| simulate_bus_route {AnglerCreelSurveySimulation} | R Documentation |
Simulate a bus route survey
Description
This function uses the output from make_anglers and
get_total_values to conduct a bus-route or traditional access point
creel survey of the population of anglers from make_anglers and
provide clerk-observed counts of anglers and their effort.
Usage
simulate_bus_route(start_time, wait_time, n_anglers, n_sites,
sampling_prob = 1, mean_catch_rate, ...)
Arguments
start_time |
The start time of the surveyor at each site. This can be a
vector of start times to simulate a bus route or one |
wait_time |
The wait time of the surveyor at each site. This can be a
vector of wait times to simulate a bus route or one |
n_anglers |
the number of anglers at each site, either a vector or a single number for single sites |
n_sites |
The number of sites being visited. |
sampling_prob |
What is the sampling probability for the survey? If all
sites will be visited during the first or second half of the fishing day,
|
mean_catch_rate |
The mean catch rate for the fishery |
... |
Arguments to be passed to other subfunctions, specifically to the
|
Details
Effort and catch are estimated from the the Bus Route Estimator equation in Robson and Jones (1989), Jones and Robson (1991; eqn. 1) and Pollock et al. 1994.
Catch rate is calculated from the Ratio of Means equation (see Malvestuto (1996) and Jones and Pollock (2012) for discussions).
The Ratio of means is calculated by
\widehat{R_1} = \frac{\sum\limits_{i=1}^n{c_i/n}}{\sum\limits_{i=1}^n{L_i/n}}
where c_i is the catch for the i^{th} sampling unit
and L_i is thelength of the fishing trip at the time of the
interview. For incomplete surveys, L_i represents in incomplete
trip.
The bus route estimator is
\widehat{E} = T\sum\limits_{i=1}^n{\frac{1}{w_{i}}}\sum\limits_{j=1}^m{\frac{e_{ij}}{\pi_{j}}}
where E = estimated total party-hours of effort; T = total time
to complete a full circuit of the route, including travelling and waiting;
w_i = waiting time at the i^{th} site
(where i = 1, ..., n sites); e_{ij} =
total time that the j^{th} car is parked at the i^{th}
site while the agent is at that site (where j = 1, ..., n sites).
Value
Estimate catch (Ehat), the catch rate calculated by the ratio of means,
the true, observed catch, and the actual catch rate (mean_lambda).
Author(s)
Steven H. Ranney
References
Jones, C. M., and D. Robson. 1991. Improving precision in angler surveys: traditional access design versus bus route design. American Fisheries Society Symposium 12:177-188.
Jones, C. M., and K. H. Pollock. 2012. Recreational survey methods: estimation of effort, harvest, and released catch. Pages 883-919 in A. V. Zale, D. L. Parrish, and T. M. Sutton, editors. Fisheries Techniques, 3rd edition. American Fisheries Society, Bethesda, Maryland.
Malvestuto, S. P. 1996. Sampling the recreational creel. Pages 591-623 in B. R. Murphy and D. W. Willis, editors. Fisheries techniques, 2nd edition. American Fisheries Society, Bethesda, Maryland.
Pollock, K. H., C. M. Jones, and T. L. Brown. 1994. Angler survey methods and their applications in fisheries management. American Fisheries Society, Special Publication 25, Bethesda, Maryland.
Robson, D., and C. M. Jones. 1989. The theoretical basis of an access site angler survey design. Biometrics 45:83-98.
See Also
Examples
# To simulate one bus route survey that takes place in the morning, these values are used
#start time at access sites
startTimeAM <- c(1, 2,3,4,5)
#wait time at access sites
waitTimeAM <- c(.5, .5, .5, .5, 2)
#the number of anglers that will visit access site throughout the day
nanglersAM <- c(10,10,10,10,50)
# the number of sites to be visited
nsitesAM <- 5
# the sampling probability. Here it is .5 because we are only conducting this
# survey during the first 50% of the fishing day
sampling_prob <- .5
# the mean catch rate. Here it is 2.5 which equals 2.5 fish/hour
mean_catch_rate <- 2.5
simulate_bus_route(start_time = startTimeAM, wait_time = waitTimeAM, n_anglers = nanglersAM,
n_sites = nsitesAM, sampling_prob = sampling_prob, mean_catch_rate = mean_catch_rate)
# To simulate one traditional access point survey where the creel clerk arrives,
# counts anglers, and interviews anglers that have completed their trips
start_time = 0.001
wait_time = 8
#nanglers can be informed by previously-collected data
n_anglers = 1000
n_sites = 1
# sampling probability here is 8/12 because we are staying at the access site
# for 8 hours of a 12-hour fishing day. To adjust the fishing day length, an
# additional 'fishing_day_length' argument needs to be passed to this function.
sampling_prob <- (8/12)
# the mean catch rate.
mean_catch_rate <- 5
simulate_bus_route(start_time, wait_time, n_anglers, n_sites, sampling_prob, mean_catch_rate)