maelle/ypr
An R package of equilibrium-based yield per recruit methods
ypr 
Introduction
ypr is an R package that
implements equilibrium-based yield per recruit methods. Yield per
recruit methods can used to estimate the optimal yield (Walters and
Martell 2004) of a fish population with one or more ecotypes. The yield
can be based on the number of fish caught (or harvested) or biomass for
all fish or just large (trophy) individuals.
The key life history parameters are
- The growth coefficient (
k) and mean maximum length (Linf) from the
Von Bertalanffy growth curve - The length at which 50% mature (
Ls) - The length at which 50% vulnerable to harvest (
Lv) - The annual interval natural mortality rate (
n) - The lifetime number of spawners per spawner at low density (
Rk)
and in the case of ecotypes
- The relative proportion of recruits that belong to that ecotype
(RPR)
The calculations do not account for stochasticity, predator-prey
dynamics, angler responses or density-dependent growth.
Installation
To install the latest release from CRAN
install.packages("ypr")To install the developmental version from
GitHub
# install.packages("remotes")
remotes::install_github("poissonconsulting/ypr")Demonstration
Schedule
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,
#> # ⁴FishingMortalityFish
ypr_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 ¹HandlingMortalitiesStock-Recruitment
ypr_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.Yield
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. 5.82
ypr_plot_yield(population)
Information
For more information see the Get
Started
vignette.
Interaction
To interactively explore the effects of altering individual parameters
on the schedule, stock-recruitment and yield see the ypr shiny
app.
Creditation
Development of ypr was partially supported by the Habitat Conservation
Trust Foundation and
the Ministry of Forests, Lands and Natural Resource
Operations.
The hex was designed by The Forest.
Contribution
Please report any
issues.
Pull requests are
always welcome.
Code of Conduct
Please note that the ypr project is released with a Contributor Code of
Conduct.
By contributing to this project, you agree to abide by its terms
References
Walters, Carl J., and Steven J. D. Martell. 2004. Fisheries Ecology and
Management. Princeton, N.J: Princeton University Press.