Published

2025-06-13 22:41:02

1 BOEM Marine Environmental Sensitivity Analysis

1.1 Updated Methodology for 2025 Planning Areas

1.1.1 Summary of Methodology

BOEM is required under Section 18(a)(2)(G) of the OCS Lands Act to consider the relative environmental sensitivity and marine productivity of the OCS when making decisions regarding the schedule of lease sales for the National OCS Program. Building upon previous assessments, this updated methodology leverages the best available species distribution models and high-resolution environmental data to provide enhanced spatial resolution and taxonomic coverage for environmental sensitivity analysis.

The current approach combines vulnerability and resilience of marine ecosystems to potential impacts from offshore energy development activities within the context of existing environmental conditions and climate change. This analysis incorporates multiple authoritative data sources downscaled to 0.05° resolution rasters before aggregating to BOEM Planning Areas by taxonomic category.

1.1.2 Updated Methodological Framework

flowchart LR
    A[Area of Analysis] --> B[Data Sources]
    A --> C[Processing]
    A --> D[Final Scoring]
    
    B --> B1[Species Distribution Models]
    B --> B2[Environmental Data]
    B --> B3[Habitat Data]
    
    B1 --> B1a[AquaMaps Global SDMs]
    B1 --> B1b[NMFS ESA Critical Habitats]
    B1 --> B1c[NCCOS Seabird Distributions]
    B1 --> B1d[NOAA GoA Cetacean/Turtle Models]
    B1 --> B1e[NCCOS Deep Sea Corals]
    B1 --> B1f[Duke Atlantic Cetacean DSMs]
    B1 --> B1g[SWFSC Pacific Cetacean DSMs]
    
    B2 --> B2a[Bio-Oracle Primary Productivity]
    B2 --> B2b[Climate Variables]
    
    B3 --> B3a[Benthic Habitats]
    B3 --> B3b[Critical Habitats]
    
    C --> C1[Downscale to 0.05° Resolution]
    C --> C2[Taxonomic Categorization]
    C --> C3[Ecoregional Scaling]
    
    C2 --> C2a[Crustaceans]
    C2 --> C2b[Fish]
    C2 --> C2c[Marine Mammals]
    C2 --> C2d[Mollusks]
    C2 --> C2e[Sea Turtles]
    C2 --> C2f[Seabirds]
    C2 --> C2g[Other Taxa]
    
    C3 --> C3a[Min/Max Scaling by Ecoregion]
    
    D --> D1[Planning Area Aggregation]
    D --> D2[Equal Weight Averaging]
    D --> D3[Flower Plot Visualization]
    
    D1 --> D1a[Taxonomic Scores]
    D1 --> D1b[Primary Productivity Score]
    D1 --> D1c[Combined Sensitivity Score]
    
    style B1 fill:#e1f5fe
    style B2 fill:#f3e5f5
    style B3 fill:#e8f5e8
    style C2 fill:#fff3e0
    style D3 fill:#ffebee

1.1.3 Data Sources and Coverage

1.1.3.1 Species Distribution Models

AquaMaps Global Species Distributions

Global coverage for ~17,500 marine species
0.5° native resolution, downscaled to 0.05°
Includes fish, invertebrates, marine mammals, and sea turtles
Habitat suitability index (0-100)

NMFS ESA Critical Habitats

Federally designated critical habitats for threatened and endangered species
Vector polygons converted to 0.05° presence/absence rasters
High conservation importance weighting

NCCOS Seabird Distributions

Atlantic and Pacific seasonal distribution models
Species-specific abundance and occurrence predictions
Integrated with eBird and survey data

NOAA Gulf of America Spatial Models

High-resolution cetacean and sea turtle density surface models
Monthly and seasonal predictions
Accounts for oceanographic variability

NCCOS Deep Sea Coral Data

Gulf of America deep-sea coral and sponge distributions
Benthic habitat associations
Vulnerability to bottom disturbance

Duke University Cetacean Models (Atlantic)

Density surface models for Atlantic cetaceans
Environmental predictors and seasonal patterns
Uncertainty quantification

SWFSC Cetacean Models (Pacific)

West Coast cetacean density surface models
California Current ecosystem focus
Integration with ecosystem indicators

1.1.3.2 Environmental Data

Bio-Oracle Primary Productivity

Global phytoplankton concentrations
Monthly climatologies (2000-2020)
0.05° native resolution
Units: mmol/m³ surface phytoplankton

1.1.4 Processing Methodology

1.1.4.1 Spatial Standardization

All data sources are processed to a common 0.05° decimal degree grid (~5.5 km resolution) using:

Bilinear interpolation for continuous variables (suitability, density, productivity)
Nearest neighbor for categorical variables (habitat types, presence/absence)
Area-weighted aggregation for polygon-to-raster conversion
Temporal averaging for multi-temporal datasets

1.1.4.2 Taxonomic Categorization

Species are classified into standardized taxonomic categories:

Crustaceans: Decapods, copepods, krill, and other arthropods
Fish: All ray-finned and cartilaginous fish species
Marine Mammals: Cetaceans, pinnipeds, sirenians, and sea otters
Mollusks: Gastropods, bivalves, cephalopods
Sea Turtles: All marine turtle species
Seabirds: Marine and coastal bird species
Other Taxa: Cnidarians, echinoderms, and remaining groups

1.1.4.3 Extinction Risk Integration

Species are weighted by IUCN Red List conservation status:

Critically Endangered (CR): 1.0
Endangered (EN): 0.8
Vulnerable (VU): 0.6
Near Threatened (NT): 0.4
Least Concern (LC): 0.2

1.1.4.4 Ecoregional Scaling

To enable comparative analysis across biogeographically distinct regions, scores are scaled using ecoregional min-max normalization:

\[Score_{scaled} = \frac{Score_{raw} - Min_{ecoregion}}{Max_{ecoregion} - Min_{ecoregion}} \times 100\]

This approach accounts for natural differences in species richness and productivity between Arctic, temperate, and tropical ecosystems.

1.1.5 BOEM Ecoregions (Updated 2025)

The analysis uses nine BOEM ecoregions that align with Large Marine Ecosystem boundaries:

Chukchi and Beaufort Seas - Arctic marine ecosystem
Eastern Bering Sea - Subarctic shelf ecosystem
Gulf of Alaska - Transitional subarctic ecosystem
Washington/Oregon - Upwelling-dominated temperate ecosystem
California Current - Eastern boundary current ecosystem
Western and Central Gulf of America - Subtropical shelf ecosystem
Eastern Gulf of America - Carbonate platform ecosystem
Southeast U.S. Continental Shelf - Western Atlantic warm temperate
Northeast U.S. Continental Shelf - Western Atlantic cold temperate

Note: Gulf of Mexico has been renamed to Gulf of America in 2025 planning documents.

1.1.6 Planning Area Aggregation

Within each planning area, taxonomic and productivity scores are calculated as:

Cell-level scoring: Combine species suitability with extinction risk weights
Taxonomic aggregation: Sum weighted scores within each taxonomic category
Spatial aggregation: Area-weighted mean across planning area cells
Final scoring: Equal-weighted average of taxonomic and productivity components

1.1.7 Visualization: Flower Plot

Results are visualized using a flower plot format where:

Petal length: Represents the scaled sensitivity score (0-100)
Petal width: Represents relative importance/weight
Petal color: Distinguishes taxonomic categories and primary productivity
Center score: Overall sensitivity index for the planning area

# Example flower plot structure
components <- c("Crustaceans", "Fish", "Marine Mammals", 
               "Mollusks", "Sea Turtles", "Seabirds", 
               "Other Taxa", "Primary Productivity")
scores <- c(45, 67, 82, 34, 91, 56, 28, 73)
weights <- rep(1, 8)  # Equal weighting

plot_flower(
  categories = components,
  heights = scores,
  widths = weights,
  title = "Alaska Planning Area Sensitivity"
)

1.1.8 Climate Change Integration

The ecosystem change vulnerability index incorporates:

Temperature change: Regional warming trends
Sea ice dynamics: Arctic and subarctic changes
Ocean acidification: pH and aragonite saturation
Sea level rise: Coastal habitat impacts
Storm intensity: Extreme weather patterns
Species composition: Range shifts and invasions
Permafrost thaw: Arctic-specific impacts

Scores range from 0-4 and are added to base sensitivity scores.

1.1.9 Model Validation and Uncertainty

The updated methodology includes:

Cross-validation with independent survey data
Uncertainty propagation from input data sources
Sensitivity analysis to key parameter assumptions
Expert review of taxonomic classifications and weights
Temporal validation using historical data splits

1.1.10 Applications and Limitations

Applications:

Comparative ranking of planning areas for lease sale scheduling
Environmental impact assessment support
Marine spatial planning and conflict identification
Research prioritization and monitoring design

Limitations:

Model resolution limits fine-scale assessment
Data gaps for some taxonomic groups and regions
Static analysis doesn’t capture dynamic oceanographic processes
Equal weighting may not reflect all stakeholder values

1.1.11 Future Enhancements

Planned improvements include:

Dynamic modeling: Seasonal and interannual variability
Ecosystem services: Quantification of marine ecosystem benefits
Cumulative impacts: Integration with existing stressors
Stakeholder weighting: Alternative value frameworks
Real-time updates: Integration with monitoring networks

1.1.12 References

Balcom, B.J., et al. (2011). A Comparison of Marine Productivity Among Outer Continental Shelf Planning Areas. Final Report.
BOEM (2014). Environmental Sensitivity Analysis for the Marine Environment. OCS Study BOEM 2014-037.
Kaschner, K., et al. (2019). AquaMaps: Predicted range maps for aquatic species. Version 10/2019.

This methodology represents a significant advancement in marine environmental sensitivity assessment, providing enhanced spatial resolution, taxonomic coverage, and scientific rigor for offshore energy planning decisions.

# BOEM Marine Environmental Sensitivity Analysis ## Updated Methodology for 2025 Planning Areas ### Summary of Methodology BOEM is required under Section 18(a)(2)(G) of the OCS Lands Act to consider the relative environmental sensitivity and marine productivity of the OCS when making decisions regarding the schedule of lease sales for the National OCS Program. Building upon previous assessments, this updated methodology leverages the best available species distribution models and high-resolution environmental data to provide enhanced spatial resolution and taxonomic coverage for environmental sensitivity analysis. The current approach combines vulnerability and resilience of marine ecosystems to potential impacts from offshore energy development activities within the context of existing environmental conditions and climate change. This analysis incorporates multiple authoritative data sources downscaled to 0.05° resolution rasters before aggregating to BOEM Planning Areas by taxonomic category. ### Updated Methodological Framework ```{mermaid} flowchart LR A[Area of Analysis] --> B[Data Sources] A --> C[Processing] A --> D[Final Scoring] B --> B1[Species Distribution Models] B --> B2[Environmental Data] B --> B3[Habitat Data] B1 --> B1a[AquaMaps Global SDMs] B1 --> B1b[NMFS ESA Critical Habitats] B1 --> B1c[NCCOS Seabird Distributions] B1 --> B1d[NOAA GoA Cetacean/Turtle Models] B1 --> B1e[NCCOS Deep Sea Corals] B1 --> B1f[Duke Atlantic Cetacean DSMs] B1 --> B1g[SWFSC Pacific Cetacean DSMs] B2 --> B2a[Bio-Oracle Primary Productivity] B2 --> B2b[Climate Variables] B3 --> B3a[Benthic Habitats] B3 --> B3b[Critical Habitats] C --> C1[Downscale to 0.05° Resolution] C --> C2[Taxonomic Categorization] C --> C3[Ecoregional Scaling] C2 --> C2a[Crustaceans] C2 --> C2b[Fish] C2 --> C2c[Marine Mammals] C2 --> C2d[Mollusks] C2 --> C2e[Sea Turtles] C2 --> C2f[Seabirds] C2 --> C2g[Other Taxa] C3 --> C3a[Min/Max Scaling by Ecoregion] D --> D1[Planning Area Aggregation] D --> D2[Equal Weight Averaging] D --> D3[Flower Plot Visualization] D1 --> D1a[Taxonomic Scores] D1 --> D1b[Primary Productivity Score] D1 --> D1c[Combined Sensitivity Score] style B1 fill:#e1f5fe style B2 fill:#f3e5f5 style B3 fill:#e8f5e8 style C2 fill:#fff3e0 style D3 fill:#ffebee ``` ### Data Sources and Coverage #### Species Distribution Models **AquaMaps Global Species Distributions** - Global coverage for ~17,500 marine species - 0.5° native resolution, downscaled to 0.05° - Includes fish, invertebrates, marine mammals, and sea turtles - Habitat suitability index (0-100) **NMFS ESA Critical Habitats** - Federally designated critical habitats for threatened and endangered species - Vector polygons converted to 0.05° presence/absence rasters - High conservation importance weighting **NCCOS Seabird Distributions** - Atlantic and Pacific seasonal distribution models - Species-specific abundance and occurrence predictions - Integrated with eBird and survey data **NOAA Gulf of America Spatial Models** - High-resolution cetacean and sea turtle density surface models - Monthly and seasonal predictions - Accounts for oceanographic variability **NCCOS Deep Sea Coral Data** - Gulf of America deep-sea coral and sponge distributions - Benthic habitat associations - Vulnerability to bottom disturbance **Duke University Cetacean Models (Atlantic)** - Density surface models for Atlantic cetaceans - Environmental predictors and seasonal patterns - Uncertainty quantification **SWFSC Cetacean Models (Pacific)** - West Coast cetacean density surface models - California Current ecosystem focus - Integration with ecosystem indicators #### Environmental Data **Bio-Oracle Primary Productivity** - Global phytoplankton concentrations - Monthly climatologies (2000-2020) - 0.05° native resolution - Units: mmol/m³ surface phytoplankton ### Processing Methodology #### Spatial Standardization All data sources are processed to a common 0.05° decimal degree grid (~5.5 km resolution) using: 1. **Bilinear interpolation** for continuous variables (suitability, density, productivity) 2. **Nearest neighbor** for categorical variables (habitat types, presence/absence) 3. **Area-weighted aggregation** for polygon-to-raster conversion 4. **Temporal averaging** for multi-temporal datasets #### Taxonomic Categorization Species are classified into standardized taxonomic categories: - **Crustaceans**: Decapods, copepods, krill, and other arthropods - **Fish**: All ray-finned and cartilaginous fish species - **Marine Mammals**: Cetaceans, pinnipeds, sirenians, and sea otters - **Mollusks**: Gastropods, bivalves, cephalopods - **Sea Turtles**: All marine turtle species - **Seabirds**: Marine and coastal bird species - **Other Taxa**: Cnidarians, echinoderms, and remaining groups #### Extinction Risk Integration Species are weighted by IUCN Red List conservation status: - **Critically Endangered (CR)**: 1.0 - **Endangered (EN)**: 0.8 - **Vulnerable (VU)**: 0.6 - **Near Threatened (NT)**: 0.4 - **Least Concern (LC)**: 0.2 #### Ecoregional Scaling To enable comparative analysis across biogeographically distinct regions, scores are scaled using ecoregional min-max normalization: $$Score_{scaled} = \frac{Score_{raw} - Min_{ecoregion}}{Max_{ecoregion} - Min_{ecoregion}} \times 100$$ This approach accounts for natural differences in species richness and productivity between Arctic, temperate, and tropical ecosystems. ### BOEM Ecoregions (Updated 2025) The analysis uses nine BOEM ecoregions that align with Large Marine Ecosystem boundaries: 1. **Chukchi and Beaufort Seas** - Arctic marine ecosystem 2. **Eastern Bering Sea** - Subarctic shelf ecosystem 3. **Gulf of Alaska** - Transitional subarctic ecosystem 4. **Washington/Oregon** - Upwelling-dominated temperate ecosystem 5. **California Current** - Eastern boundary current ecosystem 6. **Western and Central Gulf of America** - Subtropical shelf ecosystem 7. **Eastern Gulf of America** - Carbonate platform ecosystem 8. **Southeast U.S. Continental Shelf** - Western Atlantic warm temperate 9. **Northeast U.S. Continental Shelf** - Western Atlantic cold temperate *Note: Gulf of Mexico has been renamed to Gulf of America in 2025 planning documents.* ### Planning Area Aggregation Within each planning area, taxonomic and productivity scores are calculated as: 1. **Cell-level scoring**: Combine species suitability with extinction risk weights 2. **Taxonomic aggregation**: Sum weighted scores within each taxonomic category 3. **Spatial aggregation**: Area-weighted mean across planning area cells 4. **Final scoring**: Equal-weighted average of taxonomic and productivity components ### Visualization: Flower Plot Results are visualized using a flower plot format where: - **Petal length**: Represents the scaled sensitivity score (0-100) - **Petal width**: Represents relative importance/weight - **Petal color**: Distinguishes taxonomic categories and primary productivity - **Center score**: Overall sensitivity index for the planning area ```r # Example flower plot structure components <- c("Crustaceans", "Fish", "Marine Mammals", "Mollusks", "Sea Turtles", "Seabirds", "Other Taxa", "Primary Productivity") scores <- c(45, 67, 82, 34, 91, 56, 28, 73) weights <- rep(1, 8) # Equal weighting plot_flower( categories = components, heights = scores, widths = weights, title = "Alaska Planning Area Sensitivity" ) ``` ### Climate Change Integration The ecosystem change vulnerability index incorporates: - **Temperature change**: Regional warming trends - **Sea ice dynamics**: Arctic and subarctic changes - **Ocean acidification**: pH and aragonite saturation - **Sea level rise**: Coastal habitat impacts - **Storm intensity**: Extreme weather patterns - **Species composition**: Range shifts and invasions - **Permafrost thaw**: Arctic-specific impacts Scores range from 0-4 and are added to base sensitivity scores. ### Model Validation and Uncertainty The updated methodology includes: - **Cross-validation** with independent survey data - **Uncertainty propagation** from input data sources - **Sensitivity analysis** to key parameter assumptions - **Expert review** of taxonomic classifications and weights - **Temporal validation** using historical data splits ### Applications and Limitations **Applications:** - Comparative ranking of planning areas for lease sale scheduling - Environmental impact assessment support - Marine spatial planning and conflict identification - Research prioritization and monitoring design **Limitations:** - Model resolution limits fine-scale assessment - Data gaps for some taxonomic groups and regions - Static analysis doesn't capture dynamic oceanographic processes - Equal weighting may not reflect all stakeholder values ### Future Enhancements Planned improvements include: - **Dynamic modeling**: Seasonal and interannual variability - **Ecosystem services**: Quantification of marine ecosystem benefits - **Cumulative impacts**: Integration with existing stressors - **Stakeholder weighting**: Alternative value frameworks - **Real-time updates**: Integration with monitoring networks ### References - Balcom, B.J., et al. (2011). A Comparison of Marine Productivity Among Outer Continental Shelf Planning Areas. Final Report. - BOEM (2014). Environmental Sensitivity Analysis for the Marine Environment. OCS Study BOEM 2014-037. - Kaschner, K., et al. (2019). AquaMaps: Predicted range maps for aquatic species. Version 10/2019. --- *This methodology represents a significant advancement in marine environmental sensitivity assessment, providing enhanced spatial resolution, taxonomic coverage, and scientific rigor for offshore energy planning decisions.*