Protecting marine mammals, turtles, and birds by rebuilding global fisheries
This repo contains data and R code for reproducing Burgess et al. (2018), "Protecting marine mammals, turtles, and birds by rebuilding global fisheries".
Abstract: Reductions in global fishing pressure are needed to end overfishing of target species and maximize the value of fisheries. We ask whether such reductions would also be sufficient to protect non–target species threatened as bycatch. We compare changes in fishing pressure needed to maximize profits from 4713 target fish stocks—accounting for >75% of global catch—to changes in fishing pressure needed to reverse ongoing declines of 20 marine mammal, sea turtle, and seabird populations threatened as bycatch. We project that maximizing fishery profits would halt or reverse declines of approximately half of these threatened populations. Recovering the other populations would require substantially greater effort reductions or targeting improvements. Improving commercial fishery management could thus yield important collateral benefits for threatened bycatch species globally.
Click on the "fork" button at the very top right of the page to create an independent copy of the repo within your own GitHub account. Alternately, click on the green "clone or download" button just below that to download the repo to your local computer.
The main file for conducting the analysis is
R/bycatch.R. This file contains self-explanatory code for easily reproducing the results from the ten different model runs described in the paper, as well as all of the figures.
The entire analysis is conducted in the R programming environment. R is free, open-source and available for download here. We highly recommend running R in the RStudio IDE, which you can also download for free here.
You will need to install a number of external R packages to run the code successfully. These are listed at the top of the main
R/bycatch.R file. An easy way to ensure that you have the correct versions of all the packages is to run the following code chunk in your R console:
if (!require("pacman")) install.packages("pacman") pacman::p_install(c(data.table, pbapply, parallel, R.utils, truncnorm, scales, grid, rworldmap, sf, rgeos, tidyverse, forcats, cowplot, ggthemes, RColorBrewer, viridis, extrafont, here)) pacman::p_update()
Please note that the
extrafont package requires some minor setup upon first use. The main analysis will still work without this initial setup, but it is perhaps useful for reproducing some of the figures in full. See here for instructions.
The core analysis in this paper involves a series of computationally intensive Monte Carlo simulations. The code is optimized to run in parallel and will automatically exploit any multi-core capability on your machine. That being said, even on a 24 core Linux server, a single model run can take around 40 minutes to complete. (See here for a tutorial on how to set up your own personal server using Google Compute Engine, complete with R and RStudio Server.) We strongly suggest reducing the "n1" and "n2" parameters (+/- line 75 in
R/bycatch.R) before running the analysis on a local machine with few CPUs. Failing that, the saved results from our own model runs can be found in the
If you have any trouble running the code, or find any errors, please file an issue on this repo and we'll look into it.