DRCN Optimization CEA Selection

Name: DRCN Optimization CEA Selection
Creator: U.S. Fish & Wildlife Service
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LOGIC SCORING OF PREFERENCE METHOD AND OPTIMIZATION MODEL The first step in realizing the Delmarva Restoration and Conservation Network (DRCN) Strategy was to agree on our Vision for Delmarva, and outline the Goals, Objectives, and Strategies that we will employ to achieve that Vision. Next we identified and mapped the important natural resource characteristics of the landscape in order visualize restoration and conservation priorities. The third step was to use our collective professional knowledge to rank and prioritize land parcels based on those characteristics using a structured decision-making approach known as the Logic Scoring of Preference (LSP) method. The final step in the process was to include funding to further analyze the parcels by using an optimization model to identify a suite of projects that will get us the most natural resource value with a finite budget. Logic Scoring of Preference Project Methodology On October 9, 2019, DRCN convened a structured decision-making LSP workshop to help inform its Strategic Restoration and Conservation Action Plan for the Delmarva Peninsula. The LSP method helps ensure that important decision-making criteria are included in the evaluation and that project evaluation is based on the fundamental properties of human reasoning. The LSP method uses “attribute trees” with weightings and logic structures as the organizing method for ensuring that decision support models reflect the desired intent of decision makers (Appendix 1). Once areas have been evaluated for the fish and wildlife habitat, water quality, coastal resilience, and working landscape values, the DRCN partners can identify relevant sources of funding to implement projects within each category and forecast annual revenue amounts for the next few years to set as a “budget” for optimization scenarios. For more information on the LSP method and optimization, see the following <a href='http://www.willallen.com/JCP/JCP_2011_V07_6_Allen.pdf' rel='nofollow ugc'>journal article</a> and recent <a href='https://www.amazon.com/Soft-Computing-Evaluation-Logic-Applications/dp/1119256453' rel='nofollow ugc'>book</a>. Filtering the Parcel Analysis There are over 755,000 unique parcel PINs totaling over 3.68 million acres in these nine Delmarva counties. Many of these parcels are not suitable for restoration or conservation investments within the focus areas of the Delmarva RCN partners. We removed parcels less than 20 acres and those confirmed to be managed lands under fee simple protection or conservation easements. We used the owner name field in the CoreLogic parcel data that The Conservation Fund uses on a license restricted basis as well as the Protected Areas Database of the US (PADUS) and the National Conservation Easement Database (NCED) to reduce the number of parcels analyzed using the Logic Scoring of Preference (LSP) method to only 26,617 – about 3.5% of all parcels, totaling over 1.9 million acres – about 53% of the acreage. There are still 184 parcels totaling 13,234 acres (less than 1% of the analyzed acreage) with a blank owner name in the parcel database, which usually means they are tax exempt and therefore publicly owned. We have kept them in for GIS analysis purposes for this workshop but would remove those from any future optimization scenarios if we were able to confirm they were already publicly owned and/or protected. Quick summary of the analyzed parcels in the Delmarva peninsula Ø All: 26,617 parcels | 1,913,935 acres | Range: 20 – 1,286 acres Ø DE: 8,566 parcels | 564,373 acres | Range: 20-1,141 acres Ø MD: 14,653 parcels | 1,133,599 acres | Range: 20 – 1,286 acres Ø VA: 3,398 parcels | 215,963 acres | Range: 20 – 1,210 acres o PADUS in Delmarva: 706,040 acres o NCED in Delmarva: 451,952 acres The resulting plan identifies the most important places to protect and restore and describes how the DRCN partners will work together to obtain funding for on-the-ground restoration and conservation investments. Workshop Project Stations Each branch of the LSP attribute tree had its own station with 3-4 maps associated with the four restoration and conservation investment types: wildlife habitat, water quality, coastal resiliency, and working lands (Appendix 2). Feedback from workshop attendees (Appendix 3) was used to assign weightings and logic structures for the LSP attribute tree as well as to inform the optimization scenarios for restoration and conservation investments. Sample maps and models using the LSP method were developed so that stakeholders can visualize potential conservation and restoration priorities: a) 11 Fish & Wildlife Habitat (Figure 1), b) 121 Water Quality Protection Value (Figure 2), and c) 122 Water Quality Restoration Value (Figure 3). The LSP results for Climate Resiliency and Working Lands were inconclusive. Employing the LSP method to those landscape characteristics will require further refinement of the corresponding attribute trees. Table 1. Attribute Table with Weights provided by October 9, 2019 workshop Fish & Wildlife Habitat 1 Delmarva Restoration and Conservation Network – LSP Attribute Tree 11 Fish and Wildlife Habitat 40% 111 Fish & Wildlife Habitat Connected Networks 40% 1111 Acreage of Connected Network Designations* in Parcel 40% 1112 Percent of Parcel with Connected Network Designations 20% 1113 Total Parcel Size *Connected Network Designations Nature’s Network Terrestrial Cores Aquatic Cores Imperiled Species Cores Grassland Cores State Green Infrastructure Network Designations Maryland Green Infrastructure Delaware Ecological Network Virginia Conservation Vision 35% 112 Representative Species Landscape Capability 40% 1121 Number of Representative Species* 40% 1122 Acreage of Representative Species Suitable Habitat 20% 1123 Percent of Parcel with Representative Species Habitat * Representative Species Uplands: Wood Thrush, Ovenbird, Eastern Box Turtle, Prairie Warbler, Red-Shouldered Hawk Riparian Forests, Wetlands: Louisiana Woodthrush, Wood Duck, American Woodcock Marshes: American Black Duck (Breeding & Non-Breeding), Virginia Rail, Snowy Egret, Saltmarsh Sparrow Beach & Near Shore: Northern Diamond-Backed Terrapin, American Oystercatcher, Sanderling 25% 113 Proximity to existing protected/managed land (PADUS + NCED) Water Quality 1 Delmarva Restoration and Conservation Network – LSP Attribute Tree 12 Water Quality 50% 121 Protection Value 60% 1211 Nature’s Network Aquatic Buffers 30% Total acreage of aquatic buffers within the parcel 45% Percent of parcel within aquatic buffers 25% Total parcel size 40% 1212 Healthy Watershed Designations 50% 122 Restoration Value 30% 1221 Nature’s Network Habitat Condition 30% Total acreage of suitable restorable habitat in parcel 45% Percent of parcel with suitable restoration habitat 25% Total parcel size 20% 1222 EPA Impaired Waters / 303d list 25% 1223 EPA Total Maximum Daily Load stream segments 25% 1224 Important Anadromous Fish Habitat As a next step, DRCN partners will identify relevant sources of funding to implement projects within each category and forecast annual revenue amounts for the next few years to set as a “budget” for optimization scenarios. LSP attribute trees will then be refined based on the specific requirements of the funding sources. The LSP model results were used to develop the business planning section of the Strategic Restoration and Conservation Action Plan, describing how the DRCN partners will work together to obtain funding for on-the-ground restoration and conservation investments. Now stakeholders can forecast funding available from key conservation and restoration programs over the next few years that can be set as a budget. This allows the DRCN to model some optimization scenarios that inform how to get the “best bang for the buck” of those conservation and restoration investments. Optimization and cost effectiveness are two methods that can provide better conservation and restoration outcomes on the ground at the same budget level. As noted in the book The Science of Strategic Conservation: Protecting More with Less, project selection methods using cost effective techniques are superior to traditional “rank-based” methods where the top scoring projects are selected regardless of their cost. An example of how optimization and cost-effective analysis works is in a pilot project where we applied the LSP model results for fish and wildlife habitat and water quality to the project selection criteria of the Chesapeake Bay Wild program (Table 2). The Chesapeake Wild Act, if passed by Congress, will authorize the establishment of a grant program to fund fish and wildlife habitat restoration and conservation projects in the Chesapeake Bay watershed. Section 3b from the legislation summarizes how the funding should be used: <ul> <li>A) to sustain and enhance restoration and protection activities;</li> <li>B) to improve and maintain water quality to support fish and wildlife, habitats of fish and wildlife, and drinking water for people;</li> <li>C) to sustain and enhance water management for volume and flood damage mitigation improvements to benefit fish and wildlife habitat;</li> <li>D) to improve opportunities for public access and recreation in the Chesapeake Bay watershed consistent with the ecological needs of fish and wildlife habitat;</li> <li>E) to facilitate strategic planning to maximize the resilience of natural ecosystems and habitats under changing watershed conditions;</li> <li>F) to utilize green infrastructure or natural infrastructure best management practices to enhance fish and wildlife habitat;</li> <li>G) to engage the public through outreach, education, and citizen involvement to increase capacity and support for coordinated restoration and protection activities in the Chesapeake Bay watershed;</li> <li>H) to sustain and enhance vulnerable communities and fish and wildlife habitat;</li> <li>I) to conserve and restore fish, wildlife, and plant corridors; and</li> <li>J) to increase scientific capacity to support the planning, monitoring, and research activities necessary to carry out coordinated restoration and protection activities.</li></ul> We assumed that $22.5 million (i.e. $5 million, $7.5 million, and $10 million over a three-year period) would be available from the program and that it could be leveraged by 50% to support the acquisition of conservation easements. Project costs were estimated through an analysis of land values in each county. We ran the cost-effective analysis for the entire peninsula, inclusive of areas outside the Chesapeake Bay watershed, since we assumed that the equivalent and previously authorized Delaware River Basin program would have similar selection criteria. The cost-effective analysis ratio is the benefit of the project, as measured by the LSP model score, divided by the cost of purchasing the easement. As you can see In Table 2, if you used the cost-effective analysis project selection instead of the traditional rank-based method, you would be able to protect 26% more acres, complete 3 additional projects, get 19% better projects (as measured by mean LSP score), and get 36% better quality (as measured by the aggregate LSP scores). You can visually compare the difference from reviewing the two accompanying maps (Figure 4 and Figure 5). Table 2. Pilot Project Optimization Results <table border='1' cellpadding='0' cellspacing='0' style='border-collapse:collapse; border:none;'> <tbody><tr style=''> <td style='width:77.9pt; border:solid windowtext 1.0pt; padding:0in 5.4pt 0in 5.4pt;' valign='top' width='104'> Method </td> <td style='width:77.9pt; border:solid windowtext 1.0pt; border-left:none; padding:0in 5.4pt 0in 5.4pt;' valign='top' width='104'> # Projects </td> <td style='width:77.9pt; border:solid windowtext 1.0pt; border-left:none; padding:0in 5.4pt 0in 5.4pt;' valign='top' width='104'> Acres </td> <td style='width:77.9pt; border:solid windowtext 1.0pt; border-left:none; padding:0in 5.4pt 0in 5.4pt;' valign='top' width='104'> Mean LSP Score (0-100) </td> <td style='width:77.95pt; border:solid windowtext 1.0pt; border-left:none; padding:0in 5.4pt 0in 5.4pt;' valign='top' width='104'> Aggregate LSP Value </td> </tr> <tr style=''> <td style='width:77.9pt; border:solid windowtext 1.0pt; border-top:none; padding:0in 5.4pt 0in 5.4pt;' valign='top' width='104'> Cost-Effective Analysis </td> <td style='width:77.9pt; border-top:none; border-left:none; border-bottom:solid windowtext 1.0pt; border-right:solid windowtext 1.0pt; padding:0in 5.4pt 0in 5.4pt;' valign='top' width='104'> 24 </td> <td style='width:77.9pt; border-top:none; border-left:none; border-bottom:solid windowtext 1.0pt; border-right:solid windowtext 1.0pt; padding:0in 5.4pt 0in 5.4pt;' valign='top' width='104'> 12,502 </td> <td style='width:77.9pt; border-top:none; border-left:none; border-bottom:solid windowtext 1.0pt; border-right:solid windowtext 1.0pt; padding:0in 5.4pt 0in 5.4pt;' valign='top' width='104'> 86.0 </td> <td style='width:77.95pt; border-top:none; border-left:none; border-bottom:solid windowtext 1.0pt; border-right:solid windowtext 1.0pt; padding:0in 5.4pt 0in 5.4pt;' valign='top' width='104'> 2,065 </td> </tr> <tr style=''> <td style='width:77.9pt; border:solid windowtext 1.0pt; border-top:none; padding:0in 5.4pt 0in 5.4pt;' valign='top' width='104'> Rank-Based Method </td> <td style='width:77.9pt; border-top:none; border-left:none; border-bottom:solid windowtext 1.0pt; border-right:solid windowtext 1.0pt; padding:0in 5.4pt 0in 5.4pt;' valign='top' width='104'> 21 </td> <td style='width:77.9pt; border-top:none; border-left:none; border-bottom:solid windowtext 1.0pt; border-right:solid windowtext 1.0pt; padding:0in 5.4pt 0in 5.4pt;' valign='top' width='104'> 9,987 </td> <td style='width:77.9pt; border-top:none; border-left:none; border-bottom:solid windowtext 1.0pt; border-right:solid windowtext 1.0pt; padding:0in 5.4pt 0in 5.4pt;' valign='top' width='104'> 72.1 </td> <td style='width:77.95pt; border-top:none; border-left:none; border-bottom:solid windowtext 1.0pt; border-right:solid windowtext 1.0pt; padding:0in 5.4pt 0in 5.4pt;' valign='top' width='104'> 1,515 </td> </tr> </tbody></table>

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