Data Measures for Habitat

Project Area

RESTORE Goal: Habitat – Priority Attribute: Conversion

DEFINITION The project area proposed for conservation is the area of interest defined or selected by the user. Multiple areas of interest may be aggregated into one project.

Data Summary

• Data Source: Defined by user

• Unit: Acres

• Default Utility Function: The larger the project area (higher score), the better for conservation

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

• Threshold: Discrete utility values ranging from 0 to 1 (see Table 6)

Table 6. Thresholds and utility for project area.

Thresholds ( $km^2$ )	Utility
0	0
0.01-0.40	0.30
0.41-0.80	0.75
0.8-2.0	0.90
>2	1

Threat of Urbanization

RESTORE Goal: Habitat – Priority Attribute: Conversion

DEFINITION Threat of urbanization (ToU) indicates the likelihood of the given project area or area of interest (AoI) being urbanized by the year 2060. A ToU score of zero indicates the AoI is already urbanized. A ToU score of one indicates that there is absolutely no threat of urbanization. A ToU score between zero and one indicates the predicted likelihood of threat in decreasing order.

Data Summary

• Data Source: SLEUTH (Slope, Land use, Exclusion, Urban extent, Transportation and Hillshade) cellular automaton model 3.0 beta with 2060 projections [(Chaudhuri and Clarke 2013), (Clarke 2018), (Clarke 2008), (Terando et al. 2014)]

• Unit: Index

• Default Utility Function: The lower the threat of urbanization (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

  • Urbanized (0), High (>0 to 0.33), Medium (0.34 to 0.66), Low (0.67 to <1), No threat (1)

Work Flow:

1. Reclassify raster values in 16 classes from 0-1000, where Ai represents the area of the ith class within a hexagon.

2. Convert individual classes to vectors.

3. Crop the vector classes to hexagon boundaries and perform spatial joints to obtain areas of each vector class within hexagons.

4. TOU is then calculated as shown in Equation 1.

Equation 7:

$$TOU = 1 - ((\sum_{i=25}^{1000} 0.001i*A_{i})+A_1) \text{, where i } \epsilon \text{ {(25, 50, 100, 200, 300,...,900, 950, 975, 1000)}}$$

Figure 26. Work flow for threat of urbanization.

Table 7. Urbanization Classes based on SLEUTH model

Class	SLEUTH Score
NA	0
$A_{1}$	1
$A_{25}$	25
$A_{50}$	50
$A_{100}$	100
$A_{200}$	200
$A_{300}$	300
$A_{400}$	400
$A_{500}$	500
$A_{600}$	600
$A_{700}$	700
$A_{800}$	800
$A_{900}$	900
$A_{950}$	950
$A_{975}$	975
$A_{1000}$	1000

Connectivity to Existing Protected Area

RESTORE Goal: Habitat – Priority Attribute: Connectivity

DEFINITION Connectivity to existing protected area indicates if the proposed conservation area is close to an area classified as protected by Protected Areas Database of the United States (PAD-US) 2.0 data (Gergeley 2016). Protected areas included International Union for Conservation of Nature (IUCN) Categories Ia-VI and U.S. Geological Survey Gap Analysis Program (GAP) Status 1–4 areas. A binary attribute represents the spatial relationship between a hexagon and a protected area within PAD-US 2.0. Any hexagon that directly intersects or is within a 1 km2 distance of a protected area would count as one, otherwise, zero.

Data Summary

• Data Source: PAD-US 2.0 (Gergeley 2016)

• Unit: Index

• Default Utility Function: Higher connectivity to existing protected land (score = 1) is better for conservation

• Area of Interest Aggregation: The score for the area of interest is the highest binary score from the hexagons that make up the area of interest

• Threshold: Binary utility value

  • Area of interest is within 1 $km^2$ of an existing protected area = 1
  • Area of interest is beyond 1 $km^2$ of an existing protected area = 0

Work Flow:

1. Create a 1 km buffer around PAD-US data.

2. Perform spatial overlay of the PAD-US buffer with hexagon boundaries.

3. Classify the hexagons as connected (1) or not connected (0) based on the overlap between a hexagon and the buffer.

Figure 27: Work flow for connectivity for existing protected area

Connectivity of Natural Lands

RESTORE Goal: Habitat – Priority Attribute: Connectivity

DEFINITION A percent attribute that stands for the proportion of area classified as a hub or corridor, according to the Intact Habitat Cores layer from Esri’s Green Infrastructure Initiative (Perkl 2019). This attribute prioritizes large protected areas and critical corridor connections.

Data Summary

• Data Source: Esri Intact Habitat Cores (Perkl 2019)

• Unit: Percentage (%)

• Default Utility Function: A higher proportion of natural connectivity (higher score) is 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: Continuous utility value ranging from 0 to 1

Work Flow:

1. Convert the Intact Habitat Cores layer to vector format.

2. Merge Hub and Connectivity layers.

3. Perform spatial joint with hexagon boundaries to extract areas.

Figure 28: Work flow for structural connectivity

Composition of Priority Natural Lands

RESTORE Goal: Habitat – Priority Attribute: Patch Size

DEFINITION This attribute prioritizes rare habitat types and those that have been identified as conservation priorities in state and regional plans. Scores reflect the proportion (%) of each area of interest that is covered by a priority land cover.

Data Summary

• Data Source: Gap Analysis Program (GAP) (USGS 2011); Florida Cooperative Land Cover (FL CLC) (FL-FWS 2019); Texas Ecological Mapping Systems (TX EMS) (TX-TPWD 2019)

• Unit: Percentage (%)

• Default Utility Function: A higher proportion of natural lands (higher score) is 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 with increments of 0.5 (see Table 8)

  • Tier 1 lands, highest quality (1.0)

  • Tier 2 lands (0.50)

  • Tier 3 lands (0.0)

Work Flow:

1. Mosaic TX, FL, and GAP data together.

2. Compile appropriate classes within assigned categories.

3. Resample TX to 30 m resolution.

4. Rank classifications of landcover within the hexagons (see Table 2).

Table 8. Categorization (tier) of landcover classes for ranking in the composition of natural lands.

Tier Number	Land Cover
Tier 1 (score of 1)	State Wildlife Action Plan (SWAP) Tier 1 habitats Gulf Coast Vulnerability Assessment ecosystems: mangrove tidal emergent marsh oyster reef barrier islands LCC priority habitats: GCP LCC, GCPO LCC, PF LCC Other prioritized natural lands not overlapping with Tier 1
Tier 2 (score of 0.50)	Managed pine, rangelands, hay, crop lands, Low density residential
Tier 3 (score of 0)	Urban Commercial Barren Open water

References

Chaudhuri, Gargi, and Keith Clarke. 2013. “The Sleuth Land Use Change Model: A Review.” Environmental Resources Research 1 (1): 88–105. https://doi.org/10.22069/ijerr.2013.1688.

Clarke, K.C. 2018. “Project Gigalopolis-Sleuth Urbanization Model.” USGS; UCSB. http://www.ncgia.ucsb.edu/projects/gig/index.html.

Clarke, K.C. 2008. “Mapping and Modelling Land Use Change: An Application of the Sleuth Model.” In Landscape Analysis and Visualisation: Spatial Models for Natural Resource Management and Planning, edited by Christopher Pettit, William Cartwright, Ian Bishop, Kim Lowell, David Pullar, and David Duncan, 353–66. Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-69168-6_17.

FL-FWS. 2019. “Florida FWS-Cooperative Land Cover, Version 3.3.” Tallahassee, FL, USA: Florida Fish; Wildlife Conservation Commission.

Gergeley, A, K.J.; McKerrow. 2016. “PAD-Us—National Inventory of Protected Areas.” Reston, VA, USA: USGS. https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-data-download?qt-science_center_objects=0#qt-science_center_objects.

Perkl, R. 2019. “Intact Habitat Cores Map.” https://www.arcgis.com/home/item.html?id=b9155ee0caa04fcf9744e25cad76b44a.

Terando, AJ, J Costanza, C Belyea, R.R. Dunn, A. McKerrow, and J.A. Collazo. 2014. “The Southern Megalopolis: Using the Past to Predict the Future of Urban Sprawl in the Southeast U.s.” PLoS ONE. https://doi.org/10.1371/journal.pone.0102261.

TX-TPWD. 2019. “Texas Ecological Mapping Systems-Txems.” Austin, TX, USA: Texas Parks; Wildlife. https://tpwd.texas.gov/landwater/land/programs/landscape-ecology/ems/emst.

USGS. 2011. “U.S. Geological Survey Gap Analysis Program; Gap/Landfire National Terrestrial Ecosystems.” Reston, VA, USA: USGS.