CalFishTrack

Central Valley Enhanced

Acoustic Tagging Project

Wild Putah Creek Chinook salmon

2019-2020 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Study is complete, all tags are no longer active.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 <- as.data.frame(fread("qry_HexCodes.txt", stringsAsFactors = F))
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 == "Putah_Creek_CHN_2020",]
 

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 <- "Release 1"
  
  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, na.rm = T),
                         by = c("Release"))
  release_stats <- merge(release_stats,
                         aggregate(list(Mean_weight = study_tagcodes$Weight),
                             by= list(Release = study_tagcodes$Release),
                             FUN = mean, na.rm = T),
                         by = c("Release"))
  
    release_stats2<-release_stats[,-3]
  colnames(release_stats2)[2]<-"Release time"
  
  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$Last_release_time <- format(release_stats$Last_release_time, tz = "Etc/GMT+8")
  
  kable(release_stats, format = "html") %>%
          kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left")
}                       

Project began on 2020-04-21 11:00:00, see tagging details below:

Release	First_release_time	Last_release_time	Number_fish_released	Release_location	Release_rkm	Mean_length	Mean_weight
Release 1	2020-04-21 11:00:00	2020-05-22 11:30:00	60	Russell Ranch	168.9	80.2	5.9

---

2. Real-time Fish Detections

---

setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = ""))

library(cder)
library(reshape2)

detects_study <- fread(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products\\Study_detection_files\\detects_Putah_Creek_CHN_2020.csv", sep = ""), colClasses = c(DateTime_PST = "character", RelDT = "character"))


if(nrow(detects_study)>0){
  detects_study$DateTime_PST <- as.POSIXct(detects_study$DateTime_PST, format = "%Y-%m-%d %H:%M:%S", "Etc/GMT+8")
  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$RelDT), "Etc/GMT+8")
  #endtime <- as.Date(c(Sys.time()))#, max(detects_benicia$first_detect)+60*60*24)))
  #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)
  endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")), max(as.Date(detects_benicia$RelDT)+(detects_benicia$tag_life*1.5)))
 
  
  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=viridis_pal()(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= viridis_pal()(rel_num), horiz = T, title = "Release")
  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{
  plot(1:2, type = "n", xlab = "",xaxt = "n", yaxt = "n", ylab = "Number of fish arrivals per day")
  text(1.5,1.5, labels = "NO DETECTIONS YET", cex = 2)
}

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)

if (nrow(detects_benicia) == 0){
  WR.surv1 <- data.frame("Release Group"=NA, "Survival (%)"="NO DETECTIONS YET", "SE"=NA, "95% lower C.I."=NA, "95% upper C.I."=NA, "Detection efficiency (%)"=NA)
  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", "bordered"), full_width = F, position = "left"))
}else if (length(table(detects_benicia$general_location)) == 1){
   WR.surv1 <- data.frame("Release Group"=NA, "Survival (%)"="NOT ENOUGH DETECTIONS", "SE"=NA, "95% lower C.I."=NA, "95% upper C.I."=NA, "Detection efficiency (%)"=NA)
  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", "bordered"), full_width = F, position = "left"))
} 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$Release)))
  groups_w_detects <- names(table(test3$Release))

  inp[,groups] <- 0
  for (i in groups) {
    inp[as.character(inp$Release) == i, i] <- 1
  }

  inp$inp_final <- paste("1",apply(inp2, 1, paste, collapse=""),sep="")
  
  
  if(length(groups) > 1){
  ## make sure factor levels have a release that has detections first. if first release in factor order has zero #detectins, model goes haywire
    inp.df <- data.frame(ch = as.character(inp$inp_final), freq = 1, rel = inp$Release, stringsAsFactors = F)

    WR.process <- process.data(inp.df, 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)
    
    inp.df <- inp.df[inp.df$rel %in% groups_w_detects,]
    inp.df$rel <- factor(inp.df$rel, levels = groups_w_detects)
    if(length(groups_w_detects) > 1){
      WR.process <- process.data(inp.df, model="CJS", begin.time=1, groups = "rel") 
    
      WR.ddl <- make.design.data(WR.process)
      WR.mark.rel <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time*rel),p=list(formula=~time)), silent = T, output = F)
    }else{  
      WR.process <- process.data(inp.df, model="CJS", begin.time=1) 
    
      WR.ddl <- make.design.data(WR.process)
      WR.mark.rel <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)), silent = T, output = F)
    }
      
    WR.surv <- cbind(Release = "ALL",round(WR.mark.all$results$real[1,c("estimate", "se", "lcl", "ucl")] * 100,1))
    WR.surv.rel <- cbind(Release = groups_w_detects, round(WR.mark.rel$results$real[seq(from=1,to=length(groups_w_detects)*2,by = 2),c("estimate", "se", "lcl", "ucl")] * 100,1))
    WR.surv.rel <- merge(WR.surv.rel, data.frame(Release = groups), all.y = T)
    WR.surv.rel[is.na(WR.surv.rel$estimate),"estimate"] <- 0
    WR.surv <- rbind(WR.surv, WR.surv.rel)
    
  }else{
    inp.df <- data.frame(ch = as.character(inp$inp_final), freq = 1, stringsAsFactors = F)
    WRinp <- convert.inp(inp.df)
    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 <- cbind(Release = c("ALL", groups),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.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", "bordered"), 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(format(Sys.time(), "%Y-%m-%d"))) { 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) 
  
}

3.1 Minimum survival to Benicia Bridge East Span (using CJS survival model)

Release Group	Survival (%)	SE	95% lower C.I.	95% upper C.I.	Detection efficiency (%)
NA	NO DETECTIONS YET	NA	NA	NA	NA

---

4. Detections statistics at all realtime receivers

---

setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = ""))

if (nrow(detects_study) == 0){
  "No detections yet"
} else {
  study_count <- nrow(study_tagcodes)
  gen_locs <- read.csv("realtime_locs.csv", stringsAsFactors = F)
  
  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", "bordered"), full_width = F, position = "left"))
  
  for (j in sort(unique(study_tagcodes$Release))) {
    
    if(nrow(detects_study[detects_study$Release == j,]) > 0 ) {
    
      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("4.2 Detections for",j,"release groups", sep = " "),
            "html")
      
      print(kable_styling(final_stats, bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), 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")
    }
  }
}

[1] “No detections yet”

## Set fig height for next plot here, based on how long fish have been at large
figheight <- min(10,max(c(3,as.numeric(difftime(Sys.Date(), min(study_tagcodes$RelDT), units = "days")) / 4)))

4.3 Fish arrivals per day

setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = ""))

if (nrow(detects_study) == 0){
  "No detections yet"
} else {
  
  beacon_by_day <- fread("beacon_by_day.csv", stringsAsFactors = F)
  beacon_by_day$day <- as.Date(beacon_by_day$day)
  
  arrivals$day <- as.Date(format(arrivals$DateTime_PST, "%Y-%m-%d"))
  
  arrivals_per_day <- aggregate(list(New_arrivals = arrivals$TagCode), by = list(day = arrivals$day, general_location = arrivals$general_location), length)
  arrivals_per_day$day <- as.Date(arrivals_per_day$day)

  ## Now subset to only look at data for the correct beacon for that day
  beacon_by_day <- as.data.frame(beacon_by_day[which(beacon_by_day$TagCode == beacon_by_day$beacon),])
  
  ## Now only keep beacon by day for days since fish were released
  beacon_by_day <- beacon_by_day[beacon_by_day$day >= as.Date(min(study_tagcodes$RelDT)) & beacon_by_day$day <= endtime,]  
  
  beacon_by_day <- merge(beacon_by_day, gen_locs[,c("location", "general_location","rkm")], by = "location", all.x = T)

  arrivals_per_day <- merge(beacon_by_day, arrivals_per_day, all.x = T, by = c("general_location", "day"))
  
  arrivals_per_day$day <- factor(arrivals_per_day$day)
  
  ## Remove bench test and other NA locations
  arrivals_per_day <- arrivals_per_day[!arrivals_per_day$general_location == "Bench_test",]
  arrivals_per_day <- arrivals_per_day[is.na(arrivals_per_day$general_location) == F,]
  
  ## Change order of data to plot decreasing rkm
  arrivals_per_day <- arrivals_per_day[order(arrivals_per_day$rkm, decreasing = T),]
  arrivals_per_day$general_location <- factor(arrivals_per_day$general_location, unique(arrivals_per_day$general_location))
  
  
  ggplot(data=arrivals_per_day, aes(x=general_location, y=fct_rev(as_factor(day)))) +
  geom_tile(fill = "lightgray", color = "black") + 
  geom_text(aes(label=New_arrivals)) +
  labs(x="General Location", y = "Date") +
  theme(panel.background = element_blank(), axis.text.x = element_text(angle = 90, hjust = 1))
}

[1] “No detections yet”

rm(list = ls())
cleanup(ask = F)