R/SampFish.R
In JVAdams/artiFISHal: A Pelagic Fish Community Simulator
#' Survey a Fish Population
#'
#' Sample a simulated population of pelagic fish
#' (created with \code{\link{SimFish}}) with down-looking acoustics
#' (cross-lake, west-to-east) and midwater trawls (west-to-east).
#'
#' @param SimPop
#' A list with elements \code{LakeInfo}, \code{FishInfo}, \code{FishParam},
#' \code{FishPop}, typically output from \code{\link{SimFish}}.
#' @param NumEvents
#' An integer scalar, number of sampling events, i.e., number of times
#' to repeat the survey.
#' @param AcNum
#' An integer scalar > 0, number of equally-spaced, cross-lake,
#' acoustic transects (oriented west-to-east).
#' @param AcInterval
#' A numeric scalar, length (distance) of acoustic interval (in m).
#' @param AcLayer
#' A numeric scalar, depth of acoustic layer (in m).
#' @param AcAngle
#' A numeric scalar, full beam angle width of down-looking acoustic transducer
#' (in degrees).
#' @param MtNum
#' An integer scalar > 0, target number of midwater trawl tows
#' (oriented west-to-east along acoustic transects at random water depths).
#' @param MtHt
#' A numeric scalar, height of rectangular midwater trawl opening (in m).
#' @param MtWd
#' A numeric scalar, width of rectangular midwater trawl opening (in m).
#' @param MtLen
#' A numeric scalar, length (distance) of midwater trawl haul (in m).
#' @param MtMinCat
#' An integer scalar, the minimum catch (number of fish) per
#' midwater trawl tow, default 2.
#' Tows capturing fewer than \code{MtMinCat} fish will be tossed out.
#' @param MtMulti
#' An integer scalar, the initial number of midwater trawl tows is multiplied
#' by this scalar in an attempt to achieve the target number of tows,
#' \code{MtNum}, even after eliminating those tows that extend beyond
#' the boundaries of the lake or those with fewer than \code{MtMinCat},
#' default 6.
#' @param PlotsPdf
#' A character scalar, name of pdf file to store the diagnostic plots in.
#' If FALSE, the default, no plots are created.
#' @param Seed
#' An integer scalar, starting seed for stochasticity incorporated in
#' placement of acoustic transects and midwater trawl tows.
#' Use \code{Seed} to ensure the survey is conducted in the same location
#' with each call to \code{SampFish}.
#' Otherwise, if set to NULL, the default, a random seed is used,
#' resulting in a different tow location with each call to \code{SampFish}.
#'
#' @return
#' A list with 3 elements.
#' \itemize{
#' \item \code{SurvParam} is a named vector with the survey inputs supplied
#' as arguments to \code{SampFish}.
#' \item \code{Targets} is a data frame with information on fish detected
#' in the virtual acoustics survey. Each row represents a single target,
#' columns describe the specific location of fish in the lake and
#' their target strengths.
#' Three columns are included that would not be available in a real
#' acoustic survey: group (\code{G}) and size (\code{len} and \code{wt}).
#' \item \code{MtCatch} is a data frame with information on the fish
#' captured in the virtual midwater trawl survey. Each row represents
#' a single fish, columns describe the specific location of the trawl and
#' the group and size of the fish. Seven columns are included
#' that would not be available in a real midwater trawl survey:
#' the specific location of the fish (\code{f.east}, \code{f.north},
#' \code{f.d2sh}, \code{f.botdep}, \code{f.wdep}, \code{f.d2bot}) and
#' their target strengths (\code{ts}).
#' }
#'
#' @details
#' All sampling is assumed to be "perfect" with no issues of fish
#' availability, acoustic dead zones, or trawl selectivity.
#' So, the acoustic transects capture echoes from all fish that fall within
#' the triangular prism defined by the randomly selected northing of
#' the transect and the beam angle (\code{AcAngle}) of the transducer,
#' which is assumed to be at the surface of the water.
#' And, the midwater trawl tows capture all fish that fall within
#' the rectangular prism defined by the randomly selected water depth of
#' the tow, the trawl gape (\code{MtHt} and \code{MtWd}), and
#' the tow length (\code{MtLen}).
#'
#' Three diagnostic plots are produced, if \code{PlotsPdf} is not FALSE.
#' The first is a top (bird's eye) view of the acoustic transects and midwater
#' trawls in lake, drawn to scale.
#' In this plot, acoustic transects are shown as solid blue lines and
#' midwater trawl tows are shown as red rectangles.
#' Second is a side view of each acoustic transect separately, showing
#' the individual fish targets as blue circles
#' (larger circles indicate larger fish) and midwater trawl tows as
#' red rectangles with the number of fish captured written inside.
#' The acoustic transect number and the number of targets in the transect are
#' printed on the plot.
#' Third is a length frequency histogram for fish captured in each midwater
#' trawl tow. The midwater trawl tow number and the number of fish captured in
#' the tow are printed on the plot.
#'
#' @export
#' @import
#' MASS
#' @seealso
#' \code{\link{SimFish}}
#' @references
#' Yule, DL, JV Adams, DM Warner, TR Hrabik, PM Kocovsky, BC Weidel,
#' LG Rudstam, and PJ Sullivan. 2013.
#' Evaluating analytical approaches for estimating pelagic fish biomass using
#' simulated fish communities.
#' Canadian Journal of Fisheries and Aquatic Sciences 70:1845-1857.
#' \emph{http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2013-0072#.U1KYxPldXTQ}
#'
#' Yule, DL. 2000. Comparison of horizontal acoustic and purse-seine
#' estimates of salmonid densities and sizes in eleven Wyoming waters
#' North American Journal of Fisheries Management 20:759-775.
#' \emph{http://www.tandfonline.com/doi/abs/10.1577/1548-8675(2000)020\%3C0759\%3ACOHAAP\%3E2.3.CO\%3B2}
#'
#' @examples
#' # parameters for small (a) and large (A) alewife as input to the simulator
#' fishp <- data.frame(
#' G = c("a", "A", "A"),
#' Z = c(50, 140, 140), ZE = c(0.25, 0.2, 0.2),
#' LWC1 = 0.000014, LWC2 = 2.8638, LWCE = 0.18,
#' TSC1 = -64.2, TSC2 = 20.5, TSCE = c(0.02, 0.07, 0.07),
#' PropN = c(0.55, 0.25, 0.20),
#' E = c(NA, 900, 2800), EE = c(NA, 4.5, 0.3),
#' N = NA, NE = NA,
#' WD = c(5, 15, 15), WDE = c(0.5, 0.7, 0.7),
#' D2B = NA, D2BE = NA
#' )
#'
#' # simulate the fish population
#' res <- SimFish(LakeName="Clear Lake", LkWidth=3000, LkLength=2000,
#' BotDepMin=20, BotDepMax=100, FishParam=fishp, TotNFish=50000, Seed=667)
#'
#' # survey the population
#' surv <- SampFish(SimPop=res, NumEvents=2, AcNum=5, AcInterval=3000,
#' AcLayer=10, AcAngle=7, MtNum=25, MtHt=10, MtWd=10, MtLen=200, Seed=545)
#'
#' # look at the results
#' surv$SurvParam
#' head(surv$Targets)
#' head(surv$MtCatch)
SampFish <- function(SimPop, NumEvents=1, AcNum, AcInterval, AcLayer, AcAngle,
MtNum, MtHt, MtWd, MtLen, MtMinCat=2, MtMulti=6, PlotsPdf=FALSE, Seed=NULL) {
if (is.na(PlotsPdf) | length(PlotsPdf)!=1 | is.numeric(PlotsPdf)) {
stop("PlotsPdf must be either a character scalar, or FALSE")
}
if (PlotsPdf!=FALSE) {
pdf(PlotsPdf, width=9, height=6.5, title="Survey", paper="USr")
}
# For the purpose of running the code, make sure we have at least
# two sampling events.
# If only one event was requested, info from the second event is removed.
nE.orig <- NumEvents
NumEvents <- max(nE.orig, 2)
# easting (m), range, (x direction)
eastr <- c(0, SimPop$LakeInfo$LkWidth)
# northing (m), range, (y direction)
northr <- c(0, SimPop$LakeInfo$LkLength)
# distance from west and east shore excluded from lake in m (x direction)
d2shr.we <- c(0+SimPop$LakeInfo$ints[1]/SimPop$LakeInfo$slopes[1],
-SimPop$LakeInfo$ints[2]/SimPop$LakeInfo$slopes[2]-eastr[2])
### SAMPLING GEAR & PROCESSING ###
# increase the number of initial trawls, so we'll still have enough when
# we get rid of those that hit bottom and those with fewer than 2 fish
nT2 <- ceiling(MtMulti*MtNum/AcNum)*AcNum
ACid <- 1:(AcNum*NumEvents)
ACspace <- diff(northr)/AcNum
sampinfo <- as.data.frame(matrix(NA, nrow=nT2*NumEvents, ncol=10,
dimnames=list(NULL, c("Event", "ACid", "ACnorth", "MTRgrp", "MTRid",
"MTReast", "MTRbdep", "MTRwdep", "MTRd2sh", "MTRd2bot"))))
sampinfo$Event <- rep(1:NumEvents, rep(nT2, NumEvents))
sampinfo$ACid <- rep(1:(AcNum*NumEvents), rep(nT2/AcNum, AcNum*NumEvents))
sampinfo$MTRgrp <- rep(1:(nT2/AcNum), AcNum*NumEvents)
sampinfo$MTRid <- seq(along=sampinfo$MTRgrp)
if (!is.null(Seed)) set.seed(Seed)
# convert full down-looking angle from degrees to radians
angleRadians <- 2*pi*AcAngle/360
for(k in 1:NumEvents) {
sel <- sampinfo$Event==k
ACstart <- runif (1, northr[1], northr[2])
ACnorth <- sort(unique(c(seq(ACstart, northr[1], -ACspace),
seq(ACstart, northr[2], ACspace))))
sampinfo$ACnorth[sel] <- rep(ACnorth, rep(nT2/AcNum, AcNum))
sampinfo$MTReast[sel] <- runif (nT2, eastr[1]+MtLen/2, eastr[2]-MtLen/2)
# calculate midwater trawl measures for the MIDPOINT of the trawl
sampinfo$MTRbdep[sel] <- zfromx(x=sampinfo$MTReast[sel],
maxz=SimPop$LakeInfo$BotDepMax, eastr=eastr,
ints=SimPop$LakeInfo$ints, slopes=SimPop$LakeInfo$slopes)
# random selection of midwater trawl depth
# Assign NA to those cases where bottom depth < trawl height
# These will be removed later
tooshallow <- 0+(MtHt/2) > sampinfo$MTRbdep[sel]-(MtHt/2)
sampinfo$MTRwdep[sel][tooshallow] <- NA
sampinfo$MTRwdep[sel][!tooshallow] <- runif (sum(!tooshallow), 0+(MtHt/2),
sampinfo$MTRbdep[sel][!tooshallow]-(MtHt/2))
sampinfo$MTRd2sh[sel] <- dfromx(x=sampinfo$MTReast[sel], d2shr.we=d2shr.we,
eastr=eastr)
sampinfo$MTRd2bot[sel] <- sampinfo$MTRbdep[sel] - sampinfo$MTRwdep[sel]
# make sure acoustic transect cones don't overlap
mindist.allowed <- SimPop$LakeInfo$BotDepMax*tan(angleRadians/4)
mindist.observed <- min(diff(sort(ACnorth)))
if (mindist.observed < mindist.allowed) {
warning("Acoustic transects are too close together.\nTry again with fewer acoustic transects.")
}
}
# make sure that all of the acoustic transects are kept ...
# even if they have no midwater trawls associated with them
ACsampinfo <- sampinfo[first(sampinfo$ACid)==1, 1:3]
# make sure that midwater trawl doesn't hit bottom
MTRbdep1 <- zfromx(x=sampinfo$MTReast - (MtLen/2),
maxz=SimPop$LakeInfo$BotDepMax, eastr=eastr, ints=SimPop$LakeInfo$ints,
slopes=SimPop$LakeInfo$slopes)
MTRbdep2 <- zfromx(x=sampinfo$MTReast + (MtLen/2),
maxz=SimPop$LakeInfo$BotDepMax, eastr=eastr, ints=SimPop$LakeInfo$ints,
slopes=SimPop$LakeInfo$slopes)
hits.bottom <- (MTRbdep1 - (sampinfo$MTRwdep + MtHt/2)) < 0 |
(MTRbdep2 - (sampinfo$MTRwdep + MtHt/2)) < 0
# make sure that midwater trawl doesn't extend too far east/west/north/south
extends.out <- sampinfo$ACnorth - MtWd/2 < northr[1] |
sampinfo$ACnorth + MtWd/2 > northr[2] |
sampinfo$MTReast - MtLen/2 < eastr[1] |
sampinfo$MTReast + MtLen/2 > eastr[2]
sampinfo <- sampinfo[!hits.bottom & !extends.out & !is.na(sampinfo$MTRwdep), ]
rm(ACid, ACspace, sel, ACstart, ACnorth, mindist.allowed, mindist.observed,
MTRbdep1, MTRbdep2, hits.bottom, extends.out)
# acoustic transects
# select only those targets within the volume of space sampled by
# the acoustic transect (triangular prism)
sua <- sort(unique(ACsampinfo$ACid))
AC <- data.frame(matrix(NA, nrow=0, ncol=13, dimnames=list(NULL,
c("Event", "ACid", "ACnorth", "G", "f.east", "f.north", "f.d2sh",
"f.botdep", "f.wdep", "f.d2bot", "len", "wt", "ts"))))
# acoustic slice cone cushion, based on angle of ducer and maximum depth
cushion <- SimPop$LakeInfo$BotDepMax*tan(angleRadians/2)
# create a single row of missing values that looks just like
# the "fish" data frame
# this will be used to maintain a row of information on
# an acoustic transect, even if it captures no targets
nofish <- SimPop$Fish[1, ]
ncol <- dim(SimPop$Fish)[2]
nofish[1, 1:ncol] <- rep(NA, ncol)
rm(ncol)
# acoustic transects
for(j in sua) {
# make sure that the acoustic slice does not extend further north or south
# than our sample space
if (ACsampinfo$ACnorth[j] > (northr[2] - cushion) |
ACsampinfo$ACnorth[j] < (northr[1] + cushion)) {
warning(paste("\nACid = ", ACsampinfo$ACid[j], ", ACnorth = ",
ACsampinfo$ACnorth[j],
": \nThe cone of the acoustic slice extends farther north or south \nthan the boundary of our simulated lake.",
" \nTry again using a different Seed or using fewer acoustic transects."))
}
sel <- (abs(SimPop$Fish$f.north - ACsampinfo$ACnorth[j])) <
(SimPop$Fish$f.wdep*tan(angleRadians/2))
if (sum(sel)>0) {
temp <- data.frame(
ACsampinfo[rep(j, sum(sel)), c("Event", "ACid", "ACnorth")],
SimPop$Fish[sel, ]
)
AC <- rbind(AC, temp)
} else {
temp <- data.frame(ACsampinfo[rep(j, 1), c("Event", "ACid", "ACnorth")],
nofish)
AC <- rbind(AC, temp)
}
}
rm(cushion)
# midwater trawl tows
# select only those targets within the volume of space sampled by
# the midwater trawl (rectangular prism)
sut <- sort(unique(sampinfo$MTRid))
MTRbig <- data.frame(matrix(NA, nrow=0, ncol=20, dimnames=list(NULL,
c("Event", "ACid", "ACnorth", "MTRgrp", "MTRid", "MTReast", "MTRbdep",
"MTRwdep", "MTRd2sh", "MTRd2bot", "G", "f.east", "f.north", "f.d2sh",
"f.botdep", "f.wdep", "f.d2bot", "len", "wt", "ts"))))
for(m in sut) {
j <- match(m, sampinfo$MTRid)
sel <- SimPop$Fish$f.north >= (sampinfo$ACnorth[j] - MtWd/2) &
SimPop$Fish$f.north <= (sampinfo$ACnorth[j] + MtWd/2) &
SimPop$Fish$f.east >= (sampinfo$MTReast[j] - MtLen/2) &
SimPop$Fish$f.east <= (sampinfo$MTReast[j] + MtLen/2) &
SimPop$Fish$f.wdep <= (sampinfo$MTRwdep[j] + MtHt/2) &
SimPop$Fish$f.wdep >= (sampinfo$MTRwdep[j] - MtHt/2)
if (sum(sel) >= MtMinCat) {
temp <- cbind(sampinfo[rep(j, sum(sel)), ], SimPop$Fish[sel, ])
MTRbig <- rbind(MTRbig, temp)
}
}
if (dim(MTRbig)[1] > 0) {
### select only needed number of trawls
# (remember, we sampled many more than what we needed!) ##
# create an index of trawls
mtr.indx <- MTRbig[first(MTRbig$MTRid)==1,
c("Event", "ACid", "MTRgrp", "MTRid")]
mtr.indx$mtr.grp <- unlist(lapply(table(mtr.indx$ACid), sample))
# create a random variable for sorting
y <- runif (length(mtr.indx$mtr.grp))
# sort the index of trawls by event abd group number
mtr.indx.sort <- mtr.indx[order(mtr.indx$Event, mtr.indx$mtr.grp, y), ]
# count up the number of trawls in each event
f <- first(mtr.indx.sort$Event)
x <- rep(NA, length(f))
for(ix in 1:length(f)) {
x[ix] <- if (f[ix]==1) {
1
} else {
x[ix-1]+1
}
}
mtr.indx.sort$mtr.count <- x
# keep the first MtNum trawls in each event
mtr.indx.keep <- mtr.indx.sort[mtr.indx.sort$mtr.count <= MtNum, ]
MTR <- MTRbig[MTRbig$MTRid %in% mtr.indx.keep$MTRid, ]
sampinfo.sub <- sampinfo[sampinfo$MTRid %in% mtr.indx.keep$MTRid, ]
rm(mtr.indx, y, mtr.indx.sort, f, x, mtr.indx.keep)
} else {
MTR <- MTRbig[0, ]
sampinfo.sub <- sampinfo[0, ]
}
# categorize targets interval (along transect) and layer (in water column),
# both in m
intbrks <- seq(eastr[1], eastr[2] + AcInterval - 1, AcInterval)
intmids <- intbrks[-1] - AcInterval/2
AC$interval <- intmids[cut(AC$f.east, include.lowest=TRUE, breaks=intbrks,
labels=FALSE)]
laybrks <- seq(0, SimPop$LakeInfo$BotDepMax + AcLayer - 1, AcLayer)
laymids <- laybrks[-1] - AcLayer/2
AC$layer <- laymids[cut(AC$f.wdep, include.lowest=TRUE, breaks=laybrks,
labels=FALSE)]
### diagnostic plots ###
if (PlotsPdf!=FALSE) {
sel <- AC$Event==1
ts.scaled <- (AC$ts[sel] - SimPop$FishInfo$TSRange[1])/
diff(SimPop$FishInfo$TSRange)
sua <- sort(unique(AC$ACid[sel]))
sut <- sort(unique(sampinfo.sub$MTRid[sampinfo.sub$Event==1]))
# top view of acoustic transects and midwater trawls - to scale
par(mfrow=c(1, 1), oma=rep(0, 4), mar=c(5.1, 4.1, 4.1, 2.1))
eqscplot(1, 1, type="n", xlim=eastr/1000, ylim=northr/1000, axes=FALSE,
xlab="Easting (km)", ylab="Northing (km)",
main=paste(SimPop$LakeInfo$LakeName, "- Top View - drawn to scale"))
axis(1)
axis(2, las=1)
arrows(rep(eastr[1], length(sua))/1000,
AC$ACnorth[match(sua, AC$ACid[sel])]/1000,
rep(eastr[2], length(sua))/1000,
AC$ACnorth[match(sua, AC$ACid[sel])]/1000, length=0, lwd=2, col="blue")
polygon(eastr[c(1, 2, 2, 1)]/1000, northr[c(1, 1, 2, 2)]/1000)
if (length(sut)>0) {
for(m in seq(along=sut)) {
sel2 <- sampinfo.sub$MTRid==sut[m]
polygon((sampinfo.sub$MTReast[sel2]+c(-1, 1, 1, -1)*MtLen/2)/1000,
(sampinfo.sub$ACnorth[sel2]+c(1, 1, -1, -1)*MtWd/2)/1000,
border="red", lwd=3)
}
rm(sel2)
}
# plot of each acoustic transect with outline of midwater trawl tows
par(mfrow=n2mfrow(length(sua)), oma=c(2, 2, 2, 0), mar=c(3, 3, 1, 1))
catch.tots <- table(MTR$MTRid)
for(j in seq(along=sua)) {
plot(AC$f.east/1000, -AC$f.wdep, las=1, type="n", xlab="", ylab="")
sel3 <- AC$ACid==sua[j]
points(AC$f.east[sel3]/1000, -AC$f.wdep[sel3], cex=2*ts.scaled,
col="blue")
points(AC$f.east[sel3]/1000, -AC$f.botdep[sel3], pch=16, cex=0.5)
sut2 <- sort(unique(sampinfo.sub$MTRid[sampinfo.sub$ACid==sua[j]]))
mtext(paste("id=", sua[j], "\nn=", sum(sel3), sep=""), side=1, line=-1.5,
adj=0.98, font=2, cex=par("cex"))
if (length(sut2)>0) {
for(m in seq(along=sut2)) {
sel2 <- sampinfo.sub$MTRid==sut2[m]
polygon((sampinfo.sub$MTReast[sel2]+c(-1, 1, 1, -1)*MtLen/2)/1000,
-sampinfo.sub$MTRwdep[sel2]+c(1, 1, -1, -1)*MtHt/2,
border="red", col="white")
text(sampinfo.sub$MTReast[sel2]/1000, -sampinfo.sub$MTRwdep[sel2],
catch.tots[sel2], font=2, col="red")
}
rm(sel2)
}
}
mtext("Easting (km)", side=1, outer=TRUE)
mtext("Fishing depth (m)", side=2, outer=TRUE)
mtext(paste(SimPop$LakeInfo$LakeName,
"- Acoustic Transects and Midwater Trawls"), side=3, outer=TRUE, font=2)
# plot of each midwater trawl catch
if (length(sut)>0) {
barz <- tapply(!is.na(MTR$len),
list(MTR$G, 50*floor(MTR$len/50), MTR$MTRid), sum)
barz[is.na(barz)] <- 0
barz <- barz[, , dimnames(barz)[[3]] %in% sut, drop=FALSE]
barz <- barz[apply(barz, 1, sum)>0, , , drop=FALSE]
sus <- dimnames(barz)[[1]]
par(mfrow=rev(n2mfrow(length(sut)+1)), oma=c(2, 2, 2, 0),
mar=c(3, 3, 1, 1))
for(m in seq(along=sut)) {
barplot(barz[, , m], ylim=c(0, max(apply(barz, 2:3, sum))), col=1:50,
names.arg=paste(dimnames(barz)[[2]], "+", sep=""), las=1)
mtext(paste("id=", sut[m], "\nn=", sum(barz[, , m]), sep=""), side=3,
line=-2.5, adj=0.98, font=2, cex=par("cex"))
box()
}
plot(0:1, 0:1, axes=FALSE, las=1, type="n", xlab="", ylab="")
legend("center", sus, fill=seq(sus), title="Group")
mtext("Length (mm)", side=1, outer=TRUE)
mtext("Frequency", side=2, outer=TRUE)
mtext(paste(SimPop$LakeInfo$LakeName, "- Midwater Trawl Catch"), side=3,
outer=TRUE, font=2)
rm(barz, sus)
}
if (PlotsPdf!=FALSE) graphics.off()
}
cat(paste0("Mean number of fish per acoustic transect = ",
signif(mean(table(AC$ACid)), 2), "\n"))
cat(paste0("Mean number of fish per midwater trawl tow = ",
signif(mean(table(MTR$MTRid)), 2), "\n"))
ac.out.vars <- c("Event", "ACid", "ACnorth", "interval", "layer", "f.east",
"f.north", "f.d2sh", "f.botdep", "f.wdep", "f.d2bot", "ts", "G",
"len", "wt")
# setdiff(names(AC), ac.out.vars)
# 3 variables unknown to targets: "G" "len" "wt"
mtr.out.vars <- c("Event", "ACid", "ACnorth", "MTRid", "MTReast", "MTRd2sh",
"MTRbdep", "MTRwdep", "MTRd2bot", "G", "len", "wt",
"f.east", "f.north", "f.d2sh", "f.botdep", "f.wdep", "f.d2bot", "ts")
# setdiff(names(MTR), mtr.out.vars)
# 7 variables unknown to trawl catch: "f.east" "f.north" "f.d2sh"
# "f.botdep" "f.wdep" "f.d2bot" "ts"
### only save *1* sampling event, if that is what was originally specified ###
if (nE.orig==1) {
AC <- AC[AC$Event==1, ac.out.vars]
MTR <- MTR[MTR$Event==1, mtr.out.vars]
} else {
AC <- AC[, ac.out.vars]
MTR <- MTR[, mtr.out.vars]
}
SurvParam <- c(NumEvents=NumEvents, AcNum=AcNum, AcInterval=AcInterval,
AcLayer=AcLayer, AcAngle=AcAngle, MtNum=MtNum, MtHt=MtHt, MtWd=MtWd,
MtLen=MtLen, MtMinCat=MtMinCat, MtMulti=MtMulti, Seed=Seed)
list(SurvParam=SurvParam, Targets=AC, MtCatch=MTR)
}
JVAdams/artiFISHal documentation built on May 7, 2019, 10:14 a.m.
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#' Survey a Fish Population
#'
#' Sample a simulated population of pelagic fish
#' (created with \code{\link{SimFish}}) with down-looking acoustics
#' (cross-lake, west-to-east) and midwater trawls (west-to-east).
#'
#' @param SimPop
#' A list with elements \code{LakeInfo}, \code{FishInfo}, \code{FishParam},
#' \code{FishPop}, typically output from \code{\link{SimFish}}.
#' @param NumEvents
#' An integer scalar, number of sampling events, i.e., number of times
#' to repeat the survey.
#' @param AcNum
#' An integer scalar > 0, number of equally-spaced, cross-lake,
#' acoustic transects (oriented west-to-east).
#' @param AcInterval
#' A numeric scalar, length (distance) of acoustic interval (in m).
#' @param AcLayer
#' A numeric scalar, depth of acoustic layer (in m).
#' @param AcAngle
#' A numeric scalar, full beam angle width of down-looking acoustic transducer
#' (in degrees).
#' @param MtNum
#' An integer scalar > 0, target number of midwater trawl tows
#' (oriented west-to-east along acoustic transects at random water depths).
#' @param MtHt
#' A numeric scalar, height of rectangular midwater trawl opening (in m).
#' @param MtWd
#' A numeric scalar, width of rectangular midwater trawl opening (in m).
#' @param MtLen
#' A numeric scalar, length (distance) of midwater trawl haul (in m).
#' @param MtMinCat
#' An integer scalar, the minimum catch (number of fish) per
#' midwater trawl tow, default 2.
#' Tows capturing fewer than \code{MtMinCat} fish will be tossed out.
#' @param MtMulti
#' An integer scalar, the initial number of midwater trawl tows is multiplied
#' by this scalar in an attempt to achieve the target number of tows,
#' \code{MtNum}, even after eliminating those tows that extend beyond
#' the boundaries of the lake or those with fewer than \code{MtMinCat},
#' default 6.
#' @param PlotsPdf
#' A character scalar, name of pdf file to store the diagnostic plots in.
#' If FALSE, the default, no plots are created.
#' @param Seed
#' An integer scalar, starting seed for stochasticity incorporated in
#' placement of acoustic transects and midwater trawl tows.
#' Use \code{Seed} to ensure the survey is conducted in the same location
#' with each call to \code{SampFish}.
#' Otherwise, if set to NULL, the default, a random seed is used,
#' resulting in a different tow location with each call to \code{SampFish}.
#'
#' @return
#' A list with 3 elements.
#' \itemize{
#' \item \code{SurvParam} is a named vector with the survey inputs supplied
#' as arguments to \code{SampFish}.
#' \item \code{Targets} is a data frame with information on fish detected
#' in the virtual acoustics survey. Each row represents a single target,
#' columns describe the specific location of fish in the lake and
#' their target strengths.
#' Three columns are included that would not be available in a real
#' acoustic survey: group (\code{G}) and size (\code{len} and \code{wt}).
#' \item \code{MtCatch} is a data frame with information on the fish
#' captured in the virtual midwater trawl survey. Each row represents
#' a single fish, columns describe the specific location of the trawl and
#' the group and size of the fish. Seven columns are included
#' that would not be available in a real midwater trawl survey:
#' the specific location of the fish (\code{f.east}, \code{f.north},
#' \code{f.d2sh}, \code{f.botdep}, \code{f.wdep}, \code{f.d2bot}) and
#' their target strengths (\code{ts}).
#' }
#'
#' @details
#' All sampling is assumed to be "perfect" with no issues of fish
#' availability, acoustic dead zones, or trawl selectivity.
#' So, the acoustic transects capture echoes from all fish that fall within
#' the triangular prism defined by the randomly selected northing of
#' the transect and the beam angle (\code{AcAngle}) of the transducer,
#' which is assumed to be at the surface of the water.
#' And, the midwater trawl tows capture all fish that fall within
#' the rectangular prism defined by the randomly selected water depth of
#' the tow, the trawl gape (\code{MtHt} and \code{MtWd}), and
#' the tow length (\code{MtLen}).
#'
#' Three diagnostic plots are produced, if \code{PlotsPdf} is not FALSE.
#' The first is a top (bird's eye) view of the acoustic transects and midwater
#' trawls in lake, drawn to scale.
#' In this plot, acoustic transects are shown as solid blue lines and
#' midwater trawl tows are shown as red rectangles.
#' Second is a side view of each acoustic transect separately, showing
#' the individual fish targets as blue circles
#' (larger circles indicate larger fish) and midwater trawl tows as
#' red rectangles with the number of fish captured written inside.
#' The acoustic transect number and the number of targets in the transect are
#' printed on the plot.
#' Third is a length frequency histogram for fish captured in each midwater
#' trawl tow. The midwater trawl tow number and the number of fish captured in
#' the tow are printed on the plot.
#'
#' @export
#' @import
#' MASS
#' @seealso
#' \code{\link{SimFish}}
#' @references
#' Yule, DL, JV Adams, DM Warner, TR Hrabik, PM Kocovsky, BC Weidel,
#' LG Rudstam, and PJ Sullivan. 2013.
#' Evaluating analytical approaches for estimating pelagic fish biomass using
#' simulated fish communities.
#' Canadian Journal of Fisheries and Aquatic Sciences 70:1845-1857.
#' \emph{http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2013-0072#.U1KYxPldXTQ}
#'
#' Yule, DL. 2000. Comparison of horizontal acoustic and purse-seine
#' estimates of salmonid densities and sizes in eleven Wyoming waters
#' North American Journal of Fisheries Management 20:759-775.
#' \emph{http://www.tandfonline.com/doi/abs/10.1577/1548-8675(2000)020\%3C0759\%3ACOHAAP\%3E2.3.CO\%3B2}
#'
#' @examples
#' # parameters for small (a) and large (A) alewife as input to the simulator
#' fishp <- data.frame(
#' G = c("a", "A", "A"),
#' Z = c(50, 140, 140), ZE = c(0.25, 0.2, 0.2),
#' LWC1 = 0.000014, LWC2 = 2.8638, LWCE = 0.18,
#' TSC1 = -64.2, TSC2 = 20.5, TSCE = c(0.02, 0.07, 0.07),
#' PropN = c(0.55, 0.25, 0.20),
#' E = c(NA, 900, 2800), EE = c(NA, 4.5, 0.3),
#' N = NA, NE = NA,
#' WD = c(5, 15, 15), WDE = c(0.5, 0.7, 0.7),
#' D2B = NA, D2BE = NA
#' )
#'
#' # simulate the fish population
#' res <- SimFish(LakeName="Clear Lake", LkWidth=3000, LkLength=2000,
#' BotDepMin=20, BotDepMax=100, FishParam=fishp, TotNFish=50000, Seed=667)
#'
#' # survey the population
#' surv <- SampFish(SimPop=res, NumEvents=2, AcNum=5, AcInterval=3000,
#' AcLayer=10, AcAngle=7, MtNum=25, MtHt=10, MtWd=10, MtLen=200, Seed=545)
#'
#' # look at the results
#' surv$SurvParam
#' head(surv$Targets)
#' head(surv$MtCatch)
SampFish <- function(SimPop, NumEvents=1, AcNum, AcInterval, AcLayer, AcAngle,
MtNum, MtHt, MtWd, MtLen, MtMinCat=2, MtMulti=6, PlotsPdf=FALSE, Seed=NULL) {
if (is.na(PlotsPdf) | length(PlotsPdf)!=1 | is.numeric(PlotsPdf)) {
stop("PlotsPdf must be either a character scalar, or FALSE")
}
if (PlotsPdf!=FALSE) {
pdf(PlotsPdf, width=9, height=6.5, title="Survey", paper="USr")
}
# For the purpose of running the code, make sure we have at least
# two sampling events.
# If only one event was requested, info from the second event is removed.
nE.orig <- NumEvents
NumEvents <- max(nE.orig, 2)
# easting (m), range, (x direction)
eastr <- c(0, SimPop$LakeInfo$LkWidth)
# northing (m), range, (y direction)
northr <- c(0, SimPop$LakeInfo$LkLength)
# distance from west and east shore excluded from lake in m (x direction)
d2shr.we <- c(0+SimPop$LakeInfo$ints[1]/SimPop$LakeInfo$slopes[1],
-SimPop$LakeInfo$ints[2]/SimPop$LakeInfo$slopes[2]-eastr[2])
### SAMPLING GEAR & PROCESSING ###
# increase the number of initial trawls, so we'll still have enough when
# we get rid of those that hit bottom and those with fewer than 2 fish
nT2 <- ceiling(MtMulti*MtNum/AcNum)*AcNum
ACid <- 1:(AcNum*NumEvents)
ACspace <- diff(northr)/AcNum
sampinfo <- as.data.frame(matrix(NA, nrow=nT2*NumEvents, ncol=10,
dimnames=list(NULL, c("Event", "ACid", "ACnorth", "MTRgrp", "MTRid",
"MTReast", "MTRbdep", "MTRwdep", "MTRd2sh", "MTRd2bot"))))
sampinfo$Event <- rep(1:NumEvents, rep(nT2, NumEvents))
sampinfo$ACid <- rep(1:(AcNum*NumEvents), rep(nT2/AcNum, AcNum*NumEvents))
sampinfo$MTRgrp <- rep(1:(nT2/AcNum), AcNum*NumEvents)
sampinfo$MTRid <- seq(along=sampinfo$MTRgrp)
if (!is.null(Seed)) set.seed(Seed)
# convert full down-looking angle from degrees to radians
angleRadians <- 2*pi*AcAngle/360
for(k in 1:NumEvents) {
sel <- sampinfo$Event==k
ACstart <- runif (1, northr[1], northr[2])
ACnorth <- sort(unique(c(seq(ACstart, northr[1], -ACspace),
seq(ACstart, northr[2], ACspace))))
sampinfo$ACnorth[sel] <- rep(ACnorth, rep(nT2/AcNum, AcNum))
sampinfo$MTReast[sel] <- runif (nT2, eastr[1]+MtLen/2, eastr[2]-MtLen/2)
# calculate midwater trawl measures for the MIDPOINT of the trawl
sampinfo$MTRbdep[sel] <- zfromx(x=sampinfo$MTReast[sel],
maxz=SimPop$LakeInfo$BotDepMax, eastr=eastr,
ints=SimPop$LakeInfo$ints, slopes=SimPop$LakeInfo$slopes)
# random selection of midwater trawl depth
# Assign NA to those cases where bottom depth < trawl height
# These will be removed later
tooshallow <- 0+(MtHt/2) > sampinfo$MTRbdep[sel]-(MtHt/2)
sampinfo$MTRwdep[sel][tooshallow] <- NA
sampinfo$MTRwdep[sel][!tooshallow] <- runif (sum(!tooshallow), 0+(MtHt/2),
sampinfo$MTRbdep[sel][!tooshallow]-(MtHt/2))
sampinfo$MTRd2sh[sel] <- dfromx(x=sampinfo$MTReast[sel], d2shr.we=d2shr.we,
eastr=eastr)
sampinfo$MTRd2bot[sel] <- sampinfo$MTRbdep[sel] - sampinfo$MTRwdep[sel]
# make sure acoustic transect cones don't overlap
mindist.allowed <- SimPop$LakeInfo$BotDepMax*tan(angleRadians/4)
mindist.observed <- min(diff(sort(ACnorth)))
if (mindist.observed < mindist.allowed) {
warning("Acoustic transects are too close together.\nTry again with fewer acoustic transects.")
}
}
# make sure that all of the acoustic transects are kept ...
# even if they have no midwater trawls associated with them
ACsampinfo <- sampinfo[first(sampinfo$ACid)==1, 1:3]
# make sure that midwater trawl doesn't hit bottom
MTRbdep1 <- zfromx(x=sampinfo$MTReast - (MtLen/2),
maxz=SimPop$LakeInfo$BotDepMax, eastr=eastr, ints=SimPop$LakeInfo$ints,
slopes=SimPop$LakeInfo$slopes)
MTRbdep2 <- zfromx(x=sampinfo$MTReast + (MtLen/2),
maxz=SimPop$LakeInfo$BotDepMax, eastr=eastr, ints=SimPop$LakeInfo$ints,
slopes=SimPop$LakeInfo$slopes)
hits.bottom <- (MTRbdep1 - (sampinfo$MTRwdep + MtHt/2)) < 0 |
(MTRbdep2 - (sampinfo$MTRwdep + MtHt/2)) < 0
# make sure that midwater trawl doesn't extend too far east/west/north/south
extends.out <- sampinfo$ACnorth - MtWd/2 < northr[1] |
sampinfo$ACnorth + MtWd/2 > northr[2] |
sampinfo$MTReast - MtLen/2 < eastr[1] |
sampinfo$MTReast + MtLen/2 > eastr[2]
sampinfo <- sampinfo[!hits.bottom & !extends.out & !is.na(sampinfo$MTRwdep), ]
rm(ACid, ACspace, sel, ACstart, ACnorth, mindist.allowed, mindist.observed,
MTRbdep1, MTRbdep2, hits.bottom, extends.out)
# acoustic transects
# select only those targets within the volume of space sampled by
# the acoustic transect (triangular prism)
sua <- sort(unique(ACsampinfo$ACid))
AC <- data.frame(matrix(NA, nrow=0, ncol=13, dimnames=list(NULL,
c("Event", "ACid", "ACnorth", "G", "f.east", "f.north", "f.d2sh",
"f.botdep", "f.wdep", "f.d2bot", "len", "wt", "ts"))))
# acoustic slice cone cushion, based on angle of ducer and maximum depth
cushion <- SimPop$LakeInfo$BotDepMax*tan(angleRadians/2)
# create a single row of missing values that looks just like
# the "fish" data frame
# this will be used to maintain a row of information on
# an acoustic transect, even if it captures no targets
nofish <- SimPop$Fish[1, ]
ncol <- dim(SimPop$Fish)[2]
nofish[1, 1:ncol] <- rep(NA, ncol)
rm(ncol)
# acoustic transects
for(j in sua) {
# make sure that the acoustic slice does not extend further north or south
# than our sample space
if (ACsampinfo$ACnorth[j] > (northr[2] - cushion) |
ACsampinfo$ACnorth[j] < (northr[1] + cushion)) {
warning(paste("\nACid = ", ACsampinfo$ACid[j], ", ACnorth = ",
ACsampinfo$ACnorth[j],
": \nThe cone of the acoustic slice extends farther north or south \nthan the boundary of our simulated lake.",
" \nTry again using a different Seed or using fewer acoustic transects."))
}
sel <- (abs(SimPop$Fish$f.north - ACsampinfo$ACnorth[j])) <
(SimPop$Fish$f.wdep*tan(angleRadians/2))
if (sum(sel)>0) {
temp <- data.frame(
ACsampinfo[rep(j, sum(sel)), c("Event", "ACid", "ACnorth")],
SimPop$Fish[sel, ]
)
AC <- rbind(AC, temp)
} else {
temp <- data.frame(ACsampinfo[rep(j, 1), c("Event", "ACid", "ACnorth")],
nofish)
AC <- rbind(AC, temp)
}
}
rm(cushion)
# midwater trawl tows
# select only those targets within the volume of space sampled by
# the midwater trawl (rectangular prism)
sut <- sort(unique(sampinfo$MTRid))
MTRbig <- data.frame(matrix(NA, nrow=0, ncol=20, dimnames=list(NULL,
c("Event", "ACid", "ACnorth", "MTRgrp", "MTRid", "MTReast", "MTRbdep",
"MTRwdep", "MTRd2sh", "MTRd2bot", "G", "f.east", "f.north", "f.d2sh",
"f.botdep", "f.wdep", "f.d2bot", "len", "wt", "ts"))))
for(m in sut) {
j <- match(m, sampinfo$MTRid)
sel <- SimPop$Fish$f.north >= (sampinfo$ACnorth[j] - MtWd/2) &
SimPop$Fish$f.north <= (sampinfo$ACnorth[j] + MtWd/2) &
SimPop$Fish$f.east >= (sampinfo$MTReast[j] - MtLen/2) &
SimPop$Fish$f.east <= (sampinfo$MTReast[j] + MtLen/2) &
SimPop$Fish$f.wdep <= (sampinfo$MTRwdep[j] + MtHt/2) &
SimPop$Fish$f.wdep >= (sampinfo$MTRwdep[j] - MtHt/2)
if (sum(sel) >= MtMinCat) {
temp <- cbind(sampinfo[rep(j, sum(sel)), ], SimPop$Fish[sel, ])
MTRbig <- rbind(MTRbig, temp)
}
}
if (dim(MTRbig)[1] > 0) {
### select only needed number of trawls
# (remember, we sampled many more than what we needed!) ##
# create an index of trawls
mtr.indx <- MTRbig[first(MTRbig$MTRid)==1,
c("Event", "ACid", "MTRgrp", "MTRid")]
mtr.indx$mtr.grp <- unlist(lapply(table(mtr.indx$ACid), sample))
# create a random variable for sorting
y <- runif (length(mtr.indx$mtr.grp))
# sort the index of trawls by event abd group number
mtr.indx.sort <- mtr.indx[order(mtr.indx$Event, mtr.indx$mtr.grp, y), ]
# count up the number of trawls in each event
f <- first(mtr.indx.sort$Event)
x <- rep(NA, length(f))
for(ix in 1:length(f)) {
x[ix] <- if (f[ix]==1) {
1
} else {
x[ix-1]+1
}
}
mtr.indx.sort$mtr.count <- x
# keep the first MtNum trawls in each event
mtr.indx.keep <- mtr.indx.sort[mtr.indx.sort$mtr.count <= MtNum, ]
MTR <- MTRbig[MTRbig$MTRid %in% mtr.indx.keep$MTRid, ]
sampinfo.sub <- sampinfo[sampinfo$MTRid %in% mtr.indx.keep$MTRid, ]
rm(mtr.indx, y, mtr.indx.sort, f, x, mtr.indx.keep)
} else {
MTR <- MTRbig[0, ]
sampinfo.sub <- sampinfo[0, ]
}
# categorize targets interval (along transect) and layer (in water column),
# both in m
intbrks <- seq(eastr[1], eastr[2] + AcInterval - 1, AcInterval)
intmids <- intbrks[-1] - AcInterval/2
AC$interval <- intmids[cut(AC$f.east, include.lowest=TRUE, breaks=intbrks,
labels=FALSE)]
laybrks <- seq(0, SimPop$LakeInfo$BotDepMax + AcLayer - 1, AcLayer)
laymids <- laybrks[-1] - AcLayer/2
AC$layer <- laymids[cut(AC$f.wdep, include.lowest=TRUE, breaks=laybrks,
labels=FALSE)]
### diagnostic plots ###
if (PlotsPdf!=FALSE) {
sel <- AC$Event==1
ts.scaled <- (AC$ts[sel] - SimPop$FishInfo$TSRange[1])/
diff(SimPop$FishInfo$TSRange)
sua <- sort(unique(AC$ACid[sel]))
sut <- sort(unique(sampinfo.sub$MTRid[sampinfo.sub$Event==1]))
# top view of acoustic transects and midwater trawls - to scale
par(mfrow=c(1, 1), oma=rep(0, 4), mar=c(5.1, 4.1, 4.1, 2.1))
eqscplot(1, 1, type="n", xlim=eastr/1000, ylim=northr/1000, axes=FALSE,
xlab="Easting (km)", ylab="Northing (km)",
main=paste(SimPop$LakeInfo$LakeName, "- Top View - drawn to scale"))
axis(1)
axis(2, las=1)
arrows(rep(eastr[1], length(sua))/1000,
AC$ACnorth[match(sua, AC$ACid[sel])]/1000,
rep(eastr[2], length(sua))/1000,
AC$ACnorth[match(sua, AC$ACid[sel])]/1000, length=0, lwd=2, col="blue")
polygon(eastr[c(1, 2, 2, 1)]/1000, northr[c(1, 1, 2, 2)]/1000)
if (length(sut)>0) {
for(m in seq(along=sut)) {
sel2 <- sampinfo.sub$MTRid==sut[m]
polygon((sampinfo.sub$MTReast[sel2]+c(-1, 1, 1, -1)*MtLen/2)/1000,
(sampinfo.sub$ACnorth[sel2]+c(1, 1, -1, -1)*MtWd/2)/1000,
border="red", lwd=3)
}
rm(sel2)
}
# plot of each acoustic transect with outline of midwater trawl tows
par(mfrow=n2mfrow(length(sua)), oma=c(2, 2, 2, 0), mar=c(3, 3, 1, 1))
catch.tots <- table(MTR$MTRid)
for(j in seq(along=sua)) {
plot(AC$f.east/1000, -AC$f.wdep, las=1, type="n", xlab="", ylab="")
sel3 <- AC$ACid==sua[j]
points(AC$f.east[sel3]/1000, -AC$f.wdep[sel3], cex=2*ts.scaled,
col="blue")
points(AC$f.east[sel3]/1000, -AC$f.botdep[sel3], pch=16, cex=0.5)
sut2 <- sort(unique(sampinfo.sub$MTRid[sampinfo.sub$ACid==sua[j]]))
mtext(paste("id=", sua[j], "\nn=", sum(sel3), sep=""), side=1, line=-1.5,
adj=0.98, font=2, cex=par("cex"))
if (length(sut2)>0) {
for(m in seq(along=sut2)) {
sel2 <- sampinfo.sub$MTRid==sut2[m]
polygon((sampinfo.sub$MTReast[sel2]+c(-1, 1, 1, -1)*MtLen/2)/1000,
-sampinfo.sub$MTRwdep[sel2]+c(1, 1, -1, -1)*MtHt/2,
border="red", col="white")
text(sampinfo.sub$MTReast[sel2]/1000, -sampinfo.sub$MTRwdep[sel2],
catch.tots[sel2], font=2, col="red")
}
rm(sel2)
}
}
mtext("Easting (km)", side=1, outer=TRUE)
mtext("Fishing depth (m)", side=2, outer=TRUE)
mtext(paste(SimPop$LakeInfo$LakeName,
"- Acoustic Transects and Midwater Trawls"), side=3, outer=TRUE, font=2)
# plot of each midwater trawl catch
if (length(sut)>0) {
barz <- tapply(!is.na(MTR$len),
list(MTR$G, 50*floor(MTR$len/50), MTR$MTRid), sum)
barz[is.na(barz)] <- 0
barz <- barz[, , dimnames(barz)[[3]] %in% sut, drop=FALSE]
barz <- barz[apply(barz, 1, sum)>0, , , drop=FALSE]
sus <- dimnames(barz)[[1]]
par(mfrow=rev(n2mfrow(length(sut)+1)), oma=c(2, 2, 2, 0),
mar=c(3, 3, 1, 1))
for(m in seq(along=sut)) {
barplot(barz[, , m], ylim=c(0, max(apply(barz, 2:3, sum))), col=1:50,
names.arg=paste(dimnames(barz)[[2]], "+", sep=""), las=1)
mtext(paste("id=", sut[m], "\nn=", sum(barz[, , m]), sep=""), side=3,
line=-2.5, adj=0.98, font=2, cex=par("cex"))
box()
}
plot(0:1, 0:1, axes=FALSE, las=1, type="n", xlab="", ylab="")
legend("center", sus, fill=seq(sus), title="Group")
mtext("Length (mm)", side=1, outer=TRUE)
mtext("Frequency", side=2, outer=TRUE)
mtext(paste(SimPop$LakeInfo$LakeName, "- Midwater Trawl Catch"), side=3,
outer=TRUE, font=2)
rm(barz, sus)
}
if (PlotsPdf!=FALSE) graphics.off()
}
cat(paste0("Mean number of fish per acoustic transect = ",
signif(mean(table(AC$ACid)), 2), "\n"))
cat(paste0("Mean number of fish per midwater trawl tow = ",
signif(mean(table(MTR$MTRid)), 2), "\n"))
ac.out.vars <- c("Event", "ACid", "ACnorth", "interval", "layer", "f.east",
"f.north", "f.d2sh", "f.botdep", "f.wdep", "f.d2bot", "ts", "G",
"len", "wt")
# setdiff(names(AC), ac.out.vars)
# 3 variables unknown to targets: "G" "len" "wt"
mtr.out.vars <- c("Event", "ACid", "ACnorth", "MTRid", "MTReast", "MTRd2sh",
"MTRbdep", "MTRwdep", "MTRd2bot", "G", "len", "wt",
"f.east", "f.north", "f.d2sh", "f.botdep", "f.wdep", "f.d2bot", "ts")
# setdiff(names(MTR), mtr.out.vars)
# 7 variables unknown to trawl catch: "f.east" "f.north" "f.d2sh"
# "f.botdep" "f.wdep" "f.d2bot" "ts"
### only save *1* sampling event, if that is what was originally specified ###
if (nE.orig==1) {
AC <- AC[AC$Event==1, ac.out.vars]
MTR <- MTR[MTR$Event==1, mtr.out.vars]
} else {
AC <- AC[, ac.out.vars]
MTR <- MTR[, mtr.out.vars]
}
SurvParam <- c(NumEvents=NumEvents, AcNum=AcNum, AcInterval=AcInterval,
AcLayer=AcLayer, AcAngle=AcAngle, MtNum=MtNum, MtHt=MtHt, MtWd=MtWd,
MtLen=MtLen, MtMinCat=MtMinCat, MtMulti=MtMulti, Seed=Seed)
list(SurvParam=SurvParam, Targets=AC, MtCatch=MTR)
}
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