12 16 2014 Evidence of Ostrea lurida (Carpenter 1894) population structure in Puget Sound, WA PeerJ Preprint

For those following my research you can now read and review a preprint version of my manuscript at PeerJ. This is exciting as its the first time my work has become public this way. If you have any comments or questions you can comment on the PeerJ version as long as you have a PeerJ account (Its free and a great resource).

You can find the article here:

https://peerj.com/preprints/704/

12 8 2014 News and Reflections on Summer Field Work

12/8/2014

As the year comes to a close, I'm happy to announce that the manuscript from the data generated from field work this past year is coming along nicely. I'll announce here when its available for viewing.

Also thanks to everyone who has taken the time to read this blog. I've only been going for about 6 months now but its been awesome. I can't wait to see what next year brings with all the RAD-Seq data that I'm going to work on. 

Anyway to post some content lets all be nostalgic for those warm summer days as we get closer to Christmas. 

When those Winter doldrums hit, remember those warm summer days at Oyster Bay.

11 24 2014 Non Parametric Test for Differences in Size

y1boxplot.R

Jake H

Wed Nov 26 15:18:25 2014

require(ggplot2)

## Loading required package: ggplot2

require(plyr)

## Loading required package: plyr

require(splitstackshape)

## Loading required package: splitstackshape
## Loading required package: data.table

require(nparcomp)

## Loading required package: nparcomp
## Loading required package: multcomp
## Loading required package: mvtnorm
## Loading required package: survival
## Loading required package: splines
## Loading required package: TH.data

y1size=read.csv('Y1size.csv')
#creates dataframe and reads in the CSV file for sizes
View(y1size)
#check data
y1size$Date<-as.Date(y1size$Date, "%m/%d/%Y")
#make R understand dates
y1meansize<-ddply(y1size,.(Date,Site,Pop),summarise, mean_size=mean(Length.mm,na.rm=T))
#create table of ave size for outplant and year one for each pop at each site
#print it out
print(y1meansize)

##          Date       Site Pop mean_size
## 1  2013-08-16    Fidalgo  2H     10.67
## 2  2013-08-16    Fidalgo  2N     11.60
## 3  2013-08-16    Fidalgo  2S     11.25
## 4  2013-08-16 Manchester  4H     10.53
## 5  2013-08-16 Manchester  4N     13.40
## 6  2013-08-16 Manchester  4S     11.30
## 7  2013-08-16 Oyster Bay  1H     10.49
## 8  2013-08-16 Oyster Bay  1N     10.90
## 9  2013-08-16 Oyster Bay  1S     12.15
## 10 2014-09-19 Oyster Bay  1H     27.96
## 11 2014-09-19 Oyster Bay  1N     34.65
## 12 2014-09-19 Oyster Bay  1S     27.98
## 13 2014-10-17    Fidalgo  2H     24.40
## 14 2014-10-17    Fidalgo  2N     29.10
## 15 2014-10-17    Fidalgo  2S     28.91
## 16 2014-10-24 Manchester  4H     21.49
## 17 2014-10-24 Manchester  4N     24.37
## 18 2014-10-24 Manchester  4S     23.99

#now we need to create subsets for each site for out plant and end of year 1
outmany1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2013-08-16"&Site=="Manchester")
outfidy1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2013-08-16"&Site=="Fidalgo")
outoysy1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2013-08-16"&Site=="Oyster Bay")
endmany1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2014-10-24"&Site=="Manchester")
endfidy1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2014-10-17"&Site=="Fidalgo")
endoysy1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2014-09-19"&Site=="Oyster Bay")

ggplot()+
  geom_boxplot(data=outmany1,aes(x=Pop,y=Length.mm,fill=Pop))+
  scale_colour_manual(values=c("blue","purple","orange"))+
  scale_fill_manual(values=c("blue","purple","orange"))

ggplot()+
  geom_boxplot(data=endmany1,aes(x=Pop,y=Length.mm,fill=Pop))+
  scale_colour_manual(values=c("blue","purple","orange"),guide=F)+
  scale_fill_manual(values=c("blue","purple","orange"), guide=F)+
  ylim(c(0,50))+
  labs(x="Population",y="Average Length (mm)")+
  scale_x_discrete(labels=c("Dabob","Fidalgo","Oyster Bay"))+
  annotate("text", x=c("4N","4H","4S"),y=50, label=c("A","B","A"),size=10)+
  theme_bw()+
  theme(axis.text.x=element_text(size=20),
        axis.title.x=element_text(size=25),
        axis.title.y=element_text(size=25, vjust=2),
        axis.text.y=element_text(size=20))

ggplot()+
  geom_boxplot(data=outfidy1,aes(x=Pop,y=Length.mm,fill=Pop))+
  scale_colour_manual(values=c("blue","purple","orange"))+
  scale_fill_manual(values=c("blue","purple","orange"))

ggplot()+
  geom_boxplot(data=endfidy1,aes(x=Pop,y=Length.mm,fill=Pop))+
  scale_colour_manual(values=c("blue","purple","orange"),guide=F)+
  scale_fill_manual(values=c("blue","purple","orange"),guide=F)+
  ylim(c(0,50))+
  labs(x="Population",y="Average Length (mm)")+
  scale_x_discrete(labels=c("Dabob","Fidalgo","Oyster Bay"))+
  annotate("text", x=c("2N","2H","2S"),y=50, label=c("A","B","A"),size=10)+
  theme_bw()+
  theme(axis.text.x=element_text(size=20),
        axis.title.x=element_text(size=25),
        axis.title.y=element_text(size=25, vjust=2),
        axis.text.y=element_text(size=20))

## Warning: Removed 1 rows containing non-finite values (stat_boxplot).

ggplot()+
  geom_boxplot(data=outoysy1,aes(x=Pop,y=Length.mm,fill=Pop))+
  scale_colour_manual(values=c("blue","purple","orange"))+
  scale_fill_manual(values=c("blue","purple","orange"))

ggplot()+
  geom_boxplot(data=endoysy1,aes(x=Pop,y=Length.mm,fill=Pop))+
  scale_colour_manual(values=c("blue","purple","orange"),guide=F)+
  scale_fill_manual(values=c("blue","purple","orange"),guide=F)+
  ylim(c(0,50))+
  labs(x="Population",y="Average Length (mm)")+
  scale_x_discrete(labels=c("Dabob","Fidalgo","Oyster Bay"))+
  annotate("text", x=c("1N","1H","1S"),y=50, label=c("B","A","A"),size=10)+
  theme_bw()+
  theme(axis.text.x=element_text(size=20),
        axis.title.x=element_text(size=25),
        axis.title.y=element_text(size=25, vjust=2),
        axis.text.y=element_text(size=20))

## Warning: Removed 1 rows containing non-finite values (stat_boxplot).

normality<-ddply(y1size,.(Date,Site,Pop),summarize,n=length(Length.mm),sw=shapiro.test(as.numeric(Length.mm))[2])
y1size$Pop2<-y1size$Pop
y1size$Pop2<-revalue(y1size$Pop2,c("1H"="H","2H"="H","4H"="H","1N"="N","2N"="N","4N"="N","1S"="S","2S"="S","4S"="S"))
#Here we subset the data set to only include data from the end of year 1
endy1<-ddply(y1size,.(Length.mm,Site,Pop,Tray,Sample,Area,Pop2),subset,Date>="2014-09-19")
normality<-ddply(endy1,.(Date,Site,Pop),summarize,n=length(Length.mm),sw=shapiro.test(as.numeric(Length.mm))[2])
endy1$log<-log2(endy1$Length.mm)
endy1$asin<-asin(sign(endy1$Length.mm)*sqrt(abs(endy1$Length.mm)))

## Warning: NaNs produced

normality<-ddply(endy1,.(Date,Site,Pop),summarize,n=length(log),sw=shapiro.test(as.numeric(log))[2])

sizekw<-kruskal.test(endy1$Length.mm~endy1$Site,endy1)
print(sizekw)

## 
##  Kruskal-Wallis rank sum test
## 
## data:  endy1$Length.mm by endy1$Site
## Kruskal-Wallis chi-squared = 426.2, df = 2, p-value < 2.2e-16

sizekwpop<-kruskal.test(endy1$Length.mm~endy1$Pop2,endy1)
print(sizekwpop)

## 
##  Kruskal-Wallis rank sum test
## 
## data:  endy1$Length.mm by endy1$Pop2
## Kruskal-Wallis chi-squared = 230, df = 2, p-value < 2.2e-16

require(PMCMR)

## Loading required package: PMCMR

sizenemenyi1<-posthoc.kruskal.nemenyi.test(x=endy1$Length.mm,g=endy1$Site, method="Tukey")

## Warning: Ties are present, p-values are not corrected.

sizenemenyi1

## 
##  Pairwise comparisons using Tukey and Kramer (Nemenyi) test  
##                    with Tukey-Dist approximation for independent samples 
## 
## data:  endy1$Length.mm and endy1$Site 
## 
##            Fidalgo Manchester
## Manchester < 2e-16 -         
## Oyster Bay 1.1e-12 < 2e-16   
## 
## P value adjustment method: none

sizenemenyi2<-posthoc.kruskal.nemenyi.test(x=endy1$Length.mm,g=endy1$Pop2, method="Tukey")

## Warning: Ties are present, p-values are not corrected.

sizenemenyi2

## 
##  Pairwise comparisons using Tukey and Kramer (Nemenyi) test  
##                    with Tukey-Dist approximation for independent samples 
## 
## data:  endy1$Length.mm and endy1$Pop2 
## 
##   H       N      
## N < 2e-16 -      
## S 3.4e-14 4.9e-08
## 
## P value adjustment method: none

sizenemenyi3<-posthoc.kruskal.nemenyi.test(x=endy1$Length.mm,g=endy1$Site:endy1$Pop2, method="Tukey")

## Warning: Ties are present, p-values are not corrected.

sizenemenyi3

## 
##  Pairwise comparisons using Tukey and Kramer (Nemenyi) test  
##                    with Tukey-Dist approximation for independent samples 
## 
## data:  endy1$Length.mm and endy1$Site:endy1$Pop2 
## 
##              Fidalgo:H Fidalgo:N Fidalgo:S Manchester:H Manchester:N
## Fidalgo:N    < 2e-16   -         -         -            -           
## Fidalgo:S    7.8e-14   0.9995    -         -            -           
## Manchester:H 2.7e-07   < 2e-16   < 2e-16   -            -           
## Manchester:N 1.0000    9.2e-14   1.1e-13   7.3e-06      -           
## Manchester:S 0.9786    < 2e-16   8.5e-14   0.0004       0.9880      
## Oyster Bay:H 3.1e-10   0.2781    0.6352    1.2e-14      2.4e-08     
## Oyster Bay:N < 2e-16   1.5e-11   7.2e-13   < 2e-16      < 2e-16     
## Oyster Bay:S 1.8e-09   0.6813    0.9255    9.1e-14      5.7e-08     
##              Manchester:S Oyster Bay:H Oyster Bay:N
## Fidalgo:N    -            -            -           
## Fidalgo:S    -            -            -           
## Manchester:H -            -            -           
## Manchester:N -            -            -           
## Manchester:S -            -            -           
## Oyster Bay:H 9.5e-12      -            -           
## Oyster Bay:N < 2e-16      1.0e-13      -           
## Oyster Bay:S 6.0e-11      1.0000       6.9e-12     
## 
## P value adjustment method: none