Abstract

Climate warming is increasingly leading to marked changes in plant and animal biodiversity, but it remains unclear how temperatures affect microbial biodiversity, particularly in terrestrial soils. Here we show that, in accordance with metabolic theory of ecology, taxonomic and phylogenetic diversity of soil bacteria, fungi and nitrogen fixers are all better predicted by variation in environmental temperature than pH. However, the rates of diversity turnover across the global temperature gradients are substantially lower than those recorded for trees and animals, suggesting that the diversity of plant, animal and soil microbial communities show differential responses to climate change. To the best of our knowledge, this is the first study demonstrating that the diversity of different microbial groups has significantly lower rates of turnover across temperature gradients than other major taxa, which has important implications for assessing the effects of human-caused changes in climate, land use and other factors.

Introduction

Climate change is the largest anthropogenic disturbance on natural systems1,2, and predicting the response of the biosphere to shifting climate is one of the most formidable scientific and political challenges in the 21st century3,4. Since 1850, the Earth’s surface temperature has increased by 0.76 °C and is expected to increase by another 1.1– 6.4 °C by the end of this century2. As a result of climate warming, precipitation patterns at regional and global scales are and will likely continue to be altered. This magnitude of climatic change and its induced extreme climatic events will profoundly affect ecosystem functions and most likely reduce ecosystem services. Climate change has profound and diverse effects on all levels of biological organization from individual organisms to whole-biome levels5. However, the lack of a general theoretical framework to mechanistically and quantitatively link how variation in abiotic environment will influence variation in ecological processes across all taxa limits our ability to predict future impacts within and across diverse taxa6.

Since temperature directly accelerates metabolic rates and biochemical processes7,8, a promising framework for linking abiotic environmental changes to communities is to assess how temperature affects organismal metabolism and then influences their ecology and evolution. The metabolic theory of ecology (MTE)7,8 starts from first principles of biophysics to scale up the kinetic effects of temperature on metabolism to rates of evolution, community structure, gradients of diversity and ecosystem processes9. In general, MTE predicts that the metabolism of individuals, the population growth rate and the number of species increase exponentially with the environmental temperature ()7,10, where R is the rate of some process such as metabolism, population growth or speciation, e is the base of the natural logarithm, E is the ‘activation energy’ that characterizes the temperature dependence of a given biological process, k is Boltzmann’s constant and T is temperature in kelvin. Rates of ecological and evolutionary processes, including rates of population growth, species interactions, mutation and speciation, are all predicted to have approximately the same exponential temperature dependence as metabolic rate, with an ‘activation energy’ of 0.65 eV, equivalent to a Q10 of 2.5 (ref. 10).

Because the changes in ecosystem process rates are ultimately dependent on the changes in metabolic demands of individual organisms, it is expected that temperature should have a profound influence on ecological community structure, and hence MTE should provide a powerful framework for predicting the effects of climate warming on biodiversity and ecosystem processes6,7,11. However, despite a decade of intensive research, empirical11,12 and theoretical evaluations13 of MTE have produced variable, but seemingly contradictory results9,14. Further, most assessments of MTE predictions have focused on plants and animals7. As a result, a recent paper has questioned its theoretical foundations and empirical validity9. Clearly, to better enable predictive power of the consequences of forthcoming climate change across all domains of life, there is a strong need for a more complete understanding of the relationships between temperature, diversity and ecological processes in both macrobes and microbes9.

Elucidating the drivers of how biological diversity varies over space, time and environmental gradients remain as a central focus in biodiversity science. It is well documented that the microbial diversity under natural settings, particularly in soils, is extremely high, but the mechanisms controlling such high diversity are poorly understood15,16. Various studies of soil microbes suggest that pH, rather than temperature, plays an important role in shaping microbial community structure17,18,19. A recent study demonstrated that the temperature drives continental-scale distribution of two key cyanobacteria in topsoil20, implying that the variation in temperature could underlie community-level microbial diversity and distribution. However, the importance of temperature in shaping microbial community structure in general and the applicability of MTE to soil microbial communities remain unknown. Therefore, in comparison with the known temperature gradients in diversity across macrobes, we ask: (i) does temperature play a major role in shaping continental-scale soil microbial community diversity? and (ii) if so, can MTE be used to predict the diversity patterns of the microbial communities in the forest soils? To address these questions, we used next-generation sequencing technology to analyse 126 soil samples from forest sites of different latitudes in North America, with a wide range of temperature gradient from alpine coniferous forest in Colorado to evergreen tropical forest in Panama. Our results indicate that the temperature is a primary driver in shaping soil microbial community in the forest soils and microbial groups has significantly lower rates of turnover across the temperature gradients than plants.

Results

Site characteristics

To determine whether temperature is important in controlling microbial community diversity as predicted by MTE, we undertook a continental-scale survey of the microbe diversity in soil samples from six forest sites in North America, with both small (metres)- and large (thousand kilometres)-scale spatial variations (Fig. 1). Since soil environment is highly heterogeneous, the detection of the effects of environmental factors on microbial diversity could be masked. We organized our sampling design to quantify the relative role of small- and large-spatial scales. The distribution of microbial taxa can be locally adapted to edaphic characteristics at the scale of only a few metres21. Taken that high dispersal rates of microbes may not trump local processes via mass effects22 and that the local niche-based processes may play an important role in determining the patterns of microbial species richness23, we sampled microbial communities at multiple spatial scales. Thus, different from several previous studies17,18,19, a total of 21 soil samples from each forest site were collected at metre-scale using a nested design (Fig. 1). Each sample consisted of nine soil cores evenly collected from 1-m2 area (Fig. 1). As observed in previous ecosystem diversity–stability studies24, such multiple replicate sampling within a site enables us to better quantify the temperature effects on microbial biodiversity across sites and the role of local environmental heterogeneity. Further, nested sampling within and between sites enables us to assess the relative role of small-scale and large-scale processes in shaping the variation in microbial diversity. Consequently, this trade-off in sampling results in fewer among-site comparisons, but many more samples within each site.

Figure 1: Sampling strategy with nested design.
Figure 1

Samples were taken from six forest sites from North America. At each site, 21 nested samples were collected at distance of 1, 10, 50, 100 and 250 m. At each sample point (1 × 1 m), nine soil cores were collected and composited for microbial and soil analysis.

These six forested sites spanned the global range of mean annual temperatures experienced by forested ecosystems (from 2.5 to 25.7 °C, and in latitude from 9–44° N (Supplementary Table 1)). Other site characteristics such as annual mean precipitation, soil types, soil moisture, average soil pH, soil total carbon (C) and nitrogen (N) also spanned a large fraction of the global range (Supplementary Table 1). In addition, substantial differences were observed in plant types, from boreal to tropical forest, and plant diversity (Supplementary Table 1). Pairwise comparisons showed that there were strong correlations among latitudes, annual mean temperature and annual precipitation (Supplementary Table 2). Both plant richness and diversity were positively correlated with annual mean temperature and annual precipitation (Supplementary Table 2). However, such covariations present difficulties in discerning the effects of single environmental factors, such as temperature and pH on biological communities (see below).

Sequencing targets and statistics

To determine the biodiversity of microbial communities, DNA was extracted and purified from each soil sample, and three genes targeting different taxonomic groups and with different taxonomic resolutions were amplified and sequenced using Illumina MiSeq: (i) V4 region of the 16S ribosomal RNA (rRNA) genes for bacteria and archaea; (ii) internal transcribed spacer (ITS) between 5.8S and 28S rRNA genes for fungi; and (iii) bacterial nitrogenase subunit H (nifH) for nitrogen-fixing bacteria. Diversity of prokaryotic bacteria and archaea is still being assessed, but increasingly being shown to be enormous25. Among eukaryotic organisms, fungi rank second in diversity after arthropods (insects). N fixation is critical in global biogeochemical cycling in providing a major source of nutrients. Since, N-fixing bacteria are only small components of soil microbial communities (comprising perhaps <0.5% of the genomes), surveying nifH provides higher resolution than the 16S rRNA genes used to assess the overall bacterial diversity.

An average of 64,000±18,000, 39,000±11,000 and 30,000±9,000 sequence reads per sample were obtained for 16S, ITS and nifH genes, respectively (Supplementary Table 3). This level of sequencing appeared sufficient for estimating the diversity of soil microbial communities sampled here, as indicated by rarefaction curves that approached saturation at different cutoffs for the three target genes (Supplementary Fig. 1). Nonetheless, our analysis also indicated that we could have missed the many rare genotypes that occur at all of our study sites (Supplementary Fig. 1). The numbers of OTUs (operational taxonomic units) estimated with these sequences were quite high (Supplementary Table 4). For instance, with resampling, there were 190,000 and 17,000 OTUs based on 16S and ITS sequences, respectively, at 97% identity cutoff, and 87,000 OTUs based on nifH sequences at 95% identity cutoff. Our numbers of OTUs varied considerably (1.3–2.4 times) among these 126 samples based on different sequence similarity thresholds (Supplementary Table 4). These results suggested that the diversity of bacteria, archaea, fungi and N-fixing bacteria was very high in these forest soils.

Temperature-dependent latitudinal diversity patterns

Although the latitudinal diversity pattern, whereby species diversity tends to decrease as latitude increases, is well documented and intensively studied in plant and animal ecology26, it is less clear whether microbes exhibit similar latitudinal diversity gradients17,27. To determine the latitudinal diversity patterns, the relationships between taxonomic diversity and latitudes were examined. Both taxon richness and Shannon diversity based on these three target genes were strongly correlated with latitude (Supplementary Table 5), indicating that the strong latitudinal diversity patterns exist for bacteria, fungi and N-fixing bacteria.

Further statistical analysis revealed that the microbes in these forest soils exhibited strong correlations with environmental temperature (Fig. 2; r2=0.40–0.63). The observed continental-scale diversity patterns across the temperature gradient could also reflect the influence of other environmental variables. Of all soil and site variables examined, annual mean temperature, pH, annual precipitation, and tree species richness were all correlated with taxonomic diversity (taxon richness, Shannon diversity and inverse Simpson diversity) and phylogenetic diversity (Faith D and NRI, net relatedness index; Fig. 1; Supplementary Fig. 2; Supplementary Table 6; Supplementary Table 7), indicating that these factors could play important roles in shaping microbial community diversity and structure. In most cases, variation in mean annual temperature explained a greater proportion of variation in microbial diversity, regardless of the regression techniques applied (Fig. 2; Supplementary Table 6; Supplementary Table 7). It should be noted that the effects of temperature on microbial communities could be also exerted indirectly through plants and precipitation because temperature has strong correlations with plants and precipitation (Supplementary Table 2). However, it is not possible to disentangle the direct and indirect effects of temperature on microbial communities because these factors are highly correlated (Supplementary Table 2). One possible way to parse out the indirect effects of plant diversity from direct effects of temperature on microbial diversity is to examine ecosystems along a temperature gradient, in which plant diversity is identical or similar, although it is difficult to identify such ecosystems under natural settings. In addition, assessing covariation between temperature, precipitation, plant diversity and additional variables, including pH, precipitation and their interactions indicates that temperature still plays a primary role in predicting variation in microbial diversity (Supplementary Table 8). The addition of multiple covariates, in addition to temperature only slightly improved the fraction of variation in taxon richness explained, with higher r2 and lower Akaike information criterion (AIC) (Supplementary Table 8). Together, these results suggest that mean annual temperature plays a major role in shaping variation in the composition and structure of these forest microbial communities.

Figure 2: Relationships between taxon richness, and key soil and climate variables.
Figure 2

For all plots, the y axis is the taxon richness of observed OTUs for 16S, ITS or nifH data sets. Three plots in left showed the linear relationships between annual mean temperature (kelvin) and taxon richness of 16S, ITS and nifH, respectively, and three plots in right showed the relationships between pH and taxon richness. Line in each plot represents least squares regression fit and the shaded area represents its 95% confidence limits. The relationships of taxon richness with other soil and climate variables were seen in Supplementary Fig. 2. The relationships of microbial biodiversity (taxon richness, Shannon diversity and phylogenetic diversity) to all soil, climate and plants were summarized in Supplementary Table 6.

Previous studies of soil microbes suggest that pH, rather than temperature, plays an important role in shaping community structure17,18,19. To further assess the associations with temperature and pH, we performed partial Mantel tests on the soil variables selected by BIO-ENV28: temperature, pH, total C and total N. Our results showed that all three genes had considerably higher correlations with temperature than pH (Table 1). Particularly, for 16S gene, correlations with temperature (rM=0.674) were more than three times as strong as with pH (rM=0.179; Table 1), indicating that temperature played a more important role than pH in shaping bacterial community structure. Collectively, all of our results point to temperature, as the strongest correlate of taxon richness and diversity of the forest soil microbial communities (bacteria, fungi, and N fixers) as measured by these three genes.

Table 1: Partial Mantel test to evaluate the relative importance of soil and site variables in determining microbial community structure.

Variation in temperature could affect the microbial biodiversity through a variety of mechanisms. There are several distinct direct and indirect mechanisms by which increasing temperature can generate and maintain higher species diversity. First, the most important direct mechanism is that higher temperatures lead to higher rates of metabolism, growth rates and population doubling times7. These biological rates set the pace of life and underlie near all biological activities at all levels of biological organization7,12,27,29. Second, in terrestrial environments, higher temperatures are generally associated with higher rates of ecosystem productivity and hence more species can be supported7,12. Third, higher temperatures lead to more plant species. Higher plant diversity could provide more substrates, nutrients and/or physical attachments to microorganisms, and thus higher microbial diversity30. Our data support such a linkage, as we show strong significant correlations were observed between microbial taxa richness and plant species richness (r=0.55–0.81). Fourth, temperature also affects species interactions. Higher temperatures could lead to higher rates of ecological interactions (for example, rates of parasitism, predation and competition)7,31, which then differentially affects species diversity. In addition, higher temperatures are associated with high rates of evolutionary processes such as mutations and speciation32,33. For instance, very strong positive correlations have been noted between latitudinal diversity gradients and speciation rates32, suggesting the importance of temperature in generating species composition and structure. Finally, temperature could interact with other environmental factors, such as water availability, carbon and nutrient availability, and pH to indirectly affect biodiversity. For instance, warmer environments are often wetter, which is then associated with higher primary production12. Higher temperatures cause faster decompositions and hence affect the nutrient availability4,34, which could support and maintain higher plant and microbial diversity. Discerning these various temperature-dependent mechanisms that can potentially shape microbial diversity is very challenging, particularly under natural soil environmental conditions.

Metabolic theory of ecology

To apply MTE more directly to our data on forest soil microbial communities, we used the Chao1 estimator35 to quantify the diversity of microbes and used Boltzmann–Arrhenius plots to assess the functional dependence of diversity on temperature. The Chao1 estimator incorporates the number of single gene copies in a sample to estimate the total diversity, including rare genotypes that were not sampled. Boltzmann–Arrhenius plots of the natural logarithm of diversity as a linear function of inverse absolute temperature (Fig. 3; Supplementary Fig. 3) highlight the exponential effects of temperature: the slope of such plots with sign reversed gives ‘activation energy’ as a quantitative measure of temperature dependence.

Figure 3: Relationships between taxon richness of individual locations and temperature.
Figure 3

The sequences from all 21 samples in a site were pooled together and used for estimating theoretical Chao1 value for a given site at the taxonomic resolution of 97%. The natural log of the estimated Chao1 values were used for analysing the relationships between taxon richness and temperature, which was expressed as the inverse of annual mean temperature in degree of kelvin. Line represents least squares regression fit and the shaded area represents their 95% confidence limits. (a) Bacteria based on 16S rRNA gene; (b) Fungi based on ITS; and (c) N fixers based on nifH gene. (d) Plants.

Three different commonly used statistical models29, that is, a linear model11, a quadratic polynomial model36 and a piecewise relationship model37, were used to estimate the relationships between temperature and taxon richness. While there were no differences between linear and quadratic models for both 16S and ITS genes in terms of AIC, the linear model for nifH gene fit the data better than the quadratic and piecewise relationship model (Supplementary Table 9). Thus, the linear model was selected in this study. Our statistical analyses identified strong Boltzmann exponential relationships between the log-transformed Chao1-estimated taxon richness from individual samples and the reciprocal temperature (1/kT) for bacteria (Supplementary Fig. 3A), fungi (Supplementary Fig. 3B); and N fixers (Supplementary Fig. 3C). Temperature explained 42.0–65.1% (r2 values) of the variations in microbial diversity (Supplementary Fig. 3), which are consistent with those observed in plant communities12.

Next, to directly compare the estimated activation energies for microbes and plants, the sequences from all 21 samples in a site were pooled together and used for estimating the theoretical Chao1. For the plant diversity data, because of the differences of sampling strategies, richness data were only available for five 0.1-ha plots from each of the six sites. For microbes, in support of predictions from MTE the Boltzmann (linear negative) relationships between log-transformed Chao1-estimated taxon richness from individual sites and the reciprocal temperature (1/kT) for bacteria (Fig. 3a); fungi (Fig. 3b); and N fixers (Fig. 3c) explaining between 72.7–91.8% of the variation in taxon richness (Fig. 3). For plants, we also found a strong linear relationship (Fig. 3d; r2=0.919).

In this study, the estimated activation energies from the Boltzmann–Arrhenius plots varied considerably among the different genes. For instance, at the 97% taxa identity cutoff, the fitted slopes revealed that the Ea values varied between 0.184–0.249 for 16S rRNA genes, 0.169–0.230 for ITS and 0.427–0.467 for nifH gene (Table 2). The higher Ea values for nifH is most likely due to higher taxonomic resolutions of this functional gene marker compared with the ribosomal phylogenetic gene markers. In comparisons of the same gene, Ea values increased also with increased taxonomic/genetic resolution as expected (Table 2). For instance, the slopes for N-fixing bacteria increased from 0.411 to 0.467, when similarity cutoffs increased from 95 to 97% (Table 2). The Ea values determined were more or less consistent with that reported in zooplankton (Ea=0.26)38, but quite lower than that in phytoplankton (Ea=1.0)29.

Table 2: Summary of activation energy (Ea).

Across all groups of microbes and at all levels of similarity resolution, the temperature dependence of microbial diversity was considerably less than that previously reported for plant and animal species diversity. The steepest temperature dependence for microbes at our study sites was Ea=0.467 for N fixers using the nifH gene at 97% similarity (Table 2). This is half of that for tree species diversity at our same study sites (Ea=1.030; Fig. 3d). The magnitude of variation with temperature and latitude was also considerably less than that reported for species diversity of most invertebrates and vertebrates10. Our results support the emerging generalization that the diversity of microbes and macrobes increases exponentially with temperature, but there is no ‘canonical’ temperature dependence of species diversity12,36.

Our study suggests two alternatives, but not mutually exclusive, hypotheses for the observed lower-temperature dependence for diversity of forest soil microbes across the wide range of environmental temperatures. On one hand, the measured ‘activation energies’ increase with the degree of taxonomic and phylogenetic resolution. It is certain, from the number of singleton OTUs in all of our samples and the current cutoffs at 95 and 97% identity, that we have sampled only a fraction of the genotypes present in the soils at our study site. Consistent with this hypothesis, we have shown that the apparent temperature dependence of diversity increases with increased phylogenetic resolution and sampling coverage. Fuhrman et al.27 analysed geographic variation in diversity of marine planktonic bacteria, and found a pattern with temperature and latitude generally comparable to those reported here for terrestrial soil bacteria. It remains to be seen whether, with much more complete sampling and higher resolution of genetic differences, the temperature dependence of microbes would approach and perhaps even exceed that of macroorganisms, that is, much larger plants and animals. On the other hand, the observed shallower temperature dependence of microbial diversity compared with tree species diversity that we have obtained across our sites may be due in part to the fact that microbe taxa are more widely distributed than plants and animals. Wider dispersal abilities could lead to lower spatial turnover rates among different sites and communities23. Nevertheless, the addition of more different forest sites sampled across different continents will help assess whether the results observed here are applicable to other ecosystems.

Discussion

Since temperature is a primary driver of all biological processes, it is expected that temperature has important effects on ecological patterns and processes7. While the importance of temperature in controlling plant and animal communities is well documented, little is known in microbial communities, especially in soil environments. By examining 126 samples across a wide range of temperatures, our results showed that environmental temperature appeared to have a pervasive influence on variation in soil microbial diversity. This is the first demonstration that temperature plays a more primary role in shaping variation in microbial diversity in the forest soils than other proposed environmental drivers. This result is consistent with various previous analyses in soils, which showed temperature plays critical roles in controlling microbial growth and activities34. Importantly, our results show that the temperature gradient for microbes is not as steep as observed for macrobes. Microbial taxa richness increase across the observed global temperature range at a rate of 2–8 times lower than those observed for trees and many other animals, implying that temperature differentially influences species diversity in microbes and macrobes. Thus, while MTE provides a powerful framework for predicting broad large-scale biodiversity, further theoretical modelling development is needed to account for the unique characteristics of microorganisms, for example, extremely high diversity, large community size, high abundance, low extinction rates and long-distance dispersal.

Our findings have important implications for understanding and predicting ecological consequences of climate change. First, if temperature drives the increase in microbial diversity, we see from the tropics to an alpine forest, warming ecosystems should often become more diverse and active27, with enhanced processes—such as decomposition, nutrient cycling and carbon sequestration—that depend on this diversity39. Such changes in ecosystem function could, in turn, collectively shape feedbacks of ecosystems to climate warming. Also, the simple temperature-dependent Arrhenius relationships between temperature and microbial biodiversity could provide a quantitative framework for predicting how climate warming impacts diversity and ecosystem processes, though other factors (for example, pH, plant diversity and nutrient availability) should be incorporated into the MTE-based models40 to improve predictive accuracy. In a word, MTE-based kinetic models could provide powerful tools for projecting the effects of current and future climate warming on biodiversity and ecosystem processes6,7,11.

Methods

Site description and sampling

The following six forests long-term ecological research were selected for this study (Supplementary Table 1). Five belonged to the US National Science Foundation (NSF) long-term ecological research network: Niwot, Andrews, Harvard, Coweeta and Luquillo. A sixth, Barro Colorado Island, is administered by the Smithsonian institution. The selected sites provide variation in ecosystem type from boreal to tropical forest, in average annual temperature from 2.5 to 25.7 °C, and a rough gradient of latitude from 9–44° N (Supplementary Table 1). Moreover, Luquillo and Barro Colorado Island are two tropical forests, the former in the Carribean Sea, the other in the island of Panama. Hourly temperature and annual precipitation data were collected through the nearest weather stations on sites, and mean temperature and average annual precipitation were calculated thereafter.

A nested sampling design was implemented to examine the microbial diversity at each of six forest sites. At each site, we collected and homogenized nine surface soil cores (10-cm depth, Oakfield Apparatus Company model HA) from 21 individual square metre plots in the summer of 2002. The 21–1-m2 plots were laid out in a cross pattern (Fig. 1), with plots adjacent to 1, 10, 50, 100 and 200 m in each cardinal direction from a central 1-m2 plot. Soils were kept on ice in the field, then at −20 °C (LUQ, CWT, AND and NWT) or −80 °C (BCI and HFR) until shipped overnight on dry ice to the Institute for Environmental Genomics at the University of Oklahoma.

Plant diversity

Plant species were surveyed by the Enquist lab using a modified ‘Gentry plot’ methodology whereby five 0.1-ha Gentry plots were established within the 25-ha plot at each site. Each Gentry plot consisted of five 100 × 2 m transects separated by a distance of 8 m, so that each Gentry plot was located within a 42 × 100  m area. All plant stems >1-cm basal diameter that were rooted within the transects were censused and identified to species. For plants that extended outside of the transect boundaries, inclusion criteria varied by growth form: trees were censused if the centre of their stem base fell within the transect bounds, lianas were censused if rooted within the transect and hemiepiphytes were censused if any part of the aerial root of rhizome fell within the transect. Stems were tallied as separate individuals if they were not connected above ground or below ground within 10 cm of the soil surface.

Soil chemistry

The soil moisture was measured by putting 1.5 g soil into 65 °C oven until constant weight was reached. The percentage of weight loss after oven dry to the original weight was calculated as soil moisture content (%). Soil pH was measured in soil suspension with a soil:water ratio of 1:2.5 (weight:volume) according to the standard protocol described previously41. The soil C and N contents were measured by a LECO TruSpec Carbon and Nitrogen Analyzer (LECO Corporation, St Joseph, MI) in the Soil, Water and Forage Analytical Laboratory at the Oklahoma State University (Stillwater, OK). In the same analytical laboratory, the soil NH4+, NO3 contents extracted from soils with 1 M KCl based on the standard protocol described previously42 and measured by Lachat Quickchem 8500 series 2 instrument (Lachat, Loveland, CO).

DNA extraction

Soil DNA was extracted using the grinding SDS-based DNA extraction method as previously described43. The quality was assessed based on spectrometry absorbance at wavelengths of 230, 260 and 280 nm (ratios of absorbance at 260/280 nm 1.8 and 260/230 nm >1.7) detected by a NanoDrop ND-1000 Spectrophotometer (NanoDrop Technologies). DNA concentration was measured by PicoGreen using a FLUOstar OPTIMA fluorescence plate reader (BMG LABTECH, Jena, Germany).

Amplicon sequencing

For 16S rRNA genes, the V4 region was amplified with the primer pair 515F (5′- GTGCCAGCMGCCGCGGTAA -3′) and 806R (5′- GGACTACHVGGGTWTCTAAT -3′) combined with the Illumina adaptor sequence, a pad and a linker of two bases, and a barcode sequences on the reverse primers. PCR amplification was performed in 25 μl reactions containing 2.5 μl 10 × AccuPrime PCR buffer (including dNTPs) (Invitrogen, Grand Island, NY), 0.4 μM of both forward and reverse primers, 10 ng of template DNA and 0.2 μl AccuPrime High-Fidelity Taq Polymerase. Three replicates of amplifications were made for each sample and mixed after PCR amplification to minimize potential biases from amplification44. Thermal cycling conditions were as follows: initial denaturation at 94 °C for 1 min, followed by 30 cycles of 94 °C for 20 s, 53 °C for 25 s and 68 °C for 45 s, with final extension at 68 °C for 10 min

For nifH gene and fungal ITS sequencing, the phasing amplicon sequencing approach was used45. For nifH, an amplicon of 302 bp (excluding the primers) was targeted using the primers: Pol115F, TGCGAYCCSAARGCBGACTC and Pol457R, ATSGCCATCATYTCRCCGGA . For fungal ITS, an amplicon of 309 bp (not including the primers) in ITS2 region was targeted using the primers: gITS7F, GTGARTCATCGARTCTTTG and ITS4R, TCCTCCGCTTATTGATATGC 46.

A two-step PCR was performed for ITS and nifH amplicon sequencing to avoid extra PCR bias that could be introduced by the added components in the long primers used for PCR library preparation45. Phasing primers were designed and used in the second step of the two-step PCR. Spacers of different length (0–7 bases) were added between the sequencing primer and the target gene amplification to randomize base position during sequencing45. To ensure that the total length of the amplified sequences remain constant with the primer set used, the forward and reverse primers were used in a complementary manner so that all of the extended primer sets have exactly seven extra bases as the spacer for sequencing phase shift45. Both forward and reverse phasing primers include the Illumina adaptor, the Illumina sequencing primer, a spacer, and the target gene primer and a barcode of 12 bases in the reverse primer between the sequencing primer and the adaptor. In the two-step PCR, the first round PCR was carried out in a 50 μl reaction containing 5 μl 10 × PCR buffer II (including dNTPs), 0.5 U high-fidelity AccuPrimeTaq DNA polymerase (Life Technologies), 0.4 μM of both forward and reverse target only primers and 10 ng soil DNA. Reactions were performed in triplicate and the sample amplification program described above was used except that only 10 cycles were performed, and the annealing temperature was 56 °C for ITS and 62 °C for nifH. The triplicate products from the first round PCR were combined, purified with an Agencourt AMPure XP kit (Beckman Coulter, Beverly, MA, USA), eluted in 50 μl water and aliquoted into three new PCR tubes (15 μl each). The second round PCR used a 25 μl reaction containing 2.5 μl 10 × PCR buffer II (including dNTPs), 0.25 U high-fidelity AccuPrime Taq DNA polymerase (Life Technologies), 0.4 μM of both forward and reverse phasing primers and 15 μl aliquot of the first round purified PCR product. The amplifications were cycled 20 times following the above program. Positive PCR products were confirmed by agarose gel electrophoresis. PCR products from triplicate reactions were combined and quantified with PicoGreen.

PCR products from samples to be sequenced in the same MiSeq run (generally 3 × 96=288 samples) were pooled at equal molality. The pooled mixture was purified with a QIAquick Gel Extraction kit (Qiagen Sciences, Germantown, MD, USA) and re-quantified with PicoGreen. Sample libraries for sequencing were prepared according to the MiSeq Reagent Kit Preparation Guide (Illumina, San Diego, CA, USA) as described previously45,47. First, the combined sample library was diluted to 2 nM. Then, sample denaturation was performed by mixing 10 μl of the diluted library and 10 μl of 0.2 N fresh NaOH, and incubated 5 min at room temperature. A measure of 980 μl of chilled Illumina HT1 buffer was added to the denatured DNA and mixed to make a 20 pM library. Finally, the 20 pM library was further adjusted to the desired concentration (12 pM) for sequencing using chilled HT1 buffer. The library for sequencing was mixed with a certain proportion of a Phix library of the same concentration to achieve a 10% Phix spike.

A 300-cycle v1 (for 16S ribosomal DNA, rDNA) or 500-cycle v2 (for ITS or nifH) MiSeq reagent cartridge (Illumina) was thawed for 1 h in a water bath, inverted 10 times to mix the thawed reagents and stored at 4 °C for a short time until use. For 16S rDNA sequencing, customized sequencing primers for forward, reverse and index reads were added to the corresponding wells on the reagent cartridge before being loaded as described previously47. Sequencing was performed for 151, 12 and 151 cycles (for 16S rDNA), or 251, 12 and 251 cycles (for ITS and nifH) for forward, index and reverse reads, respectively.

Sequence preprocessing

The raw reads of 16S, ITS and nifH genes were collected in Miseq sequencing machine in fastq format. Their forward and reverse directions, and barcodes were generated into separated files. First, the spiked PhiX reads were removed by using BLAST against PhiX genome sequence in E value <10−5. Second, the reads were assigned to samples according to the barcodes in the barcode file with up to one mismatch allowed. For both 16S and ITS, forward and reverse reads of same sequence with at least 10 bp overlap and <5% mismatches were combined, as single sequence by using FLASH program48, while the minimum overlap length for nifH was set to 50 bp. Any joined sequences with an ambiguous base, or <240 bp for 16S, <200 bp for ITS and <229 bp for nifH were discarded. Besides, the Btrim program49 with threshold of QC >20 over 5-bp window size was used to further filter the unqualified sequences. Thereafter, U-CHIME50 was used to remove chimeras by searching against green reference data set51 for 16S data set, against UNITE/QIIME released ITS reference (http://qiime.wordpress.com/2012/11/27/uniteqiime-12_11-its-reference-otus-now-available-alpha-release/) for ITS data set and against nifH database released by Zehr Laboratory (http://pmc.ucsc.edu/~wwwzehr/research/database/) for nifH data set.

OTUs were classified using UCLUST with different similarity levels52 for all 16S, ITS and nifH genes. Thereafter, the reads of OTUs were re-assigned back to their samples and a big matrix with 126 samples as columns and all OTUs as rows was generated for each data set. Since, reliable taxonomic assignments (OTU annotations) for both ITS and nifH were still unavailable, we only got taxonomic annotation for 16S data set through Ribosomal Database Project (RDP) classifier53 with minimal 50% confidence score. All of the sequences were also reanalyzed using the recent program UPARSE, which was developed by the same author54. The main differences of these two programs are the algorithms for read quality filtering and the chimera filtering. For the 16S data, about five times of less OTUs were obtained with UPARSE than UCLUST. Although the OTU numbers obtained by these two programs were markedly different, but relationships between the OTU richness, and climate and soil variables were not changed significantly (data not shown), no matter what programs were used. Since, UPARSE requires good reference database by using UPARSE-REF algorithm to remove chimeras, reliable OTU classification could not be obtained with nifH data because of lack of comprehensive reference database. Thus, to be consistent, all of the sequence analysis results reported were based on UCLUST approach in this study.

Richness estimation and diversity calculation

In this study, we employed two ways to measure the richness through OTU table. First, the number of species for each sample was estimated by using Chao1 value35, that is,

where Sobs is the number of OTU observed in this sample and f1 and f2 are the numbers of singleton and doubleton OTUs. For each sample, f1 and f2 were counted through each column of OTU matrix. For each site, the abundance of every OTU was summed by all 21 replicates and then Sobs, f1 and f2 were measured through the pooled OTU abundances. Chao1 values represented the estimated species richness in samples or sites. Another easier way to compare the richness of different communities was to use observed richness through equal amount of sampling size. We randomly picked up the sequences from larger samples until they were reached the same size as the smallest sample. The minimum numbers of resampled sequences were 25,901 for 16S rRNA, 13,688 for ITS and 16,000 for nifH genes.

The taxonomic diversity in this study was measured by Shannon–Weaver index, that is,

where pi is the proportion of the ith OTU abundance to total abundance in certain sample. Unlike richness and Faith’s phylogenetic diversity, the Shannon index took species abundance into account and thus it reflected both richness and abundance distributions.

Another taxonomic diversity index is inverse Simpson index, that is,

where pi is still the proportion of the ith OTU abundance to total abundance in certain sample.

The phylogenetic diversity was measured by two approaches. The first is to use Faith’s approach55, which is the sum of the total phylogenetic branch length of detected OTUs in each sample. To calculate this, phylogenetic trees were firstly generated for 16S, ITS and nifH data sets, respectively. For 16S genes, a representative sequence was selected from each OTU. The selected representative sequences were aligned using PyNAST56 against with GreenGene 16S Core Set alignment. For ITS sequences, the selected OTU representative sequences were self-aligned by MUSCLE alignment program57. For nifH sequences, the OTU representative sequences were aligned using Mothur software58, with default kmer searching option (http://www.mothur.org/wiki/Align.seqs). All of the trees were constructed using by FastTree2 program59. After all trees were constructed, the Faith’s phylogenetic diversity was calculated using Picante package in R60.

The second approach is based on NRI. NRI was calculated based on abundance-weighted mean pairwise phylogenetic distance (MPD)61 and the manual of Phylocom62:

where dij is phylogenetic distance between observed taxa i and j, xi and xj are relative abundances of taxa i and j, MPDobs is observed MPD, and and s.d.(MPDnull) are the mean and s.d. of MPD in the null communities. We used the null model called phylogeny shuffle to generate 1,000 null communities as described in Webb et al.62. The bigmemory63 and snow64 packages in R were used to harness the large data sets when calculating NRI.

Statistical methods

Pearson correlations were used to reveal the linear dependence between two variables and the correlations between microbial diversity and sample traits. The significance of Pearson correlation is inferred through the Student’s t-distribution with degrees of freedom n-2. Since, some environmental variables had only single value for each site (for example, mean temperature, annual precipitation and so on), to keep consistent, the mean values for all other variables with multiple measurements (for example, pH, soil moisture total carbon and total nitrogen) were obtained and used for subsequent statistical analysis.

The goodness of fit with linear and non-linear models was assessed by the coefficient of determination (r2) and AIC. Coefficient of determination is defined as,

where SSres is the sum of squares of residuals and SStot is the total sum of squares. Since the residuals can be considered as variance of the model’s errors, the term SSres/SStot represents the unexplained proportion and thus r2 is the proportion of the explained variance of the linear or non-linear model. Meanwhile, the AIC was also used to determine which model fits best the experimental data: AIC=−2 × ln(L)+2n, where L is the probability of the data given a model and n is the number of parameters65. The smaller AIC value, the better model fits.

Mantel test was used for testing correlations between two distance matrixes, and partial Mantel were used to determine the contributions of various factors to explain community variations. The significance of the test was measured by random permutations66. For environmental variables, the best combination of environmental variables was selected by BioENV with ranked correlations28. The selected environmental variables were normalized to zero mean and one unit s.d. The distances among samples were calculated based on Euclidean dissimilarity. For community data sets, Bray–Curtis distance was used67. Partial Mantel tests were performed in R using vegan package68.

Fittings of MTE

One of the major predictions of MTE is the number of species increases exponentially with environmental temperature7. We tested this hypothesis by using Chao1 estimated microbial species richness and annual average temperatures across six sites. The linear model11 is,

where k is Boltzmann's constant, T is absolute temperature in kelvin (K)7 and a is the intercept of this linear model. The Ea also called as the activation energy that equals the inverse number of slope in the linear regression.

Two other statistical models, a quadratic polynomial model36 and a piecewise relationship model69, were also used for estimating the relationships between temperature and taxon richness. Model selection was based on AIC, followed by explained variance (r2) and parameter significance (P values). If the differences of AIC values between two models are <2, these models are considered competitive29.

Data availability

The OTU tables of 16S, ITS and nifH sequences that support the findings of this study are available in the institute website, http://ieg.ou.edu/4download. The raw sequencing data have been deposited in the NCBI Sequence Read Archive under accession code PRJNA308872.

Additional information

How to cite this article: Zhou, J. et al. Temperature mediates continental-scale diversity of microbes in forest soils. Nat. Commun. 7:12083 doi: 10.1038/ncomms12083 (2016).

Accessions

Sequence Read Archive

References

  1. 1.

    et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).

  2. 2.

    IPCC. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T.F. et al.) (Cambridge Univ. Press (2013).

  3. 3.

    et al. Consequences of climate change on the tree of life in Europe. Nature 470, 531–534 (2011).

  4. 4.

    et al. Microbial mediation of carbon-cycle feedbacks to climate warming. Nat. Clim. Change 2, 106–110 (2012).

  5. 5.

    , & Global metabolic impacts of recent climate warming. Nature 467, 704–U788 (2010).

  6. 6.

    & Climate change, biotic interactions and ecosystem services. Phil. Trans. R Soc. B 365, 2013–2018 (2010).

  7. 7.

    , , , & Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).

  8. 8.

    , , , & Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).

  9. 9.

    et al. Testing the metabolic theory of ecology. Ecol. Lett. 15, 1465–1474 (2012).

  10. 10.

    Why are there so many species in the tropics? J. Biogeogr. 41, 8–22 (2014).

  11. 11.

    , & Global biodiversity, biochemical kinetics, and the energetic-equivalence rule. Science 297, 1545–1548 (2002).

  12. 12.

    , , & Temperature dependence, spatial scale, and tree species diversity in eastern Asia and North America. Proc. Natl Acad. Sci. USA 106, 13388–13392 (2009).

  13. 13.

    , & Evolving ecological networks and the emergence of biodiversity patterns across temperature gradients. Proc. Biol. Sci 279, 1051–1060 (2012).

  14. 14.

    & Why are metabolic scaling exponents so controversial? Quantifying variance and testing hypotheses. Ecol. Lett. 13, 728–735 (2010).

  15. 15.

    et al. Stochastic assembly leads to alternative communities with distinct functions in a bioreactor microbial community. MBio 4, e00584–00512 (2013).

  16. 16.

    et al. Stochasticity, succession, and environmental perturbations in a fluidic ecosystem. Proc. Natl Acad. Sci. USA 111, E836–E845 (2014).

  17. 17.

    & The diversity and biogeography of soil bacterial communities. Proc. Natl Acad. Sci. USA 103, 626–631 (2006).

  18. 18.

    et al. The bacterial biogeography of British soils. Environ. Microbiol. 13, 1642–1654 (2011).

  19. 19.

    et al. Soil bacterial diversity in the Arctic is not fundamentally different from that found in other biomes. Environ. Microbiol. 12, 2998–3006 (2010).

  20. 20.

    , , , & Temperature drives the continental-scale distribution of key microbes in topsoil communities. Science 340, 1574–1577 (2013).

  21. 21.

    , , & An experimental test of local adaptation in soil bacteria. Evolution 57, 27–36 (2003).

  22. 22.

    & Species sorting affects bacterioplankton community composition as determined by 16S rDNA and 16S rRNA fingerprints. ISME J. 4, 729–738 (2010).

  23. 23.

    et al. Microbial biogeography: putting microorganisms on the map. Nat. Rev. Microbiol. 4, 102–112 (2006).

  24. 24.

    , & Biodiversity impacts ecosystem productivity as much as resources, disturbance, or herbivory. Proc. Natl Acad. Sci. USA 109, 10394–10397 (2012).

  25. 25.

    , , , & Global distribution of microbial abundance and biomass in subseafloor sediment. Proc. Natl Acad. Sci. USA 109, 16213–16216 (2012).

  26. 26.

    , , , & What causes latitudinal gradients in species diversity? Evolutionary processes and ecological constraints on swallowtail biodiversity. Ecol. Lett. 15, 267–277 (2012).

  27. 27.

    et al. A latitudinal diversity gradient in planktonic marine bacteria. Proc. Natl Acad. Sci. USA 105, 7774–7778 (2008).

  28. 28.

    & A method of linking multivariate community structure to environmental variables. Mar. Ecol.Prog. Ser. 92, 205–219 (1993).

  29. 29.

    et al. Metabolic dependence of phytoplankton species richness. Glob. Ecol. Biogeogr. 24, 472–482 (2015).

  30. 30.

    et al. Plant diversity increases soil microbial activity and soil carbon storage. Nat. Commun. 6, 6707 (2015).

  31. 31.

    , , & Does warming enhance the effect of microzooplankton grazing on marine phytoplankton in the ocean? Limnol. Oceanogr. 57, 519–526 (2012).

  32. 32.

    & Assessing latitudinal gradients in speciation rates and biodiversity at the global scale. Ecol. Lett. 9, 947–954 (2006).

  33. 33.

    , , & Kinetic effects of temperature on rates of genetic divergence and speciation. Proc. Natl Acad. Sci. USA 103, 9130–9135 (2006).

  34. 34.

    & Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).

  35. 35.

    Nonparametric-estimation of the number of classes in a population. Scand. J. Stat. 11, 265–270 (1984).

  36. 36.

    et al. A global evaluation of metabolic theory as an explanation for terrestrial species richness gradients. Ecology 88, 1877–1888 (2007).

  37. 37.

    Estimating regression models with unknown break-points. Stat. Med. 22, 3055–3071 (2003).

  38. 38.

    et al. Large-scale geographic patterns of diversity and community structure of pelagic crustacean zooplankton in Canadian lakes. Glob. Ecol. Biogeogr. 22, 784–795 (2013).

  39. 39.

    , , & Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 111, 5266–5270 (2014).

  40. 40.

    , , , & Interactions between temperature and nutrients across levels of ecological organization. Glob. Change Biol. 21, 1025–1040 (2015).

  41. 41.

    Ministry of Agriculture FaFM-ADASA. in The Analysis of Agricultural Materials: a Manual 735 of the Analytical Methods used by the Agricultural Development and Advisory Service 3rd edn, (HMSO 1986).

  42. 42.

    in Methods of Soil Analysis. Part 2- Chemical and Microbiological Properties (eds Black C. A. et al. 1149–1178American Society of Agronomy (1965).

  43. 43.

    , & DNA recovery from soils of diverse composition. Appl. Environ. Microbiol. 62, 316–322 (1996).

  44. 44.

    et al. Reproducibility and quantitation of amplicon sequencing-based detection. ISME J. 5, 1303–1313 (2011).

  45. 45.

    et al. Phasing amplicon sequencing on Illumina Miseq for robust environmental microbial community analysis. BMC Microbiol. 15, 125 (2015).

  46. 46.

    et al. New primers to amplify the fungal ITS2 region - evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677 (2012).

  47. 47.

    et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).

  48. 48.

    & FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).

  49. 49.

    Btrim: a fast, lightweight adapter and quality trimming program for next-generation sequencing technologies. Genomics 98, 152–153 (2011).

  50. 50.

    , , , & UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).

  51. 51.

    et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).

  52. 52.

    Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

  53. 53.

    , , & Naïve bayesian classifier for rapid assignment of rrna sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

  54. 54.

    UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).

  55. 55.

    Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992).

  56. 56.

    et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010).

  57. 57.

    MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

  58. 58.

    et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).

  59. 59.

    , & FastTree 2-approximately maximum-likelihood trees for large alignments. Plos One 5, e9490 (2010).

  60. 60.

    et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).

  61. 61.

    , , & Phylogenies and Community Ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).

  62. 62.

    , & Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 24, 2098–2100 (2008).

  63. 63.

    , & Scalable strategies for computing with massive data. J. Stat. Software 55, 1–19 (2013).

  64. 64.

    , , & Snow: Simple network of workstations. R package version 0.4-1 (2015).

  65. 65.

    & in Model Selection and Multi-Model Inference: a Practical Information-Theoretic Approach Springer (2002).

  66. 66.

    The detection of disease clustering and a generalized regression approach. Cancer Res. 27, 209–220 (1967).

  67. 67.

    & An ordination of the upland forest communities of Southern Wisconsin. Ecol. Monogr. 27, 326–349 (1957).

  68. 68.

    VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).

  69. 69.

    Estimating regression models with unknown break-points. Stat. Med. 22, 3055–3071 (2003).

Download references

Acknowledgements

This study was supported by the U.S. National Science Foundation MacroSystems Biology program under the contract (NSF EF-1065844), by the Office of the Vice President for Research at the University of Oklahoma, by the Collaborative Innovation Center for Regional Environmental Quality at the Tsinghua University and the National Science Foundation of China (41430856). Y.D. was also supported by the National Natural Science Foundation of China (grant no. 31540071), Strategic Priority Research Program of the Chinese Academy of Sciences (CAS; grant XDB15010302) and CAS 100 talent program.

Author information

Affiliations

  1. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China

    • Jizhong Zhou
    •  & Yunfeng Yang
  2. Institute for Environmental Genomics, Department of Microbiology and Plant Biology and School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, Oklahoma 73019, USA

    • Jizhong Zhou
    • , Ye Deng
    • , Lina Shen
    • , Chongqing Wen
    • , Qingyun Yan
    • , Daliang Ning
    • , Yujia Qin
    • , Kai Xue
    • , Liyou Wu
    • , Zhili He
    • , James W. Voordeckers
    •  & Joy D. Van Nostrand
  3. Earth Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94270, USA

    • Jizhong Zhou
  4. CAS Key Laboratory for Environmental Biotechnology, Research Center for Eco-Environmental Science, Chinese Academy of Sciences, Beijing, 100085, China

    • Ye Deng
  5. Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona 85721, USA

    • Vanessa Buzzard
    • , Sean T. Michaletz
    •  & Brian J. Enquist
  6. The Santa Fe Institute, USA, 1399 Hyde Park Rd, Santa Fe, New Mexico 87501, USA

    • Brian J. Enquist
  7. EEB Graduate Program, Department of Biology, University of Oklahoma, Norman, OK 73019, USA

    • Michael D. Weiser
    •  & Michael Kaspari
  8. Smithsonian Tropical Research Institute, Balboa 0843-03092, Republic of Panama

    • Michael Kaspari
  9. Department of Biology, University of New Mexico, Albuquerque, New Mexico 87131, USA

    • Robert Waide
    •  & James H. Brown

Authors

  1. Search for Jizhong Zhou in:

  2. Search for Ye Deng in:

  3. Search for Lina Shen in:

  4. Search for Chongqing Wen in:

  5. Search for Qingyun Yan in:

  6. Search for Daliang Ning in:

  7. Search for Yujia Qin in:

  8. Search for Kai Xue in:

  9. Search for Liyou Wu in:

  10. Search for Zhili He in:

  11. Search for James W. Voordeckers in:

  12. Search for Joy D. Van Nostrand in:

  13. Search for Vanessa Buzzard in:

  14. Search for Sean T. Michaletz in:

  15. Search for Brian J. Enquist in:

  16. Search for Michael D. Weiser in:

  17. Search for Michael Kaspari in:

  18. Search for Robert Waide in:

  19. Search for Yunfeng Yang in:

  20. Search for James H. Brown in:

Contributions

All authors contributed the intellectual input and assistance to this study and manuscript preparation. J.Z., J.H.B., B.J.E., Z.H., M.K. and R.W. developed the original concepts. Y.D., L.S., C.W., Y.Q., K.X., Q.Y., D.N., J.D.V., L.W., Z.H., J.W.V., V.B., S.T.M., M.D.W. and Y.Y. contributed the reagents, collected data and performed data analysis. L.S., C.W and Q.Y. did the sequencing. J.Z., Y.D. and D.N. performed the statistical analysis and data integration. J.Z. and Y.D. wrote the paper with help from J.H.B, B.J.E., M.K., S.T.M., K.X., L.Y. and M.D.W.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Jizhong Zhou or Ye Deng.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    Supplementary Figures 1-3 and Supplementary Tables 1-9

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/ncomms12083

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.