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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

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 ¹​NaturalMortality, ²​Vulnerability, ³​Retention,
#> #   ⁴​FishingMortality

Fish

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 ¹​HandlingMortalities

Stock-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.

A screenshot of the yield from the ypr shiny app
A screenshot of the yield from 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.