A quantitative framework reveals the ecological drivers of grassland soil microbial community assembly in response to warming

Unraveling the drivers controlling community assembly is a central issue in ecology. Selection, dispersal, diversification and drift are conceptually accepted as major community assembly processes. Defining their relative importance in governing biodiversity is compellingly needed, but very challenging. Here, we present a novel framework to quantitatively infer community assembly mechanisms by phylogenetic bin-based null model analysis (iCAMP). Our results with simulated microbial communities showed that iCAMP had high accuracy (0.93 - 0.99), precision (0.80 - 0.94), sensitivity (0.82 - 0.94), and specificity (0.95 - 0.98), which were 10-160% higher than those from the entire community-based approach. Applying it to grassland microbial communities in response to experimental warming, our analysis showed that homogeneous selection (38%) and “drift” (59%) played dominant roles in controlling grassland soil microbial community assembly. Interestingly, warming enhanced homogeneous selection, but decreased “drift” over time. Warming-enhanced selection was primarily imposed on Bacillales in Firmicutes, which were strengthened by increased drought and reduced plant productivity. This general framework should also be useful for plant and animal ecology.

showed significantly lower precision (<0.57, down to negative, p < 0.0001) than iCAMP across 165 all scenarios (Fig. 2a-c). 166 167 Since only QPEN and iCAMP can quantify relative importance of different ecological processes, 168 their performances were particularly compared. Overall, iCAMP had, on average, higher accuracy 169 (9.9% higher), precision (120.2%), sensitivity (61.1%), and specificity (10.6%) than QPEN (Fig.  170 2i, S9k, l). The setting of phylogenetic signal also had significant impacts on iCAMP performance. 171 When the phylogenetic signal increased from low/medium (Fig. 2i, S9k) to high (Fig. S9l), the 172 accuracy and specificity of iCAMP remained high (> 0.92) without any significant change (p > 173 0.20), but the precision and sensitivity of iCAMP increased from 0.80-0.82 to 0.90-0.94. In 174 contrast, the overall performance of QPEN did not show improvement when phylogenetic signal 175 increased. 176 9 The performance varied considerably among different ecological processes ( Fig. 2d-h, S10). In the 178 simulated communities, QPEN had lower performance (quantitative precision < 0, qualitative 179 precision < 0.13, sensitivity < 0.04, Fig. S10) in estimating HoS and HeS even under high 180 phylogenetic signal (Fig. S9f, g). iCAMP improved the estimation of all processes. Under medium 181 and high phylogenetic signals, all performance indices were higher than 0.78 for iCAMP (Fig.  182   S10). However, with low phylogenetic signal, iCAMP had low sensitivity (down to 0.17) for HoS 183 (Fig. S10), despite that it was considerably (p < 0.05) higher than QPEN (sensitivity < 0.04 for 184 HoS). These results confirmed that low phylogenetic signal of niche preference can limit the 185 capability of phylogenetic metrics to infer selection, which can be partly but not completely 186 overcome by iCAMP. Nevertheless, the quantitative performance of iCAMP remained relatively 187 high for all processes under all scenarios (quantitative accuracy and precision > 0.71). Collectively, 188 all these results indicated that iCAMP can substantially improve the quantitative estimation of 189 community assembly processes. HoS and DR were more important than other processes in bacterial community assembly, with 195 average relative importance of 37.0-38.5% and 58.3-59.9% (Fig. 3a, b), respectively. Warming 196 significantly altered the relative importance of different processes (p < 0.01, permutational 197 ANOVA). Since other processes had quite low estimated relative importance (< 3.4%), we 198 primarily focused on the effects of warming on HoS and DR in subsequent analyses. Overall,199 warming decreased the relative importance of DR and increased HoS. Significant year-to-year 200 variations were observed (Fig. 3c, d). In the first year, the communities under warming showed 201 significantly higher ratio of DR (Cohen's d = 2.9, p = 0.001), but lower ratio of HoS (Cohen's d = 202 -2.7, p < 0.001) than those under control, suggesting the bacterial community assembly was even 203 more stochastic under warming than control in the beginning. In the second year, the difference 204 between warming and control became insignificant. In the third to fifth years, the communities 205 under warming had significantly higher ratio of HoS (Cohen's d = 0.6-1.7) and lower ratio of DR 206 and DR are generally considered stochastic (neutral) processes 5 , thus the sum of their estimated 217 relative importance can be used to estimate stochasticity of community assembly. Based on 218 iCAMP results, the relative importance of stochastic processes was 62.6% under control and 219 61.3% under warming (Fig. S12). In contrast, QPEN estimated the relative importance of 220 stochastic processes was 26.7% under control and 16.7% under warming, which were much lower 221 than those estimated by other approaches (Fig. S12). For instance, variation partitioning analysis 222 explained by all measured environmental variables 38 . The tNST and pNST were on average 48.8% 224 under warming and 52.3% under control, and NP ranged from 74% to 79% in different years for 225 both warming and control (Fig. S12). Obviously, VPA, NST, and NP showed more consistent 226 results with iCAMP than QPEN. 227 228 All approaches did not reveal significant (p > 0.10) differences of the 5-year mean stochasticity 229 between warming and control, except tNST with medium effect size (p < 0.05, Fig. S12 Fig. 4a). Two of the major bins were Bacillales (Bin 1, 26.7% in total abundance of 244 HoS-controlled bins) in Firmicutes and Spartobacteria (Bin 2, 18.8%) in Verrucomicrobia (Fig.  245 4b, S13a). In contrast, DR dominated 598 bins (91% of bin numbers and 67% of relative 12 abundance, Fig. 4a), which mainly belonged to Class Alphaproteobacteria (22.2% in total 247 abundance of DR-controlled bins) and Phylum Actinobacteria (23.5%) (Fig. S13a). 248

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To understand how different lineages respond to warming, we further determined the bacterial 250 groups contributing to the warming-induced changes of HoS and DR in the third to fifth years (Fig.  251 4c, d). Our results revealed that Firmicutes contributed 58.2% of the warming-induced increases 252 in HoS (Fig. S13b). The most abundant Firmicutes bin (Bin 1, Bacillales, average 74.8% in 253 Firmicutes) was always governed by HoS (Fig. S13c). Warming gradually drove its abundance 254 significantly (p < 0.05) higher than those under controls (Fig. S13c). In contrast, the decrease of 255 DR under warming was due to similar negative responses of many bins in five phyla 256 (Proteobacteria, Verrucomicrobia, Bacteroidetes, Planctomycetes, and Acidobacteria, Fig. 4c, 257 S13b). For instance, Bin 4 of Rhizobiales in Alphaproteobacteria (Bin 4. Fig. S13b) had lower 258 relative abundance and reduced relative importance of DR under warming, especially in later 3 259 years (Fig. S13d). These results demonstrated that different bins were controlled by quite distinct 260 assembly mechanisms in response to warming. 261 262 Environmental factors influencing ecological processes. Plant and environmental factors also 263 affected the relative importance of different ecological processes (Fig. 5, S14, S15, Supplementary  264 Text B). Overall, the two major processes (HoS and DR) were significantly correlated with the 265 factors related to precipitation, plants, soil nitrogen, and temperature (Fig. 5). All these factors 266 showed distinct association strengths with HoS or DR under warming and control. 267 13 The precipitation and plant-related factors showed stronger association with HoS and DR than all 269 other factors. The precipitation and drought index in the sampling month showed significant 270 correlation with HoS (Fig. 5a, S14a) and DR (Fig. 5b), with slightly stronger association under 271 warming (R 2 = 0.52-0.57, p < 0.1, Fig. 5a, b) than control (R 2 = 0.30-0.36, p < 0.05, Fig. 5a, b). Disentangling ecological drivers controlling community assembly is crucial but difficult in 283 ecology, especially in microbial ecology. Although metagenomics and associated technologies 284 have revolutionized microbial ecology research 1 , a great challenge is how to use such massive data 285 to address compelling ecological questions such as community assembly mechanisms. Thus, in 286 this study, we described a novel framework, iCAMP, to quantify the relative importance of 287 different ecological processes underlying community diversity and dynamics based on individual 288 phylogenetic groups (bins) rather than the entire community. Various analyses demonstrated that 289 iCAMP improved performance substantially with higher precision, sensitivity, specificity, and 290 14 microbial ecology beyond biodiversity patterns towards mechanistic understanding of community 292 diversity and succession. Although this framework is tested with microbial community data, it 293 should also be applicable to plant and animal communities. 294

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To quantify selection, phylogeny-based approaches 22-25,28,29,39-41 require that the phylogenetic 296 distances among taxa reflect their niche difference, i.e. there is phylogenetic signal or niche 297 conservatism 5,23 . Although phylogenetic niche conservatism of microbial traits was reported 42,43 , 298 the signals were mostly at medium or low levels (i.e. close to or lower than Brownian Motion 299 expectation) 44 . Fortunately, significant phylogenetic signals were frequently found within a short 300 phylogenetic distance 29,40,42 , which was employed by recent microbial studies 24,28,29,41 , particularly 301 in QPEN 21,22 . However, QPEN did not perform well in inferring selection with the simulated 302 communities, possibly because it does not distinguish the differential influences of selection on 303 distinct phylogenetic groups. In contrast, iCAMP partitions selection based on individual 304 phylogenetic groups within a short phylogenetic distance, and hence it can greatly improve 305 quantitative performance with accuracy and precision > 0.71 even when the overall (across-tree) 306 phylogenetic signal is low. 307 308 A central question in ecology is to determine the relative importance of deterministic and 309 stochastic processes in controlling community diversity and succession 5,9-16 . The results from 310 iCAMP indicated that the grassland soil microbial community is more stochastic with an average 311 of ~60% stochasticity, while those from QPEN suggested that it is more deterministic with an 312 average of 8% stochasticity, down to 0% in several years. We believe that the conclusion from 313 iCAMP is more convincing due to two primary reasons. First, the iCAMP results are more 314 consistent with several other commonly used methods such as multivariate analysis (e.g. VPA), 315 null models (e.g. tNST, pNST), and neutral theory models (e.g. NP) 5 , which revealed an average 316 of 48.8% to 79.0% stochasticity. Second, the results from iCAMP are more in accordance with our 317 general expectation that stochastic processes should play more important roles under less stressful 318 environments 10-12,14,16 . Since the grassland has rich C resources (above ground biomass, 272 g dry 319 weight/m 2 on average), intermediate level of precipitation (550 to 994 mm annual precipitation), 320 mild temperature (~15.6 °C mean annual air temperature), and nearly neutral soil pH (5.6 to 7.0), 321 the grassland soil microbial communities appear to be not under very stressful conditions, and 322 hence stochastic processes should play important roles, but at least not zero as revealed by QPEN. 323

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Unraveling the drivers controlling the responses of ecological communities to climate change is 325 also a critical topic in ecology and global change biology 38 . Several previous studies demonstrated 326 that climate warming have significant impact on microbial diversity 45 , structure 38 , functional gene 327 composition 46 , and activities 46-50 , but the underlying community assembly processes were rarely 328 examined. In our grassland site, experimental warming increased the soil temperature by ~3 °C 38 , 329 thus it may gradually impose selective pressure as a deterministic force to decrease stochasticity 330 as evident by our previous studies 38, 45 . Here, iCAMP further revealed that warming gradually 331 enhanced HoS and decreased DR in bacterial community assembly, and that the warming-induced Procedure of iCAMP 358 iCAMP includes three major steps (Fig. 1). The first step is phylogenetic binning (Fig. S1a, S3). 359 Three binning algorithms were compared. One is based on the distance to abundant taxa (Fig. S3a). 360 The most abundant (i.e. the highest mean relative abundance in the regional pool) taxon is 361 designated as the first bin to serve as the core taxon. All taxa with distances to the core taxon less 362 than the phylogenetic signal threshold, ds, are assigned to this core taxon. The next bin is generated 363 from the rest taxa in the same way. Consequently, a series of bins are generated with strict radiuses 364 less than ds, so called strict bins. However, some strict bins may have too few taxa to provide 365 enough statistical power for further analysis. Thus, each small bin is merged into its nearest 366 neighbor bin until all bins reach the minimal size requirement, nmin. The second algorithm is based 367 on pairwise distances (Fig. S3b). The first bin consists the most abundant taxon, and all other taxa 368 among which all pairwise distances are lower than ds. The second bin includes the next most 369 abundant taxon among the remaining taxa. This procedure continues until all taxa are classified 370 into different bins. To ensure each bin have enough size (≥ nmin), a small bin less than nmin is merged 371 into the nearest neighbor until all bins reach the minimal requirement nmin. The third algorithm is 372 based on phylogenetic tree (Fig. S3c). The phylogenetic tree is truncated at a certain phylogenetic 373 distance (as short as necessary) to the root, by which all the rest connections between tips (taxa) 374 are lower than the threshold ds. The taxa derived from the same ancestor after the truncating point 375 are grouped to the same strict bin. Then, each small bin is merged into the bin with the nearest 376 relatives. This procedure is repeated until all merged bins have enough taxa (≥ nmin). 377

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The objective of phylogenetic binning is to obtain adequate within-bin phylogenetic signal. To 379 evaluate phylogenetic signal within each bin, the correlation between the pairwise phylogenetic 380 distances and niche preference differences were analyzed by Mantel tests, where niche preference 381 means the niche leading to optimum fitness (or relative fitness reflected by relative abundance) of 382 a taxon. The bins with Pearson correlation coefficient R > 0.1 and p < 0.05 (one tail) are considered 383 as bins with significant phylogenetic signal. In simulated communities, the niche preference 384 difference between two taxa is treated as the key trait value difference. For empirical data, an 385 index, i.e., niche value, is estimated as the relative-abundance-weighted mean of an environmental 386 factor for each taxon as previously reported 23 . For instance, if OTU1 has relative abundances of 387 10%, 20%, and 10% in three samples under the temperature of 10, 20, and 30 °C, respectively, the 388 temperature niche value of OTU1 is (10 × 10% + 20 × 20% + 3 × 10%) / (10% + 20% + 10%) = 389 20 °C. Then, the difference of niche values between taxa reflects niche difference, which are used 390 for phylogenetic signal estimation. An optimized nmin should lead to the highest number of bins 391 with significant phylogenetic signal and relatively high average correlation coefficient (average 392 R) within bins. In this study, the optimized nmin is 24 for simulated datasets and 12 for the empirical 393 data. 394

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The second step is the null model analysis within each bin shown in Fig. S1b. Accordingly, an 396 operator is defined as below to count whether a bin is governed by a process (Eq.1 to Eq. 10). 397  evaluation. In addition, three methods for reducing taxa number were tested. The method 435 "rarefaction" means to randomly draw the same number of individuals (sequences) from each 436 sample and reduce the taxa number. The method "average abundance trimming" ranks all taxa 437 from abundant to rare according to their average relative abundances across all samples and only 438 keeps the taxa before a certain rank. The method "cumulative abundance trimming" ranks taxa in 439 each sample from abundant to rare, then only keeps the abundant taxa in each sample so that every 440 sample has the same cumulative abundance. The iCAMP results from the three methods were 441 compared to that from the original simulated communities. 442

443
The third step of iCAMP is to integrate the results of different bins to assess the relative importance 444 of each process (Fig. S1c-f). Defining neutrality at individual level has been proved a key to 445 successfully develop the unified neutral theory 7 . Therefore, the relative importance of a process 446 can be quantitatively measured as abundance-weighted percentage for each bin (Eq. 11) or the 447 entire communities (Eq. 12Eq. 12 and 13). Qualitatively, for each pairwise comparison between 448 communities (samples), the process with higher relative importance than other processes is 449 regarded as the dominant process. Eq. 11 Eq. 13 Relative importance of the th ecological process in governing the turnovers of Bin k 452 among a group of communities (e.g. samples within a treatment, a region, etc.; Fig.  453 S1d). 454 Relative importance of the th ecological process in governing the turnover between 455 community u and v (Fig. S1c). 456 Relative importance of the th ecological process in governing a group of communities 457 (Pτ group in Fig. S1c) As showed in Eq. 13, the relative importance of each process is the sum of the terms , by 468 which we can define the contribution of different bins to (Eq. 14 and 15). 469 Eq. 15 Bin contribution to Process, measuring the contribution of Bin k to the relative 470 importance of th ecological process in the assembly of a group of communities (Fig.  471 S1e). 472 Bin Relative contribution to Process, measuring the relative contribution of Bin k to 473 the th ecological process (Fig. S1f). 474 475

Simulation model 476
In the simulation model (Fig. S2), all samples are from the same region sharing the same 477 metacommunity (the regional species pool) with 20 million individuals. The relative abundances 478 of species in metacommunity are simulated using metacommunity zero-sum multinomial 479 distribution model (mZSM) derived from Hubbell's Unified Neutral Theory Model 60 , using R 480 package "sads" 61 with J = 2 × 10 7 and θ = 5,000. The whole region has two separated islands of A 481 and B (Fig. S2a). For species controlled by dispersal, migration is unlimited within each island but 482 nearly impossible between islands. Each island has two plots: plot LA and HA at island A, and 483 plot LB and HB at island B. The two plots at the same island are under distinct environments. The 484 environment variable is as low as 0.05 in the north plots at each island (LA and LB), but as high 485 as 0.95 in the south plots (HA and HB), which is a critical setting for species under niche selection. 486 At each plot, 6 local communities are simulated and sampled as biological replicates. Each local 487 community contains 20,000 individuals of 100 species. 488 A phylogenetic tree was retrieved from a previous publication 22 which simulated evolution from a 490 single ancestor to the equilibrium between speciation and extinction and generated a tree with 491 1,140 species. A trait defining the optimal environment of each species (Ei) evolves along the 492 phylogenetic tree with a certain phylogenetic signal. We simulated three pools of species as three 493 scenarios to explore the performance of iCAMP under distinct levels of phylogenetic signals. (i) 494 The low-phylogenetic-signal pool was generated as described previously 22 . The Blomberg's K 495 value is as low as 0.15, close to the mean K value of 91 continuous prokaryotic traits 44  The medium-phylogenetic-signal pool was generated by simulating the trait according to 500 Brownian motion model, using the function "fastBM" in R package "phytools" 62 with an ancestral 501 state of 0.5, an instantaneous variance of Brownian process of 0.25, and the boundary from 0 to 1. 502 The final K value is 0.9, close to the mean phylogenetic signal level of 899 prokaryotic binary 503 traits 44 . (iii) The high-phylogenetic-signal pool was simulated according to Blomberg's ACDC 504 model 63 with a g value of 2000. The final K value is as high as 5.5, close to the highest phylogenetic 505 signal of prokaryotic traits to date 44 . 506 507 For each scenario, we simulated 15 situations with different levels of expected relative importance 508 of various processes (Fig. S2b). The situations can be classified into two types. In the first type, 509 all species under each situation are governed by the same kind of processes, i.e. pure selection, or 510 dispersal, or drift. In each of the other situations, species in the regional pool are assigned to 511 different types controlled by various processes. Once a species is assigned to be controlled by 512 selection or dispersal rather than drift, its nearest relatives within ds will also be assigned to the 513 same type of processes considering the phylogenetic signal of traits. Species controlled by each 514 type of processes are simulated as below. (i) To simulate strong selection without stochasticity, 515 the relative abundance of each species is determined by the difference between the environment 516 variable and their trait values (optimal environment), following a Gaussian function (Eq. 16, Fig.  517 S2d). 518 Eq. 16 Abundance of species i in local community j. 520 The value of the key environmental variable in local community j, which is 0.05 in the 521 north plots (LA and LB) and 0.95 in the south plots (HA and HB). 522 The optimum environment of species i. 523 The standard deviation, which is 0.015. 524 525 Consequently, the turnovers of these species under the same environment (i.e. within north plots, 526 or within south plots) are solely governed by homogeneous selection, and those between distinct 527 environments (i.e. between north and south plots) are governed by heterogeneous selection. 528 529 (ii) To simulate extreme dispersal without selection, we modified Sloan's simulation model 64 530 which was derived from Hubbell's neutral theory model (Fig. S2e). Each island has a unique 531 species pool, simulated as a local community under the regional metacommunity following neutral 532 theory model but with a relatively low dispersal rate (m1 = 0.01). However, the unique species 533 pools of the two islands are constrained to have no overlapped species, regarding extreme dispersal 534 limitation between the two islands. Then, the local communities in each island are simulated as 535 governed by neutral dispersal from both the regional metacommunity with a low rate (m1 = 0.01) 536 and the unique species pool of the island with a high rate (m2 = 0.99). It means 99% of dead 537 individuals in a local community are replaced by species from the small island-unique species pool 538 at each time step. Therefore, all the turnovers within an island are governed by homogenizing 539 dispersal, and those between islands are controlled by dispersal limitation. 540 541 (iii) Drift is simulated as neutral stochastic processes without dispersal limitation or homogenizing 542 dispersal. To simulate drift, all local communities are generated under neutral dispersal from the 543 regional metacommunity with a medium rate (m1=0.5, Fig. S2c). Since 50% of dead individuals 544 are replaced by species randomly drawing from a relatively large regional pool, all the turnovers 545 among local communities are neither affected by homogenizing dispersal nor under dispersal 546 limitation. 547 548 Under each situation, the dataset of the 24 local communities is simulated as a combination of 549 species governed by different ecological processes, with ratios defined by the situation setting 550 (Table S1, Fig. S2b). For each turnover between a pair of local communities, the mean relative 551 abundance of species governed by a process defines the expected relative importance of the process 552 (Eq. 17). The process with the highest relative importance is the expected dominant process of the 553 turnover. Since dispersal and drift are simulated as pure stochastic processes, the expected 554 stochasticity is defined as the sum of expected relative importance of HD, DL, and DR (Table S1). 555 Eq. 17 The expected relative importance of the th ecological process in community turnover 556 between sample u and v. 557 Total relative abundance of Bin k in community u; 558 Total relative abundance of Bin k in community v; 559 Operator, equal to 1 if the turnover of the k th bin between community u and v is 560 governed by the th ecological process, and equal to 0 if not. 561

562
We simulated 3 scenarios with different levels of phylogenetic signal, 15 situations per scenario 563 with 1 dataset per situation, thus a total of 45 datasets. In each dataset, we applied both QPEN and 564 iCAMP to estimate the relative importance of different processes (quantitative estimation) and the 565 dominant process (qualitative estimation). QPEN cannot assess relative importance of processes 566 for each turnover, but can estimate their relative importance as the percentage of turnovers 567 governed by the process in all turnovers within a plot (e.g. plot HA) or between a pair of plots (e.g. 568 plot HA vs HB). Then, the ecological stochasticity of community assembly can be quantified as 569 the relative importance of stochastic processes (i.e. HD, DL, and DR) based on QPEN and iCAMP, 570 respectively. For comparison, the ecological stochasticity in each dataset is also estimated with 571 NP 65 , tNST 37 , and pNST 37,38 , as previously described. Covariance of x and y. In our study, x and y are the expected and estimated stochasticity 580 or relative importance of a process, respectively.   Table S2 for details). The correlation was determined based on the difference (marked with 897 "Δ") or the mean (without "Δ") of a factor between each pair of samples. In this figure, drought, 898 precipitation, plant, soil temperature and other properties were measured in the sampling month, Figures   Fig. 1. Overview of iCAMP. iCAMP includes several key steps: (i) phylogenetic binning, (ii) binbased null model simulations with phylogenetic diversity for partitioning selection, and taxonomic diversity for partitioning dispersal and drift, and (iii) statistical analysis for assessing relative importance of different ecological processes and linking the processes with different environmental factors. βNRI, beta net relatedness index; RC, modified Raup-Crick metric; Here, "ecological processes" particularly mean community assembly processes, including homogeneous selection (HoS), heterogeneous selection (HeS), homogenizing dispersal (HD), dispersal limitation (DL), and "drift" (DR). See main text for detailed explanation. , and low-phylogenetic-signal (LPS) scenarios, which were assessed by quantitative accuracy (qACC) and precision (qPRC). (d-h) The performances of iCAMP and QPEN were also evaluated based on the consistency between the estimated and expected relative importance of different individual ecological processes. (i) Overall performance of iCAMP and QPEN under MPS scenario assessed with six performance indexes: qACC (χ), qPRC (ρ), qualitative accuracy (ACC), qualitative precision (PRC), sensitivity (SST), and specificity (SPC). NP, abundance-weighted neutral taxa percentage; tNST and pNST, taxonomic and phylogenetic normalized stochasticity ratio. Significance was indicated as ***, p < 0.001; **, p < 0.01; *, p < 0.05; or by different letters (p < 0.05).    Table S2 for details). The correlation was determined based on the difference (marked with "Δ") or the mean (without "Δ") of a factor between each pair of samples. In this figure, drought, precipitation, plant, soil temperature and other properties were measured in the sampling month, while moisture values were annual means. Only factors significantly correlated with HoS or DR were shown, see Table S2 for other factors. Significance was expressed as ***, p < 0.01; **, p < 0.05; *, p < 0.1.