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B_1sp_ModelJune16.R
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##########################
# B sp model
###########################
library(purrr)
library(tidyverse)
library(lubridate)
library(plyr)
library(dplyr)
library(ggplot2)
# data retrieval tool from USGS
library(dataRetrieval)
# Code for HPC - tidyverse has some issues on our HPC because one of the packages is deprecated
# We have to manually load all tidyverse packages
# library(purrr, lib.loc = "/home/ib/kurthena/R_libs/4.2.1")
# library(tibble, lib.loc = "/home/ib/kurthena/R_libs/4.2.1")
# library(tidyr, lib.loc = "/home/ib/kurthena/R_libs/4.2.1")
# library(readr, lib.loc = "/home/ib/kurthena/R_libs/4.2.1")
# library(stringr, lib.loc = "/home/ib/kurthena/R_libs/4.2.1")
# library(forcats, lib.loc = "/home/ib/kurthena/R_libs/4.2.1")
# library(lubridate, lib.loc = "/home/ib/kurthena/R_libs/4.2.1")
# library(plyr, lib.loc = "/home/ib/kurthena/R_libs/4.2.1")
# library(dplyr, lib.loc = "/home/ib/kurthena/R_libs/4.2.1")
# library(ggplot2, lib.loc = "/home/ib/kurthena/R_libs/4.2.1")
# library(dataRetrieval, lib.loc = "/home/ib/kurthena/R_libs/4.2.1")
source("1spFunctions.R")
#------------------------------------------------------------
# Set up location specific data
#-----------------------------------------------------------
#if looking at ColRiver temps read in temperature data from USGS gauge at Lees Ferry, AZ between _ to the end of last water year
# temp <- readNWISdv("09380000", "00010", "2007-10-01", "2021-09-30")
#-------------------------------------------------------------
# create a series of average 2 weekly data
#-------------------------------------------------------------
# calculate mean temperature data for each timestep (2 week interval)
# temps <- average.yearly.temp(temp, "X_00010_00003", "Date")
# Time <- c(1:1825)
# Date <- rep(c(1:365), times = 5)
# Day <- seq(as.Date("2022-01-01"), as.Date("2026-12-31"), by="days")
# Day <- Day[-which(Day == "2024-02-29")]
#
# Temperature <- -7.374528 * (cos(((2*pi)/365)*Date)) + (-1.649263* sin(2*pi/(365)*Date)) + 10.956243
#
# temp <- as.data.frame(cbind(Time, Day, Date, Temperature))
# peaklist <- 0
# peakeach <- length(temp$Temperature)
# iteration <- 10
# baselineK <- 10000
# disturbanceK <- 40000
# extinct = 50
# flow.data <- discharge
# temp.data <- temp
Bmodel <- function(flow.data, temp.data, baselineK, disturbanceK, Qmin, extinct, iteration, peaklist = NULL, peakeach = NULL){
# set up model
source("NegExpSurv.R")
Q <- as.numeric(flow.data)
temps <- temp.data
degreedays <- as.data.frame(cbind(temps$dts, temps$Temperature * 14))
colnames(degreedays) <- c("dts", "DegreeDay")
degreedays$dts <- as.Date(degreedays$dts, origin = "1970-01-01")
# need to make ramped increasing hydropeaking index
hp <- c(rep(peaklist, each = peakeach))
# specify iterations
iterations <- iteration
# baseline K in the absence of disturbance
Kb <- as.numeric(baselineK)
# max K after a big disturbance
Kd <- as.numeric(disturbanceK)
# specify baseline transition probabilities for each species at mean temps
G1 = 0.04#move to Stage2 (subimago)
G2 = 0.2 #move to Stage3 (adult)
P1 = 0.6#stay in Stage1 (larvae)
P2 = 0.6 #stay in Stage2 (subimago)
# want to run this for one year, in 14 day timesteps
timestep <- seq(2, (length(temps$Temperature) + 1), by = 1)
# create an array to put our output into
output.N.array <- array(0, dim = c(length(timestep) + 1))
output.N.list <- list(output.N.array)
# create array to put the total N of all species into
Total.N <- array(0,
dim <-c((length(timestep) +1 ), iterations),
dimnames <- list(1:(length(timestep) + 1), 1:iterations))
# create list of arrays w/ abundance data for each spp
reparray <- array(0,
dim = c(length(timestep) + 1, 3, iterations),
dimnames = list(1:(length(timestep)+1), c("S1", "S2", "S3"), 1:iterations)
)
output.N.list <- reparray
Qmin <- Qmin
a <- 0.001
g <- 1
h <- high$m$getPars()[2]
k <- high$m$getPars()[1]
extinction <- extinct
#-------------------------
# Outer Loop of Iterations
#--------------------------
for (iter in c(1:iterations)) {
K = Kb # need to reset K for each iteration
# pull random values from a uniform distribution
output.N.list[1,1:3, iter]<- runif(3, min = 1, max = (0.3*K))
# we often want to look at different parameter values after we run code, so we create some lists
# list to input Ks
Klist <- vector()
Klist[1] <- 10000
# list to imput flow morts
flowmortlist <- vector()
Flist <- vector()
emergetime <- vector()
sizelist <- vector()
#-------------------------
# Inner Loop of Timesteps
#-------------------------
for (t in timestep) {
#----------------------------------------------------------
# Calculate how many timesteps emerging adults have matured
emergetime <- append(emergetime, back.count.degreedays(t, 500, degreedays)) # value from Sweeney et al 2017
#---------------------------------------------------------
# Calculate fecundity per adult
# we start by pulling fecundities from normal distribution
# assuming 50 50 sex ration, 0.22 of egg masses 'dissapearred', and 0.2 desiccation because of rock drying
F3 = 1000 * 0.5 * hydropeaking.mortality(0.0, 0.2, h = hp[t-1])
#F3 = rnorm(1, mean = 1104.5, sd = 42.75) * 0.5 #Baetidae egg minima and maxima from Degrange, 1960, assuming 1:1 sex ratio and 50% egg mortality
# we can also relate fecundities to body mass.
# Sweeney and Vannote 1980 have recorded dry body weight between 0.9 and 2.0 mg.
# That weight is related to fecundity Y = 614X - 300
# we can "convert" emergetime to mg by multiplying to get dry weights between 0.9 - 2 mg, and then convert to fecunity
# Issue: this data is for Ephemerella spp, not Baetidae spp
#
if (t > 19) {
size <- emergetime[t-1]
sizelist <- append(sizelist, size)
F3 <- (33.333*size)+ 800 * 0.5 * hydropeaking.mortality(0.0, 0.2, h = hp[t-1])
#F3 <- (57*size)+506 * 0.5 * hydropeaking.mortality(0.0, 0.2, h = hp[t-1]) * 0.78 * 0.65
}
#--------------------------------------------------
# Calculate the disturbance magnitude-K relationship
# Sets to 0 if below the Qmin
Qf <- Qf.Function(Q[t-1], Qmin, a)
#-------------------------------------------------------------------
# Calculate K carrying capacity immediately following the disturbance
K0 <- K + ((Kd-K)*Qf)
# Calculate final K for timestep, including relationship between K and time since disturbance
K <- post.dist.K(K0, Kb, g, t, Q, Qmin)
Klist <- append(Klist, K)
#---------------------------------------------
# Calculate effect of density dependence on fecundity
# Logistic via Rogosch et al. Fish Model
# no immediate egg mortality incorporated
F3 <- Logistic.Dens.Dependence(F3, K, Total.N[t-1, iter])
#
# add F_BAET to list
Flist <- append(Flist, F3)
#-----------------------------------------------
# Calculate new transition probabilities based on temperature
# This is the growth v development tradeoff
# development measures
# in this function, we assume that if below the min temp threshold (9) no maturation occurs (slow maturation, large growth)
# if above the max temp threshold (15), no one remains more than 1 timestep in each stage (fast maturation, small growth)
# # Probabilities of remaining in stages (when temps low, high prob of remaining)
#development measures (basically, if below 10C, no development, if between 10 and 12, follows a function, if above 12, prob of transition to next stage is 0.6395)
if (5 > temps$Temperature[t-1]) {
P1 <- 1-(1/4)
P2 <- 1-(1/4)
G1 <- 0.1/4
G2 <- 0.5/4
}
if (temps$Temperature[t-1] > 20){
P1 <- 1-(1/1)
P2 <- 1-(1/1)
G1 <- 0.1/1
G2 <- 0.5/1
}
if (5 <= temps$Temperature[t-1] & temps$Temperature[t-1] <= 21 & is.na(emergetime[t] == F)){
G1 <- 0.1/((emergetime[t]-1)/2)
G2 <- 0.5/((emergetime[t]-1)/2)
P1 <- 1-(1/((emergetime[t]-1)/2))
P2 <- 1-(1/((emergetime[t]-1)/2))
}
if (5 <= temps$Temperature[t-1] & temps$Temperature[t-1] <= 21 & is.na(emergetime[t] == T)) {
G1 <- 0.1/((-0.353 * temps$Temperature[t-1]) + 10.059)
P1 <- 1-(1/((-0.353 * temps$Temperature[t-1]) + 10.059))
G2 <- 0.5/((-0.353 * temps$Temperature[t-1]) + 10.059)
P2 <- 1-(1/((--0.353 * temps$Temperature[t-1]) + 10.059))
}
# if (7 <= temps$Temperature[t-1] & temps$Temperature[t-1] <= 25) G1 <- growth.development.tradeoff(temps$Temperature[t-1], 7, 25, 0.15, 0.25)
# if (7 <= temps$Temperature[t-1] & temps$Temperature[t-1] <= 25) G2 <- growth.development.tradeoff(temps$Temperature[t-1], 7, 25, 0.15, 0.25)
#
# # growth (if below-0.353x 10C, no growth can occur - everything basically freezes, if between 10 and 11, prob of remaining in same stage = 0.6395, if above 13, prob of transition to next stage is 0 )
# P1 <- 0.55 - G1
# P2 <- 0.55 - G2
#-----------------------------------------------
# Create Lefkovitch Matrix
T1 <- c(P1, 0, F3)
T2 <- c(G1, P2, 0)
T3 <- c(0, G2, 0)
A <- rbind( T1, T2, T3)
#--------------------------------------
# Calculate abundances for each stage
output.N.list[t, 1:3, iter] <- A %*% output.N.list[t-1, 1:3, iter]
#------------------------------------------
#Calculate immediate mortality due to flows
# mortality due to flooding follows N0 = Nz*e^-hQ
#s1
output.N.list[t, 1, iter] <- flood.mortality(output.N.list[t, 1, iter], k, h, Q[t-1], Qmin)
#s2Q
output.N.list[t,2,iter] <- flood.mortality(output.N.list[t,2,iter], k, h, Q[t-1], Qmin)
output.N.list[t,3,iter] <- flood.mortality(output.N.list[t,3,iter], k, h, Q[t-1], Qmin)
flowmortlist <- append(flowmortlist, flood.mortality(1, k, h, Q[t-1], Qmin))
#------------------------------------------------------
# check extinction threshold and if below set to 0
Total.N[,iter] <- apply(output.N.list[,,iter],1,sum)
if (Total.N[t, iter] < extinction){
output.N.list[t,,iter] <- 0
Total.N[t, iter] <- 0}
} #-------------------------
# End Inner Loop
#-------------------------
} #----------------------
# End Outer Loop
#----------------------
return(output.N.list[ , 1:2, ])
}
#------------------
# Analyzing Results
#-------------------
# summarizing iterations
# out <- BAETmodel(flow.data = discharge, temp.data = temps, disturbanceK = 40000, baselineK = 10000, Qmin = 0.25, extinct = 500, iteration = 1, peaklist = 0, peakeach = length(temps$Temperature))
#
# adults <-as.data.frame(cbind(temps$dts, out[1:length(temps$dts),3,1]))
# colnames(adults) <- c("Time","Adult Baetidae")
#
# ## turning replist into a df
# means.list.BAET <- mean.data.frame(out, burnin = 0, iteration = 1)
# means.list.BAET <- cbind(means.list.BAET[1:length(means.list.BAET$mean.abund),], temps$dts[1:length(means.list.BAET$mean.abund)])
# means.list.BAET$`temps$dts` <- as.Date(means.list.BAET$`temps$dts`)
#
# # note how boom and bust this model is - K is set to be 10,000 not 100,000
# abund.trends.BAET <- ggplot(data = adults[300:500,], aes(x = Time,
# y = `Adult Baetidae`/10000, group = 1)) +
# geom_point()+
# # geom_ribbon(aes(ymin = mean.abund - 1.96 * se.abund,
# # ymax = mean.abund + 1.96 * se.abund),
# # colour = 'transparent',
# # alpha = .5,
# # show.legend = FALSE) +
# geom_line(show.legend = FALSE) +
# coord_cartesian(ylim = c(0,2)) +
# ylab('Adult Baetis Abundance/ Baseline Reproductive Limit') +
# xlab('Timestep')
#
# plot(adults$`Adult Baetidae`[300:500], adults$`Adult Baetidae`[301:501], type = "b", xlab = "Adult Baetids t", ylab = "Adult Baetids t+1")
#
# ggplot(data = means.list.BAET[300:500,], aes(x = `temps$dts`,
# y = mean.abund/10000, group = 1)) +
# geom_point()+
# # geom_ribbon(aes(ymin = mean.abund - 1.96 * se.abund,
# # ymax = mean.abund + 1.96 * se.abund),
# # colour = 'transparent',
# # alpha = .5,
# # show.legend = FALSE) +
# geom_line(show.legend = FALSE) +
# coord_cartesian(ylim = c(0,30)) +
# ylab('Baetis spp. Abundance/Reproductive Limit') +
# xlab('Timestep')
#
# plot(means.list.BAET$mean.abund[300:500], means.list.BAET$mean.abund[301:501], type = "b", xlab = "Nt", ylab = "Nt+1")
#
# ggplot(data = NULL, mapping = aes(x = temps$dts, y = Total.N[2:2003]/10000))+
# geom_line(show.legend = FALSE) +
# ylab('Baetis spp. Abundance/Reproductive Limit') +
# xlab(" ")