calc_LFI: Calculate community indicators
In LeMaRns: Length-Based Multispecies Analysis by Numerical Simulation

Description Usage Arguments Value See Also Examples

Calculates the Large Fish Indicator (LFI), Typical Length (TyL) or Length Quantile (LQ) for the community or a subset of the species.

calc_LFI(wgt, N, bry, prop)

calc_LQ(wgt, u_bound, N, prob)

calc_TyL(wgt, mid, N)

`wgt`	A matrix with dimensions `nsc` and `nfish` representing the weight of each species in each length class.
`N`	A matrix with dimensions `nsc` and `nfish` representing the number of individuals in each length class for the current time step.
`bry`	A numeric vector representing the length classes that are larger than `length_LFI`.
`prop`	A numeric value between 0 and 1 representing how far along, as a proportion, the value of the large indicator threshold is in the length class that contains it.
`u_bound`	A numeric vector of length `nsc` representing the upper bounds of the length classes.
`prob`	A numeric value or vector between 0 and 1 denoting the LQ to be calculated.
`mid`	A numeric vector of length `nfish` representing the mid-point of the length classes in the model.

calc_LFI returns a numeric value or vector representing the proportion of the biomass in the length classes above bry and in prop of the biomass in the length class bry.

calc_TYL returns a numeric value or vector representing the biomass-weighted geometric mean length of the community.

calc_LQ returns a numeric value or vector representing the length at which biomass exceeds a given proportion prob of the total biomass.

get_indicators

# Set up and run the model
NS_params <- LeMansParam(NS_par, tau=NS_tau, eta=rep(0.25, 21), L50=NS_par$Lmat, other=1e11)
effort <- matrix(0.5, 10, dim(NS_params@Qs)[3])
model_run <- run_LeMans(NS_params, years=10, effort=effort)

# Calculate the LFI for 40cm
bry <- which(NS_params@l_bound<=40 & NS_params@u_bound>40)
length_LFI <- 40
prop <- (length_LFI-NS_params@l_bound[bry])/(NS_params@u_bound[bry]-NS_params@l_bound[bry])
LFI <- calc_LFI(model_run@N[,,101], NS_params@wgt, bry, prop)

# Calculate TyL for the final time step
calc_TyL(wgt=NS_params@wgt, mid=NS_params@mid, N=model_run@N[,,101])

# Calculate the LQ for the final time step
calc_LQ(wgt=NS_params@wgt, u_bound=NS_params@u_bound, N=model_run@N[,,101], prob=0.5)