CalFishTrack
Central Valley Enhanced
Acoustic Tagging Project
Hatchery-origin San Joaquin spring-run Chinook salmon
2018-2019 Season (PROVISIONAL DATA)
1. Project Status
Study is complete, all tags are no longer active. All times in Pacific Standard Time.
Telemetry Study Template for this study can be found here
setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = ""))
tagcodes <- read.csv("qry_HexCodes.txt", stringsAsFactors = F, colClasses=c("TagID_Hex"="character"))
tagcodes$RelDT <- as.POSIXct(tagcodes$RelDT, format = "%m/%d/%Y %I:%M:%S %p", tz = "Etc/GMT+8")
latest <- read.csv("latest_download.csv", stringsAsFactors = F)
study_tagcodes <- tagcodes[tagcodes$StudyID == "SCARF_San_Joaquin_Spring_run_2019",]
if (nrow(study_tagcodes) == 0){
cat("Project has not yet begun")
}else{
cat(paste("Project began on ", min(study_tagcodes$RelDT), ", see tagging details below:", sep = ""))
study_tagcodes$Release <- "FreemontFord"
study_tagcodes[study_tagcodes$RelDT > as.POSIXct("2019-03-01"), "Release"] <- "DurhamFerry"
release_stats <- aggregate(list(First_release_time = study_tagcodes$RelDT),
by= list(Release = study_tagcodes$Release),
FUN = min)
release_stats <- merge(release_stats,
aggregate(list(Last_release_time = study_tagcodes$RelDT),
by= list(Release = study_tagcodes$Release),
FUN = max),
by = c("Release"))
release_stats <- merge(release_stats, aggregate(list(Number_fish_released =
study_tagcodes$TagID_Hex),
by= list(Release = study_tagcodes$Release),
FUN = function(x) {length(unique(x))}),
by = c("Release"))
release_stats <- merge(release_stats,
aggregate(list(Release_location = study_tagcodes$Rel_loc),
by= list(Release = study_tagcodes$Release),
FUN = function(x) {head(x,1)}),
by = c("Release"))
release_stats <- merge(release_stats,
aggregate(list(Release_rkm = study_tagcodes$Rel_rkm),
by= list(Release = study_tagcodes$Release),
FUN = function(x) {head(x,1)}),
by = c("Release"))
release_stats <- merge(release_stats,
aggregate(list(Mean_length = study_tagcodes$Length),
by= list(Release = study_tagcodes$Release),
FUN = mean),
by = c("Release"))
release_stats <- merge(release_stats,
aggregate(list(Mean_weight = study_tagcodes$Weight),
by= list(Release = study_tagcodes$Release),
FUN = mean),
by = c("Release"))
release_stats[,c("Mean_length", "Mean_weight")] <- round(release_stats[,c("Mean_length", "Mean_weight")],1)
release_stats$First_release_time <- format(release_stats$First_release_time, tz = "Etc/GMT+8")
release_stats$Last_release_time <- format(release_stats$First_release_time, tz = "Etc/GMT+8")
kable(release_stats, format = "html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F, position = "left")
} | Release | First_release_time | Last_release_time | Number_fish_released | Release_location | Release_rkm | Mean_length | Mean_weight |
|---|---|---|---|---|---|---|---|
| DurhamFerry | 2019-03-12 20:41:00 | 2019-03-12 20:41:00 | 354 | DurhamFerry | 183.78 | 80.6 | 5.2 |
| FreemontFord | 2019-02-28 17:55:00 | 2019-02-28 17:55:00 | 347 | FreemontFord | 270.93 | 80.0 | 5.1 |
2. Real-time Fish Detections
Data current as of 2025-04-22 08:00:00. All times in Pacific Standard Time.
library(reshape2)
setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = ""))
detects_study <- read.csv("C:/Users/field/Desktop/Real-time data massaging/products/Study_detection_files/detects_SCARF_San_Joaquin_Spring_run_2019.csv", stringsAsFactors = F)
detects_study$DateTime_PST <- as.POSIXct(detects_study$DateTime_PST, format = "%Y-%m-%d %H:%M:%S", "Etc/GMT+8")
if(nrow(detects_study)>0){
detects_study <- merge(detects_study, study_tagcodes[,c("TagID_Hex", "RelDT", "StudyID", "Release", "tag_life")], by.x = "TagCode", by.y = "TagID_Hex")
}
detects_benicia <- detects_study[detects_study$general_location %in% c("Benicia_west", "Benicia_east"),]
if (nrow(detects_benicia)>0) {
detects_benicia <- merge(detects_benicia,aggregate(list(first_detect = detects_benicia$DateTime_PST), by = list(TagCode= detects_benicia$TagCode), FUN = min))
detects_benicia$Day <- as.Date(detects_benicia$first_detect, "Etc/GMT+8")
starttime <- as.Date(min(detects_benicia$RelDT), "Etc/GMT+8")
endtime <- min(as.Date(c(Sys.time())), max(as.Date(detects_benicia$RelDT)+(detects_benicia$tag_life*1.5)))
#wlk_flow <- cdec_query("COL", "20", "H", starttime, endtime+1)
#wlk_flow$datetime <- as.Date(wlk_flow$datetime)
#wlk_flow_day <- aggregate(list(parameter_value = wlk_flow$parameter_value), by = list(Day = wlk_flow$datetime), FUN = mean, na.rm = T)
daterange <- data.frame(Day = seq.Date(from = starttime, to = endtime, by = "day"))
rels <- unique(study_tagcodes[study_tagcodes$StudyID == unique(detects_benicia$StudyID), "Release"])
rel_num <- length(rels)
rels_no_detects <- as.character(rels[!(rels %in% unique(detects_benicia$Release))])
tagcount <- aggregate(list(unique_tags = detects_benicia$TagCode), by = list(Day = detects_benicia$Day, Release = detects_benicia$Release ), FUN = function(x){length(unique(x))})
tagcount1 <- reshape2::dcast(tagcount, Day ~ Release)
daterange1 <- merge(daterange, tagcount1, all.x=T)
if(length(rels_no_detects)>0){
for(i in rels_no_detects){
daterange1 <- cbind(daterange1, x=NA)
names(daterange1)[names(daterange1) == 'x'] <- paste(i)
}
}
#daterange2 <- merge(daterange1, wlk_flow_day, by = "Day", all.x = T)
daterange2 <- daterange1
rownames(daterange2) <- daterange2$Day
daterange2$Day <- NULL
par(mar=c(6, 5, 2, 5) + 0.1)
barp <- barplot(t(daterange2[,1:ncol(daterange2)]), plot = FALSE, beside = T)
barplot(t(daterange2[,1:ncol(daterange2)]), beside = T, col=rainbow(rel_num),
xlab = "", ylab = "Number of fish arrivals per day",
ylim = c(0,max(daterange2[,1:ncol(daterange2)], na.rm = T)*1.2),
las = 2, xlim=c(0,max(barp)+1), cex.lab = 1.5, yaxt = "n", xaxt = "n")#,
#legend.text = colnames(daterange2[,1:ncol(daterange2)-1]),
#args.legend = list(x ='topright', bty='n', inset=c(-0.2,0)), title = "Release Group")
legend(x ='topleft', legend = colnames(daterange2)[1:ncol(daterange2)], fill= rainbow(rel_num), horiz = T, title = "Release Group")
ybreaks <- if(max(daterange2[,1:ncol(daterange2)], na.rm = T) < 4) {max(daterange2[,1:ncol(daterange2)], na.rm = T)} else {5}
xbreaks <- if(ncol(barp) > 10) {seq(1, ncol(barp), 2)} else {1:ncol(barp)}
barpmeans <- colMeans(barp)
axis(1, at = barpmeans[xbreaks], labels = rownames(daterange2)[xbreaks], las = 2)
axis(2, at = pretty(0:max(daterange2[,1:ncol(daterange2)], na.rm = T), ybreaks))
box()
#par(new=T)
#plot(x = barpmeans, daterange2$parameter_value, yaxt = "n", xaxt = "n", ylab = "", xlab = "", col = "blue", type = "l", lwd=2, xlim=c(0,max(barp)+1), ylim = c(min(daterange2$parameter_value, na.rm = T), max(daterange2$parameter_value, na.rm=T)*1.1))#, ylab = "Returning adults", xlab= "Outmigration year", yaxt="n", col="red", pch=20)
#axis(side = 4)#, labels = c(2000:2016), at = c(2000:2016))
#mtext("Flow (cfs) at Colusa Bridge", side=4, line=3, cex=1.5, col="blue")
}else{
print("No detections at Benicia yet")
}
2.1 Detections at Benicia Bridge
3. Survival and Routing Probability
setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = ""))
library(RMark)
gen_locs <- read.csv("realtime_locs.csv", stringsAsFactors = F)
study_count <- nrow(study_tagcodes)
if (nrow(detects_benicia) == 0){
"No detections yet"
} else {
benicia <- read.csv("benicia_surv.csv", stringsAsFactors = F)
benicia$RelDT <- as.POSIXct(benicia$RelDT)
## Only do survival to Benicia here
test3 <- detects_study[detects_study$rkm < 53,]
## Create inp for survival estimation
inp <- as.data.frame(reshape2::dcast(test3, TagCode ~ rkm, fun.aggregate = length))
## Sort columns by river km in descending order
# Count number of genlocs
gen_loc_sites <- ncol(inp)-1
inp <- inp[,c(1,order(names(inp[,2:(gen_loc_sites+1)]), decreasing = T)+1)]
inp <- merge(study_tagcodes, inp, by.x = "TagID_Hex", by.y = "TagCode", all.x = T)
inp2 <- inp[,(ncol(inp)-gen_loc_sites+1):ncol(inp)]
inp2[is.na(inp2)] <- 0
inp2[inp2 > 0] <- 1
inp <- cbind(inp, inp2)
groups <- as.character(sort(unique(inp$Release)))
inp[,groups] <- 0
for (i in groups) {
inp[as.character(inp$Release) == i, i] <- 1
}
if(length(groups) > 1){
inp$inp_final <- paste("1",apply(inp2, 1, paste, collapse=""), " ",apply(inp[,groups], 1, paste, collapse=" ")," ;",sep = "")
}else{
inp$inp_final <- paste("1",apply(inp2, 1, paste, collapse=""), " ",inp[,groups]," ;",sep = "")
}
write.table(inp$inp_final,"WRinp.inp",row.names = F, col.names = F, quote = F)
if(length(groups) > 1){
WRinp <- convert.inp("WRinp.inp", group.df=data.frame(rel=groups))
WR.process <- process.data(WRinp, model="CJS", begin.time=1, groups = "rel")
WR.ddl <- make.design.data(WR.process)
WR.mark.all <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)), silent = T, output = F)
WR.mark.rel <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time*rel),p=list(formula=~time)), silent = T, output = F)
WR.surv <- round(WR.mark.all$results$real[1,c("estimate", "se", "lcl", "ucl")] * 100,1)
WR.surv <- rbind(WR.surv, round(WR.mark.rel$results$real[seq(from=1,to=length(groups)*2,by = 2),c("estimate", "se", "lcl", "ucl")] * 100,1))
}else{
WRinp <- convert.inp("WRinp.inp")
WR.process <- process.data(WRinp, model="CJS", begin.time=1)
WR.ddl <- make.design.data(WR.process)
WR.mark.all <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)), silent = T, output = F)
WR.surv <- round(WR.mark.all$results$real[1,c("estimate", "se", "lcl", "ucl")] * 100,1)
}
WR.surv$Detection_efficiency <- NA
WR.surv[1,"Detection_efficiency"] <- round(WR.mark.all$results$real[gen_loc_sites+1,"estimate"] * 100,1)
WR.surv <- cbind(Release = c("ALL", groups), WR.surv)
WR.surv1 <- WR.surv
colnames(WR.surv1) <- c("Release Group", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.", "Detection efficiency (%)")
print(kable(WR.surv1, row.names = F, "html", caption = '3.1 Minimum survival to Benicia Bridge East Span (using CJS survival model)') %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F, position = "left"))
## Find mean release time per release group, and ALL
reltimes <- aggregate(list(RelDT = study_tagcodes$RelDT), by = list(Release = study_tagcodes$Release), FUN = mean)
reltimes <- rbind(reltimes, data.frame(Release = "ALL", RelDT = mean(study_tagcodes$RelDT)))
## Assign whether the results are tentative or final
quality <- "tentative"
if(endtime < as.Date(c(Sys.time()))) { quality <- "final"}
WR.surv <- merge(WR.surv, reltimes, by = "Release", all.x = T)
WR.surv$RelDT <- as.POSIXct(WR.surv$RelDT, origin = '1970-01-01')
## remove old benicia record for this studyID
benicia <- benicia[!benicia$StudyID == unique(study_tagcodes$StudyID),]
benicia <- rbind(benicia, data.frame(WR.surv, StudyID = unique(study_tagcodes$StudyID), data_quality = quality))
write.csv(benicia, "benicia_surv.csv", row.names = F, quote = F)
}| Release Group | Survival (%) | SE | 95% lower C.I. | 95% upper C.I. | Detection efficiency (%) |
|---|---|---|---|---|---|
| ALL | 6.1 | 0.9 | 4.6 | 8.2 | 100 |
| DurhamFerry | 5.9 | 1.3 | 3.9 | 8.9 | NA |
| FreemontFord | 6.3 | 1.3 | 4.2 | 9.4 | NA |
4. Detection Statistics
setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = ""))
if (nrow(detects_study) == 0){
"No detections yet"
} else {
arrivals <- aggregate(list(DateTime_PST = detects_study$DateTime_PST), by = list(general_location = detects_study$general_location, TagCode = detects_study$TagCode), FUN = min)
tag_stats <- aggregate(list(First_arrival = arrivals$DateTime_PST),
by= list(general_location = arrivals$general_location),
FUN = min)
tag_stats <- merge(tag_stats,
aggregate(list(Mean_arrival = arrivals$DateTime_PST),
by= list(general_location = arrivals$general_location),
FUN = mean),
by = c("general_location"))
tag_stats <- merge(tag_stats,
aggregate(list(Last_arrival = arrivals$DateTime_PST),
by= list(general_location = arrivals$general_location),
FUN = max),
by = c("general_location"))
tag_stats <- merge(tag_stats,
aggregate(list(Fish_count = arrivals$TagCode),
by= list(general_location = arrivals$general_location),
FUN = function(x) {length(unique(x))}),
by = c("general_location"))
tag_stats$Percent_arrived <- round(tag_stats$Fish_count/study_count * 100,2)
tag_stats <- merge(tag_stats, unique(gen_locs[,c("general_location", "rkm")]))
tag_stats <- tag_stats[order(tag_stats$rkm, decreasing = T),]
tag_stats[,c("First_arrival", "Mean_arrival", "Last_arrival")] <- format(tag_stats[,c("First_arrival", "Mean_arrival", "Last_arrival")], tz = "Etc/GMT+8")
print(kable(tag_stats, row.names = F,
caption = "4.1 Detections for all releases combined",
"html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F, position = "left"))
count=1
for (j in sort(unique(study_tagcodes$release))) {
if(nrow(detects_study[detects_study$release == j,]) > 0 ) {
count=count+1
temp <- detects_study[detects_study$release == j,]
arrivals1 <- aggregate(list(DateTime_PST = temp$DateTime_PST), by = list(general_location = temp$general_location, TagCode = temp$TagCode), FUN = min)
rel_count <- nrow(study_tagcodes[study_tagcodes$release == j,])
tag_stats1 <- aggregate(list(First_arrival = arrivals1$DateTime_PST),
by= list(general_location = arrivals1$general_location),
FUN = min)
tag_stats1 <- merge(tag_stats1,
aggregate(list(Mean_arrival = arrivals1$DateTime_PST),
by= list(general_location = arrivals1$general_location),
FUN = mean),
by = c("general_location"))
tag_stats1 <- merge(tag_stats1,
aggregate(list(Last_arrival = arrivals1$DateTime_PST),
by= list(general_location = arrivals1$general_location),
FUN = max),
by = c("general_location"))
tag_stats1 <- merge(tag_stats1,
aggregate(list(Fish_count = arrivals1$TagCode),
by= list(general_location = arrivals1$general_location),
FUN = function(x) {length(unique(x))}),
by = c("general_location"))
tag_stats1$Percent_arrived <- round(tag_stats1$Fish_count/rel_count * 100,2)
tag_stats1 <- merge(tag_stats1, unique(gen_locs[,c("general_location", "rkm")]))
tag_stats1 <- tag_stats1[order(tag_stats1$rkm, decreasing = T),]
tag_stats1[,c("First_arrival", "Mean_arrival", "Last_arrival")] <- format(tag_stats1[,c("First_arrival", "Mean_arrival", "Last_arrival")], tz = "Etc/GMT+8")
final_stats <- kable(tag_stats1, row.names = F,
caption = paste("4.",count," Detections for ",j," release groups", sep = ""),
"html")
print(kable_styling(final_stats, bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F, position = "left"))
} else {
cat("\n\n\\pagebreak\n")
print(paste("No detections for",j,"release group yet", sep=" "), quote = F)
cat("\n\n\\pagebreak\n")
}
}
}| general_location | First_arrival | Mean_arrival | Last_arrival | Fish_count | Percent_arrived | rkm |
|---|---|---|---|---|---|---|
| SJ_Hills_Ferry | 2019-03-08 06:26:40 | 2019-03-10 17:27:25 | 2019-03-14 04:27:14 | 18 | 2.57 | 257.000 |
| Old River | 2019-03-30 23:53:52 | 2019-04-12 01:13:29 | 2019-04-19 12:29:21 | 19 | 2.71 | 153.001 |
| MiddleRiver | 2019-03-21 00:29:58 | 2019-03-31 19:48:16 | 2019-04-08 10:51:52 | 3 | 0.43 | 150.000 |
| CVP_Tank | 2019-03-24 17:55:48 | 2019-04-09 19:21:27 | 2019-04-20 19:17:28 | 6 | 0.86 | 144.531 |
| CVP_UpStream_TrashRack | 2019-03-15 01:59:05 | 2019-04-07 23:09:04 | 2019-04-20 18:21:28 | 37 | 5.28 | 144.531 |
| Clifton_Court_RadGates | 2019-03-14 02:08:17 | 2019-04-01 15:07:18 | 2019-04-20 15:39:24 | 24 | 3.42 | 142.721 |
| Clifton_Court_SWP | 2019-03-14 07:35:22 | 2019-03-27 14:39:09 | 2019-04-16 10:41:31 | 35 | 4.99 | 142.721 |
| Benicia_east | 2019-03-20 19:25:33 | 2019-04-10 23:11:18 | 2019-04-27 15:39:16 | 43 | 6.13 | 52.240 |
| Benicia_west | 2019-03-20 19:31:57 | 2019-04-10 23:16:08 | 2019-04-27 15:42:43 | 43 | 6.13 | 52.040 |
rm(list = ls())
cleanup(ask = F)