get_total_values {AnglerCreelSurveySimulation}

Conduct a creel survey of a population of anglers at an access site.

Description

This function uses the output from make_anglers 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

get_total_values(
  data,
  start_time = NULL,
  end_time = NULL,
  wait_time = NULL,
  sampling_prob = 1,
  mean_catch_rate = NULL,
  scale = 1,
  ...
)

Arguments

data

The dataframe returned from make_anglers

start_time

The start time of the clerk.

end_time

the end time of the clerk.

wait_time

the wait time of the clerk.

sampling_prob

The sampling probability of the survey. The default is 1 but will need to be changed if the survey is conducted during only half of the fishing day (i.e., .5) or over longer time periods (e.g., 9.5/12, if the survey is 9.5 hours long and the fishing day length is 12 hours)

mean_catch_rate

The mean catch rate for the fishery.

scale

The scale parameter must be positive and is passed to the rgamma function to randomly generate angler trip lengths

...

Arguments to be passed to other functions.

Details

Total effort is the sum of the trip lengths from data

The total number of anglers is equal to the nrow() of the dataframe in data

Catch rates are assigned to anglers based upon the Gamma distribution with a mean of mean_catch_rate

If both end_time=NULL and wait_time=NULL then wait_time will be 0.5 (one-half hour). If a value is passed to end_time, then wait_time becomes end_time - start_time.

If start_time=NULL, then a start_time is generated from the uniform distribution between 0 and 11.5 hours into the fishing day.

If end_time=NULL, then end_time = start_time+wait_time

Incomplete trip effort is observed two ways: 1) by counting anglers that were at the site for the entire time that the surveyor was at the site and 2) counting anglers that arrived after the surveyor arrived at the site but remained at the site after the surveyor left. These anglers are counted and their effort calculated based upon surveyor start_time and end_time.

Completed trip effort is observed two ways: 1) by interviewing anglers that left while the surveyor was at the site. The surveyor can determine effort and catch. 2) by interviewing anglers that both arrived and departed while the surveyor was on site. When wait_time is short, these cases are are rare; however, when wait_time is long (e.g., all day), then these cases are much more likely.

Trip lengths of observed trips (both incomplete and complete) are scaled by the sampling_prob value. The sampling_prob is used to estimate effort and catch.

Author(s)

Steven H. Ranney

References

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.

Examples

library(dplyr)   
set.seed(256)

start_time = .001 #start of fishing day
end_time = 12 #end of fishing day
mean_catch_rate = 0.1 #this will cause VERY few fish to be caught!

make_anglers(100) %>%  
  get_total_values(start_time = start_time, 
                   end_time = end_time, mean_catch_rate = mean_catch_rate)

start_time = .001 #start of fishing day
end_time = 6 #halfway through the fishing day
sampling_prob = .5 #this needs to be .5 because we are sampling only 50% of the fishing day
mean_catch_rate = 0.1 #this will cause VERY few fish to be caught!

make_anglers(100) %>%  
  get_total_values(start_time = start_time, end_time = end_time, 
                   sampling_prob = sampling_prob, mean_catch_rate = mean_catch_rate)