Description Usage Arguments Value Examples
This function is used to calculate the effect size and timing of the next stochastic population disturbance in a SHAPE run.
1 2 3  compute_distGrowth(func_distFactor, func_growthType, func_distType,
func_growthRate, func_popSize, func_focalSize,
func_manualGenerations = NULL, func_stepDivs)

func_distFactor 
This is the expected effect size of the disturbance, it should be a named vector with elements factor, random which are each used as per the func_distType 
func_growthType 
This is the growth model of the SHAPE run 
func_distType 
This is the type of disturbance to be simulated. Currently I've implemented bottleneck, random options for constant bottlenecks or normally distributed random effect sizes 
func_growthRate 
This is the basal growth rate of the SHAPE run 
func_popSize 
This is a vector of the number of individuals in each of the populations 
func_focalSize 
This only matters if the growth model is exponential in which case the disturbance is always such that the community size is reduced to the func_focalSize value 
func_manualGenerations 
If not NULL, it will be rounded to an integer value and taken as the manually controlled number of generations between disturbances. Otherwise, the disturbance factor and growth rate are used to estimate the number of steps required for a community with relative fitness 1 to rebound. 
func_stepDivs 
This is the value that controls what proportion of a standard biological "generation" is simulated in each step of a SHAPE run. 
A named vector with three elements describing the simulated reduction factor of populations, the number of individuals lost, and the number of steps estimated until the next disturbance.
1 2 3 4 5 6 7 8 9 10 11  # This calculates the information for the next planned stochastic disturbance event.
# Consider a situation where there is a disturbance reducing populations 100 fold,
# and it occurs either in a proscriptive number of steps, or we calculate it based
# on recovery time as per the growth rate and growth model parameters.
compute_distGrowth("bottleneck","exponential","bottleneck",
2,1e4,1e2,5,1)
compute_distGrowth("bottleneck","exponential","bottleneck",
2,1e4,1e2,NULL,1)
# If growth is constant or Poisson, then disturbances are effectively supressed
compute_distGrowth("bottleneck","poisson","bottleneck",
2,1e4,1e2,NULL,1)

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