Description Usage Arguments Details Value References See Also Examples
Combine survey data from acoustic transects and midwater trawl tows (created
by SampFish
).
Apply availability to the acoustic data and catchability (availability and
selectivity) to the midwater trawl catch.
1 2 |
SimPop |
A list with elements |
AcMtSurv |
A list with elements |
AcExcl |
A numeric vector of length 2, depth of acoustic "dead" zones at the surface and at the bottom (in m), default of c(0, 0) represents 100% acoustic availability of fish. |
MtExcl |
A numeric vector of length 2, depth of zones unfishable with the midwater trawl at the surface and at the bottom (in m), default of c(0, 0) represents 100% midwater trawl availability of fish. |
PanelProps |
A numeric vector of length 4, size of the different mesh panel zones of the
midwater trawl, mouth (outermost), middle, aft, and cod (inner),
default c(0.4, 0.3, 0.2, 0.1).
Sizes are expressed as proportions of the distance from the outer edge of
the trawl to the trawl center in both the vertical and horizontal
directions, and they should add up to 1. Use |
SelecParam |
A data frame with 6 columns in which each row provides the midwater trawl
selectivity parameters for a given fish group and mesh panel zone.
All columns must be completely filled in (no missing values).
Selectivity is assumed to be 100% for any group-zone combination not
represented as a row in the data frame.
For 100% selectivity of small fish, use
|
Seed |
An integer scalar, starting seed for stochasticity incorporated in acoustic
and midwater trawl catchability.
Use |
A classification tree is used to relate the catch composition of the midwater trawl to the location of the trawl in the lake (e.g., MTReast, ACnorth, MTRd2sh, MTRbdep). This tree is then used to assign a single midwater trawl catch to each acoustic cell (interval x layer), such that the estimated acoustic densities can be assigned to specific fish groups (species, life stages). See, for example, Yule et al. (2013).
A data frame with estimated fish density (in number per ha) and biomass (in kg per ha) for each sampling event and group (species, lifestage).
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. http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2013-0072#.U1KYxPldXTQ
SimFish
, SampFish
, ViewZones
,
TuneSelec
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | # 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)
# 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)
selec <- data.frame(
G = c("A", "a", "A", "a", "A", "a"),
Zone = c("mouth", "mouth", "middle", "middle", "aft", "aft"),
MtL50Small = c(100, 100, 60, 60, 30, 30),
MtSlopeSmall = c(40, 40, 30, 30, 20, 20),
MtL50Large = c(180, 180, Inf, Inf, Inf, Inf),
MtSlopeLarge = c(20, 20, 100, 100, 100, 100)
)
AcMtEst(SimPop=res, AcMtSurv=surv, Seed=927)
AcMtEst(SimPop=res, AcMtSurv=surv, AcExcl=c(5, 10),
MtExcl=c(2, 2), SelecParam=selec, Seed=204)
|
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