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FisheRies. What I use R for. Current work: GAMMS with bayesian spikeslabGAM estimator to improve egg larvae biomass estimates Geostatistics Precision estimates of survey data Analyse acoustic data (abundance and biomass estimates) Previously: Create an online platform with catch anaysis. - PowerPoint PPT Presentation
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FisheRies
What I use R for
Current work: GAMMS with bayesian spikeslabGAM estimator to
improve egg larvae biomass estimates Geostatistics Precision estimates of survey data Analyse acoustic data (abundance and biomass
estimates) Previously:
Create an online platform with catch anaysis
What is rApache
rApache project for web application development with R statistical language (http://rapache.net, developped by Jeroen Ooms, University of Utrecht)
Apache module named mod_R that embeds the R interpreter inside the web server, bundled with libapreq (Apache module for manipulating client request data)
→ transform R into a server-side scripting environment.
R package brew: templating framework for report generation, and it's perfect for generating HTML on the fly (syntax is similar to PHP, Ruby's erb module, Java Server Pages, and Python's psp module)
rApache and the fisherman
• Combining statistics with fisherman's knowledge
• Build a web application with live data analysis of catch data
• Look at correlations between environmental information and catches
• Maps of environment and catches
• Get an insight into the value in the tank
• Social media
• Safety updates
How does it work?
• Temperature & Salinity profile
• Currents• Sediments• Waves and Wind
• Catch Information • Logbook & GPS
• Experience
Data collection Data processing
Data Presentaion1) Data collection onboard from sensors
2) Auxiliary online data colleciton
3) Logbook entry into Excel form
4) Automated data upload
5) .rhtml pages – html and R anlayse and produce graphs
<%setContentType("text/html")%><html><body><%require(ggplot2)library(ggplot2)data<-read.csv(".../data.csv")…i<-1%><form method="post"><select name="Trip"> <option value="all">Recorded Trips</option> <%for (i in 1:l){….}%><input type="submit" value="PLOT"/> </form> <% a<-as.numeric(POST$Trip) trip<-(levels(factor(data$Trip.number))[a])%><pre><h2>Trip number: <%=trip%></h2>...</body></html>
Using rApache
HTML form
R code
Install rApache on any Linux/Unix or Mac Apache server
Define script folder for .R and .rhtml files
Create .rhtml files → Normal HTML with R code embedded in <% … %> or call R script Upload data or get data from forms
Create a user interface (for example with javascript libraries such as ExtJS)
googleVis
googleVis is an R package providing an interface between R and the Google Visualisation API
html code that contains the data and references to JavaScript functions hosted by Google
http://code.google.com/p/google-motion-charts-with-r/
<%setContentType("text/html")myname <-
ifelse(is.null(GET$name),'World',GET$name)%><html><head> <title>Catch information</title></head><body><%library(ggplot2)library(googleVis)library(Hmisc)catch_3<-read.csv("/opt/local/apache2/htdocs/orion_data/catchwsed.csv")catch_3$Date<-as.Date(catch_3$Date)-(10*365+0.25*10)%><%=gvisMotionChart(catch_3, idvar="Species", timevar="Haul", options=list(width=500, height=450))$html$chart%></body></html>
</html>
Final product
Second part
Combine mackerel acoustic and egg data
Use the DIDSON acoustic camera to automatically detect fish
R for a fisheries scientist
Stock assessments 2 main types of surveys I currently work with: Egg/larvae surveys e.g. Triennial Horse mackerel/Mackerel
egg survey Acoustic surveys e.g. Acoustic herring survey, Acoustic blue
whiting survey
From egg countings to SSB
Duration Stage 1: Ln(hours)=-1.61*ln(Temp.)+7.76 Temperature at 5mð Ds_hours= e^Ln(hours)
Egg abundance per m2: Ae=Ce*S*D/V Ce=Counted eggs S=raising factor D=sampling depth V=filtered Volume
Daily egg production: DEP=24*Ae / DS hours
Area ICES square: cos(lat)*30*1853.2*30*1853.2
Total annual egg production: TAEP=DEP*72* Area
Spawning stock biomassSSB=SR*TAEP/TF SR=Sex ratio=1:1=2,Total fecundity=1401eggs/g/female
Stock information based on triennial egg survey Mackerel hard to detect acoustically as they do not have a
swimbladder
Counted eggs per m2 for the 3 survey periods in 2002 using qplot from ggplot2
Mackerel egg production curve 2002 using qplot from ggplot2
qplot(data=d.eggs.plot,Julian.Days,Daily.egg.production)+geom_line(colour="red",size=2) #Create lineplot with qplotggsave(file=paste("output/EggProductionCurve",year,".png",sep="")) #Save file as png
survey.area<-as.data.frame(map("world", xlim=c(-2,10), ylim=c(52,62),plot=FALSE)[c("x","y")]) #create map using package maps
area<-qplot(x,y,data=survey.area,geom="path") # include map in qplot q<-area + geom_text(aes(x=Lon, y=Lat, label=round(DEPR,1),colour=DEPR),size=2.5,data=data_mbl) #add values of DEPR & colour them q<-q + scale_x_continuous("Longitude",breaks=floor(min(data_mbl$Lon)):ceiling(max(data_mbl$Lon))) # define scales for X axis q<-q+scale_y_continuous("Latitude",breaks=floor(min(data_mbl$Lat)):ceiling(max(data_mbl$Lat))) # define scales for Y axis q<-q + opts(legend.position = "none") # remove legend q<-q+ opts(title = paste("Daily egg production Part",i,year,sep=" "),size=13) # Add title q
Mackerel in the North Sea
####SpikeSlabGam
library(spikeSlabGAM)...f1<-log(eggs.m2+1) ~ Lon+Lat+Temp.5mf2<-log(eggs.m2+1) ~ (Lon+Lat+Temp.5m + B.Depth)^2f3<-log(eggs.m2+1) ~ (Lon+Lat+Temp.5m + B.Depth)^3fa<-log(eggs.m2+1)~(S.depth+Vol.filt+J.Day)^2+(sm(Lon)+sm(Lat)+sm(Temp.5m))^2 mcmc<-list(nChains=8,chainlength=1000,burnin=500,thin=5)
m1<-spikeSlabGAM(formula=f1, family="gaussian", data=ddd, mcmc=mcmc)m2<-spikeSlabGAM(formula=f2, family="gaussian", data=ddd, mcmc=mcmc)m3<-spikeSlabGAM(formula=f3, family="gaussian", data=ddd, mcmc=mcmc)ma<-spikeSlabGAM(formula=fa, family="gaussian", data=ddd, mcmc=mcmc)
Calculations relatively easy but do not provide any error estimatesImprovement: model data using GAMs or GAMMs with bayesion factor estimator and combine with acoustic data
####GAM
library(mgcv)...mac.t5.bd<-gam(log(Egg.m2+1)~s(Lon,Lat,k = 10)+s(Temp.5m)+ s(B.depth), data = data05.in)mac.t5<-gam(log(Egg.m2+1)~s(Lon,Lat,k = 10)+s(Temp.5m), data = data05.in)
Packages used:GAM: mgcvTime: fieldsVisualisation: maps
Packages used:GAMM: spikeSlabGAMTime: fieldsVisualisation: ggplot (integrated in spikeSlabGAM)
Egg abundance map GAM 2002
###VISULISATION A
data$log.net.area <- 0*data$Lon # make offset columnzz[data$ind]<-(predict(mac.t5,abcd)) #predict the values
png(paste("map.GAM_t5_",fn,"_",year,".png",sep="")) #write to fileimage(lon,lat,matrix(zz,llo,lla),col=topo.colors(100), cex.lab=1,cex.axis=1,xlim=c(-4,10),ylim=c(50,60))contour(lon,lat,matrix(zz,llo,lla),add=TRUE)map('world', fill = F, col = 1:10,xlim=c(-4,10),ylim=c(50,60),add=T) #Add world map contoursdev.off()
#Define the arealon<-seq(minlon,maxlon,1/2)lat<-seq(minlat,maxlat,1/2)llo<-length(lon)lla<-length(lat)
zz<-array(NA,llo*lla)
Lat<-0Lon<-0a<-1for (i in 1:lla){ for (j in 1:llo) { Lat[a]<-lat[i] Lon[a]<-lon[j] a<-a+1 j<-j+1 } i<-i+1}
Simply plot(modelname,labels=c(“var1:var2"))Using ggplot2 within the spikeSlabGAM with labels defining the selected
variables
Egg abundance maps using spikeslabGAM and ggplot2 for 2002
plot(m2,labels=“Temp.5m") respectively Lon, Lat
plot(m2,labels=c(“Lon:Lat"))
DIDSON Acoustic camera
• Acoustic lens, 96 beams, 0.3° opening, Frequency 1.8 MHz
• Currently used to count & measure length of fishes
• Can it be used to look at the acoustic blind zone?
• Stability?
Target strength measurements
Tungsten CarbideCopper
qplot(copper$Ping_number,copper$TS_uncomp,ylab="uncompensated TS (dB)",xlab="Ping number")
qplot(copper$TS_uncomp, geom = "density",ylab="Density", xlab="TS uncompensated")
Questions?
