ICAR Sponsored Winter School on Climate change impacts and resilient options for Indian marine fisherie

Kaladharan, P

Najmudeen, T M

Ninan, Roshen G

Rojith, G

Sathianandan, T V

Zacharia, P U

CMFRI Digital Repository

ICAR Sponsored Winter School on Climate change impact5 and resilience options for Indian marine fisheries Course ~ a n u a l  mdlan Cwndl of 4grlwlhrral k w e h  Central Mathe FkMes Research lnstlMe Past k a  w. i a .  ~ ~ l a r n  N m  P.0.. m i 8 2  018 Mala .  Indk                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      ²  ²            ² ²                                                              β  β  β  β  εβ  β  β  εβ                   σ                       𝛽 𝛽′𝛽 = 1    'V XU '1 | − 𝐼|1'   XV• • •   •    •      Multivariate Statistical Methods    •    •                                 ‟                                                                                                                  0100000200000300000400000NE SE SW NWCatch (t)                    0.0 20.0 40.0 60.0 80.0SWNWSENE         0 5000 10000 15000 20000SWNWSENE   04000080000120000SW NW SE NE   02000400060008000100001200014000SW NW SE NE                                         µµ                       µ              Δ Δ                                   º        º                                                                                                                                                                                                                                                                   help(solve) ?solve help("[[") help.start() getwd() [1] " "C:/Users/HP/Documents" setwd("H:/CMFRI/Lectures delivered/Winter school_Dr. Zacharia_2018")    objects()rm()rm(x, y) Year<-c(1991,1992,1993,1994,1995,1996,1997,1998,1999,2000)   fish<-matrix(c("sardine","mackerel","tuna","shark"),nrow=2) fish catch <- c(20,30,45,15,26,30,23,29,30) CPUE <- c(1.2,1.3,1.4,1.1,1.4,1.5,1.3,1.2,1.1) column.names <- c("Sardine","Mackerel","Anchovy") row.names <- c("Jan","Feb","Mar") matrix.names <- c("2001","2002") result <- array(c(catch, CPUE),dim = c(3,3,2),dimnames = list(row.names,column.names,   matrix.names)) print(result)     Fish_type<-c("Pelagic", "Demersal", "Mollusc", "Demersal", "Pelagic", "Demersal") Fish_cat<-as.factor(Fish_type) levels(Fish_cat) Landings<-c(25,29,37,50) Type_fish<-c("sardine","mackerel","shrimp","prawn") FishData<-data.frame(Type_fish, Landings) FishData    data.frame()Year<-c(1991,1992,1993,1994,1995,1996,1997,1998,1999,2000) fish<-matrix(c("sardine","mackerel","tuna","shark"),nrow=2) Landings<-c(25,29,37,50) Type_fish<-c("sardine","mackerel","shrimp","prawn") FishData<-data.frame(Type_fish, Landings) Mylist=list(Year, fish, FishData) Mylist    Landings<-c(4434, 2194, 2737, 1263, 2068, 1385, 1319, 1352, 971, 819) mean(Landings) var(Landings) c() mean() var() c()mean(Landings) var(Landings)mean() var()help(functionname)help(aov)aov()    mean(x) median(x) var(x): sd(x): sort(x): rank(x) rank(-x) t.test(x,mu=n, alternative = “two.sided”) t.test(x,mu=n, alternative = “greater”) t.test(x,mu=n, alternative = “less”) t.test(x,y,mu=0, var.equal = TRUE, alternative = “two.sided”) t.test(x,y,mu=0, alternative = “two.sided”, paired = TRUE) cor(x,y) cor.test(x,y) lm(y~x, data = d) coefficients(a confint(a)   library()library(boot)install.packages()update.packages()search().   Year<-c(2000,2001,2002,2003,2004,2005,2006) Fish_Landings<-c(50,60,47,85,29,58,24) fishData=data.frame(Year,Fish_Landings) fishData data.frame()names(FishData) scan()y<-scan()   scan()scan()Year,Month,Catch 2000,jan,52 2000,jul,40 2001,jan,53 2001,jul,54 2002,jan,42 2002,jul,48 2003,jan,35 2003,jul,39 2004,jan,60 2004,jul,59 2005,jan,48 2005,jul,49 read.table()mydata=read.table("Landings.txt",header=TRUE,sep=",") mydata    read.table()read.table()read.table()read.csv()read.csv2() read.table() read.csv()   library(xlsx) read.xlsx("myfile.xlsx", sheetName = "Sheet1") read.table()webdata=read.table("http://cmfri.org.in/datasets/ effort.dat") Loading R commander:  install.packages(Rcmdr) library(Rcmdr)           Landings=read.csv("LandingsData.csv",header=TRUE,sep=",")    plot(Landings$Year,Landings$Crabs,main="Crabcatch[1997-2016]",xlab="year",ylab= "catch(kg)" )  hist(Landings$ IndianMackerel, main="Histogram for Indian Mackerel catch[1997-2016]", xlab="catch", col="red", breaks=5)    boxplot(Landings[,-1], main=" Catch of various species", xlab="Species", ylab="Catch(kg)")    plot(Landings$ IndianMackerel, type = "o",col = "cyan", xlab = "Year", ylab = "Catch (Kg)", main = "Time series of Catch", ylim=c(500, 60000)) # type ‘o’ means both point and lines lines(Landings$Crabs, type = "o", col = "blue") lines(Landings$BombayDuck, type = "o", col = "green") lines(Landings$Ribbon_Fishes, type = "o", col = "yellow") lines(Landings$Sharks, type = "o", col = "grey")    pairs(~BombayDuck+Crabs+IndianMackerel,data=Landings, main=" Scatterplot Matrix") len<-c(90, 128, 112, 68, 56, 58, 111, 111, 115, 65) wt<-c(9.3, 32.5, 19, 4.4, 2.1, 2.8, 16.1, 17.9, 22.7, 3.4) logL<-log(len) logW<-log(wt) lm1<-lm(logW~logL) summary(lm1) library(FSA) fitPlot(lm1,xlab="log Total Length (mm)",ylab="log Weight (g)",main="")     β ⇒ β  ⇒               Chapter 15                                  ₹₹                   Chapter 16                                  Chapter 17                                                                                                                                [ ]ta bSt tR S e𝑅𝑡 𝑆𝑡 ,log t t ttRa bSS   log ttRS( )tyε2~ (0, )t N ,t t i i t tiy a bS c Z   itZ {1,..., }i kT Tt t t ty x Z       ( , )Ta b  *(1, )t tx S1( ,..., )Tkc c  1, ,( ,..., )t t k tz Z Zk2~ (0, )t N I  Ity tzty tz(.)g tz( )t tz g z 1, ,( ,..., )t t k tz Z Z,1( ) ( )kt i i tiz g Z (.)g1( ,..., )kc c ( )Tt t t ty x g z   ( )tg zTtx    (.)g( ) ( )t t t ty f x g z                        µ                                                                                      o o                                                                            º                         𝑅𝑡 =  𝑆𝑡𝑒𝑎 − 𝑏𝑆𝑡           𝑦𝑡 =  log (𝑅𝑡𝑆𝑡) = 𝑎 − 𝑏 𝑆𝑡 +  𝜖𝑡  𝑦𝑡 =  log (𝑅𝑡𝑆𝑡) = 𝑎 − 𝑏 𝑆𝑡 +  ∑ 𝑐𝑖𝑖𝑍𝑖,𝑡 + 𝜖𝑡     𝜷 𝜖  𝜎2 𝝐 𝜎2𝜷tttttttezzxBzAy1𝒀 = 𝑿 𝜷 +  𝜺 𝒀 = 𝑿 𝜷 +  𝜺 𝜷 =  (𝑿′𝑽−𝟏𝑿)−𝟏𝑿 𝑽−𝟏𝒚   ty tzmk txrk tt eandtytx~ ~ ~ ~z z z zt t t p t p t         1 1 2 2  ( )~B zt t   ( )B B B Bpp    1 1 22 ~ ~ ~ ~z z z zt t t p t p t         1 1 2 2    𝝓(𝑩)?̃?𝒕 = 𝜷𝟎𝒙𝒕 + 𝜷𝟏𝒙𝒕−𝟏 + ⋯ + 𝜷𝒓𝒙𝒕−𝒓 + 𝜺𝒕tttqqtppqtqtttptptttBzBBBBzBBBzzzz)(~)()1(~)1(~~~~22122122112211 ttqqqtqttttBBBBz)()1(~2212211                                       µ                                                  

ICAR Sponsored Winter School on Climate change impacts and resilient options for Indian marine fisheries

http://eprints.cmfri.org.in/13577/1/Winter%20School%20on%20Climate%20Change%20Impacts%20and%20Resilience%20Options%20for%20Indian%20Marine%20Fisheries_2018_Eprints.pdf

ICAR Sponsored Winter School on Climate change impacts and resilient options for Indian marine fisheries

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