Data Measures for Living Coastal & Marine Resources

Vulnerable Areas of Terrestrial Endemic Species

RESTORE Goal: Living Coastal & Marine Resources – Priority Attribute: Biodiversity

DEFINITION This measure represents the ratio of endemic species to the amount of protected land in the contiguous U.S. The endemic taxa considered are mammals, birds, amphibians, reptiles, freshwater fish, and trees. A score of 0 represents the least vulnerable areas (low priority for conservation). A score of 10 represents the most vulnerable areas (high priority for conservation).

Data Summary

• Data Source: Jenkins et al. 2015, “US Protected Lands Mismatch Biodiversity Priorities.”

• Unit: Index

• Default Utility Function: The greater the amount of vulnerable area (higher score), the better for conservation

• Area of Interest Aggregation: Maximum score from the hexagons inside the area of interest

• Threshold: Continuous utility value ranging from 0 to 1

  • 0-3 (Low); 3-6 (Medium); 6-10 (High)

Workflow:

1. Classify the raster into 10 classes.

2. Vectorize 10 classes.

3. Perform spatial join with the hexagon boundaries.

4. Compute the mean of biodiversity values within each hexagon.

Threatened and Endangered Species - Critical Habitat Area

RESTORE Goal: Living Coastal and Marine Resources – Priority Attribute: T&E Species

DEFINITION The measure is based on the U.S. Fish and Wildlife Service designated federally threatened and endangered (T&E) critical habitat (USFWS 2018b). The value in each hexagon is the cumulative percent (%) area of critical habitats for all T&E species.

Data Summary

• Data Source: USFWS Threatened and Endangered Species IPaC (USFWS 2018b)

• Unit: Percentage (%)

• Default Utility Function: The greater amount of critical habitat (higher score), the better for conservation

• Area of Interest Aggregation: The score for the area of interest is the summed score from the hexagons inside the area of interest

• Threshold: Discrete utility value ranging from 0 to 1 (see Table 9)

Workflow:

1. Exclude the species not found in the SCA region and the areas of critical habitat not contained in the SCA region.

2. For each species i, calculate the proportion of critical habitat for species in the SCA region contained within a hexagon (Asi) using Equation 10.

3. Calculate T&E percent area using Equation 11.

Equation 10:

$$[A_{s_i} = \frac{\text{Area of critical habitat for species } i \text{ that intersects with the hexagon}}{\text{Total area of critical habitat for species }i}]$$

Equation 11:

$$[\text{Threatened and Endangered Percentage Area} = \sum_{i} A_{s_i}]$$

Table 9. Threatened and Endangered Species Critical Habitat Area

Value ($km^2$)	Utility
0	0
.001-0.02	0.75
0.021-0.06	0.9
>0.06	1

Threatened and Endangered Species - Number of Species

RESTORE Goal: Living Coastal and Marine Resources – Priority Attribute: T&E Species

DEFINITION This attribute measures the number of federally threatened and endangered (T&E) species that have habitat ranges identified within each hexagon.

Data Summary

• Data Source: USFWS Threatened and Endangered Species range maps (USFWS 2018a)

• Unit: Count (#)

• Default Utility Function: The higher the number of threatened and endangered species (higher score), the better for conservation

• Area of Interest Aggregation: Maximum score from the hexagons inside the area of interest

• Threshold: Discrete utility value ranging from 0 to 1 (see Table 10)

Workflow:

1. Perform spatial joint of the data with hexagon boundaries

2. Obtain the counts within each hexagon (Table 10).

Table 10. Threatened and Endangered Species Count.

Value (Count #)	Utility
0	0
1	0.9
2	0.95
>2	1

Light Pollution Index

RESTORE Goal: Living Coastal and Marine Resources – Priority Attribute: Urban Impacts

DEFINITION An index that measures the intensity of light pollution within each hexagon. A score of zero indicates that the sky above the hexagon is already polluted/bright, and a score of >0 to 1 indicates light pollution (LP) in decreasing order.

Data Summary

• Data Source: VIIRS 2017 produced by the The New World Atlas of Artificial Night Sky Brightness (Falchi et al. 2018)

• Unit: Index

• Default Utility Function: The less light pollution (higher score), the better for conservation

• Area of Interest Aggregation: Maximum score from the hexagons inside the area of interest

• Threshold: Discrete utility value ranging from 0 to 1 (see Table 11)

Workflow:

1. Reclassify raster values from 0–30 to zero (Z), low (L), medium (M), and high (H) as per the thresholds shown in Table 11 below.

2. Convert zero, low, medium, and high classes to vectors.

3. Crop the vector classes to hexagon boundaries and perform spatial joints to obtain areas (AZ, AL, AM, and AH).

4. The LP index is then calculated as shown in Equation 12.

Equation 12:

$$[\text{Light Pollution Index} = 1 - (0.33A_{L} + 0.66A_{M} + A_{H})]$$

Table 11. Description of discretized values used to calculate Light Pollution Index.

Class	Light Pollution Score
High (completely urban) - AZ	0
Medium - AL	1-10
Low - AM	11-20
Zero (no urbanization) - AH	21-30

Terrestrial Vertebrate Biodiversity

RESTORE Goal: Living Coastal & Marine Resources – Priority Attribute: Biodiversity

DEFINITION This measure represents the average number of mammal, bird, amphibian, and reptile species identified in an area.

Data Summary

• Data Source: Jenkins et al. 2015, “US Protected Lands Mismatch Biodiversity Priorities.”; Source data from BiodiversityMapping.org

• Unit: Count

• Default Utility Function: The greater the number of species/more biodiverse (higher score), the better for conservation

• Area of Interest Aggregation: Maximum score from the hexagons inside the area of interest

• Threshold: Discrete utility values ranging from 0 to 1

  • 0 - 25 : 0

  • 25 - 62 : 0.5

  • 62 - 70 : 0.6

  • 70 - 77 : 0.7

  • 77 - 84 : 0.8

  • 84 - 90 : 0.9

  • 90 - 100 : 1

Workflow:

1. Convert shapefiles to rasters (4 in total)

2. Add the four rasters together, then calculate the mean to determine the average number of species per hexagon

Vulnerability to Invasive Plants

RESTORE Goal: Living Coastal & Marine Resources – Priority Attribute: Invasive Species

DEFINITION This measure represents an area's average probability of invasion from 31 different invasive plant species found within the Gulf Coast Region of the United States. A score of 0 represents the lowest probability of being invaded. A score of 1 represents the highest probability of being invaded.

Data Summary

• Data Source: Lázaro-Lobo, A., Evans, K., & Ervin, G. (2020). Evaluating landscape characteristics of predicted hotspots for plant invasions. Invasive Plant Science and Management, 13(3), 163-175. doi:10.1017/inp.2020.21

• Unit: Percent (%)

• Default Utility Function: The higher the probability of being invaded (higher score), the better for conservation

• Area of Interest Aggregation: Average score from the hexagons inside the area of interest

• Threshold: Continuous utility values ranging from 0 to 1

Workflow:

1. Take an average of all 31 raster files

2. Using the output raster, run zonal statistics to determine average vulnerability to invasive plants for each hexagon

References

Falchi, F, P Cinzano, D Duriscoe, C.C.M. Kyba, C.D. Elvidge, K. Baugh, B. Portnov, N.A. Rybnikova, and R. Furgoni. 2018. “Supplement to: The New World Atlas of Artificial Night Sky Brightness. GFZ Data Services (2016).” http://dataservices.gfz-potsdam.de/contact/showshort.php?id=escidoc:1541893&contactform.

Jenkins, Clinton N., Kyle S. Van Houtan, Stuart L. Pimm, and Joseph O. Sexton. 2015. “US Protected Lands Mismatch Biodiversity Priorities.” Proceedings of the National Academy of Sciences 112 (16): 5081–6. https://doi.org/10.1073/pnas.1418034112.

USFWS. 2018a. “U.S. Fish and Wildlife Service Environmental Conservation Online System All Threatened and Endangered Species Range.” https://ecos.fws.gov/ecp/.USFWS. 2018b. “U.S. Fish and Wildlife Service Environmental Conservation Online System Critical Habitat Report.” https://ecos.fws.gov/ecp/report/table/critical-habitat.html.