---
title: "SPM (from datalowSA) Installation"
date: "06/07/2020"
output:
html_document:
theme: cerulean
pdf_document: default
word_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# datalowSA: A package to facilitate application of data poor stock assessments (datalowSA).
(This package is well described in https://rdrr.io/github/haddonm/datalowSA/f/README.md)
Surplus production model (spm https://rdrr.io/github/haddonm/datalowSA/man/spm.html) is a component of the package *datalowSA*. "It is one of the simplest analytic methods available that provides for a full fish stock assessment of the population dynamics of the stock being examined.". "SPM represents the dynamics using a Schaefer or a Fox model. The outputs include predicted Biomass, year, catch, cpue, predicted cpue, contributions to *q*, ssq, and depletion levels. Generally it would be more sensible to use *simpspm* when fitting a Schaefer model and *simpfox* when fitting a Fox model as those functions are designed to generate only the predicted cpue required by the functions *ssq* and *negLL*, but the example shows how it could be used. The function *spm* is used inside *displayModel* and could be used alone, to generate a full-list of model outputs after the model has been fitted.".
# Installation
This package is run from R as found on CRAN. R can be installed from https://cran.r-project.org/.
The recommended way to run R is using RStudio. After you have installed R, then install RStudio. It can be found at https://www.rstudio.com/products/rstudio/download/.
The supported version of SPM is part of a set of stock assessment tools within Dr Haddon's *datalowSA* package. Therefore one needs to first install * datalowSA* from within R.
Note for R4.0.0.0+:
R4.0.0.0+ is available recently. Under this version, Rtools40 and RStudio1.2.5042+ (if you use RStudio) are required. If you get the error messages of missing packages such as "backports" or "fs", you need to install the packages manually.
Set up checking/installing packages functions
```{r checking and installing functions, eval=TRUE}
# Ordinary checking/installing function
Check_Install.packages <- function(pkg){
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(new.pkg))
install.packages(new.pkg,dependencies = TRUE) else print(paste0("'",pkg,"' has been installed already!"))
sapply(pkg, require, character.only = TRUE)
}
# github checking/installing function
Github_CheckInstall.packages <- function(pkg,github_pkg,build_vignettes){
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(new.pkg))
devtools::install_github(github_pkg,build_vignettes)
sapply(pkg, require, character.only = TRUE)
}
```
Installing packages from GitHub directly.
Check and install "devtools"
```{r install_from_github, eval=TRUE}
Check_Install.packages("devtools")
# IsExst_Devtools = check.packages("devtools")
#
# if(!IsExst_Devtools) install.packages("devtools") else print("'devtools' has been installed already!")
```
Check and install *datalowSA* from github
``` {r packages install_from_github, eval=TRUE}
# Check if existed and if not, install the required package TMBhelper:
github_pkg = "haddonm/datalowSA"
Github_CheckInstall.packages("datalowSA",github_pkg=github_pkg,build_vignettes=TRUE)
#install_github("jabbamodel/JABBA")
```
NB Once you have installed *datalowSA*, you don't need to run the install.packages code chunk again. Luckily it will just give an error stating it won't re-install if your version is up to date.

```{r load package, eval=TRUE}
library(datalowSA)
```
It should be noted that there are also quite good vignettes for this package by typing *browseVignettes(package = "datalowSA")*. In that file for SPM, Dr Haddon describes several steps of which we only show a few below with our example dataset.
## Reading in the data
An simulated example dataset has been created for the whole toolbox. Let us now read in these data. We assume you have saved the data to the same directory as your working directory.
```{r load data }
Catches <- read.csv("Catches.csv")
print(head(Catches))
CPUEs <- read.csv("CPUE.csv")
print(head(CPUEs))
```
Our dataset was created using Stock Synthesis based on an age-at-maturity of roughly 3-4 years and a rate of natural mortality of 0.2 yr^{-1}. For this package, we provide the catch data in tons and cpue data in tons per effort unit.
## Making input data into the model
Input data expected as a column of years, catches and cpue (as we have set up in Example). The model also needs some idea of the population growth rate (*r*), *K* and the initial biomass (*B*_{init}) of the population if the data are not collected from start of the fishery.
In this example,
```{r CreateSPMdata}
inputDat = merge(Catches[,c("Year","Value")],
CPUEs[,c("Year","Value")],by="Year")
year <- inputDat$Year
catch <- inputDat$Value.x
cpue <- inputDat$Value.y
dat <- makespmdata(cbind(year,catch,cpue))
#pars <- c(0.241,51049) # from cMSY simulation's mean_r and mean_K
pars <- c(0.281,45236) # from CMSY actual medium r and medium K. The data collected from the beginning of the fishery, therefore no Binit given.
```
## Running the model
The syntax for using spm function as:
spm(inp=pars,indat=dat,schaefer = TRUE)

However as mentioned above, the function *spm* is used inside 'displayModel' and displayModel takes a set of parameters and the *spmdat* matrix and plots the predicted depletion, catch, the surplus production, and the CPUE and the model fit to CPUE.
### Schaefer Model
To fit catch and cpue data using Shaefer model and the model set default LRP as 0.2 and TRP as 0.48. The model results are plotted.
```{r run Schaefer Model step1}
ans <- displayModel(pars,dat,schaefer = TRUE)
str(ans)
```
To optimise the parameters and apply Schaefer *spm* modeling again with optimised parameters, and to output model results in text.
```{r run Schaefer Model step2}
bestSP <- optim(par=pars,fn=ssq,callfun=simpspm,indat=dat)
ans <- displayModel(bestSP$par,dat,schaefer=TRUE)
str(ans)
```
### Fox Model
To fit catch and cpue data using the Fox model and set the default LRP to 0.2 and TRP to 0.48, and plot the resulst:
```{r run Fox Model step1}
ans <- displayModel(pars,dat,schaefer = FALSE)
str(ans)
```
To optimise the parameters and do Fox *spm* modeling again with optimised parameters, ando to output model results in text.
```{r run Fox Model step2}
bestSP <- optim(par=pars,fn=ssq,callfun=simpfox,indat=dat)
ans <- displayModel(bestSP$par,dat,schaefer=FALSE)
str(ans)
```
## Explanation of the results
*spm* generally produces six plots for each of the model run. They include "ExploitB Depletion","Scaled CPUE","LN Residuals","Catch","Surplus Production - Biomass" and "Surplus Production - Depletion". From these graphs one can already get quite a lot of the required management information, including *MSY* and the depletion rate. We have two models examples and both use 0.2*B*_{0} and 0.482*B*_{0} as limit and target reference point proxies.
There are two options to set the parameters for the modeling for each of the models (Schaefer and Fox). Option 1 is to use given parameters (*r*, *K*); Option 2 is to optimise the parameters given the input values for *r* and *K*.
* **ExploitB Depletion** displays the trajectory of the exploitable biomass depletion rate. The red line is the LRP and the green line is the TRP. In Schaefer model results, two options are similar and
* **Catch** displays the catch series with *MSY* as the red line.
* **Scaled CPUE** displays the model fit to the CPUE data. The red line is the model predictions and the black dots are the CPUE data points.
* **Surplus Production - Biomass** displays surplus production vs biomass. Vertical red lines represent *B*_{lim}, *B*_{targ} and *B*_{MSY} and the horizontal red dash line is *MSY*.
* **LN Residuals** displays the residuals (scaled observe CPUE / scaled predicted CPUE) with a value of RMSE (root mean square error)
* **Surplus Production - Depletion** displays the surplus production vs deletion function. The vertical lines represent the LRF (red), the TRP (green) and *MSY* point (dark green). The horizontal lines represent *MSY* (red) and *C*_{targ} (green).
## Key references
### SPM Methodology
Schaefer, M.B. 1954. Some aspects of the dynamics of populations important to the management of the commercial marine fisheries. *Bulletin, Inter-American Tropical Tuna Commission* 1:25-56. https://www.iattc.org/PDFFiles/Bulletins/_English/Vol-1-No-2-1954-SCHAEFER,%20MILNER%20B._Some%20aspects%20of%20the%20dynamics%20of%20populations%20important%20to%20the%20management%20of%20the%20commercial%20marine%20fisheries.pdf
Schaefer, M.B. 1957. A study of the dynamics of the fishery for yellowfin tuna in the Eastern Tropical Pacific Ocean. *Bulletin, Inter-American Tropical Tuna Commission* 2:247-285. https://www.iattc.org/PDFFiles/Bulletins/_English/Vol-2-No-6-1957-SCHAEFER,%20MILNER%20B_A%20study%20of%20the%20dynamics%20of%20the%20fishery%20for%20yellowfin%20tuna%20in%20the%20eastern%20tropical%20Pacific%20Ocean.pdf
Walters, C.J., Martell, S.J.D. and J. Korman 2006. A stochastic approach to stock reduction analysis.* Canadian Journal of Fisheries and Aquatic Sciences* 63:212-223. https://doi.org/10.1139/f05-213
### SPM Evaluation
Froese, R., Demirel, N., Coro, G., Kleisner, K. and H. Winker. 2017. Estimating fisheries reference points from catch and resilience. *Fish and Fisheries*. 18:506-526. https://doi.org/10.1111/faf.12190.
### datalowSA package
Haddon, M., Burch, P., Dowling, N. and R. Little, R. 2019. Reducing the Number of Un-defined Species in Future Status of Australian Fish Stocks Reports: Phase Two - training in the assessment of data-poor stocks. FRDC Final Report 2017/102. CSIRO Oceans and Atmosphere and Fisheries Research Development Corporation. Hobart. 125 p.