Appendix_S2_Konza_ITS_data_analysis
Charles T. Bond
07/11/2025
Setup
options(knitr.duplicate.label = "allow")
#library(dada2)
library(phyloseq)
library(vegan)## Loading required package: permute## Loading required package: lattice## This is vegan 2.7-0library(ggplot2)
library(tidyr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(pheatmap)
library(GUniFrac)## Registered S3 method overwritten by 'rmutil':
## method from
## print.response httr#library(metagMisc)
#library(raster)
library(pals)
library(RColorBrewer)
library(ragg)
library(ggpubr)
#Set theme
theme_set(theme_bw() + theme(
plot.title = element_text(size=20, color="black"),
axis.text.x = element_text(size=15, color="black"),
axis.text.y = element_text(size=15, color="black"),
axis.title.x = element_text(size=15),
axis.title.y = element_text(size=15),
legend.text = element_text(size=12),
legend.title = element_text(size=15),
# legend.position = "bottom",
# legend.key=element_blank(),
# legend.key.size = unit(0.5, "cm"),
# legend.spacing.x = unit(0.1, "cm"),
# legend.spacing.y = unit(0.1, "cm"),
panel.background = element_blank(),
#panel.border = element_rect(colour = "black", fill=NA, size=1),
plot.background = element_blank()))Statistical Analysis-Data Curation
FUNGI-ONLY ANALYSES. full dataset, not rarified
This is will produce the fungal community tables and statistics on fungal diversity at Konza.
setwd('/Users/chunk/AIMS_Konza_synoptic_ITS_community/phyloseq_tabs/fall2023')
#Create phyloseq object
#read in files
asvtab <- read.table("practice09-15-2023.count_table", header=T, row.names=1,
check.names=F, sep="\t")
metadata_full <- read.csv("submetatab03102025.csv", row.names=1)
metadata_full$substrate <- as.factor(metadata_full$substrate)
metadata_full<-metadata_full[metadata_full$substrate!='W',]
metadata_full$burn_freq<-1/metadata_full$burn_interval
taxtab <- as.matrix(read.csv("final_fungi_tax_tab_kzsyn_09-15-2023.csv", header = T, row.names=1))
asvtab <- otu_table(asvtab, taxa_are_rows = FALSE)
taxtab <- tax_table(taxtab)
metadata <- sample_data(metadata_full)
#combine into phyloseq object
pseqtest <- phyloseq(asvtab, taxtab, metadata)
#extract easy-to-use sample data
samdftest <- data.frame(sample_data(pseqtest))
cured_asvs<- pseqtest@otu_table
summary(rowSums(cured_asvs))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 352 7732 14898 17710 25982 60014sum(rowSums(cured_asvs))## [1] 2550213ncol(cured_asvs)## [1] 7829##remove singletons, doubletons, and ASVs with less than 5 reads across all samples.
cured_asvs <- cured_asvs[,colSums(cured_asvs)>5]
summary(rowSums(cured_asvs))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 352 7706 14872 17680 25932 59985#rowSums(cured_asvs)
pseqtest <- phyloseq(cured_asvs, taxtab, metadata)
#extract easy-to-use sample data
samdftest <- data.frame(sample_data(pseqtest))
sum(rowSums(cured_asvs))## [1] 2545863ncol(cured_asvs)## [1] 6564## Examine the read depths by substrate:
###Leaf
summary(rowSums(pseqtest@otu_table[pseqtest@sam_data$substrate=="L",]))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5333 23272 28730 29143 34079 59985##L sample n
nrow(pseqtest@otu_table[pseqtest@sam_data$substrate=="L",])## [1] 49###Biofilm
summary(rowSums(pseqtest@otu_table[pseqtest@sam_data$substrate=="B",]))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 352 3791 7724 9260 12873 31449##B sample n
nrow(pseqtest@otu_table[pseqtest@sam_data$substrate=="B",])## [1] 46###Sediment
summary(rowSums(pseqtest@otu_table[pseqtest@sam_data$substrate=="S",]))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1747 7888 13105 14120 18670 39038##S sample n
nrow(pseqtest@otu_table[pseqtest@sam_data$substrate=="S",])## [1] 49###site characteristics
### subset selecting any one of three substrates to get one row of metadata per site
metadata_sitechars<-metadata_full[metadata_full$substrate=='S',]
### kmeans clustered (k=3) by annual percent wet clusters
metadata_sitechars## siteid Sample substrate Site sticID wet_dry n_wet n_total
## 01m01_S 01M01 01m01_S S 01m01 01M01_1 WET 4661 12197
## 01m02_S 01M02 01m02_S S 01m02 01M02_1 WET 3720 26368
## 01m03_S 01M03 01m03_S S 01m03 01M03_1 WET 2221 22615
## 01m04_S 01M04 01m04_S S 01m04 01M04_2 WET 6218 33864
## 01m05_S 01M05 01m05_S S 01m05 01M05_1 WET 18449 21654
## 01m06_S 01M06 01m06_S S 01m06 01M06_1 WET 8747 33852
## 02m01_S 02M01 02m01_S S 02m01 02M01_1 WET 48 12640
## 02m02_S 02M02 02m02_S S 02m02 02M02_1 WET 3016 22615
## 02m03_S 02M03 02m03_S S 02m03 02M03_1 WET 2826 22616
## 02m04_S 02M04 02m04_S S 02m04 02M04_1 WET 1571 23611
## 02m05_S 02M05 02m05_S S 02m05 02M05_1 WET 7074 12197
## 02m06_S 02M06 02m06_S S 02m06 02M06_1 WET 7423 34630
## 02m07_S 02M07 02m07_S S 02m07 02M07_1 WET 28515 34621
## 02m08_S 02M08 02m08_S S 02m08 02M08_1 WET 2595 13798
## 02m09_S 02M09 02m09_S S 02m09 02M09_1 WET 9912 34620
## 02m10_S 02M10 02m10_S S 02m10 02M10_1 WET 454 11252
## 02m11_S 02M11 02m11_S S 02m11 02M11_1 WET 21189 34813
## 04m01_S 04M01 04m01_S S 04m01 04M01_1 WET 15003 23391
## 04m02_S 04M02 04m02_S S 04m02 04M02_1 WET 15667 23393
## 04m03_S 04M03 04m03_S S 04m03 04M03_1 WET 2928 11199
## 04m04_S 04M04 04m04_S S 04m04 04M04_1 WET 19514 19514
## 04m05_S 04M05 04m05_S S 04m05 04M05_1 WET 29056 34811
## 04m06_S 04M06 04m06_S S 04m06 04M06_1 WET 1742 22648
## 04m07_S 04M07 04m07_S S 04m07 04M07_1 WET 7690 34812
## 04m08_S 04M08 04m08_S S 04m08 04M08_1 WET 33425 34810
## 04m09_S 04M09 04m09_S S 04m09 04M09_1 WET 6014 34811
## 04m10_S 04M10 04m10_S S 04m10 04M10_1 WET 5974 34812
## 04m11_S 04M11 04m11_S S 04m11 04M11_1 WET 4154 34811
## 04m12_S 04M12 04m12_S S 04m12 04M12_1 WET 7763 34839
## 04m13_S 04M13 04m13_S S 04m13 04M13_1 DRY 1895 34812
## 04t01_S 04T01 04t01_S S 04t01 04T01_1 DRY 1110 23408
## 04t02_S 04T02 04t02_S S 04t02 04T02_1 WET 1737 14001
## 04w01_S 04W01 04w01_S S 04w01 04W01_1 DRY 1406 22645
## 04w02_S 04W02 04w02_S S 04w02 04W02_1 WET 49 10295
## 04w03_S 04W03 04w03_S S 04w03 04W03_1 WET 387 12197
## 04w04_S 04W04 04w04_S S 04w04 04W04_1 WET 2323 22459
## 20m01_S 20M01 20m01_S S 20m01 20M01_1 DRY 973 22641
## 20m02_S 20M02 20m02_S S 20m02 20M02_1 WET 1506 21644
## 20m03_S 20M03 20m03_S S 20m03 20M03_1 DRY 0 2013
## 20m04_S 20M04 20m04_S S 20m04 20M04_1 WET 1830 14951
## 20m05_S 20M05 20m05_S S 20m05 20M05_1 WET 8275 34812
## sfm01_S SFM01 sfm01_S S sfm01 SFM01_1 WET 8770 22640
## sfm02_S SFM02 sfm02_S S sfm02 SFM02_1 WET 3133 22614
## sfm03_S SFM03 sfm03_S S sfm03 SFM03_1 WET 3041 24860
## sfm04_S SFM04 sfm04_S S sfm04 SFM04_1 WET 7628 34812
## sfm05_S SFM05 sfm05_S S sfm05 SFM05_1 WET 9405 22615
## sfm06_S SFM06 sfm06_S S sfm06 SFM06_1 WET 2591 12197
## sfm07_S SFM07 sfm07_S S sfm07 SFM07_1 WET 4344 34812
## Sft01_S SFT01 Sft01_S S Sft01 SFT01_1 WET 5492 9953
## Sft02_S SFT02 Sft02_S S Sft02 SFT02_1 WET 6166 19488
## prc_missing prc_wet tempC_mean LeafSpecies1 LeafSpecies2
## 01m01_S 0.7836529 0.382143150 6.984064 Elm Elm
## 01m02_S 0.5322915 0.141080097 17.589772 Populus Oak
## 01m03_S 0.5988612 0.098209153 16.937429 Locust Elm
## 01m04_S 0.3993295 0.183616820 13.943160 Populus Oak
## 01m05_S 0.6159072 0.851990394 17.020088 Elm Elm
## 01m06_S 0.3995424 0.258389460 12.771854 Elm Elm
## 02m01_S 0.7757951 0.003797468 10.924877 Oak Oak
## 02m02_S 0.5988612 0.133362812 16.571000 Elm Elm
## 02m03_S 0.5988435 0.124955784 16.688561 Populus Elm
## 02m04_S 0.5811945 0.066536784 8.749176 Populus Populus
## 02m05_S 0.7836529 0.579978683 7.051607 Elm Elm
## 02m06_S 0.3857424 0.214351718 13.764280 Populus Populus
## 02m07_S 0.3859021 0.823633055 12.515694 Elm Populus
## 02m08_S 0.7552548 0.188070735 23.540365 Populus Oak
## 02m09_S 0.3859198 0.286308492 13.351205 Oak Oak
## 02m10_S 0.8004151 0.040348383 10.233446 Oak Populus
## 02m11_S 0.3824964 0.608651940 13.726468 <NA> <NA>
## 04m01_S 0.5850968 0.641400539 14.481234 Elm Oak
## 04m02_S 0.5850613 0.669730261 14.260131 Oak Oak
## 04m03_S 0.8013552 0.261451915 22.924203 Oak Elm
## 04m04_S 0.6538659 1.000000000 17.398986 Oak Elm
## 04m05_S 0.3825319 0.834678694 12.543450 Elm Oak
## 04m06_S 0.5982759 0.076916284 15.710394 Elm Populus
## 04m07_S 0.3825141 0.220900839 13.178269 Elm Elm
## 04m08_S 0.3825496 0.960212583 12.652138 Sedge Sedge
## 04m09_S 0.3825319 0.172761483 13.987895 Populus Elm
## 04m10_S 0.3825141 0.171607492 13.181735 Elm Populus
## 04m11_S 0.3825319 0.119330097 13.750683 Grass Grass
## 04m12_S 0.3820352 0.222824995 13.046472 Grass Grass
## 04m13_S 0.3825141 0.054435252 15.048048 Elm Forb
## 04t01_S 0.5847952 0.047419686 16.858128 Elm Elm
## 04t02_S 0.7516540 0.124062567 10.815149 Grass Grass
## 04w01_S 0.5983291 0.062088761 18.465848 Populus Populus
## 04w02_S 0.8173901 0.004759592 8.747003 Elm Elm
## 04w03_S 0.7836529 0.031729114 8.024337 Elderberry none
## 04w04_S 0.6016283 0.103432922 7.663201 Elm none
## 20m01_S 0.5984001 0.042975134 17.689228 Oak Oak
## 20m02_S 0.6160846 0.069580484 14.913436 Populus Populus
## 20m03_S 0.9642939 0.000000000 20.307879 Elm Elm
## 20m04_S 0.7348032 0.122399839 22.609106 Elm Elm
## 20m05_S 0.3825141 0.237705389 14.597884 Rubrus Populus
## sfm01_S 0.5984178 0.387367491 15.483584 Elm Oak
## sfm02_S 0.5988790 0.138542496 16.544916 Elm Populus
## sfm03_S 0.5590400 0.122325020 8.424775 Oak Oak
## sfm04_S 0.3825141 0.219119844 12.655383 Oak Oak
## sfm05_S 0.5988612 0.415874420 16.812533 Elm Elm
## sfm06_S 0.7836529 0.212429286 8.022568 Oak Oak
## sfm07_S 0.3825141 0.124784557 13.666626 Elm Oak
## Sft01_S 0.8234564 0.551793429 21.420313 Elm Elm
## Sft02_S 0.6543271 0.316399836 18.611745 <NA> <NA>
## LeafSpecies3 Elm Populus Oak Locust Graminales Elderberry Rubrus Forb
## 01m01_S Oak 1 0 1 0 0 0 0 0
## 01m02_S Oak 0 1 1 0 0 0 0 0
## 01m03_S Oak 1 0 1 1 0 0 0 0
## 01m04_S Elm 1 1 1 0 0 0 0 0
## 01m05_S Oak 1 0 1 0 0 0 0 0
## 01m06_S Elm 1 0 0 0 0 0 0 0
## 02m01_S Oak 0 0 1 0 0 0 0 0
## 02m02_S Elm 1 0 0 0 0 0 0 0
## 02m03_S Elm 1 1 0 0 0 0 0 0
## 02m04_S Populus 0 1 0 0 0 0 0 0
## 02m05_S Elm 1 0 0 0 0 0 0 0
## 02m06_S Populus 0 1 0 0 0 0 0 0
## 02m07_S Populus 1 1 0 0 0 0 0 0
## 02m08_S Oak 0 1 1 0 0 0 0 0
## 02m09_S Oak 0 0 1 0 0 0 0 0
## 02m10_S Populus 0 1 1 0 0 0 0 0
## 02m11_S <NA> 0 0 0 0 0 0 0 0
## 04m01_S Populus 1 1 1 0 0 0 0 0
## 04m02_S Populus 0 1 1 0 0 0 0 0
## 04m03_S Elm 1 0 1 0 0 0 0 0
## 04m04_S Elm 1 0 1 0 0 0 0 0
## 04m05_S Populus 1 1 1 0 0 0 0 0
## 04m06_S Populus 1 1 0 0 0 0 0 0
## 04m07_S Populus 1 1 0 0 0 0 0 0
## 04m08_S Forbs 0 0 0 0 1 0 0 1
## 04m09_S Unknown 1 1 0 0 0 0 0 0
## 04m10_S Populus 1 1 0 0 0 0 0 0
## 04m11_S Shrub 0 0 0 0 1 0 0 0
## 04m12_S Elm 1 0 0 0 1 0 0 0
## 04m13_S Grass 1 0 0 0 1 0 0 1
## 04t01_S Grass 1 0 0 0 1 0 0 0
## 04t02_S Elm 1 0 0 0 1 0 0 0
## 04w01_S Oak 0 1 1 0 0 0 0 0
## 04w02_S Populus 1 1 0 0 0 0 0 0
## 04w03_S none 0 0 0 0 0 1 0 0
## 04w04_S none 1 0 0 0 0 0 0 0
## 20m01_S Oak 0 0 1 0 0 0 0 0
## 20m02_S Populus 0 1 0 0 0 0 0 0
## 20m03_S Elm 1 0 0 0 0 0 0 0
## 20m04_S Elm 1 0 0 0 0 0 0 0
## 20m05_S Elm 1 1 0 0 0 0 1 0
## sfm01_S Oak 1 0 1 0 0 0 0 0
## sfm02_S Oak 1 1 1 0 0 0 0 0
## sfm03_S Populus 0 1 1 0 0 0 0 0
## sfm04_S Elm 1 0 1 0 0 0 0 0
## sfm05_S Oak 1 0 1 0 0 0 0 0
## sfm06_S Populus 0 1 1 0 0 0 0 0
## sfm07_S Oak 1 0 1 0 0 0 0 0
## Sft01_S Oak 1 0 1 0 0 0 0 0
## Sft02_S <NA> 0 0 0 0 0 0 0 0
## Shrub Unknown Sediment.Texture.Description Percent.Rocks
## 01m01_S 0 0 Sandy 5
## 01m02_S 0 0 Sandy 80
## 01m03_S 0 0 Sandy coarse 30
## 01m04_S 0 0 FBOM find sand and clay 15
## 01m05_S 0 0 FBOM coarse sand 5
## 01m06_S 0 0 FBOM silt 20
## 02m01_S 0 0 <NA> 50
## 02m02_S 0 0 <NA> 60
## 02m03_S 0 0 <NA> 85
## 02m04_S 0 0 <NA> 60
## 02m05_S 0 0 <NA> 85
## 02m06_S 0 0 <NA> 80
## 02m07_S 0 0 <NA> 85
## 02m08_S 0 0 <NA> 85
## 02m09_S 0 0 <NA> 80
## 02m10_S 0 0 <NA> 75
## 02m11_S 0 0 <NA> 92
## 04m01_S 0 0 <NA> NA
## 04m02_S 0 0 <NA> NA
## 04m03_S 0 0 <NA> NA
## 04m04_S 0 0 <NA> NA
## 04m05_S 0 0 <NA> NA
## 04m06_S 0 0 <NA> NA
## 04m07_S 0 0 <NA> NA
## 04m08_S 0 0 <NA> NA
## 04m09_S 1 0 <NA> NA
## 04m10_S 0 0 <NA> NA
## 04m11_S 1 0 Very silty and gravel 15
## 04m12_S 0 0 Sily clay gravel 40
## 04m13_S 0 0 Silty clay loam 50
## 04t01_S 0 0 Silty clay 60
## 04t02_S 0 0 Sandy silt and OM 40
## 04w01_S 0 0 Sandy silty loam 90
## 04w02_S 0 0 Silty clay loam 60
## 04w03_S 0 0 Sandy 80
## 04w04_S 0 0 <NA> NA
## 20m01_S 0 0 Clay and sand NA
## 20m02_S 0 0 Sandy clay NA
## 20m03_S 0 0 Sandy gravel clay 96
## 20m04_S 0 0 <NA> 70
## 20m05_S 0 0 <NA> 92
## sfm01_S 0 0 Gravel fine NA
## sfm02_S 0 0 <NA> 93
## sfm03_S 0 0 <NA> 75
## sfm04_S 0 0 <NA> 80
## sfm05_S 0 0 Silt and gravel 85
## sfm06_S 0 0 More sandy 90
## sfm07_S 0 0 Sandy 94
## Sft01_S 0 0 Silt and sand 30
## Sft02_S 0 0 <NA> NA
## Percent.Leaf.Litter Percent.Sediment meanN2Ar meanO2Ar meanO2
## 01m01_S 65 30 39.10763 17.400480 240.5808
## 01m02_S 5 15 39.06296 15.942810 222.7925
## 01m03_S 1 50 38.89556 18.615640 260.6771
## 01m04_S 1 85 38.36237 18.353790 252.3483
## 01m05_S 1 95 37.71366 20.699810 282.3224
## 01m06_S 2 78 39.19682 8.391730 118.2369
## 02m01_S 5 45 37.41552 19.713560 278.0889
## 02m02_S 10 30 38.40148 19.559160 267.3011
## 02m03_S 2 13 38.93042 18.647260 240.2760
## 02m04_S 10 30 40.40274 16.808220 216.8560
## 02m05_S 5 10 40.38089 18.725840 249.3837
## 02m06_S 5 15 41.57071 19.564580 263.1396
## 02m07_S 5 10 40.42192 18.300640 262.7113
## 02m08_S 5 10 41.24604 16.580070 236.0363
## 02m09_S 5 15 43.07667 14.044110 199.9340
## 02m10_S 5 20 41.40282 16.478370 230.7487
## 02m11_S 0 8 38.29022 19.139970 260.5252
## 04m01_S NA NA 37.57460 18.989200 263.2088
## 04m02_S NA NA 38.23949 20.460710 266.7046
## 04m03_S NA NA 38.66996 21.090990 268.6730
## 04m04_S NA NA 38.53184 19.752330 263.0540
## 04m05_S NA NA 38.60799 17.753540 248.6051
## 04m06_S NA NA 38.40265 17.471470 256.1535
## 04m07_S NA NA 38.40001 19.283240 279.1496
## 04m08_S NA NA 40.41111 10.877780 233.8889
## 04m09_S NA NA 37.67970 20.823870 447.8220
## 04m10_S NA NA 36.76538 17.605280 378.5998
## 04m11_S 20 65 38.24263 21.433920 290.3476
## 04m12_S 1 30 39.19880 15.232070 216.4251
## 04m13_S 4 35 NA NA NA
## 04t01_S 1 40 NA NA NA
## 04t02_S 20 40 36.41330 20.440760 266.4446
## 04w01_S 1 10 NA NA NA
## 04w02_S 1 40 39.47468 13.027200 182.7960
## 04w03_S 1 20 38.60776 17.655170 242.7429
## 04w04_S NA NA 40.62702 8.231568 121.9809
## 20m01_S NA NA NA NA NA
## 20m02_S NA NA NA NA NA
## 20m03_S 3 1 NA NA NA
## 20m04_S 15 15 37.82488 18.272930 257.9912
## 20m05_S 1 7 37.49003 20.545300 284.1714
## sfm01_S NA NA 37.84584 17.725240 248.7184
## sfm02_S 2 5 37.37885 19.135270 257.8768
## sfm03_S 10 15 39.56851 17.270260 236.4955
## sfm04_S 1 19 41.10526 16.296770 229.6165
## sfm05_S 1 15 39.02887 18.860440 267.3889
## sfm06_S 1 9 38.29906 18.485370 255.1875
## sfm07_S 5 1 40.03592 20.560300 269.5654
## Sft01_S 40 30 39.00162 16.347990 223.8743
## Sft02_S NA NA 38.93783 17.121400 237.3192
## project date rType rep sublocation month time_cst sub_watershed
## 01m01_S AIMS 20210605 ENVD 1 SW June 15:35 01M
## 01m02_S AIMS 20210605 ENVD 1 SW June 17:15 01M
## 01m03_S AIMS 20210606 ENVD 1 SW June 9:00 01M
## 01m04_S AIMS 20210606 ENVD 1 SW June 10:37 01M
## 01m05_S AIMS 20210606 ENVD 1 SW June 12:15 01M
## 01m06_S AIMS 20210606 ENVD 1 SW June 14:04 01M
## 02m01_S AIMS 20210605 ENVD 1 SW June 9:21 02M
## 02m02_S AIMS 20210605 ENVD 1 SW June 11:06 02M
## 02m03_S AIMS 20210605 ENVD 1 SW June 13:14 02M
## 02m04_S AIMS 20210605 ENVD 1 SW June 15:00 02M
## 02m05_S AIMS 20210605 ENVD 1 SW June 16:21 02M
## 02m06_S AIMS 20210606 ENVD 1 SW June 17:50 02M
## 02m07_S AIMS 20210606 ENVD 1 SW June 10:05 02M
## 02m08_S AIMS 20210606 ENVD 1 SW June 11:30 02M
## 02m09_S AIMS 20210606 ENVD 1 SW June 12:54 02M
## 02m10_S AIMS 20210606 ENVD 1 SW June 15:00 02M
## 02m11_S AIMS 20210606 ENVD 1 SW June 16:40 02M
## 04m01_S AIMS 20210603 ENVD 1 SW June 11:15 04M
## 04m02_S AIMS 20210605 ENVD 1 SW June 14:50 04M
## 04m03_S AIMS 20210605 ENVD 1 SW June 04M
## 04m04_S AIMS 20210605 ENVD 1 SW June 16:03 04M
## 04m05_S AIMS 20210605 ENVD 1 SW June 17:25 04M
## 04m06_S AIMS 20210606 ENVD 1 SW June 8:30 04M
## 04m07_S AIMS 20210606 ENVD 1 SW June 10:00 04M
## 04m08_S AIMS 20210606 ENVD 1 SW June 11:40 04M
## 04m09_S AIMS 20210606 ENVD 1 SW June 15:30 04M
## 04m10_S AIMS 20210606 ENVD 1 SW June 16:30 04M
## 04m11_S AIMS 20210607 ENVD 1 SW June 13:45 04M
## 04m12_S AIMS 20210607 ENVD 1 SW June 14:22 04M
## 04m13_S AIMS 20210607 ENVD 1 SW June 16:13 04M
## 04t01_S AIMS 20210604 ENVD 1 SW June 9:36 04M
## 04t02_S AIMS 20210607 ENVD 1 SW June 11:00 04M
## 04w01_S AIMS 20210607 ENVD 1 SW June 8:40 04W
## 04w02_S AIMS 20210607 ENVD 1 SW June 9:44 04W
## 04w03_S AIMS 20210607 ENVD 1 SW June 10:44 04W
## 04w04_S AIMS 20210607 ENVD 1 SW June 12:00 04W
## 20m01_S AIMS 20210605 ENVD 1 SW June 15:41 20M
## 20m02_S AIMS 20210605 ENVD 1 SW June 16:27 20M
## 20m03_S AIMS 20210605 ENVD 1 SW June 17:00 20M
## 20m04_S AIMS 20210606 ENVD 1 SW June 9:25 20M
## 20m05_S AIMS 20210606 ENVD 1 SW June 10:53 20M
## sfm01_S AIMS 20210604 ENVD 1 SW June 10:54 SFM
## sfm02_S AIMS 20210604 ENVD 1 SW June 14:00 SFM
## sfm03_S AIMS 20210605 ENVD 1 SW June SFM
## sfm04_S AIMS 20210605 ENVD 1 SW June SFM
## sfm05_S AIMS 20210605 ENVD 1 SW June 10:57 SFM
## sfm06_S AIMS 20210605 ENVD 1 SW June 12:16 SFM
## sfm07_S AIMS 20210605 ENVD 1 SW June 13:51 SFM
## Sft01_S AIMS 20210606 ENVD 1 SW June 14:58 SFM
## Sft02_S AIMS 20210606 ENVD 1 SW June 13:12 SFM
## burn_area burn_interval stream_order elevation flow_state conductivity
## 01m01_S N1B 1.205882 3 373.9098 flowing 560.0
## 01m02_S N1B 1.205882 3 375.4253 flowing 562.0
## 01m03_S N1B 1.205882 3 378.8144 flowing 557.7
## 01m04_S N1B 1.205882 3 381.3559 flowing 542.8
## 01m05_S N1B 1.205882 3 384.7108 flowing 527.8
## 01m06_S N1B 1.205882 2 394.2241 flowing 575.6
## 02m01_S N1A 1.051282 1 353.3494 flowing 546.1
## 02m02_S N1A 1.051282 1 356.2749 flowing 560.4
## 02m03_S N2B 2.277778 1 358.4956 flowing 558.4
## 02m04_S N2B 2.277778 1 364.2157 flowing 558.0
## 02m05_S N2B 2.277778 1 367.7589 flowing 564.0
## 02m06_S N2B 2.277778 1 369.4064 flowing 561.2
## 02m07_S N2B 2.277778 1 373.2965 flowing 568.6
## 02m08_S N2B 2.277778 1 376.8670 flowing 566.0
## 02m09_S N2B 2.277778 1 379.9472 flowing 557.7
## 02m10_S N2B 2.277778 1 387.2234 flowing 541.4
## 02m11_S N2B 2.277778 1 401.5449 flowing 566.9
## 04m01_S N2B 2.277778 2 355.1574 flowing 534.5
## 04m02_S N2B 2.277778 2 362.0723 flowing 534.6
## 04m03_S N4D 4.100000 2 364.9916 flowing 521.4
## 04m04_S N4D 4.100000 2 367.7222 flowing 548.1
## 04m05_S N4D 4.100000 2 370.0585 flowing 559.0
## 04m06_S N4D 4.100000 2 372.0548 flowing 557.2
## 04m07_S N4D 4.100000 2 376.4349 flowing 557.3
## 04m08_S N4D 4.100000 2 380.3074 flowing 558.3
## 04m09_S N4D 4.100000 2 384.8762 flowing 546.1
## 04m10_S N4D 4.100000 2 393.9353 flowing 555.0
## 04m11_S N4D 4.100000 2 404.7170 flowing 528.5
## 04m12_S N4D 4.100000 1 407.2463 flowing 569.1
## 04m13_S N4D 4.100000 1 407.2463 dry NA
## 04t01_S N4B 3.416667 1 379.5954 dry NA
## 04t02_S N4B 3.416667 1 387.0224 flowing 546.1
## 04w01_S N2B 2.277778 1 360.5698 dry NA
## 04w02_S N2B 2.277778 1 370.9928 flowing 541.2
## 04w03_S N2B 2.277778 1 382.2555 flowing 555.9
## 04w04_S N2B 2.277778 1 391.0867 flowing 561.7
## 20m01_S N20B 6.833333 1 371.0545 dry NA
## 20m02_S N20B 6.833333 1 378.9335 pool NA
## 20m03_S N20B 6.833333 1 385.9613 dry NA
## 20m04_S N20B 6.833333 1 389.7962 flowing 544.1
## 20m05_S N20B 6.833333 1 401.0049 flowing 529.8
## sfm01_S N1A 1.051282 3 350.8112 flowing 561.1
## sfm02_S N4B 3.416667 3 352.7506 flowing 545.7
## sfm03_S N4B 3.416667 3 353.4152 flowing 574.9
## sfm04_S N4B 3.416667 3 356.1383 flowing 564.8
## sfm05_S N4B 3.416667 3 361.0678 flowing 559.5
## sfm06_S N20B 6.833333 3 365.2245 flowing 549.4
## sfm07_S N20B 6.833333 3 367.3486 flowing 528.8
## Sft01_S N4B 3.416667 1 356.8398 flowing 589.7
## Sft02_S N4B 3.416667 1 372.4689 flowing 536.1
## canopy_cover_percent long lat stic_sublocation
## 01m01_S 44.8 -96.57702 39.08667 HS
## 01m02_S 74.0 -96.57633 39.08629 HS
## 01m03_S 2.1 -96.57594 39.08471 HS
## 01m04_S 85.4 -96.57516 39.08364 LS
## 01m05_S 56.3 -96.57419 39.08264 HS
## 01m06_S 60.4 -96.57158 39.08065 HS
## 02m01_S 71.9 -96.58813 39.09188 LS
## 02m02_S 86.5 -96.58833 39.09084 HS
## 02m03_S 92.7 -96.58859 39.08991 HS
## 02m04_S 96.9 -96.58964 39.08824
## 02m05_S 28.1 -96.59082 39.08698
## 02m06_S 46.9 -96.59143 39.08724 HS
## 02m07_S 14.6 -96.59252 39.08648 HS
## 02m08_S 51.0 -96.59291 39.08557 HS
## 02m09_S 40.6 -96.59339 39.08482 HS
## 02m10_S 27.1 -96.59357 39.08251 HS
## 02m11_S 0.0 -96.59368 39.07925 HS
## 04m01_S 76.0 -96.58620 39.08984 HS
## 04m02_S 6.3 -96.58481 39.08775 HS
## 04m03_S 26.0 -96.58366 39.08685 HS
## 04m04_S 65.6 -96.58345 39.08576 HS
## 04m05_S 96.9 -96.58310 39.08502 HS
## 04m06_S 84.4 -96.58342 39.08426 HS
## 04m07_S 70.8 -96.58253 39.08278 HS
## 04m08_S 31.3 -96.58221 39.08131 HS
## 04m09_S 32.3 -96.58163 39.07994 HS
## 04m10_S 62.5 -96.58289 39.07743 HS
## 04m11_S 37.5 -96.58383 39.07472 HS
## 04m12_S 72.9 -96.58406 39.07316 HS
## 04m13_S 50.0 -96.58430 39.07081 HS
## 04t01_S 10.4 -96.58430 39.08236 HS
## 04t02_S 1.0 -96.58083 39.07970 HS
## 04w01_S 64.6 -96.58543 39.08816 HS
## 04w02_S 95.8 -96.58689 39.08609 HS
## 04w03_S 34.4 -96.58804 39.08386 HS
## 04w04_S 93.8 -96.58866 39.08223 HS
## 20m01_S 13.5 -96.57694 39.08838 HS
## 20m02_S 0.0 -96.57370 39.08921 HS
## 20m03_S 35.4 -96.57178 39.08822 HS
## 20m04_S 76.0 -96.57111 39.08709 HS
## 20m05_S 4.2 -96.56800 39.08600 HS
## sfm01_S 78.1 -96.58719 39.09228 HS
## sfm02_S 84.4 -96.58632 39.09126 HS
## sfm03_S 63.5 -96.58578 39.09099 LS
## sfm04_S 86.5 -96.58440 39.09089 HS
## sfm05_S 80.2 -96.58186 39.09019 HS
## sfm06_S 85.4 -96.57941 39.08950
## sfm07_S 97.9 -96.57846 39.08868 HS
## Sft01_S 2.1 -96.58517 39.09185 HS
## Sft02_S 4.2 -96.57852 39.09105 HS
## stream_temp_C_1wk_avg stream_temp_C_2wk_avg stream_temp_C_3wk_avg
## 01m01_S 14.99187 15.52654 15.52654
## 01m02_S 15.00088 15.62155 15.62155
## 01m03_S 16.58707 16.81508 16.89144
## 01m04_S 16.35122 16.62819 16.70815
## 01m05_S 15.99709 16.13474 16.19758
## 01m06_S 14.76880 15.05902 15.15786
## 02m01_S 15.83346 16.46933 16.46933
## 02m02_S 15.72499 16.30161 16.30161
## 02m03_S 15.89609 16.38938 16.38938
## 02m04_S NA NA NA
## 02m05_S NA NA NA
## 02m06_S 16.08588 16.22219 16.26795
## 02m07_S 15.51903 15.69292 15.73883
## 02m08_S 15.38467 15.59371 15.64881
## 02m09_S 15.52730 15.62343 15.66680
## 02m10_S 15.42985 15.63123 15.68870
## 02m11_S 18.03801 17.57932 17.58944
## 04m01_S 15.25973 15.88678 15.88678
## 04m02_S 15.27976 15.73862 15.73862
## 04m03_S 15.05235 15.62948 15.62948
## 04m04_S 14.93837 15.36597 15.36597
## 04m05_S 14.80410 15.18022 15.18022
## 04m06_S 15.11215 17.15255 17.47976
## 04m07_S 14.79667 14.99342 15.02939
## 04m08_S 14.76238 14.94428 14.97109
## 04m09_S 15.46201 15.59828 15.63621
## 04m10_S 15.49736 15.61895 15.65965
## 04m11_S 15.80325 15.53535 15.59864
## 04m12_S 15.04872 15.04683 15.21936
## 04m13_S 20.93713 19.98362 20.19477
## 04t01_S 16.21178 17.83773 17.83773
## 04t02_S 16.33662 16.26803 16.36318
## 04w01_S 19.60074 18.54883 18.55940
## 04w02_S 16.49336 16.48086 16.58824
## 04w03_S 17.07074 16.91755 16.97935
## 04w04_S 17.81425 17.18182 17.34407
## 20m01_S 16.67287 18.72120 18.72120
## 20m02_S 14.59120 15.59831 15.59831
## 20m03_S 14.70236 17.07537 17.07537
## 20m04_S 15.98811 16.32228 16.37584
## 20m05_S 15.84432 16.17126 16.24164
## sfm01_S 14.95903 15.77372 15.77372
## sfm02_S 14.99649 15.85153 15.85153
## sfm03_S 15.15370 15.87817 15.87817
## sfm04_S 15.09614 15.70999 15.70999
## sfm05_S 15.28462 15.92358 15.92358
## sfm06_S NA NA NA
## sfm07_S 15.39202 16.03691 16.03691
## Sft01_S 15.79067 16.04113 16.11101
## Sft02_S 16.26859 16.67016 16.70813
## percent_wet_1wk_avg percent_wet_2wk_avg percent_wet_3wk_avg twi
## 01m01_S 1.00000000 1.0000000 1.0000000 16.61854
## 01m02_S 1.00000000 1.0000000 1.0000000 15.35936
## 01m03_S 1.00000000 1.0000000 1.0000000 15.67691
## 01m04_S 1.00000000 1.0000000 1.0000000 15.51595
## 01m05_S 0.98216939 0.9910781 0.9916725 15.96002
## 01m06_S 1.00000000 1.0000000 1.0000000 15.75029
## 02m01_S 1.00000000 1.0000000 1.0000000 17.50626
## 02m02_S 1.00000000 1.0000000 1.0000000 16.74125
## 02m03_S 1.00000000 1.0000000 1.0000000 15.93150
## 02m04_S NA NA NA 16.02225
## 02m05_S NA NA NA 16.33888
## 02m06_S 1.00000000 1.0000000 1.0000000 15.43594
## 02m07_S 1.00000000 1.0000000 1.0000000 15.26017
## 02m08_S 1.00000000 1.0000000 1.0000000 15.93291
## 02m09_S 1.00000000 1.0000000 1.0000000 14.74790
## 02m10_S 1.00000000 1.0000000 1.0000000 14.12819
## 02m11_S 1.00000000 1.0000000 1.0000000 14.58442
## 04m01_S 1.00000000 1.0000000 1.0000000 16.12752
## 04m02_S 1.00000000 1.0000000 1.0000000 15.95807
## 04m03_S 1.00000000 1.0000000 1.0000000 17.71474
## 04m04_S 1.00000000 1.0000000 1.0000000 15.89908
## 04m05_S 1.00000000 1.0000000 1.0000000 16.17441
## 04m06_S 1.00000000 0.5940520 0.5544761 15.63759
## 04m07_S 1.00000000 1.0000000 1.0000000 17.33315
## 04m08_S 1.00000000 1.0000000 1.0000000 15.45113
## 04m09_S 1.00000000 1.0000000 1.0000000 15.01552
## 04m10_S 1.00000000 1.0000000 1.0000000 14.87435
## 04m11_S 1.00000000 1.0000000 1.0000000 13.76612
## 04m12_S 1.00000000 1.0000000 1.0000000 14.38845
## 04m13_S 0.18573551 0.1895911 0.2908263 14.38845
## 04t01_S 0.62109955 0.6869496 0.6869496 14.65861
## 04t02_S 1.00000000 1.0000000 1.0000000 13.50106
## 04w01_S 0.68499257 0.8423792 0.8620690 14.72918
## 04w02_S 1.00000000 1.0000000 1.0000000 14.14492
## 04w03_S 1.00000000 1.0000000 1.0000000 13.66540
## 04w04_S 0.08766716 0.3353160 0.4183474 14.39993
## 20m01_S 0.14561664 0.1858736 0.1858736 14.87513
## 20m02_S 1.00000000 1.0000000 1.0000000 16.03198
## 20m03_S 1.00000000 0.8848539 0.8848539 15.66395
## 20m04_S 1.00000000 1.0000000 1.0000000 15.41921
## 20m05_S 1.00000000 1.0000000 1.0000000 13.70703
## sfm01_S 1.00000000 1.0000000 1.0000000 18.44983
## sfm02_S 1.00000000 1.0000000 1.0000000 17.58335
## sfm03_S 1.00000000 1.0000000 1.0000000 18.85499
## sfm04_S 1.00000000 1.0000000 1.0000000 18.68798
## sfm05_S 1.00000000 1.0000000 1.0000000 16.93182
## sfm06_S NA NA NA 16.90306
## sfm07_S 1.00000000 1.0000000 1.0000000 17.54719
## Sft01_S 1.00000000 1.0000000 1.0000000 13.78743
## Sft02_S 1.00000000 0.9836431 0.9847328 14.25019
## distance_from_outlet drainage_area wetted_width slope_percent
## 01m01_S 1.400 28.3099 3.26 1.9659865
## 01m02_S 1.510 27.8823 1.64 6.7913133
## 01m03_S 1.700 25.9970 1 4.6210146
## 01m04_S 1.840 24.7503 5.29 5.1649304
## 01m05_S 2.000 19.8808 1.6 2.6663996
## 01m06_S 2.460 10.5589 1.7473247
## 02m01_S 0.102 24.1532 1.1 0.6906100
## 02m02_S 0.235 23.7320 1.1 1.4580085
## 02m03_S 0.360 20.3529 1.9 2.8084484
## 02m04_S 0.620 18.9885 1.615 2.3933994
## 02m05_S 0.809 17.7830 2.1 1.6336321
## 02m06_S 0.880 15.6919 1.1 3.5524391
## 02m07_S 1.040 14.1328 3.8135683
## 02m08_S 1.200 12.9671 1.3 1.7876415
## 02m09_S 1.330 9.8943 1.3 4.4537733
## 02m10_S 1.600 6.1497 1.7 5.1409352
## 02m11_S 2.000 2.6741 0.25 1.4200636
## 04m01_S 0.320 39.7602 2 4.5041414
## 04m02_S 0.660 30.2096 3 4.0557311
## 04m03_S 0.810 29.3020 1.3 0.6801648
## 04m04_S 0.950 28.4130 1.7 4.0463663
## 04m05_S 1.060 23.8689 2 2.5836580
## 04m06_S 1.200 23.3323 1.5 4.3149004
## 04m07_S 1.400 15.1506 1.7 0.5150872
## 04m08_S 1.600 13.8929 2.3 3.0987060
## 04m09_S 1.800 8.4654 1.2 2.9192216
## 04m10_S 2.100 6.5928 0.6 2.6186152
## 04m11_S 2.500 2.8759 0.57 3.4581806
## 04m12_S 2.700 2.2526 2.7 1.4551936
## 04m13_S 2.930 2.2526 1.4551936
## 04t01_S 1.450 6.6500 3.2760137
## 04t02_S 1.800 3.6848 5.7631675
## 04w01_S 0.600 7.6246 dry 3.4996900
## 04w02_S 0.880 6.1046 1.1 5.0192408
## 04w03_S 1.200 4.2006 2 5.5754928
## 04w04_S 1.400 2.9436 0.5 1.8795989
## 20m01_S 1.310 18.9453 7.4814887
## 20m02_S 1.650 16.3166 2.0370426
## 20m03_S 1.910 9.0899 1.6399257
## 20m04_S 2.060 8.6075 1.9832584
## 20m05_S 2.400 5.4440 6.9193510
## sfm01_S 0.000 132.6873 1.4764745
## sfm02_S 0.140 107.6787 2.8482142
## sfm03_S 0.180 67.0512 0.4976569
## sfm04_S 0.350 60.9054 0.5342054
## sfm05_S 0.660 58.6702 2.9770306
## sfm06_S 0.910 51.0121 2.6644572
## sfm07_S 1.100 49.9061 1.3695436
## Sft01_S 0.300 4.5053 5.2945770
## Sft02_S 1.010 4.8346 3.5823484
## weather pH
## 01m01_S clear, some clouds, hot sunny 90s 8.07
## 01m02_S sunny, clear, warm 8.00
## 01m03_S cloudy, warm, nice breeze 8.20
## 01m04_S some sun, cloudy 8.13
## 01m05_S some sun, cloudy 8.18
## 01m06_S sunny, come clouds 7.99
## 02m01_S sunny, 75deg, clear 7.94
## 02m02_S sunny, clear, 80s 8.22
## 02m03_S sunny (relentless) 8.15
## 02m04_S sunny, clear skies 8.12
## 02m05_S sunny 8.18
## 02m06_S sunny 8.16
## 02m07_S cloudy, cool, breeze 7.97
## 02m08_S overcast, breezy 7.98
## 02m09_S 75, overcast 7.71
## 02m10_S sunny 7.89
## 02m11_S BLANK 8.02
## 04m01_S hot, clear, sunny 8.33
## 04m02_S sunny! Hot! Clear sky! 8.17
## 04m03_S hot, partly cloudly 8.16
## 04m04_S sunny 8.17
## 04m05_S sunny, cooler in shade, breezy 8.04
## 04m06_S hot, cloudy, breezy 8.10
## 04m07_S overcast, mild temps 8.11
## 04m08_S overcast, mild, somewhat windy 7.73
## 04m09_S cloudy, hot 8.07
## 04m10_S cloudy, hot 8.07
## 04m11_S 80s, sunny 8.25
## 04m12_S 80s, hot as heck 8.21
## 04m13_S 80s, sunny NA
## 04t01_S 80s NA
## 04t02_S 80s 8.25
## 04w01_S hot, sunny NA
## 04w02_S warm, sunny 8.08
## 04w03_S suuny, warm 8.16
## 04w04_S sunny, hot 8.02
## 20m01_S 90s, sunny NA
## 20m02_S 90s, sunny NA
## 20m03_S 90s, sunny NA
## 20m04_S 70s, overcast 8.24
## 20m05_S 70s, overcast 8.13
## sfm01_S clear, sunny 8.01
## sfm02_S sunny, clear, hot 8.10
## sfm03_S sunny, clear, hot 7.98
## sfm04_S sunny, clear, hot 8.06
## sfm05_S ~70s, hot, sunny 8.02
## sfm06_S 80s, sunny 8.06
## sfm07_S 90s, sunny 8.01
## Sft01_S 80s, sunny 8.02
## Sft02_S 80s, partly cloudy 8.13
## crewBGC
## 01m01_S Connor Brown, Rachel Wakefield
## 01m02_S Connor Brown, Rachel Wakefield
## 01m03_S Connor Brown, Rachel Wakefield
## 01m04_S Connor Brown, Rachel Wakefield
## 01m05_S Connor Brown, Rachel Wakefield
## 01m06_S Connor Brown, Rachel Wakefield
## 02m01_S Erin Seybold, Amy Burgin
## 02m02_S Erin Seybold, Amy Burgin, Kaci Zarek
## 02m03_S Erin Seybold, Amy Burgin, Kaci Zarek
## 02m04_S Erin Seybold, Amy Burgin, Kaci Zarek
## 02m05_S Erin Seybold, Amy Burgin, Kaci Zarek
## 02m06_S Erin Seybold, Amy Burgin, Kaci Zarek
## 02m07_S Amy Burgin, Kaci Zarek
## 02m08_S Erin Seybold, Amy Burgin, Kaci Zarek
## 02m09_S Erin Seybold, Amy Burgin, Kaci Zarek
## 02m10_S Erin Seybold, Amy Burgin
## 02m11_S Erin Seybold, Amy Burgin, Kaci Zarek
## 04m01_S Jess Wilhelm, Sarah Flynn
## 04m02_S Jess Wilhelm, Sarah Flynn
## 04m03_S Jess Wilhelm, Sarah Flynn
## 04m04_S Jess Wilhelm, Sarah Flynn
## 04m05_S Jess Wilhelm, Sarah Flynn
## 04m06_S Jess Wilhelm, Sarah Flynn
## 04m07_S Jess Wilhelm, Sarah Flynn
## 04m08_S Jess Wilhelm, Sarah Flynn
## 04m09_S Jess Wilhelm, Sarah Flynn
## 04m10_S Jess Wilhelm, Sarah Flynn
## 04m11_S Eva Burke, Bri Richards
## 04m12_S Bri Richards, Eva Burke, Sam Thomas
## 04m13_S Bri Richards, Eva Burke, Sam Thomas
## 04t01_S Bri Richards, Eva Burke, Sam Thomas
## 04t02_S Bri Richards, Eva Burke, Sam Thomas
## 04w01_S Sarah Flynn, Jess Wilhelm, Kaci Zarek
## 04w02_S Sarah Flynn, Jess Wilhelm, Kaci Zarek
## 04w03_S Sarah Flynn, Jess Wilhelm, Kaci Zarek
## 04w04_S Sarah Flynn, Jess Wilhelm, Kaci Zarek
## 20m01_S Eva Burke, Bri Richards
## 20m02_S Eva Burke, Bri Richards
## 20m03_S Eva Burke, Bri Richards
## 20m04_S Eva Burke, Bri Richards
## 20m05_S Eva Burke, Bri Richards
## sfm01_S Erin Seybold, Jess Wilhelm, Connor Brown, Bri Richards, Sarah Flynn
## sfm02_S Erin Seybold, Jess Wilhelm, Connor Brown, Bri Richards, Sarah Flynn
## sfm03_S Jess Wilhelm, Connor Brown, Bri Richards, Sarah Flynn
## sfm04_S BLANK
## sfm05_S Bri Richards, Connor Brown
## sfm06_S Connor Brown, Bri Richards, Rachel Wakefield, Eva Burke
## sfm07_S Connor Brown, Bri Richards, Rachel Wakefield, Eva Burke
## Sft01_S Eva Burke, Bri Richards
## Sft02_S Eva Burke, Bri Richards
## crewMicro
## 01m01_S Ken Aho, Michael J Braus
## 01m02_S Ken Aho, Michael J Braus
## 01m03_S Ken Aho, Michael J Braus
## 01m04_S Ken Aho, Michael J Braus
## 01m05_S Ken Aho, Michael J Braus
## 01m06_S Ken Aho, Michael J Braus
## 02m01_S Kevin Kuehn, Charlie Bond, Diana Diaz, Brett Nave
## 02m02_S Brett Nave, Diana Diaz
## 02m03_S Brett Nave, Diana Diaz
## 02m04_S Brett Nave, Diana Diaz
## 02m05_S Brett Nave, Diana Diaz
## 02m06_S Brett Nave, Diana Diaz
## 02m07_S Brett Nave, Brooke Vogt
## 02m08_S Brett Nave, Brooke Vogt
## 02m09_S Brett Nave, Brooke Vogt
## 02m10_S Brett Nave, Kaci Zarek
## 02m11_S Brett Nave
## 04m01_S Kevin Kuehn, Charlie Bond
## 04m02_S Kevin Kuehn, Charlie Bond
## 04m03_S Kevin Kuehn, Charlie Bond
## 04m04_S Kevin Kuehn, Charlie Bond
## 04m05_S Kevin Kuehn, Charlie Bond
## 04m06_S Kevin Kuehn, Charlie Bond
## 04m07_S Kevin Kuehn, Charlie Bond
## 04m08_S Kevin Kuehn, Charlie Bond
## 04m09_S Kevin Kuehn, Charlie Bond
## 04m10_S Kevin Kuehn, Charlie Bond
## 04m11_S Charlie Bond, Harley-Payj Leatherman
## 04m12_S Lydia Zeglin, Charlie Bond, Harley-Payj Leatherman
## 04m13_S Lydia Zeglin, Charlie Bond, Harley-Payj Leatherman
## 04t01_S Lydia Zeglin, Charlie Bond, Harley-Payj Leatherman
## 04t02_S Lydia Zeglin, Charlie Bond, Harley-Payj Leatherman
## 04w01_S Ken Aho, Michael J Braus
## 04w02_S Ken Aho, Michael J Braus
## 04w03_S Ken Aho, Michael J Braus
## 04w04_S Ken Aho, Michael J Braus
## 20m01_S Eva Burke, Lydia Zeglin
## 20m02_S Lydia Zeglin, Harley-Payj Leatherman
## 20m03_S Lydia Zeglin, Harley-Payj Leatherman
## 20m04_S Lydia Zeglin, Harley-Payj Leatherman
## 20m05_S Lydia Zeglin, Harley-Payj Leatherman
## sfm01_S Brett Nave, Lydia Zeglin, Charlie Bond, Ken Aho, Rob Ramos, Michael J Braus
## sfm02_S Brett Nave, Lydia Zeglin, Charlie Bond, Ken Aho, Rob Ramos, Michael J Braus
## sfm03_S BLANK
## sfm04_S BLANK
## sfm05_S Lydia Zeglin, Michael J Braus, Ken Aho, Harley-Payj Leatherman
## sfm06_S Lydia Zeglin, Michael J Braus, Ken Aho, Harley-Payj Leatherman
## sfm07_S Lydia Zeglin, Michael J Braus, Ken Aho, Harley-Payj Leatherman
## Sft01_S Lydia Zeglin, Harley-Payj Leatherman
## Sft02_S Lydia Zeglin, Harley-Payj Leatherman
## wetdrybin alpha.cent page.rank imp.closeness.cent betweenness.cent
## 01m01_S 1 NA NA NA NA
## 01m02_S 1 2.227619e+09 0.022524170 0.2388125 24
## 01m03_S 1 5.510795e+06 0.019353794 0.2549933 21
## 01m04_S 1 1.438817e+04 0.015623939 0.5225321 16
## 01m05_S 1 1.231568e+02 0.011235874 0.3356343 9
## 01m06_S 1 1.000000e+00 0.006073446 0.0000000 0
## 02m01_S 1 1.429936e+23 0.033713970 1.2118946 10
## 02m02_S 1 2.297338e+21 0.032518264 0.6715845 18
## 02m03_S 1 1.447618e+19 0.031111551 0.6668311 24
## 02m04_S 1 1.106869e+17 0.029456595 0.5153950 28
## 02m05_S 1 7.389150e+14 0.027509587 0.3065895 30
## 02m06_S 1 2.383760e+12 0.025218990 0.2542547 30
## 02m07_S 1 5.599429e+09 0.022524170 0.3314081 28
## 02m08_S 1 2.516975e+07 0.019353794 0.2155983 24
## 02m09_S 1 7.677335e+04 0.015623939 0.1427609 18
## 02m10_S 1 1.508025e+02 0.011235874 0.2736938 10
## 02m11_S 1 1.000000e+00 0.006073446 0.0000000 0
## 04m01_S 1 1.499246e+26 0.036385276 0.7094577 24
## 04m02_S 1 1.038099e+24 0.035660977 0.5690639 33
## 04m03_S 1 5.556482e+21 0.034808861 0.4520184 40
## 04m04_S 1 1.686326e+19 0.033806371 0.6350717 45
## 04m05_S 1 1.199949e+17 0.032626971 0.4365059 48
## 04m06_S 1 4.511630e+14 0.031239442 0.4334246 49
## 04m07_S 1 1.758850e+12 0.029607055 0.4374748 48
## 04m08_S 1 7.518766e+09 0.027686599 0.4364054 45
## 04m09_S 1 1.872708e+07 0.019353794 0.2339300 30
## 04m10_S 1 5.049598e+04 0.015623939 0.3123012 22
## 04m11_S 1 2.742202e+02 0.011235874 0.1500621 12
## 04m12_S 1 1.000000e+00 0.006073446 0.0000000 0
## 04m13_S 0 NA NA NA NA
## 04t01_S 0 NA NA NA NA
## 04t02_S 1 1.000000e+00 0.006073446 0.0000000 0
## 04w01_S 0 NA NA NA NA
## 04w02_S 1 1.639315e+02 0.011235874 0.2516394 0
## 04w03_S 1 NA NA NA NA
## 04w04_S 1 1.000000e+00 0.006073446 0.0000000 0
## 20m01_S 0 NA NA NA NA
## 20m02_S 1 1.000000e+00 0.006073446 0.0000000 0
## 20m03_S 0 NA NA NA NA
## 20m04_S 1 2.773658e+02 0.011235874 0.1483541 0
## 20m05_S 1 1.000000e+00 0.006073446 0.0000000 0
## sfm01_S 1 8.235430e+30 0.095083882 1.6330347 0
## sfm02_S 1 2.520015e+28 0.071004190 1.3390882 24
## sfm03_S 1 1.149650e+19 0.040003835 0.6055277 20
## sfm04_S 1 2.256246e+16 0.033844659 0.5783555 24
## sfm05_S 1 1.184520e+14 0.032672016 0.6108834 28
## sfm06_S 1 NA NA NA NA
## sfm07_S 1 8.808420e+11 0.025218990 0.2500193 25
## Sft01_S 1 0.000000e+00 0.006073446 0.0000000 0
## Sft02_S 1 1.000000e+00 0.006073446 0.0000000 0
## n.nodes.in.paths n.paths upstream.network.length path.length.mean
## 01m01_S NA NA NA NA
## 01m02_S 4 4 1026.2137 780.4340
## 01m03_S 3 3 621.9854 501.6075
## 01m04_S 2 2 238.9767 177.8984
## 01m05_S 1 1 122.1568 122.1568
## 01m06_S 1 0 0.0000 0.0000
## 02m01_S 10 10 2446.4219 1117.5401
## 02m02_S 9 9 2384.1787 1172.5521
## 02m03_S 8 8 2225.4810 1140.5862
## 02m04_S 7 7 2094.6961 1154.0586
## 02m05_S 6 6 1944.8995 1171.6390
## 02m06_S 5 5 1634.9202 1033.9917
## 02m07_S 4 4 1209.2061 760.3469
## 02m08_S 3 3 986.7395 717.1738
## 02m09_S 2 2 658.8946 583.9934
## 02m10_S 1 1 149.8025 149.8025
## 02m11_S 1 0 0.0000 0.0000
## 04m01_S 12 12 2987.4094 1436.2436
## 04m02_S 11 11 2842.9871 1409.2597
## 04m03_S 10 10 2656.1605 1344.6764
## 04m04_S 9 9 2326.6582 1127.9712
## 04m05_S 8 8 2186.1251 1110.8679
## 04m06_S 7 7 1920.1574 965.6001
## 04m07_S 6 6 1663.6467 827.2711
## 04m08_S 5 5 1429.7189 712.0119
## 04m09_S 3 3 828.2233 584.6964
## 04m10_S 2 2 457.3604 320.7504
## 04m11_S 1 1 273.2202 273.2202
## 04m12_S 1 0 0.0000 0.0000
## 04m13_S NA NA NA NA
## 04t01_S NA NA NA NA
## 04t02_S 1 0 0.0000 0.0000
## 04w01_S NA NA NA NA
## 04w02_S 1 1 162.9315 162.9315
## 04w03_S NA NA NA NA
## 04w04_S 1 0 0.0000 0.0000
## 20m01_S NA NA NA NA
## 20m02_S 1 0 0.0000 0.0000
## 20m03_S NA NA NA NA
## 20m04_S 1 1 276.3658 276.3658
## 20m05_S 1 0 0.0000 0.0000
## sfm01_S 36 36 9141.8043 1544.4877
## sfm02_S 24 24 6169.6472 1419.4947
## sfm03_S 10 10 2826.7084 1258.5955
## sfm04_S 8 8 2162.1418 980.6325
## sfm05_S 7 7 1971.6645 903.0345
## sfm06_S NA NA NA NA
## sfm07_S 5 5 1421.6323 1019.7657
## Sft01_S 1 0 0.0000 0.0000
## Sft02_S 1 0 0.0000 0.0000
## path.length.var path.length.skew path.length.kurt path.degree.mean
## 01m01_S NA <NA> NA NA
## 01m02_S 54316.821 -1.248785863 1.6976796 1.500000
## 01m03_S 9519.896 0.066974863 NA 1.333333
## 01m04_S 3730.568 #NAME? NA 1.000000
## 01m05_S 0.000 <NA> NA 0.000000
## 01m06_S NA <NA> NA NA
## 02m01_S 671357.198 0.370974689 -1.3388034 1.800000
## 02m02_S 608464.589 0.271073351 -1.4227284 1.777778
## 02m03_S 539974.121 0.133092965 -1.4235345 1.750000
## 02m04_S 450631.873 -0.010853048 -1.2887412 1.714286
## 02m05_S 329631.762 -0.031515342 -1.3367751 1.666667
## 02m06_S 217368.310 0.076868398 -2.3974893 1.600000
## 02m07_S 156084.891 -0.334968534 -3.1685438 1.500000
## 02m08_S 79528.637 -1.372761625 NA 1.333333
## 02m09_S 5610.194 Inf NA 1.000000
## 02m10_S 0.000 <NA> NA 0.000000
## 02m11_S NA <NA> NA NA
## 04m01_S 685100.475 0.056099525 -1.1486650 1.833333
## 04m02_S 581881.270 0.051359207 -1.1201534 1.818182
## 04m03_S 475691.709 0.100896097 -1.2067074 1.800000
## 04m04_S 401314.439 0.04780462 -1.1157912 1.777778
## 04m05_S 314364.590 0.094403399 -1.1958385 1.750000
## 04m06_S 242725.861 0.127465125 -1.3543796 1.714286
## 04m07_S 185411.970 0.098709732 -1.5602981 1.666667
## 04m08_S 138000.872 -0.061526721 -1.5543863 1.200000
## 04m09_S 35303.942 0.570981423 NA 1.333333
## 04m10_S 18662.314 Inf NA 1.000000
## 04m11_S 0.000 <NA> NA 0.000000
## 04m12_S NA <NA> NA NA
## 04m13_S NA <NA> NA NA
## 04t01_S NA <NA> NA NA
## 04t02_S NA <NA> NA NA
## 04w01_S NA <NA> NA NA
## 04w02_S 0.000 <NA> NA 0.000000
## 04w03_S NA <NA> NA NA
## 04w04_S NA <NA> NA NA
## 20m01_S NA <NA> NA NA
## 20m02_S NA <NA> NA NA
## 20m03_S NA <NA> NA NA
## 20m04_S 0.000 <NA> NA 0.000000
## 20m05_S NA <NA> NA NA
## sfm01_S 777673.412 0.194607866 -1.1602263 1.888889
## sfm02_S 687169.878 0.129037598 -1.1487930 1.833333
## sfm03_S 476988.579 0.048611958 -1.3427457 1.600000
## sfm04_S 320238.675 0.017314743 -1.8477390 1.750000
## sfm05_S 264053.134 -0.167224111 -1.8815127 1.428571
## sfm06_S NA <NA> NA NA
## sfm07_S 140905.808 -0.918431724 -0.4568918 1.600000
## Sft01_S NA <NA> NA NA
## Sft02_S NA <NA> NA NA
## path.degree.var path.degree.skew path.degree.kurt in.eccentricity
## 01m01_S NA NA NA NA
## 01m02_S 0.2500000 0.00000000 -6.00000000 1026.2137
## 01m03_S 0.2222222 1.73205081 NA 621.9854
## 01m04_S 0.0000000 NA NA 238.9767
## 01m05_S 0.0000000 NA NA 122.1568
## 01m06_S NA NA NA 0.0000
## 02m01_S 0.1600000 -1.77878118 1.40625000 2446.4219
## 02m02_S 0.1728395 -1.61984774 0.73469388 2384.1787
## 02m03_S 0.1875000 -1.44016460 0.00000000 2225.4810
## 02m04_S 0.2040816 -1.22963409 -0.84000000 2094.6961
## 02m05_S 0.2222222 -0.96824584 -1.87500000 1944.8995
## 02m06_S 0.2400000 -0.60858062 -3.33333333 1634.9202
## 02m07_S 0.2500000 0.00000000 -6.00000000 1209.2061
## 02m08_S 0.2222222 1.73205081 NA 986.7395
## 02m09_S 0.0000000 NA NA 658.8946
## 02m10_S 0.0000000 NA NA 149.8025
## 02m11_S NA NA NA 0.0000
## 04m01_S 0.3055556 -0.06298367 0.65454546 2787.4053
## 04m02_S 0.3305785 0.02763854 0.41250000 2642.9831
## 04m03_S 0.3600000 0.13176157 0.17857143 2456.1565
## 04m04_S 0.3950617 0.25446429 -0.04017857 2126.6542
## 04m05_S 0.4375000 0.40406102 -0.22857143 1986.1211
## 04m06_S 0.4897959 0.59529405 -0.35000000 1720.1533
## 04m07_S 0.2222222 -0.96824584 -1.87500000 1463.6427
## 04m08_S 0.5600000 -0.51224083 -0.61224490 1229.7149
## 04m09_S 0.2222222 1.73205081 NA 828.2233
## 04m10_S 0.0000000 NA NA 457.3604
## 04m11_S 0.0000000 NA NA 273.2202
## 04m12_S NA NA NA 0.0000
## 04m13_S NA NA NA NA
## 04t01_S NA NA NA NA
## 04t02_S NA NA NA 0.0000
## 04w01_S NA NA NA NA
## 04w02_S 0.0000000 NA NA 162.9315
## 04w03_S NA NA NA NA
## 04w04_S NA NA NA 0.0000
## 20m01_S NA NA NA NA
## 20m02_S NA NA NA 0.0000
## 20m03_S NA NA NA NA
## 20m04_S 0.0000000 NA NA 276.3658
## 20m05_S NA NA NA 0.0000
## sfm01_S 0.2654321 -0.16232311 0.79578353 3282.2906
## sfm02_S 0.3055556 -0.05854473 0.23698615 2955.4908
## sfm03_S 0.6400000 -0.38910838 0.37039620 2256.1267
## sfm04_S 0.1875000 -1.44016460 0.00000000 1746.5857
## sfm05_S 0.5306122 -1.11454978 0.27337278 1556.1084
## sfm06_S NA NA NA NA
## sfm07_S 0.2400000 -0.60858062 -3.33333333 1421.6323
## Sft01_S NA NA NA 0.0000
## Sft02_S NA NA NA 0.0000
## mean.efficiency alpha.cent.wt flowing.upstream.length.m
## 01m01_S NA NA NA
## 01m02_S 1.420660e-04 16.7227749 1026.2137
## 01m03_S 1.516910e-04 10.2898922 621.9854
## 01m04_S 3.108460e-04 4.8215449 238.9767
## 01m05_S 1.996630e-04 0.6931472 122.1568
## 01m06_S 0.000000e+00 0.6931472 0.0000
## 02m01_S 7.209370e-04 63.6024867 2446.4219
## 02m02_S 3.995150e-04 55.8258767 2384.1787
## 02m03_S 3.966870e-04 48.1181484 2225.4810
## 02m04_S 3.066000e-04 40.4709846 2094.6961
## 02m05_S 1.823850e-04 32.8980191 1944.8995
## 02m06_S 1.512520e-04 25.4986698 1634.9202
## 02m07_S 1.971490e-04 18.4009505 1209.2061
## 02m08_S 1.282560e-04 11.5065545 986.7395
## 02m09_S 8.492620e-05 5.0225801 658.8946
## 02m10_S 1.628160e-04 0.6931472 149.8025
## 02m11_S 0.000000e+00 0.6931472 0.0000
## 04m01_S 4.220450e-04 71.9801783 2987.4094
## 04m02_S 3.385270e-04 64.0275678 2842.9871
## 04m03_S 2.688980e-04 56.1429308 2656.1605
## 04m04_S 3.777940e-04 48.3907426 2326.6582
## 04m05_S 2.596700e-04 40.7008567 2186.1251
## 04m06_S 2.578370e-04 33.1406943 1920.1574
## 04m07_S 2.602470e-04 25.7239270 1663.6467
## 04m08_S 2.596110e-04 18.4586939 1429.7189
## 04m09_S 1.391610e-04 11.7394190 828.2233
## 04m10_S 1.857830e-04 5.6175714 457.3604
## 04m11_S 8.926956e-05 0.6931472 273.2202
## 04m12_S 0.000000e+00 0.6931472 0.0000
## 04m13_S NA NA NA
## 04t01_S NA NA NA
## 04t02_S 0.000000e+00 0.6931472 0.0000
## 04w01_S NA NA NA
## 04w02_S 1.496960e-04 0.6931472 162.9315
## 04w03_S NA NA NA
## 04w04_S 0.000000e+00 0.6931472 0.0000
## 20m01_S NA NA NA
## 20m02_S 0.000000e+00 0.6931472 0.0000
## 20m03_S NA NA NA
## 20m04_S 8.825350e-05 0.6931472 276.3658
## 20m05_S 0.000000e+00 0.6931472 0.0000
## sfm01_S 9.714660e-04 88.7097372 9141.8043
## sfm02_S 7.966020e-04 79.9823402 6169.6472
## sfm03_S 3.602190e-04 46.1814551 2826.7084
## sfm04_S 3.440540e-04 38.5026005 2162.1418
## sfm05_S 3.634050e-04 30.9159671 1971.6645
## sfm06_S NA NA NA
## sfm07_S 1.487320e-04 23.6564061 1421.6323
## Sft01_S 0.000000e+00 0.6931472 0.0000
## Sft02_S 0.000000e+00 0.6931472 0.0000
## W_Chl.a_ug_per_mL rock_area B_Chl.a_ug_per_cm2 W_AFDM B_AFDM S_AFDM
## 01m01_S 0.000000 76.2 0.160 1.8 74.39 0.032
## 01m02_S 0.000000 75.0 0.267 1.9 84.73 0.063
## 01m03_S 0.000000 73.4 0.275 1.8 83.65 0.052
## 01m04_S 0.000000 61.2 0.024 1.8 72.60 0.054
## 01m05_S 0.000000 75.0 0.029 2.1 68.11 0.043
## 01m06_S 9.624198 75.0 0.055 1.9 76.48 0.041
## 02m01_S 8.178957 75.0 0.149 2.0 68.85 0.041
## 02m02_S 0.000000 75.0 0.060 2.1 59.15 0.042
## 02m03_S 0.000000 75.0 0.065 1.6 17.97 0.044
## 02m04_S 10.687007 75.0 0.025 1.7 59.57 0.051
## 02m05_S 11.022447 75.0 0.288 0.6 16.64 0.038
## 02m06_S 15.654617 75.0 0.046 2.1 64.53 0.061
## 02m07_S 197.142577 75.0 0.259 2.0 67.20 0.045
## 02m08_S 2.891405 75.0 0.040 2.0 68.05 0.000
## 02m09_S 0.000000 75.0 0.209 1.9 63.31 0.024
## 02m10_S 0.000000 75.0 0.088 2.0 62.67 0.072
## 02m11_S 47.871840 75.0 0.066 0.6 19.63 0.045
## 04m01_S 0.000000 74.9 0.269 0.6 25.18 0.047
## 04m02_S 0.000000 36.4 0.043 0.8 53.52 0.097
## 04m03_S 9.618699 56.0 0.534 0.8 33.01 0.060
## 04m04_S 0.000000 65.9 0.911 0.9 26.10 0.067
## 04m05_S 11.487364 67.8 0.414 0.7 22.24 0.050
## 04m06_S 0.000000 71.9 1.150 0.8 38.99 0.034
## 04m07_S 40.861097 71.9 1.118 0.9 32.06 0.032
## 04m08_S 110.954030 83.0 0.691 0.9 24.75 0.043
## 04m09_S 69.102034 40.5 1.668 0.8 125.89 0.038
## 04m10_S 8.865000 33.5 0.164 0.8 64.27 0.067
## 04m11_S 19.965844 54.3 0.268 0.7 41.16 0.067
## 04m12_S 138.469097 83.6 0.179 0.9 28.06 0.116
## 04m13_S NA 52.1 0.552 0.7 46.33 0.177
## 04t01_S NA 57.1 0.044 0.7 27.74 0.086
## 04t02_S 16.131531 50.9 0.389 0.7 45.68 0.085
## 04w01_S NA 75.0 0.027 NA 33.28 0.035
## 04w02_S 0.000000 56.5 0.036 0.8 38.87 0.053
## 04w03_S 29.790082 75.0 0.019 0.8 29.46 0.038
## 04w04_S 0.000000 71.2 0.021 0.8 25.21 0.073
## 20m01_S NA 75.0 0.838 0.7 26.88 0.074
## 20m02_S 235.094272 62.5 0.087 0.7 29.38 0.072
## 20m03_S NA 82.9 0.159 0.6 23.46 0.052
## 20m04_S 98.406280 66.4 0.734 0.6 35.64 0.063
## 20m05_S 24.120099 89.7 0.279 0.8 24.67 0.071
## sfm01_S 0.000000 86.8 0.140 0.6 21.74 0.037
## sfm02_S 0.000000 76.0 0.266 0.7 19.44 0.053
## sfm03_S 0.000000 75.0 0.064 0.7 29.01 0.078
## sfm04_S 21.026154 75.0 0.655 NA 43.71 0.028
## sfm05_S 4.068694 75.9 0.202 0.7 23.91 0.034
## sfm06_S 90.806642 94.6 0.185 0.7 19.34 0.038
## sfm07_S 18.039189 61.5 0.191 0.8 29.85 0.050
## Sft01_S 0.000000 79.0 0.314 0.8 26.89 NA
## Sft02_S 2.149028 91.0 0.428 0.7 20.80 NA
## LL_AFDM pw_cluster wet_dist_cluster burn_freq
## 01m01_S 0.759 2 1 0.8292685
## 01m02_S 0.857 3 1 0.8292685
## 01m03_S 1.000 3 1 0.8292685
## 01m04_S 1.000 3 1 0.8292685
## 01m05_S 1.000 1 2 0.8292685
## 01m06_S 1.000 3 1 0.8292685
## 02m01_S 0.615 3 3 0.9512196
## 02m02_S 0.750 3 3 0.9512196
## 02m03_S 0.654 3 3 0.4390243
## 02m04_S 0.619 3 3 0.4390243
## 02m05_S 0.667 2 2 0.4390243
## 02m06_S 0.696 3 3 0.4390243
## 02m07_S 0.848 1 2 0.4390243
## 02m08_S 0.882 3 3 0.4390243
## 02m09_S 1.000 3 1 0.4390243
## 02m10_S 1.000 3 1 0.4390243
## 02m11_S NA 2 2 0.4390243
## 04m01_S 0.656 2 2 0.4390243
## 04m02_S 0.444 2 2 0.4390243
## 04m03_S 0.706 3 3 0.2439024
## 04m04_S 0.480 1 2 0.2439024
## 04m05_S 0.735 1 2 0.2439024
## 04m06_S 0.800 3 3 0.2439024
## 04m07_S 0.909 3 1 0.2439024
## 04m08_S 0.500 1 2 0.2439024
## 04m09_S 1.000 3 1 0.2439024
## 04m10_S 1.000 3 1 0.2439024
## 04m11_S 1.000 3 1 0.2439024
## 04m12_S 1.000 3 1 0.2439024
## 04m13_S 0.889 3 1 0.2439024
## 04t01_S 0.302 3 1 0.2926829
## 04t02_S 1.000 3 1 0.2926829
## 04w01_S 0.962 3 3 0.4390243
## 04w02_S 1.000 3 3 0.4390243
## 04w03_S 1.000 3 3 0.4390243
## 04w04_S 1.000 3 1 0.4390243
## 20m01_S 0.952 3 1 0.1463415
## 20m02_S 1.000 3 1 0.1463415
## 20m03_S 0.905 3 1 0.1463415
## 20m04_S 1.000 3 1 0.1463415
## 20m05_S 0.917 3 1 0.1463415
## sfm01_S 0.783 2 3 0.9512196
## sfm02_S 0.833 3 3 0.2926829
## sfm03_S 0.704 3 3 0.2926829
## sfm04_S 0.788 3 3 0.2926829
## sfm05_S 0.741 2 3 0.2926829
## sfm06_S 0.526 3 3 0.1463415
## sfm07_S 0.846 3 3 0.1463415
## Sft01_S 0.655 2 2 0.2926829
## Sft02_S 1.000 3 3 0.2926829summary(metadata_sitechars$burn_interval)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.051 2.278 3.417 3.296 4.100 6.833sd(metadata_sitechars$burn_interval)## [1] 1.759497metasite_corr<- metadata_sitechars[,c("prc_wet","tempC_mean","elevation","distance_from_outlet","drainage_area","twi", "slope_percent","burn_interval","canopy_cover_percent", "B_Chl.a_ug_per_cm2")]
library(linkET)
metacorrplot50<- qcorrplot(correlate(x=metasite_corr[1:10], y=NULL, method = "spearman"), type = "lower", diag = FALSE, grid_size = 0.5) +
geom_square() +
geom_mark(sep = '\n',size=4,sig_level = c(0.05,0.01,0.001),sig_thres = 0.05,
only_mark = TRUE)+
#scale_fill_gradientn(colours = RColorBrewer::brewer.pal(11, "RdBu"))+
scale_fill_gradientn(colours = RColorBrewer::brewer.pal(11, "RdYlBu"))+
guides(fill = guide_colorbar(title = "Spearman's rho"))+
theme(legend.key.size = unit(25,"pt"),
axis.text = element_text(size = 18,color = "black"),
strip.text = element_text(size = 18,color = "black"),
plot.title=element_text(size=16),)+
theme(legend.position = "right")+
xlab(label="")+
ylab(label="")+
labs(title = "Environmental predictors (50 sites)")
metacorrplot50
metadata_sitecharsa<-metadata_sitechars[!is.na(metadata_sitechars$alpha.cent.wt),]
metasitedag_corr<- metadata_sitecharsa[,c("prc_wet","tempC_mean","elevation","distance_from_outlet","drainage_area","twi", "slope_percent","burn_interval", "canopy_cover_percent", "B_Chl.a_ug_per_cm2","W_Chl.a_ug_per_mL","alpha.cent.wt", "flowing.upstream.length.m")]
metacorrplot42<- qcorrplot(correlate(x=metasitedag_corr[1:13], y=NULL, method = "spearman"), type = "lower", diag = FALSE, grid_size = 0.5) +
geom_square() +
geom_mark(sep = '\n',size=4,sig_level = c(0.05,0.01,0.001),sig_thres = 0.05,
only_mark = TRUE)+
#scale_fill_gradientn(colours = RColorBrewer::brewer.pal(11, "RdBu"))+
scale_fill_gradientn(colours = RColorBrewer::brewer.pal(11, "RdYlBu"))+
guides(fill = guide_colorbar(title = "Spearman's rho"))+
theme(legend.key.size = unit(25,"pt"),
axis.text = element_text(size = 18,color = "black"),
strip.text = element_text(size = 18,color = "black"),
plot.title=element_text(size=16),)+
theme(legend.position = "right")+
xlab(label="")+
ylab(label="")+
labs(title = "Environmental predictors (42 wet StreamDAG sites)")
metacorrplot42
library(cowplot)##
## Attaching package: 'cowplot'## The following object is masked from 'package:ggpubr':
##
## get_legendplotout <- "envi_corrplots_03102025.tiff"
agg_tiff(filename=plotout, width=2400, height=4200, units="px",
pointsize=10, res=600, compression="lzw", scaling=0.5)
plot_grid(metacorrplot50,metacorrplot42,labels="AUTO", ncol=1)
invisible(dev.off())
detach("package:linkET", unload=TRUE)###Accumulation curves from unrarefied data
#library('rgdal')
#library('biosurvey')
### epilithon leaf sediment
myColors <- c("#9ACD32","#CD853F","#B0C4DE")
pseqtestf<-pseqtest
samdftestf<-samdftest
botu<- pseqtestf@otu_table[samdftestf$substrate=='B',]
lotu<- pseqtestf@otu_table[samdftestf$substrate=='L',]
sotu<- pseqtestf@otu_table[samdftestf$substrate=='S',]
#wotu<- pseqtestf@otu_table[samdftestf$substrate=='W',]
accum_nor_B <- specaccum(botu, method="random")
accum_nor_L <- specaccum(lotu, method="random")
accum_nor_S <- specaccum(sotu, method="random")
#accum_nor_W <- specaccum(wotu, method="random")
plot(accum_nor_S, col = myColors[3])
#then plot the rest
plot(accum_nor_L, add = TRUE, col = myColors[2]) #col is COLOUR setting, so change it to something else if you want
plot(accum_nor_B, add = TRUE, col = myColors[1])
#plot(accum_nor_W, add = TRUE, col = 4)
legend("topright",y=NULL,legend=as.factor(samdftestf$substrate),fill=NULL,col = as.factor(samdftestf$substrate))
#legend("topright",threednmds$xyz.convert(18, 0, 12), pch = 1, col = as.factor(samdftest$substrate), yjust=0, legend = as.factor(samdftest$substrate), cex = 1)
#data <- data.frame(Sites=acc$sites, Richness=acc$richness, SD=acc$sd)
dfb <-data.frame(Sites=accum_nor_B$sites, Richness=accum_nor_B$richness, SD=accum_nor_B$sd)
dfl <-data.frame(Sites=accum_nor_L$sites, Richness=accum_nor_L$richness, SD=accum_nor_L$sd)
dfs <-data.frame(Sites=accum_nor_S$sites, Richness=accum_nor_S$richness, SD=accum_nor_S$sd)
#dfw <-data.frame(Sites=accum_nor_W$sites, Richness=accum_nor_W$richness, SD=accum_nor_W$sd)
library(RColorBrewer)
#define custom color scale
#myColors <- brewer.pal(3, "Spectral")
myColors <- c("#9ACD32","#CD853F","#6495ED")
#myColors <- c("#ADFF2F","#A0522D","#FFD700")
#myColors <- c("#ADFF2F","#D2B48C"," #8B4513")
#myColors <- c("#F8766D","#7CAE00", "#00BFC4") #"#C77CFF"
names(myColors) <- levels(samdftestf$substrate)
custom_colors <- scale_colour_manual(name = samdftestf$substrate, values = myColors)
ASVaccumsub<- ggplot() +
geom_point(data=dfl, colour="#CD853F", aes(x=Sites, y=Richness, )) +
geom_line(data=dfl, colour="#CD853F",aes(x=Sites, y=Richness)) +
geom_ribbon(data=dfl,aes(x=Sites,
ymin=(Richness-2*SD),ymax=(Richness+2*SD)),alpha=0.2)+
geom_point(data=dfb, colour="#9ACD32", aes(x=Sites, y=Richness)) +
geom_line(data=dfb, colour="#9ACD32", aes(x=Sites, y=Richness)) +
geom_ribbon(data=dfb,aes(x=Sites,
ymin=(Richness-2*SD),ymax=(Richness+2*SD)),alpha=0.2)+
geom_point(data=dfs, colour="#6495ED", aes(x=Sites, y=Richness)) +
geom_line(data=dfs, colour="#6495ED", aes(x=Sites, y=Richness)) +
geom_ribbon(data=dfs,aes(x=Sites,
ymin=(Richness-2*SD),ymax=(Richness+2*SD)),alpha=0.2) +
# Color of lines and points
scale_color_discrete(name='Substrate:', labels=c('Epilithic Biofilms', 'Leaf Litter', 'Sediment'))+
#title(main = "Fungal ASV accumulation curves by substrate")+
#, 'Surface #water'))+
# geom_point(data=dfw, aes(x=Sites, y=Richness, colour='W')) +
#geom_line(data=dfw, aes(x=Sites, y=Richness,colour='W')) +
#geom_ribbon(data=dfw,aes(x=Sites,
#ymin=(Richness-2*SD),ymax=(Richness+2*SD)),alpha=0.2)+
labs(title = "Fungal ASV accumulation curves by substrate", y="ASV Richness")+
theme(text=element_text(size=12), #change font size of all text
axis.text=element_text(size=12), #change font size of axis text
axis.title=element_text(size=12), #change font size of axis titles
plot.title=element_text(size=15), #change font size of plot title
# legend.text=element_text(size=15),
# legend.title = element_text(size=15),#change font size of legend text
strip.text.x = element_text(size = 12),
#legend.title=element_text(size=9),
# legend.position = "right",
plot.margin = unit(c(1, 1, 1, 2), "lines")) #+
#theme(legend.position="bottom")
#scale_fill_discrete(labels=c('High Program',
# custom_colors
ASVaccumsub
ASVaccumsub2<- ASVaccumsub+ theme(legend.position="bottom", plot.margin = unit(c(1, 1, 2, 2), "lines"))
ASVaccumsub2
plotout <- "Fungal_ASV_accum.curve_substrate_022224.tiff"
agg_tiff(filename=plotout, width=2555, height=1464, units="px",
pointsize=10, res=600, compression="lzw", scaling=0.5)
ASVaccumsub<- ASVaccumsub + theme(legend.position = "bottom")+ scale_color_discrete(name='Substrate:', labels=c('Epilithic Biofilms', 'Leaf Litter', 'Sediment'))## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.ASVaccumsub
invisible(dev.off())Saved ASVaccumsub so I can combine it in a Beta Diversity figure with multiple other plots.
library(MicEco)
library(eulerr)
myColors <- c("#9ACD32","#CD853F","#6495ED")
venndiagram<- ps_venn(pseqtest,group="substrate", labels=c('Epilithon', 'Leaf litter', 'Sediment'), fill=myColors)
venndiagram
summary stats on taxa before rarefaction
tax_tib<-as.data.frame(pseqtest@tax_table@.Data)
unique_gen<-unique(tax_tib$Genus)
###number of genera
length(unique_gen)## [1] 989nrow(tax_tib)## [1] 6564unique_fam<-unique(tax_tib$Family)
#number of families
length(unique_fam)## [1] 413unique(tax_tib$Phylum)## [1] "p__Ascomycota" "p__Basidiomycota"
## [3] "p__Blastocladiomycota" "p__Mortierellomycota"
## [5] NA "p__Kickxellomycota"
## [7] "p__Rozellomycota" "p__Aphelidiomycota"
## [9] "p__Basidiobolomycota" "p__Mucoromycota"
## [11] "p__Chytridiomycota" "p__Calcarisporiellomycota"
## [13] "p__Olpidiomycota" "p__Glomeromycota"
## [15] "p__Neocallimastigomycota" "p__Entorrhizomycota"#unique(tax_tib$Class)
#unique(tax_tib$Family)
## number of ASV without genus assigned
sum(is.na(tax_tib$Genus))## [1] 1564### number of ASVs
nrow(tax_tib)## [1] 6564## proportion of ASVs without genus-level taxonomy:
sum(is.na(tax_tib$Genus))/nrow(tax_tib)## [1] 0.2382693### proportion of ASVs with genus-or-lower taxonomy:
1-(sum(is.na(tax_tib$Genus))/nrow(tax_tib))## [1] 0.7617307Rarefaction
NMDS of fungi, rarefied data rarefaction
summary(rowSums(cured_asvs))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 352 7706 14872 17680 25932 59985sum(rowSums(cured_asvs))## [1] 2545863rowSums(cured_asvs)## 01m01_B 01m01_L 01m01_S 01m02_B 01m02_L 01m02_S 01m03_B 01m03_L 01m03_S 01m04_B
## 3057 14839 10080 7353 43601 2549 5324 34079 12735 6781
## 01m04_L 01m04_S 01m05_B 01m05_L 01m05_S 01m06_B 01m06_L 01m06_S 02m01_B 02m01_L
## 21031 11674 6414 32460 8861 3353 33616 15291 959 33392
## 02m01_S 02m02_B 02m02_L 02m02_S 02m03_B 02m03_L 02m03_S 02m04_L 02m04_S 02m05_B
## 28833 9469 16814 21342 31449 24657 39038 25888 7663 3399
## 02m05_L 02m06_B 02m06_L 02m06_S 02m07_B 02m07_L 02m07_S 02m08_B 02m08_L 02m08_S
## 44235 13430 20384 12116 7721 59985 29320 716 43176 2953
## 02m09_B 02m09_L 02m09_S 02m10_B 02m10_L 02m10_S 02m11_B 02m11_S 04m01_B 04m01_L
## 11247 23858 6147 17150 28241 19307 13306 13981 24438 28848
## 04m01_S 04m02_B 04m02_L 04m02_S 04m03_B 04m03_L 04m03_S 04m04_B 04m04_L 04m04_S
## 17104 10799 33147 19771 14551 5333 17328 14700 22412 12689
## 04m05_B 04m05_L 04m05_S 04m06_B 04m06_L 04m06_S 04m07_L 04m07_S 04m08_B 04m08_L
## 6675 26064 7167 3384 36404 9423 23929 3607 352 11880
## 04m08_S 04m09_B 04m09_L 04m09_S 04m10_B 04m10_L 04m10_S 04m11_B 04m11_L 04m11_S
## 2659 1451 18005 3124 27744 5421 24009 11575 25477 12324
## 04m12_B 04m12_L 04m12_S 04m13_B 04m13_L 04m13_S 04t01_B 04t01_L 04t01_S 04t02_B
## 5956 19195 18670 7849 56252 15698 8776 33402 6529 15162
## 04t02_L 04t02_S 04w01_B 04w01_L 04w01_S 04w02_B 04w02_L 04w02_S 04w03_B 04w03_L
## 28756 11520 16484 43197 18909 2441 32314 24453 8313 10039
## 04w03_S 04w04_L 04w04_S 20m01_L 20m01_S 20m02_B 20m02_L 20m02_S 20m03_B 20m03_L
## 19108 31826 3018 35696 16404 7727 23272 13105 8008 40416
## 20m03_S 20m04_B 20m04_L 20m04_S 20m05_B 20m05_L 20m05_S sfm01_B sfm01_L sfm01_S
## 14905 3547 44427 34406 900 26199 6383 13850 27294 7888
## sfm02_B sfm02_L sfm02_S sfm03_B sfm03_L sfm03_S sfm04_B sfm04_L sfm04_S sfm05_B
## 7012 31527 26776 4523 33214 1747 10658 43674 13650 7260
## sfm05_L sfm05_S sfm06_B sfm06_L sfm06_S sfm07_B sfm07_L sfm07_S Sft01_B Sft01_L
## 23448 10453 1095 15116 15920 7266 25794 14319 20756 28730
## Sft01_S Sft02_B Sft02_L Sft02_S
## 17019 11569 37060 9915### NMDS no convergent solutions at the lower read depth.
set.seed(2024)
### Rarefying to 2441 reads to balance sample retention and read depth
rarefied <- Rarefy(cured_asvs, 2441)
rarefied$discard## [1] "02m01_B" "02m08_B" "04m08_B" "04m09_B" "20m05_B" "sfm03_S" "sfm06_B"## [1] "saltob1"
rarasv<-rarefied$otu.tab.rff
rarasv<- rarasv[,colSums(rarasv)>0]
summary(rowSums(rarasv))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2441 2441 2441 2441 2441 2441## look at read depths for the different substrates:
asvtab <- otu_table(rarasv, taxa_are_rows = FALSE)
taxtab <- tax_table(taxtab)
### removing sample 04w03_L, which was 1 leaf rather than 3, and sample Sft02_L, where the leaf types were missing
#asvtab <- asvtab[row.names(asvtab)!="04w03_L"|row.names(asvtab)!="Sft02_L",]
metadata<- metadata[metadata$Sample!="04w03_L",]
metadata<- metadata[metadata$Sample!="Sft02_L",]
#metadata <- sample_data(metadata_full)
#combine into phyloseq object
pseqtest <- phyloseq(asvtab, taxtab, metadata)
#extract easy-to-use sample data
samdftest <- data.frame(sample_data(pseqtest))
## Examine the read depths by substrate:
###Leaf
#summary(rowSums(pseqtest@otu_table[pseqtest@sam_data$substrate=="L",]))
count(samdftest[samdftest$substrate=="L",])## n
## 1 47###Biofilm
#summary(rowSums(pseqtest@otu_table[pseqtest@sam_data$substrate=="B",]))
count(samdftest[samdftest$substrate=="B",])## n
## 1 40###Sediment
#summary(rowSums(pseqtest@otu_table[pseqtest@sam_data$substrate=="S",]))
count(samdftest[samdftest$substrate=="S",])## n
## 1 48unique(samdftest$Site)## [1] "01m01" "01m02" "01m03" "01m04" "01m05" "01m06" "02m01" "02m02" "02m03"
## [10] "02m04" "02m05" "02m06" "02m07" "02m08" "02m09" "02m10" "02m11" "04m01"
## [19] "04m02" "04m03" "04m04" "04m05" "04m06" "04m07" "04m08" "04m09" "04m10"
## [28] "04m11" "04m12" "04m13" "04t01" "04t02" "04w01" "04w02" "04w03" "04w04"
## [37] "20m01" "20m02" "20m03" "20m04" "20m05" "sfm01" "sfm02" "sfm03" "sfm04"
## [46] "sfm05" "sfm06" "sfm07" "Sft01" "Sft02"BETA DIVERSITY
#generate distance matrices
braytest <- phyloseq::distance(pseqtest, method = "bray")
#jactest <- phyloseq::distance(pseqtest, method="jaccard", binary=TRUE)#perform NMDS
#Bray-Curtis
braymds <- metaMDS(braytest, k=2, trymax=500, wascores = T)## Run 0 stress 0.1891976
## Run 1 stress 0.1909647
## Run 2 stress 0.194675
## Run 3 stress 0.1947434
## Run 4 stress 0.1933821
## Run 5 stress 0.1941827
## Run 6 stress 0.1823702
## ... New best solution
## ... Procrustes: rmse 0.05962884 max resid 0.3333668
## Run 7 stress 0.1897989
## Run 8 stress 0.1854733
## Run 9 stress 0.1959085
## Run 10 stress 0.192988
## Run 11 stress 0.192998
## Run 12 stress 0.187779
## Run 13 stress 0.1832224
## Run 14 stress 0.1956015
## Run 15 stress 0.1866696
## Run 16 stress 0.1904082
## Run 17 stress 0.1932108
## Run 18 stress 0.1886411
## Run 19 stress 0.187856
## Run 20 stress 0.1893
## Run 21 stress 0.1899564
## Run 22 stress 0.2034321
## Run 23 stress 0.1892057
## Run 24 stress 0.1862322
## Run 25 stress 0.1891321
## Run 26 stress 0.193693
## Run 27 stress 0.187922
## Run 28 stress 0.1966583
## Run 29 stress 0.1852158
## Run 30 stress 0.1970215
## Run 31 stress 0.1870829
## Run 32 stress 0.19193
## Run 33 stress 0.1932851
## Run 34 stress 0.1786509
## ... New best solution
## ... Procrustes: rmse 0.02473061 max resid 0.1328608
## Run 35 stress 0.1943987
## Run 36 stress 0.1841554
## Run 37 stress 0.1886268
## Run 38 stress 0.1870192
## Run 39 stress 0.1857629
## Run 40 stress 0.190391
## Run 41 stress 0.1902609
## Run 42 stress 0.1985282
## Run 43 stress 0.1903108
## Run 44 stress 0.1921002
## Run 45 stress 0.1903866
## Run 46 stress 0.1880437
## Run 47 stress 0.1923011
## Run 48 stress 0.1889239
## Run 49 stress 0.1998468
## Run 50 stress 0.1913908
## Run 51 stress 0.1961096
## Run 52 stress 0.192702
## Run 53 stress 0.1914727
## Run 54 stress 0.1880673
## Run 55 stress 0.1930168
## Run 56 stress 0.1964203
## Run 57 stress 0.188099
## Run 58 stress 0.1927073
## Run 59 stress 0.1991048
## Run 60 stress 0.1933623
## Run 61 stress 0.1872033
## Run 62 stress 0.1877236
## Run 63 stress 0.188185
## Run 64 stress 0.1930705
## Run 65 stress 0.196322
## Run 66 stress 0.1892131
## Run 67 stress 0.1988549
## Run 68 stress 0.1911861
## Run 69 stress 0.1926128
## Run 70 stress 0.1978086
## Run 71 stress 0.1949472
## Run 72 stress 0.1925612
## Run 73 stress 0.2020521
## Run 74 stress 0.20001
## Run 75 stress 0.1945963
## Run 76 stress 0.1904209
## Run 77 stress 0.2025506
## Run 78 stress 0.1804386
## Run 79 stress 0.1887502
## Run 80 stress 0.189241
## Run 81 stress 0.1964436
## Run 82 stress 0.1913557
## Run 83 stress 0.1903153
## Run 84 stress 0.1889073
## Run 85 stress 0.1833801
## Run 86 stress 0.1936862
## Run 87 stress 0.1822609
## Run 88 stress 0.1940231
## Run 89 stress 0.1822199
## Run 90 stress 0.187969
## Run 91 stress 0.187106
## Run 92 stress 0.1879956
## Run 93 stress 0.1865517
## Run 94 stress 0.1909236
## Run 95 stress 0.185613
## Run 96 stress 0.1979995
## Run 97 stress 0.1901827
## Run 98 stress 0.1888313
## Run 99 stress 0.2008897
## Run 100 stress 0.1948892
## Run 101 stress 0.1976156
## Run 102 stress 0.1970096
## Run 103 stress 0.1892256
## Run 104 stress 0.1886899
## Run 105 stress 0.1912327
## Run 106 stress 0.1866122
## Run 107 stress 0.2006185
## Run 108 stress 0.1915875
## Run 109 stress 0.1957624
## Run 110 stress 0.1960796
## Run 111 stress 0.1968611
## Run 112 stress 0.1946191
## Run 113 stress 0.1940794
## Run 114 stress 0.1955224
## Run 115 stress 0.191767
## Run 116 stress 0.1878099
## Run 117 stress 0.1913789
## Run 118 stress 0.1899183
## Run 119 stress 0.1906047
## Run 120 stress 0.1945278
## Run 121 stress 0.1844739
## Run 122 stress 0.1884762
## Run 123 stress 0.1836336
## Run 124 stress 0.1888132
## Run 125 stress 0.1880465
## Run 126 stress 0.1893887
## Run 127 stress 0.1877807
## Run 128 stress 0.191328
## Run 129 stress 0.1869569
## Run 130 stress 0.1909365
## Run 131 stress 0.1966191
## Run 132 stress 0.190234
## Run 133 stress 0.1933312
## Run 134 stress 0.1895461
## Run 135 stress 0.1909229
## Run 136 stress 0.1923826
## Run 137 stress 0.1900266
## Run 138 stress 0.1890306
## Run 139 stress 0.1897782
## Run 140 stress 0.1880601
## Run 141 stress 0.1913699
## Run 142 stress 0.1899254
## Run 143 stress 0.1877806
## Run 144 stress 0.1968299
## Run 145 stress 0.1908171
## Run 146 stress 0.1909314
## Run 147 stress 0.1886333
## Run 148 stress 0.1828151
## Run 149 stress 0.1922951
## Run 150 stress 0.1819447
## Run 151 stress 0.1853189
## Run 152 stress 0.1843539
## Run 153 stress 0.1909701
## Run 154 stress 0.1939012
## Run 155 stress 0.1973056
## Run 156 stress 0.1862691
## Run 157 stress 0.1845437
## Run 158 stress 0.1849241
## Run 159 stress 0.1935671
## Run 160 stress 0.1892494
## Run 161 stress 0.1828378
## Run 162 stress 0.1887036
## Run 163 stress 0.1868185
## Run 164 stress 0.1959341
## Run 165 stress 0.1917535
## Run 166 stress 0.1863588
## Run 167 stress 0.1982453
## Run 168 stress 0.1882796
## Run 169 stress 0.1873868
## Run 170 stress 0.2029617
## Run 171 stress 0.1887465
## Run 172 stress 0.1826517
## Run 173 stress 0.1957385
## Run 174 stress 0.1944879
## Run 175 stress 0.1981288
## Run 176 stress 0.1902004
## Run 177 stress 0.192224
## Run 178 stress 0.1805073
## Run 179 stress 0.1917858
## Run 180 stress 0.1777344
## ... New best solution
## ... Procrustes: rmse 0.01910761 max resid 0.1617494
## Run 181 stress 0.1898878
## Run 182 stress 0.1859507
## Run 183 stress 0.1931142
## Run 184 stress 0.1902316
## Run 185 stress 0.1882324
## Run 186 stress 0.1902599
## Run 187 stress 0.1898895
## Run 188 stress 0.1881876
## Run 189 stress 0.1890749
## Run 190 stress 0.1822972
## Run 191 stress 0.1819856
## Run 192 stress 0.1981679
## Run 193 stress 0.196465
## Run 194 stress 0.1877572
## Run 195 stress 0.1984572
## Run 196 stress 0.1865292
## Run 197 stress 0.1891491
## Run 198 stress 0.1962862
## Run 199 stress 0.1942636
## Run 200 stress 0.1886724
## Run 201 stress 0.1973607
## Run 202 stress 0.1883056
## Run 203 stress 0.1941041
## Run 204 stress 0.1884547
## Run 205 stress 0.1883699
## Run 206 stress 0.1910796
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## *** Best solution was not repeated -- monoMDS stopping criteria:
## 62: no. of iterations >= maxit
## 430: stress ratio > sratmax
## 8: scale factor of the gradient < sfgrmin## Warning in metaMDS(braytest, k = 2, trymax = 500, wascores = T): stress is
## (nearly) zero: you may have insufficient data
brayscores <- scores(braymds)
#Jaccard
#jacmds <- metaMDS(jactest, k=3, trymax=500, wascores = T)
#jacscores <- scores(jacmds)
braymds##
## Call:
## metaMDS(comm = braytest, k = 2, trymax = 500, wascores = T)
##
## global Multidimensional Scaling using monoMDS
##
## Data: braytest
## Distance: bray
##
## Dimensions: 2
## Stress: 0.1777344
## Stress type 1, weak ties
## Best solution was not repeated after 500 tries
## The best solution was from try 180 (random start)
## Scaling: centring, PC rotation, halfchange scaling
## Species: scores missing#jacmdsstressplot(braymds)
#stressplot(jacmds)
factorfit(brayscores, samdftest$substrate, permutations = 999)## Centroids:
## NMDS1 NMDS2
## PB 0.0693 -0.0640
## PL 0.6033 -0.0651
## PS -0.6484 0.1171
##
## Goodness of fit:
## r2 Pr(>r)
## P 0.495 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999factorfit(brayscores, samdftest$flow_state, strata = samdftest$substrate, permutations = 999)## Centroids:
## NMDS1 NMDS2
## Pdry 0.2113 0.0924
## Pflowing -0.0249 -0.0116
## Ppool -0.0065 0.0235
##
## Goodness of fit:
## r2 Pr(>r)
## P 0.0107 0.232
## Blocks: strata
## Permutation: free
## Number of permutations: 999factorfit(brayscores, samdftest$pw_cluster, strata = samdftest$substrate, permutations = 999)## Centroids:
## NMDS1 NMDS2
## P1 -0.1436 -0.1575
## P2 -0.0353 -0.0043
## P3 0.0282 0.0232
##
## Goodness of fit:
## r2 Pr(>r)
## P 0.0102 0.26
## Blocks: strata
## Permutation: free
## Number of permutations: 999myColors <- c("#9ACD32","#CD853F","#6495ED")
nmdsp3 <- plot_ordination(pseqtest, braymds, type="samples", color="substrate", shape="flow_state")+
geom_point(size=2)+scale_colour_manual(values=myColors)
nmdsp3
nmds2plus<- plot_ordination(pseqtest, braymds, type="samples")
nmds2plus$layers<-nmds2plus$layers[-1]
nmds2plus<-nmds2plus+
geom_point(size=2, aes(shape=flow_state, color=substrate))+
scale_colour_manual(values=myColors, name='Substrate:', labels=c('Epilithon', 'Leaf litter', 'Sediment'))+
#scale_color_discrete(name='Substrate:', labels=c('Epilithic biofilms', 'Leaf litter', 'Benthic sediment'))+
scale_shape_discrete(name='Flow state:')+
stat_ellipse(
#inherit.aes = FALSE,
aes(x=brayscores[,1],y=brayscores[,2],color=samdftest$substrate
# ,linetype=(samdftest$flow_state)
))+
labs(title = "Bray-Curtis NMDS of fungal ASVs by substrate")+
annotate("text", x = -1.1, y = -1.4, label = sprintf("k=2, Stress=%.4f",braymds$stress), size=3.5)+
# theme(legend.text = element_text(size=10))+
theme(text=element_text(size=8), #change font size of all text
axis.text=element_text(size=10), #change font size of axis text
axis.title=element_text(size=10), #change font size of axis titles
plot.title=element_text(size=12), #change font size of plot title
legend.text=element_text(size=10), #change font size of legend text
strip.text.x = element_text(size = 10),
#legend.title=element_text(size=9),
legend.position = "none",
plot.margin = unit(c(1, 1, 1, 2), "lines")) #+
# guides(color = myColors) #+scale_colour_manual(values=myColors)
nmds2plus
### now with legend, for publishing
nmds2plusleg<-nmds2plus+
geom_point(size=2, aes(shape=flow_state, color=substrate))+
scale_colour_manual(values=myColors, name='Substrate:', labels=c('Epilithon', 'Leaf litter', 'Sediment'))+
#scale_color_discrete(name='Substrate:', labels=c('Epilithic biofilms', 'Leaf litter', 'Benthic sediment'))+
scale_shape_discrete(name='Flow state:')+
stat_ellipse(
#inherit.aes = FALSE,
aes(x=brayscores[,1],y=brayscores[,2],color=samdftest$substrate
# ,linetype=(samdftest$flow_state)
))+
labs(title = "Bray-Curtis NMDS of fungal ASVs by substrate")+
annotate("text", x = -1.1, y = -1.4, label = sprintf("k=2, Stress=%.4f",braymds$stress), size=3.5)+
# theme(legend.text = element_text(size=10))+
theme(text=element_text(size=12), #change font size of all text
axis.text=element_text(size=12), #change font size of axis text
axis.title=element_text(size=12), #change font size of axis titles
plot.title=element_text(size=15), #change font size of plot title
legend.text=element_text(size=15),
legend.title = element_text(size=15),#change font size of legend text
strip.text.x = element_text(size = 12),
#legend.title=element_text(size=9),
legend.position = "right",
plot.margin = unit(c(1, 1, 1, 2), "lines")) #+## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for shape is already present.
## Adding another scale for shape, which will replace the existing scale. # guides(color = myColors) #+scale_colour_manual(values=myColors)
nmds2plusleg
Combined beta-diversity plot:
ASVaccumsub nmds2plus venndiagram
library(cowplot)
### Golden ratio: 1.618:1, to scale the figures.
ASVaccumsub<- ASVaccumsub+ ggtitle("") #theme(plot.title = NULL)#element_text(size=11))
bottom<- plot_grid(ASVaccumsub,venndiagram,#dbRDA_B,dbRDAL,dbRDA_S,
labels=c('B','C'), ncol=2, rel_widths = c(1.618,1))
bottom
plotout <- "Beta_threeway_03.15.2025.tiff"
agg_tiff(filename=plotout, width=2200, height=2500, units="px",
pointsize=10, res=600, compression="lzw", scaling=0.5)
plot_grid(nmds2plusleg,bottom,
labels=c('A','',''), ncol=1, rel_heights = c(1.618,1))
invisible(dev.off())Table of PERMANOVA results
### ANOSIM for effects of substrate ## cut, redundant with PERMANOVA
#anosim(braytest, samdftest$substrate, permutations = 99999)
### PERMANOVA for substarte effects
adonis2(braytest ~ substrate, data = samdftest, permutations = 99999, by="terms")## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 99999
##
## adonis2(formula = braytest ~ substrate, data = samdftest, permutations = 99999, by = "terms")
## Df SumOfSqs R2 F Pr(>F)
## substrate 2 6.300 0.13147 9.9903 1e-05 ***
## Residual 132 41.618 0.86853
## Total 134 47.918 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1### testing for differences in dispersion between substrates
bds<- betadisper(braytest, samdftest$substrate)
anova(bds)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 0.00886 0.0044275 0.8442 0.4322
## Residuals 132 0.69230 0.0052447### no significant differences in dispersion between substrates.
### dispersion test for substrate
bds<- betadisper(braytest, samdftest$substrate)
permutest(bds)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.00886 0.0044275 0.8442 999 0.433
## Residuals 132 0.69230 0.0052447plot(bds)
boxplot(bds)
mod.HSD<- TukeyHSD(bds)
mod.HSD## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = distances ~ group, data = df)
##
## $group
## diff lwr upr p adj
## L-B 0.003254524 -0.03367464 0.04018369 0.9762330
## S-B 0.018457369 -0.01829450 0.05520924 0.4609375
## S-L 0.015202846 -0.02002459 0.05043028 0.5637110plot(mod.HSD)
PERMANOVA for all samples
samdftest$lndrainage_area<- log(samdftest$drainage_area)
## annual flow permanence
adonis2(braytest ~ prc_wet, strata=samdftest$substrate, data = samdftest, permutations = 9999, by="terms")## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Blocks: strata
## Permutation: free
## Number of permutations: 9999
##
## adonis2(formula = braytest ~ prc_wet, data = samdftest, permutations = 9999, by = "terms", strata = samdftest$substrate)
## Df SumOfSqs R2 F Pr(>F)
## prc_wet 1 0.483 0.01008 1.3542 0.0045 **
## Residual 133 47.435 0.98992
## Total 134 47.918 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1adonis2(braytest ~ lndrainage_area*prc_wet, strata=samdftest$substrate, data = samdftest, permutations = 9999, by="terms")## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Blocks: strata
## Permutation: free
## Number of permutations: 9999
##
## adonis2(formula = braytest ~ lndrainage_area * prc_wet, data = samdftest, permutations = 9999, by = "terms", strata = samdftest$substrate)
## Df SumOfSqs R2 F Pr(>F)
## lndrainage_area 1 0.589 0.01229 1.6561 0.0006 ***
## prc_wet 1 0.447 0.00932 1.2557 0.0115 *
## lndrainage_area:prc_wet 1 0.289 0.00604 0.8133 0.6851
## Residual 131 46.593 0.97235
## Total 134 47.918 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#################################
#adonis2(braytest ~ lndrainage_area+burn_interval+prc_wet+tempC_mean
# , strata=samdftest$substrate, data = samdftest, permutations = 9999, by="terms")
##########
### Multi-factor PERMANOVA for all samples
## bray~ drainage:annual percent wet + burn interval
adonis2(braytest ~ lndrainage_area*prc_wet+burn_interval#+tempC_mean
, strata=samdftest$substrate, data = samdftest, permutations = 99999, by="terms")## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Blocks: strata
## Permutation: free
## Number of permutations: 99999
##
## adonis2(formula = braytest ~ lndrainage_area * prc_wet + burn_interval, data = samdftest, permutations = 99999, by = "terms", strata = samdftest$substrate)
## Df SumOfSqs R2 F Pr(>F)
## lndrainage_area 1 0.589 0.01229 1.6620 0.00054 ***
## prc_wet 1 0.447 0.00932 1.2602 0.01081 *
## burn_interval 1 0.519 0.01084 1.4656 0.00080 ***
## lndrainage_area:prc_wet 1 0.289 0.00604 0.8167 0.67279
## Residual 130 46.073 0.96151
## Total 134 47.918 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1################################PERMANOVA for StreamDAG/Wet sites
####streamDAG measures on beta diversity
#packageVersion("streamDAG")
pseq_DAG<- subset_samples(pseqtest, !is.na(alpha.cent))
sam_DAG<- data.frame(sample_data(pseq_DAG))
table(sam_DAG$substrate)##
## B L S
## 34 40 40bray_DAG <- phyloseq::distance(pseq_DAG, method = "bray")
##stream temp the week before
adonis2(bray_DAG ~ alpha.cent, data = sam_DAG, strata=sam_DAG$substrate)## Permutation test for adonis under reduced model
## Blocks: strata
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = bray_DAG ~ alpha.cent, data = sam_DAG, strata = sam_DAG$substrate)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.272 0.00676 0.7621 0.765
## Residual 112 39.986 0.99324
## Total 113 40.259 1.00000adonis2(bray_DAG ~ alpha.cent.wt, data = sam_DAG, strata=sam_DAG$substrate, permutations = 9999)## Permutation test for adonis under reduced model
## Blocks: strata
## Permutation: free
## Number of permutations: 9999
##
## adonis2(formula = bray_DAG ~ alpha.cent.wt, data = sam_DAG, permutations = 9999, strata = sam_DAG$substrate)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.475 0.01181 1.3381 0.0093 **
## Residual 112 39.783 0.98819
## Total 113 40.259 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#adonis2(bray_DAG ~ alpha.cent.wt+burn_interval+prc_wet+tempC_mean, data = sam_DAG, strata=sam_DAG$substrate, permutations = 9999, by="terms")
adonis2(bray_DAG ~ alpha.cent.wt*prc_wet+burn_interval, data = sam_DAG, strata=sam_DAG$substrate, permutations = 99999, by="terms")## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Blocks: strata
## Permutation: free
## Number of permutations: 99999
##
## adonis2(formula = bray_DAG ~ alpha.cent.wt * prc_wet + burn_interval, data = sam_DAG, permutations = 99999, by = "terms", strata = sam_DAG$substrate)
## Df SumOfSqs R2 F Pr(>F)
## alpha.cent.wt 1 0.475 0.01181 1.3450 0.00817 **
## prc_wet 1 0.420 0.01043 1.1883 0.02887 *
## burn_interval 1 0.487 0.01209 1.3775 0.00455 **
## alpha.cent.wt:prc_wet 1 0.356 0.00884 1.0073 0.16258
## Residual 109 38.520 0.95683
## Total 113 40.259 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PERMANOVA for Leaf Type effects in leaf samples
# subsetting for leaf sampels only
pseqL<- subset_samples(pseqtest, substrate=='L')
samL<- data.frame(sample_data(pseqL))
table(samL$substrate)##
## L
## 47bray_L <- phyloseq::distance(pseqL, method = "bray")
adonis2(bray_L ~ Oak, data = samL, permutations = 9999, by="terms")## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
##
## adonis2(formula = bray_L ~ Oak, data = samL, permutations = 9999, by = "terms")
## Df SumOfSqs R2 F Pr(>F)
## Oak 1 0.4538 0.03197 1.4862 0.0161 *
## Residual 45 13.7418 0.96803
## Total 46 14.1957 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1adonis2(bray_L ~ Elm, data = samL, permutations = 9999, by="terms")## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
##
## adonis2(formula = bray_L ~ Elm, data = samL, permutations = 9999, by = "terms")
## Df SumOfSqs R2 F Pr(>F)
## Elm 1 0.4416 0.03111 1.4449 0.0236 *
## Residual 45 13.7540 0.96889
## Total 46 14.1957 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1adonis2(bray_L ~ Populus, data = samL, permutations = 9999, by="terms")## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
##
## adonis2(formula = bray_L ~ Populus, data = samL, permutations = 9999, by = "terms")
## Df SumOfSqs R2 F Pr(>F)
## Populus 1 0.4617 0.03252 1.5127 0.0121 *
## Residual 45 13.7340 0.96748
## Total 46 14.1957 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1adonis2(bray_L ~ Graminales, data = samL, permutations = 9999, by="terms")## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
##
## adonis2(formula = bray_L ~ Graminales, data = samL, permutations = 9999, by = "terms")
## Df SumOfSqs R2 F Pr(>F)
## Graminales 1 0.4363 0.03073 1.4269 0.0295 *
## Residual 45 13.7594 0.96927
## Total 46 14.1957 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1otherleaf<- samL
otherleaf$Other_leaf <- ifelse(otherleaf$Locust == "1" | otherleaf$Rubrus == "1" | otherleaf$Elderberry == "1" | otherleaf$Forb == "1" | otherleaf$Shrub == "1" , 1, 0)
adonis2(bray_L ~ Other_leaf, data = otherleaf)## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = bray_L ~ Other_leaf, data = otherleaf)
## Df SumOfSqs R2 F Pr(>F)
## Model 1 0.3519 0.02479 1.144 0.209
## Residual 45 13.8437 0.97521
## Total 46 14.1957 1.00000ALPHA DIVERSITY
Documentation for estimate_richness advises you do not
rarefy data beforehand, so I am testing on the beta
pseqtest version.
#setwd("/Users/chunk/Documents/DADA2/DADA2_package_test/Kz_Syn_ITS/run_2/outputs")
#calculate alpha diversity
#write.csv(samdftest, "/Users/chunk/Documents/DADA2/DADA2_package_test/Kz_Syn_ITS/run_2/outputs/metadata.csv" )
#metadata <- read.csv("~/Documents/DADA2/DADA2_package_test/Kz_Syn_ITS/metadata.csv", stringsAsFactors=TRUE)
#pseqtest <- pseqtestBetac
#samdftest <- samdftestBetac
alpha <- estimate_richness(pseqtest, split=TRUE, measures=NULL)
#write.csv(alpha, "alpha diversity temp.csv")
#alpha2 <- read.csv("~/Documents/DADA2/DADA2_package_test/Kz_Syn_ITS/alpha diversity.csv", stringsAsFactors=TRUE)
#alpha's row names got messed up, but order is conserved, so set row.names to samdftest
row.names(alpha)<- row.names(samdftest)
alpha <- merge(samdftest, alpha, by= 'row.names', all=TRUE)
row.names(alpha) <- alpha$Row.names
#alpha
#write.csv(alpha, "alpharar diversity_fungi_040723.csv")
pseq_richplot<- plot_richness(pseqtest, x = "substrate", color = "substrate", measures = c("Observed","Shannon"))+ geom_boxplot()
pseq_richplot
Playing with wet/dry effects
alpha$substrate<- as.factor(alpha$substrate)
alpha$flow_state<- as.factor(alpha$flow_state)
alpha$wet_dry<- as.factor(alpha$wet_dry)#Set theme for upcoming figures
theme_set(theme_bw() + theme(
plot.title = element_text(size=16, color="black"),#,
axis.text.x = element_text(size=12, color="black"),
axis.text.y = element_text(size=12, color="black"),
axis.title.x = element_text(size=14),
axis.title.y = element_text(size=14)#,
#legend.text = element_text(size=10),
#legend.title = element_text(size=10),
# legend.position = "bottom",
# legend.key=element_blank(),
# legend.key.size = unit(0.5, "cm"),
# legend.spacing.x = unit(0.1, "cm"),
# legend.spacing.y = unit(0.1, "cm"),
#panel.background = element_blank(),
#panel.border = element_rect(colour = "black", fill=NA, size=1),
#plot.background = element_blank()))
))alpha$flow_state<- as.factor(alpha$flow_state)
sublablist<- c('B'='Epilithic biofilms', 'L'='Leaf litter', 'S'='Benthic sediment')
sub_labeller <- function(variable,value){
return(sublablist[value])
}
library(ggpattern)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ readr 2.1.5
## ✔ lubridate 1.9.3 ✔ stringr 1.5.1
## ✔ purrr 1.0.2 ✔ tibble 3.2.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ lubridate::stamp() masks cowplot::stamp()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggpubr)
library(rstatix)##
## Attaching package: 'rstatix'
##
## The following object is masked from 'package:stats':
##
## filterstat.test <- alpha %>%
wilcox_test(Observed~substrate) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance()
stat.test## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 Observed B L 40 47 759 0.124 0.372 ns
## 2 Observed B S 40 48 569 0.001 0.003 **
## 3 Observed L S 47 48 728 0.003 0.009 **effsize <- alpha %>%
wilcox_effsize(Observed~substrate)
effsize## # A tibble: 3 × 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 Observed B L 0.165 40 47 small
## 2 Observed B S 0.349 40 48 moderate
## 3 Observed L S 0.305 47 48 moderatesubalpha_rich <- ggplot(alpha, aes(x=substrate, y=Observed)) +
geom_boxplot()+
scale_x_discrete(labels = c('Epilithon','Leaf litter','Sediment'))+
ylab("ASV Richness")+
ylim(0,600)+
geom_signif(comparisons = list(c("L","S")),y_position = 500,
map_signif_level=TRUE)+
geom_signif(comparisons = list(c("B","S")),y_position = 540,
map_signif_level=TRUE)+
geom_signif(comparisons = list(c("B","L")),y_position = 460,
map_signif_level=TRUE)+
theme(legend.position='none',axis.text.x = element_text(size=16), axis.title.x = element_blank(),strip.text.x = element_text(size = 16), plot.title = element_text(size=20, color="black"),
axis.text.y = element_text(size=16, color="black"),
axis.title.y = element_text(size=16))+
theme(plot.margin = unit(c(2, 2, 2, 2), "lines"))
subalpha_rich## Warning in wilcox.test.default(c(210, 195, 187, 216, 292, 210, 294, 262, :
## cannot compute exact p-value with ties## Warning in wilcox.test.default(c(101, 224, 219, 72, 203, 107, 149, 210, :
## cannot compute exact p-value with ties## Warning in wilcox.test.default(c(101, 224, 219, 72, 203, 107, 149, 210, :
## cannot compute exact p-value with ties
###############
##################################################
stat.test <- alpha %>%
wilcox_test(Shannon ~substrate) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance()
stat.test## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 Shannon B L 40 47 1225 0.015 4.5 e-2 *
## 2 Shannon B S 40 48 578 0.001 3 e-3 **
## 3 Shannon L S 47 48 416 0.0000000282 8.46e-8 ****effsize <- alpha %>%
wilcox_effsize(Shannon~substrate)
effsize## # A tibble: 3 × 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 Shannon B L 0.260 40 47 small
## 2 Shannon B S 0.341 40 48 moderate
## 3 Shannon L S 0.544 47 48 largesubalpha_shan <- ggplot(alpha, aes(x=substrate, y=Shannon)) +
geom_boxplot()+
scale_x_discrete(labels = c('Epilithon','Leaf litter','Sediment'))+
ylab("Shannon Diversity Index")+
ylim(0,6.5)+
geom_signif(comparisons = list(c("L","S")),y_position = 5.7,
map_signif_level=TRUE)+
geom_signif(comparisons = list(c("B","S")),y_position = 6.2,
map_signif_level=TRUE)+
geom_signif(comparisons = list(c("B","L")),y_position = 5.4,
map_signif_level=TRUE)+
theme(legend.position='none',axis.text.x = element_text(size=16), axis.title.x = element_blank(),strip.text.x = element_text(size = 16), plot.title = element_text(size=20, color="black"),
axis.text.y = element_text(size=16, color="black"),
axis.title.y = element_text(size=16))+
theme(plot.margin = unit(c(2, 2, 2, 2), "lines"))
subalpha_shan
###############
library(cowplot)
plotout <- "Alpha-2-panel_03-14-2025.tiff"
agg_tiff(filename=plotout, width=1800, height=2800, units="px",
pointsize=10, res=600, compression="lzw", scaling=0.5)
plot_grid(subalpha_rich, subalpha_shan, labels="AUTO",label_size = 18, ncol = 1)## Warning in wilcox.test.default(c(210, 195, 187, 216, 292, 210, 294, 262, :
## cannot compute exact p-value with ties## Warning in wilcox.test.default(c(101, 224, 219, 72, 203, 107, 149, 210, :
## cannot compute exact p-value with ties## Warning in wilcox.test.default(c(101, 224, 219, 72, 203, 107, 149, 210, :
## cannot compute exact p-value with tiesinvisible(dev.off())Generalized linear models for alpha diversity
### checkinig for normality of response variables (required for gaussian)
# Shapiro-Wilk test for normality
shapiro_test <- alpha %>%
group_by(substrate) %>%
summarize(p_value = shapiro.test(Observed)$p.value)
shapiro_test## # A tibble: 3 × 2
## substrate p_value
## <fct> <dbl>
## 1 B 0.321
## 2 L 0.898
## 3 S 0.296# Shapiro-Wilk test for normality
shapiro_test <- alpha %>%
group_by(substrate) %>%
summarize(p_value = shapiro.test(Shannon)$p.value)
shapiro_test## # A tibble: 3 × 2
## substrate p_value
## <fct> <dbl>
## 1 B 0.271
## 2 L 0.0456
## 3 S 0.000265So, for ASV richness, we can use a Gaussian (normal) distribution, while Shannon is non-normal and would would better with Gamma (skewed withh all positive values).
GLMs for ASV richness
### Now, we should log-transform drainage area for this, since:
summary(alpha$drainage_area)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.253 6.621 16.317 23.725 28.096 132.687summary(log(alpha$drainage_area))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.8121 1.8903 2.7922 2.6852 3.3356 4.8880## Log-transforming drainage_area
alpha$lndrainage_area<- log(alpha$drainage_area)
### Richness is normal but shannon diversity is non-normal
##################### Richness ############################
####### leaf
##### BEST FIT FOR LEAF LITTER RICHNESS ###################
glmoL<- glm(Observed ~ lndrainage_area, family = gaussian, data=alpha, subset = substrate=='L')
summary(glmoL) ##### AIC: 516.67 LOWEST FOR Leaf RICHNESS##
## Call:
## glm(formula = Observed ~ lndrainage_area, family = gaussian,
## data = alpha, subset = substrate == "L")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 157.545 24.842 6.342 9.69e-08 ***
## lndrainage_area 26.904 8.528 3.155 0.00286 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 3200.049)
##
## Null deviance: 175854 on 46 degrees of freedom
## Residual deviance: 144002 on 45 degrees of freedom
## AIC: 516.67
##
## Number of Fisher Scoring iterations: 2###########################################################
glmoL<- glm(Observed ~ lndrainage_area+prc_wet, family = gaussian, data=alpha, subset = substrate=='L')
summary(glmoL) ###### AIC: 517.49##
## Call:
## glm(formula = Observed ~ lndrainage_area + prc_wet, family = gaussian,
## data = alpha, subset = substrate == "L")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 153.914 25.045 6.145 2.06e-07 ***
## lndrainage_area 24.924 8.719 2.858 0.00648 **
## prc_wet 33.383 31.562 1.058 0.29596
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 3191.628)
##
## Null deviance: 175854 on 46 degrees of freedom
## Residual deviance: 140432 on 44 degrees of freedom
## AIC: 517.49
##
## Number of Fisher Scoring iterations: 2glmoL<- glm(Observed ~ lndrainage_area*prc_wet, family = gaussian, data=alpha, subset = substrate=='L')
summary(glmoL) ##### AIC: 517.95##
## Call:
## glm(formula = Observed ~ lndrainage_area * prc_wet, family = gaussian,
## data = alpha, subset = substrate == "L")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 118.24 38.82 3.046 0.00396 **
## lndrainage_area 38.14 14.03 2.718 0.00942 **
## prc_wet 220.79 159.48 1.384 0.17337
## lndrainage_area:prc_wet -64.27 53.62 -1.199 0.23726
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 3160.272)
##
## Null deviance: 175854 on 46 degrees of freedom
## Residual deviance: 135892 on 43 degrees of freedom
## AIC: 517.95
##
## Number of Fisher Scoring iterations: 2glmoL<- glm(Observed ~ lndrainage_area+prc_wet+burn_interval, family = gaussian, data=alpha, subset = substrate=='L')
summary(glmoL) ##### AIC: 518.06##
## Call:
## glm(formula = Observed ~ lndrainage_area + prc_wet + burn_interval,
## family = gaussian, data = alpha, subset = substrate == "L")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 131.965 31.408 4.202 0.000131 ***
## lndrainage_area 25.944 8.733 2.971 0.004844 **
## prc_wet 37.766 31.676 1.192 0.239705
## burn_interval 5.381 4.676 1.151 0.256183
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 3168.277)
##
## Null deviance: 175854 on 46 degrees of freedom
## Residual deviance: 136236 on 43 degrees of freedom
## AIC: 518.06
##
## Number of Fisher Scoring iterations: 2glmoL<- glm(Observed ~ lndrainage_area+prc_wet+burn_interval+tempC_mean, family = gaussian, data=alpha, subset = substrate=='L')
summary(glmoL) #### AIC: 520.04##
## Call:
## glm(formula = Observed ~ lndrainage_area + prc_wet + burn_interval +
## tempC_mean, family = gaussian, data = alpha, subset = substrate ==
## "L")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 135.4836 41.2379 3.285 0.00206 **
## lndrainage_area 25.9820 8.8387 2.940 0.00532 **
## prc_wet 37.8393 32.0491 1.181 0.24438
## burn_interval 5.5063 4.8222 1.142 0.25997
## tempC_mean -0.2823 2.1086 -0.134 0.89415
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 3242.329)
##
## Null deviance: 175854 on 46 degrees of freedom
## Residual deviance: 136178 on 42 degrees of freedom
## AIC: 520.04
##
## Number of Fisher Scoring iterations: 2glmoL<- glm(Observed ~ lndrainage_area*prc_wet+tempC_mean, family = gaussian, data=alpha, subset = substrate=='L')
summary(glmoL) #### AIC: 519.95##
## Call:
## glm(formula = Observed ~ lndrainage_area * prc_wet + tempC_mean,
## family = gaussian, data = alpha, subset = substrate == "L")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 118.06017 47.92236 2.464 0.0179 *
## lndrainage_area 38.13087 14.22277 2.681 0.0104 *
## prc_wet 220.71838 161.74127 1.365 0.1796
## tempC_mean 0.01336 2.07137 0.006 0.9949
## lndrainage_area:prc_wet -64.24119 54.38894 -1.181 0.2442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 3235.514)
##
## Null deviance: 175854 on 46 degrees of freedom
## Residual deviance: 135892 on 42 degrees of freedom
## AIC: 519.95
##
## Number of Fisher Scoring iterations: 2glmoL<- glm(Observed ~ lndrainage_area*prc_wet+burn_interval, family = gaussian, data=alpha, subset = substrate=='L')
summary(glmoL) #### AIC: 518.67##
## Call:
## glm(formula = Observed ~ lndrainage_area * prc_wet + burn_interval,
## family = gaussian, data = alpha, subset = substrate == "L")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 99.865 42.342 2.359 0.0231 *
## lndrainage_area 38.296 14.004 2.735 0.0091 **
## prc_wet 213.629 159.326 1.341 0.1872
## burn_interval 5.029 4.672 1.077 0.2878
## lndrainage_area:prc_wet -60.406 53.640 -1.126 0.2665
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 3148.64)
##
## Null deviance: 175854 on 46 degrees of freedom
## Residual deviance: 132243 on 42 degrees of freedom
## AIC: 518.67
##
## Number of Fisher Scoring iterations: 2#########################################################
####### bioifilm
glmoB<- glm(Observed ~ lndrainage_area, family = gaussian, data=alpha, subset = substrate=='B')
summary(glmoB) ###AIC: 485.76##
## Call:
## glm(formula = Observed ~ lndrainage_area, family = gaussian,
## data = alpha, subset = substrate == "B")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 114.14 43.34 2.634 0.0121 *
## lndrainage_area 34.65 15.04 2.303 0.0268 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 9971.903)
##
## Null deviance: 431830 on 39 degrees of freedom
## Residual deviance: 378932 on 38 degrees of freedom
## AIC: 485.76
##
## Number of Fisher Scoring iterations: 2glmoB<- glm(Observed ~ lndrainage_area+prc_wet, family = gaussian, data=alpha, subset = substrate=='B')
summary(glmoB) ## AIC: 485.48##
## Call:
## glm(formula = Observed ~ lndrainage_area + prc_wet, family = gaussian,
## data = alpha, subset = substrate == "B")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 101.00 43.61 2.316 0.0262 *
## lndrainage_area 30.10 15.14 1.989 0.0542 .
## prc_wet 88.05 59.74 1.474 0.1490
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 9673.413)
##
## Null deviance: 431830 on 39 degrees of freedom
## Residual deviance: 357916 on 37 degrees of freedom
## AIC: 485.48
##
## Number of Fisher Scoring iterations: 2glmoB<- glm(Observed ~ lndrainage_area*prc_wet, family = gaussian, data=alpha, subset = substrate=='B')
summary(glmoB) ## AIC: 487.14##
## Call:
## glm(formula = Observed ~ lndrainage_area * prc_wet, family = gaussian,
## data = alpha, subset = substrate == "B")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 73.48 66.32 1.108 0.275
## lndrainage_area 40.65 24.40 1.666 0.104
## prc_wet 208.45 225.28 0.925 0.361
## lndrainage_area:prc_wet -42.76 77.09 -0.555 0.583
##
## (Dispersion parameter for gaussian family taken to be 9857.862)
##
## Null deviance: 431830 on 39 degrees of freedom
## Residual deviance: 354883 on 36 degrees of freedom
## AIC: 487.14
##
## Number of Fisher Scoring iterations: 2glmoB<- glm(Observed ~ lndrainage_area+prc_wet+burn_interval, family = gaussian, data=alpha, subset = substrate=='B')
summary(glmoB) ### AIC: 479.97##
## Call:
## glm(formula = Observed ~ lndrainage_area + prc_wet + burn_interval,
## family = gaussian, data = alpha, subset = substrate == "B")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.671 53.540 0.087 0.93096
## lndrainage_area 33.878 14.037 2.414 0.02101 *
## prc_wet 116.613 56.115 2.078 0.04489 *
## burn_interval 25.121 9.209 2.728 0.00979 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 8239.054)
##
## Null deviance: 431830 on 39 degrees of freedom
## Residual deviance: 296606 on 36 degrees of freedom
## AIC: 479.97
##
## Number of Fisher Scoring iterations: 2###### BEST FIT FOR EPILITHON RICHNESS ###################
glmoB<- glm(Observed ~ lndrainage_area+prc_wet+burn_interval+tempC_mean, family = gaussian, data=alpha, subset = substrate=='B')
summary(glmoB) ### AIC: 476.36 #LOWEST FOR BIOFILM RICHNESS##
## Call:
## glm(formula = Observed ~ lndrainage_area + prc_wet + burn_interval +
## tempC_mean, family = gaussian, data = alpha, subset = substrate ==
## "B")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -91.796 65.791 -1.395 0.1717
## lndrainage_area 31.456 13.314 2.363 0.0238 *
## prc_wet 109.552 53.146 2.061 0.0468 *
## burn_interval 18.309 9.199 1.990 0.0544 .
## tempC_mean 8.612 3.752 2.296 0.0278 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 7365.442)
##
## Null deviance: 431830 on 39 degrees of freedom
## Residual deviance: 257790 on 35 degrees of freedom
## AIC: 476.36
##
## Number of Fisher Scoring iterations: 2##########################################################
glmoB<- glm(Observed ~ lndrainage_area*prc_wet+tempC_mean, family = gaussian, data=alpha, subset = substrate=='B')
summary(glmoB) ### AIC: 480.57 ##
## Call:
## glm(formula = Observed ~ lndrainage_area * prc_wet + tempC_mean,
## family = gaussian, data = alpha, subset = substrate == "B")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -65.91 77.30 -0.853 0.39961
## lndrainage_area 32.81 22.40 1.465 0.15187
## prc_wet 140.06 206.62 0.678 0.50230
## tempC_mean 10.90 3.77 2.892 0.00654 **
## lndrainage_area:prc_wet -18.17 70.75 -0.257 0.79888
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 8183.645)
##
## Null deviance: 431830 on 39 degrees of freedom
## Residual deviance: 286428 on 35 degrees of freedom
## AIC: 480.57
##
## Number of Fisher Scoring iterations: 2glmoB<- glm(Observed ~ lndrainage_area*prc_wet+burn_interval, family = gaussian, data=alpha, subset = substrate=='B')
summary(glmoB) ### AIC: 481.42##
## Call:
## glm(formula = Observed ~ lndrainage_area * prc_wet + burn_interval,
## family = gaussian, data = alpha, subset = substrate == "B")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -27.840 71.471 -0.390 0.69925
## lndrainage_area 46.064 22.560 2.042 0.04876 *
## prc_wet 255.500 208.174 1.227 0.22789
## burn_interval 25.336 9.281 2.730 0.00985 **
## lndrainage_area:prc_wet -49.240 71.032 -0.693 0.49275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 8359.68)
##
## Null deviance: 431830 on 39 degrees of freedom
## Residual deviance: 292589 on 35 degrees of freedom
## AIC: 481.42
##
## Number of Fisher Scoring iterations: 2#########################################################
####### sediment
###### BEST FIT FOR SEDIMENT RICHNESS ###################
glmoS<- glm(Observed ~ lndrainage_area, family = gaussian, data=alpha, subset = substrate=='S')
summary(glmoS) ## AIC: 591.47 # LOWEST IN SEDIMENT##
## Call:
## glm(formula = Observed ~ lndrainage_area, family = gaussian,
## data = alpha, subset = substrate == "S")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 308.215 44.899 6.865 1.45e-08 ***
## lndrainage_area -8.309 15.991 -0.520 0.606
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12111.69)
##
## Null deviance: 560407 on 47 degrees of freedom
## Residual deviance: 557138 on 46 degrees of freedom
## AIC: 591.47
##
## Number of Fisher Scoring iterations: 2#########################################################
glmoS<- glm(Observed ~ lndrainage_area+prc_wet, family = gaussian, data=alpha, subset = substrate=='S')
summary(glmoS) ### AIC: 593.29##
## Call:
## glm(formula = Observed ~ lndrainage_area + prc_wet, family = gaussian,
## data = alpha, subset = substrate == "S")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 311.644 46.061 6.766 2.27e-08 ***
## lndrainage_area -6.997 16.446 -0.425 0.673
## prc_wet -25.333 61.253 -0.414 0.681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12333.96)
##
## Null deviance: 560407 on 47 degrees of freedom
## Residual deviance: 555028 on 45 degrees of freedom
## AIC: 593.29
##
## Number of Fisher Scoring iterations: 2glmoS<- glm(Observed ~ lndrainage_area*prc_wet, family = gaussian, data=alpha, subset = substrate=='S')
summary(glmoS) ### AIC: 594.82##
## Call:
## glm(formula = Observed ~ lndrainage_area * prc_wet, family = gaussian,
## data = alpha, subset = substrate == "S")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 275.974 71.335 3.869 0.000358 ***
## lndrainage_area 6.742 26.647 0.253 0.801442
## prc_wet 130.923 245.391 0.534 0.596353
## lndrainage_area:prc_wet -56.055 85.208 -0.658 0.514055
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12491.41)
##
## Null deviance: 560407 on 47 degrees of freedom
## Residual deviance: 549622 on 44 degrees of freedom
## AIC: 594.82
##
## Number of Fisher Scoring iterations: 2glmoS<- glm(Observed ~ lndrainage_area+prc_wet+burn_interval, family = gaussian, data=alpha, subset = substrate=='S')
summary(glmoS) ## AIC: 592.86##
## Call:
## glm(formula = Observed ~ lndrainage_area + prc_wet + burn_interval,
## family = gaussian, data = alpha, subset = substrate == "S")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 259.473 57.084 4.545 4.26e-05 ***
## lndrainage_area -5.428 16.251 -0.334 0.740
## prc_wet -14.537 60.825 -0.239 0.812
## burn_interval 13.611 9.020 1.509 0.138
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 11993.65)
##
## Null deviance: 560407 on 47 degrees of freedom
## Residual deviance: 527721 on 44 degrees of freedom
## AIC: 592.86
##
## Number of Fisher Scoring iterations: 2glmoS<- glm(Observed ~ lndrainage_area+prc_wet+burn_interval+tempC_mean, family = gaussian, data=alpha, subset = substrate=='S')
summary(glmoS) ## AIC: 594.48 ##
## Call:
## glm(formula = Observed ~ lndrainage_area + prc_wet + burn_interval +
## tempC_mean, family = gaussian, data = alpha, subset = substrate ==
## "S")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 230.386 75.964 3.033 0.0041 **
## lndrainage_area -6.407 16.459 -0.389 0.6990
## prc_wet -16.772 61.402 -0.273 0.7860
## burn_interval 12.453 9.301 1.339 0.1876
## tempC_mean 2.471 4.215 0.586 0.5609
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12175.3)
##
## Null deviance: 560407 on 47 degrees of freedom
## Residual deviance: 523538 on 43 degrees of freedom
## AIC: 594.48
##
## Number of Fisher Scoring iterations: 2glmoS<- glm(Observed ~ lndrainage_area*prc_wet+tempC_mean, family = gaussian, data=alpha, subset = substrate=='S')
summary(glmoS) ## AIC: 596.13##
## Call:
## glm(formula = Observed ~ lndrainage_area * prc_wet + tempC_mean,
## family = gaussian, data = alpha, subset = substrate == "S")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 237.145 86.983 2.726 0.00923 **
## lndrainage_area 3.123 27.155 0.115 0.90898
## prc_wet 100.970 249.379 0.405 0.68757
## tempC_mean 3.336 4.237 0.787 0.43548
## lndrainage_area:prc_wet -45.947 86.537 -0.531 0.59818
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12600.32)
##
## Null deviance: 560407 on 47 degrees of freedom
## Residual deviance: 541814 on 43 degrees of freedom
## AIC: 596.13
##
## Number of Fisher Scoring iterations: 2glmoS<- glm(Observed ~ lndrainage_area*prc_wet+burn_interval, family = gaussian, data=alpha, subset = substrate=='S')
summary(glmoS) ## AIC: 594.42 ##
## Call:
## glm(formula = Observed ~ lndrainage_area * prc_wet + burn_interval,
## family = gaussian, data = alpha, subset = substrate == "S")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.143 77.985 2.900 0.00586 **
## lndrainage_area 7.589 26.297 0.289 0.77427
## prc_wet 133.587 242.116 0.552 0.58398
## burn_interval 13.478 9.085 1.484 0.14521
## lndrainage_area:prc_wet -53.175 84.091 -0.632 0.53050
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12159.5)
##
## Null deviance: 560407 on 47 degrees of freedom
## Residual deviance: 522858 on 43 degrees of freedom
## AIC: 594.42
##
## Number of Fisher Scoring iterations: 2#########################################################################################################GLMs for Shannon diversity
# Shannon Diversity by substrate
####### leaf
glmsL<- glm(Shannon ~ lndrainage_area, family = Gamma, data=alpha, subset = substrate=='L')
summary(glmsL) ### AIC: 84.772##
## Call:
## glm(formula = Shannon ~ lndrainage_area, family = Gamma, data = alpha,
## subset = substrate == "L")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.318906 0.018622 17.126 <2e-16 ***
## lndrainage_area -0.017426 0.006154 -2.832 0.0069 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.02280456)
##
## Null deviance: 1.2800 on 46 degrees of freedom
## Residual deviance: 1.0972 on 45 degrees of freedom
## AIC: 84.772
##
## Number of Fisher Scoring iterations: 4glmsL<- glm(Shannon ~ lndrainage_area+prc_wet, family = Gamma, data=alpha, subset = substrate=='L')
summary(glmsL) ### AIC: 84.168 ### ##
## Call:
## glm(formula = Shannon ~ lndrainage_area + prc_wet, family = Gamma,
## data = alpha, subset = substrate == "L")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.323603 0.018647 17.354 <2e-16 ***
## lndrainage_area -0.015549 0.006202 -2.507 0.0159 *
## prc_wet -0.035035 0.021061 -1.663 0.1033
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.02198014)
##
## Null deviance: 1.2800 on 46 degrees of freedom
## Residual deviance: 1.0383 on 44 degrees of freedom
## AIC: 84.168
##
## Number of Fisher Scoring iterations: 4###### BEST FIT FOR LEAF LITTER SHANNON ###################
glmsL<- glm(Shannon ~ lndrainage_area*prc_wet, family = Gamma, data=alpha, subset = substrate=='L')
summary(glmsL) ### AIC: 83.879 ### LOWEST FOR LEAF SHANNON##
## Call:
## glm(formula = Shannon ~ lndrainage_area * prc_wet, family = Gamma,
## data = alpha, subset = substrate == "L")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.356752 0.028634 12.459 7.31e-16 ***
## lndrainage_area -0.027590 0.009994 -2.761 0.00844 **
## prc_wet -0.197933 0.107940 -1.834 0.07362 .
## lndrainage_area:prc_wet 0.055484 0.036268 1.530 0.13338
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.02117977)
##
## Null deviance: 1.27998 on 46 degrees of freedom
## Residual deviance: 0.98909 on 43 degrees of freedom
## AIC: 83.879
##
## Number of Fisher Scoring iterations: 4###########################################################
glmsL<- glm(Shannon ~ lndrainage_area+prc_wet+tempC_mean, family = Gamma, data=alpha, subset = substrate=='L')
summary(glmsL) ### AIC: 84.168 ### ##
## Call:
## glm(formula = Shannon ~ lndrainage_area + prc_wet + tempC_mean,
## family = Gamma, data = alpha, subset = substrate == "L")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3267717 0.0285582 11.442 1.24e-14 ***
## lndrainage_area -0.0155518 0.0062717 -2.480 0.0171 *
## prc_wet -0.0350478 0.0212869 -1.646 0.1070
## tempC_mean -0.0002191 0.0014814 -0.148 0.8831
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.02246013)
##
## Null deviance: 1.2800 on 46 degrees of freedom
## Residual deviance: 1.0378 on 43 degrees of freedom
## AIC: 86.146
##
## Number of Fisher Scoring iterations: 4glmsL<- glm(Shannon ~ lndrainage_area+prc_wet+burn_interval, family = Gamma, data=alpha, subset = substrate=='L')
summary(glmsL) ### AIC: 84.865##
## Call:
## glm(formula = Shannon ~ lndrainage_area + prc_wet + burn_interval,
## family = Gamma, data = alpha, subset = substrate == "L")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.338942 0.023006 14.733 <2e-16 ***
## lndrainage_area -0.016228 0.006212 -2.612 0.0123 *
## prc_wet -0.038112 0.021112 -1.805 0.0780 .
## burn_interval -0.003738 0.003271 -1.143 0.2595
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.02190781)
##
## Null deviance: 1.28 on 46 degrees of freedom
## Residual deviance: 1.01 on 43 degrees of freedom
## AIC: 84.865
##
## Number of Fisher Scoring iterations: 4glmsL<- glm(Shannon ~ lndrainage_area+prc_wet+burn_interval+tempC_mean, family = Gamma, data=alpha, subset = substrate=='L')
summary(glmsL) ### AIC: 86.864 ##
## Call:
## glm(formula = Shannon ~ lndrainage_area + prc_wet + burn_interval +
## tempC_mean, family = Gamma, data = alpha, subset = substrate ==
## "L")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.383e-01 3.047e-02 11.101 4.54e-14 ***
## lndrainage_area -1.623e-02 6.285e-03 -2.582 0.0134 *
## prc_wet -3.812e-02 2.137e-02 -1.784 0.0816 .
## burn_interval -3.756e-03 3.354e-03 -1.120 0.2691
## tempC_mean 4.977e-05 1.498e-03 0.033 0.9737
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.02243281)
##
## Null deviance: 1.28 on 46 degrees of freedom
## Residual deviance: 1.01 on 42 degrees of freedom
## AIC: 86.864
##
## Number of Fisher Scoring iterations: 4glmsL<- glm(Shannon ~ lndrainage_area*prc_wet+tempC_mean, family = Gamma, data=alpha, subset = substrate=='L')
summary(glmsL) ### AIC: 85.87##
## Call:
## glm(formula = Shannon ~ lndrainage_area * prc_wet + tempC_mean,
## family = Gamma, data = alpha, subset = substrate == "L")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3585856 0.0353187 10.153 7.12e-13 ***
## lndrainage_area -0.0275660 0.0101163 -2.725 0.00934 **
## prc_wet -0.1974331 0.1092623 -1.807 0.07794 .
## tempC_mean -0.0001316 0.0014483 -0.091 0.92805
## lndrainage_area:prc_wet 0.0553092 0.0367137 1.507 0.13942
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.02167875)
##
## Null deviance: 1.27998 on 46 degrees of freedom
## Residual deviance: 0.98891 on 42 degrees of freedom
## AIC: 85.87
##
## Number of Fisher Scoring iterations: 4glmsL<- glm(Shannon ~ lndrainage_area*prc_wet+burn_interval, family = Gamma, data=alpha, subset = substrate=='L')
summary(glmsL) ### AIC: 84.755##
## Call:
## glm(formula = Shannon ~ lndrainage_area * prc_wet + burn_interval,
## family = Gamma, data = alpha, subset = substrate == "L")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.368947 0.030876 11.949 4.26e-15 ***
## lndrainage_area -0.027568 0.009973 -2.764 0.00844 **
## prc_wet -0.192827 0.108288 -1.781 0.08220 .
## burn_interval -0.003394 0.003224 -1.053 0.29847
## lndrainage_area:prc_wet 0.052758 0.036419 1.449 0.15487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.02121778)
##
## Null deviance: 1.2800 on 46 degrees of freedom
## Residual deviance: 0.9658 on 42 degrees of freedom
## AIC: 84.755
##
## Number of Fisher Scoring iterations: 4#################################################################################
####### bioifilm
glmsB<- glm(Shannon ~ lndrainage_area, family = Gamma, data=alpha, subset = substrate=='B')
summary(glmsB) ### AIC: 71.798##
## Call:
## glm(formula = Shannon ~ lndrainage_area, family = Gamma, data = alpha,
## subset = substrate == "B")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.289209 0.015696 18.426 < 2e-16 ***
## lndrainage_area -0.015228 0.005218 -2.918 0.00589 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.0196937)
##
## Null deviance: 0.92450 on 39 degrees of freedom
## Residual deviance: 0.75712 on 38 degrees of freedom
## AIC: 71.798
##
## Number of Fisher Scoring iterations: 4glmsB<- glm(Shannon ~ lndrainage_area+prc_wet, family = Gamma, data=alpha, subset = substrate=='B')
summary(glmsB) ### AIC: 73.304##
## Call:
## glm(formula = Shannon ~ lndrainage_area + prc_wet, family = Gamma,
## data = alpha, subset = substrate == "B")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.291558 0.016210 17.986 < 2e-16 ***
## lndrainage_area -0.014561 0.005357 -2.718 0.00993 **
## prc_wet -0.014164 0.020650 -0.686 0.49704
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.01994359)
##
## Null deviance: 0.92450 on 39 degrees of freedom
## Residual deviance: 0.74785 on 37 degrees of freedom
## AIC: 73.304
##
## Number of Fisher Scoring iterations: 4glmsB<- glm(Shannon ~ lndrainage_area*prc_wet, family = Gamma, data=alpha, subset = substrate=='B')
summary(glmsB) ### AIC: 73.084##
## Call:
## glm(formula = Shannon ~ lndrainage_area * prc_wet, family = Gamma,
## data = alpha, subset = substrate == "B")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.31768 0.02427 13.091 2.96e-15 ***
## lndrainage_area -0.02423 0.00850 -2.850 0.00719 **
## prc_wet -0.12511 0.07820 -1.600 0.11834
## lndrainage_area:prc_wet 0.03860 0.02641 1.462 0.15255
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.01923683)
##
## Null deviance: 0.92450 on 39 degrees of freedom
## Residual deviance: 0.70761 on 36 degrees of freedom
## AIC: 73.084
##
## Number of Fisher Scoring iterations: 4glmsB<- glm(Shannon ~ lndrainage_area+prc_wet+burn_interval, family = Gamma, data=alpha, subset = substrate=='B')
summary(glmsB) ### AIC: 71.484##
## Call:
## glm(formula = Shannon ~ lndrainage_area + prc_wet + burn_interval,
## family = Gamma, data = alpha, subset = substrate == "B")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.316110 0.020272 15.593 < 2e-16 ***
## lndrainage_area -0.015413 0.005191 -2.969 0.00529 **
## prc_wet -0.021598 0.020139 -1.072 0.29067
## burn_interval -0.006353 0.003289 -1.931 0.06134 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.01862153)
##
## Null deviance: 0.92450 on 39 degrees of freedom
## Residual deviance: 0.67994 on 36 degrees of freedom
## AIC: 71.484
##
## Number of Fisher Scoring iterations: 4glmsB<- glm(Shannon ~ lndrainage_area+prc_wet+burn_interval+tempC_mean, family = Gamma, data=alpha, subset = substrate=='B')
summary(glmsB) ### AIC: 68.913 ##
## Call:
## glm(formula = Shannon ~ lndrainage_area + prc_wet + burn_interval +
## tempC_mean, family = Gamma, data = alpha, subset = substrate ==
## "B")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.350873 0.025741 13.631 1.46e-15 ***
## lndrainage_area -0.014949 0.004964 -3.012 0.0048 **
## prc_wet -0.019667 0.019149 -1.027 0.3114
## burn_interval -0.004195 0.003287 -1.276 0.2102
## tempC_mean -0.002922 0.001401 -2.085 0.0444 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.01682868)
##
## Null deviance: 0.9245 on 39 degrees of freedom
## Residual deviance: 0.6067 on 35 degrees of freedom
## AIC: 68.913
##
## Number of Fisher Scoring iterations: 4###### BEST FIT FOR EPILITHON SHANNON ###################
glmsB<- glm(Shannon ~ lndrainage_area*prc_wet+tempC_mean, family = Gamma, data=alpha, subset = substrate=='B')
summary(glmsB) ### AIC: 68.796 ####### LOWEST FOR BIOFILM SHANNON##
## Call:
## glm(formula = Shannon ~ lndrainage_area * prc_wet + tempC_mean,
## family = Gamma, data = alpha, subset = substrate == "B")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.362652 0.029397 12.336 2.66e-14 ***
## lndrainage_area -0.022590 0.008062 -2.802 0.00822 **
## prc_wet -0.109276 0.073500 -1.487 0.14603
## tempC_mean -0.003309 0.001337 -2.475 0.01831 *
## lndrainage_area:prc_wet 0.032809 0.024800 1.323 0.19443
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.01678025)
##
## Null deviance: 0.92450 on 39 degrees of freedom
## Residual deviance: 0.60493 on 35 degrees of freedom
## AIC: 68.796
##
## Number of Fisher Scoring iterations: 4#################################################################################
glmsB<- glm(Shannon ~ lndrainage_area*prc_wet+burn_interval, family = Gamma, data=alpha, subset = substrate=='B')
summary(glmsB) ### AIC: 70.884##
## Call:
## glm(formula = Shannon ~ lndrainage_area * prc_wet + burn_interval,
## family = Gamma, data = alpha, subset = substrate == "B")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.343297 0.026650 12.882 7.65e-15 ***
## lndrainage_area -0.025260 0.008142 -3.103 0.00378 **
## prc_wet -0.136684 0.075904 -1.801 0.08037 .
## burn_interval -0.006464 0.003221 -2.007 0.05256 .
## lndrainage_area:prc_wet 0.039772 0.025455 1.562 0.12718
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.01787228)
##
## Null deviance: 0.92450 on 39 degrees of freedom
## Residual deviance: 0.63726 on 35 degrees of freedom
## AIC: 70.884
##
## Number of Fisher Scoring iterations: 4#################################################################################
####### sediment
###### BEST FIT FOR SEDIMENT SHANNON ###################
glmsS<- glm(Shannon ~ lndrainage_area, family = Gamma, data=alpha, subset = substrate=='S')
summary(glmsS) ### AIC: 112.51 LOWEST##
## Call:
## glm(formula = Shannon ~ lndrainage_area, family = Gamma, data = alpha,
## subset = substrate == "S")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.248e-01 1.446e-02 15.546 <2e-16 ***
## lndrainage_area 8.023e-05 5.150e-03 0.016 0.988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.02482373)
##
## Null deviance: 1.339 on 47 degrees of freedom
## Residual deviance: 1.339 on 46 degrees of freedom
## AIC: 112.51
##
## Number of Fisher Scoring iterations: 4##############################################################
glmsS<- glm(Shannon ~ lndrainage_area+prc_wet, family = Gamma, data=alpha, subset = substrate=='S')
summary(glmsS) ### AIC: 114.48##
## Call:
## glm(formula = Shannon ~ lndrainage_area + prc_wet, family = Gamma,
## data = alpha, subset = substrate == "S")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2252145 0.0148534 15.163 <2e-16 ***
## lndrainage_area 0.0002545 0.0053048 0.048 0.962
## prc_wet -0.0033527 0.0196470 -0.171 0.865
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.02533586)
##
## Null deviance: 1.3390 on 47 degrees of freedom
## Residual deviance: 1.3383 on 45 degrees of freedom
## AIC: 114.48
##
## Number of Fisher Scoring iterations: 4glmsS<- glm(Shannon ~ lndrainage_area*prc_wet, family = Gamma, data=alpha, subset = substrate=='S')
summary(glmsS) ### AIC: 116.25##
## Call:
## glm(formula = Shannon ~ lndrainage_area * prc_wet, family = Gamma,
## data = alpha, subset = substrate == "S")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.234025 0.023211 10.083 5.18e-13 ***
## lndrainage_area -0.003138 0.008647 -0.363 0.718
## prc_wet -0.041433 0.078140 -0.530 0.599
## lndrainage_area:prc_wet 0.013697 0.027252 0.503 0.618
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.02593494)
##
## Null deviance: 1.3390 on 47 degrees of freedom
## Residual deviance: 1.3318 on 44 degrees of freedom
## AIC: 116.25
##
## Number of Fisher Scoring iterations: 4glmsS<- glm(Shannon ~ lndrainage_area+prc_wet+burn_interval, family = Gamma, data=alpha, subset = substrate=='S')
summary(glmsS) #### AIC: 114.4##
## Call:
## glm(formula = Shannon ~ lndrainage_area + prc_wet + burn_interval,
## family = Gamma, data = alpha, subset = substrate == "S")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2420253 0.0185442 13.051 <2e-16 ***
## lndrainage_area -0.0001634 0.0052837 -0.031 0.975
## prc_wet -0.0071479 0.0196380 -0.364 0.718
## burn_interval -0.0043495 0.0028703 -1.515 0.137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.02509339)
##
## Null deviance: 1.3390 on 47 degrees of freedom
## Residual deviance: 1.2816 on 44 degrees of freedom
## AIC: 114.4
##
## Number of Fisher Scoring iterations: 4glmsS<- glm(Shannon ~ lndrainage_area+prc_wet+burn_interval+tempC_mean, family = Gamma, data=alpha, subset = substrate=='S')
summary(glmsS) ### AIC: 116.39##
## Call:
## glm(formula = Shannon ~ lndrainage_area + prc_wet + burn_interval +
## tempC_mean, family = Gamma, data = alpha, subset = substrate ==
## "S")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2436529 0.0250204 9.738 1.92e-12 ***
## lndrainage_area -0.0001205 0.0053635 -0.022 0.982
## prc_wet -0.0070380 0.0198960 -0.354 0.725
## burn_interval -0.0042881 0.0029701 -1.444 0.156
## tempC_mean -0.0001350 0.0013711 -0.098 0.922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.02566735)
##
## Null deviance: 1.3390 on 47 degrees of freedom
## Residual deviance: 1.2813 on 43 degrees of freedom
## AIC: 116.39
##
## Number of Fisher Scoring iterations: 4glmsS<- glm(Shannon ~ lndrainage_area*prc_wet+tempC_mean, family = Gamma, data=alpha, subset = substrate=='S')
summary(glmsS) ### AIC: 118.14##
## Call:
## glm(formula = Shannon ~ lndrainage_area * prc_wet + tempC_mean,
## family = Gamma, data = alpha, subset = substrate == "S")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.239426 0.028556 8.385 1.36e-10 ***
## lndrainage_area -0.002648 0.008870 -0.299 0.767
## prc_wet -0.037380 0.079923 -0.468 0.642
## tempC_mean -0.000460 0.001379 -0.334 0.740
## lndrainage_area:prc_wet 0.012320 0.027845 0.442 0.660
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.02645821)
##
## Null deviance: 1.3390 on 47 degrees of freedom
## Residual deviance: 1.3289 on 43 degrees of freedom
## AIC: 118.14
##
## Number of Fisher Scoring iterations: 4glmsS<- glm(Shannon ~ lndrainage_area*prc_wet+burn_interval, family = Gamma, data=alpha, subset = substrate=='S')
summary(glmsS) ### AIC: 116.18##
## Call:
## glm(formula = Shannon ~ lndrainage_area * prc_wet + burn_interval,
## family = Gamma, data = alpha, subset = substrate == "S")
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.250058 0.025543 9.790 1.64e-12 ***
## lndrainage_area -0.003301 0.008603 -0.384 0.703
## prc_wet -0.042758 0.078495 -0.545 0.589
## burn_interval -0.004310 0.002906 -1.483 0.145
## lndrainage_area:prc_wet 0.012788 0.027330 0.468 0.642
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.02575849)
##
## Null deviance: 1.339 on 47 degrees of freedom
## Residual deviance: 1.276 on 43 degrees of freedom
## AIC: 116.18
##
## Number of Fisher Scoring iterations: 4#########################################################################################################TAXONOMIC COMPOSITION:
PHYLUM ~ class ~ family Taxonomy table
#setwd("/Users/chunk/Documents/DADA2/DADA2_package_test/Kz_Syn_ITS/run_2/outputs")
#make taxonomy object by phylum
phyla_counts_tab <- otu_table(tax_glom(pseqtest, taxrank="Phylum"), taxa_are_rows = FALSE)
phyla_counts_tab <- t(phyla_counts_tab)
#make vector of phyla names to set as row names
phyla_tax_vec <- as.vector(tax_table(tax_glom(pseqtest, taxrank="Phylum"))[,2])
rownames(phyla_counts_tab) <- as.vector(phyla_tax_vec)
phyla_counts_tab## OTU Table: [15 taxa and 135 samples]
## taxa are rows
## 01m01_B 01m01_L 01m01_S 01m02_B 01m02_L 01m02_S
## p__Ascomycota 1454 1977 1205 2024 2259 1057
## p__Basidiomycota 295 451 606 272 178 702
## p__Blastocladiomycota 0 0 67 7 0 276
## p__Mortierellomycota 1 2 376 86 0 334
## p__Kickxellomycota 0 1 75 0 0 0
## p__Rozellomycota 0 0 4 0 0 6
## p__Aphelidiomycota 528 3 0 16 0 3
## p__Basidiobolomycota 0 0 1 0 0 0
## p__Mucoromycota 0 0 20 0 0 0
## p__Chytridiomycota 0 0 12 0 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 0 0 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## 01m03_B 01m03_L 01m03_S 01m04_B 01m04_L 01m04_S
## p__Ascomycota 1362 1828 554 1541 2003 826
## p__Basidiomycota 461 589 989 726 415 630
## p__Blastocladiomycota 48 0 67 0 0 195
## p__Mortierellomycota 82 2 230 42 7 473
## p__Kickxellomycota 14 0 57 0 0 93
## p__Rozellomycota 17 0 3 0 0 8
## p__Aphelidiomycota 37 5 3 1 0 57
## p__Basidiobolomycota 0 0 0 0 0 0
## p__Mucoromycota 8 0 3 0 0 28
## p__Chytridiomycota 0 0 0 0 0 4
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 0 0 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## 01m05_B 01m05_L 01m05_S 01m06_B 01m06_L 01m06_S
## p__Ascomycota 1731 1965 1262 1591 2246 966
## p__Basidiomycota 253 445 596 507 190 878
## p__Blastocladiomycota 29 7 55 0 0 2
## p__Mortierellomycota 170 8 298 0 3 401
## p__Kickxellomycota 0 1 69 101 0 51
## p__Rozellomycota 0 0 2 0 0 11
## p__Aphelidiomycota 83 1 4 60 0 2
## p__Basidiobolomycota 0 0 1 0 0 0
## p__Mucoromycota 35 0 30 0 0 6
## p__Chytridiomycota 4 0 0 0 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 0 0 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## 02m01_L 02m01_S 02m02_B 02m02_L 02m02_S 02m03_B
## p__Ascomycota 2074 162 2023 2180 316 2055
## p__Basidiomycota 305 2104 390 205 1938 355
## p__Blastocladiomycota 21 0 10 13 24 1
## p__Mortierellomycota 6 81 0 15 106 5
## p__Kickxellomycota 2 1 0 0 7 2
## p__Rozellomycota 0 2 0 0 2 0
## p__Aphelidiomycota 0 0 0 1 0 0
## p__Basidiobolomycota 0 0 0 0 0 0
## p__Mucoromycota 0 0 0 1 3 0
## p__Chytridiomycota 0 0 0 0 2 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 0 0 7 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## 02m03_L 02m03_S 02m04_L 02m04_S 02m05_B 02m05_L
## p__Ascomycota 1771 307 2265 1043 1366 2309
## p__Basidiomycota 632 1969 124 822 327 108
## p__Blastocladiomycota 4 3 9 21 8 0
## p__Mortierellomycota 13 73 3 408 10 1
## p__Kickxellomycota 2 15 0 29 0 0
## p__Rozellomycota 2 0 0 2 2 0
## p__Aphelidiomycota 0 0 0 0 11 0
## p__Basidiobolomycota 1 0 0 0 0 0
## p__Mucoromycota 0 0 0 4 0 0
## p__Chytridiomycota 0 0 0 0 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 0 3 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## 02m06_B 02m06_L 02m06_S 02m07_B 02m07_L 02m07_S
## p__Ascomycota 1950 2028 1443 1911 1885 1042
## p__Basidiomycota 240 366 533 247 537 1144
## p__Blastocladiomycota 24 2 68 1 0 78
## p__Mortierellomycota 35 2 203 15 2 48
## p__Kickxellomycota 5 0 47 0 0 7
## p__Rozellomycota 6 0 13 10 0 39
## p__Aphelidiomycota 3 0 1 7 0 4
## p__Basidiobolomycota 0 0 3 0 0 1
## p__Mucoromycota 8 0 1 0 0 12
## p__Chytridiomycota 1 0 0 0 0 1
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 1 0 0 0 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## 02m08_L 02m08_S 02m09_B 02m09_L 02m09_S 02m10_B
## p__Ascomycota 1695 1180 2038 2192 1465 2184
## p__Basidiomycota 723 245 274 233 516 214
## p__Blastocladiomycota 0 10 0 0 179 1
## p__Mortierellomycota 0 153 15 2 73 0
## p__Kickxellomycota 0 537 12 0 39 0
## p__Rozellomycota 0 3 0 0 17 0
## p__Aphelidiomycota 0 0 0 0 0 0
## p__Basidiobolomycota 0 0 0 0 0 0
## p__Mucoromycota 0 7 0 0 0 0
## p__Chytridiomycota 0 0 0 0 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 0 0 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## 02m10_L 02m10_S 02m11_B 02m11_S 04m01_B 04m01_L
## p__Ascomycota 2363 1785 2159 1615 1956 2011
## p__Basidiomycota 77 395 269 265 347 381
## p__Blastocladiomycota 0 12 0 197 41 5
## p__Mortierellomycota 0 141 9 174 10 18
## p__Kickxellomycota 0 14 0 24 0 3
## p__Rozellomycota 0 3 0 1 6 2
## p__Aphelidiomycota 0 19 0 1 1 0
## p__Basidiobolomycota 0 3 0 3 0 0
## p__Mucoromycota 1 0 0 2 2 0
## p__Chytridiomycota 0 0 0 0 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 0 0 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## 04m01_S 04m02_B 04m02_L 04m02_S 04m03_B 04m03_L
## p__Ascomycota 1324 1973 2358 614 2045 2073
## p__Basidiomycota 446 279 69 750 282 338
## p__Blastocladiomycota 211 12 0 275 0 0
## p__Mortierellomycota 168 13 3 363 10 4
## p__Kickxellomycota 8 0 1 25 12 0
## p__Rozellomycota 18 0 0 8 0 1
## p__Aphelidiomycota 6 15 0 6 0 0
## p__Basidiobolomycota 0 0 0 0 0 0
## p__Mucoromycota 0 0 0 0 0 0
## p__Chytridiomycota 0 0 0 0 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 0 0 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 3 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## 04m03_S 04m04_B 04m04_L 04m04_S 04m05_B 04m05_L
## p__Ascomycota 1435 840 2277 227 1472 2001
## p__Basidiomycota 528 1359 113 1967 264 429
## p__Blastocladiomycota 82 3 0 0 2 0
## p__Mortierellomycota 138 58 7 159 56 0
## p__Kickxellomycota 43 2 1 27 6 0
## p__Rozellomycota 8 9 1 11 0 0
## p__Aphelidiomycota 2 15 2 1 68 0
## p__Basidiobolomycota 4 10 0 0 0 0
## p__Mucoromycota 3 1 0 0 0 0
## p__Chytridiomycota 0 0 0 0 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 0 0 2 0
## p__Neocallimastigomycota 3 0 1 0 0 0
## p__Glomeromycota 28 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## 04m05_S 04m06_B 04m06_L 04m06_S 04m07_L 04m07_S
## p__Ascomycota 1251 1546 2269 1618 2160 1434
## p__Basidiomycota 763 616 167 496 255 485
## p__Blastocladiomycota 13 0 0 3 0 128
## p__Mortierellomycota 132 9 0 198 7 90
## p__Kickxellomycota 39 0 0 36 0 0
## p__Rozellomycota 7 0 0 18 1 8
## p__Aphelidiomycota 0 65 0 1 0 21
## p__Basidiobolomycota 0 0 0 0 0 0
## p__Mucoromycota 3 0 0 9 0 0
## p__Chytridiomycota 0 0 0 0 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 0 0 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## 04m08_L 04m08_S 04m09_L 04m09_S 04m10_B 04m10_L
## p__Ascomycota 2281 1742 2175 1851 2109 1857
## p__Basidiomycota 105 282 233 355 289 565
## p__Blastocladiomycota 0 0 3 0 0 0
## p__Mortierellomycota 7 77 6 47 10 2
## p__Kickxellomycota 0 35 0 29 0 0
## p__Rozellomycota 0 0 0 38 5 5
## p__Aphelidiomycota 8 27 1 0 0 0
## p__Basidiobolomycota 0 0 0 0 0 0
## p__Mucoromycota 0 0 2 0 0 0
## p__Chytridiomycota 0 0 0 0 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 1 13 0 0 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## 04m10_S 04m11_B 04m11_L 04m11_S 04m12_B 04m12_L
## p__Ascomycota 459 2041 2338 1680 2162 2366
## p__Basidiomycota 1307 274 89 242 246 62
## p__Blastocladiomycota 66 1 0 139 0 0
## p__Mortierellomycota 372 21 4 215 24 5
## p__Kickxellomycota 37 0 0 56 0 2
## p__Rozellomycota 121 6 0 2 0 0
## p__Aphelidiomycota 5 6 0 17 0 2
## p__Basidiobolomycota 2 0 0 7 0 0
## p__Mucoromycota 9 0 0 3 0 0
## p__Chytridiomycota 1 0 0 1 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 5 0 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 3 8 0
## 04m12_S 04m13_B 04m13_L 04m13_S 04t01_B 04t01_L
## p__Ascomycota 1265 2289 1822 1348 2210 2202
## p__Basidiomycota 762 110 617 651 138 164
## p__Blastocladiomycota 58 36 0 13 21 62
## p__Mortierellomycota 220 0 0 230 11 3
## p__Kickxellomycota 24 0 0 9 0 0
## p__Rozellomycota 2 0 0 10 0 0
## p__Aphelidiomycota 4 4 0 69 0 0
## p__Basidiobolomycota 0 0 0 4 0 0
## p__Mucoromycota 1 2 0 6 0 0
## p__Chytridiomycota 0 0 0 0 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 0 0 6 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## 04t01_S 04t02_B 04t02_L 04t02_S 04w01_B 04w01_L
## p__Ascomycota 1270 2122 2296 2155 2077 2114
## p__Basidiomycota 552 214 78 197 345 326
## p__Blastocladiomycota 34 1 1 3 6 0
## p__Mortierellomycota 252 20 6 44 4 0
## p__Kickxellomycota 150 1 0 10 0 0
## p__Rozellomycota 2 1 1 1 0 0
## p__Aphelidiomycota 0 0 0 0 0 0
## p__Basidiobolomycota 2 0 0 1 0 0
## p__Mucoromycota 0 3 0 0 0 0
## p__Chytridiomycota 0 0 0 0 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 0 0 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 2 0 0 0 0 0
## 04w01_S 04w02_B 04w02_L 04w02_S 04w03_B 04w03_S
## p__Ascomycota 1462 2282 1888 870 1936 1393
## p__Basidiomycota 554 149 495 618 505 289
## p__Blastocladiomycota 69 0 30 365 0 233
## p__Mortierellomycota 122 0 12 476 0 193
## p__Kickxellomycota 23 0 0 47 0 32
## p__Rozellomycota 1 0 0 5 0 6
## p__Aphelidiomycota 0 0 0 0 0 0
## p__Basidiobolomycota 4 0 0 0 0 2
## p__Mucoromycota 2 0 1 1 0 3
## p__Chytridiomycota 0 0 0 0 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 0 0 0 3
## p__Neocallimastigomycota 0 0 0 0 0 2
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## 04w04_L 04w04_S 20m01_L 20m01_S 20m02_B 20m02_L
## p__Ascomycota 2190 1245 1470 1032 1490 1344
## p__Basidiomycota 240 752 901 846 634 1055
## p__Blastocladiomycota 0 0 0 3 0 0
## p__Mortierellomycota 7 351 47 353 14 20
## p__Kickxellomycota 0 0 0 98 4 0
## p__Rozellomycota 0 0 1 19 0 0
## p__Aphelidiomycota 0 44 0 0 12 0
## p__Basidiobolomycota 0 0 0 2 0 0
## p__Mucoromycota 0 0 2 1 0 2
## p__Chytridiomycota 0 0 0 0 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 0 0 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 2 5 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## 20m02_S 20m03_B 20m03_L 20m03_S 20m04_B 20m04_L
## p__Ascomycota 995 2118 2337 1210 1642 2107
## p__Basidiomycota 842 260 98 534 281 308
## p__Blastocladiomycota 0 0 0 2 0 0
## p__Mortierellomycota 495 5 5 415 9 2
## p__Kickxellomycota 35 2 0 67 0 0
## p__Rozellomycota 5 2 0 12 8 0
## p__Aphelidiomycota 0 4 0 1 0 0
## p__Basidiobolomycota 0 0 0 0 0 0
## p__Mucoromycota 2 2 0 4 0 0
## p__Chytridiomycota 0 0 0 0 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 0 0 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## 20m04_S 20m05_L 20m05_S sfm01_B sfm01_L sfm01_S
## p__Ascomycota 1242 2310 1252 1879 2102 1303
## p__Basidiomycota 251 118 600 383 301 724
## p__Blastocladiomycota 15 1 38 11 1 34
## p__Mortierellomycota 614 0 119 39 16 239
## p__Kickxellomycota 150 0 291 1 0 60
## p__Rozellomycota 51 3 23 7 2 10
## p__Aphelidiomycota 2 0 4 9 0 4
## p__Basidiobolomycota 1 0 4 0 0 0
## p__Mucoromycota 21 0 1 0 0 0
## p__Chytridiomycota 8 0 0 0 0 0
## p__Calcarisporiellomycota 6 0 0 0 0 0
## p__Olpidiomycota 0 0 0 0 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 1 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## sfm02_B sfm02_L sfm02_S sfm03_B sfm03_L sfm04_B
## p__Ascomycota 1620 2329 506 1862 2192 2149
## p__Basidiomycota 509 91 1291 382 241 197
## p__Blastocladiomycota 1 0 25 0 0 0
## p__Mortierellomycota 60 1 440 17 2 3
## p__Kickxellomycota 3 0 16 0 0 0
## p__Rozellomycota 5 0 6 0 0 4
## p__Aphelidiomycota 41 0 1 0 0 0
## p__Basidiobolomycota 0 0 0 0 0 0
## p__Mucoromycota 0 1 0 0 0 0
## p__Chytridiomycota 0 0 0 0 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 0 0 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## sfm04_L sfm04_S sfm05_B sfm05_L sfm05_S sfm06_L
## p__Ascomycota 1827 1795 1700 2127 497 2015
## p__Basidiomycota 255 415 440 229 1253 366
## p__Blastocladiomycota 306 0 23 36 0 17
## p__Mortierellomycota 11 84 43 18 296 18
## p__Kickxellomycota 0 4 1 0 48 1
## p__Rozellomycota 2 7 3 0 1 1
## p__Aphelidiomycota 0 7 61 15 10 1
## p__Basidiobolomycota 0 0 0 0 0 0
## p__Mucoromycota 1 4 0 0 0 0
## p__Chytridiomycota 0 0 0 0 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 0 0 0 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## sfm06_S sfm07_B sfm07_L sfm07_S Sft01_B Sft01_L
## p__Ascomycota 1284 1763 1975 1240 1956 2055
## p__Basidiomycota 754 369 400 580 315 320
## p__Blastocladiomycota 16 29 30 2 53 1
## p__Mortierellomycota 293 54 18 506 39 11
## p__Kickxellomycota 34 3 0 16 2 0
## p__Rozellomycota 5 6 1 7 0 1
## p__Aphelidiomycota 1 46 0 0 1 0
## p__Basidiobolomycota 0 2 0 2 8 0
## p__Mucoromycota 1 0 0 1 1 3
## p__Chytridiomycota 0 0 0 1 0 0
## p__Calcarisporiellomycota 0 0 0 0 0 0
## p__Olpidiomycota 0 3 0 0 0 0
## p__Neocallimastigomycota 0 0 0 0 0 0
## p__Glomeromycota 0 0 0 0 0 0
## p__Entorrhizomycota 0 0 0 0 0 0
## Sft01_S Sft02_B Sft02_S
## p__Ascomycota 1146 2102 1726
## p__Basidiomycota 766 272 333
## p__Blastocladiomycota 41 7 0
## p__Mortierellomycota 286 13 194
## p__Kickxellomycota 36 1 53
## p__Rozellomycota 8 2 13
## p__Aphelidiomycota 2 0 0
## p__Basidiobolomycota 6 0 0
## p__Mucoromycota 1 1 11
## p__Chytridiomycota 0 0 0
## p__Calcarisporiellomycota 0 0 0
## p__Olpidiomycota 0 0 0
## p__Neocallimastigomycota 0 0 0
## p__Glomeromycota 0 0 4
## p__Entorrhizomycota 0 0 0write.csv(phyla_counts_tab, 'phyla_counts_rar_03.08.2025.csv')asv_counts <- pseqtest@otu_table
#determine the number of unclassified seqs at the phylum level
unclassified_tax_counts <- colSums(t(asv_counts)) - colSums(phyla_counts_tab)
#Add a row of "unclassified" to the phylum count table
phyla_and_unidentified_counts_tab <- rbind(phyla_counts_tab, "Unclassified_Phylum"=unclassified_tax_counts)split Ascomycota into classes
#remove p__Ascomycota for the purpose of separating it into classes later
temp_major_taxa_counts_tab <-
phyla_and_unidentified_counts_tab[!row.names(phyla_and_unidentified_counts_tab)
%in% "p__Ascomycota",]
#make count table broken down by class
class_counts_tab <- otu_table(tax_glom(t(pseqtest), taxrank="Class"))
#make table containing phylum and class data
class_tax_phy_tab <- tax_table(tax_glom(t(pseqtest), taxrank="Class"))
phy_tmp_vec <- class_tax_phy_tab[,2]
class_tmp_vec <- class_tax_phy_tab[,3]
rows_tmp <- row.names(class_tax_phy_tab)
class_tax_tab <- data.frame("Phylum"=phy_tmp_vec, "Class"=class_tmp_vec, row.names = rows_tmp)
#make vector of just the p__Ascomycota classes
asco_classes_vec <- as.vector(class_tax_tab[class_tax_tab$Phylum == "p__Ascomycota", "Class"])
row.names(class_counts_tab) <- as.vector(class_tax_tab$Class)
#make table of p__Ascomycota classes
asco_class_counts_tab <- class_counts_tab[row.names(class_counts_tab) %in% asco_classes_vec, ]
#make table of p__Ascomycota not identified to the class level
asco_no_class_annotated_counts <-
phyla_and_unidentified_counts_tab[row.names(phyla_and_unidentified_counts_tab) %in% "p__Ascomycota",]-
colSums(asco_class_counts_tab)
#Now combine the tables
major_taxa_counts_tab <- rbind(temp_major_taxa_counts_tab, asco_class_counts_tab,
"Unclassified_Ascomycetes"=asco_no_class_annotated_counts)
head(major_taxa_counts_tab)## 01m01_B 01m01_L 01m01_S 01m02_B 01m02_L 01m02_S 01m03_B
## p__Basidiomycota 295 451 606 272 178 702 461
## p__Blastocladiomycota 0 0 67 7 0 276 48
## p__Mortierellomycota 1 2 376 86 0 334 82
## p__Kickxellomycota 0 1 75 0 0 0 14
## p__Rozellomycota 0 0 4 0 0 6 17
## p__Aphelidiomycota 528 3 0 16 0 3 37
## 01m03_L 01m03_S 01m04_B 01m04_L 01m04_S 01m05_B 01m05_L
## p__Basidiomycota 589 989 726 415 630 253 445
## p__Blastocladiomycota 0 67 0 0 195 29 7
## p__Mortierellomycota 2 230 42 7 473 170 8
## p__Kickxellomycota 0 57 0 0 93 0 1
## p__Rozellomycota 0 3 0 0 8 0 0
## p__Aphelidiomycota 5 3 1 0 57 83 1
## 01m05_S 01m06_B 01m06_L 01m06_S 02m01_L 02m01_S 02m02_B
## p__Basidiomycota 596 507 190 878 305 2104 390
## p__Blastocladiomycota 55 0 0 2 21 0 10
## p__Mortierellomycota 298 0 3 401 6 81 0
## p__Kickxellomycota 69 101 0 51 2 1 0
## p__Rozellomycota 2 0 0 11 0 2 0
## p__Aphelidiomycota 4 60 0 2 0 0 0
## 02m02_L 02m02_S 02m03_B 02m03_L 02m03_S 02m04_L 02m04_S
## p__Basidiomycota 205 1938 355 632 1969 124 822
## p__Blastocladiomycota 13 24 1 4 3 9 21
## p__Mortierellomycota 15 106 5 13 73 3 408
## p__Kickxellomycota 0 7 2 2 15 0 29
## p__Rozellomycota 0 2 0 2 0 0 2
## p__Aphelidiomycota 1 0 0 0 0 0 0
## 02m05_B 02m05_L 02m06_B 02m06_L 02m06_S 02m07_B 02m07_L
## p__Basidiomycota 327 108 240 366 533 247 537
## p__Blastocladiomycota 8 0 24 2 68 1 0
## p__Mortierellomycota 10 1 35 2 203 15 2
## p__Kickxellomycota 0 0 5 0 47 0 0
## p__Rozellomycota 2 0 6 0 13 10 0
## p__Aphelidiomycota 11 0 3 0 1 7 0
## 02m07_S 02m08_L 02m08_S 02m09_B 02m09_L 02m09_S 02m10_B
## p__Basidiomycota 1144 723 245 274 233 516 214
## p__Blastocladiomycota 78 0 10 0 0 179 1
## p__Mortierellomycota 48 0 153 15 2 73 0
## p__Kickxellomycota 7 0 537 12 0 39 0
## p__Rozellomycota 39 0 3 0 0 17 0
## p__Aphelidiomycota 4 0 0 0 0 0 0
## 02m10_L 02m10_S 02m11_B 02m11_S 04m01_B 04m01_L 04m01_S
## p__Basidiomycota 77 395 269 265 347 381 446
## p__Blastocladiomycota 0 12 0 197 41 5 211
## p__Mortierellomycota 0 141 9 174 10 18 168
## p__Kickxellomycota 0 14 0 24 0 3 8
## p__Rozellomycota 0 3 0 1 6 2 18
## p__Aphelidiomycota 0 19 0 1 1 0 6
## 04m02_B 04m02_L 04m02_S 04m03_B 04m03_L 04m03_S 04m04_B
## p__Basidiomycota 279 69 750 282 338 528 1359
## p__Blastocladiomycota 12 0 275 0 0 82 3
## p__Mortierellomycota 13 3 363 10 4 138 58
## p__Kickxellomycota 0 1 25 12 0 43 2
## p__Rozellomycota 0 0 8 0 1 8 9
## p__Aphelidiomycota 15 0 6 0 0 2 15
## 04m04_L 04m04_S 04m05_B 04m05_L 04m05_S 04m06_B 04m06_L
## p__Basidiomycota 113 1967 264 429 763 616 167
## p__Blastocladiomycota 0 0 2 0 13 0 0
## p__Mortierellomycota 7 159 56 0 132 9 0
## p__Kickxellomycota 1 27 6 0 39 0 0
## p__Rozellomycota 1 11 0 0 7 0 0
## p__Aphelidiomycota 2 1 68 0 0 65 0
## 04m06_S 04m07_L 04m07_S 04m08_L 04m08_S 04m09_L 04m09_S
## p__Basidiomycota 496 255 485 105 282 233 355
## p__Blastocladiomycota 3 0 128 0 0 3 0
## p__Mortierellomycota 198 7 90 7 77 6 47
## p__Kickxellomycota 36 0 0 0 35 0 29
## p__Rozellomycota 18 1 8 0 0 0 38
## p__Aphelidiomycota 1 0 21 8 27 1 0
## 04m10_B 04m10_L 04m10_S 04m11_B 04m11_L 04m11_S 04m12_B
## p__Basidiomycota 289 565 1307 274 89 242 246
## p__Blastocladiomycota 0 0 66 1 0 139 0
## p__Mortierellomycota 10 2 372 21 4 215 24
## p__Kickxellomycota 0 0 37 0 0 56 0
## p__Rozellomycota 5 5 121 6 0 2 0
## p__Aphelidiomycota 0 0 5 6 0 17 0
## 04m12_L 04m12_S 04m13_B 04m13_L 04m13_S 04t01_B 04t01_L
## p__Basidiomycota 62 762 110 617 651 138 164
## p__Blastocladiomycota 0 58 36 0 13 21 62
## p__Mortierellomycota 5 220 0 0 230 11 3
## p__Kickxellomycota 2 24 0 0 9 0 0
## p__Rozellomycota 0 2 0 0 10 0 0
## p__Aphelidiomycota 2 4 4 0 69 0 0
## 04t01_S 04t02_B 04t02_L 04t02_S 04w01_B 04w01_L 04w01_S
## p__Basidiomycota 552 214 78 197 345 326 554
## p__Blastocladiomycota 34 1 1 3 6 0 69
## p__Mortierellomycota 252 20 6 44 4 0 122
## p__Kickxellomycota 150 1 0 10 0 0 23
## p__Rozellomycota 2 1 1 1 0 0 1
## p__Aphelidiomycota 0 0 0 0 0 0 0
## 04w02_B 04w02_L 04w02_S 04w03_B 04w03_S 04w04_L 04w04_S
## p__Basidiomycota 149 495 618 505 289 240 752
## p__Blastocladiomycota 0 30 365 0 233 0 0
## p__Mortierellomycota 0 12 476 0 193 7 351
## p__Kickxellomycota 0 0 47 0 32 0 0
## p__Rozellomycota 0 0 5 0 6 0 0
## p__Aphelidiomycota 0 0 0 0 0 0 44
## 20m01_L 20m01_S 20m02_B 20m02_L 20m02_S 20m03_B 20m03_L
## p__Basidiomycota 901 846 634 1055 842 260 98
## p__Blastocladiomycota 0 3 0 0 0 0 0
## p__Mortierellomycota 47 353 14 20 495 5 5
## p__Kickxellomycota 0 98 4 0 35 2 0
## p__Rozellomycota 1 19 0 0 5 2 0
## p__Aphelidiomycota 0 0 12 0 0 4 0
## 20m03_S 20m04_B 20m04_L 20m04_S 20m05_L 20m05_S sfm01_B
## p__Basidiomycota 534 281 308 251 118 600 383
## p__Blastocladiomycota 2 0 0 15 1 38 11
## p__Mortierellomycota 415 9 2 614 0 119 39
## p__Kickxellomycota 67 0 0 150 0 291 1
## p__Rozellomycota 12 8 0 51 3 23 7
## p__Aphelidiomycota 1 0 0 2 0 4 9
## sfm01_L sfm01_S sfm02_B sfm02_L sfm02_S sfm03_B sfm03_L
## p__Basidiomycota 301 724 509 91 1291 382 241
## p__Blastocladiomycota 1 34 1 0 25 0 0
## p__Mortierellomycota 16 239 60 1 440 17 2
## p__Kickxellomycota 0 60 3 0 16 0 0
## p__Rozellomycota 2 10 5 0 6 0 0
## p__Aphelidiomycota 0 4 41 0 1 0 0
## sfm04_B sfm04_L sfm04_S sfm05_B sfm05_L sfm05_S sfm06_L
## p__Basidiomycota 197 255 415 440 229 1253 366
## p__Blastocladiomycota 0 306 0 23 36 0 17
## p__Mortierellomycota 3 11 84 43 18 296 18
## p__Kickxellomycota 0 0 4 1 0 48 1
## p__Rozellomycota 4 2 7 3 0 1 1
## p__Aphelidiomycota 0 0 7 61 15 10 1
## sfm06_S sfm07_B sfm07_L sfm07_S Sft01_B Sft01_L Sft01_S
## p__Basidiomycota 754 369 400 580 315 320 766
## p__Blastocladiomycota 16 29 30 2 53 1 41
## p__Mortierellomycota 293 54 18 506 39 11 286
## p__Kickxellomycota 34 3 0 16 2 0 36
## p__Rozellomycota 5 6 1 7 0 1 8
## p__Aphelidiomycota 1 46 0 0 1 0 2
## Sft02_B Sft02_S
## p__Basidiomycota 272 333
## p__Blastocladiomycota 7 0
## p__Mortierellomycota 13 194
## p__Kickxellomycota 1 53
## p__Rozellomycota 2 13
## p__Aphelidiomycota 0 0write.csv(major_taxa_counts_tab, "majortaxa_count_rar_03.08.2025.csv")
#Check that all sequences are accounted for
identical(colSums(major_taxa_counts_tab), rowSums(asv_counts))## [1] TRUE## [1] TRUE
#Convert totals to relative abundance
major_taxa_proportions_tab <- apply(major_taxa_counts_tab, 2, function(x) x/sum(x)*100)
#colSums(major_taxa_proportions_tab)
write.csv(major_taxa_proportions_tab, "majortaxa_relative abundance_rar_03.08.2025col.csv")by family
#make taxonomy object by genus
family_counts_tab <- otu_table(tax_glom(pseqtest, taxrank="Family"), taxa_are_rows = FALSE)
family_counts_tab <- t(family_counts_tab)
#make vector of genus names to set as row names
family_tax_vec <- as.vector(tax_table(tax_glom(pseqtest, taxrank="Family"))[,5])
rownames(family_counts_tab) <- as.vector(family_tax_vec)
#determine the number of unclassified seqs at the family level
unclassified_family_counts <- colSums(t(asv_counts)) - colSums(family_counts_tab)
unclassified_family_counts## 01m01_B 01m01_L 01m01_S 01m02_B 01m02_L 01m02_S 01m03_B 01m03_L 01m03_S 01m04_B
## 355 39 122 208 81 112 629 31 577 377
## 01m04_L 01m04_S 01m05_B 01m05_L 01m05_S 01m06_B 01m06_L 01m06_S 02m01_L 02m01_S
## 54 189 213 48 318 331 16 223 64 98
## 02m02_B 02m02_L 02m02_S 02m03_B 02m03_L 02m03_S 02m04_L 02m04_S 02m05_B 02m05_L
## 128 60 92 52 34 98 72 148 1033 92
## 02m06_B 02m06_L 02m06_S 02m07_B 02m07_L 02m07_S 02m08_L 02m08_S 02m09_B 02m09_L
## 250 85 212 338 38 130 77 340 148 95
## 02m09_S 02m10_B 02m10_L 02m10_S 02m11_B 02m11_S 04m01_B 04m01_L 04m01_S 04m02_B
## 286 102 18 139 145 272 116 84 424 222
## 04m02_L 04m02_S 04m03_B 04m03_L 04m03_S 04m04_B 04m04_L 04m04_S 04m05_B 04m05_L
## 39 507 163 65 329 183 57 91 726 33
## 04m05_S 04m06_B 04m06_L 04m06_S 04m07_L 04m07_S 04m08_L 04m08_S 04m09_L 04m09_S
## 325 241 84 215 30 478 326 528 200 310
## 04m10_B 04m10_L 04m10_S 04m11_B 04m11_L 04m11_S 04m12_B 04m12_L 04m12_S 04m13_B
## 95 23 111 127 94 286 62 19 356 49
## 04m13_L 04m13_S 04t01_B 04t01_L 04t01_S 04t02_B 04t02_L 04t02_S 04w01_B 04w01_L
## 73 472 133 42 244 181 78 62 43 18
## 04w01_S 04w02_B 04w02_L 04w02_S 04w03_B 04w03_S 04w04_L 04w04_S 20m01_L 20m01_S
## 288 10 34 99 228 406 94 296 108 132
## 20m02_B 20m02_L 20m02_S 20m03_B 20m03_L 20m03_S 20m04_B 20m04_L 20m04_S 20m05_L
## 394 102 101 109 44 277 630 89 145 81
## 20m05_S sfm01_B sfm01_L sfm01_S sfm02_B sfm02_L sfm02_S sfm03_B sfm03_L sfm04_B
## 194 197 55 158 453 31 194 322 20 106
## sfm04_L sfm04_S sfm05_B sfm05_L sfm05_S sfm06_L sfm06_S sfm07_B sfm07_L sfm07_S
## 90 254 343 81 378 177 125 301 54 127
## Sft01_B Sft01_L Sft01_S Sft02_B Sft02_S
## 172 106 338 192 578#Add a row of "unclassified" to the family count table
family_and_unidentified_counts_tab <- rbind(family_counts_tab,
"Unclassified_family"=unclassified_family_counts)
write.csv(family_and_unidentified_counts_tab, "family raw abundance_rar.csv")
#Check that all seqs are accounted for.
identical(colSums(family_and_unidentified_counts_tab), rowSums(asv_counts))## [1] TRUE#Convert totals to relative abundance
family_proportions_tab <- apply(family_and_unidentified_counts_tab, 2, function(x) x/sum(x)*100)
write.csv(family_proportions_tab, "family relative abundance_rar.csv")#Merge metadata, phylum, and family abundances
#phy_and_fam <- merge(t(major_taxa_counts_tab), t(family_and_unidentified_counts_tab),
# by="row.names", all=TRUE)
phy_and_fam <- merge(t(major_taxa_proportions_tab), t(family_proportions_tab),
by="row.names", all=TRUE)
phyfam <- phy_and_fam[,-1]
rownames(phyfam) <- phy_and_fam[,1]
tax_merge <- merge(samdftest, phyfam,
by="row.names", all=TRUE)
taxmerge <- tax_merge[,-1]
rownames(taxmerge) <- tax_merge[,1]
write.csv(tax_merge, "1216R_03.08.2025_taxmerge.csv")summary stats about major taxa
colnames(taxmerge)## [1] "siteid"
## [2] "Sample"
## [3] "substrate"
## [4] "Site"
## [5] "sticID"
## [6] "wet_dry"
## [7] "n_wet"
## [8] "n_total"
## [9] "prc_missing"
## [10] "prc_wet"
## [11] "tempC_mean"
## [12] "LeafSpecies1"
## [13] "LeafSpecies2"
## [14] "LeafSpecies3"
## [15] "Elm"
## [16] "Populus"
## [17] "Oak"
## [18] "Locust"
## [19] "Graminales"
## [20] "Elderberry"
## [21] "Rubrus"
## [22] "Forb"
## [23] "Shrub"
## [24] "Unknown"
## [25] "Sediment.Texture.Description"
## [26] "Percent.Rocks"
## [27] "Percent.Leaf.Litter"
## [28] "Percent.Sediment"
## [29] "meanN2Ar"
## [30] "meanO2Ar"
## [31] "meanO2"
## [32] "project"
## [33] "date"
## [34] "rType"
## [35] "rep"
## [36] "sublocation"
## [37] "month"
## [38] "time_cst"
## [39] "sub_watershed"
## [40] "burn_area"
## [41] "burn_interval"
## [42] "stream_order"
## [43] "elevation"
## [44] "flow_state"
## [45] "conductivity"
## [46] "canopy_cover_percent"
## [47] "long"
## [48] "lat"
## [49] "stic_sublocation"
## [50] "stream_temp_C_1wk_avg"
## [51] "stream_temp_C_2wk_avg"
## [52] "stream_temp_C_3wk_avg"
## [53] "percent_wet_1wk_avg"
## [54] "percent_wet_2wk_avg"
## [55] "percent_wet_3wk_avg"
## [56] "twi"
## [57] "distance_from_outlet"
## [58] "drainage_area"
## [59] "wetted_width"
## [60] "slope_percent"
## [61] "weather"
## [62] "pH"
## [63] "crewBGC"
## [64] "crewMicro"
## [65] "wetdrybin"
## [66] "alpha.cent"
## [67] "page.rank"
## [68] "imp.closeness.cent"
## [69] "betweenness.cent"
## [70] "n.nodes.in.paths"
## [71] "n.paths"
## [72] "upstream.network.length"
## [73] "path.length.mean"
## [74] "path.length.var"
## [75] "path.length.skew"
## [76] "path.length.kurt"
## [77] "path.degree.mean"
## [78] "path.degree.var"
## [79] "path.degree.skew"
## [80] "path.degree.kurt"
## [81] "in.eccentricity"
## [82] "mean.efficiency"
## [83] "alpha.cent.wt"
## [84] "flowing.upstream.length.m"
## [85] "W_Chl.a_ug_per_mL"
## [86] "rock_area"
## [87] "B_Chl.a_ug_per_cm2"
## [88] "W_AFDM"
## [89] "B_AFDM"
## [90] "S_AFDM"
## [91] "LL_AFDM"
## [92] "pw_cluster"
## [93] "wet_dist_cluster"
## [94] "burn_freq"
## [95] "lndrainage_area"
## [96] "p__Basidiomycota"
## [97] "p__Blastocladiomycota"
## [98] "p__Mortierellomycota"
## [99] "p__Kickxellomycota"
## [100] "p__Rozellomycota"
## [101] "p__Aphelidiomycota"
## [102] "p__Basidiobolomycota"
## [103] "p__Mucoromycota"
## [104] "p__Chytridiomycota"
## [105] "p__Calcarisporiellomycota"
## [106] "p__Olpidiomycota"
## [107] "p__Neocallimastigomycota"
## [108] "p__Glomeromycota"
## [109] "p__Entorrhizomycota"
## [110] "Unclassified_Phylum"
## [111] "c__Dothideomycetes"
## [112] "c__Leotiomycetes"
## [113] "c__Sordariomycetes"
## [114] "c__Eurotiomycetes"
## [115] "c__Orbiliomycetes"
## [116] "c__Pezizomycotina_cls_Incertae_sedis"
## [117] "c__Saccharomycetes"
## [118] "c__Lecanoromycetes"
## [119] "c__Pezizomycetes"
## [120] "c__Laboulbeniomycetes"
## [121] "c__Taphrinomycetes"
## [122] "c__Schizosaccharomycetes"
## [123] "c__Geoglossomycetes"
## [124] "c__Candelariomycetes"
## [125] "c__Arthoniomycetes"
## [126] "Unclassified_Ascomycetes"
## [127] "f__Pleosporaceae"
## [128] "f__Cladosporiaceae"
## [129] "f__Phallaceae"
## [130] "f__Mycosphaerellaceae"
## [131] "f__Phaeosphaeriaceae"
## [132] "f__Helotiales_fam_Incertae_sedis"
## [133] "f__Didymosphaeriaceae"
## [134] "f__Cucurbitariaceae"
## [135] "f__Helotiaceae"
## [136] "f__Gnomoniaceae"
## [137] "f__Phacidiaceae"
## [138] "f__Chaetomellaceae"
## [139] "f__Calloriaceae"
## [140] "f__Blastocladiales_fam_Incertae_sedis"
## [141] "f__Sporocadaceae"
## [142] "f__Cylindrosympodiaceae"
## [143] "f__Venturiaceae"
## [144] "f__Mortierellaceae"
## [145] "f__Didymellaceae"
## [146] "f__Sebacinaceae"
## [147] "f__Hyaloscyphaceae"
## [148] "f__Filobasidiaceae"
## [149] "f__Xylariaceae"
## [150] "f__Verrucariaceae"
## [151] "f__Helicogoniaceae"
## [152] "f__Saccotheciaceae"
## [153] "f__Phanerochaetaceae"
## [154] "f__Piskurozymaceae"
## [155] "f__Tubulicrinaceae"
## [156] "f__Coniothyriaceae"
## [157] "f__Leptosphaeriaceae"
## [158] "f__Tricholomataceae"
## [159] "f__Thelephoraceae"
## [160] "f__Cystofilobasidiaceae"
## [161] "f__Microsporomycetaceae"
## [162] "f__Inocybaceae"
## [163] "f__Hymenogastraceae"
## [164] "f__Discosiaceae"
## [165] "f__Dictyosporiaceae"
## [166] "f__Sporormiaceae"
## [167] "f__Symmetrosporaceae"
## [168] "f__Mrakiaceae"
## [169] "f__Melanommataceae"
## [170] "f__Auriculariales_fam_Incertae_sedis"
## [171] "f__Leotiaceae"
## [172] "f__Teratosphaeriaceae"
## [173] "f__Kickxellomycota_fam_Incertae_sedis"
## [174] "f__Neodevriesiaceae"
## [175] "f__Hygrophoraceae"
## [176] "f__Thelebolaceae"
## [177] "f__Orbiliaceae"
## [178] "f__Trichomeriaceae"
## [179] "f__Corticiaceae"
## [180] "f__Strophariaceae"
## [181] "f__GS11_fam_Incertae_sedis"
## [182] "f__Nectriaceae"
## [183] "f__Mortierellales_fam_Incertae_sedis"
## [184] "f__Cylindriaceae"
## [185] "f__Pezizomycotina_fam_Incertae_sedis"
## [186] "f__Dothioraceae"
## [187] "f__Russulaceae"
## [188] "f__Neopyrenochaetaceae"
## [189] "f__Rhynchogastremataceae"
## [190] "f__Dothideales_fam_Incertae_sedis"
## [191] "f__Botryosphaeriaceae"
## [192] "f__Bulleribasidiaceae"
## [193] "f__Rickenellaceae"
## [194] "f__Aliquandostipitaceae"
## [195] "f__Amniculicolaceae"
## [196] "f__Tricladiaceae"
## [197] "f__Debaryomycetaceae"
## [198] "f__Erythrobasidiaceae"
## [199] "f__Rutstroemiaceae"
## [200] "f__Ascobolaceae"
## [201] "f__Irpicaceae"
## [202] "f__Sordariales_fam_Incertae_sedis"
## [203] "f__Lentitheciaceae"
## [204] "f__Pannariaceae"
## [205] "f__Diaporthaceae"
## [206] "f__Amphisphaeriaceae"
## [207] "f__Nothodactylariaceae"
## [208] "f__Trematosphaeriaceae"
## [209] "f__Gastrosporiaceae"
## [210] "f__Niessliaceae"
## [211] "f__Periconiaceae"
## [212] "f__Sebacinales_fam_Incertae_sedis"
## [213] "f__Basidiomycota_fam_Incertae_sedis"
## [214] "f__Lycoperdaceae"
## [215] "f__Serendipitaceae"
## [216] "f__Aphelidiomycota_fam_Incertae_sedis"
## [217] "f__Bionectriaceae"
## [218] "f__Pseudeurotiaceae"
## [219] "f__Geastraceae"
## [220] "f__Extremaceae"
## [221] "f__Hebelomataceae"
## [222] "f__Meruliaceae"
## [223] "f__Phaeotremellaceae"
## [224] "f__Pezizaceae"
## [225] "f__Chaetomiaceae"
## [226] "f__Psathyrellaceae"
## [227] "f__Tuberaceae"
## [228] "f__Hydnodontaceae"
## [229] "f__Catenariaceae"
## [230] "f__Herpotrichiellaceae"
## [231] "f__Tarzettaceae"
## [232] "f__Glomerellaceae"
## [233] "f__Hypocreaceae"
## [234] "f__Crepidotaceae"
## [235] "f__Physalacriaceae"
## [236] "f__Russulales_fam_Incertae_sedis"
## [237] "f__Pyxidiophoraceae"
## [238] "f__Ganodermataceae"
## [239] "f__Lasiosphaeriaceae"
## [240] "f__Acrocalymmaceae"
## [241] "f__Lophiostomataceae"
## [242] "f__Boletaceae"
## [243] "f__Clavicipitaceae"
## [244] "f__Sporidiobolaceae"
## [245] "f__Hypocreales_fam_Incertae_sedis"
## [246] "f__Massarinaceae"
## [247] "f__Podoscyphaceae"
## [248] "f__Sclerococcaceae"
## [249] "f__Sympoventuriaceae"
## [250] "f__Trichosporonaceae"
## [251] "f__Schizoporaceae"
## [252] "f__Pezizales_fam_Incertae_sedis"
## [253] "f__Buckleyzymaceae"
## [254] "f__Aspergillaceae"
## [255] "f__Agaricales_fam_Incertae_sedis"
## [256] "f__Taphrinaceae"
## [257] "f__Bartaliniaceae"
## [258] "f__Schizosaccharomycetaceae"
## [259] "f__Stachybotryaceae"
## [260] "f__Valsariaceae"
## [261] "f__Cordycipitaceae"
## [262] "f__Amorosiaceae"
## [263] "f__Dermateaceae"
## [264] "f__Cystobasidiales_fam_Incertae_sedis"
## [265] "f__Polyporaceae"
## [266] "f__Cystobasidiaceae"
## [267] "f__Hyaloriaceae"
## [268] "f__Sclerotiniaceae"
## [269] "f__Erythrobasidiales_fam_Incertae_sedis"
## [270] "f__Discinellaceae"
## [271] "f__Strelitzianaceae"
## [272] "f__Pestalotiopsidaceae"
## [273] "f__Schizoparmaceae"
## [274] "f__Exidiaceae"
## [275] "f__Peniophoraceae"
## [276] "f__Agaricomycotina_fam_Incertae_sedis"
## [277] "f__Tzeananiaceae"
## [278] "f__Nigrogranaceae"
## [279] "f__Massariaceae"
## [280] "f__Microbotryaceae"
## [281] "f__Geastrales_fam_Incertae_sedis"
## [282] "f__Omphalotaceae"
## [283] "f__Orbiliomycetes_fam_Incertae_sedis"
## [284] "f__Cystofilobasidiales_fam_Incertae_sedis"
## [285] "f__Chrysozymaceae"
## [286] "f__Pleosporales_fam_Incertae_sedis"
## [287] "f__Aplosporellaceae"
## [288] "f__Aphelidiomycetes_fam_Incertae_sedis"
## [289] "f__Hysteriaceae"
## [290] "f__Kondoaceae"
## [291] "f__Neocelosporiaceae"
## [292] "f__Stephanosporaceae"
## [293] "f__Geminibasidiaceae"
## [294] "f__Entolomataceae"
## [295] "f__Trichosphaeriaceae"
## [296] "f__Onygenales_fam_Incertae_sedis"
## [297] "f__Acrospermaceae"
## [298] "f__GS05_fam_Incertae_sedis"
## [299] "f__Steccherinaceae"
## [300] "f__Bolbitiaceae"
## [301] "f__Basidiobolomycota_fam_Incertae_sedis"
## [302] "f__Cunninghamellaceae"
## [303] "f__Magnaporthaceae"
## [304] "f__GS08_fam_Incertae_sedis"
## [305] "f__Bulleraceae"
## [306] "f__Gelatinodiscaceae"
## [307] "f__Lipomycetaceae"
## [308] "f__Microstromataceae"
## [309] "f__Naviculisporaceae"
## [310] "f__Laboulbeniomycetes_fam_Incertae_sedis"
## [311] "f__Cryphonectriaceae"
## [312] "f__Phaeoseptaceae"
## [313] "f__Malasseziaceae"
## [314] "f__Chaetothyriales_fam_Incertae_sedis"
## [315] "f__Libertasomycetaceae"
## [316] "f__Cyphellophoraceae"
## [317] "f__Geoglossaceae"
## [318] "f__Pulvinulaceae"
## [319] "f__Blastocladiomycota_fam_Incertae_sedis"
## [320] "f__GS13_fam_Incertae_sedis"
## [321] "f__Candelariaceae"
## [322] "f__Cryptocoryneaceae"
## [323] "f__Lophiotremataceae"
## [324] "f__Sordariaceae"
## [325] "f__Physciaceae"
## [326] "f__Saccharomycetaceae"
## [327] "f__Dothideomycetes_fam_Incertae_sedis"
## [328] "f__Pyronemataceae"
## [329] "f__Sclerodermataceae"
## [330] "f__Ascodesmidaceae"
## [331] "f__Gomphaceae"
## [332] "f__Endogonomycetes_fam_Incertae_sedis"
## [333] "f__Torulaceae"
## [334] "f__Tremellaceae"
## [335] "f__Hymenochaetaceae"
## [336] "f__Phaffomycetaceae"
## [337] "f__Bombardiaceae"
## [338] "f__Atheliaceae"
## [339] "f__Fomitopsidaceae"
## [340] "f__Basidiobolales_fam_Incertae_sedis"
## [341] "f__Mucoraceae"
## [342] "f__Hyponectriaceae"
## [343] "f__Pseudoperisporiaceae"
## [344] "f__Hyphodermataceae"
## [345] "f__Thyridiaceae"
## [346] "f__Capnodiaceae"
## [347] "f__Melanogastraceae"
## [348] "f__Aphelidiales_fam_Incertae_sedis"
## [349] "f__Dothideaceae"
## [350] "f__Helminthosphaeriaceae"
## [351] "f__Acrospermales_fam_Incertae_sedis"
## [352] "f__Agaricomycetes_fam_Incertae_sedis"
## [353] "f__Agaricaceae"
## [354] "f__Pluteaceae"
## [355] "f__Orbiliales_fam_Incertae_sedis"
## [356] "f__Hamatocanthoscyphaceae"
## [357] "f__Sclerogastraceae"
## [358] "f__Podosporaceae"
## [359] "f__Stictidaceae"
## [360] "f__Paraphysodermataceae"
## [361] "f__Xylariales_fam_Incertae_sedis"
## [362] "f__Saksenaeaceae"
## [363] "f__Eurotiomycetes_fam_Incertae_sedis"
## [364] "f__Halosphaeriaceae"
## [365] "f__Microstromatales_fam_Incertae_sedis"
## [366] "f__Ophiocordycipitaceae"
## [367] "f__Branch02_fam_Incertae_sedis"
## [368] "f__Calcarisporiellaceae"
## [369] "f__Tremellales_fam_Incertae_sedis"
## [370] "f__Phaeococcomycetaceae"
## [371] "f__Radulomycetaceae"
## [372] "f__Saccharomycetales_fam_Incertae_sedis"
## [373] "f__Roccellaceae"
## [374] "f__Planistromellaceae"
## [375] "f__Tubeufiales_fam_Incertae_sedis"
## [376] "f__Ophiostomataceae"
## [377] "f__Mortierellomycota_fam_Incertae_sedis"
## [378] "f__GS19_fam_Incertae_sedis"
## [379] "f__Hydnaceae"
## [380] "f__Spiculogloeaceae"
## [381] "f__Trimorphomycetaceae"
## [382] "f__Phaeomoniellaceae"
## [383] "f__Cryptococcaceae"
## [384] "f__Xenasmataceae"
## [385] "f__Acarosporaceae"
## [386] "f__Mycenastraceae"
## [387] "f__Plectosphaerellaceae"
## [388] "f__Apiosporaceae"
## [389] "f__Neophaeosphaeriaceae"
## [390] "f__Ramicandelaberaceae"
## [391] "f__Tubeufiaceae"
## [392] "f__Hypoxylaceae"
## [393] "f__Lachnaceae"
## [394] "f__Arthoniaceae"
## [395] "f__Lyophyllaceae"
## [396] "f__Incrustoporiaceae"
## [397] "f__Aporpiaceae"
## [398] "f__Tremellodendropsidales_fam_Incertae_sedis"
## [399] "f__Typhulaceae"
## [400] "f__GS07_fam_Incertae_sedis"
## [401] "f__Chaetosphaeriales_fam_Incertae_sedis"
## [402] "f__Graphostromataceae"
## [403] "f__Dacryobolaceae"
## [404] "f__Capnodiales_fam_Incertae_sedis"
## [405] "f__Olpidiaceae"
## [406] "f__Mortierellomycetes_fam_Incertae_sedis"
## [407] "f__Kriegeriaceae"
## [408] "f__Olpidiomycota_fam_Incertae_sedis"
## [409] "f__Microsphaeropsidaceae"
## [410] "f__Phomatosporaceae"
## [411] "f__Mycocaliciales_fam_Incertae_sedis"
## [412] "f__Elsinoaceae"
## [413] "f__Cortinariaceae"
## [414] "f__Pileolariaceae"
## [415] "f__Mucorales_fam_Incertae_sedis"
## [416] "f__Asterinales_fam_Incertae_sedis"
## [417] "f__Stereaceae"
## [418] "f__Tilletiaceae"
## [419] "f__Microdochiaceae"
## [420] "f__Tubakiaceae"
## [421] "f__Diversisporales_fam_Incertae_sedis"
## [422] "f__Microthyriales_fam_Incertae_sedis"
## [423] "f__Sirobasidiaceae"
## [424] "f__Thelebolales_fam_Incertae_sedis"
## [425] "f__Microbotryomycetes_fam_Incertae_sedis"
## [426] "f__Reticulascaceae"
## [427] "f__Castanediellaceae"
## [428] "f__Tremellomycetes_fam_Incertae_sedis"
## [429] "f__Fenestellaceae"
## [430] "f__Anteagloniaceae"
## [431] "f__Ploettnerulaceae"
## [432] "f__Dacrymycetaceae"
## [433] "f__Mytilinidales_fam_Incertae_sedis"
## [434] "f__Myriangiales_fam_Incertae_sedis"
## [435] "f__Mytilinidiales_fam_Incertae_sedis"
## [436] "f__Trichomonascaceae"
## [437] "f__Marasmiaceae"
## [438] "f__Dactylosporaceae"
## [439] "f__Naohideaceae"
## [440] "f__Neocallimastigomycota_fam_Incertae_sedis"
## [441] "f__Geoglossales_fam_Incertae_sedis"
## [442] "f__Porodiplodiaceae"
## [443] "f__Lachnocladiaceae"
## [444] "f__GS16_fam_Incertae_sedis"
## [445] "f__Pterulaceae"
## [446] "f__Boletinellaceae"
## [447] "f__Panaceae"
## [448] "f__Schizophyllaceae"
## [449] "f__Sakaguchiaceae"
## [450] "f__Pisorisporiales_fam_Incertae_sedis"
## [451] "f__Pervetustaceae"
## [452] "f__Lopadostomataceae"
## [453] "f__Neoschizotheciaceae"
## [454] "f__Tubariaceae"
## [455] "f__Cyclothyriellaceae"
## [456] "f__Microascaceae"
## [457] "f__Glomerellales_fam_Incertae_sedis"
## [458] "f__Chaetosphaeriaceae"
## [459] "f__Myrmecridiaceae"
## [460] "f__Auriculariaceae"
## [461] "f__Wallemiaceae"
## [462] "f__Limnoperdaceae"
## [463] "f__Pilobolaceae"
## [464] "f__Rozellomycota_fam_Incertae_sedis"
## [465] "f__Glomosporiaceae"
## [466] "f__Bloxamiaceae"
## [467] "f__Gymnoascaceae"
## [468] "f__Teichosporaceae"
## [469] "f__Pycnoraceae"
## [470] "f__Onygenaceae"
## [471] "f__Melampsoraceae"
## [472] "f__Gloeophyllaceae"
## [473] "f__Juglanconidaceae"
## [474] "f__Amphisphaeriales_fam_Incertae_sedis"
## [475] "f__Schizothyriaceae"
## [476] "f__Rhizopogonaceae"
## [477] "f__Gyroporaceae"
## [478] "f__Cyphellaceae"
## [479] "f__Carcinomycetaceae"
## [480] "f__Pleuroascaceae"
## [481] "f__Entorrhizaceae"
## [482] "f__Sphaerobolaceae"
## [483] "f__Amanitaceae"
## [484] "f__Chionosphaeraceae"
## [485] "f__Meripilaceae"
## [486] "f__Calosphaeriaceae"
## [487] "f__Coryneliaceae"
## [488] "f__Mollisiaceae"
## [489] "f__Sordariomycetes_fam_Incertae_sedis"
## [490] "f__Wiesneriomycetaceae"
## [491] "f__Umbelopsidaceae"
## [492] "f__Minutisphaeraceae"
## [493] "f__Diatrypaceae"
## [494] "f__Coniochaetaceae"
## [495] "f__Tulasnellaceae"
## [496] "f__Ceratosphaeriaceae"
## [497] "f__Testudinaceae"
## [498] "f__Corticiales_fam_Incertae_sedis"
## [499] "f__Sarcosomataceae"
## [500] "f__Sarocladiaceae"
## [501] "f__Rhizophydiales_fam_Incertae_sedis"
## [502] "f__Pichiaceae"
## [503] "f__Ramalinaceae"
## [504] "f__Clavariaceae"
## [505] "f__Sporidiobolales_fam_Incertae_sedis"
## [506] "f__Neoantennariellaceae"
## [507] "f__Agaricostilbomycetes_fam_Incertae_sedis"
## [508] "f__Neocamarosporiaceae"
## [509] "f__Pleurotaceae"
## [510] "f__Eremomycetaceae"
## [511] "f__Tympanidaceae"
## [512] "f__Lentariaceae"
## [513] "f__Xenodactylariaceae"
## [514] "f__Alphamycetaceae"
## [515] "f__Teloschistaceae"
## [516] "f__Jianyuniaceae"
## [517] "f__Venturiales_fam_Incertae_sedis"
## [518] "f__GS22_fam_Incertae_sedis"
## [519] "f__Mycenaceae"
## [520] "f__Lichtheimiaceae"
## [521] "f__Septobasidiaceae"
## [522] "Unclassified_family"### SUMMARY ACROSS ALL SAMPLES
mean(rowSums(taxmerge[,c("c__Dothideomycetes","c__Leotiomycetes","c__Sordariomycetes","c__Eurotiomycetes","c__Orbiliomycetes","c__Pezizomycotina_cls_Incertae_sedis","c__Saccharomycetes","c__Lecanoromycetes","c__Pezizomycetes","c__Laboulbeniomycetes","c__Taphrinomycetes","c__Schizosaccharomycetes","c__Candelariomycetes","c__Arthoniomycetes","c__Geoglossomycetes","Unclassified_Ascomycetes")]))## [1] 69.41205sd(rowSums(taxmerge[,c("c__Dothideomycetes","c__Leotiomycetes","c__Sordariomycetes","c__Eurotiomycetes","c__Orbiliomycetes","c__Pezizomycotina_cls_Incertae_sedis","c__Saccharomycetes","c__Lecanoromycetes","c__Pezizomycetes","c__Laboulbeniomycetes","c__Taphrinomycetes","c__Schizosaccharomycetes","c__Candelariomycetes","c__Arthoniomycetes","c__Geoglossomycetes","Unclassified_Ascomycetes")]))## [1] 21.72563### SUMMARY ACROSS LEAF SAMPLES
###mean/sd percent abundance of Ascomycota in leaf
mean(rowSums(taxmerge[taxmerge$substrate=='L',c("c__Dothideomycetes","c__Leotiomycetes","c__Sordariomycetes","c__Eurotiomycetes","c__Orbiliomycetes","c__Pezizomycotina_cls_Incertae_sedis","c__Saccharomycetes","c__Lecanoromycetes","c__Pezizomycetes","c__Laboulbeniomycetes","c__Taphrinomycetes","c__Schizosaccharomycetes","c__Candelariomycetes","c__Arthoniomycetes","c__Geoglossomycetes","Unclassified_Ascomycetes")]))## [1] 85.33998sd(rowSums(taxmerge[taxmerge$substrate=='L',c("c__Dothideomycetes","c__Leotiomycetes","c__Sordariomycetes","c__Eurotiomycetes","c__Orbiliomycetes","c__Pezizomycotina_cls_Incertae_sedis","c__Saccharomycetes","c__Lecanoromycetes","c__Pezizomycetes","c__Laboulbeniomycetes","c__Taphrinomycetes","c__Schizosaccharomycetes","c__Candelariomycetes","c__Arthoniomycetes","c__Geoglossomycetes","Unclassified_Ascomycetes")]))## [1] 9.380394######################################################
### SUMMARY ACROSS EPILITHON SAMPLES
###mean/sd percent abundance of Ascomycota in epilithon
mean(rowSums(taxmerge[taxmerge$substrate=='B',c("c__Dothideomycetes","c__Leotiomycetes","c__Sordariomycetes","c__Eurotiomycetes","c__Orbiliomycetes","c__Pezizomycotina_cls_Incertae_sedis","c__Saccharomycetes","c__Lecanoromycetes","c__Pezizomycetes","c__Laboulbeniomycetes","c__Taphrinomycetes","c__Schizosaccharomycetes","c__Candelariomycetes","c__Arthoniomycetes","c__Geoglossomycetes","Unclassified_Ascomycetes")]))## [1] 76.53626sd(rowSums(taxmerge[taxmerge$substrate=='B',c("c__Dothideomycetes","c__Leotiomycetes","c__Sordariomycetes","c__Eurotiomycetes","c__Orbiliomycetes","c__Pezizomycotina_cls_Incertae_sedis","c__Saccharomycetes","c__Lecanoromycetes","c__Pezizomycetes","c__Laboulbeniomycetes","c__Taphrinomycetes","c__Schizosaccharomycetes","c__Candelariomycetes","c__Arthoniomycetes","c__Geoglossomycetes","Unclassified_Ascomycetes")]))## [1] 12.79824######################################################
### SUMMARY ACROSS SEDIMENT SAMPLES
###mean/sd percent abundance of Ascomycota in sediment
mean(rowSums(taxmerge[taxmerge$substrate=='S',c("c__Dothideomycetes","c__Leotiomycetes","c__Sordariomycetes","c__Eurotiomycetes","c__Orbiliomycetes","c__Pezizomycotina_cls_Incertae_sedis","c__Saccharomycetes","c__Lecanoromycetes","c__Pezizomycetes","c__Laboulbeniomycetes","c__Taphrinomycetes","c__Schizosaccharomycetes","c__Candelariomycetes","c__Arthoniomycetes","c__Geoglossomycetes","Unclassified_Ascomycetes")]))## [1] 47.87911sd(rowSums(taxmerge[taxmerge$substrate=='S',c("c__Dothideomycetes","c__Leotiomycetes","c__Sordariomycetes","c__Eurotiomycetes","c__Orbiliomycetes","c__Pezizomycotina_cls_Incertae_sedis","c__Saccharomycetes","c__Lecanoromycetes","c__Pezizomycetes","c__Laboulbeniomycetes","c__Taphrinomycetes","c__Schizosaccharomycetes","c__Candelariomycetes","c__Arthoniomycetes","c__Geoglossomycetes","Unclassified_Ascomycetes")]))## [1] 18.79491###non-ascomycetes in sediment
mean(rowSums(taxmerge[taxmerge$substrate=='S',c("p__Basidiomycota","p__Blastocladiomycota","p__Mortierellomycota","p__Kickxellomycota","p__Rozellomycota","p__Aphelidiomycota","p__Basidiobolomycota","p__Chytridiomycota","p__Mucoromycota","p__Calcarisporiellomycota","p__Olpidiomycota","p__Glomeromycota","p__Neocallimastigomycota","p__Entorrhizomycota","Unclassified_Phylum")]))## [1] 52.12089sd(rowSums(taxmerge[taxmerge$substrate=='S',c("p__Basidiomycota","p__Blastocladiomycota","p__Mortierellomycota","p__Kickxellomycota","p__Rozellomycota","p__Aphelidiomycota","p__Basidiobolomycota","p__Chytridiomycota","p__Mucoromycota","p__Calcarisporiellomycota","p__Olpidiomycota","p__Glomeromycota","p__Neocallimastigomycota","p__Entorrhizomycota","Unclassified_Phylum")]))## [1] 18.79491### basidiomycota in sediment
mean(taxmerge[taxmerge$substrate=='S',c("p__Basidiomycota")])## [1] 30.31288sd(taxmerge[taxmerge$substrate=='S',c("p__Basidiomycota")])## [1] 19.14144### blastocladiomycota in sediment
mean(taxmerge[taxmerge$substrate=='S',c("p__Blastocladiomycota")])## [1] 2.66455sd(taxmerge[taxmerge$substrate=='S',c("p__Blastocladiomycota")])## [1] 3.661561### Mortierellomycota in sediment
mean(taxmerge[taxmerge$substrate=='S',c("p__Mortierellomycota")])## [1] 10.0309sd(taxmerge[taxmerge$substrate=='S',c("p__Mortierellomycota")])## [1] 5.926307### Kickxellomycota in sediment
mean(taxmerge[taxmerge$substrate=='S',c("p__Kickxellomycota")])## [1] 2.221596sd(taxmerge[taxmerge$substrate=='S',c("p__Kickxellomycota")])## [1] 3.543442### rozellomycota in sediment
mean(taxmerge[taxmerge$substrate=='S',c("p__Rozellomycota")])## [1] 0.468558sd(taxmerge[taxmerge$substrate=='S',c("p__Rozellomycota")])## [1] 0.7883253### no phylum in sediment
mean(taxmerge[taxmerge$substrate=='S',c("Unclassified_Phylum")])## [1] 5.826676sd(taxmerge[taxmerge$substrate=='S',c("Unclassified_Phylum")])## [1] 4.233832### trace levels of zoosporic fungi
#mean(taxmerge$p__Neocallimastigomycota)
#sd(taxmerge$p__Neocallimastigomycota)
mean(taxmerge$p__Rozellomycota)## [1] 0.2039237sd(taxmerge$p__Rozellomycota)## [1] 0.5154182mean(taxmerge$p__Aphelidiomycota)## [1] 0.4436555sd(taxmerge$p__Aphelidiomycota)## [1] 1.953913mean(taxmerge$p__Basidiobolomycota)## [1] 0.02245588sd(taxmerge$p__Basidiobolomycota)## [1] 0.06423416mean(taxmerge$p__Chytridiomycota)## [1] 0.01062103sd(taxmerge$p__Chytridiomycota)## [1] 0.0548174mean(taxmerge$p__Mucoromycota)## [1] 0.08496821sd(taxmerge$p__Mucoromycota)## [1] 0.224308mean(taxmerge$c__Dothideomycetes)## [1] 46.82902sd(taxmerge$c__Dothideomycetes)## [1] 19.25747#mean(taxmerge[,97])
#sd(taxmerge[,97])
### identifying sites with neocallimastigomycetes
neocalsites<- taxmerge[taxmerge$p__Neocallimastigomycota>0,]
neocalsites$siteid## [1] "04M03" "04M04" "04W03"###Phylum-Class BARPLOT
#Phylum
#Select taxa that make up >90% of the total sequences
phylatrim <- data.frame(major_taxa_proportions_tab[c("p__Basidiomycota","p__Mortierellomycota","p__Blastocladiomycota","p__Aphelidiomycota","p__Kickxellomycota","p__Rozellomycota","Unclassified_Phylum","c__Dothideomycetes","c__Leotiomycetes","c__Sordariomycetes","c__Eurotiomycetes", "Unclassified_Ascomycetes"
#, "p__Ochrophyta", "p__Oomycota"
), ])
#write.csv(phylatrim_t,"/Users/chunk/Documents/DADA2/DADA2_package_test/Kz_Syn_ITS/run_2/outputs/phylatrim.csv")
#write.csv(major_taxa_proportions_tab,"/Users/chunk/Documents/DADA2/DADA2_package_test/Kz_Syn_ITS/majortaxa.csv")
#majortaxa <- read.csv("~/Documents/DADA2/DADA2_package_test/Kz_Syn_ITS/majortaxa.csv", row.names=1)
#View(majortaxa)
#phylatrim <- read.csv("~/Documents/DADA2/DADA2_package_test/Kz_Syn_ITS/phylatrimt.csv", row.names=1)
#View(phylatrim)
#Create an "other" category for the phyla not retained
filtered_proportions <- colSums(major_taxa_proportions_tab) -
colSums(phylatrim)
phylatrim <- rbind(phylatrim, "Other"=filtered_proportions)
phylatrim## X01m01_B X01m01_L X01m01_S X01m02_B
## p__Basidiomycota 12.08521098 18.47603441 24.8258910 11.1429742
## p__Mortierellomycota 0.04096682 0.08193363 15.4035231 3.5231463
## p__Blastocladiomycota 0.00000000 0.00000000 2.7447767 0.2867677
## p__Aphelidiomycota 21.63047931 0.12290045 0.0000000 0.6554691
## p__Kickxellomycota 0.00000000 0.04096682 3.0725113 0.0000000
## p__Rozellomycota 0.00000000 0.00000000 0.1638673 0.0000000
## Unclassified_Phylum 6.67759115 0.28676772 3.0725113 1.4748054
## c__Dothideomycetes 28.55387136 30.56124539 36.0507989 22.4088488
## c__Leotiomycetes 0.61450225 37.68947153 2.7038099 1.7206063
## c__Sordariomycetes 1.59770586 8.35723064 4.1786153 2.3760754
## c__Eurotiomycetes 24.17042196 3.80991397 0.3277345 48.2998771
## Unclassified_Ascomycetes 4.38344941 0.08193363 0.7374027 5.8582548
## Other 0.24580090 0.49160180 6.7185580 2.2531749
## X01m02_L X01m02_S X01m03_B X01m03_L
## p__Basidiomycota 7.2920934 28.7587054 18.8857026 24.12945514
## p__Mortierellomycota 0.0000000 13.6829168 3.3592790 0.08193363
## p__Blastocladiomycota 0.0000000 11.3068415 1.9664072 0.00000000
## p__Aphelidiomycota 0.0000000 0.1229005 1.5157722 0.20483408
## p__Kickxellomycota 0.0000000 0.0000000 0.5735354 0.00000000
## p__Rozellomycota 0.0000000 0.2458009 0.6964359 0.00000000
## Unclassified_Phylum 0.1638673 2.5809095 16.8783286 0.69643589
## c__Dothideomycetes 66.4481770 20.9750102 34.0434248 49.40598116
## c__Leotiomycetes 6.8004916 11.8394101 2.4170422 14.91192134
## c__Sordariomycetes 17.7386317 2.7857435 3.8099140 9.91396968
## c__Eurotiomycetes 0.2048341 1.2290045 7.7017616 0.04096682
## Unclassified_Ascomycetes 0.2048341 0.0000000 6.7595248 0.28676772
## Other 1.1470709 6.4727571 1.3928718 0.32773454
## X01m03_S X01m04_B X01m04_L X01m04_S
## p__Basidiomycota 40.5161819 29.74190905 17.0012290 25.8090946
## p__Mortierellomycota 9.4223679 1.72060631 0.2867677 19.3773044
## p__Blastocladiomycota 2.7447767 0.00000000 0.0000000 7.9885293
## p__Aphelidiomycota 0.1229005 0.04096682 0.0000000 2.3351086
## p__Kickxellomycota 2.3351086 0.00000000 0.0000000 3.8099140
## p__Rozellomycota 0.1229005 0.00000000 0.0000000 0.3277345
## Unclassified_Phylum 21.9172470 5.36665301 0.6554691 5.2027857
## c__Dothideomycetes 13.6829168 53.70749693 55.9197050 21.4256452
## c__Leotiomycetes 1.8435068 0.86030315 1.4338386 2.0893077
## c__Sordariomycetes 3.6460467 1.59770586 22.9414175 6.0630889
## c__Eurotiomycetes 1.3519050 0.24580090 0.5325686 0.2048341
## Unclassified_Ascomycetes 0.7783695 5.61245391 0.1638673 1.3928718
## Other 1.5157722 1.10610406 1.0651372 3.9737812
## X01m05_B X01m05_L X01m05_S X01m06_B
## p__Basidiomycota 10.3646047 18.23023351 24.41622286 20.770176
## p__Mortierellomycota 6.9643589 0.32773454 12.20811143 0.000000
## p__Blastocladiomycota 1.1880377 0.28676772 2.25317493 0.000000
## p__Aphelidiomycota 3.4002458 0.04096682 0.16386727 2.458009
## p__Kickxellomycota 0.0000000 0.04096682 2.82671036 4.137649
## p__Rozellomycota 0.0000000 0.00000000 0.08193363 0.000000
## Unclassified_Phylum 5.5714871 0.57353544 5.07988529 7.455961
## c__Dothideomycetes 59.4018845 59.56575174 33.38795576 47.275707
## c__Leotiomycetes 4.6702171 8.35723064 2.33510856 1.392872
## c__Sordariomycetes 3.6460467 11.75747644 8.48013109 4.014748
## c__Eurotiomycetes 0.2048341 0.12290045 0.53256862 7.292093
## Unclassified_Ascomycetes 1.4748054 0.20483408 4.34248259 4.055715
## Other 3.1134781 0.49160180 3.89184760 1.147071
## X01m06_L X01m06_S X02m01_L X02m01_S
## p__Basidiomycota 7.78369521 35.96886522 12.49487915 86.19418271
## p__Mortierellomycota 0.12290045 16.42769357 0.24580090 3.31831217
## p__Blastocladiomycota 0.00000000 0.08193363 0.86030315 0.00000000
## p__Aphelidiomycota 0.00000000 0.08193363 0.00000000 0.00000000
## p__Kickxellomycota 0.00000000 2.08930766 0.08193363 0.04096682
## p__Rozellomycota 0.00000000 0.45063499 0.00000000 0.08193363
## Unclassified_Phylum 0.08193363 5.07988529 1.35190496 3.72798034
## c__Dothideomycetes 42.64645637 20.89307661 58.66448177 3.07251127
## c__Leotiomycetes 45.02253175 4.13764850 11.55264236 1.39287177
## c__Sordariomycetes 2.90864400 8.07046293 13.15034822 1.18803769
## c__Eurotiomycetes 0.28676772 1.55673904 0.24580090 0.12290045
## Unclassified_Ascomycetes 0.12290045 0.16386727 0.00000000 0.00000000
## Other 1.02417042 4.99795166 1.35190496 0.86030315
## X02m02_B X02m02_L X02m02_S X02m03_B
## p__Basidiomycota 15.9770586 8.39819746 79.39369111 14.54321999
## p__Mortierellomycota 0.0000000 0.61450225 4.34248259 0.20483408
## p__Blastocladiomycota 0.4096682 0.53256862 0.98320361 0.04096682
## p__Aphelidiomycota 0.0000000 0.04096682 0.00000000 0.00000000
## p__Kickxellomycota 0.0000000 0.00000000 0.28676772 0.08193363
## p__Rozellomycota 0.0000000 0.00000000 0.08193363 0.00000000
## Unclassified_Phylum 0.7374027 1.06513724 1.47480541 0.94223679
## c__Dothideomycetes 68.5374846 82.75297009 7.04629250 74.47767308
## c__Leotiomycetes 6.8824252 2.45800901 1.67963949 6.26792298
## c__Sordariomycetes 0.4506350 3.11347808 1.76157313 0.49160180
## c__Eurotiomycetes 3.2363785 0.04096682 0.12290045 0.73740270
## Unclassified_Ascomycetes 1.5977059 0.16386727 0.40966817 0.28676772
## Other 2.1712413 0.81933634 2.41704220 1.92544039
## X02m03_L X02m03_S X02m04_L X02m04_S
## p__Basidiomycota 25.89102827 80.6636624 5.0798853 33.67472347
## p__Mortierellomycota 0.53256862 2.9905776 0.1229005 16.71446129
## p__Blastocladiomycota 0.16386727 0.1229005 0.3687014 0.86030315
## p__Aphelidiomycota 0.00000000 0.0000000 0.0000000 0.00000000
## p__Kickxellomycota 0.08193363 0.6145023 0.0000000 1.18803769
## p__Rozellomycota 0.08193363 0.0000000 0.0000000 0.08193363
## Unclassified_Phylum 0.65546907 3.0315444 1.6386727 4.46538304
## c__Dothideomycetes 43.83449406 3.6050799 62.2695617 34.37115936
## c__Leotiomycetes 4.30151577 0.5735354 25.3174928 3.52314625
## c__Sordariomycetes 22.69561655 2.7038099 1.0651372 1.84350676
## c__Eurotiomycetes 0.61450225 0.6554691 0.0000000 0.36870135
## Unclassified_Ascomycetes 0.08193363 0.1638673 0.1229005 0.73740270
## Other 1.06513724 4.8750512 4.0147481 2.17124129
## X02m05_B X02m05_L X02m06_B X02m06_L
## p__Basidiomycota 13.39614912 4.42441622 9.8320361 14.99385498
## p__Mortierellomycota 0.40966817 0.04096682 1.4338386 0.08193363
## p__Blastocladiomycota 0.32773454 0.00000000 0.9832036 0.08193363
## p__Aphelidiomycota 0.45063499 0.00000000 0.1229005 0.00000000
## p__Kickxellomycota 0.00000000 0.00000000 0.2048341 0.00000000
## p__Rozellomycota 0.08193363 0.00000000 0.2458009 0.00000000
## Unclassified_Phylum 29.37320770 0.94223679 6.8824252 1.76157313
## c__Dothideomycetes 40.39328144 85.66161409 63.3756657 52.72429332
## c__Leotiomycetes 2.13027448 4.34248259 6.1040557 5.61245391
## c__Sordariomycetes 1.76157313 3.68701352 7.4559607 16.05899222
## c__Eurotiomycetes 0.00000000 0.32773454 0.2867677 0.36870135
## Unclassified_Ascomycetes 11.34780828 0.32773454 1.6386727 1.02417042
## Other 0.32773454 0.24580090 1.4338386 7.29209340
## X02m06_S X02m07_B X02m07_L X02m07_S
## p__Basidiomycota 21.83531340 10.11880377 21.99918066 46.8660385
## p__Mortierellomycota 8.31626383 0.61450225 0.08193363 1.9664072
## p__Blastocladiomycota 2.78574355 0.04096682 0.00000000 3.1954117
## p__Aphelidiomycota 0.04096682 0.28676772 0.00000000 0.1638673
## p__Kickxellomycota 1.92544039 0.00000000 0.00000000 0.2867677
## p__Rozellomycota 0.53256862 0.40966817 0.00000000 1.5977059
## Unclassified_Phylum 5.28471938 10.24170422 0.69643589 2.6628431
## c__Dothideomycetes 40.14748054 67.34944695 64.76853748 28.9635395
## c__Leotiomycetes 4.01474805 3.64604670 6.02212208 3.1134781
## c__Sordariomycetes 8.97173290 4.01474805 6.14502253 6.7595248
## c__Eurotiomycetes 0.90126997 0.20483408 0.04096682 0.9422368
## Unclassified_Ascomycetes 1.63867268 1.67963949 0.00000000 0.7374027
## Other 3.60507989 1.39287177 0.24580090 2.7447767
## X02m08_L X02m08_S X02m09_B X02m09_L
## p__Basidiomycota 29.6190086 10.0368701 11.22490782 9.54526833
## p__Mortierellomycota 0.0000000 6.2679230 0.61450225 0.08193363
## p__Blastocladiomycota 0.0000000 0.4096682 0.00000000 0.00000000
## p__Aphelidiomycota 0.0000000 0.0000000 0.00000000 0.00000000
## p__Kickxellomycota 0.0000000 21.9991807 0.49160180 0.00000000
## p__Rozellomycota 0.0000000 0.1229005 0.00000000 0.00000000
## Unclassified_Phylum 0.9422368 12.5358460 4.17861532 0.57353544
## c__Dothideomycetes 51.3723884 43.9164277 73.08480131 39.00040967
## c__Leotiomycetes 14.0925850 1.5567390 4.54731667 46.21056944
## c__Sordariomycetes 2.5399426 0.5325686 2.66284310 2.25317493
## c__Eurotiomycetes 0.1229005 1.1470709 0.04096682 0.94223679
## Unclassified_Ascomycetes 0.4096682 0.4916018 1.39287177 1.22900451
## Other 0.9012700 0.9832036 1.76157313 0.16386727
## X02m09_S X02m10_B X02m10_L X02m10_S
## p__Basidiomycota 21.1388775 8.76689881 3.15444490 16.1818927
## p__Mortierellomycota 2.9905776 0.00000000 0.00000000 5.7763212
## p__Blastocladiomycota 7.3330602 0.04096682 0.00000000 0.4916018
## p__Aphelidiomycota 0.0000000 0.00000000 0.00000000 0.7783695
## p__Kickxellomycota 1.5977059 0.00000000 0.00000000 0.5735354
## p__Rozellomycota 0.6964359 0.00000000 0.00000000 0.1229005
## Unclassified_Phylum 6.2269562 1.72060631 0.00000000 2.8267104
## c__Dothideomycetes 35.7640311 77.18148300 31.58541581 40.5981155
## c__Leotiomycetes 4.7931176 8.97173290 8.35723064 21.4666120
## c__Sordariomycetes 4.7931176 0.57353544 56.45227366 7.6607948
## c__Eurotiomycetes 1.4338386 0.65546907 0.04096682 0.4506350
## Unclassified_Ascomycetes 3.6870135 1.02417042 0.08193363 0.6554691
## Other 9.5452683 1.06513724 0.32773454 2.4170422
## X02m11_B X02m11_S X04m01_B X04m01_L
## p__Basidiomycota 11.0200737 10.85620647 14.21548546 15.60835723
## p__Mortierellomycota 0.3687014 7.12822614 0.40966817 0.73740270
## p__Blastocladiomycota 0.0000000 8.07046293 1.67963949 0.20483408
## p__Aphelidiomycota 0.0000000 0.04096682 0.04096682 0.00000000
## p__Kickxellomycota 0.0000000 0.98320361 0.00000000 0.12290045
## p__Rozellomycota 0.0000000 0.04096682 0.24580090 0.08193363
## Unclassified_Phylum 0.1638673 6.51372388 3.19541172 0.86030315
## c__Dothideomycetes 73.3715690 38.95944285 65.17820565 55.10036870
## c__Leotiomycetes 3.8099140 8.72593200 12.12617780 4.91601803
## c__Sordariomycetes 1.3928718 13.96968456 0.53256862 19.90987300
## c__Eurotiomycetes 5.8172880 0.36870135 0.69643589 0.04096682
## Unclassified_Ascomycetes 3.2363785 1.14707087 0.65546907 0.20483408
## Other 0.8193363 3.19541172 1.02417042 2.21220811
## X04m01_S X04m02_B X04m02_L X04m02_S
## p__Basidiomycota 18.2712003 11.4297419 2.82671036 30.7251127
## p__Mortierellomycota 6.8824252 0.5325686 0.12290045 14.8709545
## p__Blastocladiomycota 8.6439984 0.4916018 0.00000000 11.2658746
## p__Aphelidiomycota 0.2458009 0.6145023 0.00000000 0.2458009
## p__Kickxellomycota 0.3277345 0.0000000 0.04096682 1.0241704
## p__Rozellomycota 0.7374027 0.0000000 0.00000000 0.3277345
## Unclassified_Phylum 10.5284719 6.1040557 0.40966817 16.3867268
## c__Dothideomycetes 40.3113478 58.0499795 82.50716919 13.3551823
## c__Leotiomycetes 2.4580090 4.9160180 1.22900451 1.8025399
## c__Sordariomycetes 3.7689472 1.6386727 12.33101188 5.8582548
## c__Eurotiomycetes 0.9422368 13.9696846 0.04096682 0.1229005
## Unclassified_Ascomycetes 2.5399426 1.3928718 0.16386727 2.1302745
## Other 4.3424826 0.8603032 0.32773454 1.8844736
## X04m03_B X04m03_L X04m03_S X04m04_B
## p__Basidiomycota 11.5526424 13.84678410 21.63047931 55.67390414
## p__Mortierellomycota 0.4096682 0.16386727 5.65342073 2.37607538
## p__Blastocladiomycota 0.0000000 0.00000000 3.35927898 0.12290045
## p__Aphelidiomycota 0.0000000 0.00000000 0.08193363 0.61450225
## p__Kickxellomycota 0.4916018 0.00000000 1.76157313 0.08193363
## p__Rozellomycota 0.0000000 0.04096682 0.32773454 0.36870135
## Unclassified_Phylum 3.7689472 1.02417042 6.84145842 5.89922163
## c__Dothideomycetes 45.9238017 67.59524785 31.33961491 29.49610815
## c__Leotiomycetes 3.0315444 6.63662433 13.47808275 1.18803769
## c__Sordariomycetes 2.0073740 3.97378124 6.18598935 1.14707087
## c__Eurotiomycetes 30.6841458 6.06308890 0.65546907 0.00000000
## Unclassified_Ascomycetes 0.8603032 0.00000000 1.55673904 1.06513724
## Other 1.2699713 0.65546907 7.12822614 1.96640721
## X04m04_L X04m04_S X04m05_B X04m05_L
## p__Basidiomycota 4.62925031 80.58172880 10.81523966 17.57476444
## p__Mortierellomycota 0.28676772 6.51372388 2.29414175 0.00000000
## p__Blastocladiomycota 0.00000000 0.00000000 0.08193363 0.00000000
## p__Aphelidiomycota 0.08193363 0.04096682 2.78574355 0.00000000
## p__Kickxellomycota 0.04096682 1.10610406 0.24580090 0.00000000
## p__Rozellomycota 0.04096682 0.45063499 0.00000000 0.00000000
## Unclassified_Phylum 1.59770586 2.00737403 23.39205244 0.45063499
## c__Dothideomycetes 62.84309709 2.94961082 27.20196641 56.32937321
## c__Leotiomycetes 15.77222450 0.61450225 1.59770586 10.89717329
## c__Sordariomycetes 14.25645227 0.49160180 1.02417042 13.64195002
## c__Eurotiomycetes 0.00000000 0.32773454 24.62105694 0.53256862
## Unclassified_Ascomycetes 0.12290045 1.35190496 5.65342073 0.04096682
## Other 0.32773454 3.56411307 0.28676772 0.53256862
## X04m05_S X04m06_B X04m06_L X04m06_S
## p__Basidiomycota 31.2576813 25.23555920 6.8414584 20.31954117
## p__Mortierellomycota 5.4076198 0.36870135 0.0000000 8.11142974
## p__Blastocladiomycota 0.5325686 0.00000000 0.0000000 0.12290045
## p__Aphelidiomycota 0.0000000 2.66284310 0.0000000 0.04096682
## p__Kickxellomycota 1.5977059 0.00000000 0.0000000 1.47480541
## p__Rozellomycota 0.2867677 0.00000000 0.0000000 0.73740270
## Unclassified_Phylum 9.5452683 8.39819746 0.2048341 2.53994265
## c__Dothideomycetes 34.0024580 56.69807456 77.6730848 44.24416223
## c__Leotiomycetes 5.0798853 3.48217943 13.3551823 11.06104056
## c__Sordariomycetes 3.6050799 1.63867268 0.7783695 3.89184760
## c__Eurotiomycetes 1.4748054 0.69643589 0.0000000 0.86030315
## Unclassified_Ascomycetes 0.9832036 0.77836952 0.2867677 1.55673904
## Other 6.2269562 0.04096682 0.8603032 5.03891848
## X04m07_L X04m07_S X04m08_L X04m08_S
## p__Basidiomycota 10.44653830 19.8689062 4.3015158 11.5526424
## p__Mortierellomycota 0.28676772 3.6870135 0.2867677 3.1544449
## p__Blastocladiomycota 0.00000000 5.2437526 0.0000000 0.0000000
## p__Aphelidiomycota 0.00000000 0.8603032 0.3277345 1.1061041
## p__Kickxellomycota 0.00000000 0.0000000 0.0000000 1.4338386
## p__Rozellomycota 0.04096682 0.3277345 0.0000000 0.0000000
## Unclassified_Phylum 0.73740270 11.2658746 1.5977059 10.8562065
## c__Dothideomycetes 83.81810733 46.5792708 45.6780008 52.2326915
## c__Leotiomycetes 3.07251127 3.2363785 42.0319541 3.4821794
## c__Sordariomycetes 0.94223679 1.4748054 1.7206063 4.6702171
## c__Eurotiomycetes 0.45063499 0.2048341 0.0000000 0.4916018
## Unclassified_Ascomycetes 0.00000000 6.3498566 2.7447767 8.9307661
## Other 0.20483408 0.9012700 1.3109381 2.0893077
## X04m09_L X04m09_S X04m10_B X04m10_L
## p__Basidiomycota 9.54526833 14.543220 11.8394101 23.14625154
## p__Mortierellomycota 0.24580090 1.925440 0.4096682 0.08193363
## p__Blastocladiomycota 0.12290045 0.000000 0.0000000 0.00000000
## p__Aphelidiomycota 0.04096682 0.000000 0.0000000 0.00000000
## p__Kickxellomycota 0.00000000 1.188038 0.0000000 0.00000000
## p__Rozellomycota 0.00000000 1.556739 0.2048341 0.20483408
## Unclassified_Phylum 0.86030315 4.956985 1.1470709 0.49160180
## c__Dothideomycetes 43.75256043 55.673904 72.7570668 53.09299467
## c__Leotiomycetes 40.43424826 4.260549 8.9307661 22.04014748
## c__Sordariomycetes 3.64604670 7.251127 0.6145023 0.69643589
## c__Eurotiomycetes 0.00000000 0.450635 2.8267104 0.00000000
## Unclassified_Ascomycetes 1.02417042 4.588283 0.0000000 0.16386727
## Other 0.32773454 3.605080 1.2699713 0.08193363
## X04m10_S X04m11_B X04m11_L X04m11_S
## p__Basidiomycota 53.5436297 11.22490782 3.6460467 9.91396968
## p__Mortierellomycota 15.2396559 0.86030315 0.1638673 8.80786563
## p__Blastocladiomycota 2.7038099 0.04096682 0.0000000 5.69438755
## p__Aphelidiomycota 0.2048341 0.24580090 0.0000000 0.69643589
## p__Kickxellomycota 1.5157722 0.00000000 0.0000000 2.29414175
## p__Rozellomycota 4.9569848 0.24580090 0.0000000 0.08193363
## Unclassified_Phylum 2.5399426 3.76894715 0.2048341 3.11347808
## c__Dothideomycetes 9.3404342 49.36501434 70.5038918 40.47521508
## c__Leotiomycetes 1.7615731 4.79311757 19.3363376 6.88242524
## c__Sordariomycetes 4.5882835 0.20483408 2.9905776 15.15772224
## c__Eurotiomycetes 0.3277345 28.30807046 0.0000000 0.81933634
## Unclassified_Ascomycetes 0.6964359 0.73740270 0.2867677 2.86767718
## Other 2.5809095 0.20483408 2.8676772 3.19541172
## X04m12_B X04m12_L X04m12_S X04m13_B
## p__Basidiomycota 10.07783695 2.53994265 31.21671446 4.5063499
## p__Mortierellomycota 0.98320361 0.20483408 9.01269971 0.0000000
## p__Blastocladiomycota 0.00000000 0.00000000 2.37607538 1.4748054
## p__Aphelidiomycota 0.00000000 0.08193363 0.16386727 0.1638673
## p__Kickxellomycota 0.00000000 0.08193363 0.98320361 0.0000000
## p__Rozellomycota 0.00000000 0.00000000 0.08193363 0.0000000
## Unclassified_Phylum 0.04096682 0.16386727 4.30151577 0.0000000
## c__Dothideomycetes 75.62474396 25.93199508 26.75133142 29.7009422
## c__Leotiomycetes 8.23433019 70.09422368 4.50634986 2.4989758
## c__Sordariomycetes 1.35190496 0.16386727 7.70176157 1.2699713
## c__Eurotiomycetes 0.86030315 0.04096682 0.57353544 57.3945104
## Unclassified_Ascomycetes 2.49897583 0.16386727 5.57148710 1.7615731
## Other 0.32773454 0.53256862 6.75952478 1.2290045
## X04m13_L X04m13_S X04t01_B X04t01_L
## p__Basidiomycota 25.27652601 26.6693978 5.6534207 6.71855797
## p__Mortierellomycota 0.00000000 9.4223679 0.4506350 0.12290045
## p__Blastocladiomycota 0.00000000 0.5325686 0.8603032 2.53994265
## p__Aphelidiomycota 0.00000000 2.8267104 0.0000000 0.00000000
## p__Kickxellomycota 0.00000000 0.3687014 0.0000000 0.00000000
## p__Rozellomycota 0.00000000 0.4096682 0.0000000 0.00000000
## Unclassified_Phylum 0.08193363 4.1376485 2.2531749 0.40966817
## c__Dothideomycetes 34.45309299 35.7230643 47.6853748 42.93322409
## c__Leotiomycetes 38.01720606 7.4149939 2.6628431 33.18312167
## c__Sordariomycetes 1.55673904 5.4895535 2.6628431 13.23228185
## c__Eurotiomycetes 0.04096682 0.1638673 36.6653011 0.08193363
## Unclassified_Ascomycetes 0.00000000 2.7857435 0.4916018 0.45063499
## Other 0.57353544 4.0557149 0.6145023 0.32773454
## X04t01_S X04t02_B X04t02_L X04t02_S
## p__Basidiomycota 22.61368292 8.76689881 3.19541172 8.07046293
## p__Mortierellomycota 10.32363785 0.81933634 0.24580090 1.80253994
## p__Blastocladiomycota 1.39287177 0.04096682 0.04096682 0.12290045
## p__Aphelidiomycota 0.00000000 0.00000000 0.00000000 0.00000000
## p__Kickxellomycota 6.14502253 0.04096682 0.00000000 0.40966817
## p__Rozellomycota 0.08193363 0.04096682 0.04096682 0.04096682
## Unclassified_Phylum 7.25112659 3.23637853 2.41704220 1.22900451
## c__Dothideomycetes 35.14952888 73.00286768 38.46784105 84.22777550
## c__Leotiomycetes 4.17861532 3.60507989 49.20114707 1.67963949
## c__Sordariomycetes 6.47275707 1.88447358 1.51577222 1.10610406
## c__Eurotiomycetes 2.29414175 3.76894715 0.00000000 0.24580090
## Unclassified_Ascomycetes 0.69643589 2.78574355 0.36870135 0.77836952
## Other 3.40024580 2.00737403 4.50634986 0.28676772
## X04w01_B X04w01_L X04w01_S X04w02_B
## p__Basidiomycota 14.13355182 13.35518230 22.69561655 6.1040557
## p__Mortierellomycota 0.16386727 0.00000000 4.99795166 0.0000000
## p__Blastocladiomycota 0.24580090 0.00000000 2.82671036 0.0000000
## p__Aphelidiomycota 0.00000000 0.00000000 0.00000000 0.0000000
## p__Kickxellomycota 0.00000000 0.00000000 0.94223679 0.0000000
## p__Rozellomycota 0.00000000 0.00000000 0.04096682 0.0000000
## Unclassified_Phylum 0.36870135 0.04096682 8.35723064 0.4096682
## c__Dothideomycetes 50.22531749 31.70831626 45.59606719 60.9586235
## c__Leotiomycetes 1.35190496 54.11716510 2.45800901 3.1954117
## c__Sordariomycetes 0.40966817 0.28676772 5.20278574 16.9192954
## c__Eurotiomycetes 32.28185170 0.04096682 1.47480541 6.1859893
## Unclassified_Ascomycetes 0.77836952 0.16386727 0.86030315 0.0000000
## Other 0.04096682 0.28676772 4.54731667 6.2269562
## X04w02_L X04w02_S X04w03_B X04w03_S X04w04_L
## p__Basidiomycota 20.27857435 25.3174928 20.688243 11.8394101 9.8320361
## p__Mortierellomycota 0.49160180 19.5002048 0.000000 7.9065957 0.2867677
## p__Blastocladiomycota 1.22900451 14.9528882 0.000000 9.5452683 0.0000000
## p__Aphelidiomycota 0.00000000 0.0000000 0.000000 0.0000000 0.0000000
## p__Kickxellomycota 0.00000000 1.9254404 0.000000 1.3109381 0.0000000
## p__Rozellomycota 0.00000000 0.2048341 0.000000 0.2458009 0.0000000
## Unclassified_Phylum 0.61450225 2.4170422 0.000000 11.6755428 0.1638673
## c__Dothideomycetes 52.92912741 25.8090946 54.485866 43.1380582 41.5403523
## c__Leotiomycetes 22.36788202 3.1544449 7.128226 4.8340844 38.4268742
## c__Sordariomycetes 0.81933634 4.1376485 0.000000 5.6124539 8.5620647
## c__Eurotiomycetes 0.04096682 0.6964359 12.863580 0.7783695 0.4916018
## Unclassified_Ascomycetes 0.16386727 0.3687014 3.072511 1.1880377 0.4096682
## Other 1.06513724 1.5157722 1.761573 1.9254404 0.2867677
## X04w04_S X20m01_L X20m01_S X20m02_B
## p__Basidiomycota 30.807046 36.91110201 34.6579271 25.9729619
## p__Mortierellomycota 14.379353 1.92544039 14.4612864 0.5735354
## p__Blastocladiomycota 0.000000 0.00000000 0.1229005 0.0000000
## p__Aphelidiomycota 1.802540 0.00000000 0.0000000 0.4916018
## p__Kickxellomycota 0.000000 0.00000000 4.0147481 0.1638673
## p__Rozellomycota 0.000000 0.04096682 0.7783695 0.0000000
## Unclassified_Phylum 2.007374 0.73740270 3.3592790 11.7574764
## c__Dothideomycetes 27.201966 32.19991807 29.9057763 35.3543630
## c__Leotiomycetes 14.584187 14.05161819 4.0147481 2.7857435
## c__Sordariomycetes 6.063089 13.35518230 4.4653830 0.9012700
## c__Eurotiomycetes 1.515772 0.04096682 1.3109381 19.9918066
## Unclassified_Ascomycetes 1.638673 0.04096682 0.5325686 1.5567390
## Other 0.000000 0.69643589 2.3760754 0.4506350
## X20m02_L X20m02_S X20m03_B X20m03_L
## p__Basidiomycota 43.21999181 34.4940598 10.65137239 4.01474805
## p__Mortierellomycota 0.81933634 20.2785744 0.20483408 0.20483408
## p__Blastocladiomycota 0.00000000 0.0000000 0.00000000 0.00000000
## p__Aphelidiomycota 0.00000000 0.0000000 0.16386727 0.00000000
## p__Kickxellomycota 0.00000000 1.4338386 0.08193363 0.00000000
## p__Rozellomycota 0.00000000 0.2048341 0.08193363 0.00000000
## Unclassified_Phylum 0.81933634 2.7447767 1.96640721 0.04096682
## c__Dothideomycetes 49.40598116 19.7460057 71.52806227 42.15485457
## c__Leotiomycetes 5.20278574 3.5641131 1.31093814 34.86276116
## c__Sordariomycetes 0.16386727 5.2847194 2.58090946 17.08316264
## c__Eurotiomycetes 0.00000000 9.0126997 9.50430152 0.28676772
## Unclassified_Ascomycetes 0.28676772 0.1638673 0.49160180 0.12290045
## Other 0.08193363 3.0725113 1.43383859 1.22900451
## X20m03_S X20m04_B X20m04_L X20m04_S
## p__Basidiomycota 21.87628021 11.5116755 12.61777960 10.28267104
## p__Mortierellomycota 17.00122900 0.3687014 0.08193363 25.15362556
## p__Blastocladiomycota 0.08193363 0.0000000 0.00000000 0.61450225
## p__Aphelidiomycota 0.04096682 0.0000000 0.00000000 0.08193363
## p__Kickxellomycota 2.74477673 0.0000000 0.00000000 6.14502253
## p__Rozellomycota 0.49160180 0.3277345 0.00000000 2.08930766
## Unclassified_Phylum 8.02949611 20.5243753 0.98320361 3.23637853
## c__Dothideomycetes 28.06226956 57.7632118 40.96681688 23.76075379
## c__Leotiomycetes 4.79311757 2.6218763 42.27775502 8.76689881
## c__Sordariomycetes 11.22490782 1.8435068 1.88447358 12.49487915
## c__Eurotiomycetes 1.55673904 0.2458009 0.08193363 1.22900451
## Unclassified_Ascomycetes 0.49160180 3.1134781 0.24580090 0.24580090
## Other 3.60507989 1.6796395 0.86030315 5.89922163
## X20m05_L X20m05_S sfm01_B sfm01_L
## p__Basidiomycota 4.83408439 24.5800901 15.69029086 12.33101188
## p__Mortierellomycota 0.00000000 4.8750512 1.59770586 0.65546907
## p__Blastocladiomycota 0.04096682 1.5567390 0.45063499 0.04096682
## p__Aphelidiomycota 0.00000000 0.1638673 0.36870135 0.00000000
## p__Kickxellomycota 0.00000000 11.9213437 0.04096682 0.00000000
## p__Rozellomycota 0.12290045 0.9422368 0.28676772 0.08193363
## Unclassified_Phylum 0.36870135 4.4653830 4.58828349 0.77836952
## c__Dothideomycetes 65.66980746 40.3113478 66.16140926 36.41950020
## c__Leotiomycetes 19.62310528 3.2773454 4.95698484 32.15895125
## c__Sordariomycetes 5.65342073 4.9160180 1.63867268 16.63252765
## c__Eurotiomycetes 0.16386727 0.7783695 0.28676772 0.00000000
## Unclassified_Ascomycetes 0.86030315 0.6964359 1.76157313 0.12290045
## Other 2.66284310 1.5157722 2.17124129 0.77836952
## sfm01_S sfm02_B sfm02_L sfm02_S
## p__Basidiomycota 29.6599754 20.85210979 3.72798034 52.88816059
## p__Mortierellomycota 9.7910692 2.45800901 0.04096682 18.02539943
## p__Blastocladiomycota 1.3928718 0.04096682 0.00000000 1.02417042
## p__Aphelidiomycota 0.1638673 1.67963949 0.00000000 0.04096682
## p__Kickxellomycota 2.4580090 0.12290045 0.00000000 0.65546907
## p__Rozellomycota 0.4096682 0.20483408 0.00000000 0.24580090
## Unclassified_Phylum 2.7447767 8.27529701 0.77836952 6.39082343
## c__Dothideomycetes 39.6558787 50.18435068 70.09422368 7.21015977
## c__Leotiomycetes 3.6460467 2.29414175 22.98238427 1.22900451
## c__Sordariomycetes 4.3015158 1.26997132 1.51577222 6.80049160
## c__Eurotiomycetes 0.5325686 0.08193363 0.28676772 1.31093814
## Unclassified_Ascomycetes 2.0073740 8.15239656 0.00000000 0.36870135
## Other 3.2363785 4.38344941 0.57353544 3.80991397
## sfm03_B sfm03_L sfm04_B sfm04_L
## p__Basidiomycota 15.6493240 9.87300287 8.0704629 10.44653830
## p__Mortierellomycota 0.6964359 0.08193363 0.1229005 0.45063499
## p__Blastocladiomycota 0.0000000 0.00000000 0.0000000 12.53584596
## p__Aphelidiomycota 0.0000000 0.00000000 0.0000000 0.00000000
## p__Kickxellomycota 0.0000000 0.00000000 0.0000000 0.00000000
## p__Rozellomycota 0.0000000 0.00000000 0.1638673 0.08193363
## Unclassified_Phylum 7.3740270 0.24580090 3.6050799 1.59770586
## c__Dothideomycetes 66.7349447 74.72347399 59.4838181 59.48381811
## c__Leotiomycetes 1.4748054 12.33101188 8.4801311 4.83408439
## c__Sordariomycetes 1.9254404 0.16386727 1.7615731 8.52109791
## c__Eurotiomycetes 0.9422368 0.36870135 16.3047931 0.16386727
## Unclassified_Ascomycetes 4.7931176 0.00000000 0.5735354 0.28676772
## Other 0.4096682 2.21220811 1.4338386 1.59770586
## sfm04_S sfm05_B sfm05_L sfm05_S
## p__Basidiomycota 17.0012290 18.02539943 9.3814011 51.33142155
## p__Mortierellomycota 3.4412126 1.76157313 0.7374027 12.12617780
## p__Blastocladiomycota 0.0000000 0.94223679 1.4748054 0.00000000
## p__Aphelidiomycota 0.2867677 2.49897583 0.6145023 0.40966817
## p__Kickxellomycota 0.1638673 0.04096682 0.0000000 1.96640721
## p__Rozellomycota 0.2867677 0.12290045 0.0000000 0.04096682
## Unclassified_Phylum 5.1208521 6.96435887 0.6554691 13.76485047
## c__Dothideomycetes 61.2863580 56.53420729 72.5931995 11.63457599
## c__Leotiomycetes 3.5231463 2.37607538 8.6439984 2.62187628
## c__Sordariomycetes 3.1954117 4.46538304 4.5063499 3.23637853
## c__Eurotiomycetes 0.3687014 0.08193363 0.1229005 0.00000000
## Unclassified_Ascomycetes 2.9496108 5.73535436 0.3277345 0.16386727
## Other 2.3760754 0.45063499 0.9422368 2.70380991
## sfm06_L sfm06_S sfm07_B sfm07_L
## p__Basidiomycota 14.99385498 30.88897993 15.1167554 16.38672675
## p__Mortierellomycota 0.73740270 12.00327735 2.2122081 0.73740270
## p__Blastocladiomycota 0.69643589 0.65546907 1.1880377 1.22900451
## p__Aphelidiomycota 0.04096682 0.04096682 1.8844736 0.00000000
## p__Kickxellomycota 0.04096682 1.39287177 0.1229005 0.00000000
## p__Rozellomycota 0.04096682 0.20483408 0.2458009 0.04096682
## Unclassified_Phylum 0.90126997 2.17124129 6.8004916 0.69643589
## c__Dothideomycetes 56.20647276 38.71364195 56.0016387 53.50266284
## c__Leotiomycetes 17.00122900 5.48955346 2.3760754 17.45186399
## c__Sordariomycetes 2.78574355 4.01474805 7.5378943 2.41704220
## c__Eurotiomycetes 0.08193363 0.32773454 2.4170422 0.73740270
## Unclassified_Ascomycetes 4.54731667 1.14707087 2.9086440 0.20483408
## Other 1.92544039 2.94961082 1.1880377 6.59565752
## sfm07_S Sft01_B Sft01_L Sft01_S
## p__Basidiomycota 23.76075379 12.90454732 13.10938140 31.38058173
## p__Mortierellomycota 20.72920934 1.59770586 0.45063499 11.71650963
## p__Blastocladiomycota 0.08193363 2.17124129 0.04096682 1.67963949
## p__Aphelidiomycota 0.00000000 0.04096682 0.00000000 0.08193363
## p__Kickxellomycota 0.65546907 0.08193363 0.00000000 1.47480541
## p__Rozellomycota 0.28676772 0.00000000 0.04096682 0.32773454
## Unclassified_Phylum 3.52314625 2.70380991 2.04834084 6.10405571
## c__Dothideomycetes 36.00983204 64.31790250 58.74641540 31.87218353
## c__Leotiomycetes 3.44121262 4.01474805 21.95821385 4.75215076
## c__Sordariomycetes 4.79311757 3.03154445 2.29414175 4.42441622
## c__Eurotiomycetes 1.51577222 3.19541172 0.36870135 0.77836952
## Unclassified_Ascomycetes 0.61450225 0.90126997 0.24580090 1.14707087
## Other 4.58828349 5.03891848 0.69643589 4.26054896
## Sft02_B Sft02_S
## p__Basidiomycota 11.14297419 13.6419500
## p__Mortierellomycota 0.53256862 7.9475625
## p__Blastocladiomycota 0.28676772 0.0000000
## p__Aphelidiomycota 0.00000000 0.0000000
## p__Kickxellomycota 0.04096682 2.1712413
## p__Rozellomycota 0.08193363 0.5325686
## Unclassified_Phylum 1.76157313 4.3834494
## c__Dothideomycetes 77.42728390 39.5739451
## c__Leotiomycetes 2.37607538 3.8508808
## c__Sordariomycetes 2.29414175 5.5714871
## c__Eurotiomycetes 1.59770586 1.2699713
## Unclassified_Ascomycetes 0.40966817 18.0253994
## Other 2.04834084 3.0315444#Add taxa names as a column
phylatrim$Major_Taxa <- row.names(phylatrim)
#transform into long format
phylalong <- gather(phylatrim, Sample, Proportion, -Major_Taxa)
phylalong$Sample <- gsub("X","",phylalong$Sample)## [1] "substrate" "WetDry"
phylamet<-data.frame("Sample"=row.names(samdftest),
"Substrate"=samdftest$substrate,
"Wet.Dry"=samdftest$wet_dry,
stringsAsFactors=T)
#merge metadata with major taxa data
phylalong <- merge(phylalong, phylamet)
#Summarize by depth and hydration
phyla_summary <-
phylalong %>% # the names of the new data frame and the data frame to be summarised
group_by(Substrate, Wet.Dry, Major_Taxa) %>% # the grouping variable
summarise(mean_prop = mean(Proportion)) # calculates the mean of each group## `summarise()` has grouped output by 'Substrate', 'Wet.Dry'. You can override
## using the `.groups` argument.## `summarise()` has grouped output by 'Depth', 'Hydration'. You can override using the `.groups` argument.
phyla_summary## # A tibble: 78 × 4
## # Groups: Substrate, Wet.Dry [6]
## Substrate Wet.Dry Major_Taxa mean_prop
## <fct> <fct> <chr> <dbl>
## 1 B DRY Other 0.830
## 2 B DRY Unclassified_Ascomycetes 0.881
## 3 B DRY Unclassified_Phylum 1.15
## 4 B DRY c__Dothideomycetes 49.8
## 5 B DRY c__Eurotiomycetes 34.0
## 6 B DRY c__Leotiomycetes 1.96
## 7 B DRY c__Sordariomycetes 1.73
## 8 B DRY p__Aphelidiomycota 0.0819
## 9 B DRY p__Basidiomycota 8.74
## 10 B DRY p__Blastocladiomycota 0.645
## # ℹ 68 more rowsphyla_summary$Substrate <- factor(phyla_summary$Substrate,
levels=c("B","L","S")) #reorder variables
phyla_summary$Wet.Dry <- factor(phyla_summary$Wet.Dry, levels= c("WET","DRY")) #reorder variables
phyla_summary$Major_Taxa <- factor(phyla_summary$Major_Taxa, levels= rev(c("p__Basidiomycota","p__Mortierellomycota","p__Blastocladiomycota","p__Aphelidiomycota","p__Kickxellomycota","p__Rozellomycota","Unclassified_Phylum","c__Dothideomycetes","c__Leotiomycetes","c__Sordariomycetes","c__Eurotiomycetes", "Unclassified_Ascomycetes", #"p__Ochrophyta", "p__Oomycota",
"Other")))
#color palette
sublab<- c("Epilithic biofilms","Leaf litter","Benthic sediment")
names(sublab)<- c("B","L","S")
pal12 <- rev(cols25(n=13)) #set n= to the number of taxa you are plotting
#make stacked bar plot
phylum_bar <- ggplot(phyla_summary, aes(x = Wet.Dry, y = mean_prop, fill = Major_Taxa))+
geom_bar(stat = "identity", col=I("black")) +
scale_fill_manual(values=pal12)+
guides(fill=guide_legend(ncol=1))+
facet_wrap(~Substrate, labeller = labeller(Substrate=sublab), nrow=1, scales="free_x") +
labs(x=NULL,y="Relative abundance (%)",
fill="Major taxa")+
#scale_facet_discrete(labels=c('B'='Epilithic biofilms', 'L'='Leaf litter', 'S'='Benthic sediment', 'W'='Surface water'))+
theme(axis.text.x = element_text(angle=60, hjust=1), legend.position = "right")+
theme(text=element_text(size=18), #change font size of all text
axis.text=element_text(size=16), #change font size of axis text
axis.title=element_text(size=16), #change font size of axis titles
plot.title=element_text(size=16), #change font size of plot title
legend.text=element_text(size=16), #change font size of legend text
strip.text.x = element_text(size = 16),
legend.title=element_text(size=16)) #change font size of legend title
phylum_bar
#c(180,100) *
# 0.0394 * # convert mm to inch
# 600 # convert to pixels
## [1] 4255.2 2364.0
plotout <- "Phylum Barplot_fungi_WetDry_v1_03082025.tiff"
agg_tiff(filename=plotout, width=4255, height=2364, units="px",
pointsize=10, res=600, compression="lzw", scaling=0.5)
phylum_bar
invisible(dev.off())## [1] "substrate" "PRC_WET_GROUP", sites kmeans clustered by annual percent wet (k=3)
phylamet<-data.frame("Sample"=row.names(samdftest),
"Substrate"=samdftest$substrate,
"Site"=samdftest$siteid,
stringsAsFactors=T)
#merge metadata with major taxa data
phylalong <- merge(phylalong, phylamet)
phylamet<-data.frame("Sample"=row.names(samdftest),
"Drainage_area"=samdftest$drainage_area,
"Annual_Percent_Wet"=samdftest$prc_wet)
#merge metadata with major taxa data
phylalong <- merge(phylalong, phylamet)
#Summarize by depth and hydration
phyla_summary <-
phylalong %>% # the names of the new data frame and the data frame to be summarised
group_by(Substrate, Site, Major_Taxa) %>% # the grouping variable
summarise(mean_prop = mean(Proportion), drainage_area = Drainage_area) #%>%## `summarise()` has grouped output by 'Substrate', 'Site'. You can override using
## the `.groups` argument. # summarise(drainage_area = Drainage_area)# calculates the mean of each group
## `summarise()` has grouped output by 'Depth', 'Hydration'. You can override using the `.groups` argument.
phyla_summary## # A tibble: 1,755 × 5
## # Groups: Substrate, Site [135]
## Substrate Site Major_Taxa mean_prop drainage_area
## <fct> <fct> <chr> <dbl> <dbl>
## 1 B 01M01 Other 0.246 28.3
## 2 B 01M01 Unclassified_Ascomycetes 4.38 28.3
## 3 B 01M01 Unclassified_Phylum 6.68 28.3
## 4 B 01M01 c__Dothideomycetes 28.6 28.3
## 5 B 01M01 c__Eurotiomycetes 24.2 28.3
## 6 B 01M01 c__Leotiomycetes 0.615 28.3
## 7 B 01M01 c__Sordariomycetes 1.60 28.3
## 8 B 01M01 p__Aphelidiomycota 21.6 28.3
## 9 B 01M01 p__Basidiomycota 12.1 28.3
## 10 B 01M01 p__Blastocladiomycota 0 28.3
## # ℹ 1,745 more rowsphyla_summary$Substrate <- factor(phyla_summary$Substrate,
levels=c("B","L","S")) #reorder variables
phyla_summary$Site <- fct_reorder(phyla_summary$Site, phyla_summary$drainage_area)
#factor(phyla_summary$wet_dist_cluster, levels= c("1","2", "3")) #reorder variables
phyla_summary$Major_Taxa <- factor(phyla_summary$Major_Taxa, levels= rev(c("p__Basidiomycota","p__Mortierellomycota","p__Blastocladiomycota","p__Aphelidiomycota","p__Kickxellomycota","p__Rozellomycota","Unclassified_Phylum","c__Dothideomycetes","c__Leotiomycetes","c__Sordariomycetes","c__Eurotiomycetes", "Unclassified_Ascomycetes", #"p__Ochrophyta", "p__Oomycota",
"Other")))
#color palette
sublab<- c("Epilithic biofilms","Leaf litter","Benthic sediment")
names(sublab)<- c("B","L","S")
pal12 <- rev(cols25(n=13)) #set n= to the number of taxa you are plotting
#make stacked bar plot
phylum_bar <- ggplot(phyla_summary, aes(x = Site, y = mean_prop, fill = Major_Taxa))+
geom_bar(stat = "identity", col=I("black")) +
scale_fill_manual(values=pal12)+
guides(fill=guide_legend(ncol=1))+
facet_wrap(~Substrate, labeller = labeller(Substrate=sublab), nrow=1, scales="free_x") +
labs(x="Sites (ordered from lowest to highest drainage area)",y="Relative abundance (%)",
fill="Major taxa")+
#scale_facet_discrete(labels=c('B'='Epilithic biofilms', 'L'='Leaf litter', 'S'='Benthic sediment', 'W'='Surface water'))+
theme(axis.text.x = element_text(angle=60, hjust=1), legend.position = "right")+
theme(text=element_text(size=20), #change font size of all text
axis.text.x=element_text(size=7), #change font size of axis text
# axis.title=element_text(size=16), #change font size of axis titles
#plot.title=element_text(size=16), #change font size of plot title
legend.text=element_text(size=16), #change font size of legend text
# strip.text.x = element_text(size = 10),
legend.title=element_text(size=16)) #change font size of legend title
phylum_bar
#c(180,100) *
# 0.0394 * # convert mm to inch
# 600 # convert to pixels
## [1] 4255.2 2364.0
plotout <- "Phylum Barplot_fungi_sites_by_drainage_area_03142025.tiff"
agg_tiff(filename=plotout, width=6000, height=2750, units="px",
pointsize=10, res=600, compression="lzw", scaling=0.5)
phylum_bar
invisible(dev.off())Differential abundance analysis via GLLVM (generalized linear latent variable models)
library(stringr)
## making new phyloseq object to play with
pseqtestITS_spp <- pseqtest
### trimming prefixes off taxa
tax_table(pseqtestITS_spp)[,"Species"] <- sapply(str_replace(tax_table(pseqtestITS_spp)[,"Species"], "s__", ""),`[`, 1)
tax_table(pseqtestITS_spp)[,"Genus"] <- sapply(str_replace(tax_table(pseqtestITS_spp)[,"Genus"], "g__", ""),`[`, 1)
tax_table(pseqtestITS_spp)[,"Family"] <- sapply(str_replace(tax_table(pseqtestITS_spp)[,"Family"], "f__", ""),`[`, 1)
tax_table(pseqtestITS_spp)[,"Order"] <- sapply(str_replace(tax_table(pseqtestITS_spp)[,"Order"], "o__", ""),`[`, 1)
tax_table(pseqtestITS_spp)[,"Class"] <- sapply(str_replace(tax_table(pseqtestITS_spp)[,"Class"], "c__", ""),`[`, 1)
tax_table(pseqtestITS_spp)[,"Phylum"] <- sapply(str_replace(tax_table(pseqtestITS_spp)[,"Phylum"], "p__", ""),`[`, 1)
tax_table(pseqtestITS_spp)[,"Kingdom"] <- sapply(str_replace(tax_table(pseqtestITS_spp)[,"Kingdom"], "k__", ""),`[`, 1)
### Now we want to merge our taxa to the lowest possible taxonomic level (species, or genus, or family, etc.)
### Name NA spp. "sp."
na.sp = !is.na(tax_table(pseqtestITS_spp)[,"Genus"]) & is.na(tax_table(pseqtestITS_spp)[,"Species"])
tax_table(pseqtestITS_spp)[na.sp][,"Species"] <- "sp."
### good
### Now, make "Species" <--- "Genus species"
# Genus and Species is not NA
no.na <- !is.na(tax_table(pseqtestITS_spp)[,"Genus"]) & !is.na(tax_table(pseqtestITS_spp)[,"Species"])
# Replace Species with full name
tax_table(pseqtestITS_spp)[no.na][,"Species"] <- paste(tax_table(pseqtestITS_spp)[no.na][,"Genus"], tax_table(pseqtestITS_spp)[no.na][,"Species"])
### Now, if genus and species are NA, how to name it for the taxonomic ranking?View(tax_table(pseqtestITS_spp))
### agglomerate counts by pseudospecies
spp_counts_tab <- otu_table(tax_glom(pseqtestITS_spp, taxrank = "Species"), taxa_are_rows = FALSE)
spp_counts_tab <- t(spp_counts_tab)
#make vector of species names to set as row names
spp_tax_vec <- as.vector(tax_table(tax_glom(pseqtestITS_spp, taxrank="Species"))[,7])
rownames(spp_counts_tab) <- as.vector(spp_tax_vec)
#spp_counts_tab
#write.csv(spp_counts_tab, 'spp_counts_rar.csv')
asv_counts <- pseqtestITS_spp@otu_table
#determine the number of unclassified seqs at the species level
unclassified_spp_counts <- colSums(t(asv_counts)) - colSums(spp_counts_tab)
#Add a row of "unclassified" to the species count table
species_and_unidentified_counts_tab <- rbind(spp_counts_tab, "Unclassified_species"=unclassified_spp_counts)
## test all counts are accounted for
identical(colSums(species_and_unidentified_counts_tab), rowSums(asv_counts))## [1] TRUE## convert counts to percent abundance:
#species_proportions <- apply(species_and_unidentified_counts_tab, 2, function(x) x/sum(x)*100)
#Merge with metadata
species_merge <- merge(t(species_and_unidentified_counts_tab), samdftest,
by="row.names", all=TRUE)
rownames(species_merge) <- species_merge[,1]
species_merge <- species_merge[,-1]
#species_merge <- merge(species_merge, alpha[,94:102],
# by="row.names", all=TRUE)
#rownames(species_merge) <- species_merge[,1]
#species_merge <- species_merge[,-1]
write.csv(species_merge, "KzSyn_ITS_pseudospeciescountsmerge_03082025.csv")gglvm of top leaf taxa
library(mvabund)
library(gllvm)## Loading required package: TMB##
## Attaching package: 'gllvm'## The following object is masked from 'package:mvabund':
##
## coefplot## The following object is masked from 'package:vegan':
##
## ordiplot## The following object is masked from 'package:stats':
##
## simulatelibrary(tidyverse)
species_merge$annual_percent_wet<- species_merge$prc_wet
sppmerge_L<- species_merge[species_merge$substrate=='L',]
sppmerge_L<- sppmerge_L[!is.na(sppmerge_L$alpha.cent.wt),]
gn<- nrow(species_and_unidentified_counts_tab)
gn## [1] 1660spptemp_L<- sppmerge_L[,1:gn]
spptemp_L<-spptemp_L[,colSums(spptemp_L)>0]
#Reorder data table from most abundant to least abundant
spptemp_L <- spptemp_L[,order(colSums(-spptemp_L,na.rm=TRUE))]
spptemp_L <- spptemp_L[,apply(spptemp_L,2,function(x) sum(x > 0))>1]
sppmerge_L <- merge(spptemp_L, samdftest,
by="row.names", all=FALSE)
rownames(sppmerge_L) <- sppmerge_L[,1]
sppmerge_L <- sppmerge_L[,-1]
sppmerge_L<- sppmerge_L[,colnames(sppmerge_L)!="Unclassified_species"]
sppmerge_L$annual_percent_wet<- sppmerge_L$prc_wet
#detach("package:linkET", unload=TRUE)
#sppmerge_L$percentwet_11month<- sppmerge_L$percentwet_bonus
L_abun<-sppmerge_L[,1:40]
X <- as.matrix(sppmerge_L[,c("annual_percent_wet","alpha.cent.wt","tempC_mean","burn_interval")])
y<-as.matrix(as.tibble(L_abun))## Warning: `as.tibble()` was deprecated in tibble 2.0.0.
## ℹ Please use `as_tibble()` instead.
## ℹ The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.gllvm(y, family = "poisson") ## Warning in matrix(fa$scores[, 1:num.lv], n, num.lv): data length [82] is not a
## sub-multiple or multiple of the number of rows [40]## Call:
## gllvm(y = y, family = "poisson")
## family:
## [1] "poisson"
## method:
## [1] "VA"
##
## log-likelihood: -37631.93
## Residual degrees of freedom: 1481
## AIC: 75501.87
## AICc: 75521.17
## BIC: 76141.82gllvm(y, family = "negative.binomial") ## Warning in matrix(fa$scores[, 1:num.lv], n, num.lv): data length [82] is not a
## sub-multiple or multiple of the number of rows [40]## Call:
## gllvm(y = y, family = "negative.binomial")
## family:
## [1] "negative.binomial"
## method:
## [1] "VA"
##
## log-likelihood: -5445.994
## Residual degrees of freedom: 1441
## AIC: 11209.99
## AICc: 11245.32
## BIC: 12065.05###
gllvm(y, X, family = "negative.binomial", num.lv = 1,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1995)## Warning in matrix(fa$scores[, 1:num.lv], n, num.lv): data length [41] is not a
## sub-multiple or multiple of the number of rows [40]## Call:
## gllvm(y = y, X = X, formula = ~alpha.cent.wt + annual_percent_wet,
## family = "negative.binomial", num.lv = 1, seed = 1995)
## family:
## [1] "negative.binomial"
## method:
## [1] "VA"
##
## log-likelihood: -5443.119
## Residual degrees of freedom: 1400
## AIC: 11286.24
## AICc: 11343.71
## BIC: 12361.79#fit_ord<- gllvm(y, family = "negative.binomial")
#ordiplot(fit_ord, biplot=TRUE, ind.spp=14)
fit_env <- gllvm(y, X, family = "negative.binomial", num.lv = 1,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1995)## Warning in matrix(fa$scores[, 1:num.lv], n, num.lv): data length [41] is not a
## sub-multiple or multiple of the number of rows [40]coefplot(fit_env, cex.ylab = 0.7, mar = c(4, 9, 2, 1), mfrow=c(1,1))
pdf("Pseudospeciesplot_L_03.15.2025.pdf",
)
coefplot(fit_env, cex.ylab = 0.7, mar = c(4, 12, 2, 1), mfrow=c(1,1))
dev.off()## quartz_off_screen
## 2sppmerge_B<- species_merge[species_merge$substrate=='B',]
sppmerge_B<- sppmerge_B[!is.na(sppmerge_B$alpha.cent.wt),]
gn<- nrow(species_and_unidentified_counts_tab)
gn## [1] 1660spptemp_B<- sppmerge_B[,1:gn]
spptemp_B<-spptemp_B[,colSums(spptemp_B)>0]
#Reorder data table from most abundant to least abundant
spptemp_B <- spptemp_B[,order(colSums(-spptemp_B,na.rm=TRUE))]
spptemp_B <- spptemp_B[,apply(spptemp_B,2,function(x) sum(x > 0))>1]
sppmerge_B <- merge(spptemp_B, samdftest,
by="row.names", all=FALSE)
rownames(sppmerge_B) <- sppmerge_B[,1]
sppmerge_B <- sppmerge_B[,-1]
sppmerge_B<- sppmerge_B[,colnames(sppmerge_B)!="Unclassified_species"]
sppmerge_B$annual_percent_wet<- sppmerge_B$prc_wet
B_abun<-sppmerge_B[,1:40]
X <- scale(as.matrix(sppmerge_B[,c("alpha.cent.wt","annual_percent_wet","tempC_mean","burn_interval","canopy_cover_percent"
)]))
y<-as.matrix(as.tibble(B_abun))
gllvm(y, family = "poisson")## Call:
## gllvm(y = y, family = "poisson")
## family:
## [1] "poisson"
## method:
## [1] "VA"
##
## log-likelihood: -17071.4
## Residual degrees of freedom: 1241
## AIC: 34380.8
## AICc: 34403.83
## BIC: 35001.41gllvm(y, family = "negative.binomial")## Call:
## gllvm(y = y, family = "negative.binomial")
## family:
## [1] "negative.binomial"
## method:
## [1] "VA"
##
## log-likelihood: -4849.454
## Residual degrees of freedom: 1201
## AIC: 10016.91
## AICc: 10059.31
## BIC: 10846.13gllvm(y, X, family = "negative.binomial", num.lv = 1,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1995)## Call:
## gllvm(y = y, X = X, formula = ~alpha.cent.wt + annual_percent_wet,
## family = "negative.binomial", num.lv = 1, seed = 1995)
## family:
## [1] "negative.binomial"
## method:
## [1] "VA"
##
## log-likelihood: -4862.078
## Residual degrees of freedom: 1160
## AIC: 10124.16
## AICc: 10193.53
## BIC: 11167.2fit_env <- gllvm(y, X, family = "negative.binomial", num.lv = 1,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1995)
coefplot(fit_env, cex.ylab = 0.7, mar = c(4, 9, 2, 1), mfrow=c(1,1))
pdf("Pseudospeciesplot_B_03.15.2025.pdf",
)
coefplot(fit_env, cex.ylab = 0.7, mar = c(4, 12, 2, 1), mfrow=c(1,1))
dev.off()## quartz_off_screen
## 2sppmerge_S<- species_merge[species_merge$substrate=='S',]
sppmerge_S<- sppmerge_S[!is.na(sppmerge_S$alpha.cent.wt),]
gn<- nrow(species_and_unidentified_counts_tab)
gn## [1] 1660spptemp_S<- sppmerge_S[,1:gn]
spptemp_S<-spptemp_S[,colSums(spptemp_S)>0]
#Reorder data table from most abundant to least abundant
spptemp_S <- spptemp_S[,order(colSums(-spptemp_S,na.rm=TRUE))]
spptemp_S <- spptemp_S[,apply(spptemp_S,2,function(x) sum(x > 0))>1]
sppmerge_S <- merge(spptemp_S, samdftest,
by="row.names", all=FALSE)
rownames(sppmerge_S) <- sppmerge_S[,1]
sppmerge_S <- sppmerge_S[,-1]
sppmerge_S<- sppmerge_S[,colnames(sppmerge_S)!="Unclassified_species"]
sppmerge_S$annual_percent_wet<- sppmerge_S$prc_wet
S_abun<-sppmerge_S[,1:40]
X <- as.matrix(sppmerge_S[,c("annual_percent_wet","alpha.cent.wt","tempC_mean","B_Chl.a_ug_per_cm2")])
y<-as.matrix(as.tibble(S_abun))
gllvm(y, family = "poisson")## Warning in matrix(fa$scores[, 1:num.lv], n, num.lv): data length [82] is not a
## sub-multiple or multiple of the number of rows [40]## Call:
## gllvm(y = y, family = "poisson")
## family:
## [1] "poisson"
## method:
## [1] "VA"
##
## log-likelihood: -25072.04
## Residual degrees of freedom: 1481
## AIC: 50382.08
## AICc: 50401.37
## BIC: 51022.03gllvm(y, family = "negative.binomial")## Warning in matrix(fa$scores[, 1:num.lv], n, num.lv): data length [82] is not a
## sub-multiple or multiple of the number of rows [40]## Call:
## gllvm(y = y, family = "negative.binomial")
## family:
## [1] "negative.binomial"
## method:
## [1] "VA"
##
## log-likelihood: -5468.074
## Residual degrees of freedom: 1441
## AIC: 11254.15
## AICc: 11289.48
## BIC: 12109.21fit_ord<- gllvm(y, family = "negative.binomial")## Warning in matrix(fa$scores[, 1:num.lv], n, num.lv): data length [82] is not a
## sub-multiple or multiple of the number of rows [40]ordiplot(fit_ord, biplot=TRUE, ind.spp=14)
gllvm(y, X, family = "negative.binomial", num.lv = 1,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1234)## Warning in matrix(fa$scores[, 1:num.lv], n, num.lv): data length [41] is not a
## sub-multiple or multiple of the number of rows [40]## Call:
## gllvm(y = y, X = X, formula = ~alpha.cent.wt + annual_percent_wet,
## family = "negative.binomial", num.lv = 1, seed = 1234)
## family:
## [1] "negative.binomial"
## method:
## [1] "VA"
##
## log-likelihood: -5453.458
## Residual degrees of freedom: 1400
## AIC: 11306.92
## AICc: 11364.39
## BIC: 12382.47fit_env <- gllvm(y, X, family = "negative.binomial", num.lv = 1,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1234)## Warning in matrix(fa$scores[, 1:num.lv], n, num.lv): data length [41] is not a
## sub-multiple or multiple of the number of rows [40]coefplot(fit_env, cex.ylab = 0.7, mar = c(4, 9, 2, 1), mfrow=c(1,1))
pdf("Pseudospeciesplot_S_03.15.2025.pdf",
)
coefplot(fit_env, cex.ylab = 0.7, mar = c(4, 12, 2, 1), mfrow=c(1,1))
dev.off()## quartz_off_screen
## 2# TAXONOMIC COMPOSITION: Genus level
#setwd("/Users/chunk/Documents/DADA2/DADA2_package_test/Kz_Syn_ITS/run_2/outputs")
#make taxonomy object by genus
gen_counts_tab <- otu_table(tax_glom(pseqtest, taxrank = "Genus"), taxa_are_rows = FALSE)
gen_counts_tab <- t(gen_counts_tab)
#make vector of genus names to set as row names
gen_tax_vec <- as.vector(tax_table(tax_glom(pseqtest, taxrank="Genus"))[,6])
rownames(gen_counts_tab) <- as.vector(gen_tax_vec)
#gen_counts_tab
#write.csv(gen_counts_tab, 'gen_counts_rar.csv')
asv_counts <- pseqtest@otu_table
#determine the number of unclassified seqs at the genus level
unclassified_gen_counts <- colSums(t(asv_counts)) - colSums(gen_counts_tab)
#Add a row of "unclassified" to the genus count table
genus_and_unidentified_counts_tab <- rbind(gen_counts_tab, "Unclassified_genus"=unclassified_gen_counts)
## test all counts are accounted for
identical(colSums(genus_and_unidentified_counts_tab), rowSums(asv_counts))## [1] TRUE## convert counts to percent abundance:
genus_proportions <- apply(genus_and_unidentified_counts_tab, 2, function(x) x/sum(x)*100)
#Merge metadata
#phy_and_fam <- merge(t(major_taxa_counts_tab), t(family_and_unidentified_counts_tab),
# by="row.names", all=TRUE)
genus_merge <- merge(t(genus_proportions), samdftest,
by="row.names", all=TRUE)
rownames(genus_merge) <- genus_merge[,1]
genus_merge <- genus_merge[,-1]
genus_merge <- merge(genus_merge, alpha[,94:103],
by="row.names", all=TRUE)
write.csv(genus_merge, "1216R_03.08.2025_genusmerge.csv")Supplementary table: top ten families by substrate
top 20 family
fam<-t(family_proportions_tab)
#fam <- fam[,-1]
#rownames(phyfam) <- phy_and_fam[,1]
#Merge metadata, phylum, and family abundances
#phy_and_fam <- merge(t(major_taxa_counts_tab), t(family_and_unidentified_counts_tab),
# by="row.names", all=TRUE)
fam_merge <- merge(samdftest, fam,
by="row.names", all=TRUE)
rownames(fam_merge) <- fam_merge[,1]
fam_merge <- fam_merge[,-1]
### Just checking this out
### "f__Pleosporaceae" in epilithon
mean(fam_merge[fam_merge$substrate=='B',"f__Pleosporaceae"])## [1] 7.477468sd(fam_merge[fam_merge$substrate=='B',"f__Pleosporaceae"])## [1] 5.86229### "f__Pleosporaceae" in leaf
mean(fam_merge[fam_merge$substrate=='L',"f__Pleosporaceae"])## [1] 12.23688sd(fam_merge[fam_merge$substrate=='L',"f__Pleosporaceae"])## [1] 8.771812### "f__Pleosporaceae" in sediment
mean(fam_merge[fam_merge$substrate=='S',"f__Pleosporaceae"])## [1] 4.648027sd(fam_merge[fam_merge$substrate=='S',"f__Pleosporaceae"])## [1] 3.735487Now, to generate tables with mean percent abundance and standard deviation for the top 20 families and genera in each substrate, we’ll want to 1) seperate the datasets by substrate, 2) sort the families by average percent abuundance by substrate, and 3) then generate a table with means and st.dev. for each of the top 20.
##Family, biofilm
fam_merge_B <- fam_merge[fam_merge$substrate=='B',]
fammertemp<- fam_merge_B[,96:ncol(fam_merge_B)]
colnames(fammertemp) <- sapply(str_replace(colnames(fammertemp), "f__", ""),`[`, 1)
#Reorder from most abundant to least abundant
fammertemp <- fammertemp[,order(colSums(-fammertemp,na.rm=TRUE))]
fammertemp<- fammertemp[,colnames(fammertemp)!="Unclassified_family"]
fammertemp <-fammertemp[,1:20]
fam_long_B <- gather(fammertemp, key = "Family", value = "Percent_Abundance")
fam_long_B$Family <- factor(fam_long_B$Family, levels = names(sort(colMeans(fammertemp), decreasing = TRUE)))
#fammertemp$fam<-row.names(fammertemp)
#fammertemp$fam <- factor(fammertemp$fam, levels = fammertemp$fam[order(fammertemp$Mean_percent_abundance, decreasing = TRUE)])
topfam_box_B<- ggplot(fam_long_B, aes(x=Family, y=Percent_Abundance))+
geom_boxplot()+#fill = "skyblue", color = "black") +
labs(y = "Percent Abundance", title = "Epilithic biofilms") +
theme_minimal() +
scale_y_continuous(labels = scales::percent_format(scale = 1))+
theme(axis.text.x = element_text(angle = 60, hjust = 1))
topfam_box_B
#mean percent abundance for each family
Mean_percent_abundance<-colMeans(fammertemp)
St.Dev<- sapply(fammertemp, sd)
name_rab<- colnames(fammertemp)
### Table of family mean percent abundance & st. deviation
topfam_table_B<- data.frame(#name_rab,
Mean_percent_abundance,St.Dev)
#topfam_table_B<-topfam_table_B[topfam_table_B$Mean_percent_abundance>0,]
topfam_table_B## Mean_percent_abundance St.Dev
## Phaeosphaeriaceae 12.7253175 7.216881
## Mycosphaerellaceae 10.1577222 13.478017
## Verrucariaceae 7.7345350 12.559767
## Cucurbitariaceae 7.5634986 6.671809
## Pleosporaceae 7.4774683 5.862290
## Didymosphaeriaceae 4.4285129 3.167097
## Didymellaceae 4.0680049 2.323612
## Phallaceae 1.9950840 1.872980
## Trichomeriaceae 1.5546907 2.577346
## Sebacinaceae 1.4440803 2.150855
## Cladosporiaceae 1.3303974 1.554360
## Helotiales_fam_Incertae_sedis 1.2832855 1.671701
## Thelephoraceae 1.2822614 5.701749
## Bulleribasidiaceae 1.2054486 1.600488
## Psathyrellaceae 1.1317083 1.369086
## Helotiaceae 1.0999590 1.122404
## Saccotheciaceae 1.0692339 5.037862
## Mortierellaceae 1.0170012 1.314377
## Aphelidiomycota_fam_Incertae_sedis 1.0159771 3.273406
## Dictyosporiaceae 0.8582548 4.335758##Family, leaf
fam_merge_L <- fam_merge[fam_merge$substrate=='L',]
fammertemp<- fam_merge_L[,96:ncol(fam_merge_L)]
colnames(fammertemp) <- sapply(str_replace(colnames(fammertemp), "f__", ""),`[`, 1)
#Reorder from most abundant to least abundant
fammertemp <- fammertemp[,order(colSums(-fammertemp,na.rm=TRUE))]
fammertemp<- fammertemp[,colnames(fammertemp)!="Unclassified_family"]
fammertemp <-fammertemp[,1:20]
fam_long_L <- gather(fammertemp, key = "Family", value = "Percent_Abundance")
fam_long_L$Family <- factor(fam_long_L$Family, levels = names(sort(colMeans(fammertemp), decreasing = TRUE)))
#fammertemp$fam<-row.names(fammertemp)
#fammertemp$fam <- factor(fammertemp$fam, levels = fammertemp$fam[order(fammertemp$Mean_percent_abundance, decreasing = TRUE)])
topfam_box_L<- ggplot(fam_long_L, aes(x=Family, y=Percent_Abundance))+
geom_boxplot()+#fill = "skyblue", color = "black") +
labs(y = "Percent Abundance", title = "Leaf litter") +
theme_minimal() +
scale_y_continuous(labels = scales::percent_format(scale = 1))+
theme(axis.text.x = element_text(angle = 60, hjust = 1))
topfam_box_L
#mean percent abundance for each family
Mean_percent_abundance<-colMeans(fammertemp)
St.Dev<- sapply(fammertemp, sd)
name_rab<- colnames(fammertemp)
### Table of family mean percent abundance & st. deviation
topfam_table_L<- data.frame(#name_rab,
Mean_percent_abundance,St.Dev)
#topfam_table_L<-topfam_table_L[topfam_table_L$Mean_percent_abundance>0,]
topfam_table_L## Mean_percent_abundance St.Dev
## Phaeosphaeriaceae 15.254474 12.796437
## Pleosporaceae 12.236875 8.771812
## Helotiaceae 6.689794 12.110203
## Helotiales_fam_Incertae_sedis 5.913168 8.102259
## Mycosphaerellaceae 5.769348 6.989446
## Cladosporiaceae 4.023464 4.066208
## Gnomoniaceae 3.221561 8.767841
## Didymellaceae 2.607930 4.099595
## Cucurbitariaceae 2.466725 2.178688
## Sporocadaceae 1.887088 3.348231
## Sebacinaceae 1.861811 2.929838
## Venturiaceae 1.854838 4.502127
## Hyaloscyphaceae 1.674410 3.591256
## Cylindrosympodiaceae 1.316168 2.284686
## Didymosphaeriaceae 1.283046 1.321612
## Calloriaceae 1.241207 2.047502
## Filobasidiaceae 1.236849 4.964168
## Bulleribasidiaceae 1.182808 1.368445
## Leptosphaeriaceae 1.113949 1.430365
## Helicogoniaceae 1.106976 4.134265##Family, sediment
fam_merge_S <- fam_merge[fam_merge$substrate=='S',]
fammertemp<- fam_merge_S[,96:ncol(fam_merge_S)]
colnames(fammertemp) <- sapply(str_replace(colnames(fammertemp), "f__", ""),`[`, 1)
#Reorder from most abundant to least abundant
fammertemp <- fammertemp[,order(colSums(-fammertemp,na.rm=TRUE))]
fammertemp<- fammertemp[,colnames(fammertemp)!="Unclassified_family"]
fammertemp <-fammertemp[,1:20]
fam_long_S <- gather(fammertemp, key = "Family", value = "Percent_Abundance")
fam_long_S$Family <- factor(fam_long_S$Family, levels = names(sort(colMeans(fammertemp), decreasing = TRUE)))
#fammertemp$fam<-row.names(fammertemp)
#fammertemp$fam <- factor(fammertemp$fam, levels = fammertemp$fam[order(fammertemp$Mean_percent_abundance, decreasing = TRUE)])
topfam_box_S<- ggplot(fam_long_S, aes(x=Family, y=Percent_Abundance))+
geom_boxplot()+#fill = "skyblue", color = "black") +
labs(y = "Percent Abundance", title = "Benthic sediment") +
theme_minimal() +
scale_y_continuous(labels = scales::percent_format(scale = 1))+
theme(axis.text.x = element_text(angle = 60, hjust = 1))
topfam_box_S
#mean percent abundance for each family
Mean_percent_abundance<-colMeans(fammertemp)
St.Dev<- sapply(fammertemp, sd)
name_rab<- colnames(fammertemp)
### Table of family mean percent abundance & st. deviation
topfam_table_S<- data.frame(#name_rab,
Mean_percent_abundance,St.Dev)
#topfam_table_S<-topfam_table_S[topfam_table_S$Mean_percent_abundance>0,]
topfam_table_S## Mean_percent_abundance St.Dev
## Mortierellaceae 9.4872320 5.647808
## Phaeosphaeriaceae 7.9441486 4.481932
## Inocybaceae 6.8354841 15.921481
## Cucurbitariaceae 6.7825686 8.082685
## Phallaceae 6.0289499 5.114020
## Pleosporaceae 4.6480268 3.735487
## Didymosphaeriaceae 3.7441964 2.685174
## Thelephoraceae 2.4878807 4.648389
## Blastocladiales_fam_Incertae_sedis 2.2702444 3.445328
## Kickxellomycota_fam_Incertae_sedis 2.1806295 3.548990
## Didymellaceae 1.9945719 1.506620
## Nectriaceae 1.8588693 1.600924
## Psathyrellaceae 1.5089444 1.970585
## Sporormiaceae 1.4167691 1.279728
## Basidiomycota_fam_Incertae_sedis 1.3886044 2.332366
## Helotiaceae 1.3280076 1.431529
## Sebacinaceae 1.2025468 2.591431
## Lycoperdaceae 1.1786495 1.516167
## Thelebolaceae 0.8952956 2.479718
## Helotiales_fam_Incertae_sedis 0.7979995 1.318732top genera box plots
gn<- nrow(genus_and_unidentified_counts_tab)+1
library(stringr)
### leaf genera proportions
genmerge_L<- genus_merge[genus_merge$substrate=='L',]
gentemp_L<- genmerge_L[,1:gn]
colnames(gentemp_L) <- sapply(str_replace(colnames(gentemp_L), "g__", ""),`[`, 1)
#colnames(gentemp_L)
rownames(gentemp_L) <- gentemp_L[,1]
gentemp_L <- gentemp_L[,-1]
#Reorder data table from most abundant to least abundant
gentemp_L <- gentemp_L[,order(colSums(-gentemp_L,na.rm=TRUE))]
### Epilithon genera proportions
### arranging top genera counts
genmerge_B<- genus_merge[genus_merge$substrate=='B',]
gentemp_B<- genmerge_B[,1:gn]
colnames(gentemp_B) <- sapply(str_replace(colnames(gentemp_B), "g__", ""),`[`, 1)
#colnames(gentemp_B)
rownames(gentemp_B) <- gentemp_B[,1]
gentemp_B <- gentemp_B[,-1]
#Reorder data table from most abundant to least abundant
gentemp_B <- gentemp_B[,order(colSums(-gentemp_B,na.rm=TRUE))]
### Sediment genera proportions
genmerge_S<- genus_merge[genus_merge$substrate=='S',]
gentemp_S<- genmerge_S[,1:gn]
colnames(gentemp_S) <- sapply(str_replace(colnames(gentemp_S), "g__", ""),`[`, 1)
#colnames(gentemp_S)
rownames(gentemp_S) <- gentemp_S[,1]
gentemp_S <- gentemp_S[,-1]
#Reorder data table from most abundant to least abundant
gentemp_S <- gentemp_S[,order(colSums(-gentemp_S,na.rm=TRUE))]##genus, biofilm
gentemp_B<- gentemp_B[,colnames(gentemp_B)!="Unclassified_genus"]
gentemp_Bt <-gentemp_B[,1:21]
gen_long_B <- gather(gentemp_Bt, key = "Genus", value = "Percent_Abundance")
gen_long_B$Genus <- factor(gen_long_B$Genus, levels = names(sort(colMeans(gentemp_Bt), decreasing = TRUE)))
#fammertemp$fam<-row.names(fammertemp)
#fammertemp$fam <- factor(fammertemp$fam, levels = fammertemp$fam[order(fammertemp$Mean_percent_abundance, decreasing = TRUE)])
topgen_box_B<- ggplot(gen_long_B, aes(x=Genus, y=Percent_Abundance))+
geom_boxplot()+#fill = "skyblue", color = "black") +
labs(y = "Percent Abundance", title = "Epilithic biofilms") +
theme_minimal() +
scale_y_continuous(labels = scales::percent_format(scale = 1))+
theme(axis.text.x = element_text(angle = 60, hjust = 1))
topgen_box_B
#mean percent abundance for each genily
Mean_percent_abundance<-colMeans(gentemp_Bt)
St.Dev<- sapply(gentemp_Bt, sd)
name_rab<- colnames(gentemp_Bt)
### Table of family mean percent abundance & st. deviation
topgen_table_B<- data.frame(#name_rab,
Mean_percent_abundance,St.Dev)
#topfam_table_B<-topfam_table_B[topfam_table_B$Mean_percent_abundance>0,]
topgen_table_B## Mean_percent_abundance St.Dev
## Ramularia 9.1233101 12.292497
## Pyrenochaetopsis 6.7410897 6.468327
## Alternaria 6.3867268 5.770778
## Verrucaria 5.8756657 10.895814
## Paraphoma 4.7357640 3.522203
## Phaeosphaeria 3.0858255 2.056672
## Tremateia 3.0510037 2.634761
## Phallus 1.9868906 1.878962
## Neosetophoma 1.4502253 1.944351
## Cladosporium 1.2935272 1.514903
## Epicoccum 1.2894306 1.161461
## Tomentella 1.2341254 5.692617
## Stagonosporopsis 1.1880377 1.120118
## Trichomeriaceae_gen_Incertae_sedis 1.1747235 2.220119
## Mycoarthris 1.1378533 1.590363
## Didymella 1.0866448 0.995253
## Aureobasidium 1.0692339 5.037862
## Thelidium 1.0241704 2.565566
## Aphelidiomycota_gen_Incertae_sedis 1.0159771 3.273406
## Neoophiobolus 0.8869316 1.796551
## Hyphodermella 0.8060221 1.172869##genus, leaf
gentemp_L<- gentemp_L[,colnames(gentemp_L)!="Unclassified_genus"]
gentemp_Lt <-gentemp_L[,1:21]
gen_long_L <- gather(gentemp_Lt, key = "Genus", value = "Percent_Abundance")
gen_long_L$Genus <- factor(gen_long_L$Genus, levels = names(sort(colMeans(gentemp_Lt), decreasing = TRUE)))
#fammertemp$fam<-row.names(fammertemp)
#fammertemp$fam <- factor(fammertemp$fam, levels = fammertemp$fam[order(fammertemp$Mean_percent_abundance, decreasing = TRUE)])
topgen_box_L<- ggplot(gen_long_L, aes(x=Genus, y=Percent_Abundance))+
geom_boxplot()+#fill = "skyblue", color = "black") +
labs(y = "Percent Abundance", title = "Leaf litter") +
theme_minimal() +
scale_y_continuous(labels = scales::percent_format(scale = 1))+
theme(axis.text.x = element_text(angle = 60, hjust = 1))
topgen_box_L
#mean percent abundance for each genily
Mean_percent_abundance<-colMeans(gentemp_Lt)
St.Dev<- sapply(gentemp_Lt, sd)
name_rab<- colnames(gentemp_Lt)
### Table of family mean percent abundance & st. deviation
topgen_table_L<- data.frame(#name_rab,
Mean_percent_abundance,St.Dev)
#topfam_table_L<-topfam_table_L[topfam_table_L$Mean_percent_abundance>0,]
topgen_table_L## Mean_percent_abundance St.Dev
## Alternaria 11.895195 8.782032
## Mycoarthris 5.682185 8.078487
## Paraphoma 4.748664 7.000964
## Ramularia 4.372990 6.772981
## Neosetophoma 4.046127 3.653187
## Cladosporium 3.843908 3.829641
## Phaeosphaeria 3.381070 6.196825
## Tetracladium 3.262528 8.112430
## Ophiognomonia 3.161418 8.764815
## Pyrenochaetopsis 1.878372 1.914685
## Crocicreas 1.822588 8.262448
## Venturia 1.590733 4.534351
## Pezizella 1.527103 3.554895
## Cylindrosympodium 1.316168 2.284686
## Efibulobasidium 1.280431 1.804593
## Calloriaceae_gen_Incertae_sedis 1.239464 2.048242
## Stagonosporopsis 1.217673 3.578453
## Hymenoscyphus 1.166247 3.152743
## Eleutheromyces 1.106976 4.134265
## Filobasidium 1.065137 4.963575
## Phacidium 1.052934 7.042910##genus, sediment
#Reorder from most abundant to least abundant
#fammertemp <- fammertemp[,order(colSums(-fammertemp,na.rm=TRUE))]
gentemp_S<- gentemp_S[,colnames(gentemp_S)!="Unclassified_genus"]
gentemp_St <-gentemp_S[,1:21]
gen_long_S <- gather(gentemp_St, key = "Genus", value = "Percent_Abundance")
gen_long_S$Genus <- factor(gen_long_S$Genus, levels = names(sort(colMeans(gentemp_St), decreasing = TRUE)))
#fammertemp$fam<-row.names(fammertemp)
#fammertemp$fam <- factor(fammertemp$fam, levels = fammertemp$fam[order(fammertemp$Mean_percent_abundance, decreasing = TRUE)])
topgen_box_S<- ggplot(gen_long_S, aes(x=Genus, y=Percent_Abundance))+
geom_boxplot()+#fill = "skyblue", color = "black") +
labs(y = "Percent Abundance", title = "Benthic sediment") +
theme_minimal() +
scale_y_continuous(labels = scales::percent_format(scale = 1))+
theme(axis.text.x = element_text(angle = 60, hjust = 1))
topgen_box_S
#mean percent abundance for each genily
Mean_percent_abundance<-colMeans(gentemp_St)
St.Dev<- sapply(gentemp_St, sd)
name_rab<- colnames(gentemp_St)
### Table of family mean percent abundance & st. deviation
topgen_table_S<- data.frame(#name_rab,
Mean_percent_abundance,St.Dev)
#topfam_table_S<-topfam_table_S[topfam_table_S$Mean_percent_abundance>0,]
topgen_table_S## Mean_percent_abundance St.Dev
## Mortierella 7.3040421 5.1409124
## Phallus 5.9871296 5.0978485
## Pyrenochaetopsis 5.9418954 8.0990632
## Paraphoma 4.2443329 2.3495793
## Pseudosperma 3.8193022 11.0517981
## Alternaria 2.5168988 3.0641952
## Tremateia 2.4127748 1.9462608
## Blastocladiales_gen_Incertae_sedis 2.2702444 3.4453279
## Kickxellomycota_gen_Incertae_sedis 2.1806295 3.5489901
## Tomentella 2.0321248 3.3281893
## Inosperma 1.9126383 8.9702269
## Phaeosphaeria 1.4483477 1.2119029
## Curvularia 1.4329851 2.0640483
## Basidiomycota_gen_Incertae_sedis 1.3886044 2.3323657
## Fusarium 1.2989895 1.0000189
## Preussia 1.2110815 1.2170898
## Tetracladium 1.1965724 1.4487151
## Inocybe 1.0591629 1.9272290
## Sebacina 0.9473576 2.4086085
## Paraphaeosphaeria 0.7647139 1.6631427
## Cladosporium 0.6810733 0.6745644table of top families and genera in each substrate
plotout <- "Family_genus_relabun_substrate_03.15.2025.tiff"
agg_tiff(filename=plotout, width=4800, height=5000, units="px",
pointsize=10, res=600, compression="lzw", scaling=0.5)
plot_grid(topfam_box_B,topgen_box_B,topfam_box_L,topgen_box_L,topfam_box_S,topgen_box_S, labels="AUTO",label_size = 18, ncol = 2)
invisible(dev.off())
write_csv(topgen_table_L, "top20genL_03.15.2025.csv")
write_csv(topgen_table_B, "top20genB_03.15.2025.csv")
write_csv(topgen_table_S, "top20genS_03.15.2025.csv")Differential abundance tests, Wilcoxon and Spearman
Limited non-parametric tests for differential relative abundances of major taxa.
### Basidiomycetes
cor_summary <- taxmerge %>%
group_by(substrate) %>%
summarize(
spearman_rho = cor(p__Basidiomycota, lndrainage_area, method = "spearman"),
p_value = cor.test(p__Basidiomycota, lndrainage_area, method = "spearman")$p.value
)## Warning: There were 3 warnings in `summarize()`.
## The first warning was:
## ℹ In argument: `p_value = cor.test(p__Basidiomycota, lndrainage_area, method =
## "spearman")$p.value`.
## ℹ In group 1: `substrate = B`.
## Caused by warning in `cor.test.default()`:
## ! Cannot compute exact p-value with ties
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.cor_summary## # A tibble: 3 × 3
## substrate spearman_rho p_value
## <fct> <dbl> <dbl>
## 1 B 0.478 0.00183
## 2 L 0.124 0.407
## 3 S 0.365 0.0108### Asco classes
cor_summary <- taxmerge %>%
group_by(substrate) %>%
summarize(
spearman_rho = cor(c__Leotiomycetes, lndrainage_area, method = "spearman"),
p_value = cor.test(c__Leotiomycetes, lndrainage_area, method = "spearman")$p.value
)## Warning: There were 3 warnings in `summarize()`.
## The first warning was:
## ℹ In argument: `p_value = cor.test(c__Leotiomycetes, lndrainage_area, method =
## "spearman")$p.value`.
## ℹ In group 1: `substrate = B`.
## Caused by warning in `cor.test.default()`:
## ! Cannot compute exact p-value with ties
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.cor_summary## # A tibble: 3 × 3
## substrate spearman_rho p_value
## <fct> <dbl> <dbl>
## 1 B -0.217 0.180
## 2 L -0.497 0.000375
## 3 S -0.358 0.0126cor_summary <- taxmerge %>%
group_by(substrate) %>%
summarize(
spearman_rho = cor(c__Leotiomycetes, lndrainage_area, method = "spearman"),
p_value = cor.test(c__Leotiomycetes, lndrainage_area, method = "spearman")$p.value
)## Warning: There were 3 warnings in `summarize()`.
## The first warning was:
## ℹ In argument: `p_value = cor.test(c__Leotiomycetes, lndrainage_area, method =
## "spearman")$p.value`.
## ℹ In group 1: `substrate = B`.
## Caused by warning in `cor.test.default()`:
## ! Cannot compute exact p-value with ties
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.cor_summary## # A tibble: 3 × 3
## substrate spearman_rho p_value
## <fct> <dbl> <dbl>
## 1 B -0.217 0.180
## 2 L -0.497 0.000375
## 3 S -0.358 0.0126cor_summary <- taxmerge %>%
group_by(substrate) %>%
summarize(
spearman_rho = cor(f__Verrucariaceae, prc_wet, method = "spearman"),
p_value = cor.test(f__Verrucariaceae, prc_wet, method = "spearman")$p.value
)## Warning: There were 3 warnings in `summarize()`.
## The first warning was:
## ℹ In argument: `p_value = cor.test(f__Verrucariaceae, prc_wet, method =
## "spearman")$p.value`.
## ℹ In group 1: `substrate = B`.
## Caused by warning in `cor.test.default()`:
## ! Cannot compute exact p-value with ties
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.cor_summary## # A tibble: 3 × 3
## substrate spearman_rho p_value
## <fct> <dbl> <dbl>
## 1 B -0.284 0.0758
## 2 L 0.0289 0.847
## 3 S -0.167 0.256FUNGAL TRAITS
Micro-Eco package for heatmaps and FungalTraits
#install.packages('microeco')
#install.packages('file2meco')
library(microeco)
library(file2meco)
##install.packages("file2meco")
microecotest<- phyloseq2meco(pseqtest)## 25 taxa with 0 abundance are removed from the otu_table ...microecotest$tidy_dataset()
# show 40 taxa at Genus level
t1 <- trans_abund$new(dataset = microecotest, taxrank = "Family", ntaxa = 15)## No taxa_abund list found. Calculate relative abundance with cal_abund function ...## The result is stored in object$taxa_abund ...## The transformed abundance data is stored in object$data_abund ...t1$plot_heatmap(facet = "flow_state", xtext_keep = FALSE, withmargin = FALSE)## Warning in scale_fill_gradientn(colours = color_values, trans =
## plot_colorscale, : log-10 transformation introduced infinite values.
# create trans_func object
t2 <- trans_func$new(microecotest)
# fungi_database = "FungalTraits" for the FungalTraits database
t2$cal_spe_func(fungi_database = "FungalTraits")## Please also cite:## FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Diversity 105, 1-16 (2020).## Mapped raw FungalTraits result is stored in object$res_spe_func_raw_FungalTraits ...## The functional binary table is stored in object$res_spe_func ...write.csv(t2$res_spe_func,"FungalTraits_ASV_tab_microeco_03.08.2025.csv")
### How many ASVs were assigned functional traits?
## total number of ASVs in rarefied data:
ncol(pseqtest@otu_table)## [1] 5852#number of ASVs with functions assigned:
nrow(t2$res_spe_func) ##ASVs checked against fungaltraits## [1] 5827rows<- rowSums(t2$res_spe_func)
no.function<- rows[rows==0]
length(no.function) #ASVs with no traits assigned## [1] 2006nrow(t2$res_spe_func)-length(no.function)## [1] 3821((nrow(t2$res_spe_func)-length(no.function))/nrow(t2$res_spe_func)) #proportion of ASVs assigned traits## [1] 0.6557405### ~65% of ASVs assigned traits
# calculate abundance-unweighted functional redundancy of each trait for each network module
t2$cal_spe_func_perc(#use_community = TRUE
)## The result table is stored in object$res_spe_func_perc ...relabun_func<-t2$res_spe_func_perc
#t2$res_spe_func_perc
write.csv(relabun_func, "functional_group_percent_abundance_03.08.2025.csv")functional analysis
#View(func_relabun)
#relabun_func
perc_func_env<- merge(samdftest,relabun_func,by="row.names")
row.names(perc_func_env) <- perc_func_env$Row.names
### mean percent abundance of dominant groups (out of classified reads)
func_prop<- perc_func_env[96:117]/100
mean(rowSums(func_prop)) ### mean proportion of total reads classified## [1] 0.6710554func_prop<- perc_func_env[96:117]/(100*mean(rowSums(func_prop))) ### for percents out of classified reads, not total reads
mean(func_prop$`primary_lifestyle|litter_saprotroph`)## [1] 0.2690047mean(func_prop$`primary_lifestyle|plant_pathogen`)## [1] 0.3396585func_perc<- perc_func_env[96:117]/rowSums(perc_func_env[96:117])
rowSums(func_perc)## 01m01_B 01m01_L 01m01_S 01m02_B 01m02_L 01m02_S 01m03_B 01m03_L 01m03_S 01m04_B
## 1 1 1 1 1 1 1 1 1 1
## 01m04_L 01m04_S 01m05_B 01m05_L 01m05_S 01m06_B 01m06_L 01m06_S 02m01_L 02m01_S
## 1 1 1 1 1 1 1 1 1 1
## 02m02_B 02m02_L 02m02_S 02m03_B 02m03_L 02m03_S 02m04_L 02m04_S 02m05_B 02m05_L
## 1 1 1 1 1 1 1 1 1 1
## 02m06_B 02m06_L 02m06_S 02m07_B 02m07_L 02m07_S 02m08_L 02m08_S 02m09_B 02m09_L
## 1 1 1 1 1 1 1 1 1 1
## 02m09_S 02m10_B 02m10_L 02m10_S 02m11_B 02m11_S 04m01_B 04m01_L 04m01_S 04m02_B
## 1 1 1 1 1 1 1 1 1 1
## 04m02_L 04m02_S 04m03_B 04m03_L 04m03_S 04m04_B 04m04_L 04m04_S 04m05_B 04m05_L
## 1 1 1 1 1 1 1 1 1 1
## 04m05_S 04m06_B 04m06_L 04m06_S 04m07_L 04m07_S 04m08_L 04m08_S 04m09_L 04m09_S
## 1 1 1 1 1 1 1 1 1 1
## 04m10_B 04m10_L 04m10_S 04m11_B 04m11_L 04m11_S 04m12_B 04m12_L 04m12_S 04m13_B
## 1 1 1 1 1 1 1 1 1 1
## 04m13_L 04m13_S 04t01_B 04t01_L 04t01_S 04t02_B 04t02_L 04t02_S 04w01_B 04w01_L
## 1 1 1 1 1 1 1 1 1 1
## 04w01_S 04w02_B 04w02_L 04w02_S 04w03_B 04w03_S 04w04_L 04w04_S 20m01_L 20m01_S
## 1 1 1 1 1 1 1 1 1 1
## 20m02_B 20m02_L 20m02_S 20m03_B 20m03_L 20m03_S 20m04_B 20m04_L 20m04_S 20m05_L
## 1 1 1 1 1 1 1 1 1 1
## 20m05_S sfm01_B sfm01_L sfm01_S sfm02_B sfm02_L sfm02_S sfm03_B sfm03_L sfm04_B
## 1 1 1 1 1 1 1 1 1 1
## sfm04_L sfm04_S sfm05_B sfm05_L sfm05_S sfm06_L sfm06_S sfm07_B sfm07_L sfm07_S
## 1 1 1 1 1 1 1 1 1 1
## Sft01_B Sft01_L Sft01_S Sft02_B Sft02_S
## 1 1 1 1 1func_perc <- merge(samdftest,func_perc,by="row.names")
row.names(func_perc) <- func_perc$Row.names
colnames(func_perc) <- gsub("^.+\\|", "", colnames(func_perc))
library(rstatix)
### lichen by substrate
stat.test <- func_perc %>%
wilcox_test(lichenized ~ substrate) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance()
stat.test## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 lichenized B L 40 47 1574. 5.22e-8 1.57e-7 ****
## 2 lichenized B S 40 48 1569 2.97e-7 8.91e-7 ****
## 3 lichenized L S 47 48 1039 4.99e-1 1 e+0 nseffsize <- func_perc %>%
wilcox_effsize(lichenized ~substrate)
effsize## # A tibble: 3 × 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 lichenized B L 0.584 40 47 large
## 2 lichenized B S 0.547 40 48 large
## 3 lichenized L S 0.0697 47 48 small### litter sap by substrate
stat.test <- func_perc %>%
wilcox_test(litter_saprotroph ~ substrate) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance()
stat.test## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 litter_sap… B L 40 47 80 2.44e-17 7.32e-17 ****
## 2 litter_sap… B S 40 48 1462 1.53e- 5 4.59e- 5 ****
## 3 litter_sap… L S 47 48 2237 1.3 e-24 3.90e-24 ****effsize <- func_perc %>%
wilcox_effsize(litter_saprotroph ~substrate)
effsize## # A tibble: 3 × 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 litter_saprotroph B L 0.785 40 47 large
## 2 litter_saprotroph B S 0.448 40 48 moderate
## 3 litter_saprotroph L S 0.847 47 48 large### soil saprotroph by substrate
stat.test <- func_perc %>%
wilcox_test(soil_saprotroph ~ substrate) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance()
stat.test## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 soil_sapro… B L 40 47 1124 1.18e- 1 3.54e- 1 ns
## 2 soil_sapro… B S 40 48 77 8.83e-18 2.65e-17 ****
## 3 soil_sapro… L S 47 48 38 9.12e-23 2.74e-22 ****effsize <- func_perc %>%
wilcox_effsize(soil_saprotroph ~substrate)
effsize## # A tibble: 3 × 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 soil_saprotroph B L 0.168 40 47 small
## 2 soil_saprotroph B S 0.789 40 48 large
## 3 soil_saprotroph L S 0.832 47 48 large### ectomycorrhizae by substrate
stat.test <- func_perc %>%
wilcox_test(ectomycorrhizal ~ substrate) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance()
stat.test## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 ectomycorr… B L 40 47 1278 3 e- 3 9 e- 3 **
## 2 ectomycorr… B S 40 48 335 1.53e- 7 4.59e- 7 ****
## 3 ectomycorr… L S 47 48 204 3.77e-12 1.13e-11 ****effsize <- func_perc %>%
wilcox_effsize(ectomycorrhizal ~substrate)
effsize## # A tibble: 3 × 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 ectomycorrhizal B L 0.318 40 47 moderate
## 2 ectomycorrhizal B S 0.560 40 48 large
## 3 ectomycorrhizal L S 0.713 47 48 large## [1] "substrate" "PRC_WET_GROUP", sites kmeans clustered by annual percent wet (k=3)
phylamet<-data.frame("Sample"=row.names(samdftest),
"Substrate"=samdftest$substrate,
"Site"=samdftest$siteid,
stringsAsFactors=T)
#merge metadata with major taxa data
phylalong <- merge(phylalong, phylamet)
phylamet<-data.frame("Sample"=row.names(samdftest),
"Drainage_area"=samdftest$drainage_area,
"Annual_Percent_Wet"=samdftest$prc_wet)
#merge metadata with major taxa data
phylalong <- merge(phylalong, phylamet)
#Summarize by depth and hydration
phyla_summary <-
phylalong %>% # the names of the new data frame and the data frame to be summarised
group_by(Substrate, Site, Major_Taxa) %>% # the grouping variable
summarise(mean_prop = mean(Proportion), drainage_area = Drainage_area) #%>%## `summarise()` has grouped output by 'Substrate', 'Site'. You can override using
## the `.groups` argument. # summarise(drainage_area = Drainage_area)# calculates the mean of each group
## `summarise()` has grouped output by 'Depth', 'Hydration'. You can override using the `.groups` argument.
phyla_summary## # A tibble: 1,755 × 5
## # Groups: Substrate, Site [135]
## Substrate Site Major_Taxa mean_prop drainage_area
## <fct> <fct> <chr> <dbl> <dbl>
## 1 B 01M01 Other 0.246 28.3
## 2 B 01M01 Unclassified_Ascomycetes 4.38 28.3
## 3 B 01M01 Unclassified_Phylum 6.68 28.3
## 4 B 01M01 c__Dothideomycetes 28.6 28.3
## 5 B 01M01 c__Eurotiomycetes 24.2 28.3
## 6 B 01M01 c__Leotiomycetes 0.615 28.3
## 7 B 01M01 c__Sordariomycetes 1.60 28.3
## 8 B 01M01 p__Aphelidiomycota 21.6 28.3
## 9 B 01M01 p__Basidiomycota 12.1 28.3
## 10 B 01M01 p__Blastocladiomycota 0 28.3
## # ℹ 1,745 more rowsphyla_summary$Substrate <- factor(phyla_summary$Substrate,
levels=c("B","L","S")) #reorder variables
phyla_summary$Site <- fct_reorder(phyla_summary$Site, phyla_summary$drainage_area)ptab<- otu_table(pseqtest)
rowSums(ptab)## 01m01_B 01m01_L 01m01_S 01m02_B 01m02_L 01m02_S 01m03_B 01m03_L 01m03_S 01m04_B
## 2441 2441 2441 2441 2441 2441 2441 2441 2441 2441
## 01m04_L 01m04_S 01m05_B 01m05_L 01m05_S 01m06_B 01m06_L 01m06_S 02m01_L 02m01_S
## 2441 2441 2441 2441 2441 2441 2441 2441 2441 2441
## 02m02_B 02m02_L 02m02_S 02m03_B 02m03_L 02m03_S 02m04_L 02m04_S 02m05_B 02m05_L
## 2441 2441 2441 2441 2441 2441 2441 2441 2441 2441
## 02m06_B 02m06_L 02m06_S 02m07_B 02m07_L 02m07_S 02m08_L 02m08_S 02m09_B 02m09_L
## 2441 2441 2441 2441 2441 2441 2441 2441 2441 2441
## 02m09_S 02m10_B 02m10_L 02m10_S 02m11_B 02m11_S 04m01_B 04m01_L 04m01_S 04m02_B
## 2441 2441 2441 2441 2441 2441 2441 2441 2441 2441
## 04m02_L 04m02_S 04m03_B 04m03_L 04m03_S 04m04_B 04m04_L 04m04_S 04m05_B 04m05_L
## 2441 2441 2441 2441 2441 2441 2441 2441 2441 2441
## 04m05_S 04m06_B 04m06_L 04m06_S 04m07_L 04m07_S 04m08_L 04m08_S 04m09_L 04m09_S
## 2441 2441 2441 2441 2441 2441 2441 2441 2441 2441
## 04m10_B 04m10_L 04m10_S 04m11_B 04m11_L 04m11_S 04m12_B 04m12_L 04m12_S 04m13_B
## 2441 2441 2441 2441 2441 2441 2441 2441 2441 2441
## 04m13_L 04m13_S 04t01_B 04t01_L 04t01_S 04t02_B 04t02_L 04t02_S 04w01_B 04w01_L
## 2441 2441 2441 2441 2441 2441 2441 2441 2441 2441
## 04w01_S 04w02_B 04w02_L 04w02_S 04w03_B 04w03_S 04w04_L 04w04_S 20m01_L 20m01_S
## 2441 2441 2441 2441 2441 2441 2441 2441 2441 2441
## 20m02_B 20m02_L 20m02_S 20m03_B 20m03_L 20m03_S 20m04_B 20m04_L 20m04_S 20m05_L
## 2441 2441 2441 2441 2441 2441 2441 2441 2441 2441
## 20m05_S sfm01_B sfm01_L sfm01_S sfm02_B sfm02_L sfm02_S sfm03_B sfm03_L sfm04_B
## 2441 2441 2441 2441 2441 2441 2441 2441 2441 2441
## sfm04_L sfm04_S sfm05_B sfm05_L sfm05_S sfm06_L sfm06_S sfm07_B sfm07_L sfm07_S
## 2441 2441 2441 2441 2441 2441 2441 2441 2441 2441
## Sft01_B Sft01_L Sft01_S Sft02_B Sft02_S
## 2441 2441 2441 2441 2441# Remove any prefix followed by '|'
colnames(perc_func_env) <- gsub("^.+\\|", "", colnames(perc_func_env))
func_prop<- perc_func_env[97:118]/100 ###rowSums(perc_func_env[127:148])
rowSums(func_prop)## 01m01_B 01m01_L 01m01_S 01m02_B 01m02_L 01m02_S 01m03_B 01m03_L 01m03_S 01m04_B
## 0.7425 0.8570 0.7752 0.6964 0.8104 0.7247 0.7308 0.8499 0.6797 0.7779
## 01m04_L 01m04_S 01m05_B 01m05_L 01m05_S 01m06_B 01m06_L 01m06_S 02m01_L 02m01_S
## 0.8011 0.6513 0.7733 0.8319 0.6830 0.7661 0.8430 0.6835 0.7959 0.7502
## 02m02_B 02m02_L 02m02_S 02m03_B 02m03_L 02m03_S 02m04_L 02m04_S 02m05_B 02m05_L
## 0.8187 0.7977 0.6787 0.8381 0.8287 0.6753 0.7880 0.7110 0.7390 0.8317
## 02m06_B 02m06_L 02m06_S 02m07_B 02m07_L 02m07_S 02m08_L 02m08_S 02m09_B 02m09_L
## 0.7492 0.8052 0.7052 0.7844 0.8619 0.7127 0.8464 0.7085 0.8184 0.8553
## 02m09_S 02m10_B 02m10_L 02m10_S 02m11_B 02m11_S 04m01_B 04m01_L 04m01_S 04m02_B
## 0.6874 0.8214 0.8659 0.7293 0.8289 0.6462 0.7694 0.8000 0.6988 0.7654
## 04m02_L 04m02_S 04m03_B 04m03_L 04m03_S 04m04_B 04m04_L 04m04_S 04m05_B 04m05_L
## 0.8332 0.5639 0.8223 0.8208 0.6613 0.7077 0.8017 0.5869 0.6990 0.8091
## 04m05_S 04m06_B 04m06_L 04m06_S 04m07_L 04m07_S 04m08_L 04m08_S 04m09_L 04m09_S
## 0.6892 0.8154 0.8245 0.6985 0.8290 0.6726 0.7670 0.7165 0.8008 0.7728
## 04m10_B 04m10_L 04m10_S 04m11_B 04m11_L 04m11_S 04m12_B 04m12_L 04m12_S 04m13_B
## 0.8257 0.8820 0.6631 0.7977 0.8605 0.6703 0.9000 0.8644 0.6211 0.8689
## 04m13_L 04m13_S 04t01_B 04t01_L 04t01_S 04t02_B 04t02_L 04t02_S 04w01_B 04w01_L
## 0.8314 0.6689 0.7632 0.7978 0.6549 0.7388 0.7800 0.7500 0.8228 0.8649
## 04w01_S 04w02_B 04w02_L 04w02_S 04w03_B 04w03_S 04w04_L 04w04_S 20m01_L 20m01_S
## 0.7144 0.8750 0.7847 0.6856 0.8167 0.6318 0.8626 0.6972 0.8500 0.7131
## 20m02_B 20m02_L 20m02_S 20m03_B 20m03_L 20m03_S 20m04_B 20m04_L 20m04_S 20m05_L
## 0.7835 0.8469 0.7500 0.7794 0.8245 0.6627 0.7551 0.7906 0.6864 0.8151
## 20m05_S sfm01_B sfm01_L sfm01_S sfm02_B sfm02_L sfm02_S sfm03_B sfm03_L sfm04_B
## 0.7463 0.7473 0.7728 0.6967 0.7472 0.8443 0.7480 0.7884 0.8327 0.7873
## sfm04_L sfm04_S sfm05_B sfm05_L sfm05_S sfm06_L sfm06_S sfm07_B sfm07_L sfm07_S
## 0.7986 0.7650 0.7351 0.7681 0.6707 0.7848 0.7399 0.7373 0.7909 0.7199
## Sft01_B Sft01_L Sft01_S Sft02_B Sft02_S
## 0.6931 0.7657 0.6319 0.7455 0.6867#func_prop <- func_prop
#Add taxa names as a column
func_prop$functional_groups <- row.names(func_prop)
#transform into long format
func_proplong <- gather(func_prop, Sample, Proportion, -functional_groups)
colnames(func_proplong)<- c("Sample", "functional_groups", "Proportion")
## [1] "substrate" "WetDry"
funcmet<-data.frame("Sample"=row.names(perc_func_env),
"Substrate"=perc_func_env$substrate,
"Site"=perc_func_env$siteid,
stringsAsFactors=T)
row.names(funcmet)<- funcmet$Sample
func_proplong <- merge(func_proplong, funcmet, by="Sample")
funcmet<-data.frame("Sample"=row.names(perc_func_env),
"Drainage_area"=perc_func_env$drainage_area,
"Annual_Percent_Wet"=perc_func_env$prc_wet)
#merge metadata with major taxa data
func_proplong <- merge(func_proplong, funcmet, by="Sample")
#Summarize by depth and hydration
phyla_summary <-
func_proplong %>% # the names of the new data frame and the data frame to be summarised
group_by(Substrate, Site, functional_groups) %>% # the grouping variable
summarise(mean_prop = mean(Proportion), drainage_area = Drainage_area) # calculates the mean of each group## `summarise()` has grouped output by 'Substrate', 'Site'. You can override using
## the `.groups` argument.## `summarise()` has grouped output by 'Depth', 'Hydration'. You can override using the `.groups` argument.
phyla_summary## # A tibble: 2,970 × 5
## # Groups: Substrate, Site [135]
## Substrate Site functional_groups mean_prop drainage_area
## <fct> <fct> <chr> <dbl> <dbl>
## 1 B 01M01 algal_parasite 0 28.3
## 2 B 01M01 animal-associated 0 28.3
## 3 B 01M01 animal_parasite 0 28.3
## 4 B 01M01 arbuscular_mycorrhizal 0 28.3
## 5 B 01M01 arthropod-associated 0 28.3
## 6 B 01M01 dung_saprotroph 0.0198 28.3
## 7 B 01M01 ectomycorrhizal 0 28.3
## 8 B 01M01 epiphyte 0 28.3
## 9 B 01M01 foliar_endophyte 0.0297 28.3
## 10 B 01M01 lichen_parasite 0.0099 28.3
## # ℹ 2,960 more rowsphyla_summary$Substrate <- factor(phyla_summary$Substrate,
levels=c( "B", "L","S")) #reorder variables
phyla_summary$Site <- fct_reorder(phyla_summary$Site, phyla_summary$drainage_area)
#phyla_summary$Wet.Dry <- factor(phyla_summary$Wet.Dry, levels= c("WET","DRY")) #reorder variables
phyla_summary$functional_groups <- factor(phyla_summary$functional_groups)
sublab<- c("Epilithic biofilms", "Leaf litter", "Benthic sediment")
names(sublab)<- c("B","L","S")
pal12 <- rev(cols25(n=22)) #set n= to the number of taxa you are plotting
#make stacked bar plot
phylum_bar <- ggplot(phyla_summary, aes(x = Site, y = mean_prop, fill = functional_groups))+
geom_bar(stat = "identity", col=I("black")) +
scale_fill_manual(values=pal12)+
guides(fill=guide_legend(ncol=1))+
facet_wrap(~Substrate, labeller = labeller(Substrate=sublab), nrow=1, scales="free_x") +
scale_y_continuous(labels = scales::percent_format(scale = 100))+
labs(x="Substrate", y="Percentage of Identified ITS Sequence Copies",
fill="functional_groups")+
#scale_facet_discrete(labels=c('B'='Epilithic biofilms', 'L'='Leaf litter', 'S'='Benthic sediment', 'W'='Surface water'))+
theme(axis.text.x = element_text(angle=60, hjust=1), legend.position = "right")+
theme(text=element_text(size=20), #change font size of all text
axis.text.x=element_text(size=7), #change font size of axis text
# axis.title=element_text(size=16), #change font size of axis titles
#plot.title=element_text(size=16), #change font size of plot title
legend.text=element_text(size=16), #change font size of legend text
# strip.text.x = element_text(size = 10),
legend.title=element_text(size=16)) #change font size of legend title
phylum_bar
#######
#c(180,100) *
# 0.0394 * # convert mm to inch
# 600 # convert to pixels
## [1] 4255.2 2364.0
plotout <- "Functional Barplot_site_drainage_03.15.2025.tiff"
agg_tiff(filename=plotout, width=6000, height=2750, units="px",
pointsize=10, res=600, compression="lzw", scaling=0.5)
phylum_bar
invisible(dev.off())Plot aquatic!!!
func_prop<- perc_func_env[171:174]/100
rowSums(func_prop)## 01m01_B 01m01_L 01m01_S 01m02_B 01m02_L 01m02_S 01m03_B 01m03_L 01m03_S 01m04_B
## 0.7722 0.9191 0.8194 0.7144 0.8974 0.7826 0.7443 0.9251 0.6834 0.8334
## 01m04_L 01m04_S 01m05_B 01m05_L 01m05_S 01m06_B 01m06_L 01m06_S 02m01_L 02m01_S
## 0.8195 0.6639 0.8128 0.8835 0.6864 0.7851 0.9285 0.7126 0.8503 0.7651
## 02m02_B 02m02_L 02m02_S 02m03_B 02m03_L 02m03_S 02m04_L 02m04_S 02m05_B 02m05_L
## 0.8724 0.8321 0.6943 0.9191 0.9101 0.7015 0.8695 0.7535 0.7636 0.8889
## 02m06_B 02m06_L 02m06_S 02m07_B 02m07_L 02m07_S 02m08_L 02m08_S 02m09_B 02m09_L
## 0.7749 0.8615 0.7319 0.8392 0.9595 0.7311 0.8856 0.7025 0.9698 0.9408
## 02m09_S 02m10_B 02m10_L 02m10_S 02m11_B 02m11_S 04m01_B 04m01_L 04m01_S 04m02_B
## 0.6953 0.8714 0.9808 0.7829 0.8422 0.6795 0.8121 0.8983 0.7207 0.8047
## 04m02_L 04m02_S 04m03_B 04m03_L 04m03_S 04m04_B 04m04_L 04m04_S 04m05_B 04m05_L
## 0.9815 0.5917 0.8500 0.8774 0.7015 0.7465 0.8986 0.5869 0.7373 0.8893
## 04m05_S 04m06_B 04m06_L 04m06_S 04m07_L 04m07_S 04m08_L 04m08_S 04m09_L 04m09_S
## 0.6999 0.8615 0.9317 0.7083 0.9183 0.6728 0.8195 0.7305 0.8686 0.8051
## 04m10_B 04m10_L 04m10_S 04m11_B 04m11_L 04m11_S 04m12_B 04m12_L 04m12_S 04m13_B
## 0.8852 1.0108 0.7216 0.8786 0.9442 0.7064 1.0200 0.9660 0.6613 0.9167
## 04m13_L 04m13_S 04t01_B 04t01_L 04t01_S 04t02_B 04t02_L 04t02_S 04w01_B 04w01_L
## 0.8832 0.7247 0.7870 0.8853 0.6655 0.7631 0.8182 0.7755 0.8514 0.9257
## 04w01_S 04w02_B 04w02_L 04w02_S 04w03_B 04w03_S 04w04_L 04w04_S 20m01_L 20m01_S
## 0.7168 0.9250 0.8453 0.7095 0.9167 0.6533 0.9241 0.7121 0.8658 0.7228
## 20m02_B 20m02_L 20m02_S 20m03_B 20m03_L 20m03_S 20m04_B 20m04_L 20m04_S 20m05_L
## 0.8029 0.9280 0.7437 0.8256 0.9171 0.6676 0.7763 0.9012 0.7260 0.8992
## 20m05_S sfm01_B sfm01_L sfm01_S sfm02_B sfm02_L sfm02_S sfm03_B sfm03_L sfm04_B
## 0.7646 0.8007 0.8181 0.7077 0.7690 0.9517 0.7482 0.8176 0.9206 0.8189
## sfm04_L sfm04_S sfm05_B sfm05_L sfm05_S sfm06_L sfm06_S sfm07_B sfm07_L sfm07_S
## 0.8554 0.8032 0.7672 0.8610 0.7018 0.8338 0.7526 0.7565 0.8360 0.7563
## Sft01_B Sft01_L Sft01_S Sft02_B Sft02_S
## 0.7256 0.8133 0.6779 0.7807 0.6899#func_prop <- func_prop
#Add taxa names as a column
func_prop$functional_groups <- row.names(func_prop)
#transform into long format
func_proplong <- gather(func_prop, Sample, Proportion, -functional_groups)
colnames(func_proplong)<- c("Sample", "functional_groups", "Proportion")
## [1] "substrate" "WetDry"
funcmet<-data.frame("Sample"=row.names(perc_func_env),
"Substrate"=perc_func_env$substrate,
"Wet.Dry"=perc_func_env$wet_dry,
stringsAsFactors=T)
row.names(funcmet)<- funcmet$Sample
#merge metadata with major taxa data
func_proplong <- merge(func_proplong, funcmet, by="Sample")
#Summarize by depth and hydration
phyla_summary <-
func_proplong %>% # the names of the new data frame and the data frame to be summarised
group_by(Substrate, Wet.Dry, functional_groups) %>% # the grouping variable
summarise(mean_prop = mean(Proportion)) # calculates the mean of each group## `summarise()` has grouped output by 'Substrate', 'Wet.Dry'. You can override
## using the `.groups` argument.## `summarise()` has grouped output by 'Depth', 'Hydration'. You can override using the `.groups` argument.
phyla_summary## # A tibble: 24 × 4
## # Groups: Substrate, Wet.Dry [6]
## Substrate Wet.Dry functional_groups mean_prop
## <fct> <fct> <chr> <dbl>
## 1 B DRY freshwater 0.0597
## 2 B DRY marine 0.00968
## 3 B DRY non-aquatic 0.424
## 4 B DRY partly_aquatic 0.352
## 5 B WET freshwater 0.0614
## 6 B WET marine 0.0139
## 7 B WET non-aquatic 0.454
## 8 B WET partly_aquatic 0.291
## 9 L DRY freshwater 0.0938
## 10 L DRY marine 0.00594
## # ℹ 14 more rowsphyla_summary$Substrate <- factor(phyla_summary$Substrate,
levels=c( "B", "L","S")) #reorder variables
phyla_summary$Wet.Dry <- factor(phyla_summary$Wet.Dry, levels= c("WET","DRY")) #reorder variables
phyla_summary$functional_groups <- factor(phyla_summary$functional_groups)
sublab<- c("Epilithic biofilms", "Leaf litter", "Benthic sediment")
names(sublab)<- c("B","L","S")
phyla_summary$functional_groups <- factor(phyla_summary$functional_groups, levels=c("freshwater","partly_aquatic","marine","non-aquatic"))
pal12 <- cols25(n=5) #set n= to the number of taxa you are plotting
#make stacked bar plot
phylum_bar <- ggplot(phyla_summary, aes(x = Wet.Dry, y = mean_prop, fill = functional_groups))+
geom_bar(stat = "identity", col=I("black")) +
scale_fill_manual(values=pal12)+
guides(fill=guide_legend(ncol=1))+
facet_wrap(~Substrate, labeller = labeller(Substrate=sublab), nrow=1, scales="free_x") +
labs(x="Substrate", y="Percentage of Identified ITS Sequence Copies",
fill="functional_groups")+
#scale_facet_discrete(labels=c('B'='Epilithic biofilms', 'L'='Leaf litter', 'S'='Benthic sediment', 'W'='Surface water'))+
theme(axis.text.x = element_text(angle=60, hjust=1), legend.position = "right")+
theme(text=element_text(size=16), #change font size of all text
axis.text=element_text(size=11), #change font size of axis text
axis.title=element_text(size=11), #change font size of axis titles
plot.title=element_text(size=11), #change font size of plot title
legend.text=element_text(size=11), #change font size of legend text
legend.title=element_text(size=11)) #change font size of legend title
phylum_bar
#c(180,100) *
# 0.0394 * # convert mm to inch
# 600 # convert to pixels
## [1] 4255.2 2364.0
plotout <- "Functional Barplot_aquatic_WetDry002_03162025.tiff"
agg_tiff(filename=plotout, width=3200, height=1800, units="px",
pointsize=10, res=600, compression="lzw", scaling=0.5)
phylum_bar
invisible(dev.off())GLLVM of functional groups
trait_prc<- relabun_func %>% dplyr::mutate( Litter_saprotrophic= `primary_lifestyle|litter_saprotroph`+
`Secondary_lifestyle|litter_saprotroph`,
Wood_saprotrophic= `primary_lifestyle|wood_saprotroph`+
`Secondary_lifestyle|wood_saprotroph`,
Plant_pathogenic= `primary_lifestyle|plant_pathogen`+
`Secondary_lifestyle|plant_pathogen`,
Mycoparasitic= `primary_lifestyle|mycoparasite`,
Decay_substrate_Leaf.Fruit.Seed=`Decay_substrate|leaf/fruit/seed`,
Decay_substrate_Wood=`Decay_substrate|wood`,
Decay_substrate_Roots=`Decay_substrate|roots`,
Decay_substrate_Soil=`Decay_substrate|soil`,
Decay_substrate_Algae=`Decay_substrate|algal_material`,
Aquatic=`Aquatic_habitat|aquatic`,
Non.aquatic=`Aquatic_habitat|non-aquatic`,
Freshwater=`Aquatic_habitat|freshwater`,
Endophytic=`Endophytic_interaction_capability|foliar_endophyte`+
`Endophytic_interaction_capability|root_endophyte`+
`Endophytic_interaction_capability|class1_clavicipitaceous_endophyte`+
`Endophytic_interaction_capability|root_endophyte_dark_septate`,
Non.endophytic=`Endophytic_interaction_capability|no_endophytic_capacity`,
Root_associated=`Endophytic_interaction_capability|root-associated`,#)
Ectomycorrhizal= `primary_lifestyle|ectomycorrhizal`,
Soil_saprotrophic= `primary_lifestyle|soil_saprotroph`,
Epiphytic= `primary_lifestyle|epiphyte`,
Lichen= `primary_lifestyle|lichenized`,
Lichen_parasite= `primary_lifestyle|lichen_parasite`)
tn<- ncol(trait_prc)-128
tn## [1] 19#View(func_relabun)
#relabun_func
trait_prct<- merge(samdftest,trait_prc[,128:ncol(trait_prc)],by="row.names")
row.names(trait_prct) <- trait_prct$Row.names
#trait_prct <- trait_prct %>% mutate_at(c('alpha.cent.wt', 'prc_wet'), ~(scale(.) %>% as.vector))
#trait_prctTraits GLLVM Leaf
library(mvabund)
library(gllvm)
library(tidyverse)
trait_L<- trait_prct[trait_prct$substrate=='L',]
trait_L<- trait_prct[trait_prct$substrate=='L',]
trait_L<- trait_L[!is.na(trait_L$alpha.cent.wt),]
traitemp_L<- trait_L[,97:116]
traitemp_L<-traitemp_L[,colSums(traitemp_L)>0]
#Reorder data table from most abundant to least abundant
traitemp_L <- traitemp_L[,order(colSums(-traitemp_L,na.rm=TRUE))]
traitemp_L <- traitemp_L[,apply(traitemp_L,2,function(x) sum(x > 0))>1]
trait_L <- merge(traitemp_L, samdftest,
by="row.names", all=FALSE)
rownames(trait_L) <- trait_L[,1]
trait_L <- trait_L[,-1]
trait_L$annual_percent_wet<- trait_L$prc_wet
#detach("package:linkET", unload=TRUE)
#trait_L$percentwet_11month<- trait_L$percentwet_bonus
L_abun<-trait_L[,1:19]
## select functional groups that make up at least 5% of reads
L_abun<-L_abun[,colMeans(L_abun)>1]
X <- as.matrix(trait_L[,c("annual_percent_wet","alpha.cent.wt","tempC_mean","burn_interval")])
#y<-as.numeric(as.matrix(L_abun))
y<-as.matrix(as.tibble(L_abun))
gllvm(y, family = "poisson") ## Call:
## gllvm(y = y, family = "poisson")
## family:
## [1] "poisson"
## method:
## [1] "VA"
##
## log-likelihood: -1492.286
## Residual degrees of freedom: 556
## AIC: 3072.572
## AICc: 3079.707
## BIC: 3266.037#gllvm(y, family = "negative.binomial")
## selecting poisson due to lower AIC
### lv 1 has lowest AIC
gllvm(y, X, family = "poisson", num.lv = 0,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1995)## Call:
## gllvm(y = y, X = X, formula = ~alpha.cent.wt + annual_percent_wet,
## family = "poisson", num.lv = 0, seed = 1995)
## family:
## [1] "poisson"
## method:
## [1] "VA"
##
## log-likelihood: -1503.346
## Residual degrees of freedom: 555
## AIC: 3096.691
## AICc: 3104.164
## BIC: 3294.553gllvm(y, X, family = "poisson", num.lv = 1,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1995)## Call:
## gllvm(y = y, X = X, formula = ~alpha.cent.wt + annual_percent_wet,
## family = "poisson", num.lv = 1, seed = 1995)
## family:
## [1] "poisson"
## method:
## [1] "VA"
##
## log-likelihood: -1487.238
## Residual degrees of freedom: 540
## AIC: 3094.476
## AICc: 3108.057
## BIC: 3358.292gllvm(y, X, family = "poisson", num.lv = 2,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1995)## Call:
## gllvm(y = y, X = X, formula = ~alpha.cent.wt + annual_percent_wet,
## family = "poisson", num.lv = 2, seed = 1995)
## family:
## [1] "poisson"
## method:
## [1] "VA"
##
## log-likelihood: -1483.841
## Residual degrees of freedom: 526
## AIC: 3115.682
## AICc: 3136.825
## BIC: 3441.055#fit_ord<- gllvm(y, family = "negative.binomial")
#ordiplot(fit_ord, biplot=TRUE, ind.spp=14)
fit_env <- gllvm(y, X, family = "poisson", num.lv = 1,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1995)
coefplot(fit_env, cex.ylab = 0.7, mar = c(4, 9, 2, 1), mfrow=c(1,1))
pdf("GLLVM_trait_L_06.12.2025.pdf",
)
coefplot(fit_env, cex.ylab = 0.7, mar = c(4, 12, 2, 1), mfrow=c(1,1))
dev.off()## quartz_off_screen
## 2Traits GLLVM Biofilm
library(mvabund)
library(gllvm)
library(tidyverse)
trait_B<- trait_prct[trait_prct$substrate=='B',]
trait_B<- trait_B[!is.na(trait_B$alpha.cent.wt),]
traitemp_B<- trait_B[,97:116]
traitemp_B<-traitemp_B[,colSums(traitemp_B)>0]
#Reorder data table from most abundant to least abundant
traitemp_B <- traitemp_B[,order(colSums(-traitemp_B,na.rm=TRUE))]
traitemp_B <- traitemp_B[,apply(traitemp_B,2,function(x) sum(x > 0))>1]
trait_B <- merge(traitemp_B, samdftest,
by="row.names", all=FALSE)
rownames(trait_B) <- trait_B[,1]
trait_B <- trait_B[,-1]
trait_B$annual_percent_wet<- trait_B$prc_wet
#detach("package:linkET", unload=TRUE)
#trait_B$percentwet_11month<- trait_B$percentwet_bonus
B_abun<-trait_B[,1:19]
## select functional groups that make up at least 5% of reads
B_abun<-B_abun[,colMeans(B_abun)>1]
X <- as.matrix(trait_B[,c("annual_percent_wet","alpha.cent.wt","tempC_mean","burn_interval")])
#y<-as.numeric(as.matrix(B_abun))
y<-as.matrix(as.tibble(B_abun))
gllvm(y, family = "poisson") ## Call:
## gllvm(y = y, family = "poisson")
## family:
## [1] "poisson"
## method:
## [1] "VA"
##
## log-likelihood: -1443.161
## Residual degrees of freedom: 559
## AIC: 2992.322
## AICc: 3002.58
## BIC: 3226.409gllvm(y, family = "negative.binomial") ## Call:
## gllvm(y = y, family = "negative.binomial")
## family:
## [1] "negative.binomial"
## method:
## [1] "VA"
##
## log-likelihood: -1471.335
## Residual degrees of freedom: 541
## AIC: 3084.669
## AICc: 3103.603
## BIC: 3398.257## selecting poisson due to lower AIC
### lv 1 has lowest AIC
gllvm(y, X, family = "poisson", num.lv = 0,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1995)## Call:
## gllvm(y = y, X = X, formula = ~alpha.cent.wt + annual_percent_wet,
## family = "poisson", num.lv = 0, seed = 1995)
## family:
## [1] "poisson"
## method:
## [1] "VA"
##
## log-likelihood: -1493.292
## Residual degrees of freedom: 558
## AIC: 3094.584
## AICc: 3105.248
## BIC: 3333.087gllvm(y, X, family = "poisson", num.lv = 1,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1995)## Call:
## gllvm(y = y, X = X, formula = ~alpha.cent.wt + annual_percent_wet,
## family = "poisson", num.lv = 1, seed = 1995)
## family:
## [1] "poisson"
## method:
## [1] "VA"
##
## log-likelihood: -1447.652
## Residual degrees of freedom: 540
## AIC: 3039.304
## AICc: 3058.807
## BIC: 3357.309gllvm(y, X, family = "poisson", num.lv = 2,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1995)## Call:
## gllvm(y = y, X = X, formula = ~alpha.cent.wt + annual_percent_wet,
## family = "poisson", num.lv = 2, seed = 1995)
## family:
## [1] "poisson"
## method:
## [1] "VA"
##
## log-likelihood: -1426.64
## Residual degrees of freedom: 523
## AIC: 3031.28
## AICc: 3061.969
## BIC: 3424.369#fit_ord<- gllvm(y, family = "negative.binomial")
#ordiplot(fit_ord, biplot=TRUE, ind.spp=14)
fit_env <- gllvm(y, X, family = "poisson", num.lv = 1,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1995)
coefplot(fit_env, cex.ylab = 0.7, mar = c(4, 9, 2, 1), mfrow=c(1,1))
pdf("GLLVM_trait_B_06.12.2025.pdf",
)
coefplot(fit_env, cex.ylab = 0.7, mar = c(4, 12, 2, 1), mfrow=c(1,1))
dev.off()## quartz_off_screen
## 2Sediment
library(mvabund)
library(gllvm)
library(tidyverse)
trait_S<- trait_prct[trait_prct$substrate=='S',]
trait_S<- trait_S[!is.na(trait_S$alpha.cent.wt),]
traitemp_S<- trait_S[,97:116]
traitemp_S<-traitemp_S[,colSums(traitemp_S)>0]
#Reorder data table from most abundant to least abundant
traitemp_S <- traitemp_S[,order(colSums(-traitemp_S,na.rm=TRUE))]
traitemp_S <- traitemp_S[,apply(traitemp_S,2,function(x) sum(x > 0))>1]
trait_S <- merge(traitemp_S, samdftest,
by="row.names", all=FALSE)
rownames(trait_S) <- trait_S[,1]
trait_S <- trait_S[,-1]
trait_S$annual_percent_wet<- trait_S$prc_wet
#detach("package:linkET", unload=TRUE)
#trait_S$percentwet_11month<- trait_S$percentwet_bonus
S_abun<-trait_S[,1:19]
## select functional groups that make up at least 5% of reads
S_abun<-S_abun[,colMeans(S_abun)>1]
X <- as.matrix(trait_S[,c("annual_percent_wet","alpha.cent.wt","tempC_mean","burn_interval")])
#y<-as.numeric(as.matrix(S_abun))
y<-as.matrix(as.tibble(S_abun))
gllvm(y, family = "poisson") ## Call:
## gllvm(y = y, family = "poisson")
## family:
## [1] "poisson"
## method:
## [1] "VA"
##
## log-likelihood: -1571.522
## Residual degrees of freedom: 593
## AIC: 3237.045
## AICc: 3244.666
## BIC: 3446.734gllvm(y, family = "negative.binomial") ## Call:
## gllvm(y = y, family = "negative.binomial")
## family:
## [1] "negative.binomial"
## method:
## [1] "VA"
##
## log-likelihood: -1604.561
## Residual degrees of freedom: 577
## AIC: 3335.123
## AICc: 3349.123
## BIC: 3616.195## selecting poisson due to lower AIC
### lv 1 has lowest AIC
gllvm(y, X, family = "poisson", num.lv = 0,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1995)## Call:
## gllvm(y = y, X = X, formula = ~alpha.cent.wt + annual_percent_wet,
## family = "poisson", num.lv = 0, seed = 1995)
## family:
## [1] "poisson"
## method:
## [1] "VA"
##
## log-likelihood: -1593.036
## Residual degrees of freedom: 592
## AIC: 3282.072
## AICc: 3290.032
## BIC: 3496.223gllvm(y, X, family = "poisson", num.lv = 1,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1995)## Call:
## gllvm(y = y, X = X, formula = ~alpha.cent.wt + annual_percent_wet,
## family = "poisson", num.lv = 1, seed = 1995)
## family:
## [1] "poisson"
## method:
## [1] "VA"
##
## log-likelihood: -1551.05
## Residual degrees of freedom: 576
## AIC: 3230.101
## AICc: 3244.57
## BIC: 3515.635gllvm(y, X, family = "poisson", num.lv = 2,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1995)## Call:
## gllvm(y = y, X = X, formula = ~alpha.cent.wt + annual_percent_wet,
## family = "poisson", num.lv = 2, seed = 1995)
## family:
## [1] "poisson"
## method:
## [1] "VA"
##
## log-likelihood: -1551.05
## Residual degrees of freedom: 561
## AIC: 3260.101
## AICc: 3282.672
## BIC: 3612.557#fit_ord<- gllvm(y, family = "negative.binomial")
#ordiplot(fit_ord, biplot=TRUE, ind.spp=14)
fit_env <- gllvm(y, X, family = "poisson", num.lv = 1,
formula = ~ alpha.cent.wt+annual_percent_wet
, seed = 1995)
coefplot(fit_env, cex.ylab = 0.7, mar = c(4, 9, 2, 1), mfrow=c(1,1))
pdf("GLLVM_trait_S_06.12.2025.pdf",
)
coefplot(fit_env, cex.ylab = 0.7, mar = c(4, 12, 2, 1), mfrow=c(1,1))
dev.off()## quartz_off_screen
## 2