Statistical Tests for Analysis of Mimosa Creek, Toohey Forest Brisbane
Evelyn (Rosie) Lomas
2023-09-22
Data was subseted in Excel for convenience, all pond and creek measurements were put together on separate sheets in excel:
Pond Measurements:
library(readxl)
Pond_data_ <- read_excel("Pond data .xlsx")
Pond_WQ<- Pond_data_
summary(Pond_data_)## Week Date Site Reading
## Min. :1.00 Length:6 Length:6 Min. :1.0
## 1st Qu.:1.25 Class :character Class :character 1st Qu.:1.0
## Median :2.00 Mode :character Mode :character Median :1.5
## Mean :2.00 Mean :1.5
## 3rd Qu.:2.75 3rd Qu.:2.0
## Max. :3.00 Max. :2.0
## ËšC DO% DO mg/L SPC (Conductivity)
## Min. :13.10 Min. : 8.80 Min. :0.930 Min. :101.4
## 1st Qu.:13.20 1st Qu.:10.00 1st Qu.:1.025 1st Qu.:102.7
## Median :13.55 Median :13.65 Median :1.255 Median :103.8
## Mean :14.13 Mean :13.65 Mean :1.348 Mean :107.8
## 3rd Qu.:14.95 3rd Qu.:15.88 3rd Qu.:1.613 3rd Qu.:114.1
## Max. :16.10 Max. :20.40 Max. :1.970 Max. :117.8
## US/CM pH NTU
## Min. :80.90 Length:6 Min. : 2.000
## 1st Qu.:81.72 Class :character 1st Qu.: 9.775
## Median :84.15 Mode :character Median : 27.500
## Mean :85.28 Mean : 43.617
## 3rd Qu.:88.97 3rd Qu.: 82.500
## Max. :91.00 Max. :100.000Creek Measurements:
library(readxl)
Creek_Data_ <- read_excel("Creek Data .xlsx")
Creek_WQ<-Creek_Data_
summary(Creek_Data_)## Week Date Site Reading
## Min. :1.0 Length:10 Length:10 Min. :1.0
## 1st Qu.:2.0 Class :character Class :character 1st Qu.:1.0
## Median :2.0 Mode :character Mode :character Median :1.0
## Mean :2.4 Mean :1.4
## 3rd Qu.:3.0 3rd Qu.:2.0
## Max. :5.0 Max. :2.0
## ËšC DO% DO mg/L SPC (Conductivity)
## Min. :12.60 Min. :11.70 Min. :1.250 Min. : 94.5
## 1st Qu.:14.03 1st Qu.:17.73 1st Qu.:1.802 1st Qu.:104.3
## Median :14.95 Median :23.10 Median :2.135 Median :107.5
## Mean :14.53 Mean :26.72 Mean :2.638 Mean :107.4
## 3rd Qu.:15.10 3rd Qu.:32.75 3rd Qu.:3.315 3rd Qu.:114.1
## Max. :15.90 Max. :53.40 Max. :5.200 Max. :116.3
## US/CM pH NTU
## Min. :72.00 Length:10 Min. : 2.20
## 1st Qu.:83.25 Class :character 1st Qu.: 9.45
## Median :85.60 Mode :character Median : 14.60
## Mean :85.75 Mean : 29.66
## 3rd Qu.:93.33 3rd Qu.: 28.77
## Max. :94.60 Max. :113.00Welch’s t test - Temp (due 2 unequal sample size and variance)
t.test(Creek_WQ$`ËšC`, Pond_WQ$`ËšC`, var.equal= FALSE) ##
## Welch Two Sample t-test
##
## data: Creek_WQ$`ËšC` and Pond_WQ$`ËšC`
## t = 0.627, df = 9.43, p-value = 0.5455
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.024578 1.817912
## sample estimates:
## mean of x mean of y
## 14.53000 14.13333Just greater than aplha p value sig of 0.05, accept H0 - there is no significant difference between the means in the Sites- Pond/ Creek.
Box Plot of Temp
Together<- rbind(Pond_WQ, Creek_WQ)
library(ggplot2)
ggplot(Together, aes(x= Site, y=`ËšC`, fill= Site )) +
geom_boxplot()+
scale_fill_manual(values=c("seagreen3", "palevioletred"))+
geom_jitter(width = 0.2)+
xlab("Site")+
ggtitle("Boxplot of Temperature Across the Two Sites at Mimosa Creek")+
theme(text = element_text(family = "Times New Roman"))
From box plots - both very skewed, means very similar.
Welch’s t test - DO% (due 2 unequal sample size and variance)
t.test(Creek_WQ$`DO%`, Pond_WQ$`DO%`, var.equal= FALSE) ##
## Welch Two Sample t-test
##
## data: Creek_WQ$`DO%` and Pond_WQ$`DO%`
## t = 2.896, df = 12.023, p-value = 0.0134
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 3.238819 22.901181
## sample estimates:
## mean of x mean of y
## 26.72 13.65accept there is a sig difference in the means of % of Dissolved Oxygen
Box Plot DO%
library(ggplot2)
ggplot(Together, aes(x= Site, y=`DO%`, fill= Site )) +
geom_boxplot()+
scale_fill_manual(values=c("seagreen3", "palevioletred"))+
geom_jitter(width = 0.2)+
xlab("Site")+
ggtitle("Boxplot of Percentage of Dissolved Oxygen Across the Two Sites
at Mimosa Creek")+
theme(text = element_text(family = "Times New Roman"))
Welch t test for DO mg/L
t.test(Creek_WQ$`DO mg/L`, Pond_WQ$`DO mg/L`, var.equal= FALSE)##
## Welch Two Sample t-test
##
## data: Creek_WQ$`DO mg/L` and Pond_WQ$`DO mg/L`
## t = 2.9303, df = 11.748, p-value = 0.01285
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.3284608 2.2508726
## sample estimates:
## mean of x mean of y
## 2.638000 1.348333Means between the two sites significantly different p value less that 0.05.
Boxplot for DO mg/L
library(ggplot2)
ggplot(Together, aes(x= Site, y=`DO mg/L`, fill= Site )) +
geom_boxplot()+
scale_fill_manual(values=c("seagreen3", "palevioletred"))+
geom_jitter(width = 0.2)+
xlab("Site")+
ggtitle("Boxplot of Dissolved Oxygen in Mg/L Across the Two Sites at
Mimosa Creek")+
theme(text = element_text(family = "Times New Roman"))
T test for SPC
t.test(Creek_WQ$`SPC (Conductivity)`, Pond_WQ$`SPC (Conductivity)`, var.equal= FALSE) ##
## Welch Two Sample t-test
##
## data: Creek_WQ$`SPC (Conductivity)` and Pond_WQ$`SPC (Conductivity)`
## t = -0.094741, df = 11.069, p-value = 0.9262
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -9.282060 8.515393
## sample estimates:
## mean of x mean of y
## 107.4000 107.7833Means between treatments are not significantly different due to P value being greater than p critical of 0.05, at 0.05% significance.
library(ggplot2)
ggplot(Together, aes(x= Site, y=`SPC (Conductivity)`, fill= Site )) +
geom_boxplot()+
scale_fill_manual(values=c("seagreen3", "palevioletred"))+
geom_jitter(width = 0.2)+
xlab("Site")+
ggtitle("Boxplot of SPC (Conductivity) Across the Two Sites at Mimosa Creek")+
theme(text = element_text(family = "Times New Roman"))
box plot shows means pretty equal too.
T test for uS/cm
t.test(Creek_WQ$`US/CM`, Pond_WQ$`US/CM`, var.equal= FALSE) ##
## Welch Two Sample t-test
##
## data: Creek_WQ$`US/CM` and Pond_WQ$`US/CM`
## t = 0.14278, df = 13.836, p-value = 0.8885
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -6.551261 7.484594
## sample estimates:
## mean of x mean of y
## 85.75000 85.28333p value is greater that p critical of 0.05 therefore, means do not significantly differ from each other when alpha set to 0.05.
library(ggplot2)
ggplot(Together, aes(x= Site, y=`US/CM`, fill= Site )) +
geom_boxplot()+
scale_fill_manual(values=c("seagreen3", "palevioletred"))+
geom_jitter(width = 0.2)+
xlab("Site")+
ggtitle("Boxplot of uS/cm Across the Two Sites at Mimosa Creek")+
theme(text = element_text(family = "Times New Roman"))
Welch’s T test for pH
first the N/A values need to be removed from both sets of data:
Creek_WQ_NONA <- Creek_WQ[-3,]
View(Creek_WQ_NONA)Pond_WQ_NONA<-Pond_WQ[-(3:4),]
View(Pond_WQ_NONA)now these new data sets can be used to perform the t-test:
Creek_pH<-as.numeric(Creek_WQ_NONA$pH)
Pond_pH<-as.numeric(Pond_WQ_NONA$pH)
t.test(Creek_pH, Pond_pH, var.equal= FALSE)##
## Welch Two Sample t-test
##
## data: Creek_pH and Pond_pH
## t = 1.0387, df = 10.602, p-value = 0.322
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1323036 0.3667481
## sample estimates:
## mean of x mean of y
## 6.352222 6.235000The p value is greater than the critical value of 0.05 therefore the means of pH between both sites do not differ significantly.
library(ggplot2)
Together_NA<-Together[-c(3,4,9),]
Together_NA$pH <- as.numeric(Together_NA$pH)
ggplot(Together_NA, aes(x= Site, y=`pH`, fill= Site )) +
geom_boxplot()+
scale_fill_manual(values=c("seagreen3","palevioletred"))+
geom_jitter(width = 0.2)+
xlab("Site")+
ggtitle("Boxplot of pH Across the Two Sites at Mimosa Creek")+
theme(text = element_text(family = "Times New Roman"))
means very similar.
Welch’s T test for NTU:
t.test(Creek_WQ$NTU, Pond_WQ$NTU, var.equal= FALSE) ##
## Welch Two Sample t-test
##
## data: Creek_WQ$NTU and Pond_WQ$NTU
## t = -0.65184, df = 8.5964, p-value = 0.5315
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -62.74103 34.82770
## sample estimates:
## mean of x mean of y
## 29.66000 43.61667T test is greater than 0.05 level of significance therefore, the means of NTU/ Turbidity between the sites does not differ significantly.
library(ggplot2)
ggplot(Together, aes(x= Site, y=`NTU`, fill= Site )) +
geom_boxplot()+
scale_fill_manual(values=c("seagreen3", "palevioletred"))+
geom_jitter(width = 0.2)+
xlab("Site")+
ggtitle("Boxplot of NTU Turbidity Across the Two Sites at Mimosa Creek")+
theme(text = element_text(family = "Times New Roman"))
Time Series Data
For the time series data, data was averaged in excel for each week and site.
library(readxl)
Time_series_Graphs <- read_excel("Time series Graphs.xlsx")
View(Time_series_Graphs)Temperature Time Series Graph
library(ggplot2)
ggplot(Time_series_Graphs, aes(x= Week, y=`ËšC`, group= Site,
color= Site)) +
geom_line(alpha=0.9, linetype=1) +
scale_color_manual(values=c("seagreen3", "palevioletred"))+
geom_point(size=2)+
ggtitle(" Change Over Time of Temperature at Two Sites Within Mimosa
Creek")+
theme(text = element_text(family = "Times New Roman"))
library(ggplot2)
ggplot(Time_series_Graphs, aes(x= Week, y=`DO%`, group= Site,
color= Site)) +
geom_line(alpha=0.9, linetype=1) +
scale_color_manual(values=c("seagreen3", "palevioletred"))+
geom_point(size=2)+
ggtitle(" Change Over Time of Percentage of Dissolved Oxygen
at Two Sites Within Mimosa Creek")+
theme(text = element_text(family = "Times New Roman"))
DO mg/L Time Series Graph
library(ggplot2)
ggplot(Time_series_Graphs, aes(x= Week, y=`DO mg/L`, group= Site,
color= Site)) +
geom_line(alpha=0.9, linetype=1) +
scale_color_manual(values=c("seagreen3", "palevioletred"))+
geom_point(size=2)+
ggtitle(" Change Over Time of Dissolved Oxygen in mg/L at Two Sites
Within Mimosa Creek")+
theme(text = element_text(family = "Times New Roman"))
SPC (Conductivity) Time Series Graph
library(ggplot2)
ggplot(Time_series_Graphs, aes(x= Week, y=`SPC (Conductivity)`, group= Site,
color= Site)) +
geom_line(alpha=0.9, linetype=1) +
scale_color_manual(values=c("seagreen3", "palevioletred"))+
geom_point(size=2)+
ggtitle(" Change Over Time of SPC (Conductivity) at Two Sites Within Mimosa
Creek")+
theme(text = element_text(family = "Times New Roman"))
US/CM Time Series Graph
library(ggplot2)
ggplot(Time_series_Graphs, aes(x= Week, y=`US/CM`, group= Site,
color= Site)) +
geom_line(alpha=0.9, linetype=1) +
scale_color_manual(values=c("seagreen3", "palevioletred"))+
geom_point(size=2)+
ggtitle(" Change Over Time of US/CM at Two Sites Within Mimosa
Creek")+
theme(text = element_text(family = "Times New Roman"))
NTU
library(ggplot2)
ggplot(Time_series_Graphs, aes(x= Week, y=`NTU`, group= Site,
color= Site)) +
geom_line(alpha=0.9, linetype=1) +
scale_color_manual(values=c("seagreen3","palevioletred"))+
geom_point(size=2)+
ggtitle(" Change Over Time of NTU at Two Sites Within Mimosa
Creek")+
theme(text = element_text(family = "Times New Roman"))
pH for Creek Site Only
N/A for week two of Pond site, hence not graphed. Creek site was graphed and a new data set with the week 5 reading for Creek was imported.
library(readxl)
Creek_pH_time_series <- read_excel("Creek_pH_time_series.xlsx")library(ggplot2)
ggplot(Creek_pH_time_series, aes(x= Week, y=`pH`, group= Site))+
geom_line( color="seagreen3", alpha=0.9, linewidth=1, linetype=1) +
geom_point(size=2, color= "seagreen3")+
ggtitle(" Change Over Time of pH at the 'Natural' Creek Site Within Mimosa
Creek")+
theme(text = element_text(family = "Times New Roman"))
Macroinvertebrate Assemblages in Leaf Litter
Non Metric Multi Dimensional Scaling Data in excel was transformmed so that the rows were sites and collums Macroinvertebrates.
library("vegan")## Loading required package: permute## Loading required package: lattice## This is vegan 2.6-4library(readxl)
rivers_catchement <- read_excel("rivers_catchement.xlsx",
sheet = "Sheet1")
View(rivers_catchement)Step 1. subset data which contains values:
data_1<-rivers_catchement[,2:26]Step 2. subset data contains locations:
data_2<-rivers_catchement[1]set.seed(3)
library("vegan")
NMDS<-metaMDS(data_1, distance= "bray")## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.07057647
## Run 1 stress 0.07794169
## Run 2 stress 0.07057647
## ... New best solution
## ... Procrustes: rmse 1.764203e-05 max resid 3.15758e-05
## ... Similar to previous best
## Run 3 stress 0.07057647
## ... Procrustes: rmse 3.645766e-06 max resid 6.445854e-06
## ... Similar to previous best
## Run 4 stress 0.07794169
## Run 5 stress 0.07794169
## Run 6 stress 0.0779417
## Run 7 stress 0.0779417
## Run 8 stress 0.07057647
## ... Procrustes: rmse 5.574472e-06 max resid 9.175593e-06
## ... Similar to previous best
## Run 9 stress 0.07794169
## Run 10 stress 0.07057648
## ... Procrustes: rmse 2.861417e-05 max resid 5.116736e-05
## ... Similar to previous best
## Run 11 stress 0.07057647
## ... Procrustes: rmse 1.41051e-05 max resid 2.620893e-05
## ... Similar to previous best
## Run 12 stress 0.08993601
## Run 13 stress 0.07057647
## ... Procrustes: rmse 2.604887e-05 max resid 4.66071e-05
## ... Similar to previous best
## Run 14 stress 0.07057647
## ... New best solution
## ... Procrustes: rmse 4.330174e-06 max resid 7.713613e-06
## ... Similar to previous best
## Run 15 stress 0.07794172
## Run 16 stress 0.137334
## Run 17 stress 0.07057649
## ... Procrustes: rmse 2.179782e-05 max resid 5.330827e-05
## ... Similar to previous best
## Run 18 stress 0.07057647
## ... Procrustes: rmse 3.804074e-06 max resid 9.171467e-06
## ... Similar to previous best
## Run 19 stress 0.0779417
## Run 20 stress 0.07057647
## ... Procrustes: rmse 4.402455e-06 max resid 7.496248e-06
## ... Similar to previous best
## *** Best solution repeated 4 timesStep 3. Data visualisation:
NMDS$stress## [1] 0.07057647stress value is 0.07 this indicates a good fit.
set.seed(3)
library("vegan")
library("ggplot2")
scores<-scores(NMDS, display="site")
scores <- cbind(as.data.frame(scores), Habitat = data_2$Habitat)
centroids <- aggregate(cbind(NMDS1, NMDS2) ~ Habitat, data = scores, FUN = mean)
seg <- merge(scores, setNames(centroids, c('Habitat','oNMDS1','oNMDS2')),
by = 'Habitat', sort = FALSE)
ggplot(scores, aes(x = NMDS1, y = NMDS2, colour = Habitat))+
scale_color_manual(values=c("seagreen4", "olivedrab", "palegreen3",
"darkcyan", "cornflowerblue", "deepskyblue3",
"coral1", "sienna3","brown2", "indianred3",
"maroon", "palevioletred"))+
geom_segment(data = seg,
mapping = aes(xend = oNMDS1, yend = oNMDS2)) +
geom_point(data = centroids, size = 4) +
geom_point() +
geom_text(label= data_2$Habitat,
nudge_x = 0.01, nudge_y = 0.1,
check_overlap = F)+
ggtitle("NMDS To Represent Macroinvertebrate Assembleges
Across Sites Within Leaf Litter")+
theme(plot.title = element_text(hjust = 0.5),
text = element_text(family = "Times New Roman"))
Creek B1,B2,B3 Values overlap due to the same scores (could be due to variety of reasons.
Bootstraping and Testing for differences Between Groups:
fit<-adonis(data_1~ Habitat, data= data_2, premutations=999, method= "bray")## 'adonis' will be deprecated: use 'adonis2' insteadfit## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Habitat 11 1.4527 0 0 1 1
## Residuals 0 0.0000 Inf 0
## Total 11 1.4527 1
##
## $call
## adonis(formula = data_1 ~ Habitat, data = data_2, method = "bray",
## premutations = 999)
##
## $coefficients
## water mites Snail Limpet Shrimp Yabbie Beetle Diving Beetle
## (Intercept) 0.4166667 0 0 0 0 0.1666667 0
## Habitat1 -0.4166667 0 0 0 0 1.8333333 0
## Habitat2 -0.4166667 0 0 0 0 -0.1666667 0
## Habitat3 -0.4166667 0 0 0 0 -0.1666667 0
## Habitat4 -0.4166667 0 0 0 0 -0.1666667 0
## Habitat5 -0.4166667 0 0 0 0 -0.1666667 0
## Habitat6 -0.4166667 0 0 0 0 -0.1666667 0
## Habitat7 1.5833333 0 0 0 0 -0.1666667 0
## Habitat8 -0.4166667 0 0 0 0 -0.1666667 0
## Habitat9 -0.4166667 0 0 0 0 -0.1666667 0
## Habitat10 -0.4166667 0 0 0 0 -0.1666667 0
## Habitat11 -0.4166667 0 0 0 0 -0.1666667 0
## Beetle (adult) Beetle (larvae) Marsh beetle (larvae) Midge
## (Intercept) 0 0 0 50.9166667
## Habitat1 0 0 0 14.0833333
## Habitat2 0 0 0 -33.9166667
## Habitat3 0 0 0 8.0833333
## Habitat4 0 0 0 -10.9166667
## Habitat5 0 0 0 -39.9166667
## Habitat6 0 0 0 24.0833333
## Habitat7 0 0 0 -0.9166667
## Habitat8 0 0 0 95.0833333
## Habitat9 0 0 0 -19.9166667
## Habitat10 0 0 0 -25.9166667
## Habitat11 0 0 0 11.0833333
## Ghost Midge Mayfly Water meter Damselfly Dragonfly nymph
## (Intercept) 0 0 0 0.08333333 0.75
## Habitat1 0 0 0 -0.08333333 -0.75
## Habitat2 0 0 0 -0.08333333 0.25
## Habitat3 0 0 0 -0.08333333 -0.75
## Habitat4 0 0 0 -0.08333333 -0.75
## Habitat5 0 0 0 -0.08333333 -0.75
## Habitat6 0 0 0 -0.08333333 -0.75
## Habitat7 0 0 0 -0.08333333 5.25
## Habitat8 0 0 0 0.91666667 -0.75
## Habitat9 0 0 0 -0.08333333 0.25
## Habitat10 0 0 0 -0.08333333 0.25
## Habitat11 0 0 0 -0.08333333 -0.75
## Narrow-wing damselfly Dragonfly nymph Lest Stick Caddis Sand caddis
## (Intercept) 0 0 0.25 0
## Habitat1 0 0 -0.25 0
## Habitat2 0 0 0.75 0
## Habitat3 0 0 -0.25 0
## Habitat4 0 0 -0.25 0
## Habitat5 0 0 -0.25 0
## Habitat6 0 0 -0.25 0
## Habitat7 0 0 -0.25 0
## Habitat8 0 0 -0.25 0
## Habitat9 0 0 1.75 0
## Habitat10 0 0 -0.25 0
## Habitat11 0 0 -0.25 0
## Flat leaf caddis Water fleas Seed shrimp worm mosquito larvae
## (Intercept) 0 0 1 9.083333 0.1666667
## Habitat1 0 0 -1 -9.083333 -0.1666667
## Habitat2 0 0 -1 -9.083333 -0.1666667
## Habitat3 0 0 -1 -9.083333 -0.1666667
## Habitat4 0 0 -1 -9.083333 -0.1666667
## Habitat5 0 0 -1 -9.083333 -0.1666667
## Habitat6 0 0 -1 -9.083333 -0.1666667
## Habitat7 0 0 -1 -9.083333 -0.1666667
## Habitat8 0 0 1 -9.083333 -0.1666667
## Habitat9 0 0 9 -9.083333 -0.1666667
## Habitat10 0 0 -1 -9.083333 -0.1666667
## Habitat11 0 0 -1 20.916667 1.8333333
##
## $coef.sites
## 1 2 3 4 5
## (Intercept) 0.35223035 0.54559068 0.41925447 0.400025490 0.41673094
## Habitat1 -0.15223035 -0.14744253 0.02218697 0.062340101 -0.18691727
## Habitat2 0.18023719 0.25202837 -0.02242908 -0.200025490 0.28238411
## Habitat3 -0.20693120 -0.11289837 -0.02119622 0.011739215 -0.18797277
## Habitat4 -0.16855688 0.03112890 -0.15734971 -0.157601248 -0.01374586
## Habitat5 0.32892907 0.31690932 0.18074553 0.005379915 0.37374525
## Habitat6 -0.10411005 -0.21523353 0.05973712 0.104925005 -0.15045875
## Habitat7 -0.35223035 -0.02868246 -0.04670545 -0.019073109 -0.07462567
## Habitat8 0.16467787 -0.54559068 0.23877662 0.314260224 0.07298100
## Habitat9 0.02031867 0.11244041 -0.41925447 -0.142882633 0.13399370
## Habitat10 0.02872203 0.16869504 -0.16211162 -0.400025490 0.16660240
## Habitat11 -0.01012508 -0.05587874 0.13147017 0.183307843 -0.41673094
## 6 7 8 9 10
## (Intercept) 0.602009742 0.34329240 0.48866101 0.32419387 0.33449417
## Habitat1 0.062794727 -0.34329240 0.11599015 -0.26070181 -0.08215772
## Habitat2 0.138448273 0.26135877 -0.48866101 0.23990869 0.08923465
## Habitat3 0.047113065 -0.27980033 0.07544155 -0.32419387 -0.14257498
## Habitat4 0.003253416 -0.09095595 -0.06493220 -0.13227468 -0.33449417
## Habitat5 0.219128469 0.37465632 -0.22199434 0.36152041 0.23413328
## Habitat6 0.077134643 -0.25878535 0.14963686 -0.20479089 -0.03014634
## Habitat7 0.021519670 -0.14329240 0.04380652 -0.17889473 -0.15082070
## Habitat8 0.168105201 0.05485575 0.30895804 0.10849843 0.24222541
## Habitat9 0.013374873 0.09814905 -0.09183561 0.07386438 -0.07258941
## Habitat10 0.035671417 0.11907320 -0.28866101 0.08757083 -0.09206993
## Habitat11 -0.184534014 -0.11347873 0.21045403 -0.09543570 0.06849091
## 11 12
## (Intercept) 0.595318533 0.36654816
## Habitat1 0.122630185 -0.28204111
## Habitat2 -0.328651866 0.27174972
## Habitat3 0.090395753 -0.24714517
## Habitat4 -0.026691082 -0.06220033
## Habitat5 -0.595318533 0.37763789
## Habitat6 0.148867514 -0.36654816
## Habitat7 0.085840887 -0.11842786
## Habitat8 0.267181467 -0.03619101
## Habitat9 0.004681467 0.11244344
## Habitat10 -0.189913128 0.13840234
## Habitat11 0.195157658 -0.10027597
##
## $f.perms
## [,1]
## [1,] 0
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## [959,] 0
## [960,] 0
## [961,] 0
## [962,] 0
## [963,] 0
## [964,] 0
## [965,] 0
## [966,] 0
## [967,] 0
## [968,] 0
## [969,] 0
## [970,] 0
## [971,] 0
## [972,] 0
## [973,] 0
## [974,] 0
## [975,] 0
## [976,] 0
## [977,] 0
## [978,] 0
## [979,] 0
## [980,] 0
## [981,] 0
## [982,] 0
## [983,] 0
## [984,] 0
## [985,] 0
## [986,] 0
## [987,] 0
## [988,] 0
## [989,] 0
## [990,] 0
## [991,] 0
## [992,] 0
## [993,] 0
## [994,] 0
## [995,] 0
## [996,] 0
## [997,] 0
## [998,] 0
## [999,] 0
##
## $model.matrix
## (Intercept) Habitat1 Habitat2 Habitat3 Habitat4 Habitat5 Habitat6 Habitat7
## 1 1 0 0 0 0 0 0 1
## 2 1 0 0 0 0 0 0 0
## 3 1 0 0 0 0 0 0 0
## 4 1 0 0 0 0 0 0 0
## 5 1 0 0 0 0 0 0 0
## 6 1 -1 -1 -1 -1 -1 -1 -1
## 7 1 1 0 0 0 0 0 0
## 8 1 0 1 0 0 0 0 0
## 9 1 0 0 1 0 0 0 0
## 10 1 0 0 0 1 0 0 0
## 11 1 0 0 0 0 1 0 0
## 12 1 0 0 0 0 0 1 0
## Habitat8 Habitat9 Habitat10 Habitat11
## 1 0 0 0 0
## 2 1 0 0 0
## 3 0 1 0 0
## 4 0 0 1 0
## 5 0 0 0 1
## 6 -1 -1 -1 -1
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
##
## $terms
## data_1 ~ Habitat
## attr(,"variables")
## list(data_1, Habitat)
## attr(,"factors")
## Habitat
## data_1 0
## Habitat 1
## attr(,"term.labels")
## [1] "Habitat"
## attr(,"order")
## [1] 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"Pr is not significant as it is greater than the critical value of 0.05 at 95% significance, therefore the macroinvertebrate assemblages do not differ to a statistical significance across sites.
NMDS For Macroinvertebrates Collected From Students in Waders
Data was transforrmed in excel again.
library(readxl)
Species_List <- read_excel("Species_List.xlsx",
sheet = "Sheet2")
View(Species_List)Step 1. Subset data which contains values:
data_1_Waders<-Species_List[,2:23]Step 2. Subset Data which contains locations
data_2_Waders<-Species_List[1]set.seed(3)
library("vegan")
NMDS_2<-metaMDS(data_1_Waders, distance= "bray")## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.06459876
## Run 1 stress 0.1242155
## Run 2 stress 0.06459929
## ... Procrustes: rmse 0.0005157377 max resid 0.001008514
## ... Similar to previous best
## Run 3 stress 0.06459895
## ... Procrustes: rmse 0.0002134384 max resid 0.0004169879
## ... Similar to previous best
## Run 4 stress 0.06459874
## ... New best solution
## ... Procrustes: rmse 3.533919e-05 max resid 6.85688e-05
## ... Similar to previous best
## Run 5 stress 0.06459912
## ... Procrustes: rmse 0.000366216 max resid 0.0007081986
## ... Similar to previous best
## Run 6 stress 0.06459864
## ... New best solution
## ... Procrustes: rmse 0.0001452467 max resid 0.0002840196
## ... Similar to previous best
## Run 7 stress 0.06459886
## ... Procrustes: rmse 0.001639206 max resid 0.00319812
## ... Similar to previous best
## Run 8 stress 0.06459896
## ... Procrustes: rmse 0.001747953 max resid 0.003412246
## ... Similar to previous best
## Run 9 stress 0.06459865
## ... Procrustes: rmse 1.800576e-05 max resid 3.534549e-05
## ... Similar to previous best
## Run 10 stress 0.06459938
## ... Procrustes: rmse 0.0007149274 max resid 0.00140369
## ... Similar to previous best
## Run 11 stress 0.06459904
## ... Procrustes: rmse 0.0004789946 max resid 0.0009377635
## ... Similar to previous best
## Run 12 stress 0.06459876
## ... Procrustes: rmse 0.001530376 max resid 0.002987498
## ... Similar to previous best
## Run 13 stress 0.06459897
## ... Procrustes: rmse 0.0004087109 max resid 0.0008008491
## ... Similar to previous best
## Run 14 stress 0.06459867
## ... Procrustes: rmse 5.377294e-05 max resid 0.0001040124
## ... Similar to previous best
## Run 15 stress 0.1242157
## Run 16 stress 0.06459872
## ... Procrustes: rmse 0.0001186798 max resid 0.000232371
## ... Similar to previous best
## Run 17 stress 0.1160901
## Run 18 stress 0.09441759
## Run 19 stress 0.0645988
## ... Procrustes: rmse 0.0002110877 max resid 0.0004145088
## ... Similar to previous best
## Run 20 stress 0.06459881
## ... Procrustes: rmse 0.0002401664 max resid 0.0004689299
## ... Similar to previous best
## *** Best solution repeated 12 times## Warning in postMDS(out$points, dis, plot = max(0, plot - 1), ...): skipping
## half-change scaling: too few points below thresholdCheck fit of Model
NMDS_2$stress## [1] 0.06459864Graphing
library("vegan")
library("ggplot2")
scores2 <-scores(NMDS_2, display="sites")
scores_df<- cbind(as.data.frame(scores2), Habitat = data_2_Waders$Habitat)
centroids2 <- aggregate(cbind(NMDS1, NMDS2) ~ Habitat, data = scores_df, FUN = mean)
seg2 <- merge(scores_df, setNames(centroids2, c('Habitat','oNMDS1','oNMDS2')),
by = 'Habitat', sort = FALSE)
ggplot(scores_df, aes(x = NMDS1, y = NMDS2, colour = Habitat))+
scale_color_manual(values=c("seagreen4", "olivedrab", "palegreen3","coral1",
"sienna3", "darkcyan", "cornflowerblue"))+
geom_segment(data = seg2,
mapping = aes(xend = oNMDS1, yend = oNMDS2)) +
geom_point(data = centroids2, size = 4) +
geom_point() +
geom_text(label= data_2_Waders$Habitat,
nudge_x = 0.015, nudge_y = 0.05,
check_overlap = F)+
ggtitle("NMDS To Represent Macroinvertebrate Assembleges
Across Sites Collected Instream")+
theme(plot.title = element_text(hjust = 0.5),
text = element_text(family = "Times New Roman"))
Percentage Weight Change, Leaf Litter:
library(readxl)
Leaf_litter_weights_ <- read_excel("Leaf litter weights .xlsx",
sheet = "Sheet1")
View(Leaf_litter_weights_)Creating a Bar Graph:
library(ggplot2)
ggplot(data=Leaf_litter_weights_, aes(x= Sample, y= `Percentage Weight Loss`,
fill= Sample))+
geom_bar(stat="identity", width=0.7)+
scale_fill_manual(values=c("seagreen3", "seagreen3", "seagreen3",
"seagreen3", "seagreen3", "seagreen3",
"palevioletred", "palevioletred", "palevioletred",
"palevioletred",
"palevioletred", "palevioletred"))+
theme(legend.position="none")+
ggtitle("Percentage Weight Loss of Leaf Litter Across Sites in Mimosa Creek, Brisbane Meanjin") +
xlab("Sites") + ylab("% Weight Loss")+
theme(text=element_text(family="Times New Roman"))