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timeseries_gams.R
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timeseries_gams.R
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# start by cleaning data
#######################################
#let's do it all at once
MCSE<-read.csv("data/MCSE_working_alldata.txt",
header=T)
Forest<-read.csv("data/Forest_working_alldata.txt",
header=T)
#fix unmatched names
names(Forest)<-names(MCSE)
#append the two tables together
all_lb<-rbind(MCSE, Forest)
names(all_lb)<-toupper(names(all_lb)) #make column names consistent with previous formatting
#Fit date format
library(lubridate)
all_lb$newdate<-mdy_hms(all_lb$DATE)
all_lb$year<-year(all_lb$newdate)
all_lb$DOY<-yday(all_lb$newdate)
#cut NA data
all_lb<-na.omit(all_lb)
#now we fix the known typos and give workable treatment names
all_lb$TREAT_DESC<-gsub("Early succesional community", "Early successional", all_lb$TREAT_DESC)
all_lb$TREAT_DESC<-gsub("Early successional community", "Early successional", all_lb$TREAT_DESC)
all_lb$TREAT_DESC<-gsub("Early Successional Community", "Early successional", all_lb$TREAT_DESC)
all_lb$TREAT_DESC<-gsub("Early sucessional community", "Early successional", all_lb$TREAT_DESC)
all_lb$TREAT_DESC<-gsub("poplar trees", "Poplar trees", all_lb$TREAT_DESC)
all_lb$TREAT_DESC<-gsub("Succesional", "Successional", all_lb$TREAT_DESC)
all_lb$TREAT_DESC<-gsub("Sucessional", "Successional", all_lb$TREAT_DESC)
all_lb$TREAT_DESC<-gsub("Alfalfa", "Alfalfa*", all_lb$TREAT_DESC)
all_lb$TREAT_DESC<-gsub("Switchgrass", "Alfalfa*", all_lb$TREAT_DESC)
all_lb$TREAT_DESC<-gsub("Biologically based \\(organic\\)", "Organic", all_lb$TREAT_DESC)
all_lb$TREAT_DESC<-gsub("Conventional till", "Conventional", all_lb$TREAT_DESC)
all_lb$TREAT_DESC<-as.factor(all_lb$TREAT_DESC)
all_lb$HABITAT<-as.factor(all_lb$HABITAT)
all_lb$REPLICATE<-as.factor(all_lb$REPLICATE)
all_lb$STATION<-as.factor(all_lb$STATION)
str(all_lb)
#need to aggregate the data in a meaningful way by species
###########
#subset the data to include only data before August 10th or the 222 DOY
all_lb= subset(all_lb, DOY > 0 & DOY < 222)
#subset the data to include 1993 or later, because sampling changed to add forests then
all_lb= subset(all_lb, year >= 1993)
#let's start by making the individual data frames like we had before
all_lb1<-all_lb
all_lb1$SPID<-NULL
library(reshape2)
library(plyr)
ABIPN<-all_lb1[which(all_lb$SPID=="ABIPN"),]
BURSI<-all_lb1[which(all_lb$SPID=="BURSI"),]
C7<-all_lb1[which(all_lb$SPID=="C7"),]
CMAC<-all_lb1[which(all_lb$SPID=="CMAC"),]
CSTIG<-all_lb1[which(all_lb$SPID=="CSTIG"),]
CTRIF<-all_lb1[which(all_lb$SPID=="CTRIF"),]
CYCSP<-all_lb1[which(all_lb$SPID=="CYCSP"),]
H13<-all_lb1[which(all_lb$SPID=="H13"),]
HAXY<-all_lb1[which(all_lb$SPID=="HAXY"),]
HCONV<-all_lb1[which(all_lb$SPID=="HCONV"),]
HGLAC<-all_lb1[which(all_lb$SPID=="HGLAC"),]
HPARN<-all_lb1[which(all_lb$SPID=="HPARN"),]
HVAR<-all_lb1[which(all_lb$SPID=="HVAR"),]
PQUA<-all_lb1[which(all_lb$SPID=="PQUA"),]
#####################################################################################
#PQUA
#tell R where the data is by melting it, assigning IDs to the columns
#cast the data to count up the bugs
PQUA2<-dcast(PQUA, year+TREAT_DESC+REPLICATE~., value.var="ADULTS", sum)
#cast the data to count the traps
PQUA3<-dcast(PQUA, year+TREAT_DESC+REPLICATE~., value.var="ADULTS", length)
#let's rename these new vectors within the data frame
names(PQUA2)[4]<-"ADULTS"
names(PQUA3)[4]<-"TRAPS"
#rename the data frame and combine the number of traps we counted into it from PQUA3
PQUA_summary<-PQUA2
PQUA_summary$TRAPS<-PQUA3$TRAPS
#create a new variable to account for trapping effort in a given year
PQUA_summary$pertrap<-PQUA_summary$ADULTS/PQUA_summary$TRAPS
####Creating a new treatment classification
PQUA_summary$TREAT_CAT=ifelse(PQUA_summary$TREAT_DESC=="Coniferous"|
PQUA_summary$TREAT_DESC=="Deciduous"|
PQUA_summary$TREAT_DESC=="Successional", "Forest",
ifelse(PQUA_summary$TREAT_DESC=="Alfalfa*"|
PQUA_summary$TREAT_DESC=="Early successional"|
PQUA_summary$TREAT_DESC=="Poplar trees", "Perennial",
ifelse(PQUA_summary$TREAT_DESC=="Conventional"|
PQUA_summary$TREAT_DESC=="No till"|
PQUA_summary$TREAT_DESC=="Organic"|
PQUA_summary$TREAT_DESC=="Reduced input", "Annual", "Check")))
# First make a dataframe for error bars
PQUA_agg <- ddply(PQUA_summary, .(TREAT_CAT, year), summarise,
mean.bugs = mean(pertrap),
sd.bugs.pt = sd(pertrap),
N = length(pertrap),
SE = sd.bugs.pt / sqrt(N))
#####################################################################################
#HVAR
#cast the data to count up the fireflies
HVAR2<-dcast(HVAR, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", sum)
#cast the data to count the traps
HVAR3<-dcast(HVAR, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", length)
#let's rename these new vectors within the data frame
names(HVAR2)[4]<-"ADULTS"
names(HVAR3)[4]<-"TRAPS"
#rename the data frame and combine the number of traps we counted into it from HVAR3
HVAR_summary<-HVAR2
HVAR_summary$TRAPS<-HVAR3$TRAPS
#create a new variable to account for trapping effort in a given year
HVAR_summary$pertrap<-HVAR_summary$ADULTS/HVAR_summary$TRAPS
####Creating a new treatment classification
HVAR_summary$TREAT_CAT=ifelse(HVAR_summary$TREAT_DESC=="Coniferous"|
HVAR_summary$TREAT_DESC=="Deciduous"|
HVAR_summary$TREAT_DESC=="Successional", "Forest",
ifelse(HVAR_summary$TREAT_DESC=="Alfalfa*"|
HVAR_summary$TREAT_DESC=="Early successional"|
HVAR_summary$TREAT_DESC=="Poplar trees", "Perennial",
ifelse(HVAR_summary$TREAT_DESC=="Conventional"|
HVAR_summary$TREAT_DESC=="No till"|
HVAR_summary$TREAT_DESC=="Organic"|
HVAR_summary$TREAT_DESC=="Reduced input", "Annual", "Check")))
# First make a dataframe for error bars
HVAR_agg <- ddply(HVAR_summary, .(TREAT_CAT, year), summarise,
mean.bugs = mean(pertrap),
sd.bugs.pt = sd(pertrap),
N = length(pertrap),
SE = sd.bugs.pt / sqrt(N))
######################################################################################
#HPARN
#cast the data to count up the fireflies
HPARN2<-dcast(HPARN, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", sum)
#cast the data to count the traps
HPARN3<-dcast(HPARN, year+TREAT_DESC+REPLICATE~., value.var="ADULTS",length)
#let's rename these new vectors within the data frame
names(HPARN2)[4]<-"ADULTS"
names(HPARN3)[4]<-"TRAPS"
#rename the data frame and combine the number of traps we counted into it from HPARN3
HPARN_summary<-HPARN2
HPARN_summary$TRAPS<-HPARN3$TRAPS
#create a new variable to account for trapping effort in a given year
HPARN_summary$pertrap<-HPARN_summary$ADULTS/HPARN_summary$TRAPS
####Creating a new treatment classification
HPARN_summary$TREAT_CAT=ifelse(HPARN_summary$TREAT_DESC=="Coniferous"|
HPARN_summary$TREAT_DESC=="Deciduous"|
HPARN_summary$TREAT_DESC=="Successional", "Forest",
ifelse(HPARN_summary$TREAT_DESC=="Alfalfa*"|
HPARN_summary$TREAT_DESC=="Early successional"|
HPARN_summary$TREAT_DESC=="Poplar trees", "Perennial",
ifelse(HPARN_summary$TREAT_DESC=="Conventional"|
HPARN_summary$TREAT_DESC=="No till"|
HPARN_summary$TREAT_DESC=="Organic"|
HPARN_summary$TREAT_DESC=="Reduced input", "Annual", "Check")))
# First make a dataframe for error bars
HPARN_agg <- ddply(HPARN_summary, .(TREAT_CAT, year), summarise,
mean.bugs = mean(pertrap),
sd.bugs.pt = sd(pertrap),
N = length(pertrap),
SE = sd.bugs.pt / sqrt(N))
######################################################################################
#HGLAC
#cast the data to count up the fireflies
HGLAC2<-dcast(HGLAC, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", sum)
#cast the data to count the traps
HGLAC3<-dcast(HGLAC, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", length)
#let's rename these new vectors within the data frame
names(HGLAC2)[4]<-"ADULTS"
names(HGLAC3)[4]<-"TRAPS"
#rename the data frame and combine the number of traps we counted into it from HGLAC3
HGLAC_summary<-HGLAC2
HGLAC_summary$TRAPS<-HGLAC3$TRAPS
#create a new variable to account for trapping effort in a given year
HGLAC_summary$pertrap<-HGLAC_summary$ADULTS/HGLAC_summary$TRAPS
####Creating a new treatment classification
HGLAC_summary$TREAT_CAT=ifelse(HGLAC_summary$TREAT_DESC=="Coniferous"|
HGLAC_summary$TREAT_DESC=="Deciduous"|
HGLAC_summary$TREAT_DESC=="Successional", "Forest",
ifelse(HGLAC_summary$TREAT_DESC=="Alfalfa*"|
HGLAC_summary$TREAT_DESC=="Early successional"|
HGLAC_summary$TREAT_DESC=="Poplar trees", "Perennial",
ifelse(HGLAC_summary$TREAT_DESC=="Conventional"|
HGLAC_summary$TREAT_DESC=="No till"|
HGLAC_summary$TREAT_DESC=="Organic"|
HGLAC_summary$TREAT_DESC=="Reduced input", "Annual", "Check")))
# First make a dataframe for error bars
HGLAC_agg <- ddply(HGLAC_summary, .(TREAT_CAT, year), summarise,
mean.bugs = mean(pertrap),
sd.bugs.pt = sd(pertrap),
N = length(pertrap),
SE = sd.bugs.pt / sqrt(N))
######################################################################################
#HCONV
#cast the data to count up the fireflies
HCONV2<-dcast(HCONV, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", sum)
#cast the data to count the traps
HCONV3<-dcast(HCONV, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", length)
#let's rename these new vectors within the data frame
names(HCONV2)[4]<-"ADULTS"
names(HCONV3)[4]<-"TRAPS"
#rename the data frame and combine the number of traps we counted into it from HCONV3
HCONV_summary<-HCONV2
HCONV_summary$TRAPS<-HCONV3$TRAPS
#create a new variable to account for trapping effort in a given year
HCONV_summary$pertrap<-HCONV_summary$ADULTS/HCONV_summary$TRAPS
####Creating a new treatment classification
HCONV_summary$TREAT_CAT=ifelse(HCONV_summary$TREAT_DESC=="Coniferous"|
HCONV_summary$TREAT_DESC=="Deciduous"|
HCONV_summary$TREAT_DESC=="Successional", "Forest",
ifelse(HCONV_summary$TREAT_DESC=="Alfalfa*"|
HCONV_summary$TREAT_DESC=="Early successional"|
HCONV_summary$TREAT_DESC=="Poplar trees", "Perennial",
ifelse(HCONV_summary$TREAT_DESC=="Conventional"|
HCONV_summary$TREAT_DESC=="No till"|
HCONV_summary$TREAT_DESC=="Organic"|
HCONV_summary$TREAT_DESC=="Reduced input", "Annual", "Check")))
# First make a dataframe for error bars
HCONV_agg <- ddply(HCONV_summary, .(TREAT_CAT, year), summarise,
mean.bugs = mean(pertrap),
sd.bugs.pt = sd(pertrap),
N = length(pertrap),
SE = sd.bugs.pt / sqrt(N))
######################################################################################
#HAXY
#cast the data to count up the fireflies
HAXY2<-dcast(HAXY, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", sum)
#cast the data to count the traps
HAXY3<-dcast(HAXY, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", length)
#let's rename these new vectors within the data frame
names(HAXY2)[4]<-"ADULTS"
names(HAXY3)[4]<-"TRAPS"
#rename the data frame and combine the number of traps we counted into it from HAXY3
HAXY_summary<-HAXY2
HAXY_summary$TRAPS<-HAXY3$TRAPS
#create a new variable to account for trapping effort in a given year
HAXY_summary$pertrap<-HAXY_summary$ADULTS/HAXY_summary$TRAPS
####Creating a new treatment classification
HAXY_summary$TREAT_CAT=ifelse(HAXY_summary$TREAT_DESC=="Coniferous"|
HAXY_summary$TREAT_DESC=="Deciduous"|
HAXY_summary$TREAT_DESC=="Successional", "Forest",
ifelse(HAXY_summary$TREAT_DESC=="Alfalfa*"|
HAXY_summary$TREAT_DESC=="Early successional"|
HAXY_summary$TREAT_DESC=="Poplar trees", "Perennial",
ifelse(HAXY_summary$TREAT_DESC=="Conventional"|
HAXY_summary$TREAT_DESC=="No till"|
HAXY_summary$TREAT_DESC=="Organic"|
HAXY_summary$TREAT_DESC=="Reduced input", "Annual", "Check")))
# First make a dataframe for error bars
HAXY_agg <- ddply(HAXY_summary, .(TREAT_CAT, year), summarise,
mean.bugs = mean(pertrap),
sd.bugs.pt = sd(pertrap),
N = length(pertrap),
SE = sd.bugs.pt / sqrt(N))
######################################################################################
#H13
#cast the data to count up the fireflies
H132<-dcast(H13, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", sum)
#cast the data to count the traps
H133<-dcast(H13, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", length)
#let's rename these new vectors within the data frame
names(H132)[4]<-"ADULTS"
names(H133)[4]<-"TRAPS"
#rename the data frame and combine the number of traps we counted into it from H133
H13_summary<-H132
H13_summary$TRAPS<-H133$TRAPS
#create a new variable to account for trapping effort in a given year
H13_summary$pertrap<-H13_summary$ADULTS/H13_summary$TRAPS
####Creating a new treatment classification
H13_summary$TREAT_CAT=ifelse(H13_summary$TREAT_DESC=="Coniferous"|
H13_summary$TREAT_DESC=="Deciduous"|
H13_summary$TREAT_DESC=="Successional", "Forest",
ifelse(H13_summary$TREAT_DESC=="Alfalfa*"|
H13_summary$TREAT_DESC=="Early successional"|
H13_summary$TREAT_DESC=="Poplar trees", "Perennial",
ifelse(H13_summary$TREAT_DESC=="Conventional"|
H13_summary$TREAT_DESC=="No till"|
H13_summary$TREAT_DESC=="Organic"|
H13_summary$TREAT_DESC=="Reduced input", "Annual", "Check")))
# First make a dataframe for error bars
H13_agg <- ddply(H13_summary, .(TREAT_CAT, year), summarise,
mean.bugs = mean(pertrap),
sd.bugs.pt = sd(pertrap),
N = length(pertrap),
SE = sd.bugs.pt / sqrt(N))
######################################################################################
#CYCSP
#cast the data to count up the fireflies
CYCSP2<-dcast(CYCSP, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", sum)
#cast the data to count the traps
CYCSP3<-dcast(CYCSP, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", length)
#let's rename these new vectors within the data frame
names(CYCSP2)[4]<-"ADULTS"
names(CYCSP3)[4]<-"TRAPS"
#rename the data frame and combine the number of traps we counted into it from CYCSP3
CYCSP_summary<-CYCSP2
CYCSP_summary$TRAPS<-CYCSP3$TRAPS
#create a new variable to account for trapping effort in a given year
CYCSP_summary$pertrap<-CYCSP_summary$ADULTS/CYCSP_summary$TRAPS
####Creating a new treatment classification
CYCSP_summary$TREAT_CAT=ifelse(CYCSP_summary$TREAT_DESC=="Coniferous"|
CYCSP_summary$TREAT_DESC=="Deciduous"|
CYCSP_summary$TREAT_DESC=="Successional", "Forest",
ifelse(CYCSP_summary$TREAT_DESC=="Alfalfa*"|
CYCSP_summary$TREAT_DESC=="Early successional"|
CYCSP_summary$TREAT_DESC=="Poplar trees", "Perennial",
ifelse(CYCSP_summary$TREAT_DESC=="Conventional"|
CYCSP_summary$TREAT_DESC=="No till"|
CYCSP_summary$TREAT_DESC=="Organic"|
CYCSP_summary$TREAT_DESC=="Reduced input", "Annual", "Check")))
# First make a dataframe for error bars
CYCSP_agg <- ddply(CYCSP_summary, .(TREAT_CAT, year), summarise,
mean.bugs = mean(pertrap),
sd.bugs.pt = sd(pertrap),
N = length(pertrap),
SE = sd.bugs.pt / sqrt(N))
######################################################################################
#CTRIF
#cast the data to count up the fireflies
CTRIF2<-dcast(CTRIF, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", sum)
#cast the data to count the traps
CTRIF3<-dcast(CTRIF, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", length)
#let's rename these new vectors within the data frame
names(CTRIF2)[4]<-"ADULTS"
names(CTRIF3)[4]<-"TRAPS"
#rename the data frame and combine the number of traps we counted into it from CTRIF3
CTRIF_summary<-CTRIF2
CTRIF_summary$TRAPS<-CTRIF3$TRAPS
#create a new variable to account for trapping effort in a given year
CTRIF_summary$pertrap<-CTRIF_summary$ADULTS/CTRIF_summary$TRAPS
####Creating a new treatment classification
CTRIF_summary$TREAT_CAT=ifelse(CTRIF_summary$TREAT_DESC=="Coniferous"|
CTRIF_summary$TREAT_DESC=="Deciduous"|
CTRIF_summary$TREAT_DESC=="Successional", "Forest",
ifelse(CTRIF_summary$TREAT_DESC=="Alfalfa*"|
CTRIF_summary$TREAT_DESC=="Early successional"|
CTRIF_summary$TREAT_DESC=="Poplar trees", "Perennial",
ifelse(CTRIF_summary$TREAT_DESC=="Conventional"|
CTRIF_summary$TREAT_DESC=="No till"|
CTRIF_summary$TREAT_DESC=="Organic"|
CTRIF_summary$TREAT_DESC=="Reduced input", "Annual", "Check")))
# First make a dataframe for error bars
CTRIF_agg <- ddply(CTRIF_summary, .(TREAT_CAT, year), summarise,
mean.bugs = mean(pertrap),
sd.bugs.pt = sd(pertrap),
N = length(pertrap),
SE = sd.bugs.pt / sqrt(N))
######################################################################################
#CSTIG
#cast the data to count up the fireflies
CSTIG2<-dcast(CSTIG, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", sum)
#cast the data to count the traps
CSTIG3<-dcast(CSTIG, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", length)
#let's rename these new vectors within the data frame
names(CSTIG2)[4]<-"ADULTS"
names(CSTIG3)[4]<-"TRAPS"
#rename the data frame and combine the number of traps we counted into it from CSTIG3
CSTIG_summary<-CSTIG2
CSTIG_summary$TRAPS<-CSTIG3$TRAPS
#create a new variable to account for trapping effort in a given year
CSTIG_summary$pertrap<-CSTIG_summary$ADULTS/CSTIG_summary$TRAPS
####Creating a new treatment classification
CSTIG_summary$TREAT_CAT=ifelse(CSTIG_summary$TREAT_DESC=="Coniferous"|
CSTIG_summary$TREAT_DESC=="Deciduous"|
CSTIG_summary$TREAT_DESC=="Successional", "Forest",
ifelse(CSTIG_summary$TREAT_DESC=="Alfalfa*"|
CSTIG_summary$TREAT_DESC=="Early successional"|
CSTIG_summary$TREAT_DESC=="Poplar trees", "Perennial",
ifelse(CSTIG_summary$TREAT_DESC=="Conventional"|
CSTIG_summary$TREAT_DESC=="No till"|
CSTIG_summary$TREAT_DESC=="Organic"|
CSTIG_summary$TREAT_DESC=="Reduced input", "Annual", "Check")))
# First make a dataframe for error bars
CSTIG_agg <- ddply(CSTIG_summary, .(TREAT_CAT, year), summarise,
mean.bugs = mean(pertrap),
sd.bugs.pt = sd(pertrap),
N = length(pertrap),
SE = sd.bugs.pt / sqrt(N))
######################################################################################
#CMAC
#cast the data to count up the fireflies
CMAC2<-dcast(CMAC, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", sum)
#cast the data to count the traps
CMAC3<-dcast(CMAC, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", length)
#let's rename these new vectors within the data frame
names(CMAC2)[4]<-"ADULTS"
names(CMAC3)[4]<-"TRAPS"
#rename the data frame and combine the number of traps we counted into it from CMAC3
CMAC_summary<-CMAC2
CMAC_summary$TRAPS<-CMAC3$TRAPS
#create a new variable to account for trapping effort in a given year
CMAC_summary$pertrap<-CMAC_summary$ADULTS/CMAC_summary$TRAPS
####Creating a new treatment classification
CMAC_summary$TREAT_CAT=ifelse(CMAC_summary$TREAT_DESC=="Coniferous"|
CMAC_summary$TREAT_DESC=="Deciduous"|
CMAC_summary$TREAT_DESC=="Successional", "Forest",
ifelse(CMAC_summary$TREAT_DESC=="Alfalfa*"|
CMAC_summary$TREAT_DESC=="Early successional"|
CMAC_summary$TREAT_DESC=="Poplar trees", "Perennial",
ifelse(CMAC_summary$TREAT_DESC=="Conventional"|
CMAC_summary$TREAT_DESC=="No till"|
CMAC_summary$TREAT_DESC=="Organic"|
CMAC_summary$TREAT_DESC=="Reduced input", "Annual", "Check")))
# First make a dataframe for error bars
CMAC_agg <- ddply(CMAC_summary, .(TREAT_CAT, year), summarise,
mean.bugs = mean(pertrap),
sd.bugs.pt = sd(pertrap),
N = length(pertrap),
SE = sd.bugs.pt / sqrt(N))
######################################################################################
#C7
#cast the data to count up the fireflies
C72<-dcast(C7, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", sum)
#cast the data to count the traps
C73<-dcast(C7, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", length)
#let's rename these new vectors within the data frame
names(C72)[4]<-"ADULTS"
names(C73)[4]<-"TRAPS"
#rename the data frame and combine the number of traps we counted into it from C73
C7_summary<-C72
C7_summary$TRAPS<-C73$TRAPS
#create a new variable to account for trapping effort in a given year
C7_summary$pertrap<-C7_summary$ADULTS/C7_summary$TRAPS
####Creating a new treatment classification
C7_summary$TREAT_CAT=ifelse(C7_summary$TREAT_DESC=="Coniferous"|
C7_summary$TREAT_DESC=="Deciduous"|
C7_summary$TREAT_DESC=="Successional", "Forest",
ifelse(C7_summary$TREAT_DESC=="Alfalfa*"|
C7_summary$TREAT_DESC=="Early successional"|
C7_summary$TREAT_DESC=="Poplar trees", "Perennial",
ifelse(C7_summary$TREAT_DESC=="Conventional"|
C7_summary$TREAT_DESC=="No till"|
C7_summary$TREAT_DESC=="Organic"|
C7_summary$TREAT_DESC=="Reduced input", "Annual", "Check")))
# First make a dataframe for error bars
C7_agg <- ddply(C7_summary, .(TREAT_CAT, year), summarise,
mean.bugs = mean(pertrap),
sd.bugs.pt = sd(pertrap),
N = length(pertrap),
SE = sd.bugs.pt / sqrt(N))
######################################################################################
#BURSI
#cast the data to count up the fireflies
BURSI2<-dcast(BURSI, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", sum)
#cast the data to count the traps
BURSI3<-dcast(BURSI, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", length)
#let's rename these new vectors within the data frame
names(BURSI2)[4]<-"ADULTS"
names(BURSI3)[4]<-"TRAPS"
#rename the data frame and combine the number of traps we counted into it from BURSI3
BURSI_summary<-BURSI2
BURSI_summary$TRAPS<-BURSI3$TRAPS
#create a new variable to account for trapping effort in a given year
BURSI_summary$pertrap<-BURSI_summary$ADULTS/BURSI_summary$TRAPS
####Creating a new treatment classification
BURSI_summary$TREAT_CAT=ifelse(BURSI_summary$TREAT_DESC=="Coniferous"|
BURSI_summary$TREAT_DESC=="Deciduous"|
BURSI_summary$TREAT_DESC=="Successional", "Forest",
ifelse(BURSI_summary$TREAT_DESC=="Alfalfa*"|
BURSI_summary$TREAT_DESC=="Early successional"|
BURSI_summary$TREAT_DESC=="Poplar trees", "Perennial",
ifelse(BURSI_summary$TREAT_DESC=="Conventional"|
BURSI_summary$TREAT_DESC=="No till"|
BURSI_summary$TREAT_DESC=="Organic"|
BURSI_summary$TREAT_DESC=="Reduced input", "Annual", "Check")))
# First make a dataframe for error bars
BURSI_agg <- ddply(BURSI_summary, .(TREAT_CAT, year), summarise,
mean.bugs = mean(pertrap),
sd.bugs.pt = sd(pertrap),
N = length(pertrap),
SE = sd.bugs.pt / sqrt(N))
######################################################################################
#ABIPN
#cast the data to count up the fireflies
ABIPN2<-dcast(ABIPN, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", sum)
#cast the data to count the traps
ABIPN3<-dcast(ABIPN, year+TREAT_DESC+REPLICATE~.,value.var="ADULTS", length)
#let's rename these new vectors within the data frame
names(ABIPN2)[4]<-"ADULTS"
names(ABIPN3)[4]<-"TRAPS"
#rename the data frame and combine the number of traps we counted into it from ABIPN3
ABIPN_summary<-ABIPN2
ABIPN_summary$TRAPS<-ABIPN3$TRAPS
#create a new variable to account for trapping effort in a given year
ABIPN_summary$pertrap<-ABIPN_summary$ADULTS/ABIPN_summary$TRAPS
####Creating a new treatment classification
ABIPN_summary$TREAT_CAT=ifelse(ABIPN_summary$TREAT_DESC=="Coniferous"|
ABIPN_summary$TREAT_DESC=="Deciduous"|
ABIPN_summary$TREAT_DESC=="Successional", "Forest",
ifelse(ABIPN_summary$TREAT_DESC=="Alfalfa*"|
ABIPN_summary$TREAT_DESC=="Early successional"|
ABIPN_summary$TREAT_DESC=="Poplar trees", "Perennial",
ifelse(ABIPN_summary$TREAT_DESC=="Conventional"|
ABIPN_summary$TREAT_DESC=="No till"|
ABIPN_summary$TREAT_DESC=="Organic"|
ABIPN_summary$TREAT_DESC=="Reduced input", "Annual", "Check")))
# First make a dataframe for error bars
ABIPN_agg <- ddply(ABIPN_summary, .(TREAT_CAT, year), summarise,
mean.bugs = mean(pertrap),
sd.bugs.pt = sd(pertrap),
N = length(pertrap),
SE = sd.bugs.pt / sqrt(N))
######################################
#get all this data back into a single dataframe
#create weighted by predation potential variable- use values based on Bahlai et al 2013
HAXY_summary$pred<-0.672*HAXY_summary$ADULTS
C7_summary$pred<-0.628*C7_summary$ADULTS
HVAR_summary$pred<-0.237*HVAR_summary$ADULTS
PQUA_summary$pred<-0.200*PQUA_summary$ADULTS
HPARN_summary$pred<-0.320*HPARN_summary$ADULTS
HGLAC_summary$pred<-0.440*HGLAC_summary$ADULTS
HCONV_summary$pred<-0.440*HCONV_summary$ADULTS
H13_summary$pred<-0.189*H13_summary$ADULTS
CYCSP_summary$pred<-0.384*CYCSP_summary$ADULTS
CTRIF_summary$pred<-0.320*CTRIF_summary$ADULTS
CSTIG_summary$pred<-0*CSTIG_summary$ADULTS
CMAC_summary$pred<-0.350*CMAC_summary$ADULTS
BURSI_summary$pred<-0.2*BURSI_summary$ADULTS #based on body size, no literature value found
ABIPN_summary$pred<-0.172*ABIPN_summary$ADULTS
invasive<-rbind(HAXY_summary, C7_summary,HVAR_summary, PQUA_summary)
native<-rbind(HPARN_summary,HGLAC_summary,HCONV_summary,H13_summary,CYCSP_summary,
CTRIF_summary,CSTIG_summary, CMAC_summary,BURSI_summary,ABIPN_summary)
all_lb<-rbind(invasive, native)
invasivetot <- ddply(invasive, .(year, TREAT_DESC, TREAT_CAT, REPLICATE), summarise,
ADULTS = sum(ADULTS),
TRAPS=max(TRAPS))
invasivetot$pertrap<-invasivetot$ADULTS/invasivetot$TRAPS
nativetot <- ddply(native, .(year, TREAT_DESC, TREAT_CAT, REPLICATE), summarise,
ADULTS = sum(ADULTS),
TRAPS=max(TRAPS))
nativetot$pertrap<-nativetot$ADULTS/nativetot$TRAPS
all_tot <- ddply(all_lb, .(year, TREAT_DESC, TREAT_CAT, REPLICATE), summarise,
ADULTS = sum(ADULTS),
TRAPS=max(TRAPS))
all_tot$pertrap<-all_tot$ADULTS/all_tot$TRAPS
pred_tot <- ddply(all_lb, .(year, TREAT_DESC, TREAT_CAT, REPLICATE), summarise,
pred = sum(pred),
TRAPS=max(TRAPS))
pred_tot$pertrap<-pred_tot$pred/pred_tot$TRAPS
######################################################################################
#now let's make a stacked timeseries figure with a GAM for decade for each of these species
#gam will include sampling correction but that's it- we want to see the raw
#population trend for each species
library(mgcv)
library(visreg)
library(ggplot2)
library(tidymv)
library(grid)
source("broken_window_script.R")
#graphical parameters
smooth.param<-0.5
##########################################
# make ABIPN figure
#get average number of traps per year per observation
newd <- with(ABIPN_summary,
data.frame(year = seq(min(year), max(year), length = 1000),
TRAPS = 50))
knots<-round(length(unique(ABIPN_summary$year))/6) #only allow max of 1 knot every ~6 years
ABIPN.gam0<-gam(ADULTS~s(year, sp=smooth.param, k=knots)+offset(log(TRAPS)),
data=ABIPN_summary, family="quasipoisson")
summary(ABIPN.gam0)
ABIPN.pred<-predict.gam(ABIPN.gam0, newd, se.fit = T, type="response")
ABIPN.pred<-cbind(newd,ABIPN.pred)
ABIPN.pred$lower<-ABIPN.pred$fit-2*ABIPN.pred$se.fit
ABIPN.pred$upper<-ABIPN.pred$fit+2*ABIPN.pred$se.fit
jitter<-position_jitter(width = 0.1, height = 0.02)
ABIPN.year<-ggplot(data=ABIPN.pred, aes(year, fit))+
geom_point(data=ABIPN_summary, aes(year, ADULTS), position = jitter, pch=21, size=1, fill="lightgrey")+
geom_ribbon(aes(ymin=lower, ymax=upper), fill='paleturquoise', alpha=0.6)+
geom_line()+
theme_classic()+
xlim(1993, 2023)+
xlab(NULL)+ylab("")+
scale_y_continuous(trans='pseudo_log',limits=c(-0.13, NA))
ABIPN.year
#######
#by plant community or (or community group)
ABIPN.gam1<-gam(ADULTS~s(year, sp=smooth.param, k=knots, by=TREAT_DESC)+offset(log(TRAPS)),
data=ABIPN_summary, family="quasipoisson")
summary(ABIPN.gam1)
visreg(ABIPN.gam1, "year", "TREAT_DESC", ylab="residual captures", gg=TRUE)+
scale_y_continuous(trans='pseudo_log')+
facet_wrap(~TREAT_DESC, ncol = 4)
ABIPN.gam2<-gam(ADULTS~s(year, sp=smooth.param, k=knots, by=as.factor(TREAT_CAT))+offset(log(TRAPS)),
data=ABIPN_summary, family="quasipoisson")
summary(ABIPN.gam2)
visreg(ABIPN.gam2, "year", "TREAT_CAT", ylab="residual captures", gg=TRUE)+
scale_y_continuous(trans='pseudo_log')+
facet_wrap(~TREAT_CAT, ncol = 4)
########Calculations for table #################
#last 10 year trend
ABIPN10<-subset(ABIPN_summary, year >= 2013)
abi10mod<-lm(pertrap~year, data=ABIPN10)
summary(abi10mod)
#whole timeseries trend
abiallmod<-lm(pertrap~year, data=ABIPN_summary)
summary(abiallmod)
#stability time
stability_time(ABIPN_summary[,c(1,6)])
#detection frequency in first five vs last five years
ddply(ABIPN_summary, .(year), summarise,
ADULTS = sum(ADULTS))
out<-ddply(ABIPN_summary, .(year), summarise,
ADULTS = sum(ADULTS),
TRAPS=sum(TRAPS))
out
sum(out$ADULTS)/sum(out$TRAPS)
##########################################
# make BURSI figure
#get average number of traps per year per observation
newd <- with(BURSI_summary,
data.frame(year = seq(min(year), max(year), length = 1000),
TRAPS = 50))
knots<-round(length(unique(BURSI_summary$year))/6) #only allow max of 1 knot every ~6 years
BURSI.gam0<-gam(ADULTS~s(year, sp=smooth.param, k=knots)+offset(log(TRAPS)),
data=BURSI_summary, family="quasipoisson")
summary(BURSI.gam0)
BURSI.pred<-predict.gam(BURSI.gam0, newd, se.fit = T, type="response")
BURSI.pred<-cbind(newd,BURSI.pred)
BURSI.pred$lower<-BURSI.pred$fit-2*BURSI.pred$se.fit
BURSI.pred$upper<-BURSI.pred$fit+2*BURSI.pred$se.fit
jitter<-position_jitter(width = 0.1, height = 0.02)
BURSI.year<-ggplot(data=BURSI.pred, aes(year, fit))+
geom_point(data=BURSI_summary, aes(year, ADULTS), position = jitter, pch=21, size=1, fill="lightgrey")+
geom_ribbon(aes(ymin=lower, ymax=upper), fill='paleturquoise', alpha=0.6)+
geom_line()+
theme_classic()+
xlim(1993, 2023)+
xlab(NULL)+ylab("")+
scale_y_continuous(trans='pseudo_log',limits=c(-0.1, NA))
BURSI.year
#######
#by plant community or (or community group)
BURSI.gam1<-gam(ADULTS~s(year, sp=smooth.param, k=knots, by=TREAT_DESC)+offset(log(TRAPS)),
data=BURSI_summary, family="quasipoisson")
summary(BURSI.gam1)
visreg(BURSI.gam1, "year", "TREAT_DESC", ylab="residual captures", gg=TRUE)+
scale_y_continuous(trans='pseudo_log')+
facet_wrap(~TREAT_DESC, ncol = 4)
BURSI.gam2<-gam(ADULTS~s(year, sp=smooth.param, k=knots, by=as.factor(TREAT_CAT))+offset(log(TRAPS)),
data=BURSI_summary, family="quasipoisson")
summary(BURSI.gam2)
visreg(BURSI.gam2, "year", "TREAT_CAT", ylab="residual captures", gg=TRUE)+
scale_y_continuous(trans='pseudo_log')+
facet_wrap(~TREAT_CAT, ncol = 4)
########Calculations for table #################
#last 10 year trend
BURSI10<-subset(BURSI_summary, year >= 2013)
BURSI10mod<-lm(pertrap~year, data=BURSI10)
summary(BURSI10mod)
#whole timeseries trend
BURSIallmod<-lm(pertrap~year, data=BURSI_summary)
summary(BURSIallmod)
#stability time
stability_time(BURSI_summary[,c(1,6)])
#detection frequency in first five vs last five years
ddply(BURSI_summary, .(year), summarise,
ADULTS = sum(ADULTS))
out<-ddply(BURSI_summary, .(year), summarise,
ADULTS = sum(ADULTS),
TRAPS=sum(TRAPS))
out
sum(out$ADULTS)/sum(out$TRAPS)
##########################################
# make C7 figure
#get average number of traps per year per observation