| leslie.sim {FSA} | R Documentation |
Constucts hypothetical catch and effort data given choices for removal events, initial population size, effort, catchability, survival, and recruitment for a hypothetical depletion fishery. Catch-per-unit effort is then plotted against total catch (i.e., a Leslie model) and slider bars can be used to change parameter values. This function can be used to explore the effects of a parameter on the model.
leslie.sim(use.rand=FALSE,ricker.mod=FALSE)
use.rand |
A logical indicating whether randomization should be incorporated into the model. See details. |
ricker.mod |
A boolean value indicating whether to use the modification proposed by Ricker (=TRUE) or not (=FALSE, default). |
A slider object is created from which the parameters of the model can be changed. The number of removal events, initial population size, effort, and catchability are defined as usual with details in the FSA text. The remaining “parameter” choices on the slider are specific to modeling assumption violations. Each of these items is described below. The range of values allowed for each of the parameters were chosen to allow a wide variety of model values. However, it is highly likely that these ranges do not encompass every possible set of values that a user may wish to view.
The ‘q factor’ value is a constant that modifies the catchability coefficient for each subsequent sample. For example, if ‘q.factor’ is set to 0.8 then the catchability decreases by a constant multiplier of 0.8 for each sample. In other words, the catchability set with the catchability slider is multiplied by the vector c(1,0.8,0.8^2,0.8^3,...) to determine a catchability for each sample.
The ‘Survival’ value is a constant used as a proportion of fish alive at time t that survive to time t+1 or, if use.rand=TRUE, is the probability that a fish survives from time t to time t+1. The survival function is applied to the population after the catch at time t has alread been removed from the population.
The ‘Recruitment’ value is a constant used to determine the number of “new” fish to recruit to the population from time t to time t+1. The number to recruit is equal to the recruitment portion of the extant number of fish alive at time t. For example, if 100 fish are alive at time t and the recruitment factor is 0.2 then 100*0.2=20 fish will be added to the population just before time t+1. The number of fish to recruit is computed after the catch at time t and any natural mortality at time t have been removed from the population.
Different plots are presented depending on whether use.rand=TRUE or use.rand=FALSE. If randomization is not used then a deterministic plot is shown by as many as three lines. The gray line is the Leslie model for the default values (these values can be returned to by using the ‘Reset’ button). This line is used simply as a basis for examining parameter changes. The blue line is the Leslie model for current choices of ‘Removals’, ‘Initial Size’, ‘Effort’, and ‘Catchability’ but NOT for ‘q factor’, ‘Survival’, or ‘Recruitment’. In other words, the blue line reflects the model for other than default parameter choices but NO assumption violations. This line serves as a basis for different parameter choices without assumption violations. The red line is the Leslie model for all current choices of parameters in the slider box. The lines are plotted in the order of “gray”, “red”, “blue” so, if any two are equal then the color first plotted will not be seen.
if use.rand=TRUE then binomial stochasticity is added to the model in the catch and survival functions. In this situation the graphic is simply the traditional Leslie model graphic with the “random” catch-per-unit-effort values plotted against total catch with a best-fit linear regression line shown in blue. The current estimaes of q and No are also printed on the graph. Finally, a ‘Re-Randomize’ button is included on the slider when use.rand=TRUE is used. Pressing this button will keep the slider choices the same as the current values but will evaluate the model using a different random seed.
The slider object has a tendency to “disappear” when focus is put on the plot. The slider object can be brought back to the foreground by finding the object listed on the Windows toolbar. The slider object should be “exit”ed when finished exploring the model.
None. An interactive graphic is produced.
Derek H. Ogle, dogle@northland.edu
## Not run: ## These examples cannot be run by examples() but should work in an interactive R session ## Not run: leslie.sim() # Deterministic exploration of model dynamics ## Not run: leslie.sim(use.rand=TRUE) # Stochastic exploration of model dynamics