Implements a framework to generate individual-level estimates of fish productivity, with a focus on reef fishes. This individual approach works by combining estimates of somatic growth through the von Bertalanffy Growth Model, and deterministic or stochastic natural mortality using instantaneous mortality rates.
You can install rfishprod from GitHub with:
devtools::install_github("renatoamorais/rfishprod")To get
devtools,
simply use install.packages("devtools").
Although not a dependency, I recommend using R 3.6.3 or higher. This is
mainly because xgboost,
the machine behind internal prediction, demands R 3.6.3 in its most
recent version.
library(rfishprod)
# Check dataset repdata #
(repdata <- rfishprod:::repdata)
# Getting levels ready #
repdata <- tidytrait (repdata, db)
# Formula from Morais and Bellwood (2018) #
fmod <- formula (~ sstmean + MaxSizeTL + Diet + Position + Method)
# Predicting Kmax, the standardised VBGF parameter (Recommendation: use 100s to 1000s iterations) #
datagr <- predKmax (repdata,
dataset = db,
fmod = fmod,
niter = 10,
return = 'pred')
datagr <- datagr$pred
# Predicting M/Z: the instantaneous mortality rate (Recommendation: see help file for) #
datagr$Md <- with (datagr,
predM (Lmeas = Size,
Lmax = MaxSizeTL,
Kmax = Kmax,
method = 'Gislason'))
# Positioning your fish in their growth trajectory #
# aka. what's the size they're supposed to have on the next day? #
with (datagr, applyVBGF (Lmeas = Size,
Lmax = MaxSizeTL,
Kmax = Kmax))
# Compare with their size on the previous day #
datagr$Size
# Estimating gross somatic growth (g) #
with(datagr, somaGain (a = a,
b = b,
Lmeas = Size,
Lmax = MaxSizeTL,
Kmax = Kmax))
# Applying stochastic mortality #
applyMstoch (datagr$Md)
# Alternatively, estimating per capita mass loss due to mortality #
with(datagr, somaLoss (M = Md,
Lmeas = Size,
a = a,
b = b))Please, if you’re using rfishprod, the relevant citation for the
package can be obtained from:
citation("rfishprod")Please report issues or
bugs or shoot me an
email (just hit ?rfishprod)