Metadata for SST

Datafile was created by the code found here: “C:Labsalmondataarea SST.R”

File contains monthly mean SST in each migration area.

SST was extracted from all cells within the migration area. The mean monthly SST was calculated based on all cells within the migration area. The value is recorded in degrees C.

Metadata for circuli

FMC (num) circuli number identifying First marine circulus

FMC.spacing (num) FMC intercirculi spacing

smolt.incr (num) smolt increment (focus to last circuli before FMC)

marine.growth (num) marine growth = FMC to end

total.growth (num) entire scale from 1st circuli to the end

smolt.circ (num) number of smolt circuli

marine.circ (num) number of marine circuli

total.circ (num) number of total circuli

FS.incr (num) first summer at sea growth (FMC to FSM)

FW.incr (num) first winter at sea growth (first circuli after FSM to M1)

PS.incr (num) post-smolt growth (FMC to M1)

FSM.spacing (num) first summer maximum intercirculi spacing

M1.spacing (num) M1 intercirculi spacing

FS.circ (num) First summer, number of circuli

FW.circ (num) First winter, number of circuli

PS.circ (num) Post-smolt, number of circuli

sst<-read_csv(paste(sst.path,"/migrationareasERSST.csv",sep = ""))
## Parsed with column specification:
## cols(
##   Dates = col_date(format = ""),
##   Area = col_character(),
##   ERSST = col_double()
## )
Carlin_full_growth<-readRDS(paste(growth.path,"/Carlin_growth_fulldata.rds",sep = ""))
Carlin_sst<-sst %>% 
  filter(Dates >= "1963-01-01", 
         Dates < "1992-01-01") %>% 
  mutate(Year = year(Dates)) %>% 
  mutate(Month = month(Dates))

Annual

Wide

Calculate annual means by smolt year

Carlin_growth_summarized<- Carlin_full_growth %>% 
  filter(!is.na(ReleaseYear)) %>% 
  dplyr::select(JoinID, ReleaseYear, RecaptureYear, FMC, M1, M2, smolt.incr, FS.incr, FW.incr, PS.incr, 
         smolt.circ, FS.circ, FW.circ, PS.circ) %>% 
  gather(key = "marker", value = "value", 4:14) %>% 
  group_by(ReleaseYear, marker) %>% 
  dplyr::summarize(mean = mean(value, na.rm = TRUE)) %>% 
  spread(key = "marker", value = "mean") %>% 
  dplyr::rename("SmoltYear" = "ReleaseYear")
sst_summarized <- Carlin_sst %>% 
  group_by(Year, Area) %>% 
  dplyr::summarize(AnnualSST = mean(ERSST)) %>% 
  spread(key = "Area", value = "AnnualSST") %>% 
  dplyr::rename("SmoltYear" = "Year")

## extract_numeric() is deprecated: please use readr::parse_number() instead

Annual

Long

Carlin_growth_long<- Carlin_full_growth %>% 
  filter(!is.na(ReleaseYear)) %>% 
  dplyr::select(JoinID, ReleaseYear, RecaptureYear, FMC, M1, M2, smolt.incr, FS.incr, FW.incr, PS.incr, 
         smolt.circ, FS.circ, FW.circ, PS.circ) %>% 
  gather(key = "responsevar", value = "value", 4:14) %>% 
  group_by(ReleaseYear, responsevar) %>% 
  dplyr::summarize(meanvalue = mean(value, na.rm = TRUE)) %>% 
  dplyr::rename("SmoltYear" = "ReleaseYear")
sst_long <- Carlin_sst %>%  
  filter(!is.na(Area)) %>% 
  group_by(Year, Area) %>% 
  dplyr::summarize(AnnualSST = mean(ERSST)) %>% 
  dplyr::rename("SmoltYear" = "Year") 
modelinglong<- Carlin_growth_long %>% 
  left_join(sst_long, by = "SmoltYear")
modelinglong<- Carlin_growth_long %>% 
  left_join(sst_long, by = "SmoltYear") %>% 
  rename("predictvar" = "Area", "meanmarker" = "meanvalue", "meanSST" = "AnnualSST")

responses<-as.vector(unique(as.character(modelinglong$responsevar)))
predictors<-as.vector(unique(as.character(modelinglong$predictvar)))

AIClist<-list()
r2list<-list()
pvlist<-list()

for(i in responses){
  for(j in predictors){
    
temp<-modelinglong %>% 
  filter(responsevar == i & predictvar == j)


glancesummary<-glance(lm(meanmarker ~ meanSST, data = temp))

AIClist[[i]][j]<-glancesummary$AIC
r2list[[i]][j]<-glancesummary$r.squared
pvlist[[i]][j]<-glancesummary$p.value
  }
}

AIClist<-do.call(rbind,AIClist)
r2list<-do.call(rbind,r2list)
pvlist<-do.call(rbind,pvlist)