16_field_ANOVA-microplankton
2023-08-28
This code compiles the field microplankton flow cytometry data into dinoflagellates and diatoms. It then performs an anova comparing microplankton biovolume by inlet, location, year, and month.
Report is located here: https://rpubs.com/HailaSchultz/field_ANOVA-microplankton
load packages
library(janitor)
library(reshape2)
library(ggplot2)
library(dplyr)
library(stringr)
library(forcats)Data wrangling
Load data
load raw biovolume data - make sure to read without headers for easier wrangling in the future
data_2019 <- read.csv("/Users/hailaschultz/Dropbox/Other studies/Aurelia project/Data Analysis/data/current_data/Microplankton/Microplankton_biovolume_2019.csv", header=FALSE)
data_2020 <- read.csv("/Users/hailaschultz/Dropbox/Other studies/Aurelia project/Data Analysis/data/current_data/Microplankton/Microplankton_biovolume_2020.csv", header=FALSE)
data_Jul_2021 <- read.csv("/Users/hailaschultz/Dropbox/Other studies/Aurelia project/Data Analysis/data/current_data/Microplankton/Microplankton_biovolume_July_2021.csv", header=FALSE)
data_Aug_2021 <- read.csv("/Users/hailaschultz/Dropbox/Other studies/Aurelia project/Data Analysis/data/current_data/Microplankton/Microplankton_biovolume_August_2021.csv", header=FALSE)Load taxa tables - for 2020 and 2021, these files match the taxa to either a diatom or dinoflagellate category
taxa_2020 <- read.csv("/Users/hailaschultz/Dropbox/Other studies/Aurelia project/Data Analysis/data/current_data/Microplankton/Microplankton_2020_taxa.csv")
taxa_2021 <- read.csv("/Users/hailaschultz/Dropbox/Other studies/Aurelia project/Data Analysis/data/current_data/Microplankton/Microplankton_2021_taxa.csv")2019
extract taxa category information
#remove excess columns
taxa_2019 <- data_2019[, -c(1:9)]
#remove excess rows
taxa_2019 <- taxa_2019[-(3:45),]
#change from wide to long format
taxa_2019<-t(taxa_2019)
#remove row names
row.names(taxa_2019) <- NULL
#rename columns
colnames(taxa_2019)[1] ="group"
colnames(taxa_2019)[2] ="taxa"
#convert to dataframe
taxa_2019<-as.data.frame(taxa_2019)reformat dataframe
#remove first row
data_2019_reformat <- data_2019[-(1),]
#make first row column names
data_2019_reformat<-data_2019_reformat %>%
row_to_names(row_number = 1)
#wide to long format
data_2019_reformat<-data_2019_reformat %>%
melt(id.vars=c(1:9))subset
data_2019_reformat<-subset(data_2019_reformat,Type=="Field")
#remove unnecessary columns
data_2019_reformat <- data_2019_reformat[, -c(2,4,6:8)]Add category
data_2019_reformat$group <- taxa_2019$group[match(data_2019_reformat$variable, taxa_2019$taxa)]
#recode so all DF categories are the same
data_2019_reformat$group <- gsub("DF-all", "DF", data_2019_reformat$group)
data_2019_reformat$group <- gsub("DF-h", "DF", data_2019_reformat$group)
data_2019_reformat$group <- gsub("DF-m", "DF", data_2019_reformat$group)reformat
#split date and time into two columns
data_2019_reformat[c('Date', 'Time')] <- str_split_fixed(data_2019_reformat$COLLECTDATE, ' ', 2)
data_2019_reformat <- data_2019_reformat[, -c(4)]
#add month
data_2019_reformat<-data_2019_reformat %>%
mutate(Month = case_when(
startsWith(Date, "9") ~ "Sep",
startsWith(Date, "8") ~ "Aug"
))
#rename columns
colnames(data_2019_reformat)[1] ="Station"
colnames(data_2019_reformat)[2] ="Inlet"
colnames(data_2019_reformat)[3] ="Location"
colnames(data_2019_reformat)[4] ="taxa"
colnames(data_2019_reformat)[5] ="biovolume"
#add year
data_2019_reformat$Year="2019"
#reorder columns
data_2019_reformat <- data_2019_reformat[, c(1,2, 3,10, 9, 7,8,6, 4,5)]
#recode location
data_2019_reformat$Location <- gsub("Out of smack", "OS", data_2019_reformat$Location)
data_2019_reformat$Location <- gsub("In smack", "IN", data_2019_reformat$Location)
data_2019_reformat$Location <- gsub("Outside bay", "OB", data_2019_reformat$Location)
#recode inlets
data_2019_reformat$Inlet <- gsub("Quartermaster", "QM", data_2019_reformat$Inlet)
data_2019_reformat$Inlet <- gsub("Sinclair", "SC", data_2019_reformat$Inlet)remove incomplete rows
data_2019_reformat <- data_2019_reformat[-which(data_2019_reformat$biovolume == ""), ]2020
reformat taxa table
#remove excess columns
taxa_2020 <- taxa_2020[, -c(2,3,5:26)]
#remove excess rows
taxa_2020 <- taxa_2020[-c(1:7,55:999),]
#rename columns
colnames(taxa_2020)[1] ="taxa"
colnames(taxa_2020)[2] ="group"reformat dataframe
#remove excess rows at bottom of dataframe
data_2020_reformat <- data_2020[-(27:1000),]
#remove first row
data_2020_reformat <- data_2020_reformat[-(1),]
#make first row column names
data_2020_reformat<-data_2020_reformat %>%
row_to_names(row_number = 1)
#wide to long format
data_2020_reformat<-data_2020_reformat %>%
melt(id.vars=c(1:4))remove unnecessary columns
data_2020_reformat <- data_2020_reformat[, -c(1,2)]list of station names from database
Database <- read.csv("/Users/hailaschultz/Dropbox/Other studies/Aurelia project/Data Analysis/data/current_data/Final_Aurelia_Database_Jan11_2023.csv")
#subset
FieldData<-subset(Database, Trial.Type=="Field")
#remove commas from jelly density
FieldData$Jelly.Density <- as.numeric(gsub(",","",FieldData$Jelly.Density..g.m3.))
Stations<-FieldData %>%
group_by(Site,Location,Station,Sample.Date,Sample.Time,Sample.Year,Latitude, Longitude)%>%
summarise(jelly_biomass=mean(Jelly.Density))rename stations
data_2020_reformat$Station <-recode(data_2020_reformat$Station,
'Budd 1S'='BUDD1s',
'Budd 2C'='BUDD2a',
'Budd 2S'='BUDD2s',
'Budd 3C'='BUDD3',
'Budd 3S'='BUDD3s',
'Budd 4C'='BUDD4',
'Budd 4S'='BUDD4s',
'Budd 5C'='BUDD5',
'Budd 7C'='BUDD7',
'Eld 1C'='Eld1',
'Eld 1S'='Eld1s',
'Eld 2C'='Eld2',
'Eld 2S'='Eld2s',
'Eld 3C'='Eld3',
'Eld 3S'='Eld3s',
'Eld 4C'='Eld4',
'Eld 4S'='Eld4s',
'Eld 5C'='Eld5',
'Qmaster 3C'='QM3a',
'Qmaster 4C'='QM4a',
'Qmaster 5C'='QM5a',
'Qmaster 7C'='QM7a',
'Qmaster 8C'='QM8a',
'Totten 1S'='Totten'
)add inlet and location
#Inlet
data_2020_reformat$Inlet <- Stations$Site[match(data_2020_reformat$Station, Stations$Station)]
data_2020_reformat$Location <- Stations$Location[match(data_2020_reformat$Station, Stations$Station)]Add category
data_2020_reformat$group <- taxa_2020$group[match(data_2020_reformat$variable, taxa_2020$taxa)]
#recode so all DF categories are the same
data_2020_reformat$group <- gsub("DF-all", "DF", data_2020_reformat$group)
data_2020_reformat$group <- gsub("DF-h", "DF", data_2020_reformat$group)
data_2020_reformat$group <- gsub("DF-m", "DF", data_2020_reformat$group)reformat
#split date and time into two columns
data_2020_reformat[c('Date', 'Time')] <- str_split_fixed(data_2020_reformat$COLLECTDATE, ' ', 2)
data_2020_reformat <- data_2020_reformat[, -c(2)]
#add month
data_2020_reformat<-data_2020_reformat %>%
mutate(Month = case_when(
startsWith(Date, "9") ~ "Sep",
startsWith(Date, "8") ~ "Aug",
startsWith(Date,"7") ~ "Jul"
))
#add year
data_2020_reformat$Year="2020"
#remove station
data_2020_reformat <- data_2020_reformat[, -c(1)]
#rename columns
colnames(data_2020_reformat)[1] ="taxa"
colnames(data_2020_reformat)[2] ="biovolume"
#reorder columns
data_2020_reformat <- data_2020_reformat[, c(3,4,5,9,10,7,8,6,1,2)]subset to Quartermaster
data_2020_reformat<-subset(data_2020_reformat,Inlet=="QM")remove incomplete rows
data_2020_reformat <- data_2020_reformat[-which(data_2020_reformat$biovolume == ""), ]2021
reformat taxa table
#remove excess columns
taxa_2021 <- taxa_2021[, -c(2:3)]
#remove excess rows
taxa_2021 <- taxa_2021[-c(1:7),]
#rename columns
colnames(taxa_2021)[1] ="taxa"
colnames(taxa_2021)[2] ="group"reformat dataframes
July
#remove first row
data_2021_Jul_reformat <- data_Jul_2021[-(1),]
#make first row column names
data_2021_Jul_reformat<-data_2021_Jul_reformat %>%
row_to_names(row_number = 1)
#wide to long format
data_2021_Jul_reformat<-data_2021_Jul_reformat %>%
melt(id.vars=c(1:2))Aug
#remove first row
data_2021_Aug_reformat <- data_Aug_2021[-(1),]
#make first row column names
data_2021_Aug_reformat<-data_2021_Aug_reformat %>%
row_to_names(row_number = 1)
#wide to long format
data_2021_Aug_reformat<-data_2021_Aug_reformat %>%
melt(id.vars=c(1:2))I decided only to use August sampes (because of poor-quality samples in July)
data_2021_reformat<-data_2021_Aug_reformatmatch with stations
#import data
Stations_2021 <- read.csv("/Users/hailaschultz/Dropbox/Other studies/Aurelia project/Data Analysis/data/current_data/Microplankton/Microplankton_2021_Stations.csv")
data_2021_reformat$Station <- Stations_2021$Station[match(data_2021_reformat$LABSAMPLENUM, Stations_2021$Sample_num)]rename stations
data_2021_reformat$Station <-recode(data_2021_reformat$Station,
'QM 10'='QM10',
'QM 5'='QM5c',
'QM 6'='QM6c',
'QM 8'='QM8c',
'QM 9'='QM9',
'QMH 1'='QM1',
'QMH 3'='QM3',
'QMH 5'='QM5',
'SC 4'='SC4c',
'SC 5'='SC5d',
'SC 6'='SC6c',
'SC 7'='SC7',
'SC 8'='SC8',
'Sinclair 1'='SC1a',
'Sinclair 3'='SC3a',
'Sinclair 6'='SC6a'
)remove old station column
data_2021_reformat <- data_2021_reformat[, -c(1)]add inlet and location
#Inlet
data_2021_reformat$Inlet <- Stations$Site[match(data_2021_reformat$Station, Stations$Station)]
data_2021_reformat$Location <- Stations$Location[match(data_2021_reformat$Station, Stations$Station)]Add category
data_2021_reformat$group <- taxa_2021$group[match(data_2021_reformat$variable, taxa_2021$taxa)]
#recode so all DF categories are the same
data_2021_reformat$group <- gsub("DF-all", "DF", data_2021_reformat$group)
data_2021_reformat$group <- gsub("DF-h", "DF", data_2021_reformat$group)
data_2021_reformat$group <- gsub("DF-m", "DF", data_2021_reformat$group)
#make NA in group DF
data_2021_reformat$group <- replace(data_2021_reformat$group, is.na(data_2021_reformat$group), "DF")reformat
#split date and time into two columns
data_2021_reformat[c('Date', 'Time')] <- str_split_fixed(data_2021_reformat$COLLECTDATE, ' ', 2)
data_2021_reformat <- data_2021_reformat[, -c(1)]
#add month
data_2021_reformat<-data_2021_reformat %>%
mutate(Month = case_when(
startsWith(Date, "9") ~ "Sep",
startsWith(Date, "8") ~ "Aug",
startsWith(Date,"7") ~ "Jul"
))
#add year
data_2021_reformat$Year="2021"
#rename columns
colnames(data_2021_reformat)[1] ="taxa"
colnames(data_2021_reformat)[2] ="biovolume"
#reorder columns
data_2021_reformat <- data_2021_reformat[, c(3,4,5,9,10,7,8,6,1,2)]remove incomplete rows
data_2021_reformat <- data_2021_reformat[-which(data_2021_reformat$biovolume == ""), ]
data_2021_reformat <- data_2021_reformat[complete.cases(data_2021_reformat), ]Add years together
Microplankton_QMSC<-rbind(data_2019_reformat,data_2020_reformat,data_2021_reformat)2-way ANOVA:location and group
subset
#just diatoms and dinoflagellates
Microplankton_QMSC<-subset(Microplankton_QMSC, group==c("DF","DT"))add up diatom and dinoflagellates per Location
Microplankton_QMSC$biovolume<-as.numeric(Microplankton_QMSC$biovolume)
Microplankton_QMSC_sum<-aggregate(data=Microplankton_QMSC, biovolume~Year+Month+Inlet+Location+Station+group, FUN=sum)unransformed model
Micro_mod1 <- aov(biovolume ~ group+Location+Month+Inlet+Year+group:Location+Location:Month+Location:Inlet+Location:Year, data = Microplankton_QMSC_sum)
summary(Micro_mod1)## Df Sum Sq Mean Sq F value Pr(>F)
## group 1 82.5 82.52 2.144 0.156
## Location 2 358.8 179.42 4.662 0.019 *
## Month 1 60.9 60.88 1.582 0.220
## Inlet 1 21.9 21.88 0.568 0.458
## Year 2 23.3 11.67 0.303 0.741
## group:Location 2 24.5 12.25 0.318 0.730
## Location:Month 2 40.5 20.23 0.526 0.598
## Location:Inlet 2 51.6 25.79 0.670 0.521
## Location:Year 3 36.2 12.06 0.313 0.816
## Residuals 25 962.2 38.49
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Micro_mod1, which = "Location")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = biovolume ~ group + Location + Month + Inlet + Year + group:Location + Location:Month + Location:Inlet + Location:Year, data = Microplankton_QMSC_sum)
##
## $Location
## diff lwr upr p adj
## OB-IN -6.321283 -12.785583 0.1430165 0.0561636
## OS-IN -5.545330 -10.930047 -0.1606130 0.0426505
## OS-OB 0.775953 -6.072694 7.6246000 0.9571216check normality assumptions
ggplot(Microplankton_QMSC_sum, aes(x=biovolume)) + geom_histogram()
qqnorm(Micro_mod1$residuals)
qqline(Micro_mod1$residuals)
shapiro.test(Micro_mod1$residuals)##
## Shapiro-Wilk normality test
##
## data: Micro_mod1$residuals
## W = 0.91217, p-value = 0.0034Histogram is logarithmic, qqplot looks weird, and shapiro test is significant, need to transform
sqrt transformation
Micro_mod2 <- aov(sqrt(biovolume) ~ group+Location+Month+Inlet+Year+group:Location+Location:Month+Location:Inlet+Location+Year, data = Microplankton_QMSC_sum)check normality assumptions
ggplot(Microplankton_QMSC_sum, aes(x=sqrt(biovolume))) + geom_histogram()
qqnorm(Micro_mod2$residuals)
qqline(Micro_mod2$residuals)
shapiro.test(Micro_mod2$residuals)##
## Shapiro-Wilk normality test
##
## data: Micro_mod2$residuals
## W = 0.96136, p-value = 0.1652boxplot(sqrt(biovolume) ~ Location, xlab='Location', ylab='Density', data=Microplankton_QMSC_sum)
boxplot(sqrt(biovolume) ~ group, xlab='group', ylab='Density', data=Microplankton_QMSC_sum)
looks good!!!
summary(Micro_mod2)## Df Sum Sq Mean Sq F value Pr(>F)
## group 1 3.74 3.736 2.829 0.10369
## Location 2 18.45 9.226 6.986 0.00346 **
## Month 1 3.95 3.949 2.990 0.09480 .
## Inlet 1 1.78 1.785 1.351 0.25487
## Year 2 0.09 0.046 0.035 0.96607
## group:Location 2 0.62 0.311 0.236 0.79156
## Location:Month 2 1.97 0.983 0.744 0.48421
## Location:Inlet 2 3.67 1.837 1.391 0.26544
## Residuals 28 36.98 1.321
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Micro_mod2, which = "Location")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = sqrt(biovolume) ~ group + Location + Month + Inlet + Year + group:Location + Location:Month + Location:Inlet + Location + Year, data = Microplankton_QMSC_sum)
##
## $Location
## diff lwr upr p adj
## OB-IN -1.4481444 -2.637702 -0.2585872 0.0146027
## OS-IN -1.2462532 -2.237146 -0.2553602 0.0114726
## OS-OB 0.2018912 -1.058393 1.4621758 0.9172959none of the interaction terms are significant, so it looks like we can interpret main effects. Biovolume is higher inside aggregation than outside of aggregation and outside of bay.
without interaction terms
Micro_mod3 <- aov(sqrt(biovolume) ~ group+Location+Month+Inlet+Year, data = Microplankton_QMSC_sum)check normality assumptions
qqnorm(Micro_mod3$residuals)
qqline(Micro_mod3$residuals)
shapiro.test(Micro_mod3$residuals)##
## Shapiro-Wilk normality test
##
## data: Micro_mod3$residuals
## W = 0.97317, p-value = 0.4194summary(Micro_mod3)## Df Sum Sq Mean Sq F value Pr(>F)
## group 1 3.74 3.736 2.938 0.09564 .
## Location 2 18.45 9.226 7.254 0.00238 **
## Month 1 3.95 3.949 3.105 0.08705 .
## Inlet 1 1.78 1.785 1.403 0.24441
## Year 2 0.09 0.046 0.036 0.96478
## Residuals 34 43.24 1.272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Micro_mod3, which = "Location")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = sqrt(biovolume) ~ group + Location + Month + Inlet + Year, data = Microplankton_QMSC_sum)
##
## $Location
## diff lwr upr p adj
## OB-IN -1.4481444 -2.604215 -0.2920737 0.0113916
## OS-IN -1.2462532 -2.209252 -0.2832543 0.0087819
## OS-OB 0.2018912 -1.022916 1.4266982 0.9142241make plot
get sample sizes
#count sample sizes per category
Microplankton_QMSC_sum %>%
group_by(group,Location) %>%
summarize(count = n()) ## # A tibble: 6 × 3
## # Groups: group [2]
## group Location count
## <chr> <chr> <int>
## 1 DF IN 10
## 2 DF OB 4
## 3 DF OS 7
## 4 DT IN 10
## 5 DT OB 4
## 6 DT OS 7#recode stations
Microplankton_QMSC_sum <- Microplankton_QMSC_sum %>%
mutate(Location = fct_recode(Location,
"Inside Aggregation" = "IN",
"Outside Aggregation" = "OS",
"Outside Bay"="OB"))
#recode group
Microplankton_QMSC_sum <- Microplankton_QMSC_sum %>%
mutate(group = fct_recode(group,
"Dinoflagellates" = "DF",
"Diatoms" = "DT"))
Microplankton_QMSC_sum$Location <- factor(Microplankton_QMSC_sum$Location,
c("Inside Aggregation", "Outside Aggregation", "Outside Bay"))
micro_plot<- ggplot(Microplankton_QMSC_sum, aes(x=group, y=sqrt(biovolume), fill=Location)) +
geom_boxplot() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))+theme_classic()+
xlab(bquote('Microplankton Taxon'))+
ylab(expression(plain('sqrt (Biovolume)') ~ plain('(' ~ mm^3 ~ '/L)')))+
scale_fill_manual(values = c("Inside Aggregation" = "#619CFF", "Outside Aggregation" = "#F8766D", "Outside Bay" = "#00BA38"))+
theme(axis.text.y=element_text(size=15,colour="black"),
legend.position=c(0.28, 0.85),
legend.text=element_text(size=15,colour="black"),
legend.title=element_text(size=15,colour="black"),
axis.title.y=element_text(size=20,colour="black"),
axis.line = element_line(colour = "black"),
axis.title.x=element_blank(),
axis.text.x=element_text(size=15,colour="black"))+
annotate("text",x=0.75, y=4.1, label= "10",size=6)+
annotate("text",x=1, y=1.4, label= "7",size=6)+
annotate("text",x=1.25, y=1.3, label= "4",size=6)+
annotate("text",x=1.75, y=5.7, label= "10",size=6)+
annotate("text",x=2, y=3.4, label= "7",size=6)+
annotate("text",x=2.25, y=1.7, label= "4",size=6)+
annotate("text",x=0.75, y=3.7, label= "*",size=10)+
annotate("text",x=1.75, y=5.3, label= "*",size=10)+
scale_y_continuous(breaks=seq(0,7,1),expand = c(0, 0), limits = c(0, 7))+
theme(plot.margin=margin(0.5, 0.5, 0.5, 0.5, unit = "cm"))
micro_plot
save plot
setwd("/Users/hailaschultz/Dropbox/Other studies/Aurelia project/Data Analysis/output")
ggsave(filename = "Field_Microplankton_ANOVA.png", plot = micro_plot, width = 164, height = 164, units="mm", device='png', dpi=600)
ggsave(filename = "Field_Microplankton_ANOVA.tif", plot = micro_plot, width = 164, height = 164, units="mm", device='tiff', dpi=600)Other factors
Inlet
ANOVA
#remove 2019
Micro_mod3 <- aov(sqrt(biovolume) ~ group+Inlet, data = Microplankton_QMSC_sum)
summary(Micro_mod3)## Df Sum Sq Mean Sq F value Pr(>F)
## group 1 3.74 3.736 2.294 0.138
## Inlet 1 3.99 3.994 2.452 0.125
## Residuals 39 63.53 1.629TukeyHSD(Micro_mod3, which = "Inlet")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = sqrt(biovolume) ~ group + Inlet, data = Microplankton_QMSC_sum)
##
## $Inlet
## diff lwr upr p adj
## SC-QM 0.6350403 -0.18522 1.455301 0.1254383TukeyHSD(Micro_mod3, which = "group")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = sqrt(biovolume) ~ group + Inlet, data = Microplankton_QMSC_sum)
##
## $group
## diff lwr upr p adj
## Diatoms-Dinoflagellates 0.5965339 -0.2001372 1.393205 0.137948Month
ANOVA
#remove 2019
Micro_mod4 <- aov(sqrt(biovolume) ~ group+Month, data = Microplankton_QMSC_sum)
summary(Micro_mod4)## Df Sum Sq Mean Sq F value Pr(>F)
## group 1 3.74 3.736 2.380 0.1310
## Month 1 6.30 6.298 4.012 0.0522 .
## Residuals 39 61.22 1.570
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Micro_mod4, which = "Month")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = sqrt(biovolume) ~ group + Month, data = Microplankton_QMSC_sum)
##
## $Month
## diff lwr upr p adj
## Sep-Aug -1.106594 -2.224105 0.01091744 0.0521703TukeyHSD(Micro_mod4, which = "group")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = sqrt(biovolume) ~ group + Month, data = Microplankton_QMSC_sum)
##
## $group
## diff lwr upr p adj
## Diatoms-Dinoflagellates 0.5965339 -0.185561 1.378629 0.1309593Year
ANOVA
#remove 2019
Micro_mod3 <- aov(sqrt(biovolume) ~ group+Year, data = Microplankton_QMSC_sum)
summary(Micro_mod3)## Df Sum Sq Mean Sq F value Pr(>F)
## group 1 3.74 3.736 2.184 0.148
## Year 2 2.52 1.260 0.736 0.485
## Residuals 38 65.00 1.711TukeyHSD(Micro_mod3, which = "Year")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = sqrt(biovolume) ~ group + Year, data = Microplankton_QMSC_sum)
##
## $Year
## diff lwr upr p adj
## 2020-2019 0.1673280 -1.1533304 1.487986 0.9487996
## 2021-2019 0.5483470 -0.5882947 1.684989 0.4741963
## 2021-2020 0.3810191 -0.8770128 1.639051 0.7422302TukeyHSD(Micro_mod3, which = "group")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = sqrt(biovolume) ~ group + Year, data = Microplankton_QMSC_sum)
##
## $group
## diff lwr upr p adj
## Diatoms-Dinoflagellates 0.5965339 -0.2205534 1.413621 0.1476607