Tutorial 1: Identifying Protected Areas Affected by Subsea Infrastructure in the North Sea

Overview

This tutorial introduces the use of the emodnet.wfs R package to retrieve and analyse marine vector data from EMODnet’s Web Feature Services (WFS). Using the North Sea as a case study, users will explore how to identify marine protected areas (MPAs) that spatially overlap with subsea infrastructure, such as pipelines and telecommunication cables.

The tutorial provides a practical entry point to accessing and working with EMODnet WFS data entirely within R. It focuses on combining multiple layers representing protected areas and human activities, and applying spatial intersection techniques to highlight potential areas of conflict or concern.

Background & Motivation

The North Sea is one of Europe’s most heavily industrialised marine regions, featuring dense networks of energy and communication infrastructure alongside a growing network of marine protected areas. Understanding where these features intersect is crucial for marine spatial planning, environmental impact assessments, and compliance with EU directives such as the Marine Strategy Framework Directive and the Habitats Directive.

This tutorial demonstrates how open-access EMODnet data can be used to identify and quantify overlaps between protected areas and marine infrastructure, offering a foundation for data-driven policy and environmental decision-making.

Learning Objectives

• Learn how to access and retrieve multiple WFS layers using the emodnet.wfs R package
• Load and combine spatial datasets representing MPAs and subsea infrastructure
• Harmonise coordinate reference systems and prepare geometries for analysis
• Apply sf::st_intersects() to detect spatial overlaps
• Summarise and visualise the extent of intersection between protected areas and human activities

EMODnet Data Sources Used

Protected Areas (Seabed Habitats): - cddaareas - natura2000areas - marineprotectedareas

Subsea Infrastructure (Human Activities): - platforms - pipelines - pcablesbshcontis - pcablesshom - pcablesrijks - pcablesnve

Methods Summary

All layers are queried via WFS and imported as sf objects. The tutorial demonstrates how to standardise CRS, validate geometries, and use spatial intersection (sf::st_intersects()) to detect overlaps between protected areas and infrastructure features. Users will generate summary outputs and maps highlighting intersecting features.

Intended Outcome

By the end of the tutorial, users will have produced a dataset and map identifying all protected areas in the North Sea that intersect with pipelines, platforms, or submarine cables. This analysis can serve as a basis for further exploration of spatial conflict, impact assessment, or policy evaluation.

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Tutorial 2: Exploring the Overlap Between Zooplankton Distributions and Predicted Plaice Nursery Grounds

Overview

In this tutorial, we demonstrate how to access and analyse gridded geospatial data using the emodnet.wcs R package. We explore the spatial overlap between predicted Essential Fish Habitat (EFH) for plaice (Pleuronectes platessa) nursery areas and the relative abundance of key copepod species, which are important prey for plaice larvae and juveniles during early life stages.

Understanding the availability and distribution of zooplankton during the nursery period provides insight into ecological suitability, feeding potential, and larval survival in these critical habitats. While the layers used here represent relative abundance proxies rather than absolute biomass, they still offer valuable information for exploring spatial co-occurrence and potential trophic support patterns.

By combining species-specific WCS layers from EMODnet Biology with habitat suitability layers from EMODnet Seabed Habitats, users will learn how to:

• Retrieve and subset multi-band raster datasets from EMODnet WCS services using emdn_get_coverage()
• Pre-process and compare copepod species distributions (e.g. Acartia spp., Temora longicornis, Calanus finmarchicus)
• Mask copepod rasters using plaice EFH nursery predictions (GRAY_INDEX band) to explore potential overlap
• Visualise and summarise relative co-occurrence patterns using the terra package

Learning Objectives

• Understand how to identify and retrieve relevant raster layers using describe_coverages()
• Use emdn_get_coverage() to download WCS data for a specific area and (optionally) time
• Apply raster alignment, masking, and standardisation for comparative analysis
• Interpret spatial patterns in relative abundance and their ecological relevance

Data Used

Dataset	Source	Layer ID
Predicted EFH for plaice nursery grounds (GRAY_INDEX band)	EMODnet Seabed Habitats	emodnet_open_maplibrary__DK004013_EFH_Plaice_Nursery grounds
Relative abundance of Acartia spp. (small copepods)	EMODnet Biology	Emodnetbio__aca_spp_19582016_L1
Relative abundance of Temora longicornis (medium copepods)	EMODnet Biology	Emodnetbio__tem_lon_19582016_L1_err
Relative abundance of Calanus finmarchicus (large copepods)	EMODnet Biology	Emodnetbio__cal_fin_19582016_L1_err

Geographic and Temporal Scope

This analysis focuses on the Eastern English Channel, a known nursery area for plaice.
Bounding box: xmin = 0.0, ymin = 49.5, xmax = 2.5, ymax = 51.0

For copepod data, we subset the time period to April–May, corresponding to the peak of the nursery phase when food availability is most critical for larval and juvenile plaice.

Suggested Workflow

1. Define spatial extent
   Set the bounding box for the Eastern English Channel.

2. Download copepod layers
   Use emdn_get_coverage() to retrieve relative abundance rasters for Acartia spp., Temora longicornis, and Calanus finmarchicus for April–May.

3. Retrieve EFH nursery habitat
   Use emdn_get_coverage() to access the plaice nursery habitat raster.
   Extract the GRAY_INDEX band, which reflects relative suitability on a continuous scale.

4. Pre-process rasters

   • Align all layers to a common extent and resolution
   • Mask each copepod raster using the nursery ground raster
   • Optionally rescale values (e.g. 0–1) for visual comparison

5. Visualise spatial overlap

   • Plot masked rasters to highlight regions of high copepod abundance within nursery grounds
   • Optionally generate maps of differences or ratios across species

6. Interpret ecological patterns

   • Discuss spatial co-occurrence as a proxy for potential prey availability
   • Reflect on implications for nursery habitat quality across different zooplankton communities

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Tutorial 3: Relating Marine Biodiversity Change to Depth in the North Sea

Overview

In this tutorial, we combine vector and raster data using the emodnet.wfs and emodnet.wcs packages to investigate whether changes in marine biodiversity over time are associated with water depth.

We use a gridded dataset of diversity change metrics derived from benthic species observations, comparing different time periods across the North Sea. These include measures of species loss, species gain, and turnover (β-diversity components). To explore potential environmental drivers, we extract bathymetric values from a mean depth raster and assess their relationship with observed biodiversity change.

Understanding how species communities respond to environmental gradients such as depth helps us contextualise change and prioritise conservation areas where communities may be most vulnerable or resilient.

Learning Objectives

• Use emdn_get_features() to access spatiotemporal diversity metrics from EMODnet Biology (WFS)
• Use emdn_get_coverage() to retrieve raster bathymetry from EMODnet Bathymetry (WCS)
• Perform spatial joins and point-based extraction from raster datasets
• Visualise and summarise patterns of species gain/loss across depth gradients

Data Used

WFS (vector)

• Layer ID: all_diversity_measures\
• Source: EMODnet Biology\
• Key variables:
  • sp_lost_beta_repl: Number of species lost between two time periods\
  • sp_gained_beta_repl: Number of species gained\
  • beta_total: Overall β-diversity\
  • period: Time periods compared (e.g. pre1990_v_post2015)

WCS (raster)

• Layer ID: emodnet__mean_2020\
• Source: EMODnet Bathymetry\
• Description: Mean depth (m), regularly gridded

Geographic Scope

Region: North Sea

Bounding box (WGS84):\

• xmin = 1.5\
• ymin = 53\
• xmax = 6.5\
• ymax = 58

This bounding box is used for both WFS and WCS queries.

Suggested Workflow

1. Use emdn_get_features() to filter the "all_diversity_measures" layer by one or more variables and time periods.
2. Use emdn_get_coverage() to retrieve bathymetry raster over the same bounding box.
3. Use terra::extract() to retrieve depth values for each biodiversity grid point.
4. Visualise results using:
   • Boxplots of species loss/gain across depth bands
   • Scatterplots or GAMs showing trends of β-diversity with depth
5. Optionally: aggregate values spatially or by habitat if combined with additional layers.

Tutorial 4: Exploring Functional Traits Across Seabed Habitats in the Aegean Sea

Overview

This tutorial demonstrates how to integrate EMODnet WFS and WCS services with external EU-based data from the Copernicus Marine Service and the WoRMS trait database to explore the functional composition of benthic communities in the Aegean Sea. Specifically, it investigates how body size, a key ecological trait, varies across different seabed habitats and environmental gradients such as depth and bottom temperature.

By combining habitat mapping, species occurrences, trait data, and environmental rasters, this tutorial highlights practical workflows for multi-source marine ecological analysis using open-access EU marine data infrastructures.

Aims

• Demonstrate combined use of emodnet.wfs, emodnet.wcs, and CopernicusMarine packages
• Retrieve and visualise body size traits for benthic species using the WoRMS API
• Explore trait–environment relationships across habitat types
• Highlight practical geospatial workflows including raster extraction and spatial joins

Data Sources

EMODnet WFS\

• eb185_benthos_full_matched: Benthic species records in the Aegean\
• eu_seabed_habitats_2019 (or equivalent): Seabed habitat polygons

EMODnet WCS\

• emodnet__mean_2020: Bathymetry raster (mean depth of seafloor)

Copernicus Marine Service\

• GLOBAL_MULTIYEAR_PHY_001_030: Bottom temperature and/or salinity reanalysis data, accessed via the CopernicusMarine R package\
• Spatial and temporal subsetting performed using cms_download_subset(), as outlined in pepijn-devries.github.io/CopernicusMarine

External Resources\

• WoRMS traits via worrms::wm_attr_data(): Used to assign body size categories to observed species

Why These Variables?

• Body size is a fundamental trait linked to metabolism, feeding strategy, and ecological role. Its distribution can reveal patterns in community function and response to environmental gradients.
• Seabed habitats structure benthic biodiversity and are central to ecosystem function and conservation assessment.
• Bathymetry provides crucial context on environmental filtering and species zonation.
• Bottom temperature, derived from Copernicus Marine reanalysis, offers insight into thermal niches and habitat suitability, especially for sedentary or temperature-sensitive benthic fauna.

Suggested Workflow

1. Define spatial extent
   Specify a bounding box around the Aegean Sea to spatially filter all layers.

2. Retrieve and filter WFS layers
   Use emodnet.wfs to download benthic species records and habitat polygons, filtering to the Aegean region.

3. Download WCS rasters
   Use emodnet.wcs::emdn_get_coverage() to get bathymetry.
   Use CopernicusMarine::cms_download_subset() to download bottom temperature rasters for the same area.

4. Link WoRMS traits
   For each species, use worrms::wm_attr_data() to retrieve body size traits.

5. Spatial operations

   • Join species points with habitat polygons\
   • Extract raster values (depth, temperature) to species points\
   • Group by habitat type and summarise trait distribution

6. Visualise patterns

   • Map trait-environment relationships\
   • Compare trait distributions across habitat classes and depth zones

7. Interpret ecological signals
   Discuss how trait variation aligns with environmental gradients and habitat types

Issues/ Questions

• What format should the tutorials webpage take? Is a Quarto website ok associated with a GitHub repo? Where should it live (when published and also during development)
• Who will review pull requests? Would Maëlle be available and willing to do this?
• Who should I contact about issues with the data I uncover?
• There is a problem with a number of WCS layers, including the bathymetry layers I'm hoping to use, emanating from OWS4r. Maëlle has already opened issues and linked to an issue already opened by others and discussed in detail here. It will need to be resolved to proceed with atleast one of the proposals.