marea: Curated Ecosystem Data for the Maritimes Region

December 5, 2025 · View on GitHub

marea provides curated data sets to support an Ecosystem Approach to Fisheries Management (EAFM) in Canada’s Maritimes Region. It offers standardized, analysis-ready time series of oceanographic, environmental, and biological data crucial for research and stock assessment.

Philosophy

A Single, Simple Data Structure: All time series data in marea are stored in simple and robust eaclass objects. This provides a consistent, predictable format, whether you are working with temperature, survey indices, or commercial catch.
User-Controlled Plotting: We provide a basic, clean plot for every dataset. From there, you are in control. Because our plots are standard ggplot2 objects, you can easily customize them, add new layers, and create the exact visualization you need for your analysis or report.

Installation

You can install the development version of marea from GitHub:

# install.packages("remotes")
remotes::install_github("MarEcosystemApproaches/marea")

For users on the DFO network who may experience connection timeouts:

# Set a longer timeout period
options(timeout = 1200)
remotes::install_github("MarEcosystemApproaches/marea")

Quick Start: A Simple Workflow

The workflow for any dataset in marea is the same: load, inspect, plot, and customize.

library(marea)

## Thank you for using marea. Type citation('marea') for citation information.

library(ggplot2) # For customization

# 1. Load a dataset of interest (e.g., grey seal abundance)
data("grey_seals")

# 2. Inspect the object - it's a clean 'ea_data' object
ea.print(grey_seals)

## --- Ecosystem Approach (EA) Data Object --- 
## Class:  ea_data 
## Data Type:  Grey Seal Abundance 
## Species:   grey seal 
## Location:    ( Scotian Shelf  Region ) 
## Time Range:   1960  -  2021 
## Units:  number of seals 
## --------------------------------------------
## Data Preview:
##   year    lower median_value    upper
## 1 1960 1.652570     1.860824 2.396134
## 2 1961 2.032521     2.263192 2.809171
## 3 1962 2.407304     2.659018 3.214911
## 4 1963 2.700793     2.972982 3.541202
## 5 1964 2.785795     3.088971 3.684344
## 6 1965 3.126289     3.449040 4.053713

# 3. Create a simple plot
 p <- plot(grey_seals)

You can use the style argument to create a default whichis an appropriate base for your data, then customize it even further by chaining on ggplot graphics grammar.

# 4. Customize it! Add a confidence ribbon and improve the labels.
# The 'low' and 'high' columns are right there in the data frame.
custom_plot <- plot(grey_seals, style = 'ribbon') +
  labs(
   title = "Grey Seal Abundance on Sable Island",
    y = "Estimated Pup Production (count)"
  ) +
  theme_bw()

Available Data

marea includes a growing list of curated data products. Use marea_metadata() to see what’s available.

library(knitr)
kable(marea_metadata())

Dataset	Region	TimeSpan	Source
amo	Northern Hemisphere (0-60N)	1854-2025	NOAA , https://www1.ncdc.noaa.gov/pub/data/cmb/ersst/v5/index/ersst.v5.amo.dat
ao	Northern Hemisphere	1950-2025	NOAA CPC, https://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/
azmp_bottom_temperature	Scotian Shelf (4X, 4V, 4W)	1950-2024	DFO Atlantic Zone Monitoring Program via azmpdata
azmp_salinity	Scotian Shelf (4X, 4V, 4W)	1948-2024	DFO Atlantic Zone Monitoring Program via azmpdata
azmp_satellite_temperature	Scotian Shelf (4X, 4V, 4W)	1982-2024	DFO Atlantic Zone Monitoring Program via azmpdata
azmp_stratification	Scotian Shelf (4X, 4V, 4W)	1948-2024	DFO Atlantic Zone Monitoring Program via azmpdata
azmp_surface_temperature	Scotian Shelf (4X, 4V, 4W)	1948-2024	DFO Atlantic Zone Monitoring Program via azmpdata
coastline	Unknown	Unknown	Unknown
eco_indicators	Maritimes	1970-2022	Bundy et al. 2017
glorys_bottom_temperature	Northwest Atlantic	Unknown	CMEMS Global Ocean Physics Reanalysis
grey_seals	Scotian Shelf	1960-2021	den Heyer, C. E., Mosnier, A., Stenson, G. B., Lidgard, D. C., Bowen, W. D., & Hammill, M. O. (2024). Grey seal pup production in Canada (DFO Can. Sci. Advis. Sec. Res. Doc. 2023/078). Fisheries and Oceans Canada, Canadian Science Advisory Secretariat.
grey_seals_2021	Maritimes	1960-2021	No citation provided
nao	North Atlantic	1951-2024	NOAA NCEP via azmpdata; https://www.ncei.noaa.gov/access/monitoring/nao/
npgo	North Pacific Gyre	1950-2025	Di Lorenzo et al., http://www.o3d.org/npgo/
oni	Niño 3.4 Region (Pacific)	1950-2025	NOAA CPC, https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml
pdo	North Pacific	1854-2025	NOAA ERSST, https://www.ncei.noaa.gov/access/monitoring/pdo/
satellite_ocean_colour	Northwest Atlantic	Unknown	SOPhyE group, DFO
soi	Equatorial Pacific	1951-2025	NOAA CPC, https://www.cpc.ncep.noaa.gov/data/indices/soi

Documentation

For detailed examples, data sources, and methodologies, please see our vignettes:

Understanding Generic EA Data Classes: A guide to the ea classes and the package philosophy.

Plotting EA Classes: Examples and details of how to plot ea class objects.

Citation

If you use marea in a publication, please cite it. You can get the current citation information by running:

citation("marea")

Contributing

We welcome contributions! If you have suggestions, find a bug, or would like to contribute a new dataset, please see our contribution guidelines and open an issue on GitHub.

CONTRIBUTING

This package is part of a coordinated effort across DFO regions to standardize access to ecosystem data for fisheries management:

pacea - Pacific ecosystem data

gslea - Gulf of St. Lawrence ecosystem data

Acknowledgments

We acknowledge that this work is done in the traditional and unceded territory of indigenous people who have cared for this land and water for time immemorial. We thank Fisheries and Oceans Canada for funding and acknowledge the many data providers and scientists whose work makes this package possible.

Special thanks to the oce package team for inspiring the design of the ea class system.

Kelley D, Richards C (2025). oce: Analysis of Oceanographic Data. R package version 1.8-4, https://dankelley.github.io/oce/.