Introduction

The Tuna Regional Fisheries Management Organisations (tRFMOs¹) are intergovernmental organisations that are responsible for data collection, provision of scientific, and the management of tuna and tuna-like species. As a step towards harmonisation the tRFMOs have agreed on a common management advice framework, known as the Kobe Framework (Kell et al. (2016)).

Under the Kobe Framework two main main visualisation tools are used, a phase plot and a strategy matrix. The phase plots shows current stock status and exploitation rate relative to target reference points such as $B_{MSY}$ and $F_{MSY}$. While the strategy matrix presents the the probability of meeting management objectives under different management options such as a total allowable catch (TAC).

Assessment advice within the tRFMOs is based on a range of models; for example integrated models such as Stock Synthesis (SS; Methot and Wetzel (2013)) and Multifan-CL (Hampton and Fournier (2001)), virtual population analysis (VPA), and biomass dynamic models. This requires a variety of text files to be read into R.

The kobe R package has methods for reading in results from the main stock assessment methods and summarising these as phase plots and strategey matrices. In addition it includes a variety of tools for summarising results from Managment Strategy Evaluation (MSE).

Installation

The simplest way to obtain kobe is to install it from CRAN, e.g. by using the following command in the R console:

install.packages("kobe", repos = "http://cloud.r-project.org/")

The repos options can be changed depending on use preferences. Other options such as the directories where to install the packages can also be specified, see help(install.packages) for more details.

Quick Start

The intention of this section is to give a general overview of the package, the various elements, what they do, and some basic usage. So that users may have a better idea after reading this section what functions are available, which one to choose, or where to seek help. More details are given in later sections.

First, load the kobe package:

library(kobe)

There is an example dataset for Atlantic yellowfin, which can be used for illustration, alternatively users can load their own data.

load("/home/laurie/Desktop/flr/kobe/data/yft.RData")
#data(yft)

head(yft)

  scenario method iter year stock harvest
1        1  ASPIC    1 1950  2.45 0.00386
2        1  ASPIC    1 1951  2.49 0.00430
3        1  ASPIC    1 1952  2.52 0.00873
4        1  ASPIC    1 1953  2.55 0.01116
5        1  ASPIC    1 1954  2.57 0.01048
6        1  ASPIC    1 1955  2.59 0.01314

Plotting

Plotting is done using ggplot2 which provides a powerful alternative paradigm for creating both simple and complex plots in R using the ideas the Grammar of Graphics² The idea of the grammar is to specify the individual building blocks of a plot and then to combine them to create the graphic desired³.

The ggplot functions expects a data.frame for its first argument, data; then a geometric object geom that specifies the actual marks put on to a plot and an aesthetic that is “something you can see” have to be provided. Examples of geometic Objects (geom) include points (geom_point, for scatter plots, dot plots, etc), lines (geom_line, for time series, trend lines, etc) and boxplot (geom_boxplot, for, well, boxplots!). Aesthetic mappings are set with the aes() function and, examples include, position (i.e., on the x and y axes), color (“outside” color), fill (“inside” color), shape (of points), linetype and size.

The phase plot plots stock status against fishing mortality relative to target reference points as a two-dimensional phase plot.

kobePhase(subset(yft,year==2014))+
  geom_point(aes(stock,harvest))

[1] "yellow" "green"  "red"

Split-apply-combine

plyr[https://www.r-bloggers.com/a-fast-intro-to-plyr-for-r/]

Advice Framework

Stock Assessment

Current Status

The phase plot identifies quadrants (regions) where the stock is overfished (biomass or SSB is less than $B_{MSY}$) or overfishing is occurring ($F < F_{MSY}$ ) and a target region (where both $SSB > SSB_{MSY} and F < F_{MSY}). In the case of biomass dynamic stock assessment model results biomass may be used instead of SSB. The target region is also called the green quadrant, referring to the colour scheme typically used. The plots can be used to indicate for example when management plans to recover the stock to the target region should be implemented.

yft2014=subset(yft,year==2014)
kobePhase(yft2014)+
  geom_point(aes(stock,harvest,col=method))+
  facet_grid(method~scenario)

[1] "yellow" "green"  "red"

library(plyr)
trks=ddply(yft,.(method,scenario,year), 
           function(x) kobeTrks(x$stock,x$harvest,prob=c(0.5)))

We can then add the medians of the historic assessments to the phase plots by adding layers to the object , i.e. adds an extra layer plotting the time series medians and the medians in the last assessment year. We then plot the results by assessment using to split them into multiple panels. Finally we get rid of the legend for since runs are plotted by panel.

kobePhase() + 
   geom_point(aes(stock,harvest), data=subset(yft,year==2014),col="cyan")+
   geom_path( aes(stock,harvest), data=trks) +
   facet_wrap(method~scenario)

[1] "yellow" "green"  "red"

\caption{Phase plot of fishng mortality and stock status reletive to $F_{MSY}$ and $B_{MSY}$, large point and lines are the medians from the assessment and the panels correspond to each run.}

\end{center}\end{figure}

Densities

kobeTrks
kobeShade
kobeSmry
kobeP
kobeDens
kobeFreq
kobeInterp
kobeProb
recovered

Marginal Densities

kobe:::kobePhaseMar(transform(yft2014,run=paste(scenario,method))[,c("stock","harvest","run")])

[1] "yellow" "green"  "red"

kobe:::kobePhaseMar2(transform(yft2014,run=paste(scenario,method))[,c("stock","harvest","run")])

[1] "yellow" "green"  "red"

kobe:::kobePhaseMar3(transform(yft2014,run=paste(scenario,method))[,c("stock","harvest","run")])

[1] "yellow" "green"  "red"

Projections

Strategy Matrix

The strategy matrix lays out the probability of meeting management objectives under different options, this may include if desired ending overfishing or rebuilding overfished stocks. An intention is to facilitate the application of the PA by providing Commissions with a basis to evaluate and adopt management options at various levels of risk, enabling management taken accounting for uncertainty.

aav
pid

Decision Tables

Management Strategy Evaluation

Harvest Control Rules

hcr= data.frame(stock  =c(0.0 ,0.1 , 0.6,2.0), 
                harvest=c(0.01,0.01, 0.7,0.7))
kobePhase()+
  geom_line(aes(stock,harvest),data=hcr,col="orange",size=2)

[1] "yellow" "green"  "red"

Summary Statistics

When using MSE a variety of performance measures (related to catch levels and catch variability) as well as stock status are required.

Example

…

More information

You can submit bug reports, questions or suggestions on FLPKG at the FLPKG issue page,⁴ or on the FLR mailing list.
Or send a pull request to https://github.com/flr/FLPKG/
For more information on the FLR Project for Quantitative Fisheries Science in R, visit the FLR webpage.⁵
The latest version of FLPKG can always be installed using the devtools package, by calling

    library(devtools)
    install_github('flr/FLPKG')

Software Versions

R version 3.3.1 (2016-06-21)
FLCore: 2.6.0.2016919
FLPKG:
Compiled: Mon Nov 14 13:58:44 2016
Git Hash: 124aea1

Author information

Laurence KELL. laurie@kell.es

References

Hampton, John, and David A Fournier. 2001. “A Spatially Disaggregated, Length-Based, Age-Structured Population Model of Yellowfin Tuna (Thunnus Albacares) in the Western and Central Pacific Ocean.” Marine and Freshwater Research 52 (7). CSIRO: 937–63.

Kell, Laurence T, Polina Levontin, Campbell R Davies, Shelton Harley, Dale S Kolody, Mark N Maunder, Iago Mosqueira, Graham M Pilling, and Rishi Sharma. 2016. “The Quantification and Presentation of Risk.” Management Science in Fisheries: An Introduction to Simulation-Based Methods. Oxford, UK: Earthscan (Routledge), 348.

Methot, Richard D, and Chantell R Wetzel. 2013. “Stock Synthesis: A Biological and Statistical Framework for Fish Stock Assessment and Fishery Management.” Fish. Res. 142. Elsevier: 86–99.

Commission for the Conservation of Southern Bluefin Tuna (CCSBT), Inter-American Tropical Tuna Commission (IATTC), International Commission for the Conservation of Atlantic Tunas (ICCAT), Indian Ocean Tuna Commission (IOTC) and Western and Central Pacific Fisheries Commission (WCPFC).↩
Wilkinson, L. 1999. The Grammar of Graphics, Springer. doi 10.1007/978-3-642-21551-3_13.↩
http://tutorials.iq.harvard.edu/R/Rgraphics/Rgraphics.html ↩
https://github.com/flr/FLPKG/issues ↩
http://flr-project.org ↩

Kobe Advice Framework

Laurence Kell

14 noviembre, 2016