20200914 15073200 1.0 TRUE Map 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> Delmarva Optimization Cost Effective Analysis. Logic Scoring of Preference Conservation Restoration This map shows the results of the DRCN Logic Scoring of Preference Results for Fish and Wildlife Habitat Protection, Water Quality Restoration, and Water Quality Protection, as well as an example Optimization Cost Effective Analysis for Habitat Protection and Water Quality Protection. The results are in deciles, with the highest rated 10% corresponding with the highest numbers and darkest colors. Will Allen, The Conservation Fund Although these data and information have been processed successfully on a computer system of the USFWS, no warrantee expressed or implied is made regarding the accuracy or utility of the data or information on any other system or for scientific purposes , nor shall the act of distribution constitute any such warranty. This disclaimer both to the individual use of the data, and information and aggregate use with other data and information. It is also strongly recommended that careful attention be paid to the contents of the metadata file associated with these data and information. The USFWS shall not be held liable for improper or incorrect use of the data and information described and/or contained herein. /9j/4AAQSkZJRgABAQEAAAAAAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCACFAMgDAREA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD2HHGa 77nmWHJuJ2pjcT3+hrOola7NKd72RZ+yxvGiSjeEYOvPRhyDXLKeumh1xjZamTdWFxqt4Ckn2WK3 LqoX7+8jh+PrWkZxgtdWx2uNuLSfR9MeXT7cXV8CSzSSHc7Y9+vsKIyjUnaTsgaaWhJHeWcLPMzR 2c7xx3F2oGZBngBh6dqThJ6brZBdBrsVqipezQXsrIQVFuXIBHQlQcH8aKXM/dVgnZassPYPNHay yLDPNbcpJNGAxPcj+7xmlz8t0uorc24WQeyu2juAN1y2VdYwMnGdpI6nHc0pWkrroWtCcTxz3MqM nlyRv5ccjAZJK5+X8P5VNrICprF3Jp4idYvMRkZSSeC/G0fjzVQSkDdjSt2Y20bBcAqDtIwR+FZv caJCQyECgCn/AGnZtCQL2FHwVyWHynp0p9RdBtg9oi+RDdpPKfnd8ruc+vFEnd3BKxlXsN3q4kWO Y28G/wCUupyCD94dx0wPxqo1IwfvIh3ZHbaTBpsrNLeNNanYVW5wQsg6MGPepnVlP4Vr5Ao2F1TV pwq2eAplJ/fINwXDDg+/+Na0qatzMbfQtx2Vu0zXH7xpZ3VpEaTAwQFGV/CpcmtOw7amq/mJIhhj VsnDsTggVCt1HqRLbvFJNKJC8zZAL9BzwOO3SldXHYs7xGo39T37VUYOWxEpqO5WklErFQOD0reN Nw94wlUU/dIK6DmCgRJbslx58AV1khfa25CASRng9/wrnlOzUlszqjSvGz3AxPGVJGAT1rTnjK6R l7OUbNlpUco4EpyxypIHA9K45I7VqOXYpboOck02tLii9bFPUJIRFMkk0SOy5jMnQEdDj2JFKDsy 2tDFtTB9va5E0UWo3O2KVicrIFPBAz1wP0rod3G3REWs79TVMOoXd1PBNIqWe4FJIzh+g49uai8I pNbhq3YvtJ5YCbXYDjOM5rAsR7uNGVHBBclVB6sR2HrxzTswMyPS/st2t1BcukTOXl+0MTsXGMLn oK05+ZWaJtYsagy3lhL9m8qWRG/d5IwJB/hUrR2YPXYgv5Nbh0y2OnxW89yWAl3HaAMckfjTpqnd 82wnzJaF8G4afy5IgE8oEyq38XcAVLtYspXGn2q4kaBCEOXXyt29evT1pp30Ey20cMDhre1j8wjA dUAA+pqRmZKI9Pt5bK1Uu7RswLAkbjkgZ6Adf0rT4rORm3rZFe1tpZFiZ5IFSSIJMwmLBiAQAozg Y4ORRdK9hxQ6WCbTtODAwtBbRk5ZCzcck4FJNylp1K0SK5mvZdOt7gS2l4JJFZWxkdcjn2zwKqzU mtid0bEd3LNpkU7xvDM4+ZWIRhg89fzrNrldtxrXUikv5jIsYgDA4JPmDA78mi3Uofe26XscN4Fk M8DbkSOfCsfQ44NaUpOD0MqiTWo5n8tS5BO0ZwoyfwFde6OJaMbDOlzCsyK6q4yFddrD2I7GlHbU c17w+qINBmw2Aec9K4Yx01PQlLUZKPMjUA8nmqi+SVyZLnja5GsjoQjDnoK0cIyXMjNTlF8rHSwx yweTKgdH4KnoaxTcne5t8KSI5LOGWZTNErgZxkZxWZoZWu2GkyNaxTlI2WQSLGi/M2O4xyMetdNC U1dxM6iTRsyf6NAjGfZGhyxYZLCsN2XshbW7ju7GO6iDeXKgdQVIOD7GiUXF8rBO6uUTqNpOkd1K FMQb9zvjIKyDIOT/AAntWnJJe6hXvqWVuor6wkeMkgAo2F3YPfHrUOLix7ozru4itY4bbz4lkhwX Mi7c5GNw4IyM5xTTV22TZ2LNtqdnHbJbtfK7qNvmMeTx1qXq7ll+CeG4jDwyBwDgsPWkA88Sc+lI DMhab7VPLIHB3eXEuw4wBnOauVnFJElAWVxr1hfxanD5CS5SKLgkDGA2QeapyUJRcHsRyuSfMM0X w5Hp1jFEqcAA+WRtGRnJxyMniqq1pSlcqEFFWNeIrfwhzCPLIKkHIPB5H6Vk7xdi9GU5dMs7C0CW 8ZSJ7hJG+cAIc9ea19pKbu+xNkjVaCN9zMA+7nB7VhdlFRLR0kGGTYTyPLFK4C6jY2txClvJvijL q58ptoYjnBIrWnKSfu7kTtb3iSWRCCqIMetdMIS3bOWpOO0UVIIHiMzEpseTcoUYxx3qot3syZ2a TRLVmRcQgO5b5mB7dhXLJaJI7ItJtvUi3sJRsU8c49a05Vy6mfM+bRE6kSfMwII9RWEo8uiN4vm1 YjcyYDBQvOTTirR23FJ3lvsSejZzis2aorG3guroXasGeMGNDj7pzz/L9KpScVyiauVDdTnVma5e 3is4U4VhlmY8Zz2FW4e77upKnd2ZorIxgDPF5T5ICEg/TpWTWuhdyC6Ef2ZoCYVZhwpOAT60J63C w/YDZp+8xkDLIcD3xR1GVNOuxfSzYhDQxsVD4yGPse/SqlHlS7iTuXAkH2krtjJZd2MDIqOgyYIk anYoXnJwOppAUL9riRoxboHIbJUvt/Wqi+oMjP26VBDeW0awynY5ickgEH/63NJb3QmtLEmoW80d oiWLFWDopxg/Lnnr7fjTglzXYmuxfBCrucgHFIozLeb7Ajx/vJw7s8IU7iV64z+fWrfvPsJaE94T JAU8kypOu3ym+XHHc0o6O9weqsVra5fTNLhint5nkUFSqN5p/PinL3pNoS0WpHFrEkl7LE9jOkIC +VKRneSOcjtilyK247voWjc+Yiknah9jmtqahG7OebnNqJHqqPBprXVsSzxDcEH/AC07YNOlUcql pbMqVGKjoWfLPkKSCCVyQR0NSqj9or7EumlTdiCuo5CYyhJnIHB4rNQvFXNnU5ZtoasgE288j3py heNiYztPmZZMha3ZuB6YrnUUppHS5twbBImY72wcjvTlNL3UEYN+8yrqv2z7GpsbqC3KuvmNKm4F c8gc8Gphy83vq5fT3S3Esgdi0gKYAC7eh7nPvWRoZ+q6XPewSiKSIvIyEecmQqjqBj1ranUUWrkS jzFBNXvf7YhsIbUOIh/pEsjc4x/AO5rWdGKi5t+n/BIhJuxv+TEw3SLuPX5xyK5DYzL66024jSCZ jtWQDaMgDB7+1XBuLuhSV0SQMyxgw3DNDNJ+7KxKBF7e4P8AWldMSVhbfUbO/vZxb5+0WrBJdykH B6fhVShKKV9mNNN2LlxcrAPuMxIJ9uPUnpWaVwbsVtMtnt7IRyy+ZMxZzznGTnA9hnFXUkpO8UKK tuaAYFcn8agoQDcdx6dhQBDeW8d1A8TA5IIyB0pxfK7iepz6XK2t5NbzssUk2I7cQgB0HTGOfc5q /ekrroRezsy48drDPAbq8ZpbYbt7nJ9Oe3P0pR55J2W5Vu4anrdto0ksl8VithEHR+pfnn6dRVQp OaXLuKU1Hc2I5BIgZfukAiseupZWtbkXE91H5TI8Mm3LD7wxwR7VpNWS1Jity2BkDk9KgpDJOB97 6ilcRRYbWI9K9GLurnnSXK7DpSrOSvepgmlZlVGnK6GVZmLuO3b2pcqvcrmdrFiGQyYjJx3+tY1I KPvI6Kc3L3WIA376Uxb2OMRjj5R0HJxnOefpWLXc3vrZFmNi3JUqSAcHtWZZz99Y6ne6yt0dQ8vT YCPLigYhnYdd59M9q6qLpqPLbX+tjnrcy1T0Nq3WGRVmQKWAxu281jUbvqawVlYnxuXaenesizN1 lvI03KxKyl0VuM8bhWlJXkTPYV9Vs7SzmnYOkNs2whlwT0HGeo96I0pSkkuouZWuW0trcGaeKNVk uAGdwOWwMDP4VLb2fQpJbmHHqa6lHNbweY7NvHmyAEIwJBQj2x37VUocruyHK+xd0y9xbA3ULW1w R+8VuQSBglfbiiUdfdd0OMtNS5JcARl4gX4yAP4vSosWRx3d1JKFNmyIWwXMg4HrigRJNLK9pL9k UCYEqN4xT0T1D0MWHTZo5d/2eYMuP3nmDJ/SlJ3V0xWLyW5W4zNbSSiQgEuwYLjoaIuS6gT3sdhc u1rdpGzSx7CrDkqTj+dVFyWsQdnoyxAskMWwqgVeEVc8KOmSaltNjQQxSLJK7yhw5BUYxtHpQ2rA iYfdyKQyj9nmheRpLl5gzFlyoGwenFXbmasjKbt1GvzhvWuynpock9dRtWZhg07jsOZCrbT1qVJN XG4tOw+SExoGJ5PUVEKnM7Gk6bgkxokdVK5I5puEW7kqckrEyXPTcMEd89axnR6o3p1r6MdLJmPy 1HJ549KKcLS5mFSd48q3Ibq6uLVbQQwK4kmWOQltu0HuPX6Vm0pSd2axuoon+026SxwtMokkzsXd y2OtRyu1y7lU6nbXMTxJJslO4KkgAY7epGeoHXNPka1FzJlDUZorWVmvnje2kiRY5HTcu4Z5bt1x j8a0p6/DuJ6bmst0jWKzwsLhCBhoiDntkVk0+azHzaXMu30IxyxyQ3BhljkLOVQfvUJyQ3vnPNaS mmmmieTqbaxKowwDe5FYpWNBG2hRuIGTtWmApwYskcgUhMptOy6jFaqjlZIzIsgGVGMZBPrzxRyO 10xX1sOMjwq86n7SzsFCIQAAPQetXptsO4+GWb7EZrry43GW4zgDtn8OtJ2vZB6jILlpLEXDxo84 DfLH/EQf4c0SjZ2EtrlTStTvJrZTq9rHYXEkhWKIShtwxn86qpCKfuO6FFtr3lY0VhhW5knXIkkU K3zHGB04/GoburFW1JGdB1YD8aLDIpLjauUjd88E9h9auMbuzJk7K5mT3cisVSBiMnkdBWynJSsY OnBxumWK6DkHxNhxxkZqZq6NKbsx961351qtsimMzATE9VXGcj+X41yRtZts7XqxtkbaNfsocSSq Sz8HOc5zzTfNuT7r6D5Li1S6aKaRFkUB8Fv4Txk+nPH4UozkloOUIt3ZFdwTs2LcqpHOWGRRGrJa BKlF6kQt9UQsUmgZh0yhFEqjkrBCkou402OqSzh5r4LCyjdHEuCGHcH6UKUUttSmnc5me2/taxm0 iG7t/wC0YncwYiwy467iB8uR6Yrp5uSSm07Myfve6txurXum2d1Z6M8BiucLCgmDFQXI+ZD1bG38 qKcJyTqLYUpRTUS3r2k6mNOmtrCAXVtMwYIJOQ2M7gT90ArwB/eqKU4cyctGipxdrIvaNZXNpBbW whNsbba84XASZmHJH41NWSld73CEWnY24/O+2mRZEe2kX5CvUH09xWGnL5myLActI6+Ww24wx6Nx 2pWAitv9JtlaYBmDZweqkGqlo9AJ0XKkGpAyHk/snTJ5MBYYWbaq5cqueCB7dcVpb2kl3J2Rn6BH aX7QalLqSXd3GGZdsmBGG45XPHStK3NC8FGyJjaWtzodyuskbv5qtnhV6D0rA0sZSwQaWkMduN01 vEwjWeXHyk85P41bbnq+pNkh7W15cSW13qEsUEcLFyiOcLxgZPQ//XoTirqPUPNlqCykWRPMKsqj GcAE++RWdyi8iIgwAq0asV+5gazq8Wkarbpd3REd0CIkCdCOuSPqPzrroR5ovTYwreRoKQ0TYIOc EVUl76ZhF+40NrQyHxFUk3EckYznpUTi2jWnNR3Itktux8uVpFkkLOJDnbxjC+mKhRuaOdkyR47h 9rh0ZArK8TcbgcY57Hr+dRJ2drFwWl2xl/HbxW2+WOMWqbfPeViNqLyCPoaiKd/M05k/QvrNAXRR Ipd13KueSPUCsrMu6Obs/GdneeLrrSEmgVIcRgu213lzyoB64ronhpRpKpbf8jJVU5uJ0okja5KC T94q5KZ6A98VhZ2ua31OUBuLPVwt1DbxX2oTPAlxB/rBCFJDEeucV0vlcfdei1+Znqnruyxqegvq cdk96fty2cMh+Q7WlkIAGPwz7VMKvI3y6XBw5rX6DNJjl0rS9OjtpJmt4laJopXUkc8EkdcdOKc2 pyk5LUIrlSSJpbWa9M0Mlq8VuJtryyPlnUHIK49z/TtUcyVnfUaj0NFLmQfKICkUZAiQrgsO59B3 /Ss7FouJcrKMxAuoJGR0yOMVNn1GMhi+zRiO2t0jQsSQD0J6mqbcndsEkVNT1GTTYFk2PIWkCBIk 3Ennj9KqnDndhSdjDh8Q213fFLkXNpgsG3odh9iR354/CtJUJJXi7k862NHTLGz0Y3n2W0uJfmVi fKGWDZPB/i681E5yqWuwilG9jWgu0kKbo5YWforoRz6VDi0+47lLVdPtbqGSfyIpGAKTbwfmUdQQ OtXTnJOwNIfdypd+HWlsJYRGYg0by8JgeuegwOtKKtUtIG/d0NC3dpraORwgLDOEbcPwPes2rMpC SAMM4yfemptLQlwT3KVxa20rI8sEcrJnZvUHbkYOK3pTlPRswqRjDVCg/KQFAHtWzVmtTFSvF6Da syDoaBkU8jxGKY4MXmhZR3weBj8cfhWc5qKsa04c2rI112x+3W1qksbidWKTbuCVOCo984/WsmpN ORulFaFfxCBrfhsx298bUyuqhwu7nPRh2pUnyVLtXLmuaNrlW2mF1PLam+hOs2YNs0wTayoQG3BD 149KqSsk0vdepKs3vqjmdNtrbVL94bC0/swWE5cahcRHzpBgnJz65J57V0VJSgvefNfotjOKUnor W6l3UPF09jqd3ILW7vYplHk+TGdij3xzmlHDqcEk0mty5VeV7XLd14q0/wDeWVnIW1mZj5sESkFs ZGFLDAJHP4VnGhPSUvhB1I7LcoaF4ofStCWKKyZZ0k8tLSRuWz8xdmx8oAzxWlejz1Lt/MmE3GOx PHqsOoJaXr3CNAAzSQI5zCp5PH8RGOuKXs3BONtQ5lJptl3SNXQy+QbiWWGc7baaQbkZ8nAyOnTO OtZTp6XsaKZ1GlRxfYUcFNxzu2PlQ2ecH61zSVnY0T0LSKsRIVVCkk5AxyaLgZ72MlnNNeWtxK4M TYtiwKs/JHJ6c1pzqS5ZL5it1Ras2kkhhNyY2nMY8zZ93d3xWcrX0Gr9SG90a2urK7g5X7SQxbP3 WGMEemMCqjNpp9hOKsLbyHTbCKK4uDcvH8ssx459TQ1zPRDWi1LqtuPBXB6VmPQCmQeg4pgZIvLS T7dpt/HHGkC5dW4jMR6HP0rTlkrSj1/Mi61TH6dr2mahDusZ1ljWNX+UYCqRke3QdKU6c4P3kOM4 y2NCK4SVGYdPXIP8qgogtru1vofNtpVmUHB29jVaw0tYh2kRXCtsKxnbnHNbwk527mE4xhfsJXQc pMLdprZ/Lk8uU/dfGcfhWFWbi7HVRgmuYzU1DTNS1Ce1icNc2DnepyCpI6+4561jiFUilfZlw5Ht ucToryf2xe2tsrGW0gmmt5VYFXDkj8DnH5GumrpTjJ9Wk/kZxfvNI3NGtr6Twr9lv4SDcRGbzLdt rhyScFicluR2xxWFWrBVbx6dzSF+WzM/wy+vSNFPfWiR3EQ8oyzr80kY75H8X+FPFSpJ2g9PLuTT 5vtEuoXuo6jZi60WSJg0m2e1jIO5y2CWf0xSioU58tb5P/gDk21eJZ0zxHF4d1O60/XnaFpmEsE2 P3bKcDavfjPpWzpe2pqdLp94KooStIw9VjOq6+NRsZYbWTzg+XYCQ7VyCpHBGOcVtTapw5ZailrK 6Oj0/R4NYsI0e6kEK5aSM/LJu3cZ9iM+/Nc1So4SvY0S5kO8Qw2+nRQWdtZRtti+U7fn2g8jd9M0 6Lk/ebFJK9i+L1fsUVpZWJt7qONXghmUbCxBPBHUjvWfLrzSd11KvpZI0Eu1sgEmdcqoD7I8BpWP b368e9ZtX2GtNybS9Rh1WyW6hSVELFcSptOQcHj6iicXB8rHFqSuW2VAc4FQUZ8t3LA4SGxaZTzk MBj1602CFt724lkRJbKSIZ5YsCBQBZVYVuZGVkyy5kG7njvinrYRkeJb0W2lG5gw7glVZCCRkc45 64rSjHmnZkzelzK0DXo7zw08V1Nc2k2wgSTr8wPPI55wfpWtWny1dNfQUZXjroV9W8Nvrk808V3d fZL6FY22J98DnOSeBzwD6U6df2dtNUROnz9dzoPC9vYN4Ys1s4tsHlheV2kkcEn16Gsq/N7R825p TtyqxpWxgCBoWQo5yCp649Kxs72LurFeC0FjPeXSzqUuCGEYUABunH14/Krk+ZJW2JStqV7Mytaq 84IkYliCMEZPcZNdUIpXscVWXMyetDMkilMTZHTuKicFNF06jgzEhs4R4tu7mRIhK6AQOqFWZcfM Cehxke9Y1pNUlH7zpjFOXMihZeB5rTxTeax9ukht3GEtoXIGCcnJPbOTgU6mK5qKpqN33JVFqblc qa5qNxB4jhshd3dvC8Z/dwIr5TGA47jnI59KdCC9k5OKfr+QT+K1zYjuLSS8ayieIyoisY3DbgD6 /jWHLpzWsittLklklnA0uyOOFQ38CEbiee9ZVW52KirGu+n2d6I3uLSKZojmNnQEr9KdKUoppMpx TPPNE0G0u/iRrloVZLe1KSKitgBsg9MdK9OrUksPCXVnPCKdWS7HUa/qejaY0WmNdMt9JIJYYEPL MeBng4GfX0rlpU6k/ftobTlFPlvqaV9Mkwtorm2cyS4AjHB6ZILUQXLF2JlrKzGQ3MzPbXN5aRW0 gbYVSTzTtIO3kAYNZuMdVF3/AALTe7RGGjsLdorFFnmeRpRLId4ZyeSxH5UN3eo0tNDXgS2tIREr Ii7mcjd3JLH9SahpydylZDllDq+xWADFcnvRZLVg3fRChflzjnsKkZX1O7bT9Oe4W3luXBGUjxnk 9eewq4R53a9hN8quZV5oqyxrPA6W95LlpihJMiDqo/HbmtI1GtHqiJRvscvp+rQ6BeXFvfypM8sS SmBnA8o7SSOh5xjB75rplTdWKcUZqai7M0NRiTXdDsLjTbYQJP8ANFJOfY/eHpx+VRB+xqNSZTfP FOJU1HxTrVtGujNYxLdcI8tvJsWNTkLjPc7eOfSqhQpv95fQmVWS92xs+GvELTa9e+H/ALOqJYou xgNpbgZJA46k9KyrUrQVW+44T95w7Fq58RaLaapPZX2qW0f8CwFdrISOeffNTGjUlHmjEt1Ip2bK 6jTxq9x4bUXMitD9p8xm3LCQRgA/rWnvtKr8ieaN3E2QMADOeOtaLdnHLoKRg4pxkpK6Bpp2YlMk rag90lqJLOFJpo5FYIwydufmxz125rOUYv4jalJ3sjRmmZrATbTEGTLKy5Kgj09a5YwvKyOuUlFX Zi2Phuzup9O1PUJPtmpWkW1Z1O0MuSVyo9M1pKtKKlCOiZKpptSerRrG3Vbq5u3s4zIAFR4+XdcD g/jWD1SVy7a3MiWfV5rG6NlbRPKGHkuxH3T32+q9ME9q1hCmmuZkPmtoX9PxaWjq+qvfStITufbk HpgBRwBzV2UpL3bIltxg9S1aJbrdSTCCNJ5fvSKuC31NFWm0tHoTSq8zs9zkoPAkLa9e61rDyTSt dia3VZDgKBwGrdYlyiqcNFbUUqai3ORvXLWuo3ZtruKUvGn2iNwp2rg9iO/t71nNOnH3QpvnleRh RaTrhintY7uOzA4E6L+8nCgEHB9jj86TnTTTauXyy22M/wAHaVcWkl7fD7dEPPJhsnPyuNo3cH3P BrbEzUrR0em5FKDV2dZZa3bXdtNLbWFyqQPtdHtyGY5x8vrzXJOEk1d7+ZtGaa0Rqygu0biVo1A+ aPA+bPr349qyNClfaza6aiyO4l3SeXtjYHacd6uFJyE5IyL7xfaPEsbWdyYZcxy5Xay5XIx7YzWs aD3uQ5ohgg1Cw0VU0aaBsMzBpG3FcsMLk9OnNU5QnO9RCs1H3TSuX0G1uHvtS+xRXiKqSyMBuG4c Lnr2NZRVWS5YXsD5FqznfEcF1qcaNoqwnTbW3ePyweJCeCVAPOBnHvXRQcYu1Tdsiacvh2Oe0zwR L4r0q51SfUhBIU8pYoWJQMgwu7OSOeo9zXVPFKhJQir/APBMVQ9om29TqfDOjXXhzwle3FokV9q7 gthWwGI4ABPPTmuWvUjVrJPSJvCDhBtas5jQ9MtPGliY9Qs7mG9tJ/8Aj6bDcA8pu/i5z1ziuyXN Rl7j0aOVuM4+8tUeqlo44AFUb2XBbHNeeqblO72N3UUYWW5XUkjmtKTbjdmE9y0YkKHg5A5I71y0 ZuLsjsqU01crV3HAKCQQR1FJq+g07O6HTTNNAYWUkP8AKSvWs1TUJcyOj2nPHlZBpdpBpMIt7eLa nG4nlm9ye9OrH2nvE06jg7MuXV35G0mF3jbALLzjJArk5dbHZzaXOU8Un7FpqXel6jDo6s4ieSVS A4A+Xb1x+H4104f3pcslzGVV8qunYt6JoSaRbxsqxGeZfMmnQ5Lk84Gex61Tm6krvZGVS0I26sm1 3Wx4dsPtzWs1xsdQUjHIBPU+gq4xVV8iZklKHvtGxcTiWKLbkLIA3IwcVhQja7ZtXlskVdWu/slp Bb/Zp5muCV/cttK98lu1R8UnK+xrFcsEixDdI8oSaMLJHEsm4ndjOc4P4Vk0+hdzIjnvF1qeOdhL BGfPgjUHcFOeSc/XA9635Y8iaJ15iHSdXFwLrVZ5ZGBkeOJFUhUUeuOv1NVVpctoJBGV9SxrbLYa fN5sl3LCoDssabmC5O7B9cZ/AVFNc0lawTdlc53VLyx0/wAO3eoW2nvBbXOx41Zj+8yQpYrnjHH5 10QjKdRRlK7RDkoxcrGvZR2Oop9p1FPkCqPLmQ5j4AGGHrn9awk5R0iVo9WdDdWm1TcW0Qa4jRvL QttQsfUDr0rnT6Mto57U41vNRt7e6CRidT5qLGGk3bRypPQDJya6YPljdGVrysylo9zpWmSXNstj Ei7DJJcBh8ofCqpI4BIA6cd6c1Umk7/IceWJN4ZSTR/tCf2fB/ZpVpDdRHe8hLE7Wx1IBx+FOvL2 lnfXsFNct+xoT6LEsl/fC4+x/aYFTcTtRX/vbT35A61lGo5KMWr2LcbNsqeE9N07T9JJsLcw+Y7G UecZMsDjOTXbOU5O8nc4ZxjF2ib5YkAHt0rNpJMV27ID1qaXwIqp8TLErBd208e1ccIXnY7JztFs rV6B54UAKGxg9waTVyk7Dy+JC479PapUbxsy3K0uZASk8TW8sYeNxhlPcHrWc6f2rl06j+GxHLpt jqWmrY30EU9umMIy4HB447YrN89OXNFm8ZRqKzEkuNOhgt0imRFZVWFB3XoMD06c1dKU72aM60It XRIYob0RRXKeYqSCRQehI6Z9fWicOW8oipVL+7IlvSNyf3gefpRQvZhiLXRnaglve6nbWL3VzBMs XmK0TEBwGHHoen1qVdXdjVWslcvO0NojLNIJAuWLSlQEGOR+WalQur7Dc9bIW1W3khee1ljZ5lDe YmDhT938KiTadmUu5y0l017oA03T2EF1cO4kzH5bFlb942BwfQ10W5Z889Uv6Rm3eNluMhu9Z8Ra TFeRX8enhyH8ssoJUDkZx0J9aGqdKfK1cScpxvsdHeQW114fntGSDy2t2XYuNo47Vzxk4zUjRq6s cZDb6oY7e2t7u+gjaUxQvKWOyPHAfJ9Rwa6+enduSTMkmtEeiRMVVI5HUybf++sdSBXC+6Nl2MHW razj1m1u7i68t3R0RC5XLAdR+BOfX8K3pOTi4pGc0lK7MTw5r2l2lkmnXkbfbzi3dxFkSqCwTnoe MjPrXRWpTk+aO24qc4pWe5fSy1aw8PXFppFu0V9PIZDcMVK7mPLYPtWLqQlUUp7FuLUbR3E1O81+ fT5jJoZkiGB9nDKzuCpye44OOlOnGmpL3hScrbDfB7vJoYeW3aCYyN5kbR7CD9K66tubR3Rxzbb1 OgH3hWFX4GFP4kB+8aKXwIJ/EyKK8W5LBdwI5IZcVKcI6mrjOWjJKqnU51sRUp8j3CtDIKAGuGKE I21ux9DSaurFRdnceruhUuymQqMkeveoUU42ZcpNSuiG4lniiZ7ePzJOy5xzWlk9GQnrckttRS5D W80e27jQbgcckjkr7Vz+ycJJ30OuU1KGgvTpXQcY+R/MQA5LetRGNnpsXKfMlfcTeBEQVG/gbgOc elHJ73MNVPc5StdaZBqq26XEe5YZPNweQT70pNQu2yoNy0RPZ6TFYXEjWqQxRvjAVSCBySPzPFck p8y1OpRS2LAs4ZLqO4URsEVguEBIJPODU8ztYdtbmZq8+k6PZ7r5oYU4igLR7gp7AAdhWlOEqj01 FKSitTkPt1jqiX409vL8jZbyTRYAaUtgOeThOO/HNdLpOHLzLfX5GSqKV7Fe5kkudNmgBmdExHd7 Y15+YH74+XjrjPrVQiozuvl/W4m3JWYXPiltItYTdWs135MoNtehC8cQP8Oe5I75pxw/tG7O3ddS ZVOXdFCSa3bVrjX5dSu5tMeAywxuxD+axKhFGe2OvpWqT5VSUVzfoTf3udvQ6DRUhvXg8QxkWiSS KJ/NI3YUEHqcAE46VhVbinReppCzfMtDodf1q7i06FtFhe6kml2eZGu5UUA5Pp9PWuelTi2/aO1j WpJpe6XNO1hrxgscTSpnAmAIBG0EN0xzmolT5dxxlcWIYiU+UIifmZB2J5Ndq2OCTu7jx1FKp8LC PxIU9TSp6RQ5/ExCck/WvO5nLc9PlUdg713UPgODEP3wrYwCgAoAqXen293IskqtvVeCrEcehx1F OLaGmR6Pp506y8g3M1wSxffKcke30ok7jZeVQsolAAcfLnHaolFS3HGbjsKTls1SJeoUCK0d4Drs entHkPE0m/d0wemKiq3GN0bUoqTszajRUjwoxXHOTk9TshFRVkBjVpFcj5l4BrMZjXlxNaa0tvA+ 1J4i2CMhCvoPeuiEU4XfQT0ZPrWn2+paatrdJvjcqDzjrUQm4Sug5VJWZjx+EbLQtImGlyPbmSdX mOA3mAduelbOvKrJc+pl7NU0+UbpejxXfhe3lZ9i3Y8yWJFAUgk/LgducUpzaqNdggrx9Rk/h6DW rC80i7nl+xLMrRRphRGFGQo46VUarptVI7jcFO8HsXNKs9PudLayNigtreQxpG3zAbeMjjjNYzqT Uua+o0la1jQSzs7m2kMlpETCSq5H907h+Ge1RGUl13K5UZ3hL7XcaAl3PePJNPcu7llGMAkbQOw4 reuoxnZLZE0byjds19Uvm0+xeSNFJVeB0AqKVNTepU3yrQz9I8xtJtTLIZHMYJYjrXZLQ4Jbl5Rz XPXk1G3c0oq7uNrdaIyZ/9k=