Carnegie Science InsituteMar 20 2019 UTC

Figure 1D - Leslie fly fitness matrix

Fitness calculations using a Leslie matrix reveals roughly constant fitness across different microbiomes. Error bars are standard error of the estimate.

Setup

library(popbio)
popbio, gdtools, svglite, ....
FlyFitnessRawData.csv
DevelopmentData.csv

Fitness Matrix with Variable Development time per vial

# FITNESS MATRIX WITH VARIABLE DEVELOPMENT TIME PER VIAL (100x) 
fitdat<-read.csv(FlyFitnessRawData.csv, header=TRUE, sep=",") 
devdat<-read.csv(DevelopmentData.csv, header=TRUE, sep=",") 

uniq <- unique(unlist(fitdat$treat))
uniq1 <- unique(unlist(fitdat$vial))
fit_rep<-vector("numeric", 100)
fit_vial<-vector("numeric", 5)
d<-matrix(0, nrow=5, length(uniq), byrow=F)
d<-as.data.frame(d)
V1V2V3V4V5V6V7V8V9V10V11V12V13V14V15V16V17V18V19V20V21V22V23V24V25V26V27V28V29V30V31V32V33
1000000000000000000000000000000000
2000000000000000000000000000000000
3000000000000000000000000000000000
4000000000000000000000000000000000
5000000000000000000000000000000000
5 items

Raw Data Procesing

# iterate through treatments 
for(m in 1:length(uniq)){

trt<-subset(fitdat, treat == uniq[m])

# iterate through vials 
for(n in 1:length(uniq1)){

	vial<-subset(trt, vial == uniq1[n])
	fecund<-vector("numeric", 39)
	surv<-vector("numeric", 39)

# select development time within treatment to use 	
	for (i in 1:100){	
	
		trt2<-subset(devdat, treatment == uniq1[n])
		uniq2<-unique(unlist(trt2$replicate))
		r<-sample(uniq2, 1, replace=TRUE)
		rep<-subset(trt2, replicate==r)
		dev<-rep$development

		fecund<-vial$fecundity_adj
		f_add<-rep(0,dev+6)
		fecundity<-c(f_add, fecund)
		time<-seq(0, length(fecundity)-1, by=1)
	
		surv<-vial$survival
		s_add<-rep(1,dev+5)
		survival<-c(s_add, surv)

#create matrix of all zeros
		fitM<-matrix(0, nrow=length(fecundity), length(fecundity), byrow=T)

#populate matirx with fecundity and survival values
		for (k in 1:length(fecundity)){
				
		fitM[1,k]<-fecundity[k]

		for (l in 1:length(survival)){
			
			fitM[l+1,l] <- survival[l]
				
			}
		}		
		
#calculate dominant eigenvalue
	fit<-lambda(fitM)

#store results in vector
	fit_rep[i]<-fit
	
	}
	
fit_vial[n]<-mean(fit_rep)

}

#store results in dataframe	
d[,m]<-fit_vial

}

d
FlyFitness.csv
check <- read.csv(FlyFitness.csv)

Plot Fly Fitness

# PLOT FLY FITNESS (from fitness dataframe above (d) modified in Excel)

mydata<-read.csv(FlyFitness.csv, header=TRUE, sep=",")
data<-subset(mydata, treat != "33")
attach(data)

st.err <- function(x) {
		sd(x)/sqrt(length(x))
		}

stderr<-aggregate(fitness, by=list(treat), FUN="st.err")
meanFit<-aggregate(fitness, by=list(treat), FUN="mean")


spacing<-c(0.92, 1.08, 0.85, 1, 1.15, 1.62, 1.59, 2.28, 1.75, 2.19, 2.05, 1.95, 2.11, 1.71, 1.87, 2.78, 2.94, 3.02, 3.1, 3.2, 3.28, 2.66, 3.375, 2.56, 3.55, 3.9, 3.99, 4.08, 4.17, 4.33, 5, 0)
points<-c(1:32)
points<-as.character(points)

par(mar=c(5,5,2,2))
plot(spacing, meanFit$x, ylim=c(1.15, 1.25), ylab="Fitness", xlab="Number of species", pch=21, cex=3.5, cex.axis=1.4, cex.lab=1.6)
text(spacing, meanFit$x, labels=points)
arrows(spacing,meanFit$x-stderr$x,spacing,meanFit$x+stderr$x, code=3, length=0.02, angle = 90)

Leslie matrix shows constant fitness across different microbiomes. Error bars are standard error of the estimate.