mdd,FLQuant,FLPar-method | R Documentation |
Lorenzen natural mortality relationship where M is a function of weight, modified to explicitly included M as a function of numbers in a cohort, i.e. density dependence
## S4 method for signature 'FLQuant,FLPar'
mdd(object, params, scale, k = 1, m = gislason)
object |
mass at which M is to be predicted |
params |
an |
scale |
reference |
k |
rate of change in density dependence |
m |
function with mortality model, by default gisalson |
... |
other arguments, such as scale, e.g. stock numbers now relative to a reference level, e.g. at virgin biomass and k steepness of relationship |
@details
The Lorenzen natural mortality relationship is a function of mass-at-age i.e. M=a*wt^b
The relationship can be explained by population density, since as fish grow they also die and so there is potentially less competition for resources between larger and older fish. Density dependence can be modelled by a logistic function, a sigmoid curve (or S shaped) curve, with equation
f(x)=L/(1+exp(-k(x-x0)))
where e is the natural logarithm base (also known as Euler's number), x0 is the x-value of the sigmoid's midpoint, L is the curve's maximum value, and k the steepness of the curve.
Combining the two functions gives
M=aL/(1+exp(-k(n-ref)))*wt^b;
lorenzen
## Not run:
library(FLBRP)
library(FLife)
data(teleost)
par=teleost[,"Hucho hucho"]
par=lhPar(par)
hutchen=lhEql(par)
scale=stock.n(hutchen)[,25]%*%stock.wt(hutchen)
scale=(stock.n(hutchen)%*%stock.wt(hutchen)%-%scale)%/%scale
m=mdd(wt2len(stock.wt(hutchen),par),params=par,scale,k=.9)
ggplot(as.data.frame(m))+
geom_line(aes(age,data,col=factor(year)))+
theme(legend.position="none")+
scale_x_continuous(limits=c(0,15))
m=mdd(stock.wt(hutchen),params=FLPar(m1=3,m2=-0.288),scale,k=1.2,m=lorenzen)
library(FLife)
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.