pacea

An R package of Pacific ecosystem information to help facilitate an ecosystem approach to fisheries management.

We wrangle the data so you don’t have to

pacea stands for PACific Ecosystem Approach, and is pronounced ‘pac-ee-a’, with pac as in Pacific.

pacea is a R package containing a variety of data sets and model output. We wrangle the data sets behind the scenes to get them into usable formats in R, and provide helpful plotting functions. All data sets include documentation regarding the original sources and authors, who should be cited and consulted as appropriate.

Please continue reading through this README file, and especially see the vignettes section get started.

What is in pacea?

Currently, pacea contains:

• 206,901 calculations of daily sea surface temperature based on data from 19 buoys. Updated monthly.

• outputs from the spatial British Columbia continental margin (BCCM) model, the coupled physical-biogeochemical model by Peña et al. (2019). Variables are available for (a) 40,580 spatial cells across Canada’s Pacific Exclusive Economic Zone, and NEW (b) 161,025 spatial cells across a larger domain, extending into US waters. Variables are given as 27 years of monthly means (from 1993 to 2019). The variables are:

  • dissolved oxygen concentration
  • pH
  • salinity
  • temperature
  • depth-integrated phytoplankton
  • depth-integrated primary production.

  For applicable variables these are given for

  • sea surface
  • 0-40 m integration
  • 40-100 m integration
  • 100 m to the sea bottom
  • sea bottom.

• NOAA’s spatial Optimum Interpolation Sea Surface Temperature (OISST) record, that incorporates observations from different platforms (satellites, ships, buoys, and Argo floats):

  • provided as weekly and monthly means from Sep 1981 to Jul 2024. Updated monthly.

• outputs from the Hindcast of the Salish Sea (HOTSSea) physical oceanography model that recreates conditions throughout the Salish Sea from 1980 to 2018 (Oldford et al., in review). Statistics regarding temperature and salinity are available at the same 2 km x 2 km spatial resolution as the full BCCM results. Specifically, values of minimum, mean, maximum, and standard deviation (over each month) are available for:

  • sea surface
  • 0-30 m integration
  • 30-150 m integration
  • 150 m to the sea bottom
  • sea bottom.

• 9 climatic and oceanographic indices, such as the Pacific Decadal Oscillation and those related to El Niño. Updated monthly.

• estimates of abundances for Harbour Seals.

• estimates of spawning stock biomass and annual recruitments for Pacific Hake and Pacific Herring. Updated annually.

• zooplankton biomass anomalies in the Strait of Georgia, for 25 species groups, from 1996 onwards. Updated annually.

Plotting and helper functions are provided, and demonstrated in the detailed vignettes. All data and model outputs are fully documented and referenced.

What’s new (since the original release)?

The values highlighed above with Updated monthly have been updated each month since the release of pacea, as documented in the NEWS, which it is advisable to check when you update your pacea installation (see below). Since the initial release we have also added the following in response to user requests (some are documented in the Issues, with latest first):

• added results from the Hindcast of the Salish Sea (HOTSSea) physical oceanography model that recreates conditions throughout the Salish Sea from 1980 to 2018. See the new hotssea vignette

• added results from the BCCM model over its full original domain (rather than just restricted to Canada’s Exclusive Economic Zone), see the new bccm_full vignette

• added depths for the grid26 model domain, see ?grid26_depth

• updated citation information (incremented the year, added a DOI through Zenodo, and added two new co-authors: Kelsey Flynn and Greig Oldford), see `citation(“pacea”) or scroll down below.

• added Pacific Herring stock assessment results, for spawning stock biomass and age-2 recruitment for each of the five major stock assessment regions; see the populations.html vignette.

• added helper function a(), shorthand for as.data.frame(), see examples in ?a.

• extended calculations of the Aleutian Low Pressure Index (ALPI) up to 2022; they were originally only available to 2015. The Aleutian Low was mentioned in several talks at the 2024 State of the Pacific Ocean meeting, motivating us to update the values here.

• added zooplankton biomass anomalies for the Strait of Georgia, with a new vignette: zooplankton.html.

• updated the estimates of Pacific Hake abundance and recruitment with values from the 2024 assessment (retaining the original estimates from the 2023 assessments for reproducibility); see the populations.html vignette.

Brief examples of some questions that can be quickly visualised (see vignettes for full details)

How does this year’s sea surface temperature (red curve) in West Dixon Entrance compare to previous years, based on the buoy there?

plot(buoy_sst)    # Buoy C46205 is plotted by default

Are we experiencing a phase of El Niño, based on the Oceanic Niño Index? (If the last bars are red and above 0.5, then ‘yes’)

plot(oni)

How has the status of the Pacific Decadal Oscillation changed over time?

plot(pdo)

What were the differences in spatial pattern of sea surface temperature between June 2022 and 2023 (using OISST data)?

plot(oisst_month,
     months.plot = "June",
     years.plot = c(2022, 2023))

How did the upper 40m of dissolved oxygen differ between January and June, in 2015 (using BCCM results restricted to Canada’s Exclusive Economic Zone)?

plot(bccm_avg0to40m_oxygen(force = TRUE),
     months.plot = c(1, 6),
     years.plot = 2015)

How did depth-integrated primary production vary spatially and with each month throughout 2018, using the BCCM results for its full spatial domain?

plot(bccm_primaryproduction_full(),
     months = 1:12)

How did estimated maximum (over each month) depth-integrated temperature over the top 30 m change in the Salish Sea for January and September in three different years?

plot(hotssea_avg0to30m_temperature_max(),
     months = c("January", "September"),
     years = c(1995, 2010, 2018),
     eez = FALSE)

How has estimated Pacific Herring recruitment changed over time in each of the five major assessment regions?

plot(herring_recruitment)

How has estimated Pacific Hake spawning biomass (from California to BC) changed through time?

plot(hake_biomass)

What is the estimated abundance of Pacific Harbour Seals for each of seven regions?

plot(harbour_seals)

How has zooplankton biomass in the Strait of Georgia changed since 1996?

plot(zooplankton_sog)

You can view and analyse all these data sets in the usual way in R. For example:

oni
#> # A tibble: 895 × 4
#>     year month value anomaly
#>    <dbl> <dbl> <dbl>   <dbl>
#>  1  1950     1  24.7   -1.53
#>  2  1950     2  25.2   -1.34
#>  3  1950     3  25.8   -1.16
#>  4  1950     4  26.1   -1.18
#>  5  1950     5  26.3   -1.07
#>  6  1950     6  26.3   -0.85
#>  7  1950     7  26.2   -0.54
#>  8  1950     8  26.0   -0.42
#>  9  1950     9  25.8   -0.39
#> 10  1950    10  25.6   -0.44
#> # ℹ 885 more rows

What climatic and oceanographic indices are currently available, and over what years?

knitr::kable(pacea_indices)

Object	Description	Resolution	Start year	End year
pdo	Pacific Decadal Oscillation	monthly	1854	2024
npi_monthly	North Pacific Index (monthly)	monthly	1899	2024
npi_annual	North Pacific Index (annual)	annual	1899	2024
alpi	Aleutian Low Pressure Index	annual	1900	2022
oni	Oceanic Niño Index	monthly	1950	2024
npgo	North Pacific Gyre Oscillation	monthly	1950	2024
ao	Arctic Oscillation	monthly	1950	2024
soi	Southern Oscillation Index	monthly	1951	2024
mei	Multivariate El Niño Southern Oscillation Index	monthly	1979	2024

Vignettes

For more detailed explanations of the data sets and functionality see the vignettes, which are already rendered here (note that we do not automatically update them when the data in pacea are updated, so if you run them locally you may get more up-to-date values):

• indices.html Climatic and oceanographic indices and associated plotting functions.
• buoys.html Calculated daily mean sea surface temperatures and associated plotting functions.
• populations.html Estimates of animal populations and associated plotting functions.
• oisst.html OISST data and calculations and associated plotting functions.
• bccm.html BCCM model results restricted to Canada’s Exclusive Economic Zone, calculations, and associated plotting functions.
• bccm_full.html BCCM model results over the full model domain, calculations, and associated plotting functions.
• hotssea.html HOTSSea model results and associated plotting functions.
• zooplankton.html Zooplankton anomalies for the Strait of Georgia, calculations, and associated plotting functions.

The presentations where we introduced pacea are available as .pdf. Here is the DFO version (10th November 2023) and here is the UVic Ecostats version (21st November 2023). The code to build them is here as .Rmd.

Installation

Okay, if you’re convinced this package might be useful for you, then to install the latest version just:

install.packages("remotes")    # If you do not already have the "remotes" package

remotes::install_github("pbs-assess/pacea")

If you get an error like

Error in utils::download.file(....)

then the connection may be timing out (happens to us on the DFO network). Try

options(timeout = 1200)

and then try and install again. If you get a different error then post an Issue or contact Andy or Travis for help.

Updates

We plan to continually enhance pacea by updating current data sets and adding new ones, as well as adding functionality. To explain these, we will document them by date in the NEWS.

We plan to update the indices, buoy temperatures, and OISST values on the 20th of every month (or soon after), which should capture updates to those data.

To check what date you installed pacea locally on your machine, we have the helper command:

pacea_installed()

You last locally installed pacea on 2023-11-01 which is 8 days ago.

Compare that with the updates in the NEWS file (and you can also check dates of the latest commits at
https://github.com/pbs-assess/pacea/commits/main)

The commits tell you when we last changed something, while the NEWS will give a more concise summary of changes by date. Then you may wish to update pacea by running remotes::install_github("pbs-assess/pacea") again.

Audience and motivation

The primary audience is stock assessment scientists who want to analyse environmental variables in the context of their stock assessment (but we anticipate other interested users). The Fisheries Act requires management of fisheries to take into account “the biology of the fish and the environmental conditions affecting the stock”. Such an Ecosystem Approach to Fisheries Management requires data.

A comprehensive 2022 analysis of Canadian stock assessments found that availability of data on environmental variables was the leading cause of not integrating such information into assessments. pacea aims to help make data availability more streamlined, and avoid each individual assessment scientist having to figure out themselves where to get appropriate data.

This work is strongly motivated by, and based on, the GSLea R package by Dan Duplisea and colleagues for the Gulf of St Lawrence.

Citation

If you use pacea in your work then please cite it as (NOTE this has been updated since the original release):

Edwards A.M., Tai T.C., Watson J., Peña M.A., Hilborn A., Hannah C.G., Rooper C.N., Flynn K.L., and Oldford, G.L. (2024). pacea: An R package of Pacific ecosystem information to help facilitate an ecosystem approach to fisheries management. https://github.com/pbs-assess/pacea, https://zenodo.org/doi/10.5281/zenodo.13840804

You may wish to add the date you installed it (using pacea_installed()). Use citation("pacea") to get a version for LaTeX and R Markdown bibliographies. Although the DOI badge at the top of this page ends in …805, the …804 DOI is preferable because that will not change with any future updates to the GitHub tag. So …804 seems simpler and more consistent to use (thanks!).

Also let us know of any applications. This will help us devote effort into maintaining pacea.

Acknowledgments

This work is funded by a Competitive Science Research Fund grant from Fisheries and Oceans Canada (project 21-FS-03-13).

We thank the following for contributing ideas, example code for ROMS output, model output, and/or helping with ideas and coding issues:

• Jessica Nephin, Lindsay Davidson, Strahan Tucker, Brianna Wright, Patrick Thompson, Matt Grinnell, Sean Anderson, Philina English, Chris Grandin, Jennifer Boldt, Kelly Young, and others.

And a big thanks to Carley Colclough for expertly designing the pacea logo.

Notes for developers

1. Please work on your own development branch to ensure that the main branch is never broken (so users can always download the package). See below for details on branching.

2. Edit and render the README.Rmd file, not the README.Md file. If you add any new figures then commit and push them (they will be in man/figures/README-) so they show up on the GitHub README. Always render the .Rmd so that it and the rendered .Md stay in sync (see #44 (comment) for how to have Git check that README.Md is newer than README.Rmd).

3. Every data set is built using code in the raw-data/ directory, with documentation in R/data.R. Document everything!

4. If you add new functions please add tests as well so we retain good code coverage (as indicated in the badge at the top of the README on GitHub).

5. Use the standard lower_case_and_underscores for naming variables and functions.

6. Citation information is in inst/CITATION, so if you update DESCRIPTION file (e.g. add an author) then update that also and update the citation info above.

7. Logo included by running use_logo("inst/pacea-logo-from-carley.png"). The .png was expertly designed by Carley Colclough. There is also inst/pacea-logo-for-printing.jpg for printing purposes.

Branching in Git

To ensure the main branch is never broken (and so users can always download the package), when editing package code please work on your own branch and then merge it in. If just updating the README then we can stick with the main branch (because messing something up should not void the package being installable).

Your own branch should be called dev-yourname, e.g. dev-andy and dev-travis. If you don’t yet have a branch you need to:

• create it locally (and ‘check it out’, i.e. move into it): git checkout -b dev-yourname
• tell Git where to push to: git push --set-upstream origin dev-yourname (Git will tell you this command if you do a git push without having run it)
• update GitHub Actions so that pushes of your branch get properly tested on GitHub. Just add your branch to both lists of branches at the top of:
  • .github/workflows/R-CMD-check.yaml
  • .github/workflows/test-coverage.yaml
• Do that as your first commit and push to check everything’s working.

Once you have your branch set up then the workflow is:

1. Make sure you are all caught up on the main branch:

• git checkout main
• git fetch
• git rebase

2. Switch to your development branch and merge the main branch into it

• git checkout dev-yourname
• git merge main
• for magit users (probably just Andy), this is just b b and m m (it recommends arguments)

3. Do some new commits in your branch and push them in the usual way. GitHub Actions will work on your branch.

So from a quick test, the R-CMD-check badge only refers to the main branch, which is great as it should never show as failing because we should only be breaking things on own branch. Always having the green passing badge should inspire consumer confidence. On GitHub you can select your branch and see the tickmark, which will tell you if the build has failed (or if your changes have caused the code coverage to decline), and you’ll probably get an automatic email also, just to drive home the message. So the badges on the README on your branch still relate to the main branch.

You can click on the codecov badge on GitHub, and then on the codecov page you can select your branch. There are ways to check code coverage locally, but Andy found these all take a long time (and it’s easier to push and let GitHub check the coverage). See commit 6c872da for the commands Andy tried (now deleting to keep this README file cleaner).

4. When you are happy with your changes and ready to merge them into the main branch, you need to

• add any changes that users should be aware of to the NEWS file.

No need to mention things that are only of interest to developers (like the commiting of these instructions). We want the NEWS to be succinct and quickly readable so that people look at it.

Then you need to double check that the main branch is up-to-date with the version on GitHub (someone may have pushed some changes since you last fetched), then merge any changes back into your branch. You could just check on GitHub (if you’re the last person to have pushed to main then you are probably fine), or do these:

• git checkout main

• git fetch

• git rebase

• git checkout dev-yourname

• git merge main

If that does merge in some new commits then you should

• git push to run the GitHub Actions checks again on GitHub. If the merge says something like you are all caught up, no merging is needed (i.e. no-one committed anything to the main branch recenetly), then no need for the git push, since this entire step will not have added any new code.

5. Now you are ready to merge your changes into the main branch, so that people will get them when they install/update pacea:

• git checkout main
• git merge dev-yourname
• git push

And you are done! If you want, we have some aliases (such as git co for git checkout and git p for git push listed in this example .gitconfig file: https://raw.githubusercontent.com/quantitative-biology/module-1-git/main/misc/.gitconfig from our e-book https://www.quantitative-biology.ca/git-and-github.html#save-our-template-.gitconfig-file You can put those into your .gitconfig file if you like.

This can help understanding on branches: https://www.atlassian.com/git/tutorials/using-branches/git-merge

Oldford, G.L., Jarníková, T., Christensen, V., and Dunphy, M. (in review). HOTSSea v1: a NEMO-based physical Hindcast of the Salish Sea (1980–2018) supporting ecosystem model development. Preprint. https://doi.org/10.5194/gmd-2024-58 .

Peña, M.A., Fine, I. and Callendar, W. (2019). Interannual variability in primary production and shelf-offshore transport of nutrients along the northeast Pacific Ocean margin. Deep-Sea Research II, doi:10.1016/j.dsr2.2019.104637. https://www.sciencedirect.com/science/article/pii/S0967064519300220