ypr isan R package that implements equilibrium-based yield per recruitmethods. Yield per recruit methods can used to estimate the optimalyield (Walters and Martell 2004) of a fish population with one or moreecotypes. The yield can be based on the number of fish caught (orharvested) or biomass for all fish or just large (trophy)individuals.
The key life history parameters are
k) and mean maximum length(Linf) from the Von Bertalanffy growth curveLs)Lv)n)Rk)and in the case of ecotypes
RPR)The calculations do not account for stochasticity, predator-preydynamics, angler responses or density-dependent growth.
To install the latest release fromCRAN
install.packages("ypr")To install the developmental version fromGitHub
# install.packages("remotes")remotes::install_github("poissonconsulting/ypr")library(ypr)population<-ypr_population(Rk =5,Ls =50,Rmax =100,rho =0.6)ypr_plot_schedule(population,x ="Length",y ="Spawning")
ypr_tabulate_schedule(population)#> # A tibble: 20 × 11#> Age Length Weight Fecundity Spawning NaturalMor…¹ Vulner…² Reten…³ Fishin…⁴#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 1 13.9 27.0 27.0 3.13e-56 0.2 3.13e-56 0.4 2.50e-57#> 2 2 25.9 174. 174. 2.91e-29 0.2 2.91e-29 0.4 2.33e-30#> 3 3 36.2 476. 476. 1.04e-14 0.2 1.04e-14 0.4 8.35e-16#> 4 4 45.1 918. 918. 3.46e- 5 0.2 3.46e- 5 0.4 2.77e- 6#> 5 5 52.8 1469. 1469. 9.95e- 1 0.2 9.95e- 1 0.4 7.96e- 2#> 6 6 59.3 2090. 2090. 1.00e+ 0 0.2 1.00e+ 0 0.4 8.00e- 2#> 7 7 65.0 2747. 2747. 1.00e+ 0 0.2 1.00e+ 0 0.4 8.00e- 2#> 8 8 69.9 3412. 3412. 1.00e+ 0 0.2 1.00e+ 0 0.4 8.00e- 2#> 9 9 74.1 4065. 4065. 1 e+ 0 0.2 1 e+ 0 0.4 8 e- 2#> 10 10 77.7 4689. 4689. 1 e+ 0 0.2 1 e+ 0 0.4 8 e- 2#> 11 11 80.8 5274. 5274. 1 e+ 0 0.2 1 e+ 0 0.4 8 e- 2#> 12 12 83.5 5816. 5816. 1 e+ 0 0.2 1 e+ 0 0.4 8 e- 2#> 13 13 85.8 6310. 6310. 1 e+ 0 0.2 1 e+ 0 0.4 8 e- 2#> 14 14 87.8 6758. 6758. 1 e+ 0 0.2 1 e+ 0 0.4 8 e- 2#> 15 15 89.5 7160. 7160. 1 e+ 0 0.2 1 e+ 0 0.4 8 e- 2#> 16 16 90.9 7518. 7518. 1 e+ 0 0.2 1 e+ 0 0.4 8 e- 2#> 17 17 92.2 7836. 7836. 1 e+ 0 0.2 1 e+ 0 0.4 8 e- 2#> 18 18 93.3 8116. 8116. 1 e+ 0 0.2 1 e+ 0 0.4 8 e- 2#> 19 19 94.2 8363. 8363. 1 e+ 0 0.2 1 e+ 0 0.4 8 e- 2#> 20 20 95.0 8580. 8580. 1 e+ 0 0.2 1 e+ 0 0.4 8 e- 2#> # … with 2 more variables: Survivorship <dbl>, FishedSurvivorship <dbl>, and#> # abbreviated variable names ¹NaturalMortality, ²Vulnerability, ³Retention,#> # ⁴FishingMortality#> # ℹ Use `colnames()` to see all variable namesypr_plot_fish(population,color ="white")ypr_tabulate_fish(population)#> # A tibble: 20 × 8#> Age Survivors Spawners Caught Harvested Released HandlingMortal…¹ Ecotype#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>#> 1 1 70.1 2.19e-54 4.39e-55 1.76e-55 2.63e-55 0 <NA>#> 2 2 56.1 1.63e-27 3.26e-28 1.30e-28 1.96e-28 0 <NA>#> 3 3 44.9 4.68e-13 9.36e-14 3.74e-14 5.62e-14 0 <NA>#> 4 4 35.9 1.24e- 3 2.48e- 4 9.93e- 5 1.49e- 4 0 <NA>#> 5 5 28.7 2.86e+ 1 5.72e+ 0 2.29e+ 0 3.43e+ 0 0 <NA>#> 6 6 21.1 2.11e+ 1 4.23e+ 0 1.69e+ 0 2.54e+ 0 0 <NA>#> 7 7 15.6 1.56e+ 1 3.11e+ 0 1.24e+ 0 1.87e+ 0 0 <NA>#> 8 8 11.5 1.15e+ 1 2.29e+ 0 9.16e- 1 1.37e+ 0 0 <NA>#> 9 9 8.43 8.43e+ 0 1.69e+ 0 6.74e- 1 1.01e+ 0 0 <NA>#> 10 10 6.20 6.20e+ 0 1.24e+ 0 4.96e- 1 7.44e- 1 0 <NA>#> 11 11 4.57 4.57e+ 0 9.13e- 1 3.65e- 1 5.48e- 1 0 <NA>#> 12 12 3.36 3.36e+ 0 6.72e- 1 2.69e- 1 4.03e- 1 0 <NA>#> 13 13 2.47 2.47e+ 0 4.95e- 1 1.98e- 1 2.97e- 1 0 <NA>#> 14 14 1.82 1.82e+ 0 3.64e- 1 1.46e- 1 2.18e- 1 0 <NA>#> 15 15 1.34 1.34e+ 0 2.68e- 1 1.07e- 1 1.61e- 1 0 <NA>#> 16 16 0.986 9.86e- 1 1.97e- 1 7.89e- 2 1.18e- 1 0 <NA>#> 17 17 0.726 7.26e- 1 1.45e- 1 5.81e- 2 8.71e- 2 0 <NA>#> 18 18 0.534 5.34e- 1 1.07e- 1 4.27e- 2 6.41e- 2 0 <NA>#> 19 19 0.393 3.93e- 1 7.86e- 2 3.15e- 2 4.72e- 2 0 <NA>#> 20 20 0.289 2.89e- 1 5.79e- 2 2.31e- 2 3.47e- 2 0 <NA>#> # … with abbreviated variable name ¹HandlingMortalitiesypr_plot_sr(population)
ypr_tabulate_sr(population)#> # A tibble: 3 × 7#> Type pi u Eggs Recruits Spawners Fecundity#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 unfished 0 0 286350. 80 159. 3600.#> 2 actual 0.2 0.08 167831. 70.1 108. 3112.#> 3 optimal 0.458 0.183 84129. 54.0 63.7 2641.ypr_tabulate_yield(population)#> # A tibble: 2 × 8#> Type pi u Yield Age Length Weight Effort#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 actual 0.2 0.08 8.63 7.67 65.8 3112. 2.12#> 2 optimal 0.458 0.183 11.7 6.87 62.6 2641. 5.82ypr_plot_yield(population)
For more information see theGetStarted vignette.
To interactively explore the effects of altering individualparameters on the schedule, stock-recruitment and yield see the yprshinyapp.
Development of ypr was partially supported by theHabitatConservation Trust Foundation and theMinistry ofForests, Lands and Natural Resource Operations.
The hex was designed byTheForest.
Please report anyissues.
Pullrequests are always welcome.
Please note that the ypr project is released with aContributorCode of Conduct. By contributing to this project, you agree to abideby its terms