# SimulateBusRoute: Simulate a bus route survey In stevenranney/creelSurvey: Simulate a Bus Route Creel Survey of Anglers

## Description

This function uses the output from MakeAnglers and GetTotalValues to conduct a bus-route or traditional access point creel survey of the population of anglers from MakeAnglers and provide clerk-observed counts of anglers and their effort.

## Usage

 1 2 SimulateBusRoute(startTime, waitTime, nanglers, nsites, samplingProb = 1, meanCatchRate, ...) 

## Arguments

 startTime The start time of the surveyor at each site. This can be a vector of start times to simulate a bus route or one startTime to simulate a traditional access survey. waitTime The wait time of the surveyor at each site. This can be a vector of wait times to simulate a bus route or one waitTime to simulate a traditional access survey. nanglers The number of anglers at each site, either a vector or a single number. nsites How many sites are being visited? samplingProb What is the sampling probability for the survey? If all sites will be visited during the first or second half of the fishing day, samplingProb=0.5. If the survey will take the entire fishing day, then samplingProb=1. meanCatchRate The mean catch rate for the fishery. ... Arguments to be passed to other subfunctions, specifically to the MakeAnglers function, including meanTripLength and fishingDayLength.

## Details

Effort and catch are estimated from the the Bus Route Estimator equation in Robson and Jones (1989), Jones and Robson (1991) 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{∑\limits_{i=1}^n{c_i/n}}{∑\limits_{i=1}^n{L_i/n}}

where c_i is the catch for the i^{th} sampling unit and L_i is the length 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∑\limits_{i=1}^n{\frac{1}{w_{i}}}∑\limits_{j=1}^m{\frac{e_{ij}}{π_{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).

Steven 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.

MakeAnglers GetTotalValues
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 # 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 samplingProb <- .5 # the mean catch rate. Here it is 2.5 which equals 2.5 fish/hour meanCatchRate <- 2.5 SimulateBusRoute(startTimeAM, waitTimeAM, nanglersAM, nsitesAM, samplingProb, meanCatchRate) # To simulate one traditional access point survey where the creel clerk arrives, # counts anglers, and interviews anglers that have completed their trips startTime = 0.001 waitTime = 8 #nanglers can be informed by previously-collected data nanglers = 1000 nsites = 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 'fishingDayLength' argument needs to be passed to this function. samplingProb <- (8/12) # the mean catch rate. meanCatchRate <- 5 SimulateBusRoute(startTime, waitTime, nanglers, nsites, samplingProb, meanCatchRate)