leslieSim {FSATeach} | R Documentation |

Constucts hypothetical catch and effort data given choices for number of removal events, initial population size, effort, catchability, survival, and recruitment for a hypothetical depletion fishery. Various plots are produced (see details) with corresponding slider bars that allow the user to modify parameter values to explore the effect of these modifications on the Leslie model dynamics or results. This function is used primarily to explore the effects of a parameter on the model and the effects of assumptions violations on modely dynamics.

leslieSim(type = c("deterministic", "random", "resampling", "montecarlo"), ricker.mod = FALSE, rsmpls = 100)

`type` |
A single string indicating the type or version of simulation that should be used. See the details. |

`ricker.mod` |
A single logical value that indicates
whether the modification proposed by Ricker should be
used ( |

`rsmpls` |
A single numeric for the number of simulations to run. |

Three versions of the simulation are allowed. First, the
model is purely ** deterministic** (i.e.,
without randomization) such that the shape of the Leslie
model can be easily explored. Second, the model includes
a

`random`

`rsmpls`

times, the q and No are computed
from each resample, and these results are plotted. This
version, called the `resampling`

In the `random`

and `resampling`

versions,
randomness is included in the model by including binomial
stochasticity in the catch and survival functions.
Specifically, the number captured is, effectively,
computed by assigning a uniform random number from
between 0 and 1 to each individual in the population and
then “catching” those individuals where this value
is less than q*E (where E is effort expended). A similar
method is used for survivorship, but with q*E replaced
with the user chosen probability of survival. For each
version, a set of plots is produced that are linked to a
set of slider bars that allows the user to change model
parameters or create assumption violations. A slider is
created to control the number of removal events, initial
population size (No), effort, and catchability coefficent
(q). The remaining sliders allow for simulating specific
violations to the assumption of a Leslie model. These
sliders are further described below.

The **‘q factor’** value is a constant that
modifies the catchability coefficient (q) 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 already 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 proportion 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.

None. An interactive graphic with corresponding slider
bars, which differs depending on the version (as defined
by `type`

) of simulation used, is produced.

In the deterministic and random versions a plot of
catch-per-unit-effort (CPE) against cumulative catch
(i.e., the Leslie plot) is displayed. In the
deterministic version, as many as three lines may be
seen. The gray line is the Leslie model for the default
values from the slider bars. This line is used simply as
a basis for examining changes in parameters. 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 with NO
assumption violations. This line serves as a basis for
judging different parameter choices without any
assumption violations. The red line is the Leslie model
for all current choices of sliders. 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.

In the random version, the graphic is simply the
traditional Leslie model graphic (see
`depletion`

) 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 from the random data are
also printed on the graph. A **‘Re-Randomize’**
button is included with the sliders which can be used to
evaluate the model again (with a different random seed)
at the current slider choices.

In the resampling version, three side-by-side graphs will be produced. The left-most graph is a histogram of the estimates of the initial population size (No) from all resamples. The middle graph is a histogram of the estiamtes of the catchability coefficient from all resamples. Both histograms will have a vertical red dashed line at the true value of the parameter (No or q, as provided by the user) and a vertical blue solid line at the mean value of the estimate from all resamples. The right-most graph is a scatterplot of the paired catchability and initial population size estimates with red lines showing the true values of the catchability and initial population size and blue lines at the means of the respective estimates.

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. Thus, this simulation should not be used for research-grade simulations.

if (interactive()) { ## Deterministic exploration of model dynamics leslieSim() ## Stochastic exploration of model dynamics -- Leslie model plot leslieSim(type="random") ## Stochastic exploration of model dynamics -- sampling distribution plots leslieSim(type="resampling") } # end if interactive

[Package *FSATeach* version 0.0.1 Index]