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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# mmrefpoints: Projecting long-term marine mammal abundance with bycatch
<!-- badges: start -->
[](https://github.com/mcsiple/mmrefpoints/issues)
[](https://doi.org/10.5281/zenodo.6336011)
<!-- badges: end -->
`mmrefpoints` is an R package that generates marine mammal population projections based on starting abundance, life history, and bycatch rates, based on the BALEEN II population dynamics model.
## Authors
Margaret C. Siple
André E. Punt
Tessa B. Francis
Philip S. Hammond
Dennis Heinemann
Kristy J. Long
Jeffrey E. Moore
Maritza Sepúlveda
Randall R. Reeves
Guðjón Már Sigurðsson
Gísli Víkingsson
Paul R. Wade
Rob Williams
Alexandre N. Zerbini
## Contents
- [Need](#need)
- [Details](#details)
- [Installation](#installation)
- [Contributing](#contributing)
- [References](#references)
<!-- end toc -->
## Need
Stakeholders involved in the management of marine mammal bycatch in marine fisheries need tools to simulate the effects of management decisions on marine mammal populations. Population models are a key part of this process. This package contains the tools to simulate marine mammal populations and an app that shows model outputs in a user-friendly way.
## Details
This R package contains the functions used in the Marine Mammal Bycatch Impacts Exploration Tool (MMBIET), a Shiny app built by Margaret Siple, André Punt, and the Ocean Modeling Forum's [Marine Mammal Bycatch Working Group](https://oceanmodelingforum.org/working-groups/marine-mammal-bycatch-working-group/).
The functions in this package, and the Shiny app, are intended to be used in cases where data on bycatch and/or population status are sparse or unavailable.
Our target audience is stakeholders interested in projecting marine mammal populations to examine the impacts of bycatch. Those code could also be used as a teaching tool, or for anyone who is more familiar with R than FORTRAN and wants to use some components of the BALEEN II model (Punt 1999).
## Installation
This package can be downloaded directly from GitHub:
```
devtools::install_github("mcsiple/mmrefpoints")
```
* NOTE: For Linux users, if you run into an error about the `magick` package, try installing `magick` first using the instructions [here](https://cran.r-project.org/web/packages/magick/vignettes/intro.html).
## Contributing [](https://github.com/dwyl/esta/issues)
### Community guidelines
We would like this package to be sustainable in the long term and welcome contributions. If you are interested in contributing, please check out our [Contribution Guide](https://github.com/mcsiple/mmrefpoints/blob/master/CONTRIBUTING.md).
Bugs and enhancements are tracked through GitHub issues. If you have a bug to report, there is a [bug report template](https://github.com/mcsiple/mmrefpoints/blob/master/.github/ISSUE_TEMPLATE/bug_report.md) to help maximize the benefit of your report for everyone. The same goes for requests for [enhancements](https://github.com/mcsiple/mmrefpoints/blob/master/.github/ISSUE_TEMPLATE/feature_request.md).
## Accessing the MMBIET Shiny app
The functions in this package can also be accessed through the Shiny app for this project, which is located online [here](https://msiple.shinyapps.io/mmrefpoints/). The app provides an easy way to explore outcomes and print out a report with inputs and outputs.
The mmBIET Shiny app can also be accessed through the R package:
```{r eval=FALSE}
library(mmrefpoints)
run_app()
```
<img src="https://github.com/mcsiple/ltbycatch/blob/master/docs/screenshot1.png" alt="screenshot1" width="400">
## Functionality
The foundation of this package is an age-structured population projection model with bycatch mortality. Key functions in this package:
```{r eval=TRUE,echo=FALSE, results='asis'}
x <- data.frame("Function" = c("dynamics()",
"projections()"),
"Purpose" = c("Generate a single trajectory for marine mammal population size",
"Generate several trajectories for marine mammal population size"))
knitr::kable(x)
```
To create a single projection for a marine mammal population, use the `dynamics()` function:
```{r message=FALSE}
x <- mmrefpoints::dynamics(lh.params = list(S0 = 0.944, S1plus = 0.99,
K1plus = 9000, AgeMat = 17, z = 2.39, nages = 25, lambdaMax = 1.04),
InitDepl = 0.6,
ConstantCatch = NA,
ConstantF = rep(0.01, times = 100),
nyears = 100)
plot(1:100, x$TotalPop, type = 'l', xlab = "Year", ylab = "Population size")
```
Variation in the model is introduced through variation in bycatch mortality over time and uncertainty in the estimate of starting abundance.
```{r message=FALSE}
x <- mmrefpoints::projections(
NOut = 1,
ConstantBycatch = list(Catch = 50, CV = 0.7),
InitDepl = 0.6,
lh.params = list(
S0 = 0.944, S1plus = 0.99,
K1plus = 9000, AgeMat = 18, nages = 25, z = 2.39, lambdaMax = 1.04
),
nyears = 100, obs_CV = 0.1
)
# One trajectory with bycatch uncertainty and an observation CV:
plot(x$trajectories, type = "l", xlab = "Year", ylab = "Population size")
```
Projections shown in the app are based on simulation parameters provided by the user. They include a "high", "medium", and "low" bycatch level based on a user-determined range.
```{r message=FALSE}
x_lo <- mmrefpoints::projections(
NOut = 100,
ConstantBycatch = list(Catch = 0, CV = 0),
InitDepl = 0.6,
lh.params = list(
S0 = 0.944, S1plus = 0.99,
K1plus = 9000, AgeMat = 18, nages = 25, z = 2.39, lambdaMax = 1.04
),
nyears = 100, obs_CV = 0.1
)
x_med <- mmrefpoints::projections(
NOut = 100,
ConstantBycatch = list(Catch = 50, CV = 0.7),
InitDepl = 0.6,
lh.params = list(
S0 = 0.944, S1plus = 0.99,
K1plus = 9000, AgeMat = 18, nages = 25, z = 2.39, lambdaMax = 1.04
),
nyears = 100, obs_CV = 0.1
)
x_hi <- mmrefpoints::projections(
NOut = 100,
ConstantBycatch = list(Catch = 200, CV = 0.7),
InitDepl = 0.6,
lh.params = list(
S0 = 0.944, S1plus = 0.99,
K1plus = 9000, AgeMat = 18, nages = 25, z = 2.39, lambdaMax = 1.04
),
nyears = 100, obs_CV = 0.1
)
mmrefpoints::plot_proj(high = x_hi,
med = x_med,
low = x_lo,
years.plot = 100,
ylims = c(0, 9000),
K1plus = 9000)
```
## References
Punt, A. E. 1999. Annex R: A full description of the standard Baleen II model and some variants thereof. Division of Marine Research, CSIRO Marine Laboratories, Hobart, Australia. Available from https://duwamish.lib.washington.edu/uwnetid/illiad.dll?Action=10&Form=75&Value=1651729 (accessed August 7, 2018).
## How to cite
To cite this package or the MMBIET Shiny app, please use the following citation:
> Margaret C. Siple, André E. Punt, Tessa B. Francis, Philip S. Hammond, Dennis Heinemann, Kristy J. Long, Jeffrey E. Moore,
Randall R. Reeves, Sepúlveda Maritza, Guðjón Már Sigurðsson, Gísli Víkingsson, Paul R. Wade, Rob Williams and Alexandre N.
Zerbini (NA). mmrefpoints: Project Marine Mammal Populations and Calculate Reference Points. R package version 1.0.1.
url: <https://github.com/mcsiple/mmrefpoints> doi: 10.5281/zenodo.4758401
NOTE that if you want to cite all versions of the software, you can use the doi [10.5281/zenodo.4758401](https://zenodo.org/record/5949332#.Yf1infXMI-Q). When additional releases happen, there will be a doi for each new release as well.