DispersalPerRecruitModel {ConnMatTools}  R Documentation 
Population dynamics model based on lifetimeeggproduction
Description
This function implements the marine population dynamics model described in Kaplan et al. (2006). This model is most appropriate for examining equilibrium dynamics of agestructured populations or temporal dynamics of semelparous populations.
Usage
DispersalPerRecruitModel(
LEP,
conn.mat,
recruits0,
timesteps = 10,
settler.recruit.func = hockeyStick,
...
)
Arguments
LEP 
a vector of lifetimeeggproduction (LEP; also known as eggsperrecruit (EPR)) for each site. 
conn.mat 
a square connectivity matrix. 
recruits0 
a vector of initial recruitment values for each site. 
timesteps 
a vector of timesteps at which to record egg production, settlement and recruitment. 
settler.recruit.func 
a function to calculate recruitment from the
number of settlers at each site. Defaults to 
... 
additional arguments to settler.recruit.func. Typically

Value
A list with the following elements:
eggs 
egg production for
the timesteps in 
settlers 
Similar for settlement 
recruits 
Similar for recruitment 
Author(s)
David M. Kaplan dmkaplan2000@gmail.com
References
Kaplan, D. M., Botsford, L. W., and Jorgensen, S. 2006. Dispersal per recruit: An efficient method for assessing sustainability in marine reserve networks. Ecological Applications, 16: 22482263.
See Also
See also BevertonHolt
, hockeyStick
Examples
library(ConnMatTools)
data(chile.loco)
# Get appropriate collapse slope
# critical.FLEP=0.2 is just an example
slope < settlerRecruitSlopeCorrection(chile.loco,critical.FLEP=0.2)
# Make the middle 20 sites a reserve
# All other sites: scorched earth
n < dim(chile.loco)[2]
LEP < rep(0,n)
nn < round(n/2)9
LEP[nn:(nn+19)] < 1
Rmax < 1
recruits0 < rep(Rmax,n)
# Use DPR model
ret < DispersalPerRecruitModel(LEP,chile.loco,recruits0,1:20,slope=slope,Rmax=Rmax,
settler.recruit.func=BevertonHolt)
image(1:n,1:20,ret$settlers,xlab="sites",ylab="timesteps",
main=c("Settlement","click to proceed"))
locator(1)
plot(ret$settlers[,20],xlab="sites",ylab="equilibrium settlement",
main="click to proceed")
locator(1)
# Same, but with a uniform Laplacian dispersal matrix and hockeyStick
cm < laplacianConnMat(n,10,0,"circular")
ret < DispersalPerRecruitModel(LEP,cm,recruits0,1:20,slope=1/0.35,Rmax=Rmax)
image(1:n,1:20,ret$settlers,xlab="sites",ylab="timesteps",
main=c("Settlement","click to proceed"))
locator(1)
plot(ret$settlers[,20],xlab="sites",ylab="equilibrium settlement")