Enhancing Greenhouse Soil Quality through Ecological Intensification (EI). A Case Study

Analysis_SJSS Rafael Hernández 2025-11-28 Contents 1 1. Exploring data 1 2 2. Hierarchichal model based on LMM 5 3 3. Correlations 21 1 1. Exploring data library(readxl) ## Warning: package 'readxl' was built under R version 4.5.2 datos <- read_excel("D:/Analisis/SJSS/20251125_SJSS_forR.xlsx") # Conversion to factors datos$Greenhouse <- factor(datos$Greenhouse) datos$Position <- factor(datos$Position) #The parameter WSOC change from mg to g datos$WSOC <-datos$WSOC/1000 ####Summarizing ##### str(datos) ## tibble [18 x 24] (S3: tbl_df/tbl/data.frame) ## $ Greenhouse : Factor w/ 2 levels "C","EI": 2 2 2 2 2 2 2 2 2 1 ... ## $ Code : chr [1:18] "S0M1_4N" "S0M2_8N" "S0M3_3S" "S1M1_4N" ... ## $ Position : Factor w/ 9 levels "P1","P2","P3",..: 1 2 3 4 5 6 7 8 9 1 ... ## $ TOC : num [1:18] 36.8 30.6 30.5 30.8 27.8 ... ## $ WSOC : num [1:18] 0.713 0.666 0.691 0.698 0.673 ... ## $ TN : num [1:18] 3.22 2.76 4.4 3.1 4.04 3.9 2.8 4.56 3.8 2.28 ... ## $ NH4 : num [1:18] 31.8 29.3 29.9 26.3 26.3 ... ## $ NO3 : num [1:18] 4.52 6.77 11.61 8.87 9.19 ... ## $ ratioC_N : num [1:18] 11.44 11.1 6.93 9.94 6.88 ... 1 ## $ pH : num [1:18] 7.9 7.7 7.8 7.9 7.9 7.8 7.9 8 7.8 8.5 ... ## $ EC : num [1:18] 2.79 2.91 4.56 2.64 8.37 7.79 0.596 4.13 6.86 0.58 ... ## $ Ca : num [1:18] 14.98 6.74 6.34 15.86 7.12 ... ## $ Mg : num [1:18] 6.82 4.68 7.56 7.08 4 ... ## $ Na : num [1:18] 6.09 5.44 8.88 6.95 5.22 ... ## $ K : num [1:18] 3.41 2.36 2.89 3.54 3.02 6.55 4.45 3.41 5.76 1.06 ... ## $ SRA : num [1:18] 50.3 42.1 42.2 32.6 27.7 ... ## $ Bacteria_16S: num [1:18] 1.25e+09 1.06e+09 2.22e+09 2.73e+09 1.06e+09 ... ## $ ITS_Fungi : num [1:18] 1.02e+09 1.74e+09 2.11e+08 6.13e+09 4.40e+09 ... ## $ Ratio_F_B : num [1:18] 0.8175 1.6381 0.0949 2.246 4.1343 ... ## $ nosZ1 : num [1:18] 25716127 12705377 12918855 2533377 551081 ... ## $ nosZ2 : num [1:18] 2.72e+10 1.54e+10 1.32e+10 1.73e+09 2.04e+08 ... ## $ nirS : num [1:18] 18169770 28812082 16131219 367888 46132 ... ## $ nirK : num [1:18] 1.33e+10 1.49e+10 1.23e+10 4.14e+08 1.37e+07 ... ## $ nos_nir : num [1:18] 2.05 1.03 1.07 4.17 14.87 ... summary(datos) ## Greenhouse Code Position TOC WSOC ## C :9 Length:18 P1 :2 Min. : 8.773 Min. :0.04821 ## EI:9 Class :character P2 :2 1st Qu.:12.934 1st Qu.:0.23262 ## Mode :character P3 :2 Median :23.038 Median :0.48544 ## P4 :2 Mean :22.869 Mean :0.45485 ## P5 :2 3rd Qu.:30.764 3rd Qu.:0.68241 ## P6 :2 Max. :37.102 Max. :0.71314 ## (Other):6 ## TN NH4 NO3 ratioC_N ## Min. :1.980 Min. :25.06 Min. : 3.548 Min. : 3.387 ## 1st Qu.:2.400 1st Qu.:26.28 1st Qu.: 4.919 1st Qu.: 5.444 ## Median :2.710 Median :29.33 Median : 7.177 Median : 6.907 ## Mean :2.993 Mean :29.37 Mean : 8.280 Mean : 7.543 ## 3rd Qu.:3.655 3rd Qu.:29.95 3rd Qu.: 9.315 3rd Qu.: 9.646 ## Max. :4.560 Max. :41.56 Max. :20.968 Max. :12.204 ## ## pH EC Ca Mg ## Min. :7.700 Min. :0.457 Min. : 6.34 Min. : 1.980 ## 1st Qu.:7.900 1st Qu.:0.584 1st Qu.: 7.31 1st Qu.: 4.010 ## Median :8.200 Median :0.816 Median :10.50 Median : 5.240 ## Mean :8.222 Mean :2.562 Mean :11.21 Mean : 5.753 ## 3rd Qu.:8.575 3rd Qu.:3.825 3rd Qu.:15.07 3rd Qu.: 7.015 ## Max. :8.800 Max. :8.370 Max. :18.48 Max. :13.660 ## ## Na K SRA Bacteria_16S ## Min. : 1.780 Min. :0.400 Min. :10.13 Min. :2.287e+08 ## 1st Qu.: 2.748 1st Qu.:1.320 1st Qu.:22.98 1st Qu.:9.914e+08 ## Median : 4.685 Median :2.100 Median :26.85 Median :1.245e+09 ## Mean : 6.698 Mean :2.575 Mean :27.24 Mean :1.498e+09 ## 3rd Qu.: 7.595 3rd Qu.:3.410 3rd Qu.:31.60 3rd Qu.:2.161e+09 ## Max. :28.970 Max. :6.550 Max. :50.33 Max. :2.975e+09 ## ## ITS_Fungi Ratio_F_B nosZ1 nosZ2 ## Min. :9.378e+07 Min. :0.03418 Min. : 356829 Min. :2.042e+08 ## 1st Qu.:1.949e+08 1st Qu.:0.14430 1st Qu.: 760316 1st Qu.:1.847e+09 ## Median :4.673e+08 Median :0.52541 Median : 2159699 Median :5.423e+09 2 ## Mean :1.187e+09 Mean :0.87906 Mean : 4968308 Mean :8.026e+09 ## 3rd Qu.:1.441e+09 3rd Qu.:1.32068 3rd Qu.: 6884828 3rd Qu.:1.298e+10 ## Max. :6.134e+09 Max. :4.13434 Max. :25716128 Max. :2.723e+10 ## ## nirS nirK nos_nir ## Min. : 46132 Min. :1.373e+07 Min. : 0.5109 ## 1st Qu.: 1487779 1st Qu.:4.529e+08 1st Qu.: 1.5761 ## Median : 6633812 Median :1.641e+09 Median : 3.5413 ## Mean : 9778176 Mean :4.233e+09 Mean : 8.5188 ## 3rd Qu.:15058067 3rd Qu.:4.117e+09 3rd Qu.:10.6071 ## Max. :28812082 Max. :1.490e+10 Max. :29.5265 ## library(dplyr) ## Warning: package 'dplyr' was built under R version 4.5.2 ## ## Adjuntando el paquete: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union # Select only numeric columns cols_num <- sapply(datos, is.numeric) # Summary by Management resumen <- datos %>% group_by(Greenhouse) %>% summarise(across(where(is.numeric), list(mean = ~mean(.x, na.rm = TRUE), sd = ~sd(.x, na.rm = TRUE), median = ~median(.x, na.rm = TRUE), min = ~min(.x, na.rm = TRUE), max = ~max(.x, na.rm = TRUE)), .names = "{col}_{fn}")) # Results resumen ## # A tibble: 2 x 106 ## Greenhouse TOC_mean TOC_sd TOC_median TOC_min TOC_max WSOC_mean WSOC_sd ## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 C 14.1 4.78 12.2 8.77 22.3 0.230 0.0838 ## 2 EI 31.6 4.25 30.8 23.8 37.1 0.680 0.0234 ## # i 98 more variables: WSOC_median <dbl>, WSOC_min <dbl>, WSOC_max <dbl>, ## # TN_mean <dbl>, TN_sd <dbl>, TN_median <dbl>, TN_min <dbl>, TN_max <dbl>, 3 ## # NH4_mean <dbl>, NH4_sd <dbl>, NH4_median <dbl>, NH4_min <dbl>, ## # NH4_max <dbl>, NO3_mean <dbl>, NO3_sd <dbl>, NO3_median <dbl>, ## # NO3_min <dbl>, NO3_max <dbl>, ratioC_N_mean <dbl>, ratioC_N_sd <dbl>, ## # ratioC_N_median <dbl>, ratioC_N_min <dbl>, ratioC_N_max <dbl>, ## # pH_mean <dbl>, pH_sd <dbl>, pH_median <dbl>, pH_min <dbl>, ... options(dplyr.width = Inf)# To show all tables print(resumen) ## # A tibble: 2 x 106 ## Greenhouse TOC_mean TOC_sd TOC_median TOC_min TOC_max WSOC_mean WSOC_sd ## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 C 14.1 4.78 12.2 8.77 22.3 0.230 0.0838 ## 2 EI 31.6 4.25 30.8 23.8 37.1 0.680 0.0234 ## WSOC_median WSOC_min WSOC_max TN_mean TN_sd TN_median TN_min TN_max NH4_mean ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 0.230 0.0482 0.339 2.37 0.209 2.4 1.98 2.66 30.1 ## 2 0.685 0.632 0.713 3.62 0.673 3.8 2.76 4.56 28.6 ## NH4_sd NH4_median NH4_min NH4_max NO3_mean NO3_sd NO3_median NO3_min NO3_max ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 5.05 29.3 25.1 41.6 5.59 1.83 5.16 3.55 9.35 ## 2 2.22 29.3 25.1 31.8 11.0 4.96 9.19 4.52 21.0 ## ratioC_N_mean ratioC_N_sd ratioC_N_median ratioC_N_min ratioC_N_max pH_mean ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 5.98 1.97 6.14 3.39 9.29 8.59 ## 2 9.10 2.38 9.76 5.21 12.2 7.86 ## pH_sd pH_median pH_min pH_max EC_mean EC_sd EC_median EC_min EC_max Ca_mean ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 0.136 8.6 8.4 8.8 0.608 0.154 0.58 0.457 0.936 13.5 ## 2 0.0882 7.9 7.7 8 4.52 2.64 4.13 0.596 8.37 8.96 ## Ca_sd Ca_median Ca_min Ca_max Mg_mean Mg_sd Mg_median Mg_min Mg_max Na_mean ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 3.44 12.3 8.88 18.5 4.18 1.24 4.04 1.98 5.76 2.78 ## 2 3.69 7.22 6.34 15.9 7.33 2.85 7.08 4 13.7 10.6 ## Na_sd Na_median Na_min Na_max K_mean K_sd K_median K_min K_max SRA_mean ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 0.876 2.64 1.78 4.15 1.22 0.447 1.32 0.4 1.84 21.3 ## 2 7.51 7.81 5.22 29.0 3.93 1.39 3.41 2.36 6.55 33.2 ## SRA_sd SRA_median SRA_min SRA_max Bacteria_16S_mean Bacteria_16S_sd ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 7.74 25.5 10.1 28.5 1339989351. 982132527. ## 2 10.4 32.6 17.2 50.3 1656214840. 689652048. ## Bacteria_16S_median Bacteria_16S_min Bacteria_16S_max ITS_Fungi_mean ## <dbl> <dbl> <dbl> <dbl> ## 1 1006430676. 228735624. 2975358518. 271376884. ## 2 1248874119. 986376176. 2731126700. 2101969296. ## ITS_Fungi_sd ITS_Fungi_median ITS_Fungi_min ITS_Fungi_max Ratio_F_B_mean ## <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 186951304. 189439743. 93782895. 636355439. 0.415 ## 2 1987598095. 1580931517. 211177129. 6134030081. 1.34 ## Ratio_F_B_sd Ratio_F_B_median Ratio_F_B_min Ratio_F_B_max nosZ1_mean nosZ1_sd ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 0.567 0.149 0.0342 1.85 2375315. 3246681. ## 2 1.25 0.818 0.0949 4.13 7561302. 8173505. 4 ## nosZ1_median nosZ1_min nosZ1_max nosZ2_mean nosZ2_sd nosZ2_median ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 1072645. 356829. 8475450. 4239599609. 4212299515. 2452850114. ## 2 4653749. 551081. 25716127. 11812445562. 8427452017. 13183644983. ## nosZ2_min nosZ2_max nirS_mean nirS_sd nirS_median nirS_min nirS_max ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 1194479820. 12373352516. 9146418. 9056126. 7225960. 415045. 27677622. ## 2 204184800. 27230096539. 10409934. 9482330. 6041665. 46132. 28812082. ## nirK_mean nirK_sd nirK_median nirK_min nirK_max nos_nir_mean ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 1602354630. 1745293286. 586448466. 40051578. 4220207327. 11.8 ## 2 6863707875. 6551336546. 3626471454. 13726668. 14903296301. 5.24 ## nos_nir_sd nos_nir_median nos_nir_min nos_nir_max ## <dbl> <dbl> <dbl> <dbl> ## 1 13.4 3.84 1.06 29.5 ## 2 5.34 2.26 0.511 14.9 2 2. Hierarchichal model based on LMM In the mixed model(LMM), Management (Greenhouse C vs EI) was considered as a fixed factor, and the sampling points (Position) as a random factor. library(lme4) ## Warning: package 'lme4' was built under R version 4.5.2 ## Cargando paquete requerido: Matrix library(lmerTest) ## Warning: package 'lmerTest' was built under R version 4.5.2 ## ## Adjuntando el paquete: 'lmerTest' ## The following object is masked from 'package:lme4': ## ## lmer ## The following object is masked from 'package:stats': ## ## step #Normalizing the copy genes abundances in log10 x gr soil datos <- datos %>% mutate(across(c(Bacteria_16S, ITS_Fungi, nosZ1, nosZ2, nirK, nirS), log10)) # saving to excel_format() #Variable TOC modelo1 <- lmer(TOC ~Greenhouse +(1|Position), data = datos) summary(modelo1) 5 ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [ ## lmerModLmerTest] ## Formula: TOC ~ Greenhouse + (1 | Position) ## Data: datos ## ## REML criterion at convergence: 98.1 ## ## Scaled residuals: ## Min 1Q Median 3Q Max ## -1.6822 -0.7614 -0.1672 0.8018 1.8083 ## ## Random effects: ## Groups Name Variance Std.Dev. ## Position (Intercept) 0.8647 0.9299 ## Residual 19.5910 4.4262 ## Number of obs: 18, groups: Position, 9 ## ## Fixed effects: ## Estimate Std. Error df t value Pr(>|t|) ## (Intercept) 14.122 1.508 15.971 9.367 6.87e-08 *** ## GreenhouseEI 17.495 2.087 8.000 8.385 3.11e-05 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Correlation of Fixed Effects: ## (Intr) ## GreenhousEI -0.692 BIC(modelo1) ## [1] 109.6398 #Variable WSOC modelo2 <- lmer(WSOC ~Greenhouse +(1|Position), data = datos) summary(modelo2) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [ ## lmerModLmerTest] ## Formula: WSOC ~ Greenhouse + (1 | Position) ## Data: datos ## ## REML criterion at convergence: -39.5 ## ## Scaled residuals: ## Min 1Q Median 3Q Max ## -2.8119 -0.3149 0.1187 0.2831 1.7268 ## ## Random effects: ## Groups Name Variance Std.Dev. ## Position (Intercept) 0.0003698 0.01923 ## Residual 0.0034177 0.05846 ## Number of obs: 18, groups: Position, 9 ## 6 ## Fixed effects: ## Estimate Std. Error df t value Pr(>|t|) ## (Intercept) 0.22964 0.02051 15.84888 11.19 6.19e-09 *** ## GreenhouseEI 0.45041 0.02756 8.00000 16.34 1.98e-07 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Correlation of Fixed Effects: ## (Intr) ## GreenhousEI -0.672 BIC(modelo2) ## [1] -27.93151 #Variable TN modelo3 <- lmer(TN ~Greenhouse +(1|Position), data = datos) ## boundary (singular) fit: see help('isSingular') summary(modelo3) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [ ## lmerModLmerTest] ## Formula: TN ~ Greenhouse + (1 | Position) ## Data: datos ## ## REML criterion at convergence: 27.5 ## ## Scaled residuals: ## Min 1Q Median 3Q Max ## -1.72644 -0.67084 0.06692 0.53366 1.88704 ## ## Random effects: ## Groups Name Variance Std.Dev. ## Position (Intercept) 8.451e-23 9.193e-12 ## Residual 2.481e-01 4.981e-01 ## Number of obs: 18, groups: Position, 9 ## ## Fixed effects: ## Estimate Std. Error df t value Pr(>|t|) ## (Intercept) 2.3667 0.1660 16.0000 14.253 1.64e-10 *** ## GreenhouseEI 1.2533 0.2348 16.0000 5.337 6.67e-05 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Correlation of Fixed Effects: ## (Intr) ## GreenhousEI -0.707 ## optimizer (nloptwrap) convergence code: 0 (OK) ## boundary (singular) fit: see help('isSingular') 7 BIC(modelo3) ## [1] 39.06161 #Variable NH4 modelo4 <- lmer(NH4 ~Greenhouse +(1|Position), data = datos) summary(modelo4) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [ ## lmerModLmerTest] ## Formula: NH4 ~ Greenhouse + (1 | Position) ## Data: datos ## ## REML criterion at convergence: 91.5 ## ## Scaled residuals: ## Min 1Q Median 3Q Max ## -1.21818 -0.46251 0.00715 0.38676 2.37633 ## ## Random effects: ## Groups Name Variance Std.Dev. ## Position (Intercept) 6.941 2.634 ## Residual 8.268 2.875 ## Number of obs: 18, groups: Position, 9 ## ## Fixed effects: ## Estimate Std. Error df t value Pr(>|t|) ## (Intercept) 30.149 1.300 13.242 23.192 4.13e-12 *** ## GreenhouseEI -1.562 1.356 8.000 -1.152 0.283 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Correlation of Fixed Effects: ## (Intr) ## GreenhousEI -0.521 BIC(modelo4) ## [1] 103.0441 #Variable NO3 modelo5 <- lmer(NO3 ~Greenhouse +(1|Position), data = datos) summary(modelo5) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [ ## lmerModLmerTest] ## Formula: NO3 ~ Greenhouse + (1 | Position) ## Data: datos ## ## REML criterion at convergence: 91.9 ## 8 ## Scaled residuals: ## Min 1Q Median 3Q Max ## -1.6688 -0.5047 -0.1579 0.4773 2.6042 ## ## Random effects: ## Groups Name Variance Std.Dev. ## Position (Intercept) 1.031 1.015 ## Residual 12.929 3.596 ## Number of obs: 18, groups: Position, 9 ## ## Fixed effects: ## Estimate Std. Error df t value Pr(>|t|) ## (Intercept) 5.591 1.245 15.913 4.489 0.000376 *** ## GreenhouseEI 5.376 1.695 8.000 3.172 0.013159 * ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Correlation of Fixed Effects: ## (Intr) ## GreenhousEI -0.680 BIC(modelo5) ## [1] 103.4974 #Variable ratio Carbono to Nitrogeno modelo6 <- lmer(ratioC_N ~Greenhouse +(1|Position), data = datos) summary(modelo6) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [ ## lmerModLmerTest] ## Formula: ratioC_N ~ Greenhouse + (1 | Position) ## Data: datos ## ## REML criterion at convergence: 74.8 ## ## Scaled residuals: ## Min 1Q Median 3Q Max ## -1.7620 -0.7285 0.1361 0.7696 1.5186 ## ## Random effects: ## Groups Name Variance Std.Dev. ## Position (Intercept) 0.09692 0.3113 ## Residual 4.68540 2.1646 ## Number of obs: 18, groups: Position, 9 ## ## Fixed effects: ## Estimate Std. Error df t value Pr(>|t|) ## (Intercept) 5.984 0.729 15.993 8.209 3.97e-07 *** ## GreenhouseEI 3.118 1.020 8.000 3.055 0.0157 * ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## 9 ## Min 1Q Median 3Q Max ## -2.04357 -0.74347 0.00953 0.70129 1.67533 ## ## Random effects: ## Groups Name Variance Std.Dev. ## Position (Intercept) 0.0000 0.0000 ## Residual 0.1548 0.3934 ## Number of obs: 18, groups: Position, 9 ## ## Fixed effects: ## Estimate Std. Error df t value Pr(>|t|) ## (Intercept) 8.3403 0.1311 16.0000 63.598 < 2e-16 *** ## GreenhouseEI 0.7884 0.1855 16.0000 4.251 0.00061 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Correlation of Fixed Effects: ## (Intr) ## GreenhousEI -0.707 ## optimizer (nloptwrap) convergence code: 0 (OK) ## boundary (singular) fit: see help('isSingular') BIC(modelo15) ## [1] 31.51011 #Variable Ratio Fungi to Bacteria modelo16 <- lmer(Ratio_F_B ~Greenhouse +(1|Position), data = datos) ## boundary (singular) fit: see help('isSingular') summary(modelo16) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [ ## lmerModLmerTest] ## Formula: Ratio_F_B ~ Greenhouse + (1 | Position) ## Data: datos ## ## REML criterion at convergence: 48.8 ## ## Scaled residuals: ## Min 1Q Median 3Q Max ## -1.2896 -0.5057 -0.2781 0.1657 2.8842 ## ## Random effects: ## Groups Name Variance Std.Dev. ## Position (Intercept) 0.0000 0.0000 ## Residual 0.9366 0.9678 ## Number of obs: 18, groups: Position, 9 ## ## Fixed effects: ## Estimate Std. Error df t value Pr(>|t|) 16 ## (Intercept) 0.4151 0.3226 16.0000 1.287 0.2165 ## GreenhouseEI 0.9280 0.4562 16.0000 2.034 0.0589 . ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Correlation of Fixed Effects: ## (Intr) ## GreenhousEI -0.707 ## optimizer (nloptwrap) convergence code: 0 (OK) ## boundary (singular) fit: see help('isSingular') BIC(modelo16) ## [1] 60.3143 #Variable nosZ1 modelo17 <- lmer(nosZ1 ~Greenhouse +(1|Position), data = datos) summary(modelo17) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [ ## lmerModLmerTest] ## Formula: nosZ1 ~ Greenhouse + (1 | Position) ## Data: datos ## ## REML criterion at convergence: 24.4 ## ## Scaled residuals: ## Min 1Q Median 3Q Max ## -1.4724 -0.5502 0.1604 0.6878 1.1255 ## ## Random effects: ## Groups Name Variance Std.Dev. ## Position (Intercept) 0.1795 0.4237 ## Residual 0.0926 0.3043 ## Number of obs: 18, groups: Position, 9 ## ## Fixed effects: ## Estimate Std. Error df t value Pr(>|t|) ## (Intercept) 6.0547 0.1739 11.1485 34.822 9.91e-13 *** ## GreenhouseEI 0.5869 0.1435 8.0000 4.091 0.00348 ** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Correlation of Fixed Effects: ## (Intr) ## GreenhousEI -0.413 BIC(modelo17) ## [1] 35.96657 17 #Variable nosZ2 modelo18 <- lmer(nosZ2 ~Greenhouse +(1|Position), data = datos) summary(modelo18) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [ ## lmerModLmerTest] ## Formula: nosZ2 ~ Greenhouse + (1 | Position) ## Data: datos ## ## REML criterion at convergence: 30 ## ## Scaled residuals: ## Min 1Q Median 3Q Max ## -2.5659 -0.5136 0.2126 0.6788 0.9552 ## ## Random effects: ## Groups Name Variance Std.Dev. ## Position (Intercept) 0.05967 0.2443 ## Residual 0.23654 0.4864 ## Number of obs: 18, groups: Position, 9 ## ## Fixed effects: ## Estimate Std. Error df t value Pr(>|t|) ## (Intercept) 9.4632 0.1814 15.3760 52.162 <2e-16 *** ## GreenhouseEI 0.3703 0.2293 8.0000 1.615 0.145 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Correlation of Fixed Effects: ## (Intr) ## GreenhousEI -0.632 BIC(modelo18) ## [1] 41.56389 #Variable nirS modelo19 <- lmer(nirS ~Greenhouse +(1|Position), data = datos) summary(modelo19) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [ ## lmerModLmerTest] ## Formula: nirS ~ Greenhouse + (1 | Position) ## Data: datos ## ## REML criterion at convergence: 44.1 ## ## Scaled residuals: ## Min 1Q Median 3Q Max ## -2.2434 -0.7881 0.4286 0.5680 0.9030 ## ## Random effects: 18 ## Groups Name Variance Std.Dev. ## Position (Intercept) 0.07481 0.2735 ## Residual 0.62765 0.7922 ## Number of obs: 18, groups: Position, 9 ## ## Fixed effects: ## Estimate Std. Error df t value Pr(>|t|) ## (Intercept) 6.58720 0.27938 15.82056 23.578 9.48e-14 *** ## GreenhouseEI 0.02586 0.37347 8.00000 0.069 0.947 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Correlation of Fixed Effects: ## (Intr) ## GreenhousEI -0.668 BIC(modelo19) ## [1] 55.62019 #Variable nirK modelo20 <- lmer(nirK ~Greenhouse +(1|Position), data = datos) summary(modelo20) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [ ## lmerModLmerTest] ## Formula: nirK ~ Greenhouse + (1 | Position) ## Data: datos ## ## REML criterion at convergence: 47.4 ## ## Scaled residuals: ## Min 1Q Median 3Q Max ## -2.1914 -0.6707 0.4931 0.6145 0.7217 ## ## Random effects: ## Groups Name Variance Std.Dev. ## Position (Intercept) 0.2197 0.4687 ## Residual 0.6689 0.8178 ## Number of obs: 18, groups: Position, 9 ## ## Fixed effects: ## Estimate Std. Error df t value Pr(>|t|) ## (Intercept) 8.7004 0.3142 15.0781 27.69 2.41e-14 *** ## GreenhouseEI 0.6591 0.3855 8.0000 1.71 0.126 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Correlation of Fixed Effects: ## (Intr) ## GreenhousEI -0.613 19 modelo_simple <- lm(nirK ~Greenhouse, data = datos) summary(modelo_simple) ## ## Call: ## lm(formula = nirK ~ Greenhouse, data = datos) ## ## Residuals: ## Min 1Q Median 3Q Max ## -2.2220 -0.5983 0.1339 0.7840 0.9249 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 8.7004 0.3142 27.690 6.04e-15 *** ## GreenhouseEI 0.6591 0.4444 1.483 0.157 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 0.9426 on 16 degrees of freedom ## Multiple R-squared: 0.1209, Adjusted R-squared: 0.06593 ## F-statistic: 2.2 on 1 and 16 DF, p-value: 0.1575 BIC(modelo_simple) ## [1] 55.50628 BIC(modelo20) ## [1] 58.967 #Variable nos_nir modelo21 <- lmer(nos_nir ~Greenhouse +(1|Position), data = datos) summary(modelo21) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [ ## lmerModLmerTest] ## Formula: nos_nir ~ Greenhouse + (1 | Position) ## Data: datos ## ## REML criterion at convergence: 123.5 ## ## Scaled residuals: ## Min 1Q Median 3Q Max ## -0.9473 -0.6846 -0.1787 0.1275 1.6780 ## ## Random effects: ## Groups Name Variance Std.Dev. ## Position (Intercept) 26.83 5.180 ## Residual 76.69 8.757 ## Number of obs: 18, groups: Position, 9 ## 20 ## Fixed effects: ## Estimate Std. Error df t value Pr(>|t|) ## (Intercept) 11.796 3.391 14.993 3.478 0.00337 ** ## GreenhouseEI -6.554 4.128 8.000 -1.588 0.15101 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Correlation of Fixed Effects: ## (Intr) ## GreenhousEI -0.609 BIC(modelo21) ## [1] 135.0411 3 3. Correlations ##Correlation analysis## #Selecting numeric variables# num_df <- datos[sapply(datos, is.numeric)] library(Hmisc) ## Warning: package 'Hmisc' was built under R version 4.5.2 ## ## Adjuntando el paquete: 'Hmisc' ## The following objects are masked from 'package:dplyr': ## ## src, summarize ## The following objects are masked from 'package:base': ## ## format.pval, units # rcorr generates Spearman � y p-values corr_res <- rcorr(as.matrix(num_df), type = "spearman") rho <- corr_res$r rho ## TOC WSOC TN NH4 NO3 ## TOC 1.00000000 0.8477027 0.68972647 -0.01147091 0.55033564 ## WSOC 0.84770275 1.0000000 0.83522727 0.19875781 0.41219008 ## TN 0.68972647 0.8352273 1.00000000 0.11946335 0.43595041 ## NH4 -0.01147091 0.1987578 0.11946335 1.00000000 -0.43716284 ## NO3 0.55033564 0.4121901 0.43595041 -0.43716284 1.00000000 ## ratioC_N 0.89060888 0.6639134 0.37067635 -0.02085619 0.36551373 21 ## pH -0.84810295 -0.8626484 -0.81196587 -0.16781024 -0.46345799 ## EC 0.66357069 0.7320600 0.84047507 0.03232710 0.45327833 ## Ca -0.48400413 -0.5214250 -0.62570994 -0.05214048 -0.58853906 ## Mg 0.68833849 0.5379453 0.56375846 -0.10636658 0.50387203 ## Na 0.82188963 0.6616736 0.74070248 -0.18049922 0.70351240 ## K 0.85581498 0.7011379 0.72957640 -0.15927521 0.63030023 ## SRA 0.46130031 0.5709861 0.30975740 0.22003282 0.12390296 ## Bacteria_16S 0.19298246 0.1662365 -0.03510584 -0.24401744 0.41817249 ## ITS_Fungi 0.70072239 0.6566857 0.51729485 -0.29928634 0.50180698 ## Ratio_F_B 0.52941176 0.4987094 0.43779046 -0.34204154 0.20134231 ## nosZ1 0.56522012 0.6545853 0.51579665 0.52721079 -0.04919747 ## nosZ2 0.51215898 0.4710155 0.46066351 0.43336408 -0.02380957 ## nirS 0.01345064 0.1086959 0.13561106 0.58914514 -0.41822036 ## nirK 0.43766313 0.4109740 0.31987646 0.52955104 -0.03312636 ## nos_nir -0.33212734 -0.3136653 -0.29089089 -0.33822263 0.05279514 ## ratioC_N pH EC Ca Mg ## TOC 0.89060888 -0.84810295 0.66357069 -0.48400413 0.68833849 ## WSOC 0.66391336 -0.86264842 0.73205998 -0.52142495 0.53794535 ## TN 0.37067635 -0.81196587 0.84047507 -0.62570994 0.56375846 ## NH4 -0.02085619 -0.16781024 0.03232710 -0.05214048 -0.10636658 ## NO3 0.36551373 -0.46345799 0.45327833 -0.58853906 0.50387203 ## ratioC_N 1.00000000 -0.70918953 0.38286894 -0.33746130 0.47574819 ## pH -0.70918953 1.00000000 -0.78230186 0.73530108 -0.57758735 ## EC 0.38286894 -0.78230186 1.00000000 -0.66563467 0.46749226 ## Ca -0.33746130 0.73530108 -0.66563467 1.00000000 -0.17027864 ## Mg 0.47574819 -0.57758735 0.46749226 -0.17027864 1.00000000 ## Na 0.56582351 -0.73097828 0.72070221 -0.48218902 0.87558091 ## K 0.63152531 -0.71813552 0.67803699 -0.35865676 0.86305013 ## SRA 0.53560372 -0.53476442 0.10010320 -0.30856553 0.17234262 ## Bacteria_16S 0.18885449 -0.03028939 0.01960784 0.04231166 0.31888545 ## ITS_Fungi 0.62641899 -0.60474336 0.55005160 -0.43653251 0.28586171 ## Ratio_F_B 0.48194014 -0.47523010 0.51289990 -0.29618163 0.10835913 ## nosZ1 0.54244568 -0.67734483 0.38405982 -0.37474301 0.36853180 ## nosZ2 0.44490578 -0.54609755 0.29281008 -0.37040993 0.40248453 ## nirS 0.02690128 -0.29791899 0.09208515 -0.24211152 0.06932253 ## nirK 0.39317255 -0.58798423 0.40869252 -0.38903389 0.41696984 ## nos_nir -0.26901280 0.52515421 -0.53492160 0.32902335 -0.41696984 ## Na K SRA Bacteria_16S ITS_Fungi ## TOC 0.821889629 0.8558150 0.46130031 0.19298246 0.70072239 ## WSOC 0.661673554 0.7011379 0.57098614 0.16623647 0.65668568 ## TN 0.740702479 0.7295764 0.30975740 -0.03510584 0.51729485 ## NH4 -0.180499215 -0.1592752 0.22003282 -0.24401744 -0.29928634 ## NO3 0.703512397 0.6303002 0.12390296 0.41817249 0.50180698 ## ratioC_N 0.565823514 0.6315253 0.53560372 0.18885449 0.62641899 ## pH -0.730978279 -0.7181355 -0.53476442 -0.03028939 -0.60474336 ## EC 0.720702213 0.6780370 0.10010320 0.01960784 0.55005160 ## Ca -0.482189016 -0.3586568 -0.30856553 0.04231166 -0.43653251 ## Mg 0.875580912 0.8630501 0.17234262 0.31888545 0.28586171 ## Na 1.000000000 0.9488113 0.20753746 0.28394428 0.58234391 ## K 0.948811262 1.0000000 0.20361782 0.28837244 0.60981985 ## SRA 0.207537457 0.2036178 1.00000000 0.18679051 0.43446852 ## Bacteria_16S 0.283944282 0.2883724 0.18679051 1.00000000 0.19711042 ## ITS_Fungi 0.582343908 0.6098199 0.43446852 0.19711042 1.00000000 ## Ratio_F_B 0.379969075 0.4113700 0.25077399 -0.19917441 0.86790506 22 ## nosZ1 0.373900787 0.3120792 0.62319141 0.04244327 0.29606768 ## nosZ2 0.425466750 0.3321245 0.40765786 -0.06414921 0.22452222 ## nirS -0.008281591 -0.1352332 0.31246871 -0.40558853 -0.18106631 ## nirK 0.375777203 0.2575131 0.29798341 -0.12002109 0.07139186 ## nos_nir -0.390269988 -0.2637307 -0.07863451 0.14899170 -0.07139186 ## Ratio_F_B nosZ1 nosZ2 nirS nirK ## TOC 0.52941176 0.56522012 0.51215898 0.013450640 0.43766313 ## WSOC 0.49870941 0.65458531 0.47101550 0.108695885 0.41097397 ## TN 0.43779046 0.51579665 0.46066351 0.135611057 0.31987646 ## NH4 -0.34204154 0.52721079 0.43336408 0.589145137 0.52955104 ## NO3 0.20134231 -0.04919747 -0.02380957 -0.418220358 -0.03312636 ## ratioC_N 0.48194014 0.54244568 0.44490578 0.026901280 0.39317255 ## pH -0.47523010 -0.67734483 -0.54609755 -0.297918989 -0.58798423 ## EC 0.51289990 0.38405982 0.29281008 0.092085150 0.40869252 ## Ca -0.29618163 -0.37474301 -0.37040993 -0.242111519 -0.38903389 ## Mg 0.10835913 0.36853180 0.40248453 0.069322529 0.41696984 ## Na 0.37996907 0.37390079 0.42546675 -0.008281591 0.37577720 ## K 0.41137000 0.31207917 0.33212453 -0.135233233 0.25751309 ## SRA 0.25077399 0.62319141 0.40765786 0.312468712 0.29798341 ## Bacteria_16S -0.19917441 0.04244327 -0.06414921 -0.405588527 -0.12002109 ## ITS_Fungi 0.86790506 0.29606768 0.22452222 -0.181066307 0.07139186 ## Ratio_F_B 1.00000000 0.19254751 0.13967972 -0.071391858 0.03414393 ## nosZ1 0.19254751 1.00000000 0.88842773 0.649714670 0.79398039 ## nosZ2 0.13967972 0.88842773 1.00000000 0.688796680 0.78215768 ## nirS -0.07139186 0.64971467 0.68879668 1.000000000 0.80082988 ## nirK 0.03414393 0.79398039 0.78215768 0.800829876 1.00000000 ## nos_nir -0.14588771 -0.56460828 -0.50622407 -0.699170124 -0.87759336 ## nos_nir ## TOC -0.33212734 ## WSOC -0.31366527 ## TN -0.29089089 ## NH4 -0.33822263 ## NO3 0.05279514 ## ratioC_N -0.26901280 ## pH 0.52515421 ## EC -0.53492160 ## Ca 0.32902335 ## Mg -0.41696984 ## Na -0.39026999 ## K -0.26373071 ## SRA -0.07863451 ## Bacteria_16S 0.14899170 ## ITS_Fungi -0.07139186 ## Ratio_F_B -0.14588771 ## nosZ1 -0.56460828 ## nosZ2 -0.50622407 ## nirS -0.69917012 ## nirK -0.87759336 ## nos_nir 1.00000000 pval <- corr_res$P # ADjusted p-values with FDR (Benjamini-Hochberg) pval_fdr <- matrix(p.adjust(pval, method = "fdr"), 23 nrow = nrow(pval), dimnames = dimnames(pval)) pval_fdr ## TOC WSOC TN NH4 NO3 ## TOC NA 0.0001703116 0.0098026780 0.96858074 0.061061203 ## WSOC 1.703116e-04 NA 0.0002595494 0.55629860 0.175423470 ## TN 9.802678e-03 0.0002595494 NA 0.73479162 0.151127881 ## NH4 9.685807e-01 0.5562986012 0.7347916176 NA 0.151127881 ## NO3 6.106120e-02 0.1754234699 0.1511278807 0.15112788 NA ## ratioC_N 5.923602e-05 0.0144199137 0.2223773353 0.95204629 0.228154297 ## pH 1.703116e-04 0.0001076813 0.0005984441 0.61739076 0.124423017 ## EC 1.441991e-02 0.0051598257 0.0002208613 0.93426357 0.134376323 ## Ca 1.070896e-01 0.0761575940 0.0244783704 0.90160443 0.040716236 ## Mg 9.802678e-03 0.0686254162 0.0518805134 0.76557177 0.087734258 ## Na 4.296291e-04 0.0146062574 0.0045991943 0.58841848 0.008392894 ## K 1.380017e-04 0.0083928937 0.0051598257 0.63706526 0.023555007 ## SRA 1.254606e-01 0.0499713204 0.3210489529 0.50869488 0.728295242 ## Bacteria_16S 5.650815e-01 0.6187460250 0.9342635689 0.44885083 0.170328348 ## ITS_Fungi 8.392894e-03 0.0157345527 0.0792074168 0.33965341 0.088851425 ## Ratio_F_B 7.157469e-02 0.0911147016 0.1511278807 0.27240310 0.555244240 ## nosZ1 5.188051e-02 0.0160061434 0.0796524728 0.07263613 0.906735374 ## nosZ2 8.122453e-02 0.1170602307 0.1254606377 0.15204577 0.947855010 ## nirS 9.669639e-01 0.7631440208 0.6952677236 0.04071624 0.170328348 ## nirK 1.511279e-01 0.1754234699 0.3089318053 0.07157469 0.934263569 ## nos_nir 2.855581e-01 0.3179037626 0.3474554267 0.27810779 0.901604431 ## ratioC_N pH EC Ca Mg ## TOC 5.923602e-05 0.0001703116 0.0144199137 0.107089578 9.802678e-03 ## WSOC 1.441991e-02 0.0001076813 0.0051598257 0.076157594 6.862542e-02 ## TN 2.223773e-01 0.0005984441 0.0002208613 0.024478370 5.188051e-02 ## NH4 9.520463e-01 0.6173907554 0.9342635689 0.901604431 7.655718e-01 ## NO3 2.281543e-01 0.1244230172 0.1343763225 0.040716236 8.773426e-02 ## ratioC_N NA 0.0076373200 0.2096990413 0.278107787 1.129342e-01 ## pH 7.637320e-03 NA 0.0013858411 0.005069443 4.606943e-02 ## EC 2.096990e-01 0.0013858411 NA 0.014419914 1.203623e-01 ## Ca 2.781078e-01 0.0050694429 0.0144199137 NA 6.132259e-01 ## Mg 1.129342e-01 0.0460694260 0.1203622623 0.613225909 NA ## Na 5.188051e-02 0.0051598257 0.0062173003 0.107089578 8.149791e-05 ## K 2.355501e-02 0.0063744149 0.0117472714 0.239770553 1.076813e-04 ## SRA 6.862542e-02 0.0686254162 0.7820692863 0.321539772 6.103297e-01 ## Bacteria_16S 5.730038e-01 0.9362393915 0.9520462877 0.920188748 3.089318e-01 ## ITS_Fungi 2.447837e-02 0.0329452578 0.0610612033 0.151127881 3.549704e-01 ## Ratio_F_B 1.070896e-01 0.1129342275 0.0812245299 0.339653406 7.631440e-01 ## nosZ1 6.571716e-02 0.0117472714 0.2092817731 0.219348341 2.241612e-01 ## nosZ2 1.452081e-01 0.0634895885 0.3451684039 0.222377335 1.832633e-01 ## nirS 9.425462e-01 0.3396534058 0.8043994199 0.451252605 8.581604e-01 ## nirK 1.979215e-01 0.0407162355 0.1776266786 0.201908308 1.703283e-01 ## nos_nir 3.925456e-01 0.0735761487 0.0686254162 0.290290536 1.703283e-01 ## Na K SRA Bacteria_16S ITS_Fungi ## TOC 4.296291e-04 1.380017e-04 0.12546064 0.5650815 0.0083928937 ## WSOC 1.460626e-02 8.392894e-03 0.04997132 0.6187460 0.0157345527 ## TN 4.599194e-03 5.159826e-03 0.32104895 0.9342636 0.0792074168 24 ## NH4 5.884185e-01 6.370653e-01 0.50869488 0.4488508 0.3396534058 ## NO3 8.392894e-03 2.355501e-02 0.72829524 0.1703283 0.0888514251 ## ratioC_N 5.188051e-02 2.355501e-02 0.06862542 0.5730038 0.0244783704 ## pH 5.159826e-03 6.374415e-03 0.06862542 0.9362394 0.0329452578 ## EC 6.217300e-03 1.174727e-02 0.78206929 0.9520463 0.0610612033 ## Ca 1.070896e-01 2.397706e-01 0.32153977 0.9201887 0.1511278807 ## Mg 8.149791e-05 1.076813e-04 0.61032971 0.3089318 0.3549703692 ## Na NA 4.236951e-07 0.54308443 0.3572828 0.0436271651 ## K 4.236951e-07 NA 0.55169177 0.3512139 0.0308881257 ## SRA 5.430844e-01 5.516918e-01 NA 0.5759149 0.1518724481 ## Bacteria_16S 3.572828e-01 3.512139e-01 0.57591486 NA 0.5579270600 ## ITS_Fungi 4.362717e-02 3.088813e-02 0.15187245 0.5579271 NA ## Ratio_F_B 2.133169e-01 1.754235e-01 0.43305760 0.5562986 0.0001069091 ## nosZ1 2.193483e-01 3.179038e-01 0.02505728 0.9201887 0.3396534058 ## nosZ2 1.629278e-01 2.855581e-01 0.17774487 0.8708565 0.4986370627 ## nirS 9.739827e-01 6.952677e-01 0.31790376 0.1796150 0.5884184791 ## nirK 2.193483e-01 4.175746e-01 0.33965341 0.7347916 0.8557486462 ## nos_nir 2.014202e-01 4.037348e-01 0.84496931 0.6661911 0.8557486462 ## Ratio_F_B nosZ1 nosZ2 nirS nirK ## TOC 0.0715746938 5.188051e-02 8.122453e-02 0.9669638963 1.511279e-01 ## WSOC 0.0911147016 1.600614e-02 1.170602e-01 0.7631440208 1.754235e-01 ## TN 0.1511278807 7.965247e-02 1.254606e-01 0.6952677236 3.089318e-01 ## NH4 0.2724030993 7.263613e-02 1.520458e-01 0.0407162355 7.157469e-02 ## NO3 0.5552442398 9.067354e-01 9.478550e-01 0.1703283479 9.342636e-01 ## ratioC_N 0.1070895781 6.571716e-02 1.452081e-01 0.9425461970 1.979215e-01 ## pH 0.1129342275 1.174727e-02 6.348959e-02 0.3396534058 4.071624e-02 ## EC 0.0812245299 2.092818e-01 3.451684e-01 0.8043994199 1.776267e-01 ## Ca 0.3396534058 2.193483e-01 2.223773e-01 0.4512526053 2.019083e-01 ## Mg 0.7631440208 2.241612e-01 1.832633e-01 0.8581603947 1.703283e-01 ## Na 0.2133169107 2.193483e-01 1.629278e-01 0.9739826600 2.193483e-01 ## K 0.1754234699 3.179038e-01 2.855581e-01 0.6952677236 4.175746e-01 ## SRA 0.4330576023 2.505728e-02 1.777449e-01 0.3179037626 3.396534e-01 ## Bacteria_16S 0.5562986012 9.201887e-01 8.708565e-01 0.1796149684 7.347916e-01 ## ITS_Fungi 0.0001069091 3.396534e-01 4.986371e-01 0.5884184791 8.557486e-01 ## Ratio_F_B NA 5.650815e-01 6.886198e-01 0.8557486462 9.342636e-01 ## nosZ1 0.5650814553 NA 5.923602e-05 0.0171817562 1.031872e-03 ## nosZ2 0.6886198298 5.923602e-05 NA 0.0098026780 1.385841e-03 ## nirS 0.8557486462 1.718176e-02 9.802678e-03 NA 8.561777e-04 ## nirK 0.9342635689 1.031872e-03 1.385841e-03 0.0008561777 NA ## nos_nir 0.6723836148 5.188051e-02 8.632634e-02 0.0084189769 8.149791e-05 ## nos_nir ## TOC 2.855581e-01 ## WSOC 3.179038e-01 ## TN 3.474554e-01 ## NH4 2.781078e-01 ## NO3 9.016044e-01 ## ratioC_N 3.925456e-01 ## pH 7.357615e-02 ## EC 6.862542e-02 ## Ca 2.902905e-01 ## Mg 1.703283e-01 ## Na 2.014202e-01 ## K 4.037348e-01 ## SRA 8.449693e-01 25