Angie poop analysis
Georgia Harrison
2023-10-30
Data analysis for Langevin manuscript
impact of avian guano on cliffs
overview of analysis steps:
- Import and wrangle data
- Calculated indiciators i.e. richness and diversity
- Use paired t tests to test for differences in N and indicators by nest/no nest
- NMDS so assess plant community composition
- Rank abundance species lists and indicator species analysis
notes to future self -
core of analysis was with only cliff face plots.
ecological indicators were calculated for each sammple quadrat, then averaged to the plot level (2 quad per plot)
NMDS was with only plots below nests
Step 0 get set up
library required packages
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors#install.packages("vegan")
library(vegan)## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.6-4#install.packages("BiodiversityR")
library(BiodiversityR)## Loading required package: tcltk
## BiodiversityR 2.15-4: Use command BiodiversityRGUI() to launch the Graphical User Interface;
## to see changes use BiodiversityRGUI(changeLog=TRUE, backward.compatibility.messages=TRUE)#install.packages("indicspecies")
library(indicspecies)
#install.packages("ggpubr")
library(ggpubr)
#install.packages("RColorBrewer")
library(RColorBrewer)Also set working directory.
Step 1 import and wrangle data
read in the core files we will be working with
cover = quad level cover for all species sp_code = species code for each of the species #
cover = read.csv("Raw data input/All Quadrats - Short Cod Master.csv", header = T)
sp_codes = read.csv("Raw data input/Species Codes - Species Codes.csv", header = T)Remove quads with no veg cover
# cover$total_cover <- cover$BryoC + cover$LichC + cover$VascC
# summary(cover$total_cover)
#
# cover_low <- cover %>%
# filter(total_cover < 1)
#
# cover <- cover %>%
# filter(total_cover > 1)add meaningful cols for abio data
i.e. translate cliff position to a word value instead of a number. can always go back to number later.
# first tank the currrent columns and rename them
cover <- cover %>%
mutate(
quad_num = Transect.Code,
Site_num = Site,
plot_num = plot,
clfpos_num = clfpos,
L3R4_num = L3R4)
# site - translate numbers to meaningful values
# SNA (Snake Mountain) # 1
# BUZ (Buzzards Roost) # 2
# PIL (Pilot Mountain # 3
# TAB (Table Rock) # 4
# BLC (Big Lost Cove) # 5
# CHM (Chimney Rock) # 6
# NRS (New River State Park) # 7
# GOA (Goat Rock) # 8
# BLC2 (Big Lost Cove site 2) # 9
cover <- cover %>%
mutate(Site_name = case_when(
Site_num == 1 ~ "SNA", Site_num == 2 ~ "BUZ", Site_num == 3 ~ "PIL",
Site_num == 4 ~ "TAB", Site_num == 5 ~ "BLC", Site_num == 6 ~ "CHM",
Site_num == 7 ~ "NRS",Site_num == 8 ~ "GOA", Site_num == 9 ~ "BLC2",
TRUE ~ as.character(Site_num) # Handle other values
))
# quad pairs
# for L3R4 make 3= L; 4 = R
cover <- cover %>%
mutate(Quad_pos = case_when(
L3R4_num == 3 ~ "L",
L3R4_num == 4 ~ "R",
TRUE ~ as.character(L3R4) # Handle other values
))
#for clfpos, create new column called cliff position based on clfpos
# 8 = Edge, # 1, 2, 3, 4, = Face, # 15 = Talus
cover <- cover %>%
mutate(Cliff_Position = case_when(
clfpos == 8 ~ "Edge",
clfpos %in% c(1, 2, 3, 4) ~ "Face",
clfpos == 15 ~ "Talus",
TRUE ~ as.character(clfpos)))
# then assign plot_location based on clfpos
# Edge = 8
# P1 1, # P2 2, # P3 3, # P4 4, # TALUS 15
cover <- cover %>%
mutate(Plot_Location = case_when(
clfpos == 8 ~ "Edge", clfpos == 1 ~ "P1",
clfpos == 2 ~ "P2", clfpos == 3 ~ "P3",
clfpos == 4 ~ "P4", clfpos == 15 ~ "TALUS",
TRUE ~ as.character(clfpos) # Handle other values
))
# NP1P2 is poop or no poop
#1 = control #2 = nest
cover <- cover %>%
mutate(Control_or_treatment =
ifelse(NP1P2 == 1, "Control",
ifelse(NP1P2 == 2, "Nest",as.character(NP1P2))))
#Nest Position: This is the postion of the sample (quadrat) relative to the nest (or their equivalent on the control)
cover <- cover %>%
mutate(Nest_Position = case_when(
cover$plot %in% c(
2, 3, 4, 7, 8, 9, 10, 13, 14, 15, 17, 18, 19, 20, 23, 24, 25, 29, 30, 31, 34, 35, 38, 39, 40, 41, 43, 44, 45, 48, 49, 50, 53, 54, 55, 56, 58, 59, 60, 63, 67, 68, 71, 72, 73, 76, 77, 78, 79, 82, 83, 84, 85, 88, 89, 90
) ~ "below_nest",
TRUE ~ "above_nest"))filter out edge, and talus plots also remove the few above nest plots
#save a version of cover with the edge and talus plots
cover_OG_withEdgeTalus <- cover
cover <- cover %>%filter(Cliff_Position != "Edge")
cover <- cover %>%filter(Cliff_Position != "Talus")Create columns for unique ID based on transect, plot or quad
# create unique identified to each quad - make this the first column
# merge site_name, Control_or_treament, plot number, quad number
# seperate values by underscores
cover <- cover %>%
mutate(Quad_Identifier =
paste(Site_name, Control_or_treatment, plot_num, quad_num, Quad_pos, sep = "_")) %>%
select(Quad_Identifier, everything())
# create unique identified for each plot
# merge site_name, Control_or_treament, plot number,
cover <- cover %>%
mutate(Plot_Identifier =
paste(Site_name, Control_or_treatment, plot_num, sep = "_")) %>%
select(Plot_Identifier, everything())
# identify transect pairs
cover <- cover %>%
mutate(T_Pair_identifer = paste(Site_name, Cliff_Position, sep = "_")) %>%
select(T_Pair_identifer, everything())
#identify transect names
cover <- cover %>%
mutate(Transect_name = paste(Site_name, Control_or_treatment, sep = "_")) %>%
select(Transect_name, everything())Create a plot metadata / abio sheet for future reference
also seperate out cover only and link by quad and plot ID
# select these cols
plot_metadata <- cover %>%
select(
Transect_name, T_Pair_identifer, Quad_Identifier, Plot_Identifier,
Site_name, Quad_pos, Cliff_Position,
Plot_Location,Nest_Position, Control_or_treatment,
plot_num,quad_num)
# select those same cols and add on
plot_enviro <- cover %>%
select(
Transect_name, T_Pair_identifer, Quad_Identifier, Plot_Identifier,
Site_name, Quad_pos, Cliff_Position,
Plot_Location,Nest_Position, Control_or_treatment,
plot_num,quad_num,
Heter, SL, AS, wpn, wpw, transdist, NH4, NO31, NO32)
# create subset data with just species cover values and Quad ID to use for future indicies calculations
cover_all <- cover %>%
select(Quad_Identifier, Ssp1:Ssp192)summarize number of plots, transects, etc for methods
# Calculate counts of plots, transects, and quadrats
count_summary <- cover %>%
summarise(
Number_of_Plots = n_distinct(Plot_Identifier),
Number_of_Transects = n_distinct(Quad_Identifier),
Number_of_Quadrats = n_distinct(quad_num))
count_summary## Number_of_Plots Number_of_Transects Number_of_Quadrats
## 1 62 122 122# Calculate counts by "Cliff_Position" and "Control_or_treatment"
count_by_cliff_control <- plot_metadata %>%
group_by(Control_or_treatment, Cliff_Position) %>%
summarise(Coun_quadst = n_distinct(quad_num))## `summarise()` has grouped output by 'Control_or_treatment'. You can override
## using the `.groups` argument.count_by_cliff_control## # A tibble: 2 × 3
## # Groups: Control_or_treatment [2]
## Control_or_treatment Cliff_Position Coun_quadst
## <chr> <chr> <int>
## 1 Control Face 62
## 2 Nest Face 60# Calculate counts by "Cliff_Position" and "Control_or_treatment"
count_by_plot_control <- plot_metadata %>%
group_by(Control_or_treatment, Cliff_Position, Plot_Location) %>%
summarise(Coun_quadst = n_distinct(quad_num))## `summarise()` has grouped output by 'Control_or_treatment', 'Cliff_Position'.
## You can override using the `.groups` argument.count_by_plot_control## # A tibble: 8 × 4
## # Groups: Control_or_treatment, Cliff_Position [2]
## Control_or_treatment Cliff_Position Plot_Location Coun_quadst
## <chr> <chr> <chr> <int>
## 1 Control Face P1 18
## 2 Control Face P2 18
## 3 Control Face P3 18
## 4 Control Face P4 8
## 5 Nest Face P1 18
## 6 Nest Face P2 18
## 7 Nest Face P3 16
## 8 Nest Face P4 8# Calculate counts sucessful traps
# Count the number of "ND" values for NH4 by "Control_or_treatment"
count_ND_NH4 <- plot_enviro %>%
group_by(Control_or_treatment, Cliff_Position) %>%
summarise(ND_NH4 = sum(NH4 == "ND"))## `summarise()` has grouped output by 'Control_or_treatment'. You can override
## using the `.groups` argument.# Count the number of "ND" values for NO32 by "Control_or_treatment"
count_ND_NO32 <- plot_enviro %>%
group_by(Control_or_treatment, Cliff_Position) %>%
summarise(ND_NO32 = sum(NO32 == "ND"))## `summarise()` has grouped output by 'Control_or_treatment'. You can override
## using the `.groups` argument.# Merge the two data frames to combine the counts
count_ND_NH4_NO32 <- full_join(count_ND_NH4, count_ND_NO32, by = c("Control_or_treatment", "Cliff_Position"))
count_ND_NH4_NO32 <- full_join(count_ND_NH4_NO32, count_by_cliff_control, by = c("Control_or_treatment", "Cliff_Position"))
# Print the resulting data frame
count_ND_NH4_NO32## # A tibble: 2 × 5
## # Groups: Control_or_treatment [2]
## Control_or_treatment Cliff_Position ND_NH4 ND_NO32 Coun_quadst
## <chr> <chr> <int> <int> <int>
## 1 Control Face 24 24 62
## 2 Nest Face 24 24 60write.csv(count_ND_NH4_NO32, "Intermediate data files/count_of_quadrats_by_trt_trapsucess.csv", row.names = F)Subset quad data into analysis groups: all taxa, lichen, bryo, plants
also remove the algae
# transofrm the cover_all to long format for subsetting
cover_all_long <- cover_all %>%
pivot_longer(cols = starts_with("Ssp"),
names_to = "species_code",
values_to = "species_cover")
# Remove rows with species_cover equal to 0
cover_all_long <- cover_all_long %>%
filter(species_cover != 0)
# prepare to assign taxa to each species
# Remove spaces and periods from the "Number" column in the sp_codes data frame
sp_codes <- sp_codes %>%
mutate(Number = gsub("[ .]", "", Number))
# Remove spaces and periods from the "species_code" column in the cover_all_long data frame
cover_all_long <- cover_all_long %>%
mutate(species_code = gsub("[ .]", "", species_code))
# Join the cleaned data frames
cover_all_long_with_taxa <- cover_all_long %>%
left_join(sp_codes, by = c("species_code" = "Number"))
# summary(as.factor(cover_all_long_with_taxa$taxa)) # cool
# remove the algae
cover_all_long_with_taxa <- cover_all_long_with_taxa %>%
filter(taxa != "A")Subset data based on taxa, spread back to wide
alltaxa_long <- cover_all_long_with_taxa
# Subset data based on taxa
lichens_long <- alltaxa_long %>% filter(taxa == "L")
plants_long <- alltaxa_long %>% filter(taxa == "V")
bryo_long <- alltaxa_long %>% filter(taxa == "B")
# Spread back to wide format
alltaxa_wide <- alltaxa_long %>%
select(-taxa,-Species, -Notes) %>%
pivot_wider(names_from = species_code, values_from = species_cover,
values_fill = 0)
lichens_wide <- lichens_long %>%
select(-taxa,-Species, -Notes) %>%
pivot_wider(names_from = species_code, values_from = species_cover,
values_fill = 0)
plants_wide <- plants_long %>%
select(-taxa,-Species, -Notes) %>%
pivot_wider(names_from = species_code, values_from = species_cover,
values_fill = 0)
bryo_wide <- bryo_long %>%
select(-taxa,-Species, -Notes) %>%
pivot_wider(names_from = species_code, values_from = species_cover,
values_fill = 0)Step 2 calculate indicators
Calculate cover, eveness, richness and divestiy for each funcational group at each quad
# create output df
indic <- alltaxa_wide %>% select(Quad_Identifier)
l_indic <- lichens_wide %>% select(Quad_Identifier)
p_indic <- plants_wide %>% select(Quad_Identifier)
b_indic <- bryo_wide %>% select(Quad_Identifier)
# turn wide format species cover to matrix by assigning the quad ID col to row names
all.matrix <- alltaxa_wide %>% column_to_rownames(var = "Quad_Identifier")
lichen.matrix <- lichens_wide %>% column_to_rownames(var = "Quad_Identifier")
plant.matrix <- plants_wide %>% column_to_rownames(var = "Quad_Identifier")
bryo.matrix <- bryo_wide %>% column_to_rownames(var = "Quad_Identifier")
### calculate indicators for each taxa group
# all
indic$All_Richness <- rowSums(all.matrix > 0)
indic$All_Shannon <- diversity(all.matrix)
indic$All_Simpson <- diversity(all.matrix, index = "simpson")
indic$All_Cover <- rowSums(all.matrix)
indic$All_Evenness <- indic$All_Shannon / log(indic$All_Richness)
# lichens
l_indic$Lichen_Richness <- rowSums(lichen.matrix > 0)
l_indic$Lichen_Shannon <- diversity(lichen.matrix)
l_indic$Lichen_Simpson <- diversity(lichen.matrix, index = "simpson")
l_indic$Lichen_Cover <- rowSums(lichen.matrix)
l_indic$Lichen_Evenness <- l_indic$Lichen_Shannon / log(l_indic$Lichen_Richness)
# plants
p_indic$Plant_Richness <- rowSums(plant.matrix > 0)
p_indic$Plant_Shannon <- diversity(plant.matrix)
p_indic$Plant_Simpson <- diversity(plant.matrix, index = "simpson")
p_indic$Plant_Cover <- rowSums(plant.matrix)
p_indic$Plant_Evenness <- p_indic$Plant_Shannon / log(p_indic$Plant_Richness)
# bryos
b_indic$Bryo_Richness <- rowSums(bryo.matrix > 0)
b_indic$Bryo_Shannon <- diversity(bryo.matrix)
b_indic$Bryo_Simpson <- diversity(bryo.matrix, index = "simpson")
b_indic$Bryo_Cover <- rowSums(bryo.matrix)
b_indic$Bryo_Evenness <- b_indic$Bryo_Shannon / log(b_indic$Bryo_Richness)
# merge all the indicator df by the row name which is Quad_Identifier
# Merge data frames based on Quad_Identifier
merged_indic <- merge(indic, l_indic, by = "Quad_Identifier", all = TRUE)
merged_indic <- merge(merged_indic, p_indic, by = "Quad_Identifier", all = TRUE)
merged_indic <- merge(merged_indic, b_indic, by = "Quad_Identifier", all = TRUE)
# If there are NAs, replace them with 0
merged_indic[is.na(merged_indic)] <- 0
# Create data frames for the two new plots with indicator values filled with 0
new_plots <- data.frame(
Quad_Identifier = c("GOA_nest_76_151_L", "BLC2_control_91_182_R"),
All_Richness = 0, All_Shannon = 0, All_Simpson = 0, All_Cover = 0, All_Evenness = 0,
Lichen_Richness = 0, Lichen_Shannon = 0, Lichen_Simpson = 0, Lichen_Cover = 0, Lichen_Evenness = 0,
Plant_Richness = 0, Plant_Shannon = 0, Plant_Simpson = 0, Plant_Cover = 0, Plant_Evenness = 0,
Bryo_Richness = 0, Bryo_Shannon = 0, Bryo_Simpson = 0, Bryo_Cover = 0, Bryo_Evenness = 0)
# Concatenate the new_plots with merged_indic
merged_indic <- rbind(merged_indic, new_plots)
# add in metadata
merged_indic <- merged_indic %>%
left_join(plot_metadata, by = "Quad_Identifier")
quad_level_indicators <- merged_indic
#save this as csv file
#write.csv(merged_indic, "Intermediate data files/all_indicators.csv")Average indicators to plot level
# add a plot-level indicator
head(merged_indic)## Quad_Identifier All_Richness All_Shannon All_Simpson All_Cover
## 1 BLC_Control_48_95_L 6 1.363419 0.6831276 27.0
## 2 BLC_Control_48_96_R 8 1.620561 0.7387958 47.0
## 3 BLC_Control_49_97_R 11 1.357773 0.5954556 77.2
## 4 BLC_Control_49_98_L 6 1.164674 0.5827219 65.0
## 5 BLC_Control_50_100_L 7 1.246507 0.6286003 58.2
## 6 BLC_Control_50_99_R 6 1.152739 0.5558633 57.0
## All_Evenness Lichen_Richness Lichen_Shannon Lichen_Simpson Lichen_Cover
## 1 0.7609385 5 1.118541 0.5467128 17.0
## 2 0.7793249 7 1.550585 0.7277883 46.0
## 3 0.5662354 9 1.252040 0.5740048 75.2
## 4 0.6500169 6 1.164674 0.5827219 65.0
## 5 0.6405779 7 1.246507 0.6286003 58.2
## 6 0.6433560 5 0.999133 0.5082305 54.0
## Lichen_Evenness Plant_Richness Plant_Shannon Plant_Simpson Plant_Cover
## 1 0.6949883 1 0 0 10
## 2 0.7968431 1 0 0 1
## 3 0.5698281 1 0 0 1
## 4 0.6500169 0 0 0 0
## 5 0.6405779 0 0 0 0
## 6 0.6207963 0 0 0 0
## Plant_Evenness Bryo_Richness Bryo_Shannon Bryo_Simpson Bryo_Cover
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 1 0 0 1
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 1 0 0 3
## Bryo_Evenness Transect_name T_Pair_identifer Plot_Identifier Site_name
## 1 0 BLC_Control BLC_Face BLC_Control_48 BLC
## 2 0 BLC_Control BLC_Face BLC_Control_48 BLC
## 3 0 BLC_Control BLC_Face BLC_Control_49 BLC
## 4 0 BLC_Control BLC_Face BLC_Control_49 BLC
## 5 0 BLC_Control BLC_Face BLC_Control_50 BLC
## 6 0 BLC_Control BLC_Face BLC_Control_50 BLC
## Quad_pos Cliff_Position Plot_Location Nest_Position Control_or_treatment
## 1 L Face P1 below_nest Control
## 2 R Face P1 below_nest Control
## 3 R Face P2 below_nest Control
## 4 L Face P2 below_nest Control
## 5 L Face P3 below_nest Control
## 6 R Face P3 below_nest Control
## plot_num quad_num
## 1 48 95
## 2 48 96
## 3 49 97
## 4 49 98
## 5 50 100
## 6 50 99merged_indic$Plot_Identifier <- as.factor(merged_indic$Plot_Identifier)
# Summarize indicator values to the plot level
plot_average_indic <- merged_indic %>%
group_by(Plot_Identifier) %>%
summarize(
All_Richness = mean(All_Richness),
All_Shannon = mean(All_Shannon),
All_Simpson = mean(All_Simpson),
All_Cover = mean(All_Cover),
All_Evenness = mean(All_Evenness),
Lichen_Richness = mean(Lichen_Richness),
Lichen_Shannon = mean(Lichen_Shannon),
Lichen_Simpson = mean(Lichen_Simpson),
Lichen_Cover = mean(Lichen_Cover),
Lichen_Evenness = mean(Lichen_Evenness),
Plant_Richness = mean(Plant_Richness),
Plant_Shannon = mean(Plant_Shannon),
Plant_Simpson = mean(Plant_Simpson),
Plant_Cover = mean(Plant_Cover),
Plant_Evenness = mean(Plant_Evenness),
Bryo_Richness = mean(Bryo_Richness),
Bryo_Shannon = mean(Bryo_Shannon),
Bryo_Simpson = mean(Bryo_Simpson),
Bryo_Cover = mean(Bryo_Cover),
Bryo_Evenness = mean(Bryo_Evenness)
)
# Keep only one row per unique Plot_Identifier
plot_metadata_unique <- plot_metadata %>% distinct(Plot_Identifier, .keep_all = TRUE)
plot_average_indic <- plot_average_indic %>%
left_join(plot_metadata_unique, by = "Plot_Identifier")
# remove rows with NA in the control col
# Remove rows with NA in the Control_or_treatment column
plot_average_indic <- plot_average_indic[!is.na(plot_average_indic$Control_or_treatment), ]
# Check which Plot_Identifier are not common between plot_metadata and plot_average_indic
write.csv(plot_average_indic, "Intermediate data files/plot_level_indicators.csv")Step 3 stats tests and figs for paired t tests
Tests for differences in N
average plot level N
# Create N_enviro by filtering rows where NH4 is not equal to "ND"
NH4_enviro <- plot_enviro %>%
filter(NH4 != "ND")
N03_enviro <- plot_enviro %>%
filter(NO32 != "ND")Now we need to ensure that only pairs with both sucessful trap data are compared means by plot and transect levels
# Convert NH4 and NO32 to numeric
NH4_enviro$NH4 <- as.numeric(NH4_enviro$NH4)
N03_enviro$NO32 <- as.numeric(N03_enviro$NO32)
#calclate transect means
transect_nh4 <- NH4_enviro %>%
group_by(Transect_name) %>%
summarize(transect_mean_nh4 = mean(NH4))
t_nh4 <- transect_nh4 %>%
mutate(Transect_name = as.character(Transect_name)) %>%
separate(Transect_name, into = c("site", "control_or_treatment"), sep = "_")
t_nh4$Transect_name <- transect_nh4$Transect_name
transect_no3 <- N03_enviro %>%
group_by(Transect_name) %>%
summarize(transect_mean_no3 = mean(NO32))
t_no3 <- transect_no3 %>%
mutate(Transect_name = as.character(Transect_name)) %>%
separate(Transect_name, into = c("site", "control_or_treatment"), sep = "_")
t_no3$Transect_name <- transect_no3$Transect_name
# average N to plot levels
p_NH4 <- NH4_enviro %>%
group_by(Plot_Identifier) %>%
summarize(Avg_NH4 = mean(NH4))
plot_NH4 <- p_NH4 %>%
mutate(Plot_Identifier = as.character(Plot_Identifier)) %>%
separate(Plot_Identifier, into = c("site", "control_or_treatment", "plot_num"), sep = "_")
plot_NH4$Plot_Identifier <- p_NH4$Plot_Identifier
p_NO3 <- N03_enviro %>%
group_by(Plot_Identifier) %>%
summarize(avg_NO32 = mean(NO32))
plot_NO3 <- p_NO3 %>%
mutate(Plot_Identifier = as.character(Plot_Identifier)) %>%
separate(Plot_Identifier, into = c("site", "control_or_treatment", "plot_num"), sep = "_")
plot_NO3$Plot_Identifier <- p_NO3$Plot_IdentifierTest for differences in N At both plot and transect level using paired and unpaired t tests
# need to remove transects which do not have pairs
t_no3 <- t_no3 %>% filter(site != "CHM" & site != "PIL")
# need to remove transects which do not have pairs
t_nh4 <- t_nh4 %>% filter(site != "PIL")
# Test for differences in transect-level data (paired)
no3_ttest_transect_paired <- t.test(transect_mean_no3 ~ control_or_treatment, data = t_no3, paired = TRUE)
no3_ttest_transect_unpaired <- t.test(transect_mean_no3 ~ control_or_treatment, data = t_no3, paired = FALSE)
nh4_ttest_transect_paired <- t.test(transect_mean_nh4 ~ control_or_treatment, data = t_nh4, paired = TRUE)
nh4_ttest_transect_unpaired <- t.test(transect_mean_nh4 ~ control_or_treatment, data = t_nh4, paired = FALSE)
# Create data frames for transect-level results
transect_results_paired <- data.frame(
Variable = c("NO3", "NH4"),
Test = "Paired t-test",
Mean_Control = c(no3_ttest_transect_paired$estimate[1], nh4_ttest_transect_paired$estimate[1]),
mean_nest = c(no3_ttest_transect_paired$estimate[2], nh4_ttest_transect_paired$estimate[2]),
P_Value = c(no3_ttest_transect_paired$p.value, nh4_ttest_transect_paired$p.value)
)
transect_results_unpaired <- data.frame(
Variable = c("NO3", "NH4"),
Test = "Unpaired t-test",
Mean_Control = c(no3_ttest_transect_unpaired$estimate[1], nh4_ttest_transect_unpaired$estimate[1]),
mean_nest = c(no3_ttest_transect_unpaired$estimate[2], nh4_ttest_transect_unpaired$estimate[2]),
P_Value = c(no3_ttest_transect_unpaired$p.value, nh4_ttest_transect_unpaired$p.value)
)
# Test for differences in plot-level data (paired)
no3_ttest_plot_paired <- t.test(avg_NO32 ~ control_or_treatment, data = plot_NO3, paired = TRUE)
no3_ttest_plot_unpaired <- t.test(avg_NO32 ~ control_or_treatment, data = plot_NO3, paired = FALSE)
nh4_ttest_plot_paired <- t.test(Avg_NH4 ~ control_or_treatment, data = plot_NH4, paired = TRUE)
nh4_ttest_plot_unpaired <- t.test(Avg_NH4 ~ control_or_treatment, data = plot_NH4, paired = FALSE)
# Create data frames for plot-level results
plot_results_paired <- data.frame(
Variable = c("NO3", "NH4"),
Test = "Paired t-test",
Mean_Control = c(no3_ttest_plot_paired$estimate[1], nh4_ttest_plot_paired$estimate[1]),
mean_nest = c(no3_ttest_plot_paired$estimate[2], nh4_ttest_plot_paired$estimate[2]),
P_Value = c(no3_ttest_plot_paired$p.value, nh4_ttest_plot_paired$p.value)
)
plot_results_unpaired <- data.frame(
Variable = c("NO3", "NH4"),
Test = "Unpaired t-test",
Mean_Control = c(no3_ttest_plot_unpaired$estimate[1], nh4_ttest_plot_unpaired$estimate[1]),
mean_nest = c(no3_ttest_plot_unpaired$estimate[2], nh4_ttest_plot_unpaired$estimate[2]),
P_Value = c(no3_ttest_plot_unpaired$p.value, nh4_ttest_plot_unpaired$p.value)
)
######## save the output tables
# Combine the results into two output tables
N_results_paired <- rbind(transect_results_paired, plot_results_paired)
N_results_unpaired <- rbind(transect_results_unpaired, plot_results_unpaired)
# Combine the results with the 'Test Type' column
N_test_combined_results <- bind_rows(N_results_paired, N_results_unpaired)
N_test_combined_results## Variable Test Mean_Control mean_nest P_Value
## 1 NO3 Paired t-test -1.980319e-06 NA 0.34936726
## 2 NH4 Paired t-test -5.502129e-07 NA 0.07889930
## 3 NO3 Paired t-test -1.111711e-06 NA 0.27665261
## 4 NH4 Paired t-test -3.598220e-07 NA 0.06100970
## 5 NO3 Unpaired t-test 1.972884e-06 3.953204e-06 0.23881810
## 6 NH4 Unpaired t-test 3.731589e-07 9.233719e-07 0.05506528
## 7 NO3 Unpaired t-test 2.003926e-06 3.115636e-06 0.29226573
## 8 NH4 Unpaired t-test 4.027068e-07 7.625289e-07 0.06017415# note that for the paired tests, only mean differences are reported
write.csv(N_test_combined_results, "Output figs and tables/N ttest results.csv", row.names = F)Figure for N
# Convert NH4 data from g to ug
NH4_enviro$NH4_ug <- NH4_enviro$NH4 * 1e6
# Calculate min and max values for NH4_ug
nh4_min <- min(NH4_enviro$NH4_ug)
nh4_max <- max(NH4_enviro$NH4_ug)
# Convert NO3 data from g to ug
N03_enviro$NO32_ug <- N03_enviro$NO32 * 1e6
# Calculate min and max values for NO32_ug
no3_min <- min(N03_enviro$NO32_ug)
no3_max <- max(N03_enviro$NO32_ug)
# Define the Dark2 color palette with as many colors as needed
dark2_palette <- brewer.pal(8, "Dark2") # You can adjust the number of colors as needed
# Create a box plot of NH4 levels with customized y-axis limits and ug labels
nh4_fig <- ggplot(NH4_enviro, aes(x = Control_or_treatment, y = NH4_ug, fill = Control_or_treatment)) +
geom_boxplot() +
labs(x = "Control or Treatment", y = expression("NH"[4] ~ (ug/cm^2))) +
scale_y_continuous(labels = scales::number_format(scale = 1e-6)) +
scale_fill_manual(values = dark2_palette) + # Use the Dark2 color palette
theme_classic() +
ylim(0, 15) +
theme(legend.position = "none") # Remove the legend## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.nh4_fig## Warning: Removed 2 rows containing non-finite values (`stat_boxplot()`).
# Create a box plot of NO3 levels with customized y-axis limits and ug labels
no3_fig <- ggplot(N03_enviro, aes(x = Control_or_treatment, y = NO32_ug, fill = Control_or_treatment)) +
geom_boxplot() +
labs(x = "Control or Treatment", y = expression("NO"[3] ~ (ug/cm^2))) +
scale_y_continuous(labels = scales::number_format(scale = 1e-6)) +
scale_fill_manual(values = dark2_palette) + # Use the Dark2 color palette
theme_classic() +
ylim(0, 15) +
theme(legend.position = "none") # Remove the legend## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.no3_fig
# Combine the NH4 and NO3 box plots
combined_n_plot <- ggarrange(
nh4_fig, no3_fig,
ncol = 2, nrow = 1,
common.legend = FALSE)## Warning: Removed 2 rows containing non-finite values (`stat_boxplot()`).# Display the combined plot
combined_n_plot
# Save the combined plot as an image
ggsave("Output figs and tables/combined_n_plot.png", plot = combined_n_plot, width = 8, height = 4, dpi = 300)Summary table with means and SE
tests for differences in plot level indicators by poop
I think there is an issue with there being some empty data or plots??
# Define the indicator variables
indicator_variables <- c("All_Shannon", "All_Simpson", "All_Cover", "All_Evenness",
"Lichen_Richness", "Lichen_Shannon", "Lichen_Simpson", "Lichen_Cover", "Lichen_Evenness",
"Plant_Richness", "Plant_Shannon", "Plant_Simpson", "Plant_Cover", "Plant_Evenness",
"Bryo_Richness", "Bryo_Simpson", "Bryo_Cover", "Bryo_Evenness")
# Create a data frame to store the paired t-test results
results_df_paired <- data.frame(Variable = indicator_variables,
p_value = numeric(length(indicator_variables)),
t_statistic = numeric(length(indicator_variables)),
degrees_of_freedom = numeric(length(indicator_variables)),
conf_interval = character(length(indicator_variables)),
mean_difference = numeric(length(indicator_variables)))
# Create a data frame to store the non-paired t-test results
results_df <- data.frame(Variable = indicator_variables,
p_value = numeric(length(indicator_variables)),
t_statistic = numeric(length(indicator_variables)),
degrees_of_freedom = numeric(length(indicator_variables)),
conf_interval = character(length(indicator_variables)),
mean_difference = numeric(length(indicator_variables)))
# Perform paired and non-paired t-tests for each indicator variable and save the results
for (i in 1:length(indicator_variables)) {
variable <- indicator_variables[i]
plot_average_indic$Control_or_treatment <- as.factor(plot_average_indic$Control_or_treatment)
# Paired t-test
res_paired <- t.test(plot_average_indic[[variable]] ~ Control_or_treatment,
data = plot_average_indic, paired = TRUE)
# Non-paired t-test
res <- t.test(plot_average_indic[[variable]] ~ Control_or_treatment, data = plot_average_indic)
results_df_paired$p_value[i] <- res_paired$p.value
results_df_paired$t_statistic[i] <- res_paired$statistic
results_df_paired$degrees_of_freedom[i] <- res_paired$parameter
results_df_paired$conf_interval[i] <- paste(res_paired$conf.int[1], res_paired$conf.int[2], sep = " - ")
results_df_paired$mean_difference[i] <- res_paired$estimate
results_df$p_value[i] <- res$p.value
results_df$t_statistic[i] <- res$statistic
results_df$degrees_of_freedom[i] <- res$parameter
results_df$conf_interval[i] <- paste(res$conf.int[1], res$conf.int[2], sep = " - ")
results_df$mean_difference[i] <- res$estimate
}## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement length
## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement length
## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement length
## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement length
## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement length
## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement length
## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement length
## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement length
## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement length
## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement length
## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement length
## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement length
## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement length
## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement length
## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement length
## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement length
## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement length
## Warning in results_df$mean_difference[i] <- res$estimate: number of items to
## replace is not a multiple of replacement lengthresults_df_paired## Variable p_value t_statistic degrees_of_freedom
## 1 All_Shannon 0.10495230 1.6718587 30
## 2 All_Simpson 0.10528274 1.6701996 30
## 3 All_Cover 0.79035434 -0.2682349 30
## 4 All_Evenness 0.36061311 0.9283942 30
## 5 Lichen_Richness 0.05671790 1.9818954 30
## 6 Lichen_Shannon 0.06261036 1.9338850 30
## 7 Lichen_Simpson 0.06012180 1.9536595 30
## 8 Lichen_Cover 0.39506424 -0.8628526 30
## 9 Lichen_Evenness 0.12825088 1.5642358 30
## 10 Plant_Richness 0.51095283 0.6652821 30
## 11 Plant_Shannon 0.84018138 0.2034186 30
## 12 Plant_Simpson 0.75608434 0.3134812 30
## 13 Plant_Cover 0.62216620 0.4979268 30
## 14 Plant_Evenness 0.51069763 0.6656869 30
## 15 Bryo_Richness 0.60693283 0.5199230 30
## 16 Bryo_Simpson 0.71573481 -0.3676237 30
## 17 Bryo_Cover 0.13738976 1.5263823 30
## 18 Bryo_Evenness 0.89390173 -0.1345048 30
## conf_interval mean_difference
## 1 -0.0334478244999803 - 0.335380626749656 0.15096640
## 2 -0.0131798835451799 - 0.131506368425532 0.05916324
## 3 -25.374425980664 - 19.4828130774382 -2.94580645
## 4 -0.042096562930675 - 0.112269775437644 0.03508661
## 5 -0.0240766364375026 - 1.60472179772783 0.79032258
## 6 -0.00813621616176478 - 0.298474237043392 0.14516901
## 7 -0.00285414822176339 - 0.12870551954658 0.06292569
## 8 -25.7582964468183 - 10.4573287048828 -7.65048387
## 9 -0.0189819811280552 - 0.143207983108934 0.06211300
## 10 -0.267068922796073 - 0.525133438925105 0.12903226
## 11 -0.103496245307525 - 0.126394270712645 0.01144901
## 12 -0.059987354474083 - 0.0817423360052674 0.01087749
## 13 -3.53626888112508 - 5.81659146177024 1.14016129
## 14 -0.0841162850874729 - 0.165469900695506 0.04067681
## 15 -0.330583846798216 - 0.55639029841112 0.11290323
## 16 -0.0861224955717563 - 0.0598469505147911 -0.01313777
## 17 -1.20474335679817 - 8.33377561486268 3.56451613
## 18 -0.124649599548425 - 0.10924519927714 -0.00770220results_df## Variable p_value t_statistic degrees_of_freedom
## 1 All_Shannon 0.2139215 1.2569513 56.73442
## 2 All_Simpson 0.1744291 1.3786510 47.73617
## 3 All_Cover 0.8238691 -0.2236793 52.86223
## 4 All_Evenness 0.3739828 0.8992944 39.23051
## 5 Lichen_Richness 0.2313405 1.2093298 59.23962
## 6 Lichen_Shannon 0.2000526 1.2961309 58.13301
## 7 Lichen_Simpson 0.1556666 1.4410076 51.23431
## 8 Lichen_Cover 0.4704838 -0.7276449 46.43967
## 9 Lichen_Evenness 0.1315454 1.5401859 39.22000
## 10 Plant_Richness 0.5786634 0.5583812 59.96053
## 11 Plant_Shannon 0.8434649 0.1983324 59.16445
## 12 Plant_Simpson 0.7598566 0.3071364 57.11793
## 13 Plant_Cover 0.6609226 0.4408717 58.80223
## 14 Plant_Evenness 0.5172409 0.6519222 53.53019
## 15 Bryo_Richness 0.6580872 0.4447995 59.18110
## 16 Bryo_Simpson 0.7792703 -0.2815346 59.92911
## 17 Bryo_Cover 0.1342158 1.5238813 47.16670
## 18 Bryo_Evenness 0.9201544 -0.1006623 59.95464
## conf_interval mean_difference
## 1 -0.0895645379837867 - 0.391497340233462 1.48030776
## 2 -0.0271331854869785 - 0.145459670367331 0.68921987
## 3 -29.362641775915 - 23.4710288726892 59.64870968
## 4 -0.0438152812476329 - 0.113988493754602 0.77958630
## 5 -0.517259469814372 - 2.0979046311047 6.14516129
## 6 -0.0790160585722377 - 0.369354079453865 1.31337022
## 7 -0.0247314224306002 - 0.150582793755416 0.64252338
## 8 -28.8087334264965 - 13.5077656845611 47.53258065
## 9 -0.019443956726723 - 0.143669958707601 0.77462616
## 10 -0.333208320373466 - 0.591272836502499 0.66129032
## 11 -0.104054504326389 - 0.126952529731509 0.11803579
## 12 -0.0600383132068852 - 0.0817932947380697 0.07466886
## 13 -4.03508139385956 - 6.31540397450472 5.66451613
## 14 -0.0844431632923039 - 0.165796778900337 0.14549179
## 15 -0.394975906373918 - 0.620782357986821 0.74193548
## 16 -0.106483674988612 - 0.080208129931647 0.08488107
## 17 -1.14071436794831 - 8.26974662601282 6.45161290
## 18 -0.160757873906328 - 0.145353473635043 0.14787764indic_paired_results<- results_df_paired
write.csv(indic_paired_results, "Output figs and tables/Indicator paired ttest results.csv", row.names = F)Figures
reformat the data
colnames(plot_average_indic)## [1] "Plot_Identifier" "All_Richness" "All_Shannon"
## [4] "All_Simpson" "All_Cover" "All_Evenness"
## [7] "Lichen_Richness" "Lichen_Shannon" "Lichen_Simpson"
## [10] "Lichen_Cover" "Lichen_Evenness" "Plant_Richness"
## [13] "Plant_Shannon" "Plant_Simpson" "Plant_Cover"
## [16] "Plant_Evenness" "Bryo_Richness" "Bryo_Shannon"
## [19] "Bryo_Simpson" "Bryo_Cover" "Bryo_Evenness"
## [22] "Transect_name" "T_Pair_identifer" "Quad_Identifier"
## [25] "Site_name" "Quad_pos" "Cliff_Position"
## [28] "Plot_Location" "Nest_Position" "Control_or_treatment"
## [31] "plot_num" "quad_num"# Define the columns for each indicator category
richness_columns <- c("All_Richness", "Lichen_Richness", "Plant_Richness", "Bryo_Richness")
shannon_columns <- c("All_Shannon", "Lichen_Shannon", "Plant_Shannon", "Bryo_Simpson")
cover_columns <- c("All_Cover", "Lichen_Cover", "Plant_Cover", "Bryo_Cover")
evenness_columns <- c("All_Evenness", "Lichen_Evenness", "Plant_Evenness", "Bryo_Evenness")
Simpson_columns <- c("All_Simpson", "Lichen_Simpson", "Plant_Simpson", "Bryo_Simpson")
# Specify the data frame and columns to pivot for each indicator category
long_richness <- plot_average_indic %>%
pivot_longer(cols = all_of(richness_columns),
names_to = "Indicator",
values_to = "Richness")
long_shannon <- plot_average_indic %>%
pivot_longer(cols = all_of(shannon_columns),
names_to = "Indicator",
values_to = "Shannon")
long_cover <- plot_average_indic %>%
pivot_longer(cols = all_of(cover_columns),
names_to = "Indicator",
values_to = "Cover")
long_evenness <- plot_average_indic %>%
pivot_longer(cols = all_of(evenness_columns),
names_to = "Indicator",
values_to = "Evenness")
long_simpson <- plot_average_indic %>%
pivot_longer(cols = all_of(Simpson_columns),
names_to = "Indicator",
values_to = "Simpson")
# Extract the taxon from the Indicator column for each indicator category
long_richness <- long_richness %>%
mutate(Taxon = sub(".*?([A-Za-z]+).*", "\\1", Indicator))
long_shannon <- long_shannon %>%
mutate(Taxon = sub(".*?([A-Za-z]+).*", "\\1", Indicator))
long_cover <- long_cover %>%
mutate(Taxon = sub(".*?([A-Za-z]+).*", "\\1", Indicator))
long_evenness <- long_evenness %>%
mutate(Taxon = sub(".*?([A-Za-z]+).*", "\\1", Indicator))
long_simpson <- long_simpson %>%
mutate(Taxon = sub(".*?([A-Za-z]+).*", "\\1", Indicator))FIgure for cover
# Order the 'Taxon' factor variable
long_cover$Taxon <- factor(long_cover$Taxon, levels = c("All", "Lichen", "Bryo", "Plant"))
# Create the grouped box plot with ordered Taxon levels
gg_boxplot_cover <- ggplot(long_cover, aes(x = Control_or_treatment, y = Cover, fill = Taxon)) +
geom_boxplot() +
scale_fill_brewer(palette = "Dark2") +
theme_classic() +
labs(x = "Control or Treatment", y = "Cover (%)") +
theme(axis.title.x = element_blank()) +
theme(legend.position = "top")+
scale_y_continuous(expand = expansion(add = 0, mult = 0))
gg_boxplot_cover
Figures for richness, diversity, and eveness
# Order the 'Taxon' factor variable
long_cover$Taxon <- factor(long_cover$Taxon, levels = c("All", "Lichen", "Bryo", "Plant"))
long_richness$Taxon <- factor(long_richness$Taxon, levels = c("All", "Lichen", "Bryo", "Plant"))
long_shannon$Taxon <- factor(long_shannon$Taxon, levels = c("All", "Lichen", "Bryo", "Plant"))
long_evenness$Taxon <- factor(long_evenness$Taxon, levels = c("All", "Lichen", "Bryo", "Plant"))
long_simpson$Taxon <- factor(long_simpson$Taxon, levels = c("All", "Lichen", "Bryo", "Plant"))
# Create the grouped box plot with ordered Taxon levels
gg_boxplot_cover <- ggplot(long_cover, aes(x = Control_or_treatment, y = Cover, fill = Taxon)) +
geom_boxplot() +scale_fill_brewer(palette = "Dark2") +
theme_classic() + labs(x = "Control or Treatment", y = "Cover (%)") +
theme(axis.title.x = element_blank()) + theme(legend.position = "top")+
scale_y_continuous(expand = expansion(add = 0, mult = 0))
gg_boxplot_cover
gg_boxplot_richness <- ggplot(long_richness, aes(x = Control_or_treatment, y = Richness, fill = Taxon)) +
geom_boxplot() +scale_fill_brewer(palette = "Dark2") +
theme_classic() + labs(x = "Control or Treatment", y = "Richness") +
theme(axis.title.x = element_blank()) + theme(legend.position = "top")+
scale_y_continuous(expand = expansion(add = 0, mult = 0))
gg_boxplot_richness
gg_boxplot_shannon <- ggplot(long_shannon, aes(x = Control_or_treatment, y = Shannon, fill = Taxon)) +
geom_boxplot() +scale_fill_brewer(palette = "Dark2") +
theme_classic() + labs(x = "Control or Treatment", y = "Shannon Diversity Index") +
theme(axis.title.x = element_blank()) + theme(legend.position = "top")+
scale_y_continuous(expand = expansion(add = 0, mult = 0))
gg_boxplot_shannon
gg_boxplot_simpson <- ggplot(long_simpson, aes(x = Control_or_treatment, y = Simpson, fill = Taxon)) +
geom_boxplot() +scale_fill_brewer(palette = "Dark2") +
theme_classic() + labs(x = "Control or Treatment", y = "Simpson Diversity Index") +
theme(axis.title.x = element_blank()) + theme(legend.position = "top")+
scale_y_continuous(expand = expansion(add = 0, mult = 0))
gg_boxplot_simpson
gg_boxplot_evenness <- ggplot(long_evenness, aes(x = Control_or_treatment, y = Evenness, fill = Taxon)) +
geom_boxplot() +scale_fill_brewer(palette = "Dark2") +
theme_classic() + labs(x = "Control or Treatment", y = "Evenness") +
theme(axis.title.x = element_blank()) + theme(legend.position = "top")+
scale_y_continuous(expand = expansion(add = 0, mult = 0))
gg_boxplot_evenness
# Display the grouped box plots
combined_indicies<- ggarrange(gg_boxplot_cover, gg_boxplot_richness, gg_boxplot_evenness, gg_boxplot_shannon, gg_boxplot_simpson,
common.legend = TRUE, legend = "top")
combined_indicies
ggsave("Output figs and tables/combined_indicies_plot.png", plot = combined_indicies, width = 8, height = 6, dpi = 300)Step 4 NMDS
– all species NMDS – just lichen nmds
– just below nest for both too
First we need to create environmental and community df with only sucessful traps
# Create the trap_success column
plot_enviro <- plot_enviro %>%
mutate(trap_success = ifelse(NH4 == "ND" | NO32 == "ND", "failure", "success"))
# Filter plot_enviro to select only "success" rows
plot_enviro_success <- plot_enviro %>%
filter(trap_success == "success")
# Extract Quad_Identifier values
matching_quad_ids <- plot_enviro_success$Quad_Identifier
# Filter rows in all.matrix based on matching Quad_Identifier values
all.matrix_filtered <- all.matrix[rownames(all.matrix) %in% matching_quad_ids, ]
# Filter rows in all.matrix based on matching Quad_Identifier values
lichen.matrix_filtered <- lichen.matrix[rownames(lichen.matrix) %in% matching_quad_ids, ]
# Filter rows in plot_enviro_success based on matching Quad_Identifier values
plot_enviro_success_filtered <- plot_enviro_success[plot_enviro_success$Quad_Identifier %in% rownames(all.matrix_filtered), ]# bring in all community data
m_com = as.matrix(all.matrix_filtered)
# Filter plot_enviro_success to select only relevant environmental columns
env <- plot_enviro_success_filtered[, c("NH4", "NO32")]
env$NO32 <- as.numeric(env$NO32)
env$NH4 <- as.numeric(env$NH4)
colnames(env)[2] <- "NO3"
# Ensure 'env' and 'en2nmds' have the same row dimensions
matching_quad_ids <- plot_enviro_success_filtered$Quad_Identifier
m_com_filtered <- m_com[rownames(m_com) %in% matching_quad_ids, ]
# Perform NMDS with the filtered community data
set.seed(123)
en2nmds = metaMDS(m_com_filtered, k = 2, distance = "bray")## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1321824
## Run 1 stress 0.1357896
## Run 2 stress 0.1334558
## Run 3 stress 0.1332019
## Run 4 stress 0.1416852
## Run 5 stress 0.1321777
## ... New best solution
## ... Procrustes: rmse 0.002254197 max resid 0.01540606
## Run 6 stress 0.1330466
## Run 7 stress 0.1336268
## Run 8 stress 0.1335211
## Run 9 stress 0.1335211
## Run 10 stress 0.1329827
## Run 11 stress 0.1324714
## ... Procrustes: rmse 0.02607983 max resid 0.11947
## Run 12 stress 0.1438811
## Run 13 stress 0.1332012
## Run 14 stress 0.1332013
## Run 15 stress 0.1349237
## Run 16 stress 0.1323636
## ... Procrustes: rmse 0.02821341 max resid 0.1183386
## Run 17 stress 0.1323634
## ... Procrustes: rmse 0.028167 max resid 0.1183997
## Run 18 stress 0.1449351
## Run 19 stress 0.1321781
## ... Procrustes: rmse 0.0002133179 max resid 0.0007503009
## ... Similar to previous best
## Run 20 stress 0.1347276
## *** Best solution repeated 1 times# Perform environmental fit with matching dimensions
en2 = envfit(en2nmds, env, permutations = 999, na.rm = TRUE)
en2##
## ***VECTORS
##
## NMDS1 NMDS2 r2 Pr(>r)
## NH4 -0.001176 1.000000 0.0346 0.335
## NO3 0.210500 -0.977590 0.0445 0.205
## Permutation: free
## Number of permutations: 999LICHENS
# bring in all community data
m_com = as.matrix(lichen.matrix_filtered)
# Filter plot_enviro_success to select only relevant environmental columns
env <- plot_enviro_success_filtered[, c("NH4", "NO32")]
env$NO32 <- as.numeric(env$NO32)
env$NH4 <- as.numeric(env$NH4)
colnames(env)[2] <- "NO3"
# Ensure 'env' and 'en2nmds' have the same row dimensions
matching_quad_ids <- plot_enviro_success$Quad_Identifier
m_com_filtered <- m_com[rownames(m_com) %in% matching_quad_ids, ]
# Perform NMDS with the filtered community data
set.seed(123)
en2nmds = metaMDS(m_com_filtered, k = 2, distance = "bray")## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1257683
## Run 1 stress 0.1264156
## Run 2 stress 0.1269873
## Run 3 stress 0.1270916
## Run 4 stress 0.1270123
## Run 5 stress 0.1257637
## ... New best solution
## ... Procrustes: rmse 0.00436288 max resid 0.02154982
## Run 6 stress 0.1257869
## ... Procrustes: rmse 0.00454245 max resid 0.02591011
## Run 7 stress 0.1316656
## Run 8 stress 0.12677
## Run 9 stress 0.1267703
## Run 10 stress 0.126383
## Run 11 stress 0.1265093
## Run 12 stress 0.1400032
## Run 13 stress 0.1267407
## Run 14 stress 0.1271221
## Run 15 stress 0.1267665
## Run 16 stress 0.1264158
## Run 17 stress 0.1259911
## ... Procrustes: rmse 0.02445718 max resid 0.1068502
## Run 18 stress 0.1264892
## Run 19 stress 0.1257808
## ... Procrustes: rmse 0.003026036 max resid 0.01994196
## Run 20 stress 0.1267671
## *** Best solution was not repeated -- monoMDS stopping criteria:
## 1: no. of iterations >= maxit
## 19: stress ratio > sratmax# Perform environmental fit with matching dimensions
en2 = envfit(en2nmds, env, permutations = 999, na.rm = TRUE)
en2##
## ***VECTORS
##
## NMDS1 NMDS2 r2 Pr(>r)
## NH4 0.32770 0.94478 0.0406 0.267
## NO3 -0.08341 -0.99652 0.0414 0.236
## Permutation: free
## Number of permutations: 999only below nests
# Create the trap_success column
plot_enviro <- plot_enviro %>%
mutate(trap_success = ifelse(NH4 == "ND" | NO32 == "ND", "failure", "success"))
# Filter plot_enviro to select only "success" rows
plot_enviro_success <- plot_enviro %>%
filter(trap_success == "success")
# Add an additional filter layer for Nest_Position
plot_enviro_success_belowNest <- plot_enviro_success %>%
filter(Nest_Position == "below_nest")
# Extract Quad_Identifier values
matching_quad_ids <- plot_enviro_success_belowNest$Quad_Identifier
# Filter rows in all.matrix based on matching Quad_Identifier values
all.matrix_filtered_belownest <- all.matrix[rownames(all.matrix) %in% matching_quad_ids, ]
# Filter rows in lichen.matrix based on matching Quad_Identifier values
lichen.matrix_filtered_belownest <- lichen.matrix[rownames(lichen.matrix) %in% matching_quad_ids, ]
# Filter rows in plot_enviro_success based on matching Quad_Identifier values
plot_enviro_success_belowNest_filtered <- plot_enviro_success_belowNest[plot_enviro_success_belowNest$Quad_Identifier %in% rownames(all.matrix_filtered), ]all - below nest only
# bring in all community data
m_com = as.matrix(all.matrix_filtered_belownest)
# Filter plot_enviro_success to select only relevant environmental columns
env <- plot_enviro_success_belowNest_filtered[, c("NH4", "NO32", "Site_name")]
env$NO32 <- as.numeric(env$NO32)
env$NH4 <- as.numeric(env$NH4)
colnames(env)[2] <- "NO3"
# Ensure 'env' and 'en2nmds' have the same row dimensions
matching_quad_ids <- plot_enviro_success_belowNest_filtered$Quad_Identifier
m_com_filtered <- m_com[rownames(m_com) %in% matching_quad_ids, ]
# Perform NMDS with the filtered community data
set.seed(123)
en2nmds = metaMDS(m_com_filtered, k = 2, distance = "bray")## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1347773
## Run 1 stress 0.1388983
## Run 2 stress 0.1348449
## ... Procrustes: rmse 0.003359053 max resid 0.02018058
## Run 3 stress 0.1368306
## Run 4 stress 0.1383077
## Run 5 stress 0.133843
## ... New best solution
## ... Procrustes: rmse 0.06127205 max resid 0.1607576
## Run 6 stress 0.1352497
## Run 7 stress 0.1354224
## Run 8 stress 0.1360223
## Run 9 stress 0.1337889
## ... New best solution
## ... Procrustes: rmse 0.007908966 max resid 0.03208174
## Run 10 stress 0.1352666
## Run 11 stress 0.1381898
## Run 12 stress 0.1338766
## ... Procrustes: rmse 0.006117337 max resid 0.03217183
## Run 13 stress 0.1359186
## Run 14 stress 0.1347772
## Run 15 stress 0.1471259
## Run 16 stress 0.1430228
## Run 17 stress 0.1386782
## Run 18 stress 0.1465816
## Run 19 stress 0.1620193
## Run 20 stress 0.1353333
## *** Best solution was not repeated -- monoMDS stopping criteria:
## 2: no. of iterations >= maxit
## 17: stress ratio > sratmax
## 1: scale factor of the gradient < sfgrmin# Perform environmental fit with matching dimensions
en2 = envfit(en2nmds, env, permutations = 999, na.rm = TRUE)
en2##
## ***VECTORS
##
## NMDS1 NMDS2 r2 Pr(>r)
## NH4 -0.02163 0.99977 0.1723 0.004 **
## NO3 -0.53136 0.84715 0.1103 0.028 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999
##
## ***FACTORS:
##
## Centroids:
## NMDS1 NMDS2
## Site_nameBLC -0.5479 -0.1667
## Site_nameBUZ -0.0505 0.5615
## Site_nameCHM 0.3181 -0.2065
## Site_nameGOA -1.1617 -0.0764
## Site_nameNRS 0.9503 -0.8695
## Site_namePIL -0.3692 1.2769
## Site_nameSNA 1.0329 0.4863
## Site_nameTAB 0.3245 -0.6215
##
## Goodness of fit:
## r2 Pr(>r)
## Site_name 0.8061 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999# summary into to save for manuscipt
#Lichen only NMDS
# vectors nh4 r2 0.17229 , p 0.004 **
# vectors no3 r2 0.11029 , p 0.028 **
# goodness of fit for factor (site)
# site r2 0.80606, p 0.001 ***
# Extract NMDS site scores
e2.data.scores <- as.data.frame(scores(en2nmds$points))
e2.data.scores$Region = plot_enviro_success_belowNest_filtered$Site_name
e2.data.scores$Trt = plot_enviro_success_belowNest_filtered$Control_or_treatment
e2.data.scores$NH4 = plot_enviro_success_belowNest_filtered$NH4
e2.data.scores$NO32 = plot_enviro_success_belowNest_filtered$NO32
en_coord_cont = as.data.frame(scores(en2, "vectors")) * ordiArrowMul(en2)
en_coord_cat = as.data.frame(scores(en2, "factors")) * ordiArrowMul(en2)
theme_set(
theme(axis.title = element_text(size = 10, face = "bold", colour = "black"),
panel.background = element_blank(),
panel.border = element_rect(fill = NA, colour = "black"),
axis.ticks = element_blank(),
axis.text = element_blank(),
legend.key = element_blank(),
legend.title = element_text(size = 10, face = "bold", colour = "black"),
legend.text = element_text(size = 9, colour = "black")))
####### figures
# Define a color palette using RColorBrewer
dark2_palette <- brewer.pal(8, "Dark2")
# Scatter plot colored by Site using the Dark2 palette
alltaxa_siteNMDS <- ggplot(data = e2.data.scores, aes(x = MDS1, y = MDS2,
color = Region)) +
geom_point(size = 2, alpha = 0.6) +
scale_color_manual(values = dark2_palette) + # Apply the Dark2 color palette
labs(color = "Site")+
labs(title = "All taxa - below nest")
alltaxa_siteNMDS
# Scatter plot colored by Site using the Dark2 palette
alltaxa_siteNMDS_withTrt <-
ggplot(data = e2.data.scores, aes(x = MDS1, y = MDS2, color = Region, shape=Trt)) +
geom_point(size = 2, alpha = 0.6) +
scale_color_manual(values = dark2_palette) + # Apply the Dark2 color palette
labs(color = "Site", shape = "Guano")+
labs(title = "All taxa - below nest")
alltaxa_siteNMDS_withTrt
# # Scatter plot with ellipses and colored by Site using the Dark2 palette
# lichen_siteNMDS_ellipse <- ggplot(data = e2.data.scores, aes(x = MDS1, y = MDS2, color = Region)) +
# geom_point(size = 2, alpha = 0.6) +
# stat_ellipse(size = 1.5) +
# scale_color_manual(values = dark2_palette) + # Apply the Dark2 color palette
# labs(color = "Site")
# lichen_siteNMDS_ellipse
# Combine both scatter plots into one
alltaxa_siteNMDS_withN <- alltaxa_siteNMDS +
geom_segment(aes(x = 0, y = 0, xend = NMDS1, yend = NMDS2),
data = en_coord_cont, size = 1, alpha = 0.7, color = "black") +
geom_text(data = en_coord_cont, aes(x = NMDS1, y = NMDS2),
check_overlap = TRUE, color = "black",
fontface = "bold", label = row.names(en_coord_cont), vjust = "outward") +
labs(title = "All Taxa - below nest")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.alltaxa_siteNMDS_withN
# Combine the ggplot figures into a single plot
# remove fig title
alltaxa_nmds_combined_plot <- ggarrange(
alltaxa_siteNMDS + labs(title = NULL),
alltaxa_siteNMDS_withN + labs(title = NULL),
ncol = 2,
common.legend = TRUE,
legend = "top"
)
# Display the combined plot
alltaxa_nmds_combined_plot
ggsave("Output figs and tables/alltaxa_nmds_combined_plot.png", plot = alltaxa_nmds_combined_plot, width = 8, height = 4, dpi = 300)lichens - below nest only LICHENS
# bring in all community data
m_com = as.matrix(lichen.matrix_filtered_belownest)
# Filter plot_enviro_success to select only relevant environmental columns
env <- plot_enviro_success_belowNest_filtered[, c("NH4", "NO32", "Site_name")]
env$NO32 <- as.numeric(env$NO32)
env$NH4 <- as.numeric(env$NH4)
colnames(env)[2] <- "NO3"
# Ensure 'env' and 'en2nmds' have the same row dimensions
matching_quad_ids <- plot_enviro_success_belowNest_filtered$Quad_Identifier
m_com_filtered <- m_com[rownames(m_com) %in% matching_quad_ids, ]
# Perform NMDS with the filtered community data
set.seed(123)
en2nmds = metaMDS(m_com_filtered, k = 2, distance = "bray")## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1283553
## Run 1 stress 0.1403796
## Run 2 stress 0.131287
## Run 3 stress 0.1283551
## ... New best solution
## ... Procrustes: rmse 0.0001477325 max resid 0.0008964656
## ... Similar to previous best
## Run 4 stress 0.1326637
## Run 5 stress 0.1422671
## Run 6 stress 0.129115
## Run 7 stress 0.1309624
## Run 8 stress 0.1280076
## ... New best solution
## ... Procrustes: rmse 0.05011856 max resid 0.1353979
## Run 9 stress 0.1279845
## ... New best solution
## ... Procrustes: rmse 0.00439645 max resid 0.02122266
## Run 10 stress 0.129054
## Run 11 stress 0.1314952
## Run 12 stress 0.128019
## ... Procrustes: rmse 0.004738878 max resid 0.0212096
## Run 13 stress 0.1333126
## Run 14 stress 0.1283552
## ... Procrustes: rmse 0.04773489 max resid 0.1365068
## Run 15 stress 0.1280065
## ... Procrustes: rmse 0.003994851 max resid 0.02038142
## Run 16 stress 0.1396275
## Run 17 stress 0.1332443
## Run 18 stress 0.1365667
## Run 19 stress 0.1577363
## Run 20 stress 0.1286015
## *** Best solution was not repeated -- monoMDS stopping criteria:
## 3: no. of iterations >= maxit
## 17: stress ratio > sratmax# Perform environmental fit with matching dimensions
en2 = envfit(en2nmds, env, permutations = 999, na.rm = TRUE)
en2##
## ***VECTORS
##
## NMDS1 NMDS2 r2 Pr(>r)
## NH4 0.03738 0.99930 0.2048 0.002 **
## NO3 -0.60392 0.79705 0.1082 0.032 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999
##
## ***FACTORS:
##
## Centroids:
## NMDS1 NMDS2
## Site_nameBLC -0.6255 0.0169
## Site_nameBUZ -0.0324 0.6821
## Site_nameCHM 0.3203 -0.3264
## Site_nameGOA -1.3516 -0.2257
## Site_nameNRS 0.9306 -1.0707
## Site_namePIL -0.3776 1.5288
## Site_nameSNA 1.2587 0.4768
## Site_nameTAB 0.3690 -0.6609
##
## Goodness of fit:
## r2 Pr(>r)
## Site_name 0.8194 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999# summary into to save for manuscipt
#Lichen only NMDS
# vectors nh4 r2 0.17229, p 0.004 **
# vectors no3 r2 0.11029 , p 0.028 **
# goodness of fit for factor (site)
# site r2 0.80606, p 0.001 ***
# Extract NMDS site scores
e2.data.scores <- as.data.frame(scores(en2nmds$points))
e2.data.scores$Region = plot_enviro_success_belowNest_filtered$Site_name
e2.data.scores$Trt = plot_enviro_success_belowNest_filtered$Control_or_treatment
e2.data.scores$NH4 = plot_enviro_success_belowNest_filtered$NH4
e2.data.scores$NO32 = plot_enviro_success_belowNest_filtered$NO32
en_coord_cont = as.data.frame(scores(en2, "vectors")) * ordiArrowMul(en2)
en_coord_cat = as.data.frame(scores(en2, "factors")) * ordiArrowMul(en2)
####### figures
# Define a color palette using RColorBrewer
dark2_palette <- brewer.pal(8, "Dark2")
# Scatter plot colored by Site using the Dark2 palette
lichen_siteNMDS <- ggplot(data = e2.data.scores, aes(x = MDS1, y = MDS2,
color = Region)) +
geom_point(size = 2, alpha = 0.6) +
scale_color_manual(values = dark2_palette) + # Apply the Dark2 color palette
labs(color = "Site")+
labs(title = "Only lichens - below nest")
lichen_siteNMDS
# Scatter plot colored by Site using the Dark2 palette
lichen_siteNMDS_withTrt <-
ggplot(data = e2.data.scores, aes(x = MDS1, y = MDS2, color = Region, shape=Trt)) +
geom_point(size = 2, alpha = 0.6) +
scale_color_manual(values = dark2_palette) + # Apply the Dark2 color palette
labs(color = "Site", shape = "Guano")+
labs(title = "Only lichens - below nest")
lichen_siteNMDS_withTrt
# # Scatter plot with ellipses and colored by Site using the Dark2 palette
# lichen_siteNMDS_ellipse <- ggplot(data = e2.data.scores, aes(x = MDS1, y = MDS2, color = Region)) +
# geom_point(size = 2, alpha = 0.6) +
# stat_ellipse(size = 1.5) +
# scale_color_manual(values = dark2_palette) + # Apply the Dark2 color palette
# labs(color = "Site")
# lichen_siteNMDS_ellipse
# Combine both scatter plots into one
lichen_siteNMDS_withN <- lichen_siteNMDS +
geom_segment(aes(x = 0, y = 0, xend = NMDS1, yend = NMDS2),
data = en_coord_cont, size = 1, alpha = 0.7, color = "black") +
geom_text(data = en_coord_cont, aes(x = NMDS1, y = NMDS2),
check_overlap = TRUE, color = "black",
fontface = "bold", label = row.names(en_coord_cont), vjust = "outward") +
labs(title = "Only lichens - below nest")
lichen_siteNMDS_withN
# Combine the ggplot figures into a single plot
# remove fig title
lichen_nmds_combined_plot <- ggarrange(
lichen_siteNMDS + labs(title = NULL),
lichen_siteNMDS_withN + labs(title = NULL),
ncol = 2,
common.legend = TRUE,
legend = "top"
)
# Display the combined plot
lichen_nmds_combined_plot
ggsave("Output figs and tables/lichen_nmds_combined_plot.png", plot = lichen_nmds_combined_plot, width = 8, height = 4, dpi = 300)
#
#
# lichen_nmds_combined_plot <- ggarrange(
# lichen_siteNMDS + labs(title = NULL),
# lichen_siteNMDS_withTrt + labs(title = NULL),
# lichen_siteNMDS_withN + labs(title = NULL),
# ncol = 3,
# common.legend = TRUE,
# legend = "bottom"
# )Step 5 indicator species and rank abundance species lists
#recall the community matricies
head(all.matrix)## Ssp122 Ssp150 Ssp117 Ssp119 Ssp138 Ssp53 Ssp81 Ssp96
## PIL_Nest_22_44_R 0.1 0.1 0.0 0 0.0 0.0 0 0
## PIL_Nest_23_46_R 0.2 0.1 1.0 0 0.0 0.0 0 0
## PIL_Nest_22_43_L 1.0 2.0 0.0 1 0.5 0.0 0 0
## NRS_Nest_63_126_R 0.0 0.0 0.0 0 0.0 0.5 2 3
## PIL_Nest_23_45_L 2.4 1.0 1.7 0 0.0 0.0 0 0
## CHM_Control_56_112_L 0.0 0.0 0.0 2 0.0 0.0 0 0
## Ssp131 Ssp20 Ssp139 Ssp12 Ssp30 Ssp162 Ssp31 Ssp49 Ssp85
## PIL_Nest_22_44_R 0.0 0.0 0 0 0 0 0 0 0
## PIL_Nest_23_46_R 0.0 0.0 0 0 0 0 0 0 0
## PIL_Nest_22_43_L 0.0 0.0 0 0 0 0 0 0 0
## NRS_Nest_63_126_R 0.5 0.0 0 0 0 0 0 0 0
## PIL_Nest_23_45_L 0.0 0.1 3 0 0 0 0 0 0
## CHM_Control_56_112_L 0.0 0.0 0 4 1 2 0 0 0
## Ssp114 Ssp116 Ssp104 Ssp5 Ssp100 Ssp2 Ssp29 Ssp4 Ssp159
## PIL_Nest_22_44_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_46_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_22_43_L 0 0 0 0 0 0 0 0 0
## NRS_Nest_63_126_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_45_L 0 0 0 0 0 0 0 0 0
## CHM_Control_56_112_L 0 0 0 0 0 0 0 0 0
## Ssp10 Ssp61 Ssp62 Ssp65 Ssp69 Ssp82 Ssp118 Ssp146 Ssp171
## PIL_Nest_22_44_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_46_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_22_43_L 0 0 0 0 0 0 0 0 0
## NRS_Nest_63_126_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_45_L 0 0 0 0 0 0 0 0 0
## CHM_Control_56_112_L 0 0 0 0 0 0 0 0 0
## Ssp156 Ssp126 Ssp9 Ssp13 Ssp16 Ssp26 Ssp103 Ssp136 Ssp143
## PIL_Nest_22_44_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_46_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_22_43_L 0 0 0 0 0 0 0 0 0
## NRS_Nest_63_126_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_45_L 0 0 0 0 0 0 0 0 0
## CHM_Control_56_112_L 0 0 0 0 0 0 0 0 0
## Ssp169 Ssp177 Ssp19 Ssp44 Ssp1 Ssp124 Ssp163 Ssp15 Ssp40
## PIL_Nest_22_44_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_46_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_22_43_L 0 0 0 0 0 0 0 0 0
## NRS_Nest_63_126_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_45_L 0 0 0 0 0 0 0 0 0
## CHM_Control_56_112_L 0 0 0 0 0 0 0 0 0
## Ssp80 Ssp127 Ssp157 Ssp14 Ssp21 Ssp56 Ssp95 Ssp106 Ssp128
## PIL_Nest_22_44_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_46_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_22_43_L 0 0 0 0 0 0 0 0 0
## NRS_Nest_63_126_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_45_L 0 0 0 0 0 0 0 0 0
## CHM_Control_56_112_L 0 0 0 0 0 0 0 0 0
## Ssp155 Ssp107 Ssp111 Ssp125 Ssp141 Ssp187 Ssp160 Ssp25
## PIL_Nest_22_44_R 0 0 0 0 0 0 0 0
## PIL_Nest_23_46_R 0 0 0 0 0 0 0 0
## PIL_Nest_22_43_L 0 0 0 0 0 0 0 0
## NRS_Nest_63_126_R 0 0 0 0 0 0 0 0
## PIL_Nest_23_45_L 0 0 0 0 0 0 0 0
## CHM_Control_56_112_L 0 0 0 0 0 0 0 0
## Ssp168 Ssp181 Ssp129 Ssp149 Ssp3 Ssp18 Ssp32 Ssp66 Ssp158
## PIL_Nest_22_44_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_46_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_22_43_L 0 0 0 0 0 0 0 0 0
## NRS_Nest_63_126_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_45_L 0 0 0 0 0 0 0 0 0
## CHM_Control_56_112_L 0 0 0 0 0 0 0 0 0
## Ssp8 Ssp186 Ssp27 Ssp130 Ssp55 Ssp35 Ssp38 Ssp94 Ssp151
## PIL_Nest_22_44_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_46_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_22_43_L 0 0 0 0 0 0 0 0 0
## NRS_Nest_63_126_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_45_L 0 0 0 0 0 0 0 0 0
## CHM_Control_56_112_L 0 0 0 0 0 0 0 0 0
## Ssp54 Ssp88 Ssp98 Ssp112 Ssp72 Ssp73 Ssp77 Ssp57 Ssp120
## PIL_Nest_22_44_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_46_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_22_43_L 0 0 0 0 0 0 0 0 0
## NRS_Nest_63_126_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_45_L 0 0 0 0 0 0 0 0 0
## CHM_Control_56_112_L 0 0 0 0 0 0 0 0 0
## Ssp39 Ssp93 Ssp132 Ssp154 Ssp113 Ssp78 Ssp179 Ssp17 Ssp71
## PIL_Nest_22_44_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_46_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_22_43_L 0 0 0 0 0 0 0 0 0
## NRS_Nest_63_126_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_45_L 0 0 0 0 0 0 0 0 0
## CHM_Control_56_112_L 0 0 0 0 0 0 0 0 0
## Ssp90 Ssp108 Ssp133 Ssp144 Ssp172 Ssp191 Ssp43 Ssp45
## PIL_Nest_22_44_R 0 0 0 0 0 0 0 0
## PIL_Nest_23_46_R 0 0 0 0 0 0 0 0
## PIL_Nest_22_43_L 0 0 0 0 0 0 0 0
## NRS_Nest_63_126_R 0 0 0 0 0 0 0 0
## PIL_Nest_23_45_L 0 0 0 0 0 0 0 0
## CHM_Control_56_112_L 0 0 0 0 0 0 0 0
## Ssp115 Ssp67 Ssp101 Ssp121 Ssp76 Ssp109 Ssp167 Ssp28 Ssp48
## PIL_Nest_22_44_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_46_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_22_43_L 0 0 0 0 0 0 0 0 0
## NRS_Nest_63_126_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_45_L 0 0 0 0 0 0 0 0 0
## CHM_Control_56_112_L 0 0 0 0 0 0 0 0 0
## Ssp68 Ssp142 Ssp123 Ssp148 Ssp182 Ssp178 Ssp24 Ssp23 Ssp83
## PIL_Nest_22_44_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_46_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_22_43_L 0 0 0 0 0 0 0 0 0
## NRS_Nest_63_126_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_45_L 0 0 0 0 0 0 0 0 0
## CHM_Control_56_112_L 0 0 0 0 0 0 0 0 0
## Ssp110 Ssp189 Ssp64 Ssp6 Ssp7 Ssp137 Ssp42 Ssp50 Ssp46
## PIL_Nest_22_44_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_46_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_22_43_L 0 0 0 0 0 0 0 0 0
## NRS_Nest_63_126_R 0 0 0 0 0 0 0 0 0
## PIL_Nest_23_45_L 0 0 0 0 0 0 0 0 0
## CHM_Control_56_112_L 0 0 0 0 0 0 0 0 0
## Ssp188 Ssp164 Ssp192 Ssp47 Ssp99
## PIL_Nest_22_44_R 0 0 0 0 0
## PIL_Nest_23_46_R 0 0 0 0 0
## PIL_Nest_22_43_L 0 0 0 0 0
## NRS_Nest_63_126_R 0 0 0 0 0
## PIL_Nest_23_45_L 0 0 0 0 0
## CHM_Control_56_112_L 0 0 0 0 0head(cover)## Transect_name T_Pair_identifer Plot_Identifier Quad_Identifier
## 1 PIL_Nest PIL_Face PIL_Nest_22 PIL_Nest_22_44_R
## 2 PIL_Nest PIL_Face PIL_Nest_23 PIL_Nest_23_46_R
## 3 GOA_Nest GOA_Face GOA_Nest_76 GOA_Nest_76_151_L
## 4 PIL_Nest PIL_Face PIL_Nest_22 PIL_Nest_22_43_L
## 5 NRS_Nest NRS_Face NRS_Nest_63 NRS_Nest_63_126_R
## 6 PIL_Nest PIL_Face PIL_Nest_23 PIL_Nest_23_45_L
## Transect.Code Site plot clfpos L3R4 NP1P2 BryoC LichC VascC Heter SL AS
## 1 44 3 22 1 4 2 0 0.2 0 3 90 298
## 2 46 3 23 2 4 2 0 1.3 0 2 89 250
## 3 151 8 76 1 3 2 0 0.0 0 4 95 356
## 4 43 3 22 1 3 2 0 4.5 0 3 98 298
## 5 126 7 63 2 4 2 2 10.0 3 3 95 253
## 6 45 3 23 2 3 2 0 8.2 0 2 91 310
## wpn wpw transdist NH4 NO31 NO32 ALL.SUM Ssp1
## 1 36.34156 80.48754 10 ND ND ND 0.2 0
## 2 36.34156 80.48754 10 ND ND ND 1.3 0
## 3 36.29108 81.78748 5 1.38278E-06 3.26746E-07 4.54435E-07 4.0 0
## 4 36.34156 80.48754 10 ND ND ND 4.5 0
## 5 36.45817 81.33304 5 ND 1.6378E-05 7.28661E-06 6.0 0
## 6 36.34156 80.48754 10 ND ND ND 8.2 0
## Ssp2 Ssp3 Ssp4 Ssp5 Ssp6 Ssp7 Ssp8 Ssp9 Ssp10 Ssp11 Ssp12 Ssp13 Ssp14 Ssp15
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Ssp16 Ssp17 Ssp18 Ssp19 Ssp20 Ssp21 Ssp22 Ssp23 Ssp24 Ssp25 Ssp26 Ssp27 Ssp28
## 1 0 0 0 0 0.0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0.0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0.0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0.0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0.0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0.1 0 0 0 0 0 0 0 0
## Ssp29 Ssp30 Ssp31 Ssp32 Ssp33 Ssp34 Ssp35 Ssp36 Ssp37 Ssp38 Ssp39 Ssp40 Ssp41
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## Ssp42 Ssp43 Ssp44 Ssp45 Ssp46 Ssp47 Ssp48 Ssp49 Ssp50 Ssp51 Ssp52 Ssp53 Ssp54
## 1 0 0 0 0 0 0 0 0 0 0 0 0.0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0.0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0.0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0.0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0.5 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0.0 0
## Ssp55 Ssp56 Ssp57 Ssp58 Ssp59 Ssp60 Ssp61 Ssp62 Ssp63 Ssp64 Ssp65 Ssp66 Ssp67
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## Ssp68 Ssp69 Ssp70 Ssp71 Ssp72 Ssp73 Ssp74 Ssp75 Ssp76 Ssp77 Ssp78 Ssp79 Ssp80
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## Ssp81 Ssp82 Ssp83 Ssp84 Ssp85 Ssp86 Ssp87 Ssp88 Ssp89 Ssp90 Ssp91 Ssp92 Ssp93
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 4 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 2 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## Ssp94 Ssp95 Ssp96 Ssp97 Ssp98 Ssp99 Ssp100 Ssp101 Ssp102 Ssp103 Ssp104 Ssp105
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 3 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## Ssp106 Ssp107 Ssp108 Ssp109 Ssp110 Ssp111 Ssp112 Ssp113 Ssp114 Ssp115 Ssp116
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0
## Ssp117 Ssp118 Ssp119 Ssp120 Ssp121 Ssp122 Ssp123 Ssp124 Ssp125 Ssp126 Ssp127
## 1 0.0 0 0 0 0 0.1 0 0 0 0 0
## 2 1.0 0 0 0 0 0.2 0 0 0 0 0
## 3 0.0 0 0 0 0 0.0 0 0 0 0 0
## 4 0.0 0 1 0 0 1.0 0 0 0 0 0
## 5 0.0 0 0 0 0 0.0 0 0 0 0 0
## 6 1.7 0 0 0 0 2.4 0 0 0 0 0
## Ssp128 Ssp129 Ssp130 Ssp131 Ssp132 Ssp133 Ssp134 Ssp135 Ssp136 Ssp137 Ssp138
## 1 0 0 0 0.0 0 0 0 0 0 0 0.0
## 2 0 0 0 0.0 0 0 0 0 0 0 0.0
## 3 0 0 0 0.0 0 0 0 0 0 0 0.0
## 4 0 0 0 0.0 0 0 0 0 0 0 0.5
## 5 0 0 0 0.5 0 0 0 0 0 0 0.0
## 6 0 0 0 0.0 0 0 0 0 0 0 0.0
## Ssp139 Ssp140 Ssp141 Ssp142 Ssp143 Ssp144 Ssp145 Ssp146 Ssp147 Ssp148 Ssp149
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 3 0 0 0 0 0 0 0 0 0 0
## Ssp150 Ssp151 Ssp152 Ssp153 Ssp154 Ssp155 Ssp156 Ssp157 Ssp158 Ssp159 Ssp160
## 1 0.1 0 0 0 0 0 0 0 0 0 0
## 2 0.1 0 0 0 0 0 0 0 0 0 0
## 3 0.0 0 0 0 0 0 0 0 0 0 0
## 4 2.0 0 0 0 0 0 0 0 0 0 0
## 5 0.0 0 0 0 0 0 0 0 0 0 0
## 6 1.0 0 0 0 0 0 0 0 0 0 0
## Ssp161 Ssp162 Ssp163 Ssp164 Ssp165 Ssp166 Ssp167 Ssp168 Ssp169 Ssp170 Ssp171
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0
## Ssp172 Ssp173 Ssp174 Ssp175 Ssp176 Ssp177 Ssp178 Ssp179 Ssp180 Ssp181 Ssp182
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0
## Ssp183 Ssp184 Ssp185 Ssp186 Ssp187 Ssp188 Ssp189 Ssp190 Ssp191 Ssp192
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## quad_num Site_num plot_num clfpos_num L3R4_num Site_name Quad_pos
## 1 44 3 22 1 4 PIL R
## 2 46 3 23 2 4 PIL R
## 3 151 8 76 1 3 GOA L
## 4 43 3 22 1 3 PIL L
## 5 126 7 63 2 4 NRS R
## 6 45 3 23 2 3 PIL L
## Cliff_Position Plot_Location Control_or_treatment Nest_Position
## 1 Face P1 Nest above_nest
## 2 Face P2 Nest below_nest
## 3 Face P1 Nest below_nest
## 4 Face P1 Nest above_nest
## 5 Face P2 Nest below_nest
## 6 Face P2 Nest below_nestenv <- cover[,c("Quad_Identifier","Control_or_treatment", "Site_name", "Nest_Position")]
# Define the list of matching Quad Identifiers
matching_quad_ids <- env$Quad_Identifier
# Filter env to keep rows with matching Quad Identifiers
env_filtered <- env %>%
filter(Quad_Identifier %in% matching_quad_ids)
# Filter rows in env based on matching Quad_Identifier values
env_filtered <- env[env$Quad_Identifier %in% rownames(all.matrix), ]
# Use the filtered Quad Identifiers from env to filter all.matrix
all.matrix_filtered <- all.matrix %>%
filter(row.names(all.matrix) %in% env_filtered$Quad_Identifier)
env_filtered$Control_or_treatment <- as.factor(env_filtered$Control_or_treatment)
env_filtered$Site_name <- as.factor(env_filtered$Site_name)
env_filtered$Nest_Position <- as.factor(env_filtered$Nest_Position)
#overall rank abundance
RankAbun1 <- rankabundance(all.matrix_filtered, y=env_filtered)
RankAbun1## rank abundance proportion plower pupper accumfreq logabun rankfreq
## Ssp90 1 427.0 5.7 2.2 9.2 5.7 2.6 0.7
## Ssp103 2 417.7 5.6 3.0 8.1 11.3 2.6 1.4
## Ssp17 3 395.5 5.3 1.8 8.8 16.6 2.6 2.1
## Ssp150 4 352.2 4.7 2.4 7.1 21.3 2.5 2.7
## Ssp187 5 281.4 3.8 1.2 6.3 25.1 2.4 3.4
## Ssp2 6 213.0 2.8 1.1 4.6 27.9 2.3 4.1
## Ssp32 7 213.0 2.8 0.0 5.7 30.7 2.3 4.8
## Ssp146 8 207.0 2.8 0.5 5.0 33.5 2.3 5.5
## Ssp137 9 203.0 2.7 0.7 4.8 36.2 2.3 6.2
## Ssp80 10 194.2 2.6 0.7 4.5 38.8 2.3 6.8
## Ssp177 11 193.0 2.6 1.4 3.7 41.4 2.3 7.5
## Ssp118 12 186.0 2.5 -0.1 5.1 43.9 2.3 8.2
## Ssp69 13 174.5 2.3 -0.2 4.9 46.2 2.2 8.9
## Ssp31 14 172.0 2.3 0.6 4.0 48.5 2.2 9.6
## Ssp119 15 145.3 1.9 1.0 2.9 50.5 2.2 10.3
## Ssp104 16 138.0 1.8 0.2 3.5 52.3 2.1 11.0
## Ssp95 17 135.0 1.8 0.8 2.8 54.1 2.1 11.6
## Ssp18 18 135.0 1.8 0.3 3.3 55.9 2.1 12.3
## Ssp162 19 118.0 1.6 0.5 2.7 57.5 2.1 13.0
## Ssp29 20 116.5 1.6 0.3 2.8 59.1 2.1 13.7
## Ssp14 21 116.0 1.6 0.5 2.6 60.6 2.1 14.4
## Ssp117 22 114.4 1.5 0.7 2.3 62.1 2.1 15.1
## Ssp154 23 110.2 1.5 0.3 2.6 63.6 2.0 15.8
## Ssp159 24 106.2 1.4 0.3 2.6 65.0 2.0 16.4
## Ssp156 25 101.0 1.4 0.1 2.6 66.4 2.0 17.1
## Ssp13 26 98.0 1.3 0.4 2.2 67.7 2.0 17.8
## Ssp49 27 98.0 1.3 -0.1 2.7 69.0 2.0 18.5
## Ssp149 28 89.0 1.2 0.3 2.1 70.2 1.9 19.2
## Ssp71 29 88.2 1.2 0.5 1.9 71.4 1.9 19.9
## Ssp26 30 84.0 1.1 0.1 2.2 72.5 1.9 20.5
## Ssp101 31 83.0 1.1 0.1 2.2 73.6 1.9 21.2
## Ssp143 32 70.0 0.9 0.1 1.8 74.5 1.8 21.9
## Ssp54 33 69.0 0.9 0.1 1.8 75.5 1.8 22.6
## Ssp191 34 68.0 0.9 0.0 1.8 76.4 1.8 23.3
## Ssp43 35 61.5 0.8 0.1 1.5 77.2 1.8 24.0
## Ssp73 36 56.0 0.7 -0.4 1.9 77.9 1.7 24.7
## Ssp108 37 56.0 0.7 0.0 1.5 78.7 1.7 25.3
## Ssp158 38 52.5 0.7 -0.1 1.5 79.4 1.7 26.0
## Ssp115 39 52.0 0.7 0.2 1.2 80.1 1.7 26.7
## Ssp114 40 51.0 0.7 0.1 1.2 80.8 1.7 27.4
## Ssp130 41 50.0 0.7 -0.4 1.8 81.4 1.7 28.1
## Ssp78 42 47.5 0.6 0.1 1.2 82.1 1.7 28.8
## Ssp189 43 42.0 0.6 -0.5 1.7 82.6 1.6 29.5
## Ssp112 44 41.0 0.5 0.0 1.1 83.2 1.6 30.1
## Ssp142 45 41.0 0.5 -0.3 1.4 83.7 1.6 30.8
## Ssp7 46 39.3 0.5 -0.2 1.2 84.3 1.6 31.5
## Ssp21 47 39.0 0.5 -0.2 1.2 84.8 1.6 32.2
## Ssp94 48 39.0 0.5 0.0 1.0 85.3 1.6 32.9
## Ssp66 49 38.4 0.5 -0.3 1.3 85.8 1.6 33.6
## Ssp192 50 38.0 0.5 -0.5 1.5 86.3 1.6 34.2
## Ssp6 51 37.2 0.5 0.0 1.0 86.8 1.6 34.9
## Ssp129 52 37.0 0.5 0.1 0.9 87.3 1.6 35.6
## Ssp55 53 33.5 0.4 0.0 0.9 87.8 1.5 36.3
## Ssp25 54 31.5 0.4 0.0 0.8 88.2 1.5 37.0
## Ssp1 55 30.0 0.4 -0.2 1.0 88.6 1.5 37.7
## Ssp121 56 29.2 0.4 0.0 0.8 89.0 1.5 38.4
## Ssp111 57 29.0 0.4 0.0 0.7 89.4 1.5 39.0
## Ssp3 58 29.0 0.4 0.0 0.8 89.8 1.5 39.7
## Ssp132 59 27.0 0.4 0.0 0.8 90.1 1.4 40.4
## Ssp122 60 26.5 0.4 0.0 0.7 90.5 1.4 41.1
## Ssp85 61 26.0 0.3 0.0 0.7 90.8 1.4 41.8
## Ssp8 62 26.0 0.3 0.0 0.7 91.2 1.4 42.5
## Ssp16 63 25.0 0.3 0.0 0.7 91.5 1.4 43.2
## Ssp113 64 25.0 0.3 0.0 0.7 91.8 1.4 43.8
## Ssp20 65 24.2 0.3 -0.2 0.8 92.2 1.4 44.5
## Ssp141 66 22.5 0.3 0.0 0.6 92.5 1.4 45.2
## Ssp10 67 22.0 0.3 -0.2 0.8 92.8 1.3 45.9
## Ssp139 68 22.0 0.3 0.0 0.6 93.0 1.3 46.6
## Ssp82 69 21.0 0.3 0.0 0.6 93.3 1.3 47.3
## Ssp57 70 21.0 0.3 -0.1 0.7 93.6 1.3 47.9
## Ssp171 71 19.0 0.3 0.0 0.5 93.9 1.3 48.6
## Ssp182 72 18.0 0.2 -0.2 0.7 94.1 1.3 49.3
## Ssp100 73 18.0 0.2 0.0 0.5 94.3 1.3 50.0
## Ssp160 74 17.5 0.2 0.0 0.5 94.6 1.2 50.7
## Ssp186 75 16.0 0.2 0.0 0.4 94.8 1.2 51.4
## Ssp109 76 15.0 0.2 0.0 0.4 95.0 1.2 52.1
## Ssp188 77 15.0 0.2 -0.2 0.6 95.2 1.2 52.7
## Ssp163 78 14.0 0.2 -0.1 0.5 95.4 1.1 53.4
## Ssp181 79 13.5 0.2 -0.1 0.4 95.6 1.1 54.1
## Ssp151 80 13.0 0.2 -0.2 0.5 95.7 1.1 54.8
## Ssp4 81 13.0 0.2 0.0 0.3 95.9 1.1 55.5
## Ssp133 82 12.0 0.2 0.0 0.4 96.1 1.1 56.2
## Ssp12 83 12.0 0.2 -0.1 0.4 96.2 1.1 56.8
## Ssp19 84 12.0 0.2 -0.1 0.4 96.4 1.1 57.5
## Ssp167 85 10.5 0.1 0.0 0.3 96.5 1.0 58.2
## Ssp45 86 10.0 0.1 -0.1 0.4 96.7 1.0 58.9
## Ssp164 87 10.0 0.1 -0.1 0.4 96.8 1.0 59.6
## Ssp39 88 10.0 0.1 0.0 0.2 96.9 1.0 60.3
## Ssp168 89 10.0 0.1 -0.1 0.3 97.1 1.0 61.0
## Ssp65 90 9.0 0.1 -0.1 0.3 97.2 1.0 61.6
## Ssp96 91 9.0 0.1 -0.1 0.3 97.3 1.0 62.3
## Ssp123 92 9.0 0.1 -0.1 0.3 97.4 1.0 63.0
## Ssp50 93 9.0 0.1 -0.1 0.4 97.5 1.0 63.7
## Ssp179 94 9.0 0.1 -0.1 0.3 97.7 1.0 64.4
## Ssp128 95 9.0 0.1 0.0 0.2 97.8 1.0 65.1
## Ssp72 96 8.0 0.1 0.0 0.2 97.9 0.9 65.8
## Ssp106 97 8.0 0.1 -0.1 0.3 98.0 0.9 66.4
## Ssp5 98 7.5 0.1 0.0 0.2 98.1 0.9 67.1
## Ssp27 99 7.0 0.1 0.0 0.2 98.2 0.8 67.8
## Ssp47 100 7.0 0.1 -0.1 0.3 98.3 0.8 68.5
## Ssp77 101 6.0 0.1 0.0 0.2 98.4 0.8 69.2
## Ssp15 102 6.0 0.1 0.0 0.2 98.4 0.8 69.9
## Ssp23 103 6.0 0.1 -0.1 0.2 98.5 0.8 70.5
## Ssp169 104 6.0 0.1 -0.1 0.2 98.6 0.8 71.2
## Ssp38 105 6.0 0.1 -0.1 0.2 98.7 0.8 71.9
## Ssp64 106 5.0 0.1 -0.1 0.2 98.8 0.7 72.6
## Ssp42 107 5.0 0.1 -0.1 0.2 98.8 0.7 73.3
## Ssp155 108 5.0 0.1 0.0 0.2 98.9 0.7 74.0
## Ssp35 109 5.0 0.1 -0.1 0.2 99.0 0.7 74.7
## Ssp67 110 5.0 0.1 -0.1 0.2 99.0 0.7 75.3
## Ssp81 111 4.0 0.1 0.0 0.1 99.1 0.6 76.0
## Ssp107 112 4.0 0.1 0.0 0.1 99.1 0.6 76.7
## Ssp120 113 4.0 0.1 0.0 0.1 99.2 0.6 77.4
## Ssp148 114 4.0 0.1 0.0 0.1 99.2 0.6 78.1
## Ssp46 115 4.0 0.1 -0.1 0.2 99.3 0.6 78.8
## Ssp125 116 4.0 0.1 0.0 0.1 99.3 0.6 79.5
## Ssp88 117 4.0 0.1 0.0 0.1 99.4 0.6 80.1
## Ssp127 118 3.5 0.0 0.0 0.1 99.4 0.5 80.8
## Ssp172 119 3.0 0.0 0.0 0.1 99.5 0.5 81.5
## Ssp61 120 3.0 0.0 0.0 0.1 99.5 0.5 82.2
## Ssp157 121 3.0 0.0 0.0 0.1 99.6 0.5 82.9
## Ssp136 122 3.0 0.0 0.0 0.1 99.6 0.5 83.6
## Ssp76 123 3.0 0.0 0.0 0.1 99.6 0.5 84.2
## Ssp9 124 2.5 0.0 0.0 0.1 99.7 0.4 84.9
## Ssp116 125 2.0 0.0 0.0 0.1 99.7 0.3 85.6
## Ssp44 126 2.0 0.0 0.0 0.1 99.7 0.3 86.3
## Ssp30 127 2.0 0.0 0.0 0.1 99.8 0.3 87.0
## Ssp24 128 2.0 0.0 0.0 0.1 99.8 0.3 87.7
## Ssp144 129 2.0 0.0 0.0 0.1 99.8 0.3 88.4
## Ssp56 130 1.5 0.0 0.0 0.0 99.8 0.2 89.0
## Ssp124 131 1.0 0.0 0.0 0.0 99.8 0.0 89.7
## Ssp53 132 1.0 0.0 0.0 0.0 99.9 0.0 90.4
## Ssp98 133 1.0 0.0 0.0 0.0 99.9 0.0 91.1
## Ssp68 134 1.0 0.0 0.0 0.0 99.9 0.0 91.8
## Ssp126 135 1.0 0.0 0.0 0.0 99.9 0.0 92.5
## Ssp131 136 1.0 0.0 0.0 0.0 99.9 0.0 93.2
## Ssp28 137 1.0 0.0 0.0 0.0 99.9 0.0 93.8
## Ssp178 138 1.0 0.0 0.0 0.0 99.9 0.0 94.5
## Ssp48 139 1.0 0.0 0.0 0.0 100.0 0.0 95.2
## Ssp93 140 0.5 0.0 0.0 0.0 100.0 -0.3 95.9
## Ssp138 141 0.5 0.0 0.0 0.0 100.0 -0.3 96.6
## Ssp110 142 0.5 0.0 0.0 0.0 100.0 -0.3 97.3
## Ssp62 143 0.5 0.0 0.0 0.0 100.0 -0.3 97.9
## Ssp40 144 0.5 0.0 0.0 0.0 100.0 -0.3 98.6
## Ssp83 145 0.5 0.0 0.0 0.0 100.0 -0.3 99.3
## Ssp99 146 0.5 0.0 0.0 0.0 100.0 -0.3 100.0# Convert the matrix to a data frame
RankAbun1 <- as.data.frame(RankAbun1)
RankAbun1 <- RankAbun1 %>%
rownames_to_column(var = "Species")
head(RankAbun1)## Species rank abundance proportion plower pupper accumfreq logabun rankfreq
## 1 Ssp90 1 427.0 5.7 2.2 9.2 5.7 2.6 0.7
## 2 Ssp103 2 417.7 5.6 3.0 8.1 11.3 2.6 1.4
## 3 Ssp17 3 395.5 5.3 1.8 8.8 16.6 2.6 2.1
## 4 Ssp150 4 352.2 4.7 2.4 7.1 21.3 2.5 2.7
## 5 Ssp187 5 281.4 3.8 1.2 6.3 25.1 2.4 3.4
## 6 Ssp2 6 213.0 2.8 1.1 4.6 27.9 2.3 4.1RA_withSPID <- merge(RankAbun1, sp_codes, by.x = "Species", by.y = "Number", all.x = TRUE)
write.csv(RA_withSPID, "Output figs and tables/RankAbundance_overall.csv", row.names = F)
# rank abundance by treatment
RA_poop <- rankabuncomp(all.matrix_filtered, y=env_filtered, factor="Control_or_treatment",
scale='proportion', legend=FALSE)
RA_poop## Grouping species labelit rank abundance proportion plower pupper accumfreq
## 1 Control Ssp17 TRUE 1 238.0 6.4 0.9 12.0 6.4
## 2 Control Ssp90 TRUE 2 211.0 5.7 1.2 10.2 12.1
## 3 Control Ssp103 TRUE 3 190.7 5.2 1.9 8.5 17.3
## 4 Control Ssp118 FALSE 4 169.0 4.6 -0.5 9.6 21.9
## 5 Control Ssp146 FALSE 5 157.0 4.2 0.1 8.4 26.1
## 6 Control Ssp80 FALSE 6 129.7 3.5 0.1 6.9 29.6
## 7 Control Ssp177 FALSE 7 123.0 3.3 1.2 5.4 32.9
## 8 Control Ssp162 FALSE 8 118.0 3.2 1.0 5.4 36.1
## 9 Control Ssp2 FALSE 9 111.0 3.0 -0.2 6.2 39.1
## 10 Control Ssp150 FALSE 10 108.0 2.9 1.1 4.7 42.1
## 11 Control Ssp49 FALSE 11 98.0 2.6 -0.3 5.6 44.7
## 12 Control Ssp31 FALSE 12 97.0 2.6 0.1 5.2 47.3
## 13 Control Ssp95 FALSE 13 94.0 2.5 0.7 4.4 49.9
## 14 Control Ssp14 FALSE 14 91.0 2.5 0.5 4.4 52.3
## 15 Control Ssp156 FALSE 15 81.0 2.2 -0.1 4.5 54.5
## 16 Control Ssp26 FALSE 16 81.0 2.2 0.0 4.3 56.7
## 17 Control Ssp119 FALSE 17 78.8 2.1 0.8 3.4 58.8
## 18 Control Ssp54 FALSE 18 69.0 1.9 0.2 3.5 60.7
## 19 Control Ssp191 FALSE 19 68.0 1.8 0.1 3.6 62.5
## 20 Control Ssp13 FALSE 20 66.0 1.8 0.2 3.4 64.3
## 21 Control Ssp29 FALSE 21 60.0 1.6 -0.6 3.8 66.0
## 22 Control Ssp108 FALSE 22 56.0 1.5 0.1 2.9 67.5
## 23 Control Ssp187 FALSE 23 52.4 1.4 0.4 2.5 68.9
## 24 Control Ssp114 FALSE 24 51.0 1.4 0.4 2.4 70.3
## 25 Control Ssp115 FALSE 25 45.0 1.2 0.2 2.3 71.5
## 26 Control Ssp189 FALSE 26 42.0 1.1 -1.1 3.4 72.6
## 27 Control Ssp142 FALSE 27 41.0 1.1 -0.6 2.8 73.7
## 28 Control Ssp7 FALSE 28 39.3 1.1 -0.3 2.4 74.8
## 29 Control Ssp21 FALSE 29 36.0 1.0 -0.4 2.3 75.8
## 30 Control Ssp6 FALSE 30 36.0 1.0 0.1 1.9 76.7
## 31 Control Ssp71 FALSE 31 34.7 0.9 0.2 1.7 77.7
## 32 Control Ssp18 FALSE 32 32.0 0.9 -0.2 1.9 78.5
## 33 Control Ssp129 FALSE 33 31.0 0.8 0.0 1.7 79.4
## 34 Control Ssp143 FALSE 34 30.0 0.8 0.1 1.5 80.2
## 35 Control Ssp1 FALSE 35 30.0 0.8 -0.4 2.0 81.0
## 36 Control Ssp78 FALSE 36 29.5 0.8 0.1 1.5 81.8
## 37 Control Ssp132 FALSE 37 27.0 0.7 -0.1 1.5 82.5
## 38 Control Ssp85 FALSE 38 26.0 0.7 -0.1 1.5 83.2
## 39 Control Ssp43 FALSE 39 24.5 0.7 -0.1 1.5 83.9
## 40 Control Ssp113 FALSE 40 24.0 0.6 0.0 1.3 84.5
## 41 Control Ssp25 FALSE 41 24.0 0.6 -0.1 1.4 85.2
## 42 Control Ssp69 FALSE 42 22.5 0.6 -0.1 1.3 85.8
## 43 Control Ssp141 FALSE 43 22.5 0.6 -0.1 1.3 86.4
## 44 Control Ssp104 FALSE 44 22.0 0.6 -0.1 1.3 87.0
## 45 Control Ssp94 FALSE 45 20.0 0.5 -0.1 1.2 87.5
## 46 Control Ssp117 FALSE 46 19.2 0.5 0.1 0.9 88.1
## 47 Control Ssp101 FALSE 47 19.0 0.5 -0.5 1.5 88.6
## 48 Control Ssp121 FALSE 48 18.2 0.5 -0.2 1.2 89.1
## 49 Control Ssp100 FALSE 49 18.0 0.5 0.0 1.0 89.6
## 50 Control Ssp57 FALSE 50 18.0 0.5 -0.3 1.3 90.0
## 51 Control Ssp182 FALSE 51 18.0 0.5 -0.5 1.5 90.5
## 52 Control Ssp158 FALSE 52 17.5 0.5 -0.1 1.1 91.0
## 53 Control Ssp154 FALSE 53 17.2 0.5 -0.4 1.3 91.5
## 54 Control Ssp112 FALSE 54 17.0 0.5 -0.3 1.2 91.9
## 55 Control Ssp188 FALSE 55 15.0 0.4 -0.4 1.2 92.3
## 56 Control Ssp149 FALSE 56 15.0 0.4 -0.2 1.0 92.7
## 57 Control Ssp111 FALSE 57 13.0 0.4 0.0 0.7 93.1
## 58 Control Ssp171 FALSE 58 12.0 0.3 -0.1 0.7 93.4
## 59 Control Ssp12 FALSE 59 12.0 0.3 -0.2 0.8 93.7
## 60 Control Ssp19 FALSE 60 11.0 0.3 -0.2 0.8 94.0
## 61 Control Ssp163 FALSE 61 11.0 0.3 -0.3 0.8 94.3
## 62 Control Ssp159 FALSE 62 10.2 0.3 0.0 0.5 94.6
## 63 Control Ssp45 FALSE 63 10.0 0.3 -0.3 0.8 94.9
## 64 Control Ssp82 FALSE 64 10.0 0.3 -0.2 0.8 95.2
## 65 Control Ssp16 FALSE 65 10.0 0.3 -0.2 0.8 95.4
## 66 Control Ssp122 FALSE 66 9.3 0.3 -0.1 0.6 95.7
## 67 Control Ssp50 FALSE 67 9.0 0.2 -0.2 0.7 95.9
## 68 Control Ssp130 FALSE 68 9.0 0.2 -0.2 0.7 96.2
## 69 Control Ssp32 FALSE 69 8.0 0.2 -0.2 0.7 96.4
## 70 Control Ssp167 FALSE 70 8.0 0.2 -0.1 0.6 96.6
## 71 Control Ssp137 FALSE 71 8.0 0.2 -0.1 0.5 96.8
## 72 Control Ssp160 FALSE 72 8.0 0.2 -0.1 0.5 97.0
## 73 Control Ssp5 FALSE 73 7.5 0.2 -0.1 0.5 97.2
## 74 Control Ssp128 FALSE 74 7.0 0.2 -0.1 0.4 97.4
## 75 Control Ssp65 FALSE 75 7.0 0.2 -0.2 0.6 97.6
## 76 Control Ssp39 FALSE 76 7.0 0.2 0.0 0.4 97.8
## 77 Control Ssp96 FALSE 77 6.0 0.2 -0.2 0.5 98.0
## 78 Control Ssp169 FALSE 78 6.0 0.2 -0.2 0.5 98.1
## 79 Control Ssp67 FALSE 79 5.0 0.1 -0.1 0.4 98.3
## 80 Control Ssp42 FALSE 80 5.0 0.1 -0.1 0.4 98.4
## 81 Control Ssp107 FALSE 81 4.0 0.1 -0.1 0.3 98.5
## 82 Control Ssp181 FALSE 82 4.0 0.1 -0.1 0.3 98.6
## 83 Control Ssp46 FALSE 83 4.0 0.1 -0.1 0.3 98.7
## 84 Control Ssp179 FALSE 84 3.0 0.1 -0.1 0.2 98.8
## 85 Control Ssp20 FALSE 85 3.0 0.1 -0.1 0.2 98.9
## 86 Control Ssp109 FALSE 86 3.0 0.1 -0.1 0.2 99.0
## 87 Control Ssp168 FALSE 87 3.0 0.1 -0.1 0.2 99.0
## 88 Control Ssp125 FALSE 88 3.0 0.1 0.0 0.2 99.1
## 89 Control Ssp136 FALSE 89 2.5 0.1 0.0 0.1 99.2
## 90 Control Ssp66 FALSE 90 2.5 0.1 0.0 0.2 99.3
## 91 Control Ssp133 FALSE 91 2.0 0.1 -0.1 0.2 99.3
## 92 Control Ssp88 FALSE 92 2.0 0.1 -0.1 0.2 99.4
## 93 Control Ssp144 FALSE 93 2.0 0.1 0.0 0.1 99.4
## 94 Control Ssp24 FALSE 94 2.0 0.1 0.0 0.1 99.5
## 95 Control Ssp30 FALSE 95 2.0 0.1 0.0 0.1 99.5
## 96 Control Ssp10 FALSE 96 1.0 0.0 0.0 0.1 99.6
## 97 Control Ssp124 FALSE 97 1.0 0.0 0.0 0.1 99.6
## 98 Control Ssp15 FALSE 98 1.0 0.0 0.0 0.1 99.6
## 99 Control Ssp139 FALSE 99 1.0 0.0 0.0 0.1 99.6
## 100 Control Ssp155 FALSE 100 1.0 0.0 0.0 0.1 99.7
## 101 Control Ssp126 FALSE 101 1.0 0.0 0.0 0.1 99.7
## 102 Control Ssp4 FALSE 102 1.0 0.0 0.0 0.1 99.7
## 103 Control Ssp127 FALSE 103 1.0 0.0 0.0 0.1 99.7
## 104 Control Ssp178 FALSE 104 1.0 0.0 0.0 0.1 99.8
## 105 Control Ssp116 FALSE 105 1.0 0.0 0.0 0.1 99.8
## 106 Control Ssp48 FALSE 106 1.0 0.0 0.0 0.1 99.8
## 107 Control Ssp68 FALSE 107 1.0 0.0 0.0 0.1 99.9
## 108 Control Ssp120 FALSE 108 1.0 0.0 0.0 0.1 99.9
## 109 Control Ssp76 FALSE 109 1.0 0.0 0.0 0.1 99.9
## 110 Control Ssp172 FALSE 110 1.0 0.0 0.0 0.1 99.9
## 111 Control Ssp27 FALSE 111 1.0 0.0 0.0 0.1 100.0
## 112 Control Ssp110 FALSE 112 0.5 0.0 0.0 0.0 100.0
## 113 Control Ssp56 FALSE 113 0.5 0.0 0.0 0.0 100.0
## 114 Control Ssp83 FALSE 114 0.5 0.0 0.0 0.0 100.0
## 115 Nest Ssp150 TRUE 1 244.2 6.5 2.1 10.9 6.5
## 116 Nest Ssp187 TRUE 2 229.0 6.1 1.5 10.7 12.5
## 117 Nest Ssp103 TRUE 3 227.0 6.0 2.1 9.9 18.5
## 118 Nest Ssp90 FALSE 4 216.0 5.7 0.2 11.2 24.2
## 119 Nest Ssp32 FALSE 5 205.0 5.4 -0.3 11.2 29.7
## 120 Nest Ssp137 FALSE 6 195.0 5.2 1.6 8.7 34.8
## 121 Nest Ssp17 FALSE 7 157.5 4.2 -0.2 8.5 39.0
## 122 Nest Ssp69 FALSE 8 152.0 4.0 -1.1 9.1 43.0
## 123 Nest Ssp104 FALSE 9 116.0 3.1 -0.2 6.3 46.1
## 124 Nest Ssp18 FALSE 10 103.0 2.7 -0.1 5.5 48.8
## 125 Nest Ssp2 FALSE 11 102.0 2.7 1.0 4.4 51.5
## 126 Nest Ssp159 FALSE 12 96.0 2.5 0.2 4.8 54.0
## 127 Nest Ssp117 FALSE 13 95.2 2.5 0.9 4.1 56.5
## 128 Nest Ssp154 FALSE 14 93.0 2.5 0.5 4.5 59.0
## 129 Nest Ssp31 FALSE 15 75.0 2.0 -0.2 4.2 61.0
## 130 Nest Ssp149 FALSE 16 74.0 2.0 0.2 3.7 62.9
## 131 Nest Ssp177 FALSE 17 70.0 1.9 0.7 3.0 64.8
## 132 Nest Ssp119 FALSE 18 66.5 1.8 0.4 3.2 66.6
## 133 Nest Ssp80 FALSE 19 64.5 1.7 0.1 3.3 68.3
## 134 Nest Ssp101 FALSE 20 64.0 1.7 -0.1 3.5 70.0
## 135 Nest Ssp29 FALSE 21 56.5 1.5 0.3 2.7 71.4
## 136 Nest Ssp73 FALSE 22 56.0 1.5 -0.8 3.8 72.9
## 137 Nest Ssp71 FALSE 23 53.5 1.4 0.3 2.6 74.3
## 138 Nest Ssp146 FALSE 24 50.0 1.3 -0.5 3.2 75.7
## 139 Nest Ssp95 FALSE 25 41.0 1.1 0.3 1.9 76.8
## 140 Nest Ssp130 FALSE 26 41.0 1.1 -1.0 3.2 77.8
## 141 Nest Ssp143 FALSE 27 40.0 1.1 -0.6 2.7 78.9
## 142 Nest Ssp192 FALSE 28 38.0 1.0 -0.9 2.9 79.9
## 143 Nest Ssp43 FALSE 29 37.0 1.0 -0.2 2.2 80.9
## 144 Nest Ssp66 FALSE 30 35.9 0.9 -0.7 2.6 81.8
## 145 Nest Ssp158 FALSE 31 35.0 0.9 -0.7 2.5 82.8
## 146 Nest Ssp55 FALSE 32 33.5 0.9 -0.1 1.8 83.6
## 147 Nest Ssp13 FALSE 33 32.0 0.8 0.0 1.7 84.5
## 148 Nest Ssp3 FALSE 34 29.0 0.8 -0.1 1.6 85.3
## 149 Nest Ssp8 FALSE 35 26.0 0.7 0.0 1.4 85.9
## 150 Nest Ssp14 FALSE 36 25.0 0.7 -0.2 1.5 86.6
## 151 Nest Ssp112 FALSE 37 24.0 0.6 -0.1 1.3 87.2
## 152 Nest Ssp20 FALSE 38 21.2 0.6 -0.5 1.6 87.8
## 153 Nest Ssp139 FALSE 39 21.0 0.6 -0.1 1.2 88.4
## 154 Nest Ssp10 FALSE 40 21.0 0.6 -0.5 1.6 88.9
## 155 Nest Ssp156 FALSE 41 20.0 0.5 -0.2 1.3 89.4
## 156 Nest Ssp94 FALSE 42 19.0 0.5 -0.3 1.3 89.9
## 157 Nest Ssp78 FALSE 43 18.0 0.5 -0.4 1.4 90.4
## 158 Nest Ssp122 FALSE 44 17.2 0.5 -0.2 1.1 90.9
## 159 Nest Ssp118 FALSE 45 17.0 0.4 -0.1 1.0 91.3
## 160 Nest Ssp111 FALSE 46 16.0 0.4 -0.2 1.0 91.7
## 161 Nest Ssp186 FALSE 47 16.0 0.4 0.0 0.8 92.2
## 162 Nest Ssp16 FALSE 48 15.0 0.4 -0.1 0.9 92.6
## 163 Nest Ssp151 FALSE 49 13.0 0.3 -0.4 1.0 92.9
## 164 Nest Ssp4 FALSE 50 12.0 0.3 0.0 0.6 93.2
## 165 Nest Ssp109 FALSE 51 12.0 0.3 -0.1 0.8 93.5
## 166 Nest Ssp82 FALSE 52 11.0 0.3 -0.1 0.7 93.8
## 167 Nest Ssp121 FALSE 53 11.0 0.3 -0.3 0.9 94.1
## 168 Nest Ssp133 FALSE 54 10.0 0.3 -0.1 0.6 94.4
## 169 Nest Ssp164 FALSE 55 10.0 0.3 -0.2 0.8 94.7
## 170 Nest Ssp181 FALSE 56 9.5 0.3 -0.2 0.8 94.9
## 171 Nest Ssp160 FALSE 57 9.5 0.3 -0.2 0.7 95.2
## 172 Nest Ssp123 FALSE 58 9.0 0.2 -0.1 0.6 95.4
## 173 Nest Ssp72 FALSE 59 8.0 0.2 0.0 0.5 95.6
## 174 Nest Ssp106 FALSE 60 8.0 0.2 -0.2 0.6 95.8
## 175 Nest Ssp25 FALSE 61 7.5 0.2 -0.2 0.6 96.0
## 176 Nest Ssp171 FALSE 62 7.0 0.2 -0.1 0.5 96.2
## 177 Nest Ssp168 FALSE 63 7.0 0.2 -0.2 0.6 96.4
## 178 Nest Ssp47 FALSE 64 7.0 0.2 -0.2 0.5 96.6
## 179 Nest Ssp115 FALSE 65 7.0 0.2 -0.1 0.4 96.8
## 180 Nest Ssp38 FALSE 66 6.0 0.2 -0.2 0.5 96.9
## 181 Nest Ssp179 FALSE 67 6.0 0.2 -0.2 0.5 97.1
## 182 Nest Ssp23 FALSE 68 6.0 0.2 -0.2 0.5 97.2
## 183 Nest Ssp77 FALSE 69 6.0 0.2 -0.1 0.4 97.4
## 184 Nest Ssp129 FALSE 70 6.0 0.2 -0.1 0.4 97.5
## 185 Nest Ssp27 FALSE 71 6.0 0.2 -0.1 0.4 97.7
## 186 Nest Ssp15 FALSE 72 5.0 0.1 -0.1 0.4 97.8
## 187 Nest Ssp35 FALSE 73 5.0 0.1 -0.1 0.4 98.0
## 188 Nest Ssp64 FALSE 74 5.0 0.1 -0.1 0.4 98.1
## 189 Nest Ssp81 FALSE 75 4.0 0.1 0.0 0.3 98.2
## 190 Nest Ssp148 FALSE 76 4.0 0.1 -0.1 0.3 98.3
## 191 Nest Ssp155 FALSE 77 4.0 0.1 -0.1 0.3 98.4
## 192 Nest Ssp120 FALSE 78 3.0 0.1 -0.1 0.2 98.5
## 193 Nest Ssp157 FALSE 79 3.0 0.1 -0.1 0.2 98.6
## 194 Nest Ssp26 FALSE 80 3.0 0.1 -0.1 0.2 98.7
## 195 Nest Ssp61 FALSE 81 3.0 0.1 -0.1 0.2 98.7
## 196 Nest Ssp21 FALSE 82 3.0 0.1 0.0 0.2 98.8
## 197 Nest Ssp39 FALSE 83 3.0 0.1 0.0 0.2 98.9
## 198 Nest Ssp96 FALSE 84 3.0 0.1 -0.1 0.2 99.0
## 199 Nest Ssp163 FALSE 85 3.0 0.1 -0.1 0.2 99.1
## 200 Nest Ssp57 FALSE 86 3.0 0.1 -0.1 0.2 99.1
## 201 Nest Ssp9 FALSE 87 2.5 0.1 -0.1 0.2 99.2
## 202 Nest Ssp167 FALSE 88 2.5 0.1 -0.1 0.2 99.3
## 203 Nest Ssp127 FALSE 89 2.5 0.1 0.0 0.2 99.3
## 204 Nest Ssp128 FALSE 90 2.0 0.1 0.0 0.1 99.4
## 205 Nest Ssp65 FALSE 91 2.0 0.1 -0.1 0.2 99.4
## 206 Nest Ssp88 FALSE 92 2.0 0.1 -0.1 0.2 99.5
## 207 Nest Ssp44 FALSE 93 2.0 0.1 -0.1 0.2 99.5
## 208 Nest Ssp76 FALSE 94 2.0 0.1 -0.1 0.2 99.6
## 209 Nest Ssp172 FALSE 95 2.0 0.1 0.0 0.2 99.6
## 210 Nest Ssp6 FALSE 96 1.2 0.0 0.0 0.1 99.7
## 211 Nest Ssp19 FALSE 97 1.0 0.0 0.0 0.1 99.7
## 212 Nest Ssp28 FALSE 98 1.0 0.0 0.0 0.1 99.7
## 213 Nest Ssp116 FALSE 99 1.0 0.0 0.0 0.1 99.8
## 214 Nest Ssp56 FALSE 100 1.0 0.0 0.0 0.1 99.8
## 215 Nest Ssp98 FALSE 101 1.0 0.0 0.0 0.1 99.8
## 216 Nest Ssp131 FALSE 102 1.0 0.0 0.0 0.1 99.8
## 217 Nest Ssp125 FALSE 103 1.0 0.0 0.0 0.1 99.9
## 218 Nest Ssp53 FALSE 104 1.0 0.0 0.0 0.1 99.9
## 219 Nest Ssp113 FALSE 105 1.0 0.0 0.0 0.1 99.9
## 220 Nest Ssp40 FALSE 106 0.5 0.0 0.0 0.0 99.9
## 221 Nest Ssp93 FALSE 107 0.5 0.0 0.0 0.0 99.9
## 222 Nest Ssp138 FALSE 108 0.5 0.0 0.0 0.0 100.0
## 223 Nest Ssp62 FALSE 109 0.5 0.0 0.0 0.0 100.0
## 224 Nest Ssp136 FALSE 110 0.5 0.0 0.0 0.0 100.0
## 225 Nest Ssp99 FALSE 111 0.5 0.0 0.0 0.0 100.0
## logabun rankfreq
## 1 2.4 0.9
## 2 2.3 1.8
## 3 2.3 2.6
## 4 2.2 3.5
## 5 2.2 4.4
## 6 2.1 5.3
## 7 2.1 6.1
## 8 2.1 7.0
## 9 2.0 7.9
## 10 2.0 8.8
## 11 2.0 9.6
## 12 2.0 10.5
## 13 2.0 11.4
## 14 2.0 12.3
## 15 1.9 13.2
## 16 1.9 14.0
## 17 1.9 14.9
## 18 1.8 15.8
## 19 1.8 16.7
## 20 1.8 17.5
## 21 1.8 18.4
## 22 1.7 19.3
## 23 1.7 20.2
## 24 1.7 21.1
## 25 1.7 21.9
## 26 1.6 22.8
## 27 1.6 23.7
## 28 1.6 24.6
## 29 1.6 25.4
## 30 1.6 26.3
## 31 1.5 27.2
## 32 1.5 28.1
## 33 1.5 28.9
## 34 1.5 29.8
## 35 1.5 30.7
## 36 1.5 31.6
## 37 1.4 32.5
## 38 1.4 33.3
## 39 1.4 34.2
## 40 1.4 35.1
## 41 1.4 36.0
## 42 1.4 36.8
## 43 1.4 37.7
## 44 1.3 38.6
## 45 1.3 39.5
## 46 1.3 40.4
## 47 1.3 41.2
## 48 1.3 42.1
## 49 1.3 43.0
## 50 1.3 43.9
## 51 1.3 44.7
## 52 1.2 45.6
## 53 1.2 46.5
## 54 1.2 47.4
## 55 1.2 48.2
## 56 1.2 49.1
## 57 1.1 50.0
## 58 1.1 50.9
## 59 1.1 51.8
## 60 1.0 52.6
## 61 1.0 53.5
## 62 1.0 54.4
## 63 1.0 55.3
## 64 1.0 56.1
## 65 1.0 57.0
## 66 1.0 57.9
## 67 1.0 58.8
## 68 1.0 59.6
## 69 0.9 60.5
## 70 0.9 61.4
## 71 0.9 62.3
## 72 0.9 63.2
## 73 0.9 64.0
## 74 0.8 64.9
## 75 0.8 65.8
## 76 0.8 66.7
## 77 0.8 67.5
## 78 0.8 68.4
## 79 0.7 69.3
## 80 0.7 70.2
## 81 0.6 71.1
## 82 0.6 71.9
## 83 0.6 72.8
## 84 0.5 73.7
## 85 0.5 74.6
## 86 0.5 75.4
## 87 0.5 76.3
## 88 0.5 77.2
## 89 0.4 78.1
## 90 0.4 78.9
## 91 0.3 79.8
## 92 0.3 80.7
## 93 0.3 81.6
## 94 0.3 82.5
## 95 0.3 83.3
## 96 0.0 84.2
## 97 0.0 85.1
## 98 0.0 86.0
## 99 0.0 86.8
## 100 0.0 87.7
## 101 0.0 88.6
## 102 0.0 89.5
## 103 0.0 90.4
## 104 0.0 91.2
## 105 0.0 92.1
## 106 0.0 93.0
## 107 0.0 93.9
## 108 0.0 94.7
## 109 0.0 95.6
## 110 0.0 96.5
## 111 0.0 97.4
## 112 -0.3 98.2
## 113 -0.3 99.1
## 114 -0.3 100.0
## 115 2.4 0.9
## 116 2.4 1.8
## 117 2.4 2.7
## 118 2.3 3.6
## 119 2.3 4.5
## 120 2.3 5.4
## 121 2.2 6.3
## 122 2.2 7.2
## 123 2.1 8.1
## 124 2.0 9.0
## 125 2.0 9.9
## 126 2.0 10.8
## 127 2.0 11.7
## 128 2.0 12.6
## 129 1.9 13.5
## 130 1.9 14.4
## 131 1.8 15.3
## 132 1.8 16.2
## 133 1.8 17.1
## 134 1.8 18.0
## 135 1.8 18.9
## 136 1.7 19.8
## 137 1.7 20.7
## 138 1.7 21.6
## 139 1.6 22.5
## 140 1.6 23.4
## 141 1.6 24.3
## 142 1.6 25.2
## 143 1.6 26.1
## 144 1.6 27.0
## 145 1.5 27.9
## 146 1.5 28.8
## 147 1.5 29.7
## 148 1.5 30.6
## 149 1.4 31.5
## 150 1.4 32.4
## 151 1.4 33.3
## 152 1.3 34.2
## 153 1.3 35.1
## 154 1.3 36.0
## 155 1.3 36.9
## 156 1.3 37.8
## 157 1.3 38.7
## 158 1.2 39.6
## 159 1.2 40.5
## 160 1.2 41.4
## 161 1.2 42.3
## 162 1.2 43.2
## 163 1.1 44.1
## 164 1.1 45.0
## 165 1.1 45.9
## 166 1.0 46.8
## 167 1.0 47.7
## 168 1.0 48.6
## 169 1.0 49.5
## 170 1.0 50.5
## 171 1.0 51.4
## 172 1.0 52.3
## 173 0.9 53.2
## 174 0.9 54.1
## 175 0.9 55.0
## 176 0.8 55.9
## 177 0.8 56.8
## 178 0.8 57.7
## 179 0.8 58.6
## 180 0.8 59.5
## 181 0.8 60.4
## 182 0.8 61.3
## 183 0.8 62.2
## 184 0.8 63.1
## 185 0.8 64.0
## 186 0.7 64.9
## 187 0.7 65.8
## 188 0.7 66.7
## 189 0.6 67.6
## 190 0.6 68.5
## 191 0.6 69.4
## 192 0.5 70.3
## 193 0.5 71.2
## 194 0.5 72.1
## 195 0.5 73.0
## 196 0.5 73.9
## 197 0.5 74.8
## 198 0.5 75.7
## 199 0.5 76.6
## 200 0.5 77.5
## 201 0.4 78.4
## 202 0.4 79.3
## 203 0.4 80.2
## 204 0.3 81.1
## 205 0.3 82.0
## 206 0.3 82.9
## 207 0.3 83.8
## 208 0.3 84.7
## 209 0.3 85.6
## 210 0.1 86.5
## 211 0.0 87.4
## 212 0.0 88.3
## 213 0.0 89.2
## 214 0.0 90.1
## 215 0.0 91.0
## 216 0.0 91.9
## 217 0.0 92.8
## 218 0.0 93.7
## 219 0.0 94.6
## 220 -0.3 95.5
## 221 -0.3 96.4
## 222 -0.3 97.3
## 223 -0.3 98.2
## 224 -0.3 99.1
## 225 -0.3 100.0# now assign functional group and species ID
RA_poop_withSPID <- merge(RA_poop, sp_codes, by.x = "species", by.y = "Number", all.x = TRUE)
write.csv(RA_poop_withSPID, "Output figs and tables/RankAbundance_byTreatment.csv", row.names = F)
# rank abundance by nest location
RA_nest <- rankabuncomp(all.matrix_filtered, y=env_filtered, factor="Nest_Position",
scale='proportion', legend=FALSE)
RA_nest## Grouping species labelit rank abundance proportion plower pupper
## 1 above_nest Ssp103 TRUE 1 49.0 13.3 -3.3 30.0
## 2 above_nest Ssp130 TRUE 2 40.0 10.9 -10.4 32.2
## 3 above_nest Ssp118 TRUE 3 34.0 9.2 -5.9 24.4
## 4 above_nest Ssp146 FALSE 4 27.0 7.3 -4.4 19.0
## 5 above_nest Ssp54 FALSE 5 25.0 6.8 -2.8 16.4
## 6 above_nest Ssp85 FALSE 6 19.0 5.2 -4.2 14.5
## 7 above_nest Ssp94 FALSE 7 18.0 4.9 -0.5 10.3
## 8 above_nest Ssp122 FALSE 8 13.2 3.6 -3.4 10.5
## 9 above_nest Ssp151 FALSE 9 13.0 3.5 -4.6 11.7
## 10 above_nest Ssp109 FALSE 10 12.0 3.3 -0.5 7.0
## 11 above_nest Ssp177 FALSE 11 10.0 2.7 -1.1 6.5
## 12 above_nest Ssp65 FALSE 12 9.0 2.4 -1.8 6.7
## 13 above_nest Ssp117 FALSE 13 9.0 2.4 -0.6 5.5
## 14 above_nest Ssp82 FALSE 14 8.0 2.2 -2.0 6.4
## 15 above_nest Ssp111 FALSE 15 8.0 2.2 -2.5 6.8
## 16 above_nest Ssp13 FALSE 16 8.0 2.2 -2.1 6.4
## 17 above_nest Ssp96 FALSE 17 6.0 1.6 -2.0 5.3
## 18 above_nest Ssp38 FALSE 18 6.0 1.6 -2.1 5.4
## 19 above_nest Ssp150 FALSE 19 5.1 1.4 -1.1 3.9
## 20 above_nest Ssp171 FALSE 20 5.0 1.4 -1.8 4.5
## 21 above_nest Ssp35 FALSE 21 5.0 1.4 -1.8 4.5
## 22 above_nest Ssp119 FALSE 22 3.1 0.8 -0.7 2.4
## 23 above_nest Ssp114 FALSE 23 3.0 0.8 -1.2 2.8
## 24 above_nest Ssp69 FALSE 24 3.0 0.8 -0.6 2.2
## 25 above_nest Ssp49 FALSE 25 3.0 0.8 -0.7 2.4
## 26 above_nest Ssp61 FALSE 26 3.0 0.8 -1.2 2.8
## 27 above_nest Ssp57 FALSE 27 3.0 0.8 -0.8 2.4
## 28 above_nest Ssp167 FALSE 28 2.5 0.7 -0.8 2.1
## 29 above_nest Ssp27 FALSE 29 2.0 0.5 -0.6 1.7
## 30 above_nest Ssp112 FALSE 30 2.0 0.5 -0.1 1.2
## 31 above_nest Ssp76 FALSE 31 2.0 0.5 -0.6 1.7
## 32 above_nest Ssp10 FALSE 32 2.0 0.5 -0.8 1.9
## 33 above_nest Ssp128 FALSE 33 1.5 0.4 -0.1 0.9
## 34 above_nest Ssp148 FALSE 34 1.0 0.3 -0.3 0.8
## 35 above_nest Ssp21 FALSE 35 1.0 0.3 -0.3 0.8
## 36 above_nest Ssp155 FALSE 36 1.0 0.3 -0.3 0.9
## 37 above_nest Ssp159 FALSE 37 1.0 0.3 -0.3 0.9
## 38 above_nest Ssp125 FALSE 38 1.0 0.3 -0.3 0.8
## 39 above_nest Ssp187 FALSE 39 1.0 0.3 -0.3 0.8
## 40 above_nest Ssp139 FALSE 40 0.5 0.1 -0.2 0.4
## 41 above_nest Ssp62 FALSE 41 0.5 0.1 -0.2 0.5
## 42 above_nest Ssp56 FALSE 42 0.5 0.1 -0.2 0.4
## 43 above_nest Ssp138 FALSE 43 0.5 0.1 -0.2 0.5
## 44 above_nest Ssp127 FALSE 44 0.5 0.1 -0.1 0.4
## 45 below_nest Ssp90 TRUE 1 427.0 6.0 2.3 9.7
## 46 below_nest Ssp17 TRUE 2 395.5 5.6 1.9 9.2
## 47 below_nest Ssp103 TRUE 3 368.7 5.2 2.6 7.7
## 48 below_nest Ssp150 FALSE 4 347.1 4.9 2.4 7.4
## 49 below_nest Ssp187 FALSE 5 280.4 3.9 1.3 6.6
## 50 below_nest Ssp32 FALSE 6 213.0 3.0 0.0 6.0
## 51 below_nest Ssp2 FALSE 7 213.0 3.0 1.1 4.9
## 52 below_nest Ssp137 FALSE 8 203.0 2.9 0.7 5.0
## 53 below_nest Ssp80 FALSE 9 194.2 2.7 0.8 4.7
## 54 below_nest Ssp177 FALSE 10 183.0 2.6 1.4 3.8
## 55 below_nest Ssp146 FALSE 11 180.0 2.5 0.2 4.9
## 56 below_nest Ssp31 FALSE 12 172.0 2.4 0.6 4.2
## 57 below_nest Ssp69 FALSE 13 171.5 2.4 -0.3 5.1
## 58 below_nest Ssp118 FALSE 14 152.0 2.1 -0.5 4.8
## 59 below_nest Ssp119 FALSE 15 142.2 2.0 1.0 3.0
## 60 below_nest Ssp104 FALSE 16 138.0 1.9 0.2 3.7
## 61 below_nest Ssp95 FALSE 17 135.0 1.9 0.8 3.0
## 62 below_nest Ssp18 FALSE 18 135.0 1.9 0.3 3.5
## 63 below_nest Ssp162 FALSE 19 118.0 1.7 0.5 2.8
## 64 below_nest Ssp29 FALSE 20 116.5 1.6 0.3 2.9
## 65 below_nest Ssp14 FALSE 21 116.0 1.6 0.5 2.8
## 66 below_nest Ssp154 FALSE 22 110.2 1.5 0.3 2.8
## 67 below_nest Ssp117 FALSE 23 105.4 1.5 0.6 2.3
## 68 below_nest Ssp159 FALSE 24 105.2 1.5 0.3 2.7
## 69 below_nest Ssp156 FALSE 25 101.0 1.4 0.1 2.7
## 70 below_nest Ssp49 FALSE 26 95.0 1.3 -0.2 2.8
## 71 below_nest Ssp13 FALSE 27 90.0 1.3 0.3 2.2
## 72 below_nest Ssp149 FALSE 28 89.0 1.3 0.3 2.2
## 73 below_nest Ssp71 FALSE 29 88.2 1.2 0.5 2.0
## 74 below_nest Ssp26 FALSE 30 84.0 1.2 0.1 2.3
## 75 below_nest Ssp101 FALSE 31 83.0 1.2 0.1 2.3
## 76 below_nest Ssp143 FALSE 32 70.0 1.0 0.1 1.9
## 77 below_nest Ssp191 FALSE 33 68.0 1.0 0.0 1.9
## 78 below_nest Ssp43 FALSE 34 61.5 0.9 0.1 1.6
## 79 below_nest Ssp73 FALSE 35 56.0 0.8 -0.4 2.0
## 80 below_nest Ssp108 FALSE 36 56.0 0.8 0.0 1.5
## 81 below_nest Ssp158 FALSE 37 52.5 0.7 -0.1 1.6
## 82 below_nest Ssp115 FALSE 38 52.0 0.7 0.2 1.3
## 83 below_nest Ssp114 FALSE 39 48.0 0.7 0.1 1.2
## 84 below_nest Ssp78 FALSE 40 47.5 0.7 0.1 1.3
## 85 below_nest Ssp54 FALSE 41 44.0 0.6 -0.1 1.4
## 86 below_nest Ssp189 FALSE 42 42.0 0.6 -0.6 1.8
## 87 below_nest Ssp142 FALSE 43 41.0 0.6 -0.3 1.4
## 88 below_nest Ssp7 FALSE 44 39.3 0.6 -0.2 1.3
## 89 below_nest Ssp112 FALSE 45 39.0 0.5 0.0 1.1
## 90 below_nest Ssp66 FALSE 46 38.4 0.5 -0.3 1.4
## 91 below_nest Ssp192 FALSE 47 38.0 0.5 -0.5 1.6
## 92 below_nest Ssp21 FALSE 48 38.0 0.5 -0.2 1.2
## 93 below_nest Ssp6 FALSE 49 37.2 0.5 0.0 1.0
## 94 below_nest Ssp129 FALSE 50 37.0 0.5 0.1 1.0
## 95 below_nest Ssp55 FALSE 51 33.5 0.5 0.0 1.0
## 96 below_nest Ssp25 FALSE 52 31.5 0.4 0.0 0.9
## 97 below_nest Ssp1 FALSE 53 30.0 0.4 -0.2 1.0
## 98 below_nest Ssp121 FALSE 54 29.2 0.4 0.0 0.9
## 99 below_nest Ssp3 FALSE 55 29.0 0.4 0.0 0.9
## 100 below_nest Ssp132 FALSE 56 27.0 0.4 0.0 0.8
## 101 below_nest Ssp8 FALSE 57 26.0 0.4 0.0 0.7
## 102 below_nest Ssp113 FALSE 58 25.0 0.4 0.0 0.7
## 103 below_nest Ssp16 FALSE 59 25.0 0.4 0.0 0.7
## 104 below_nest Ssp20 FALSE 60 24.2 0.3 -0.2 0.9
## 105 below_nest Ssp141 FALSE 61 22.5 0.3 0.0 0.7
## 106 below_nest Ssp139 FALSE 62 21.5 0.3 0.0 0.7
## 107 below_nest Ssp94 FALSE 63 21.0 0.3 -0.1 0.7
## 108 below_nest Ssp111 FALSE 64 21.0 0.3 0.0 0.6
## 109 below_nest Ssp10 FALSE 65 20.0 0.3 -0.3 0.8
## 110 below_nest Ssp100 FALSE 66 18.0 0.3 0.0 0.5
## 111 below_nest Ssp57 FALSE 67 18.0 0.3 -0.2 0.7
## 112 below_nest Ssp182 FALSE 68 18.0 0.3 -0.2 0.8
## 113 below_nest Ssp160 FALSE 69 17.5 0.2 0.0 0.5
## 114 below_nest Ssp186 FALSE 70 16.0 0.2 0.0 0.4
## 115 below_nest Ssp188 FALSE 71 15.0 0.2 -0.2 0.6
## 116 below_nest Ssp171 FALSE 72 14.0 0.2 0.0 0.4
## 117 below_nest Ssp163 FALSE 73 14.0 0.2 -0.1 0.5
## 118 below_nest Ssp181 FALSE 74 13.5 0.2 -0.1 0.5
## 119 below_nest Ssp122 FALSE 75 13.3 0.2 0.0 0.4
## 120 below_nest Ssp4 FALSE 76 13.0 0.2 0.0 0.4
## 121 below_nest Ssp82 FALSE 77 13.0 0.2 -0.1 0.5
## 122 below_nest Ssp133 FALSE 78 12.0 0.2 0.0 0.4
## 123 below_nest Ssp19 FALSE 79 12.0 0.2 -0.1 0.4
## 124 below_nest Ssp12 FALSE 80 12.0 0.2 -0.1 0.4
## 125 below_nest Ssp45 FALSE 81 10.0 0.1 -0.1 0.4
## 126 below_nest Ssp39 FALSE 82 10.0 0.1 0.0 0.3
## 127 below_nest Ssp168 FALSE 83 10.0 0.1 -0.1 0.4
## 128 below_nest Ssp164 FALSE 84 10.0 0.1 -0.1 0.4
## 129 below_nest Ssp130 FALSE 85 10.0 0.1 -0.1 0.4
## 130 below_nest Ssp123 FALSE 86 9.0 0.1 -0.1 0.3
## 131 below_nest Ssp50 FALSE 87 9.0 0.1 -0.1 0.4
## 132 below_nest Ssp179 FALSE 88 9.0 0.1 -0.1 0.3
## 133 below_nest Ssp72 FALSE 89 8.0 0.1 0.0 0.2
## 134 below_nest Ssp106 FALSE 90 8.0 0.1 -0.1 0.3
## 135 below_nest Ssp167 FALSE 91 8.0 0.1 -0.1 0.3
## 136 below_nest Ssp5 FALSE 92 7.5 0.1 0.0 0.3
## 137 below_nest Ssp128 FALSE 93 7.5 0.1 0.0 0.2
## 138 below_nest Ssp47 FALSE 94 7.0 0.1 -0.1 0.3
## 139 below_nest Ssp85 FALSE 95 7.0 0.1 0.0 0.2
## 140 below_nest Ssp15 FALSE 96 6.0 0.1 0.0 0.2
## 141 below_nest Ssp169 FALSE 97 6.0 0.1 -0.1 0.3
## 142 below_nest Ssp23 FALSE 98 6.0 0.1 -0.1 0.3
## 143 below_nest Ssp77 FALSE 99 6.0 0.1 0.0 0.2
## 144 below_nest Ssp64 FALSE 100 5.0 0.1 -0.1 0.2
## 145 below_nest Ssp42 FALSE 101 5.0 0.1 -0.1 0.2
## 146 below_nest Ssp67 FALSE 102 5.0 0.1 -0.1 0.2
## 147 below_nest Ssp27 FALSE 103 5.0 0.1 0.0 0.2
## 148 below_nest Ssp81 FALSE 104 4.0 0.1 0.0 0.1
## 149 below_nest Ssp155 FALSE 105 4.0 0.1 0.0 0.1
## 150 below_nest Ssp120 FALSE 106 4.0 0.1 0.0 0.1
## 151 below_nest Ssp46 FALSE 107 4.0 0.1 -0.1 0.2
## 152 below_nest Ssp88 FALSE 108 4.0 0.1 0.0 0.1
## 153 below_nest Ssp107 FALSE 109 4.0 0.1 0.0 0.1
## 154 below_nest Ssp136 FALSE 110 3.0 0.0 0.0 0.1
## 155 below_nest Ssp172 FALSE 111 3.0 0.0 0.0 0.1
## 156 below_nest Ssp157 FALSE 112 3.0 0.0 0.0 0.1
## 157 below_nest Ssp96 FALSE 113 3.0 0.0 0.0 0.1
## 158 below_nest Ssp127 FALSE 114 3.0 0.0 0.0 0.1
## 159 below_nest Ssp125 FALSE 115 3.0 0.0 0.0 0.1
## 160 below_nest Ssp148 FALSE 116 3.0 0.0 0.0 0.1
## 161 below_nest Ssp109 FALSE 117 3.0 0.0 0.0 0.1
## 162 below_nest Ssp9 FALSE 118 2.5 0.0 0.0 0.1
## 163 below_nest Ssp116 FALSE 119 2.0 0.0 0.0 0.1
## 164 below_nest Ssp30 FALSE 120 2.0 0.0 0.0 0.1
## 165 below_nest Ssp24 FALSE 121 2.0 0.0 0.0 0.1
## 166 below_nest Ssp44 FALSE 122 2.0 0.0 0.0 0.1
## 167 below_nest Ssp144 FALSE 123 2.0 0.0 0.0 0.1
## 168 below_nest Ssp56 FALSE 124 1.0 0.0 0.0 0.0
## 169 below_nest Ssp68 FALSE 125 1.0 0.0 0.0 0.0
## 170 below_nest Ssp53 FALSE 126 1.0 0.0 0.0 0.0
## 171 below_nest Ssp131 FALSE 127 1.0 0.0 0.0 0.0
## 172 below_nest Ssp126 FALSE 128 1.0 0.0 0.0 0.0
## 173 below_nest Ssp28 FALSE 129 1.0 0.0 0.0 0.0
## 174 below_nest Ssp178 FALSE 130 1.0 0.0 0.0 0.0
## 175 below_nest Ssp48 FALSE 131 1.0 0.0 0.0 0.0
## 176 below_nest Ssp124 FALSE 132 1.0 0.0 0.0 0.0
## 177 below_nest Ssp98 FALSE 133 1.0 0.0 0.0 0.0
## 178 below_nest Ssp76 FALSE 134 1.0 0.0 0.0 0.0
## 179 below_nest Ssp40 FALSE 135 0.5 0.0 0.0 0.0
## 180 below_nest Ssp93 FALSE 136 0.5 0.0 0.0 0.0
## 181 below_nest Ssp110 FALSE 137 0.5 0.0 0.0 0.0
## 182 below_nest Ssp83 FALSE 138 0.5 0.0 0.0 0.0
## 183 below_nest Ssp99 FALSE 139 0.5 0.0 0.0 0.0
## accumfreq logabun rankfreq
## 1 13.3 1.7 2.3
## 2 24.2 1.6 4.5
## 3 33.4 1.5 6.8
## 4 40.8 1.4 9.1
## 5 47.6 1.4 11.4
## 6 52.7 1.3 13.6
## 7 57.6 1.3 15.9
## 8 61.2 1.1 18.2
## 9 64.7 1.1 20.5
## 10 68.0 1.1 22.7
## 11 70.7 1.0 25.0
## 12 73.2 1.0 27.3
## 13 75.6 1.0 29.5
## 14 77.8 0.9 31.8
## 15 80.0 0.9 34.1
## 16 82.1 0.9 36.4
## 17 83.8 0.8 38.6
## 18 85.4 0.8 40.9
## 19 86.8 0.7 43.2
## 20 88.1 0.7 45.5
## 21 89.5 0.7 47.7
## 22 90.4 0.5 50.0
## 23 91.2 0.5 52.3
## 24 92.0 0.5 54.5
## 25 92.8 0.5 56.8
## 26 93.6 0.5 59.1
## 27 94.4 0.5 61.4
## 28 95.1 0.4 63.6
## 29 95.7 0.3 65.9
## 30 96.2 0.3 68.2
## 31 96.7 0.3 70.5
## 32 97.3 0.3 72.7
## 33 97.7 0.2 75.0
## 34 98.0 0.0 77.3
## 35 98.2 0.0 79.5
## 36 98.5 0.0 81.8
## 37 98.8 0.0 84.1
## 38 99.0 0.0 86.4
## 39 99.3 0.0 88.6
## 40 99.5 -0.3 90.9
## 41 99.6 -0.3 93.2
## 42 99.7 -0.3 95.5
## 43 99.9 -0.3 97.7
## 44 100.0 -0.3 100.0
## 45 6.0 2.6 0.7
## 46 11.6 2.6 1.4
## 47 16.8 2.6 2.2
## 48 21.6 2.5 2.9
## 49 25.6 2.4 3.6
## 50 28.6 2.3 4.3
## 51 31.6 2.3 5.0
## 52 34.4 2.3 5.8
## 53 37.2 2.3 6.5
## 54 39.7 2.3 7.2
## 55 42.3 2.3 7.9
## 56 44.7 2.2 8.6
## 57 47.1 2.2 9.4
## 58 49.2 2.2 10.1
## 59 51.2 2.2 10.8
## 60 53.2 2.1 11.5
## 61 55.1 2.1 12.2
## 62 57.0 2.1 12.9
## 63 58.6 2.1 13.7
## 64 60.3 2.1 14.4
## 65 61.9 2.1 15.1
## 66 63.4 2.0 15.8
## 67 64.9 2.0 16.5
## 68 66.4 2.0 17.3
## 69 67.8 2.0 18.0
## 70 69.2 2.0 18.7
## 71 70.4 2.0 19.4
## 72 71.7 1.9 20.1
## 73 72.9 1.9 20.9
## 74 74.1 1.9 21.6
## 75 75.3 1.9 22.3
## 76 76.2 1.8 23.0
## 77 77.2 1.8 23.7
## 78 78.1 1.8 24.5
## 79 78.9 1.7 25.2
## 80 79.6 1.7 25.9
## 81 80.4 1.7 26.6
## 82 81.1 1.7 27.3
## 83 81.8 1.7 28.1
## 84 82.5 1.7 28.8
## 85 83.1 1.6 29.5
## 86 83.7 1.6 30.2
## 87 84.2 1.6 30.9
## 88 84.8 1.6 31.7
## 89 85.3 1.6 32.4
## 90 85.9 1.6 33.1
## 91 86.4 1.6 33.8
## 92 87.0 1.6 34.5
## 93 87.5 1.6 35.3
## 94 88.0 1.6 36.0
## 95 88.5 1.5 36.7
## 96 88.9 1.5 37.4
## 97 89.3 1.5 38.1
## 98 89.7 1.5 38.8
## 99 90.1 1.5 39.6
## 100 90.5 1.4 40.3
## 101 90.9 1.4 41.0
## 102 91.2 1.4 41.7
## 103 91.6 1.4 42.4
## 104 91.9 1.4 43.2
## 105 92.3 1.4 43.9
## 106 92.6 1.3 44.6
## 107 92.9 1.3 45.3
## 108 93.1 1.3 46.0
## 109 93.4 1.3 46.8
## 110 93.7 1.3 47.5
## 111 93.9 1.3 48.2
## 112 94.2 1.3 48.9
## 113 94.4 1.2 49.6
## 114 94.7 1.2 50.4
## 115 94.9 1.2 51.1
## 116 95.1 1.1 51.8
## 117 95.3 1.1 52.5
## 118 95.5 1.1 53.2
## 119 95.6 1.1 54.0
## 120 95.8 1.1 54.7
## 121 96.0 1.1 55.4
## 122 96.2 1.1 56.1
## 123 96.3 1.1 56.8
## 124 96.5 1.1 57.6
## 125 96.7 1.0 58.3
## 126 96.8 1.0 59.0
## 127 96.9 1.0 59.7
## 128 97.1 1.0 60.4
## 129 97.2 1.0 61.2
## 130 97.3 1.0 61.9
## 131 97.5 1.0 62.6
## 132 97.6 1.0 63.3
## 133 97.7 0.9 64.0
## 134 97.8 0.9 64.7
## 135 97.9 0.9 65.5
## 136 98.0 0.9 66.2
## 137 98.1 0.9 66.9
## 138 98.2 0.8 67.6
## 139 98.3 0.8 68.3
## 140 98.4 0.8 69.1
## 141 98.5 0.8 69.8
## 142 98.6 0.8 70.5
## 143 98.7 0.8 71.2
## 144 98.7 0.7 71.9
## 145 98.8 0.7 72.7
## 146 98.9 0.7 73.4
## 147 99.0 0.7 74.1
## 148 99.0 0.6 74.8
## 149 99.1 0.6 75.5
## 150 99.1 0.6 76.3
## 151 99.2 0.6 77.0
## 152 99.2 0.6 77.7
## 153 99.3 0.6 78.4
## 154 99.3 0.5 79.1
## 155 99.4 0.5 79.9
## 156 99.4 0.5 80.6
## 157 99.5 0.5 81.3
## 158 99.5 0.5 82.0
## 159 99.6 0.5 82.7
## 160 99.6 0.5 83.5
## 161 99.6 0.5 84.2
## 162 99.7 0.4 84.9
## 163 99.7 0.3 85.6
## 164 99.7 0.3 86.3
## 165 99.8 0.3 87.1
## 166 99.8 0.3 87.8
## 167 99.8 0.3 88.5
## 168 99.8 0.0 89.2
## 169 99.8 0.0 89.9
## 170 99.9 0.0 90.6
## 171 99.9 0.0 91.4
## 172 99.9 0.0 92.1
## 173 99.9 0.0 92.8
## 174 99.9 0.0 93.5
## 175 99.9 0.0 94.2
## 176 99.9 0.0 95.0
## 177 100.0 0.0 95.7
## 178 100.0 0.0 96.4
## 179 100.0 -0.3 97.1
## 180 100.0 -0.3 97.8
## 181 100.0 -0.3 98.6
## 182 100.0 -0.3 99.3
## 183 100.0 -0.3 100.0# now assign functional group and species ID
RA_nest_withSPID <- merge(RA_nest, sp_codes, by.x = "species", by.y = "Number", all.x = TRUE)
write.csv(RA_poop_withSPID, "Output figs and tables/RankAbundance_bynestLocation.csv", row.names = F)indicator species anlaysis note that this is will all the data - maybe should be face only
head(env)## Quad_Identifier Control_or_treatment Site_name Nest_Position
## 1 PIL_Nest_22_44_R Nest PIL above_nest
## 2 PIL_Nest_23_46_R Nest PIL below_nest
## 3 GOA_Nest_76_151_L Nest GOA below_nest
## 4 PIL_Nest_22_43_L Nest PIL above_nest
## 5 NRS_Nest_63_126_R Nest NRS below_nest
## 6 PIL_Nest_23_45_L Nest PIL below_nestenv_vector <- env_filtered$Control_or_treatment
indval_trt <- multipatt(all.matrix_filtered, env_vector,
control = how(nperm=999))
summary(indval_trt, alpha = .1)##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: IndVal.g
## Significance level (alpha): 0.1
##
## Total number of species: 146
## Selected number of species: 22
## Number of species associated to 1 group: 22
##
## List of species associated to each combination:
##
## Group Control #sps. 11
## stat p.value
## Ssp162 0.458 0.001 ***
## Ssp49 0.359 0.005 **
## Ssp26 0.352 0.020 *
## Ssp114 0.336 0.010 **
## Ssp132 0.311 0.038 *
## Ssp85 0.284 0.060 .
## Ssp141 0.284 0.070 .
## Ssp54 0.284 0.058 .
## Ssp108 0.284 0.062 .
## Ssp191 0.284 0.065 .
## Ssp7 0.284 0.055 .
##
## Group Nest #sps. 11
## stat p.value
## Ssp117 0.520 0.006 **
## Ssp159 0.411 0.050 *
## Ssp3 0.319 0.011 *
## Ssp186 0.319 0.011 *
## Ssp32 0.313 0.030 *
## Ssp137 0.313 0.077 .
## Ssp149 0.292 0.068 .
## Ssp55 0.291 0.026 *
## Ssp139 0.285 0.085 .
## Ssp4 0.280 0.059 .
## Ssp8 0.260 0.046 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#
#
# cover
#
# cover_onlyTrt <- cover %>%
# filter(Control_or_treatment == "Nest")
#
# env_vector_nestpos <- cover_onlyTrt$Nest_Position
#
# species_onlytrt <- cover_onlyTrt %>%
# select(Ssp1:Ssp192)
#
#
# #
# #
#
# # summary(env_vector)
# indval_nest <- multipatt(species_onlytrt, env_vector_nestpos,
# control = how(nperm=999))
# summary(indval_nest, alpha = 1)
# indval$cluster
# indval$comb
# indval$str
# indval$A
# indval$B
# indval$signI saved this outside of R to clean it up. Lets bring it back in and add species ID and taxa
indic_sp<- read.csv("Intermediate data files/indic_sp_alpha0.1.csv")
indic_sp## Grouping Species Stat P.values Sig
## 1 Control Ssp162 0.458 0.001 ***
## 2 Control Ssp49 0.359 0.005 **
## 3 Control Ssp26 0.352 0.020 *
## 4 Control Ssp114 0.336 0.010 **
## 5 Control Ssp132 0.311 0.038 *
## 6 Control Ssp85 0.284 0.060 .
## 7 Control Ssp141 0.284 0.070 .
## 8 Control Ssp54 0.284 0.058 .
## 9 Control Ssp108 0.284 0.062 .
## 10 Control Ssp191 0.284 0.065 .
## 11 Nest Ssp117 0.520 0.006 **
## 12 Nest Ssp159 0.411 0.050 *
## 13 Nest Ssp3 0.319 0.011 *
## 14 Nest Ssp186 0.319 0.011 *
## 15 Nest Ssp32 0.313 0.030 *
## 16 Nest Ssp137 0.313 0.077 .
## 17 Nest Ssp149 0.292 0.068 .
## 18 Nest Ssp55 0.291 0.026 *
## 19 Nest Ssp139 0.285 0.085 .
## 20 Nest Ssp4 0.280 0.059 .indic_sp_with_spID <- merge(indic_sp, sp_codes, by.x = "Species", by.y = "Number", all.x = TRUE)
indic_sp_with_spID## Species Grouping Stat P.values Sig Species.y taxa
## 1 Ssp108 Control 0.284 0.062 . Lecidea sp. L
## 2 Ssp114 Control 0.336 0.010 ** Lepraria sp. "Light Grey" L
## 3 Ssp117 Nest 0.520 0.006 ** Lepraria sp. ("Green Lepraria") L
## 4 Ssp132 Control 0.311 0.038 * Myelochroa aurulenta L
## 5 Ssp137 Nest 0.313 0.077 . Parmelia omphalodes L
## 6 Ssp139 Nest 0.285 0.085 . Parmelinopsis minarum L
## 7 Ssp141 Control 0.284 0.070 . Parmotrema crinitum L
## 8 Ssp149 Nest 0.292 0.068 . Physcia caesia L
## 9 Ssp159 Nest 0.411 0.050 * Ramalina intermedia L
## 10 Ssp162 Control 0.458 0.001 *** Rhizocarpon sp. L
## 11 Ssp186 Nest 0.319 0.011 * Usnea sp. L
## 12 Ssp191 Control 0.284 0.065 . Woodsia sp. V
## 13 Ssp26 Control 0.352 0.020 * "Tan Crust" L
## 14 Ssp3 Nest 0.319 0.011 * "Black Crust" L
## 15 Ssp32 Nest 0.313 0.030 * "White-Grey Crust" L
## 16 Ssp4 Nest 0.280 0.059 . "Black Crust 2" L
## 17 Ssp49 Control 0.359 0.005 ** Buellia sp. "Black Hypothallus" L
## 18 Ssp54 Control 0.284 0.058 . Bryum cf. caespiticium B
## 19 Ssp55 Nest 0.291 0.026 * Caloplaca citrina L
## 20 Ssp85 Control 0.284 0.060 . Dimelaena sp. L
## Notes
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## 20write.csv(indic_sp_with_spID, "Output figs and tables/Indicator species by treatment.csv", row.names = F)