7 99.9
NND + RaoQ + T −63.844 11 151.888 237.033 <0.001 41.7 99.9
S + RaoQ + C + T −62.660 12 151.942 237.087 <0.001 46.3 99.9
NND + FDis + T −63.922 11 152.045 237.189 <0.001 42.0 99.9
H’ + LN + P + N + T −61.490 13 152.064 237.209 <0.001 50.1 99.9
S + FDis + N + T −62.854 12 152.330 237.475 <0.001 46.9 99.9
NND + T −65.273 10 152.364 237.509 <0.001 41.1 99.9
LN + P + N + T −62.874 12 152.369 237.513 <0.001 49.1 99.9
H’ + LN + N + T −62.944 12 152.510 237.654 <0.001 39.4 99.9
S + RaoQ + N + T −62.960 12 152.542 237.687 <0.001 46.6 99.9
NND + RaoQ + C + T −63.067 12 152.757 237.901 <0.001 42.2 99.9
NND + FDis + C + T −63.112 12 152.845 237.99 <0.001 42.5 99.9
NND + C + T −64.367 11 152.934 238.078 <0.001 41.6 99.9
NND + RaoQ + N + T −63.714 12 154.050 239.194 <0.001 41.9 99.9
NND + FDis + N + T −63.784 12 154.190 239.334 <0.001 42.2 99.9
NND + N + T −65.159 11 154.519 239.663 <0.001 41.3 99.9
H’ + RaoQ + P + T −65.054 12 156.730 241.874 <0.001 47.4 99.9
RaoQ + P + T −66.341 11 156.882 242.027 <0.001 46.3 99.9
H’ + FDis + P + T −65.162 12 156.947 242.091 <0.001 47.3 99.9
RaoQ + T −67.614 10 157.047 242.192 <0.001 36.3 99.9
RaoQ + C + T −66.522 11 157.245 242.389 <0.001 37.2 99.9
FDis + P + T −66.568 11 157.336 242.481 <0.001 46.1 99.9
FDis + T −67.792 10 157.403 242.547 <0.001 36.5 99.9
FDis + C + T −66.634 11 157.469 242.613 <0.001 37.4 99.9
H’ + RaoQ + T −66.890 11 157.980 243.125 <0.001 37.4 99.9
H’ + FDis + T −66.955 11 158.109 243.254 <0.001 37.7 99.9
H’ + RaoQ + C + T −65.963 12 158.549 243.693 <0.001 38.1 99.9
H’ + FDis + C + T −65.991 12 158.603 243.747 <0.001 38.4 99.9
RaoQ + P + C + T −66.035 12 158.693 243.837 <0.001 46.6 99.9
H’ + RaoQ + P + C + T −64.863 13 158.812 243.956 <0.001 47.5 99.9
H’ + P + T −67.316 11 158.831 243.976 <0.001 45.5 99.9
RaoQ + N + T −67.379 11 158.958 244.103 <0.001 36.6 99.9
H’ + FDis + P + C + T −64.937 13 158.959 244.104 <0.001 47.5 99.9
FDis + P + C + T −66.201 12 159.024 244.169 <0.001 46.4 99.9
H’ + RaoQ + P + N + T −65.053 13 159.191 244.336 <0.001 47.4 99.9
FDis + N + T −67.540 11 159.280 244.425 <0.001 36.8 99.9
RaoQ + P + N + T −66.331 12 159.283 244.427 <0.001 46.3 99.9
H’ + FDis + P + N + T −65.159 13 159.404 244.548 <0.001 47.3 99.9
H’ + T −68.852 10 159.523 244.667 <0.001 36.1 99.9
FDis + P + N + T −66.548 12 159.719 244.863 <0.001 46.2 99.9
H’ + C + T −67.783 11 159.765 244.91 <0.001 37.0 99.9
H’ + RaoQ + N + T −66.703 12 160.028 245.172 <0.001 37.7 99.9
C + T −69.120 10 160.058 245.203 <0.001 35.1 99.9
H’ + FDis + N + T −66.758 12 160.137 245.281 <0.001 38.0 99.9
H’ + P + C + T −67.013 12 160.648 245.792 <0.001 45.8 99.9
H’ + P + N + T −67.315 12 161.252 246.396 <0.001 45.5 99.9
H’ + N + T −68.691 11 161.582 246.727 <0.001 36.4 99.9
P + T −69.946 10 161.710 246.855 <0.001 43.3 99.9
P + C + T −69.089 11 162.377 247.522 <0.001 36.2 99.9
N + T −70.296 10 162.410 247.555 <0.001 34.3 99.9
P + N + T −69.926 11 164.052 249.197 <0.001 43.3 99.9
1 −80.940 5 172.356 257.501 <0.001 0.0 91.1
Open in a new tab

Fixed factors are number of species (S), Shannon's evenness (H’), and phylogenetic diversity (IAC, imbalance of abundance at the clade; MNND, mean nearest neighbor distance), and community‐level mean of single functional traits (H max, plot‐specific maximum plant height; LN, mean leaf nitrogen content value for individual species used for all plots where the species is found) or multivariate functional trait indices (RaoQ, Quadratic entropy; FDis, Functional dispersion: weighted distances from a weighted centroid in multitrait space), and experimental treatments (T: T C = control, T W = warming, T N = nitrogen addition, T WN = warming and nitrogen addition, and T G = livestock‐grazing), and soil resources (C, soil carbon content; N, soil total nitrogen content; P, soil total phosphorus content). Hierarchical random factor is elevation (2700 m, 3200 m and 3400 m), treatment, and plot. Values are shown for the estimated number of model parameters (k), maximum log‐likelihood (LL), and the information‐theoretic Akaike's information criterion corrected for small samples (AICc), change in AICc relative to the top‐ranked model (ΔAICc), AICc weight (wAICc, model probability), and the marginal and total variance explained (Rm 2, Rc 2) as a measure of the model's goodness of fit.

Liu J, Liu D, Xu K, et al. Biodiversity explains maximum variation in productivity under experimental warming, nitrogen addition, and grazing in mountain grasslands. Ecol Evol. 2018;8:10094–10112. 10.1002/ece3.4483

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.5b02c11

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