Scale Dynamics
LGCarlson
8/19/2019
releaserecap<-readRDS(paste(shared.path, "/Salmon/Data/Growth/Data/releaserecaptdata.rds",sep = "")) %>%
filter(RdayInt < 20)
growthdata <- readRDS(paste(shared.path, "/Salmon/Data/Mark-recap/Carlin_growth_fulldata.rds", sep = ""))
releasedata <- readRDS(paste(shared.path, "/Salmon/Data/Mark-recap/Carlin_all_releases.rds", sep = "")) %>%
dplyr::select(Batch_TagID, BatchType, LifeStage)
recapdata <- read_csv(paste(shared.path, "/Salmon/Data/Mark-recap/Carlin_distantrecoveries.csv", sep = "")) %>%
dplyr::select(BatchNo, TagNo, DateEstimated, RecDateType, ReturnSource) %>%
mutate(Batch_TagID = paste(BatchNo, TagNo, sep = "_"))## Parsed with column specification:
## cols(
## .default = col_character(),
## BatchNo = col_double(),
## TagNo = col_double(),
## Lat = col_double(),
## Long = col_double(),
## RecaptureMonth = col_double(),
## RecaptureDay = col_double(),
## RecaptureYear = col_double(),
## Length1_cm = col_double(),
## Length2_cm = col_double(),
## Weight_kg = col_double(),
## ScaleSamp = col_logical()
## )## See spec(...) for full column specifications.Visualize annulus presence by day at sea.
releaserecap %>%
dplyr::select(Batch_TagID, RecaptureMonth, DaysatSea) %>%
left_join(growthdata, by = "Batch_TagID") %>%
dplyr::select(Batch_TagID, RecaptureMonthNum, marine.circ, FMC, SeaAge, M1, M2, DaysatSea) %>%
mutate(noannuli = ifelse(SeaAge == 0 & is.na(M1) == TRUE, 0, NA)) %>%
mutate(firstannulus = ifelse(is.na(M1) == FALSE & is.na(M2) == TRUE, 1, NA)) %>%
mutate(secondannulus = ifelse(is.na(M2) == FALSE, 2, NA)) %>%
gather(key = "annulustype", value = "numannuli", 9:11) %>%
filter(!is.na(numannuli)) %>%
mutate(SeaDayGroup = 20*DaysatSea %/% 20) %>%
dplyr::group_by(SeaDayGroup, numannuli) %>%
dplyr::summarise(n = n()) %>%
mutate(relfreq = n/sum(n)) %>%
filter(!is.na(SeaDayGroup)) %>%
ggplot(aes(SeaDayGroup, relfreq, fill = as.factor(numannuli))) + geom_bar(position = "stack", stat = "identity") +
labs(x = "Recapture Month", y = "Relative Frequency", fill = "Number of Annuli")
releaseonly<- releaserecap %>%
dplyr::select(Batch_TagID, RecaptureDate, ReleaseDate, RdayInt)longannuli<-releaserecap %>%
dplyr::select(Batch_TagID, RecaptureMonth, DaysatSea) %>%
left_join(growthdata, by = "Batch_TagID") %>%
dplyr::select(Batch_TagID, RecaptureMonthNum, marine.circ, FMC, SeaAge, M1, M2, DaysatSea) %>%
mutate(noannuli = ifelse(SeaAge == 0 & is.na(M1) == TRUE, 0, NA)) %>%
mutate(firstannulus = ifelse(is.na(M1) == FALSE & is.na(M2) == TRUE, 1, NA)) %>%
mutate(secondannulus = ifelse(is.na(M2) == FALSE, 2, NA)) %>%
gather(key = "annulustype", value = "numannuli", 9:11) %>%
filter(!is.na(numannuli)) Mistake 1: 1 annulus after too few days at sea
mistakeFA <- longannuli %>%
filter(annulustype == "firstannulus" & DaysatSea < 200) %>%
mutate(mistake = "1 annulus after too few days at sea") %>%
left_join(releaseonly, by = "Batch_TagID") %>%
left_join(releasedata, by = "Batch_TagID") %>%
left_join(recapdata, by = "Batch_TagID")
mistakeFA## # A tibble: 3 x 21
## Batch_TagID RecaptureMonthN~ marine.circ FMC SeaAge M1 M2
## <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
## 1 6811_6931 7 41 0.965 0 2.40 NA
## 2 7005_44155 7 30 1.37 1 1.94 NA
## 3 8223_119600 7 41 0.622 1 2.18 NA
## # ... with 14 more variables: DaysatSea <dbl>, annulustype <chr>,
## # numannuli <dbl>, mistake <chr>, RecaptureDate <date>,
## # ReleaseDate <date>, RdayInt <dbl>, BatchType <fct>, LifeStage <fct>,
## # BatchNo <dbl>, TagNo <dbl>, DateEstimated <chr>, RecDateType <chr>,
## # ReturnSource <chr>Mistake 2: no annulus, but at see more than a year
mistakeNA <- longannuli %>%
filter(annulustype == "noannuli" & DaysatSea > 250) %>%
mutate(mistake = "no annulus, but at see more than a year") %>%
left_join(releaseonly, by = "Batch_TagID") %>%
left_join(releasedata, by = "Batch_TagID") %>%
left_join(recapdata, by = "Batch_TagID")
mistakeNA## # A tibble: 2 x 21
## Batch_TagID RecaptureMonthN~ marine.circ FMC SeaAge M1 M2
## <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
## 1 6818_2201 11 5 0.940 0 NA NA
## 2 6801_5873 8 13 1.51 0 NA NA
## # ... with 14 more variables: DaysatSea <dbl>, annulustype <chr>,
## # numannuli <dbl>, mistake <chr>, RecaptureDate <date>,
## # ReleaseDate <date>, RdayInt <dbl>, BatchType <fct>, LifeStage <fct>,
## # BatchNo <dbl>, TagNo <dbl>, DateEstimated <chr>, RecDateType <chr>,
## # ReturnSource <chr>Mistake 3: 2 marine annuli in less than 700 days
mistakeSA <- longannuli %>%
filter(annulustype == "secondannulus" & DaysatSea < 580) %>%
mutate(mistake = "2 marine annuli in less than 700 days") %>%
left_join(releaseonly, by = "Batch_TagID") %>%
left_join(releasedata, by = "Batch_TagID") %>%
left_join(recapdata, by = "Batch_TagID")
mistakeSA## # A tibble: 7 x 21
## Batch_TagID RecaptureMonthN~ marine.circ FMC SeaAge M1 M2
## <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
## 1 7624_4494 9 76 1.16 2 2.87 4.87
## 2 6909_9366 10 73 0.922 2 3.12 4.36
## 3 8403_55278 8 81 0.607 1 2.38 3.74
## 4 7301_66686 7 77 0.940 1 2.46 3.83
## 5 7504_89317 7 43 0.972 1 2.73 3.30
## 6 7905_152340 8 59 0.612 1 2.02 3.34
## 7 8101_285462 7 48 0.866 1 2.18 2.98
## # ... with 14 more variables: DaysatSea <dbl>, annulustype <chr>,
## # numannuli <dbl>, mistake <chr>, RecaptureDate <date>,
## # ReleaseDate <date>, RdayInt <dbl>, BatchType <fct>, LifeStage <fct>,
## # BatchNo <dbl>, TagNo <dbl>, DateEstimated <chr>, RecDateType <chr>,
## # ReturnSource <chr>Mistake 4: at sea more than 730 days with only 1 annulus
mistakeFA2 <- longannuli %>%
filter(annulustype == "firstannulus" & DaysatSea > 730) %>%
mutate(mistake = "at sea more than 730 days with only 1 annulus") %>%
left_join(releaseonly, by = "Batch_TagID") %>%
left_join(releasedata, by = "Batch_TagID") %>%
left_join(recapdata, by = "Batch_TagID")
mistakeFA2## # A tibble: 26 x 21
## Batch_TagID RecaptureMonthN~ marine.circ FMC SeaAge M1 M2
## <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
## 1 6907_5314 10 51 1.10 1 2.87 NA
## 2 7201_6446 9 53 1.39 1 3.12 NA
## 3 6816_7138 6 55 1.60 2 2.62 NA
## 4 7201_8910 6 40 0.870 1 2.57 NA
## 5 7003_20310 9 64 0.636 1 3.51 NA
## 6 7203_31365 10 54 1.45 1 3.26 NA
## 7 7204_53511 9 63 1.08 1 3.45 NA
## 8 7204_56068 9 42 0.868 1 2.15 NA
## 9 7301_61966 5 53 1.19 0 2.47 NA
## 10 7101_74901 8 66 1.48 1 3.13 NA
## # ... with 16 more rows, and 14 more variables: DaysatSea <dbl>,
## # annulustype <chr>, numannuli <dbl>, mistake <chr>,
## # RecaptureDate <date>, ReleaseDate <date>, RdayInt <dbl>,
## # BatchType <fct>, LifeStage <fct>, BatchNo <dbl>, TagNo <dbl>,
## # DateEstimated <chr>, RecDateType <chr>, ReturnSource <chr>Mistake 5: at sea more than 1000 days with only 2 marine annuli
mistakeSA2 <- longannuli %>%
filter(annulustype == "secondannulus" & DaysatSea > 1000) %>%
mutate(mistake = "at sea more than 1000 days with only 2 marine annuli") %>%
left_join(releaseonly, by = "Batch_TagID") %>%
left_join(releasedata, by = "Batch_TagID") %>%
left_join(recapdata, by = "Batch_TagID")
mistakeSA2## # A tibble: 8 x 21
## Batch_TagID RecaptureMonthN~ marine.circ FMC SeaAge M1 M2
## <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
## 1 7601_2885 7 63 1.27 2 2.74 4.49
## 2 6908_9727 6 75 0.955 2 2.73 4.26
## 3 7303_77606 6 66 1.56 2 3.09 5.15
## 4 7903_183355 8 66 1.32 2 3.04 4.13
## 5 8604_381027 7 66 1.20 2 2.57 4.00
## 6 8604_381027 7 66 1.20 2 2.57 4.00
## 7 8604_381027 7 66 1.20 2 2.57 4.00
## 8 8604_381027 7 66 1.20 2 2.57 4.00
## # ... with 14 more variables: DaysatSea <dbl>, annulustype <chr>,
## # numannuli <dbl>, mistake <chr>, RecaptureDate <date>,
## # ReleaseDate <date>, RdayInt <dbl>, BatchType <fct>, LifeStage <fct>,
## # BatchNo <dbl>, TagNo <dbl>, DateEstimated <chr>, RecDateType <chr>,
## # ReturnSource <chr>mistakes<-rbind(mistakeFA, mistakeNA, mistakeSA,mistakeFA2, mistakeSA2)Saved using: write_csv(mistakes,“mistakendates.csv”)
Dataset without mistaken annuli scales
longannuli %>%
anti_join(mistakes, by = "Batch_TagID") %>%
mutate(SeaDayGroup = 20*DaysatSea %/% 20) %>%
dplyr::group_by(SeaDayGroup, numannuli) %>%
dplyr::summarise(n = n()) %>%
filter(!is.na(SeaDayGroup)) %>%
ggplot(aes(SeaDayGroup, n, fill = as.factor(numannuli))) + geom_histogram(stat = "identity") +
labs(x = "Number of Days at Sea", y = "Number of Individuals", fill = "Number of Annuli")
Same plot but with removed impossible annuli scales.
longannuli %>%
anti_join(mistakes, by = "Batch_TagID") %>%
mutate(SeaDayGroup = 20*DaysatSea %/% 20) %>%
dplyr::group_by(SeaDayGroup, numannuli) %>%
dplyr::summarise(n = n()) %>%
mutate(relfreq = n/sum(n)) %>%
filter(!is.na(SeaDayGroup)) %>%
ggplot(aes(SeaDayGroup, relfreq, fill = as.factor(numannuli))) + geom_bar(position = "stack", stat = "identity") +
labs(x = "Recapture Month", y = "Relative Frequency", fill = "Number of Annuli")
If this was a linear process…
longannuli %>%
anti_join(mistakes, by = "Batch_TagID") %>%
ggplot(aes(DaysatSea, marine.circ)) + geom_point() + geom_smooth(method = "lm")
goodannuli<-longannuli %>%
anti_join(mistakes, by = "Batch_TagID")
summary(lm(marine.circ ~ DaysatSea, data = goodannuli))##
## Call:
## lm(formula = marine.circ ~ DaysatSea, data = goodannuli)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.1632 -3.4937 -0.1495 3.2226 30.3667
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.172988 0.757465 16.07 <0.0000000000000002 ***
## DaysatSea 0.065116 0.001561 41.72 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.665 on 1415 degrees of freedom
## Multiple R-squared: 0.5516, Adjusted R-squared: 0.5513
## F-statistic: 1741 on 1 and 1415 DF, p-value: < 0.00000000000000022goodannuli$DaysGroup = NA
goodannuli$DaysGroup[goodannuli$DaysatSea < 250] = "as0SW"
goodannuli$DaysGroup[goodannuli$DaysatSea > 250 & goodannuli$DaysatSea < 750] = "as1SW"
goodannuli$DaysGroup[goodannuli$DaysatSea > 750] = "as2SW"
goodannuli %>%
mutate(dayspercirc = DaysatSea/marine.circ) %>%
ggplot(aes(dayspercirc)) + geom_histogram() ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
goodannuli %>%
mutate(dayspercirc = DaysatSea/marine.circ) %>%
group_by(DaysGroup) %>%
summarise(meancirday = mean(dayspercirc), n = n(), meandaysatsea = mean(DaysatSea), sddays = sd(DaysatSea))## # A tibble: 3 x 5
## DaysGroup meancirday n meandaysatsea sddays
## <chr> <dbl> <int> <dbl> <dbl>
## 1 as0SW 9.43 37 84.7 27.7
## 2 as1SW 11.1 1333 475. 39.9
## 3 as2SW 13.8 47 812. 32.5