README.md

In katherinekaplan/MPApopulationmodels: Population responses to marine protected areas (MPAs)

MPApopulationmodels

The R package 'MPApopulationmodels' contains several functions that can be used to project population responses to marine protected areas (MPAs). Each function generates a Leslie matrix that is used to project changes in the population over time. Input parameters for a species including life history characteristics and a prior fishing mortality rate are required to use the functions described. Each function is also documented in R and can be searched once the package is downloaded into R by typing ??function_name. The input and output for each function can be found in the R documentation. Below I provide a brief summary of the utility of each function.

The following functions are available from this package:

closedpop_metrics: calculates the ratio change in a fished population after a marine protected area is implemented assuming a closed population (self-seeding). It provides P1, the period of oscillations; rho, the rate of return to the stable age distribution in the MPA; theta, the angle between the fished state population vector and the stable age distribution in the MPA; Nratio, the total abundance ratio change over time; Bratio, the biomass ratio change over time; and transient_length, the approximate length of the transient period calculated from rho. See White et al. 2013 "Transient responses to marine reserves" for more information on metrics calculated.

closedpop_plot: creates a plot using ggplot that shows the changes in abundance and biomass expected following MPA implementation assuming a closed population model

closedpop_ratio: returns a data frame with time, abundance ratio (Nratio), and biomass ratio (Bratio) for a population response to a MPA assuming a closed population, which is used by the closedpop_plot function to generate the plot shown

openpop_ratio: outputs a data frame that contains changes in the population abundance and biomass overtime since MPA implementation as shown with the openpop_plots function. The data frame includes a column for time, deterministic abundance ratio changes (Nratio), deterministic biomass ratio changes (Bratio), the mean of simulations specified for abundance changes with stochasticity in recruitment inside an MPA (Nrat.sim.mean), the lower quartile for the distribution of simulated runs for changes in abundance in an MPA with stochasticity in recruitment (Nrat.lowerCI.MPA), the upper quartile for the distribution of simulated runs for changes in abundance in an MPA with stochasticity in recruitment (Nrat.upperCI.MPA), the mean of simulations of changes in abundance with stochasticity in recruitment for a control (fished site) not inside an MPA (Nrat.mean.noMPA), the lower quartile for the distribution of simulated runs for changes in abundance in a control (fished site) with stochasticity in recruitment (Nrat.lowerCI.noMPA), the upper quartile for the distribution of simulated runs for changes in abundance in a control (fished site) with stochasticity in recruitment (Nrat.upperCI.noMPA),the mean of simulations of biomass changes with stochasticity in recruitment inside an MPA (Bratio.sim.mean), the lower quartile for the distribution of simulated runs for changes in biomass in an MPA with stochasticity in recruitment (Brat.lowerCI.MPA), the upper quartile for the distribution of simulated runs for changes in biomass in an MPA with stochasticity in recruitment (Brat.upperCI.MPA), the mean of simulations specified for biomass changes with stochasticity in recruitment in a control (fished) site (Brat.mean.noMPA),the lower quartile for the distribution of simulated runs for changes in biomass in a control site with stochasticity in recruitment (Brat.lowerCI.noMPA), and the upper quartile for the distribution of simulated runs for changes in biomass in an MPA with stochasticity in recruitment (Brat.upperCI.noMPA).

openpop_plots: returns 3 plots, the first is the deterministic population projection of abundance and biomass ratios changing over time assuming an open population with constant recruitment (entering the population from external sources), the second plot is a population projection with stochastic recruitment and returns the upper and lower quartile for changes in abundance overtime since MPA implementation based on the number of simulations specified in the input of the function, the third plot is a stochastic population projection also but calculates changes in biomass over time.

openpop_time: provides outputs based on determinstic population projections based on the time reach 95% of the final equilibrium ratio in response to an MPA. Outputs include the abundance time to equilbirium (Ntime.to.equil), the biomass time to equilibrium (Btime.to.equil), the final abundance ratio change (final.N.ratio), and the final biomass ratio change (final.B.ratio).

openpop_ROC generates a receiver-operating characteristic curve (ROC) and calculates the area under this curve at different time points since MPA implementation using the fished population state as a control to compare to the MPA population for simulations with stochastic recruitment. The area under the ROC curve provides a measure of statistical power, where values closer to 0.5 indicate very low ability to detect population changes due to MPAs and values closer to 1 indicate a strong ability to detect populations changes due the MPA

katherinekaplan/MPApopulationmodels documentation built on May 22, 2019, 8:51 p.m.