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Supplementary informationTable S1. Description of three field experimental sites ADDIN EN.CITE <EndNote><Cite><Author>Wu</Author><Year>2019</Year><RecNum>1545</RecNum><DisplayText>(Wu et al., 2019)</DisplayText><record><rec-number>1545</rec-number><foreign-keys><key app="EN" db-id="vstz9z5euwfetnefdtk52exrdwtd2xt0xf9t">1545</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Wu, M.</author><author>Zhang, J. W.</author><author>Bao, Y. Y.</author><author>Liu, M.</author><author>Jiang, C. Y.</author><author>Feng, Y. Z.</author><author>Li, Z. P.</author></authors></contributors><titles><title>Long-term fertilization decreases chemical composition variation of soil humic substance across geographic distances in subtropical China</title><secondary-title>Soil and Tillage Research</secondary-title></titles><pages>105-111</pages><volume>186</volume><dates><year>2019</year></dates><isbn>01671987</isbn><urls></urls><electronic-resource-num>10.1016/j.still.2018.10.014</electronic-resource-num></record></Cite></EndNote>(Wu et al., 2019). Sampling siteChongqing (CQ)Changshu (CS)Yingtan (YT)ProvinceChongqingJiangsuJiangxiLongitude106°25'120°42'116°55'Latitude29°49'31°33'28°15'Altitude (m)200320Climatesubtropical monsoonsubtropical monsoonsubtropical monsoonMAP (mm)110613211795MAT (°C）18.416.617.6Fertilization(ha-1 yr-1)285 kg N120 kg P2O5120 kg K2O360 kg N150 kg P2O5300 kg K2O230 kg N136 kg P2O584 kg K2OStraw(ha-1 yr-1)7500 kg4500 kg4500 kgSoil classificationgrayish brown purple soil developed from Shaximiao FormationGleyic-Stagnic Anthrosols (WRB-FAO) developed from lake sedimentred paddy soil derived from Quaternary red claySoil texture 27.0% sand, 51.7% silt and 21.3% clay13.3% sand, 54.8% silt and 31.9% clay20.6% sand, 41.0% silt and 38.4% clayMAP, mean annual precipitation; MAT, mean annual temperature. Table S2. Soil chemical properties of three experimental sites ADDIN EN.CITE <EndNote><Cite><Author>Wu</Author><Year>2019</Year><RecNum>1545</RecNum><DisplayText>(Wu et al., 2019)</DisplayText><record><rec-number>1545</rec-number><foreign-keys><key app="EN" db-id="vstz9z5euwfetnefdtk52exrdwtd2xt0xf9t">1545</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Wu, M.</author><author>Zhang, J. W.</author><author>Bao, Y. Y.</author><author>Liu, M.</author><author>Jiang, C. Y.</author><author>Feng, Y. Z.</author><author>Li, Z. P.</author></authors></contributors><titles><title>Long-term fertilization decreases chemical composition variation of soil humic substance across geographic distances in subtropical China</title><secondary-title>Soil and Tillage Research</secondary-title></titles><pages>105-111</pages><volume>186</volume><dates><year>2019</year></dates><isbn>01671987</isbn><urls></urls><electronic-resource-num>10.1016/j.still.2018.10.014</electronic-resource-num></record></Cite></EndNote>(Wu et al., 2019). Experimental siteSOMTotal NTotal PTotal KAvailable NAvailable PAvailable KpHg kg-1g kg-1mg kg-1g kg-1mg kg-1mg kg-1mg kg-11:2.5CQ27.2±2.11.4 ±0.1775 ±1825.4 ±0.694 ±2.733.2 ±1.0106 ±47.0 ±0.1CS36.4 ±2.32.1 ±0.1870 ±1922.3 ±0.6134 ±8.124.8 ±2.1141 ±67.2 ±0.3YT18.4 ±2.21.1 ±0.1534 ±5715.7 ±0.784 ±9.12.6 ±0.470 ±75.2 ±0.1Data are means of 3 replicates with standard deviation. Soil organic matter was determined using a volumetric K2Cr2O7-heating method. Soil total N was determined by Kjeldahl digestion. Soil total P and K were first digested by hydrofluoric acid (HF)-perchloric acid (HClO4) and then determined by molybdenum-blue colorimetry and flame photometry, respectively. Available N content was measured using the alkaline hydrolysis method. Available P in the soil was extracted by sodium bicarbonate and determined using the molybdenum-blue method. Available K in the soil was extracted by ammonium acetate and determined by flame photometry. Soil pH was determined from soil-water suspensions (1:2.5 v/v). Table S3. Concentration of functional groups (% of signal intensity) detected by 13C NMR over 16-week decomposition of straw. Experimental siteTime (Week)Easily decomposition groupsRecalcitrant decomposition groupsO-CH3/NCHO-alkyl CO-C-O anomeric CCarbonylAlkyl CAromatic CAromatic C-OCQ16.56±1.05a57.21±1.19c14.5±0.56c3.9±0.61a9.1±1.43a5.61±0.75a3.14±0.42a26.74±1.75a55.49±0.36bc14.21±1.14bc4.33±0.98ab10.45±2.07ab5.61±1.13a3.18±0.75a47.5±1.23ab55.29±0.43bc13.59±0.47b3.9±0.56a11.1±1.69b5.64±1.19a3±0.35a86.6±0.46a53.35±2.78b13.97±0.82bc4.76±0.51b10.71±1.73ab6.81±0.49b3.81±0.6b167.92±1.13b46.17±6.47a12.6±1.22a6.07±1.2c14.64±3.24c8.17±1.44c4.44±0.78cCS18.84±0.92b54.4±0.69d13.19±0.49c4.25±0.42a10.76±1.3a5.35±0.73a3.21±0.27a28.48±0.77ab53.4±0.52cd13.37±0.21c4.57±0.31ab10.93±0.17a5.87±0.16b3.4±0.04ab48.1±0.52a52.7±0.74c13.26±0.36c4.8±0.35bc11.47±0.43a6.17±0.45b3.5±0.32b88.67±0.09b49.66±1.29b11.96±0.35b5.04±0.24c14.09±1.1b6.79±0.27c3.8±0.16c169.04±0.67b42.72±2.14a10.94±0.54a6.08±0.67d18.12±2.11c8.36±0.69d4.75±0.48dYT17.8±0.54a54.83±0.49d13.45±0.45c4.08±0.45a12.34±0.27a4.88±0.49a2.63±0.22a28.02±0.9a53.13±1.01c12.76±0.79b4.32±0.68a12.97±1.44ab5.74±0.5b3.07±0.35b48.69±0.97b52.39±0.25c12.7±0.86b4.23±0.44a13.38±1.4b5.58±0.77b3.03±0.29b87.82±0.49a50.43±1.93b12.66±0.27b4.63±0.89a14.5±0.31c6.32±0.91c3.65±0.36c167.78±0.67a44.77±1.33a11.92±0.19a6.44±0.58b15.39±1.27c8.61±0.07d5.11±0.27dData are means of replicates with standard deviation. Different letters denotes significant differences (P < 0.05). Table S4. Concentration of straw chemical components over 16-week decomposition stages ADDIN EN.CITE <EndNote><Cite><Author>Bao</Author><Year>2020</Year><RecNum>1828</RecNum><DisplayText>(Bao et al., 2020)</DisplayText><record><rec-number>1828</rec-number><foreign-keys><key app="EN" db-id="vstz9z5euwfetnefdtk52exrdwtd2xt0xf9t">1828</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Bao, Y. Y.</author><author>Guo, Z.Y.</author><author>Chen, R.R.</author><author>Wu, M.</author><author>Li, Z.P.</author><author>Lin, X.G.</author><author>Feng, Y.Z.</author></authors></contributors><titles><title>Functional community composition has less environmental variability than taxonomic composition in straw-degrading bacteria</title><secondary-title>Biology and Fertility of Soils. Bao2020</keyword></keywords><dates><year>2020</year></dates><isbn>- 1432-0789</isbn><urls><related-urls><url>- ;(Bao et al., 2020). Experimental siteTime (Week)Cellulose (%)Lignin (%)Hemicellulose (%)WSP (%)CQ134.52±5.3e9.50±0.44e11.24±1.33c0.29±0.01bc231.16±2.55d7.86±0.93d10.99±0.9c0.31±0.05c421.06±3.16c6.14±0.48c7.99±0.56b0.29±0.02bc814.51±1.38b5.26±0.68b8.04±1.58b0.27±0.03b168.74±1.36a3.18±0.38a4.61±1.24a0.20±0.05aCS130.53±3.34d7.14±0.39d12.40±1.48c0.25±0.02d230.18±1.98d6.94±0.26d12.11±2.25c0.26±0.02d425.58±1.77c6.07±0.45c8.97±1.02b0.22±0.02c819.26±1.00b4.14±0.31b9.06±0.93b0.19±0.01b1614.33±2.57a3.45±0.12a6.53±0.86a0.17±0.01aYT139.22±7.16d13.39±0.77e14.32±0.82e0.48±0.05d242.63±6.08d12.33±0.26d13.62±0.64d0.42±0.06c428.30±2.21c8.38±0.66c10.49±0.85c0.33±0.01b818.24±1.28b6.19±0.58b8.42±0.32b0.31±0.02b1614.36±1.98a5.22±0.72a6.70±0.77a0.26±0.04aDifferent letters denotes significant differences (P < 0.05). Table S5. PERMANOVA showing the community dissimilarities between soil and straw and between different decomposition stages based on weighted βMNTD distance. PairsWeighted βMNTD distanceF. ModelR2Pr (>F)CQSoil vs Straw672.9920.8510.001CQ_1 W vs CQ_16 W238.9820.9160.001CQ_1 W vs CQ_2 W22.9200.5100.001CQ_1 W vs CQ_4 W42.1060.6570.001CQ_1 W vs CQ_8 W239.7700.9160.001CQ_16 W vs CQ_2 W158.2440.8780.001CQ_16 W vs CQ_4 W195.9570.8990.001CQ_16 W vs CQ_8 W201.0690.9010.001CQ_2 W vs CQ_4 W17.3800.4410.001CQ_2 W vs CQ_8 W95.5440.8130.001CQ_4 W vs CQ_8 W49.2070.6910.001CSSoil vs Straw1497.3260.9270.001CS_1 W vs CS_16 W117.6710.8420.001CS_1 W vs CS_2 W7.1580.2450.001CS_1 W vs CS_4 W18.5750.4580.001CS_1 W vs CS_8 W239.8130.9160.001CS_16 W vs CS_2 W88.0000.8000.001CS_16 W vs CS_4 W28.4450.5640.001CS_16 W vs CS_8 W86.1500.7970.001CS_2 W vs CS_4 W12.7960.3680.001CS_2 W vs CS_8 W187.3600.8950.001CS_4 W vs CS_8 W15.7550.4170.001YTSoil vs Straw1827.5780.9390.001YT_1 W vs YT_16 W157.2180.8770.001YT_1 W vs YT_2 W9.8370.3090.001YT_1 W vs YT_4 W59.8320.7310.001YT_1 W vs YT_8 W139.9590.8640.001YT_16 W vs YT_2 W99.7140.8190.001YT_16 W vs YT_4 W84.7680.7940.001YT_16 W vs YT_8 W78.4620.7810.001YT_2 W vs YT_4 W11.7520.3480.001YT_2 W vs YT_8 W95.6910.8130.001YT_4 W vs YT_8 W86.7700.7980.001Table S6. Mantel test between βNTI and straw functional groups and straw chemical components at three experimental sites. VariableCQCSYTrprprpAll factors combined0.3540.0010.3890.0010.4180.001Alkyl C0.2770.0010.3370.0010.1380.009O-CH3/NCH0.0130.386-0.0400.7590.0640.084O-alkyl C0.4490.0010.3850.0010.3540.001O-C-O anomeric C0.2180.0010.3990.0010.1000.040Aromatic C0.3410.0010.3100.0010.3650.001Aromatic C-O0.3150.0010.3260.0010.4050.001Carbonyl0.3790.0010.2720.0010.3580.001All factors combined0.4770.0010.3750.0010.1830.002Cellulose0.4640.0010.3620.0010.1530.004Lignin0.4970.0010.4550.0010.1550.001Hemicellulose0.4660.0010.0980.0310.2040.001WSP0.4080.0010.3080.0010.0700.127“All factors combined” were calculated using 7 functional groups and 4 chemical components of straw samples (Tables S3 and S4) based on Euclidean distances respectively. Figure S1. The distribution of the genera among degradation stages (middle column) at Chongqing (A), Changshu (B) and Yingtan (C). Each point represents one independent bacterial genus (728, 759 and 622 respectively). In the right column were unique to one stage, whereas those in the left belonged to multiple stages. These phenomena indicate that the bacterial taxonomic compositions were largely consistent among degradation stages. Figure S2. Phylogenetic mantel correlogram showing significant phylogenetic signal across short phylogenetic distances. Y-axis indicates Mantel test correlation coefficients between phylogenetic distances and OTU niche differences. Each point indicates the Mantel correlation coefficient of each given range of phylogenetic distances. Red, solid and open symbols denote highly significant (P < 0.01), significant (P < 0.05) and insignificant (P > 0.05) correlations, respectively, relating between-OTU niche differences to between-OTU phylogenetic distances within a given range of phylogenetic distances. Figure S3. Venn diagram of OTUs observed from straw and soil within each experimental site. Figure S4. The number of genera against the cross-validation error curve. Figure S5. The richness of straw-decomposing bacteria change during straw decomposition at each experimental site. Main analysis codes associated with this paperMantel correlograms analysis: PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5EaW5pejwvQXV0aG9yPjxZZWFyPjIwMTA8L1llYXI+PFJl
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ADDIN EN.CITE.DATA (Diniz-Filho et al., 2010)library('dplyr')otu <- read.table("table_even_8000.txt", row.names = 1, header = T, check.names = F)map = read.table('map.txt',row.names = 1, header = T, sep = '\t')otu <- otu[,rownames(map)] %>% subset(rowSums(.) != 0) %>% t()apply(otu, 1, sum)env <- read.table("ENV.txt", header = T, row.names = 1, check.names = F,sep = '\t')env <- scale(env,center = TRUE, scale = TRUE)library("picante")phy <- read.tree("phylo")# how many tips does our phylogeny have?Ntip(phy)# plot(phy)# check for mismatches/missing speciescombined <- match.m(phy, otu)# the resulting object is a list with $phy and $comm elements, replace our original data with the sorted/matched dataphy <- combined$phyotu <- combined$commidx = intersect(rownames(env),rownames(otu))otu <- otu[idx, ] %>% t() %>% as.data.frame()env <- env[idx, ]result <- (as.matrix(otu[1,]) %*% as.matrix(env)) / (sum(otu[1,]))for(i in 2:nrow(otu)){ tmp <- (as.matrix(otu[i,]) %*% as.matrix(env)) / (sum(otu[i,])) result <- rbind(result,tmp)}write.csv(result, "otu_niche.csv")niche.eud <- vegdist(result, method = "euclidean", upper = FALSE)phy.dist <- cophenetic(phy)#normalization,scale to 0~1phy.dist <- (phy.dist - min(phy.dist))/ (max(phy.dist) - min(phy.dist))otu.correlog <- mantel.correlog(niche.eud, phy.dist, nperm = 1000, mult = "bonferroni", n.class=100, cutoff = FALSE)sink('correlog.txt')otu.correlogsink()plot(otu.correlog) + title(main = ", n.class=100, n.permutation=1000")quit("no")βNIT analysis: ADDIN EN.CITE <EndNote><Cite><Author>Stegen</Author><Year>2013</Year><RecNum>1759</RecNum><DisplayText>(Stegen et al., 2013)</DisplayText><record><rec-number>1759</rec-number><foreign-keys><key app="EN" db-id="vstz9z5euwfetnefdtk52exrdwtd2xt0xf9t">1759</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Stegen, J. C.</author><author>Lin, X. J.</author><author>Fredrickson, J. K.</author><author>Chen, X. Y.</author><author>Kennedy, D. W.</author><author>Murray, C. J.</author><author>Rockhold, M. L.</author><author>Konopka, A.</author></authors></contributors><auth-address>Pacific NW Natl Lab, Div Biol Sci, Richland, WA 99352 USA
Georgia Inst Technol, Sch Biol, Atlanta, GA 30332 USA
Pacific NW Natl Lab, Hydrol Grp, Richland, WA 99352 USA
Pacific NW Natl Lab, Dept Geosci, Richland, WA 99352 USA</auth-address><titles><title>Quantifying community assembly processes and identifying features that impose them</title><secondary-title>ISME Journal</secondary-title><alt-title>Isme J
Isme J</alt-title></titles><pages>2069-2079</pages><volume>7</volume><number>11</number><keywords><keyword>metacommunity assembly</keyword><keyword>hanford site</keyword><keyword>neutral theory</keyword><keyword>niche theory</keyword><keyword>null models</keyword><keyword>phylogenetic beta diversity</keyword><keyword>beta-diversity</keyword><keyword>ecological communities</keyword><keyword>phylogenetic structure</keyword><keyword>microbial community</keyword><keyword>niche conservatism</keyword><keyword>dynamics</keyword><keyword>neutrality</keyword><keyword>environment</keyword><keyword>patterns</keyword><keyword>drivers</keyword></keywords><dates><year>2013</year><pub-dates><date>Nov</date></pub-dates></dates><isbn>1751-7362</isbn><accession-num>WOS:000326090800002</accession-num><urls><related-urls><url><Go to ISI>://WOS:000326090800002</url></related-urls></urls><language>English</language></record></Cite></EndNote>(Stegen et al., 2013)library(picante)## read in OTU tableotu = read.csv("bacteria-abundance of OTU.csv",header=T,row.names=1);dim(otu); # this gives the dimensionsotu[1:5,1:5]; # this gives a look at the first 5 rows and columns## read in the phylogenyphylo = read.tree("bacteria-phylogeny.txt");phylo; # a summary of the phylogenyplot.phylo(phylo,typ="fan"); # a quick plot## make sure the names on the phylogeny are ordered the same as the names in otu tablematch.phylo.otu = match.phylo.data(phylo, t(otu));str(match.phylo.otu);## calculate empirical betaMNTDbeta.mntd.weighted=as.matrix(comdistnt(t(match.phylo.otu$data),cophenetic(match.phylo.otu$phy),abundance.weighted=T));dim(beta.mntd.weighted);beta.mntd.weighted[1:5,1:5];write.csv(beta.mntd.weighted,'betaMNTD_weighted.csv',quote=F);identical(colnames(match.phylo.otu$data),colnames(beta.mntd.weighted)); # just a check, should be TRUEidentical(colnames(match.phylo.otu$data),rownames(beta.mntd.weighted)); # just a check, should be TRUE# calculate randomized betaMNTDbeta.reps = 999; # number of randomizationsrand.weighted.p=array(c(-999),dim=c(ncol(match.phylo.otu$data),ncol(match.phylo.otu$data),beta.reps));dim(rand.weighted.p);for (rep in 1:beta.reps) {rand.weighted.p[,,rep]=as.matrix(comdistnt(t(match.phylo.otu$data),taxaShuffle(cophenetic(match.phylo.otu$phy)),abundance.weighted=T,exclude.conspecifics = F)); print(c(date(),rep));}weighted.bNTI=matrix(c(NA),nrow=ncol(match.phylo.otu$data),ncol=ncol(match.phylo.otu$data));dim(weighted.bNTI);for (columns in 1:(ncol(match.phylo.otu$data)-1)) { for (rows in (columns+1):ncol(match.phylo.otu$data)) { rand.vals = rand.weighted.p[rows,columns,]; weighted.bNTI[rows,columns] = (beta.mntd.weighted[rows,columns] - mean(rand.vals)) / sd(rand.vals); rm("rand.vals"); };};rownames(weighted.bNTI) = colnames(match.phylo.otu$data);colnames(weighted.bNTI) = colnames(match.phylo.otu$data);weighted.bNTI;write.csv(weighted.bNTI,"weighted_bNTI.csv",quote=F);pdf("weighted_bNTI_Histogram.pdf")hist(weighted.bNTI)dev.off()RCbray analysis: ADDIN EN.CITE <EndNote><Cite><Author>Stegen</Author><Year>2013</Year><RecNum>1759</RecNum><DisplayText>(Stegen et al., 2013)</DisplayText><record><rec-number>1759</rec-number><foreign-keys><key app="EN" db-id="vstz9z5euwfetnefdtk52exrdwtd2xt0xf9t">1759</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Stegen, J. C.</author><author>Lin, X. J.</author><author>Fredrickson, J. K.</author><author>Chen, X. Y.</author><author>Kennedy, D. W.</author><author>Murray, C. J.</author><author>Rockhold, M. L.</author><author>Konopka, A.</author></authors></contributors><auth-address>Pacific NW Natl Lab, Div Biol Sci, Richland, WA 99352 USA
Georgia Inst Technol, Sch Biol, Atlanta, GA 30332 USA
Pacific NW Natl Lab, Hydrol Grp, Richland, WA 99352 USA
Pacific NW Natl Lab, Dept Geosci, Richland, WA 99352 USA</auth-address><titles><title>Quantifying community assembly processes and identifying features that impose them</title><secondary-title>ISME Journal</secondary-title><alt-title>Isme J
Isme J</alt-title></titles><pages>2069-2079</pages><volume>7</volume><number>11</number><keywords><keyword>metacommunity assembly</keyword><keyword>hanford site</keyword><keyword>neutral theory</keyword><keyword>niche theory</keyword><keyword>null models</keyword><keyword>phylogenetic beta diversity</keyword><keyword>beta-diversity</keyword><keyword>ecological communities</keyword><keyword>phylogenetic structure</keyword><keyword>microbial community</keyword><keyword>niche conservatism</keyword><keyword>dynamics</keyword><keyword>neutrality</keyword><keyword>environment</keyword><keyword>patterns</keyword><keyword>drivers</keyword></keywords><dates><year>2013</year><pub-dates><date>Nov</date></pub-dates></dates><isbn>1751-7362</isbn><accession-num>WOS:000326090800002</accession-num><urls><related-urls><url><Go to ISI>://WOS:000326090800002</url></related-urls></urls><language>English</language></record></Cite></EndNote>(Stegen et al., 2013)raup_crick_abundance = function(spXsite, plot_names_in_col1=TRUE, classic_metric=FALSE, split_ties=TRUE, reps=9999, set_all_species_equal=FALSE, as.distance.matrix=TRUE, report_similarity=FALSE){ if(plot_names_in_col1){ row.names(spXsite)<-spXsite[,1] spXsite<-spXsite[,-1] } ## count number of sites and total species richness across all plots (gamma) n_sites<-nrow(spXsite) gamma<-ncol(spXsite) ##build a site by site matrix for the results, with the names of the sites in the row and col names: results<-matrix(data=NA, nrow=n_sites, ncol=n_sites, dimnames=list(row.names(spXsite), row.names(spXsite))) ##make the spXsite matrix into a new, pres/abs. matrix: ceiling(spXsite/max(spXsite))->spXsite.inc ##create an occurrence vector- used to give more weight to widely distributed species in the null model: occur<-apply(spXsite.inc, MARGIN=2, FUN=sum) ##create an abundance vector- used to give more weight to abundant species in the second step of the null model: abundance<-apply(spXsite, MARGIN=2, FUN=sum) ##make_null: ##looping over each pairwise community combination: for(null.one in 1:(nrow(spXsite)-1)){ for(null.two in (null.one+1):nrow(spXsite)){ null_bray_curtis<-NULL for(i in 1:reps){ ##two empty null communities of size gamma: com1<-rep(0,gamma) com2<-rep(0,gamma) ##add observed number of species to com1, weighting by species occurrence frequencies: com1[sample(1:gamma, sum(spXsite.inc[null.one,]), replace=FALSE, prob=occur)]<-1 com1.samp.sp = sample(which(com1>0),(sum(spXsite[null.one,])-sum(com1)),replace=TRUE,prob=abundance[which(com1>0)]); com1.samp.sp = cbind(com1.samp.sp,1); # head(com1.samp.sp); com1.sp.counts = as.data.frame(tapply(com1.samp.sp[,2],com1.samp.sp[,1],FUN=sum)); colnames(com1.sp.counts) = 'counts'; # head(com1.sp.counts); com1.sp.counts$sp = as.numeric(rownames(com1.sp.counts)); # head(com1.sp.counts); com1[com1.sp.counts$sp] = com1[com1.sp.counts$sp] + com1.sp.counts$counts; # com1; #sum(com1) - sum(spXsite[null.one,]); ## this should be zero if everything work properly rm('com1.samp.sp','com1.sp.counts'); ##same for com2: com2[sample(1:gamma, sum(spXsite.inc[null.two,]), replace=FALSE, prob=occur)]<-1 com2.samp.sp = sample(which(com2>0),(sum(spXsite[null.two,])-sum(com2)),replace=TRUE,prob=abundance[which(com2>0)]); com2.samp.sp = cbind(com2.samp.sp,1); # head(com2.samp.sp); com2.sp.counts = as.data.frame(tapply(com2.samp.sp[,2],com2.samp.sp[,1],FUN=sum)); colnames(com2.sp.counts) = 'counts'; # head(com2.sp.counts); com2.sp.counts$sp = as.numeric(rownames(com2.sp.counts)); # head(com2.sp.counts); com2[com2.sp.counts$sp] = com2[com2.sp.counts$sp] + com2.sp.counts$counts; # com2; # sum(com2) - sum(spXsite[null.two,]); ## this should be zero if everything work properly rm('com2.samp.sp','com2.sp.counts'); null.spXsite = rbind(com1,com2); # null.spXsite; ##calculate null bray curtis null_bray_curtis[i] = distance(null.spXsite,method='bray-curtis'); }; # end reps loop ## empirically observed bray curtis obs.bray = distance(spXsite[c(null.one,null.two),],method='bray-curtis'); ##how many null observations is the observed value tied with? num_exact_matching_in_null = sum(null_bray_curtis==obs.bray); ##how many null values are smaller than the observed *dissimilarity*? num_less_than_in_null = sum(null_bray_curtis<obs.bray); rc = (num_less_than_in_null )/reps; # rc; if(split_ties){ rc = ((num_less_than_in_null +(num_exact_matching_in_null)/2)/reps) }; if(!classic_metric){ ##our modification of raup crick standardizes the metric to range from -1 to 1 instead of 0 to 1 rc = (rc-.5)*2 }; results[null.two,null.one] = round(rc,digits=2); ##store the metric in the results matrix print(c(null.one,null.two,date())); }; ## end null.two loop }; ## end null.one loop if(as.distance.matrix){ ## return as distance matrix if so desired results<-as.dist(results) } return(results)}; ## end functionReferences ADDIN EN.REFLIST Bao, Y.Y., Guo, Z.Y., Chen, R.R., Wu, M., Li, Z.P., Lin, X.G., Feng, Y.Z., 2020. Functional community composition has less environmental variability than taxonomic composition in straw-degrading bacteria. Biology and Fertility of Soils. , J.A.F., Terribile, L.C., da Cruz, M.J.R., Vieira, L.C.G., 2010. Hidden patterns of phylogenetic non-stationarity overwhelm comparative analyses of niche conservatism and divergence. Global Ecology and Biogeography 19, 916-926.Stegen, J.C., Lin, X.J., Fredrickson, J.K., Chen, X.Y., Kennedy, D.W., Murray, C.J., Rockhold, M.L., Konopka, A., 2013. Quantifying community assembly processes and identifying features that impose them. ISME Journal 7, 2069-2079.Wu, M., Zhang, J.W., Bao, Y.Y., Liu, M., Jiang, C.Y., Feng, Y.Z., Li, Z.P., 2019. Long-term fertilization decreases chemical composition variation of soil humic substance across geographic distances in subtropical China. Soil and Tillage Research 186, 105-111. ................
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