20191119 10082100 1.0 FALSE Survey_Sites_PC_Shapefile 002 445000.001700 561999.999400 95215.004200 145999.997200 1 0.000 Projected GCS_OSGB_1936 Linear Unit: Meter (1.000000) British_National_Grid <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/2.9.0'><WKT>PROJCS[&quot;British_National_Grid&quot;,GEOGCS[&quot;GCS_OSGB_1936&quot;,DATUM[&quot;D_OSGB_1936&quot;,SPHEROID[&quot;Airy_1830&quot;,6377563.396,299.3249646]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;False_Easting&quot;,400000.0],PARAMETER[&quot;False_Northing&quot;,-100000.0],PARAMETER[&quot;Central_Meridian&quot;,-2.0],PARAMETER[&quot;Scale_Factor&quot;,0.9996012717],PARAMETER[&quot;Latitude_Of_Origin&quot;,49.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,27700]]</WKT><XOrigin>-5220400</XOrigin><YOrigin>-15524400</YOrigin><XYScale>450481592.76709664</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>27700</WKID><LatestWKID>27700</LatestWKID></ProjectedCoordinateSystem> 20220928 17233100 20220928 17233100 North American Profile of ISO19115 2003 Esri ArcGIS 12.9.0.32739 Survey_Sites_PC_Shapefile 1 -1.361997 0.318516 51.210913 50.733356 SDFBI Linnet_SDFBI, all years abundance data Shapefile 0.000 dataset EPSG 6.3(3.0.1) 0 Simple FALSE 0 FALSE FALSE Survey_Sites_PC_Shapefile Feature Class 0 FID FID OID 4 0 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape Shape Geometry 0 0 0 Feature geometry. Esri Coordinates defining the features. Join_Count Join_Count Integer 10 10 0 TARGET_FID TARGET_FID Integer 10 10 0 x x Integer 10 10 0 y y Integer 10 10 0 Present Present String 254 0 0 L_2014 L_2014 Integer 10 10 0 L_2015 L_2015 Integer 10 10 0 L_2016 L_2016 Integer 10 10 0 L_2017 L_2017 Integer 10 10 0 L_2018 L_2018 Integer 10 10 0 L_2019 L_2019 Integer 10 10 0 L_2020 L_2020 Integer 10 10 0 L_2021 L_2021 Integer 10 10 0 L_2022 L_2022 Integer 10 10 0 L_All_Year L_All_Year Integer 10 10 0 CB_2014 CB_2014 Integer 10 10 0 CB_2015 CB_2015 Integer 10 10 0 CB_2016 CB_2016 Integer 10 10 0 CB_2017 CB_2017 Integer 10 10 0 CB_2018 CB_2018 Integer 10 10 0 CB_2019 CB_2019 Integer 10 10 0 CB_2020 CB_2020 Integer 10 10 0 CB_2021 CB_2021 Integer 10 10 0 CB_2022 CB_2022 Integer 10 10 0 CB_All_Yea CB_All_Yea Integer 10 10 0 P_2014 P_2014 Integer 10 10 0 P_2015 P_2015 Integer 10 10 0 P_2016 P_2016 Integer 10 10 0 P_2017 P_2017 Integer 10 10 0 P_2018 P_2018 Integer 10 10 0 P_2019 P_2019 Integer 10 10 0 P_2020 P_2020 Integer 10 10 0 P_2021 P_2021 Integer 10 10 0 P_2022 P_2022 Integer 10 10 0 P_All_Year P_All_Year Integer 10 10 0 S_2014 S_2014 Integer 10 10 0 S_2015 S_2015 Integer 10 10 0 S_2016 S_2016 Integer 10 10 0 S_2017 S_2017 Integer 10 10 0 S_2018 S_2018 Integer 10 10 0 S_2019 S_2019 Integer 10 10 0 S_2020 S_2020 Integer 10 10 0 S_2021 S_2021 Integer 10 10 0 S_2022 S_2022 Integer 10 10 0 S_All_Year S_All_Year Integer 10 10 0 Y_2014 Y_2014 Integer 10 10 0 Y_2015 Y_2015 Integer 10 10 0 Y_2016 Y_2016 Integer 10 10 0 Y_2017 Y_2017 Integer 10 10 0 Y_2018 Y_2018 Integer 10 10 0 Y_2019 Y_2019 Integer 10 10 0 Y_2020 Y_2020 Integer 10 10 0 Y_2021 Y_2021 Integer 10 10 0 Y_2022 Y_2022 Integer 10 10 0 Y_All_Year Y_All_Year Integer 10 10 0 LI_2014 LI_2014 Integer 10 10 0 LI_2015 LI_2015 Integer 10 10 0 LI_2016 LI_2016 Integer 10 10 0 LI_2017 LI_2017 Integer 10 10 0 LI_2018 LI_2018 Integer 10 10 0 LI_2019 LI_2019 Integer 10 10 0 LI_2020 LI_2020 Integer 10 10 0 LI_2021 LI_2021 Integer 10 10 0 LI_2022 LI_2022 Integer 10 10 0 LI_All_Yea LI_All_Yea Integer 10 10 0 KT_2014 KT_2014 Integer 10 10 0 KT_2015 KT_2015 Integer 10 10 0 KT_2016 KT_2016 Integer 10 10 0 KT_2017 KT_2017 Integer 10 10 0 KT_2018 KT_2018 Integer 10 10 0 KT_2019 KT_2019 Integer 10 10 0 KT_2020 KT_2020 Integer 10 10 0 KT_2021 KT_2021 Integer 10 10 0 KT_2022 KT_2022 Integer 10 10 0 KT_All_Yea KT_All_Yea Integer 10 10 0 BZ_2014 BZ_2014 Integer 10 10 0 BZ_2015 BZ_2015 Integer 10 10 0 BZ_2016 BZ_2016 Integer 10 10 0 BZ_2017 BZ_2017 Integer 10 10 0 BZ_2018 BZ_2018 Integer 10 10 0 BZ_2019 BZ_2019 Integer 10 10 0 BZ_2020 BZ_2020 Integer 10 10 0 BZ_2021 BZ_2021 Integer 10 10 0 BZ_2022 BZ_2022 Integer 10 10 0 BZ_All_Yea BZ_All_Yea Integer 10 10 0 W_2014 W_2014 Integer 10 10 0 W_2015 W_2015 Integer 10 10 0 W_2016 W_2016 Integer 10 10 0 W_2017 W_2017 Integer 10 10 0 W_2018 W_2018 Integer 10 10 0 W_2019 W_2019 Integer 10 10 0 W_2020 W_2020 Integer 10 10 0 W_All_Year W_All_Year Integer 10 10 0 MP_2014 MP_2014 Integer 10 10 0 MP_2015 MP_2015 Integer 10 10 0 MP_2016 MP_2016 Integer 10 10 0 MP_2017 MP_2017 Integer 10 10 0 MP_2018 MP_2018 Integer 10 10 0 MP_2019 MP_2019 Integer 10 10 0 MP_2020 MP_2020 Integer 10 10 0 MP_2021 MP_2021 Integer 10 10 0 MP_2022 MP_2022 Integer 10 10 0 MP_All_Yea MP_All_Yea Integer 10 10 0 X_500 X_500 Integer 10 10 0 Y_500 Y_500 Integer 10 10 0 Shape_Leng Shape_Leng Double 19 0 0 Shape_Area Shape_Area Double 19 0 0 Area of feature in internal units squared. Esri Positive real numbers that are automatically generated. 20220928 /9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADIASwDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3TqDT VOAeBmoYL6GeaSJdyugDEOMcHOP5GnllVtxYYbOP0/wrKFSFSKlB3T6oe25Kv3ad2qJXyMgg/Sng 4OKq4hSOKQ8gEU7rTWIApgMZQyEHoaRc98MfWlO5h0xSSOsKbjn2A6k+1SIZP5SwlpcD0PfPtVUO zW7bAQhB3E9PfFPlVyytIQZCflTsv+J96mKAeXCPugZPvQBCFMKxoO/P4VZViPpio5VLSp64NOyA SOuOT9aEMkTAGe1BI696afmH9KdRcQ0nkD14pRgjI4U8j6dqQ8fXoKd3GaAE756UppMZ47Up4oAO wpRQOBmhemaAHVEoDTyngjhcf5+tSA5qPayMXQZB+8vr71QyUKFXCgAegFB+8KYsyFtpOD6MMU9u KAGn7xp461HnrTxwBSQC9TQf0FNLccUSHYhPXAoAa3zLmkUHIPbP+f5Cs6XUhCmZriFWEKzvGsbu VQ/xHHbg5PaprS88+ZQsiSIWZSVVlIIAPftgg575FFgNGoHPmTheyc/if8mpW5GOlRQje7Seo2r9 KYEwGAKCcD3oB9TzSEjNACEZxmnAcUmQKQt1pAB6Gq7yYbhtvtSySkY+YKD3bv8AQVEZYB1dM+5o YilbQ3P2yWaZI13oiKsbluhY9wP71XZCNxAOQowKImBO4HHBwT61E4JuYbcPsZ3wcYyBgmuXD4eF CkqVJWSG25askVmQHacVJFOQ21uQe/pWPBqMF2gNtJdFXjkdJGEewlCflODnOAD06Ed8gaEcTvGJ JiY0xnAPJNb8rQjR3ED2pHmiRQXdR7Z5qmiRyMFcyuD/AAs/FWkt4oz8kaAjocc1adxkZneQ4hjJ H99+B/iaRExKHd/MfnBxwv0FLPJg7QefSq/O7dnmocrCuTPzLvIOPug1AL0gCV2to9yFwrykNtBx nG31I/OpPOLbg4BXb09TWTLYLOoVmuI5VTZ5sM4XKfNxgqRzuPbNVFpgaC6ikjlIbmxklG7CpOSc jr/D2/nVqH5oFc8blBrBg0G0jvlu0jlM4d3UtMuNzDB6IP05xx04rctjshWORgGQbaG10AnHb2oO ahM6BWwQSOMe9NSZ5JCCQNwIHHQ0rq4E5ZV+Zjx2zSKwbkHNU+WOSST6mrsKqYRikndgO7UNyKOj GlxxVANB7UvUUgGOaBzQApOBTxwMe9NZcxH1xSrzz7n+dNDBwGXaQCD1zUZiKAGLoBypPB+npUrd qQkBSScDuTTAYrI4yDj1B7UryR45dR+NMMYmJcCIgcAsm7P605Yz3K4HZV2/1osK40XES5O9T9Dm mtcLJGQNxz6KakaKLGXQNjnnmk+4wyuU6cDof8/yFAGNLpVvO6vPYp52FUzrNJGzABQFJGDt+VTt 6ZGav2enray7ooIoUMjyuEcsXdsZJyParjqskLbSORwfehJRIMDhu49KNRiuu4HJ61ClwkKLHJkO B0C5zU8jJGhZzhR3qK3Rmdp3BUtwFPYf40AILobhuhmUHjJSpElikYhXUkHkdx+FS4qGeKKRf3iA n17jvwaAEe5hQ7Q29v7qcmm+VLOMysUX+4n9TU0SLEgVFA+gpS2OaAIVtrdVJESk+pGf50rWsLHI iT/vmnBvnx68mn7vQ0rgZeWZ2WM/KPvN6H0FDQKSpTaCpzlhkHgjn86dGAu4DgDio7xHmsLmNBl3 iZVGcZJBrO+uhKKyvYzTu0EunG4kTYXhjTzCuMdQc9MVeLPIqKeAox9a8M8K+FfENl40064utLuo 4I5wzuy/KBzyTXuoGBzUqbktTrxeHhQmowmpJq91+XUbyKufaFCYUHPaqhPFBdQMZGQM007HIDgk 7s81GGJOBwfSnPKqpu657UyIEZZupOaQCgN1ZvyFNPExbsuBUrHHQVFtwzs3I4z+FJbgOx1HY8im TyYw8hJIOBSxqwjAbqDVPU7u2t/KWe4iiy2cO4XOPrSEWId3JfqTmrAJHI6iqlpcw3Ue+3mjlUHB aNgwB9OKtdjQMllx5jds84pYpShO3nNNnH75x71SljE+owQs8gTypGISRkyQUA5BHqaqUrO4pO2p rRuZASRgg1LWfYDy7u4iDMUQLt3sWI49TzWhxWkXdXGndXGHsDQvDUH73NKBmmMeOmKavHHsP5Cn DimkY5H4VQwdwgyQT9Bmo9wmVRuKq+RjjOfT8s1L1XI5zTIlGfmB3r1OeD700S7k1NBAY+lKTwaZ jjJqWygc/Nx2/n/nn8KMYA9BTR1OeMdaaZlK/IC/ptHH59KAHuqAF244+8KjhbdMdx+ZRgZGCR60 7DyY3gKmc4ByTRNF5i5wCR0zTEQh/tMvmMP3UbYUHufWrmeM4bH0NVrVM2keGG0jJC9/xp5xEQQe D+Y+lAXJNx7D8OaQAnk8D3oGV6Yx+lKGBB/lSGOJA4qI5MuPbIpRy2ewpvWUt2AwaVxDuFBPU0uB /EeaTOACevYUAD+LrQBRjB3Oexbj8hUVzOtpbTXDhikSM7BepAGeKsYAAA4xVK/h+22s9puKLLGy MwHTIxWT3JRxeifFKz1jW7bTRpssTTvsD+YDg/TArvWmAOB8x9q4PRvhfp+j6xb6gl/cSPA24Iyg Amu8Cj0qY3tqdeL+r86+r3tbr3IxvYNk4J9O1JIUt7eSVwSsaFmx1IAzU4AqlcWt9caJdXH2m2SM xSnZ5DEgDI67+uB6VcYtnLYtSpMiqZbSRU3Bc7kIGT7NTitLJNezqqTPblNwb5IyDx9WNNZ8HmiV ugMM1FdTpbW8k0mfLiRpGx1wozTlcMfaob+3N5YXNvGwBlheMMemSMUkBU1LXINHsJL2/trmG3TG 58KwGeB0Y1m6D400XV9dEtmbiYQ20iuFgJOWZMHH/ATUniXStV8SeH59L2WduJCv7zzmbGCD02D0 rE8C+BL3wpqdzc3N1bzJND5YEe7IOQe49qmMppptHRSjReHlKcrVL6Lo1p/wep2kcgudRurpI5I4 pAiqJEKE4HPB+tWCPm4oPp6U/HQ1Td3c5x8x/eMf73zD8earSwRTjEsSSAdnUGrJHmLgffTp7iou 9Et7gxbGCGAssMSRqeSEUAfpV8HCkmqUR2yCroG7g9KuGw0AGTk0KMGnHsKB1zVjFIwKQ/cJ9OaO uKOn0pgNXAyB0BwKIhlS3Xcc/h2pGG3GPw/z9M0gOyTaOhBJHvxQJkpGRUcrFAoC7ix6VIOlRA75 WfsPlX+v+fagY1IN3Mh3H9BUp44pyjAppwST36CkAY+XHcGlBytIAduO/c04ADimBWObRywGYGPz D+6fX6VNKuVH1olUPE6kZyDTbdt9rESc5UfjQAgUhflkZQOwx/UUhTg7mLAnP0/L+VTbPmJ9aYTk 4X6ZpaisRlAB8pKn0B4pyxlx8znA7KMZpe54pcke2Tj3/KgLAY8Aks31zUexCTuyT7jP9alwe9Lt HpQFkZ7NnpTSARginDDZ9KjuJUt4WmkOI0XLHGeKwaYhQpAxkGkDkEAjFRi6XzVjeC5jLnaDJAyr nBPUjHQGpyM8dqbTQCbvmxURtmMD2/2qfyXDAxhhjB6jpnvUc13FFIyBbhnQ4Yx28jgHAOMhSOhH 51YilEsKyIDhhnkEH8jyKNUAqSFiQR0pzYximouHY4qKe8t7dwkrkORkKqljj1wBSs2A5IgOhIB7 UpDIT/EBiiGaKYbonDBeGx1BxnBHY4I4pzypEskkrqkaDLOxwAOuSapLSwDPNxntSq4YcVXt9T0y 8lMVtqFrPIeiRzKxP4A1IibJDj7v8qmwNNblgAYpw6fSmBucU8cD3poBfukEH5h0rjfHHi288NXt slpBbyLPHvPmBuD7YIrsT7nmsnXPDem65LDJfQGRo0AUhyOMClNNxajuKSbi1Hcq+D9cuPEGjG+u I445BMyBYwcYAHqT611MDM4JPasbSdHs9Es/slijJFuLkM2eT/8AqrXSVEQKAaqndJXHG6WpYIzS FlH3iBiqtxI8s9nFDM0QklKuVC5wEY9we4FRTxvb3bq1zJNlFYeZt4JJH8IFava6KLRnydqDPOMm nNLn5UUuR1x2qmgy6gnPOOOKvKMLhVAHbFKLuIY0yFRgkn+7g5/Edqj80rNmRSOD7nt6VMHyvCk/ TvQiBUVc5I6n1NUG5GjvKxAOxQcH1/8ArUoAhcJ82w425557806Fcbz6uaWcfIG67SGwKBkg5FM5 JJFOByvFIAcEUmAAkUK2ck0YNNA60tRDs8nNQ2eBbjGdoZgp9Rmi6JNtIRngc46471IjDYoUDaBx jpincYrsePSnKoUYpg5Jp4z3oQCMATimYHnKO+Tn3OBT+j0wgl8/U/y/wNADyRkD1NOxTFyMZ696 UEnP1ouBnEMBnIJ744qtfQfarGeBDtkdCBvYgZp1uZEur63kmaYQThFdgAcGNG5wAOrGrOAfcVnd pk7DLq5nvGgBt41SOQuxEpP8LD+6PWlG3Py4RvQjrTzzTFtprudo45kiCqGO6PdnJPuPSndyYakc N/Pp81yosJZ1lk8wPHIgGNijHzEc/LTbVilrGJEZG28qecflSwpKs9zBPKsvkyhQyrtyCit6n+96 1PtHv+Zob6MNRhk28EY9M96giuXs9Ue6a1lljaER/uimVbcSc7mHbHSrGCB/eHv1pp3B87TjuOP8 aFpqLVENrI013fTtE8InmDokmN2BGi54J7qara9azX3h7UbW2XfNNCyqucZOMYq7Idxzgg9qSRvK iBT95KSFUZxkkge/HNG5UZNO55L4D8Fa/ovi61vb+x8q3RZAX8xT1UgdDXrzAFQR3ptwl5bSW6zJ BtlkMeUckg7WbOCB/dp5JKkEdPSoVPlVjoxWKniantKm/kNQFjz1HWpc4wMVwHiL4nQ+HNcuNMk0 uSZ4gpMgmCg5UHpj3qvafFq0vNThsf7KnR5JRHu80EAk4z0qeZJ2uUsBiHDnUHa1/kehSOQ4A71P 9+MN/Eowfp2NZ15eC2aFPLLNKxAz8uMDPpTrLUDNPNGY9jRIrZ3ZBDbh/wCymoVemqvsW/eavbyO NNXsXM4BNc5EmkyaTBDDDbpqBjjVXMO1xJxznGc5710aqMAnJPUZ/nSuoYcjOOxrVxCUbkM0UFwu 2ZA4U5HGcGo4raGFmaKMJnGfepgh2kA/So1Yj7wxzii4xF1CxhuNk15bxsvVXlUEH8TWlGY5m3ja 6FQVIOcg/wBOlcmFuIJbgG0nYNM7hkAIIJyO9b+lvJFY2kcqFWjiVGzj0HH51z4PEValSpCpDlUX o++/9fMhSbdmjTxkelRyEooxyx4Ue9SBgQD2qOP945lPTon09a7jUdGvlqF6+57080jEDFHWgCFS Ifkbhc/Ke2PSpgaZNIsMEkrglEUs2FLHA9hyfpWDaaqBHF5r+aZXIQW7DaAGClgc8ruZcY556cGm B0RpuOKof2hpzKd02QHCfNuPJYqOvqVP5Z6VPB9lnUyQY2hmQlcryDg/qKAJiu5SPXiobU5toj/s 094yiOyyyLhSeuf51HbxyiCPbtI2g4Ix+tKwFhRTj6VH++JAGxfzP+FG2T/noM/7tNAOcE9OvQfW lUDBPr/LtUUnnoNwZWI5wFx/WnKZduAij6tQA/k0zDrwOlGJ/wC9GfbB/wAaMy/3E/77P+FKwGdF EkTSLGGy7bnZmLFjgDJJ5PAA/Cn7lRGZ2CqvJYnAFSxv5qPuA3Bc5xg9RVa7ANqykHbkEgDJxkZ4 +lZPuIWC6guGIhmjkx1KMDTjGrSbw8isBj5JGXP1weaqSTQT3lmII5A0cpZiYGUAbGHUgdyKrat4 l0bQp0i1K/jt5JF3IrAnIzjPAqISbV2rF8jcuWOpqpEsbOy7i0jbmLMWJOAO/sBTz9eBVTTtSs9W s0vLGdZoHyFdQccHB61Q8WRTT+F7+K2jkllaMBUjUljyOgFU2RJNbmypB4BFBANeUeBNK1m18UwS 3tlfRQBHBaaNlX7p7mvUZbuGKQxu/wA4GSoUnA/D6VFOfPHmasRCXOr2JGAQ7h34/SvFtK+IPiO+ 8VWenSXsfkS3qRkrAmcbxz0r2WKWO6VmjYMAdp9j3yKoJ4Y0OG4juYtLtkmjYOjqmCGBzmrld2sz sw1alTUlUhzXWnkXGhu5ZomuL+SVYmLKpjQc4IzwB2JqwEHv+dKDgZNJk5z0puTOU4vW/hlpWuat cajc3l4JZSCQrDAwMYGR7VVs/hVo9rqkd6t9etJFKJQjFcHnPpXel+OPxoxnqB9CKi0b3sdKxuIU eRTdrW+RV1Cxa8aBkmETxMSCU3A5GOmRRpmmyW91NLNKk7SIoAEe0DbuI6k55arAyCVBwvoBUyL5 cb5wN4wozyeeT+lSsPSdX21veStfyOVJXvY8u8S/FLVdB8S3ulRWNtJFbuFHmbsngH+tel2cxu9N t7plCySRozKp4yRnisK/8C+HNUvpb2800SXMpy8nmuMnp0BxW46pDYxW0IMa74YQVPIXeoPX2yM1 pG7bTZ24iph5QgqUbNLXzenn6kw4GKjlGQM9M0s9ibO8t0iuLiQyh8o5DcADnpSkZyPzpyi0chDH uZypOB/Or4tyUHzckdKr2yL9oIYfw8ZNXmKxoWboOgqox0EkRsM7YQe3zY7D/wCvUxwo9qbChRSz /eY5NK/YDp3rQoaTmnL0NJsCigkrj0pAOqJ7eCWSOSSGN5IiTGzKCUz1we1S9qKYFIaXBGqiAvCQ 5kBB3fNsKDrnoDwPYU2Kxex0+eK0YGZslCeMHAVfyAH1P1q/SjrQBz3l3umWyW9vCoSV+UO5wi5V SN3QEjc2T1b1qey1+CWCTEM3lwkJvC5DZxtx9c5+gz0IJ0W/0i4Kf8soz83+03p+FOmtoZ0KSxqw Of1GP5EigBtvf2tyY1hnRmkiEyr0Yoehwee9WCKrQ2EcF/PdiSVnlVV2sRtQD049++fwqz0HvQA0 /e/z/n0p4qP/AJafn/SpM4FAAeBTaU9QaMGgCgGCA5Gdwwcda82s/inbalrUOmppUqvPMIFkaYcE nGcYr0ggAEdzXCWHwp02x1eDUI9Qn82GUTKrAYJBzg1zvmdrHThnh7S9te9tLdztJogJUI4yNuQc c15/488Eap4o1K1ubKWAJFCYz5rEHO4nsK9GmQhPmBBHNLt+Xjp2olG+jMqFedCoqlPdHNeC9Mn8 PeG4NMvSvnRsxJU8EE5rozIgUHIwfekHQHGQKY0amEKyA5PTFC0IqTc5Oct3qSh1wBnrVPfNb6jP MtrLPHJGigxsgwQWzncw9RUjRlJdwPBHAI6VKhOw9M+1DipKzFGVndDbTd5Bd42jZndtrEEgFiex IqLVdSttH02fULssLeEBnKrk8kDp+NWmDKoCYIHbPasbxHpsuu+G7zTQ3lPOuAxGQMMD/Snayshx 5XNc219TM0/xzp3iDUbe10iSaRgd0qGLBK5A6kEAc89Prxg6smoX32ditm5lKEp+7bGQpPI7cgD3 zxXF+EvAmo+FdXN811byqyBCBuBwWGe3pk/hjvVjxD4zvtD1IWsiM5G2QYZQMZBwcDkcEdRweanm 5Y3nodksIq+I9jgrz7d/PsdfBPfGzvDKhDL5hhYRENje4X5T14CkfWohf6mi/wDHi0oALA7SpdQX 59A2FX5e5ftjFVdD1HUNW0mC/LgJLGxVMrkn5xzx6hcdvXNaPkXstiyPMVmaRDuDYwuFz0I9G4FU cMouLcZboiS+1TdJu08KqAc4Y7jh8ngdMoPqGFascrXVvHK6MrOgYqy7SvHTHb6Vk+VrmZozOBbs u1ZPl3MQH9Bxk+WfUZP1rUgab7On2gqZsHeU+7nPb29KeyEPwUPAyCOg9aZKsc8RieISKequvB/P 3rjbv4o6Np+rXOnXNteedBMYcoikEg4z94V2qNvCt2IzSUkzSpQqU0nNWvsQxWlvbSb47aFGxjek YBpJdZsreXybi7h3AZKu3I/HtVnrXK6xpd/Lq088NsZI5ApDB1HQAdyKxxNapTpuVOPM+xeGpwqV OWcuVdzzXXr6+Pie/ktbi5+ztcuYyjnG3d2xXsEOolo4oXk1ISiIlJHVDGzqjEjOOcbDn0OPrXJa X4x0rSoXsLieeKdZHWRPIWQElm6Eg9jg12wtSqvHE6puUoxSCNeD1GQta0OVXd9X+Hkd+ZVqkoUo TpcnKrJ2tzLTXz/4JqpPuIHOT0zUuQw3djzVSLhwfQE/kKkinxhX6djWql3PKuWcU1x0NO9xR161 YxvYCl6UhU7+KAcnFAC+9R3EpjUbBl2O1R70s0qwqC2eeAB1J9qiQSPKJpV24+4men196AJIo/Kh VM5PUn1Penn1puSQBTh0pAOpKPSgdaYDVGGx+H+f8+tP701h0I69KUHIzQAtAoopAYti0ItVMLho y7hCG3Ajce9I2oWcjpGt3AzN0AkByfzqxL8zgenNeAeF5EPi3TcSKx+1L8oPJ5rlnNxaVr3PQwGX xxNKrOVTl5Ff1303XbzPoaM8qjcqSAQelRj0PY4oRgwyD/8AWpxlyRuUMPXofzrY84iA5wfWhs7x 6CpAEZVkXOCSMHqCKaoLEk9KloQxhnGaYAfMGOgqtqH2T7Zb/bYkeDy5Pvx7gGymO3XGf1ptmLRb mVrJUSFtgKou1cj2x15/lUp+/wAtiuX3b3L546d6aRxzT5GCKWbpTZpI4IjJK6qPVjgZqrEg2MAd q5zXPBema/fC8u3uVl2hAInAGB9Qa2/t1mMB7y3BbnBkA/rUsM8NwGMMqSAHBKMDj8qlxurNGlGv VoT56UnF90U9M0a30zS4bCEuY4QQrOfmPOe31qxiRJMb8gDoRUrSETKv8OMj3NLbwliZZM7M8DuT 6U0iG3Jtvdi3jGODbEAFOGGeSaZCTJEDgj3NSXLs0bFQOmB6CmWwxAvvTerEzzfXfhlBc3+oa2dS kVmd7nyvLHXlsZzXok9te6dFBJLc28sZdIyiwMp5467z/Ksu4tYLrQ7u8a4usvFK4X7QwAHOBtzj pjitJreWRIhPdTzCMhgjsMZHToKUHDXQ0niZ1klN35dEWM4bgZPpSnqCfmY9AKB99fof6UpO1s9u /tVIzZ5vffDG7udXlu01GEJJKZMFDkZOcV6QqkOzFs5AHSnUVEYqN7dTqxOMr4lQVaV+VWW2i+Q9 Mb9ucZBGfqMU6KHzFOcjHSvP/iP4v1HwsNOGmtBvn8zzFlXcQBtwevHU1u/D7xHfeJfDp1DUFjEp ndB5a7QQMev1NXGSb5QeFqKgq7+Fu39fcdaOBgDgcUtIG9f0Of5UDAPXg1sc4hznrxSjApJGCruP QVDOzHy40OC7YyOuOpoAARJfMc5EaAD2Jzn+QqU02JFiTYowB+tPIJIpMBtOBGKQg0lIQ7vTugpq 9AaXrzVDA/w/UfzpFPyrStnseeo9qYMkkjj6f4UAPNKDxTd2QM8UGRF4J5pAZ1wrtC/lELLtO0kZ APvXlOgfC7U9L8RWWpTX1o8UEwkZULbjj6ivWEbcCD1HWmcqgx95jWDSerNqOKq0YyjB2UtGC/LK VXoevsafxu9x2pI12qfU9aSVxHE7E8gdaZzixNm2OP8Ano4/lRKdsQxXOR6jqEenSSpLCAhdgrRE 9Ce+72ropOTz92uehi6WIco03dxdn6iUlLYDJ8ygdCKYI2JYkYLepqSNdqbiOT60sgztx0JGa6di hroJDhm4Bziq94JfOtJURnSKdXZUxnAz6mrKjd5nrnFJ91kB7DmhO2ogbUPM1e3uvslysccEsZyq 5JZoyO/+wahy81/eXIjZFlKBd/B4Xk4/GplADn+71pMjoeccU5TbQXEWLzZ1U/dBAFSSu2QwHAO3 HtS264dTn7p3H6DmmFiSB6Cp2QDWTLPGfuH5voawLrxfo2j3hsNRv44blcFlYHjPI7elb4+9k+le ZeNvh3quv+IZ9UtJ7URyIgWN2IbgAHtj9alt20OnCQo1KnLWlyrud1Ha6bf+VexW9rNFN84k8kfN nv0p968IurKO/RGt1uMvvTcuNj4yPrik0Oyl03QbKzn2mSGFUbacgGtKZFdASOV+bIq09TB2voUV TTW1uOTSooEiW3cSmCPaNxZducDrgN+tX1BdcsTg9hVN7idZdkNrJcEKGbY6jAJOPvEZ6GprO688 OjwyRNG2wrJtznAP8JI6EU3JN6bktPd7FhmSKMu7BEUZJJwAKgiv7OeQRxXcEjnoqSAn8s0XwMln MiKWcxtgDqTisOSaSC6tJJbK4CiU8YHPyN71wYnFVKNWEVC8XvLpH1JlJprQ0dT0HSdbdG1GyiuW jBCF85AP0qzpem2ei2f2TToRb24YtsUnGT1PNV7S8gvJZIlhmikRQxD8ZBz6E+lXvLCj7zk/WuuE 4zXNF3XdGnO3G19Cx9obAJAJ9ao6lIZbizhMkkUbytuKOUJARjyQR3xT/KdZEKkso4IJpksUV2Gj uYI5olbIWRQwz681bbtYl7DrUJHdTxC5ldNiN+8laTBy3qeOgqzBMrTmR3+WMFVPrnqf0qjb29ta Fhb20UIfqYowucdM4/GrEORCpC5Iz+fenF2QR0Rc+0rg5VvbpThcrt5RgfwrPedlyfKJAGetOjlW VNwPFPnHc0Y5FkGVPTg+1O9q8/8AEcudcAMhASBCo3EYO5ske/T9K3/DOoMdJ3Xc5f8AevteSTcc Z7559awp4pTryo8r0V79DolQ5aMat1r06nRcD8qF5FRfaYO08eP98VKCCAQcjHUV1mAjNtFRiaNV yM9en6Uk80UfyyOg46Fsf56Gs68m8rT7ieNwNkTOGHIGAamTsDNA3CH+E/WmFkY5ZD+DYrEltzbW lvcLd3JlMkIKtKWB3OoIx9Ca181mp8yIjLmWgwD94cdO9KfvAd6xpPE9jbSTRyR3GYnZWYICCQcH v7VsKP4j35FRGcZX5XexpKEo25la4jYU7tzfh3qII0xkWQnnoO2KlI3Nk9MU4D97n2qiDM/srT9r IwYq2dy+e4Bz14zWpKoKqy/dJ/L2rk7uNVsLlXtZfMCSfN9nY+vfGPxrolvo3IQLOpc8B4HUEjnq RiuHA11UlNOlyWf3+eyJg730LZPaueuHnl1K8Q3U0aROqoiNgAbFP8ya6AHKgiqsum2E8zyzWVvJ I3VniUk9upFa4yhUr0uSnPkfdf0gkm1ZOxDp0kj6Skhcu+D8x6nDEfyFXOGjYg8HpWZrsJj8K6hF bR7StuwRY1xjjsBXkvw5/tY+NbQXZuzDskz5hbbnYfWt1eKSep2UcN7SlOpzJcvTv6Htn8GfbApS PbnGacBwg7U4IGl8xvuKMfX2qrHLYXG2Eg/efp9KiPyruPTFPkYtISewoOGAH8P86bARRyPSkl+5 n3/SojcQWvyzzxxj+HzHAz+dPjljni8yN1dCOGU5BpWAdgbT3B6UhO2NgckY7elLnIAXnH5Ux+Eb BzwQT68U1owKrG6jcy2zQ8gIyyKexPOQfep7OGWHz5J2RpZn3nywcDgDHP0qmY/tV/KnnSxKkaNi NscktnP5Cp7VgsbxiV38tyu525xXBSxtGpi54aKfPFXfbp/maSUlBPocJY/Eq4utVhszp0ISWYR5 DnIycZrvNSt5bhYfJ2Zjl3EOxAPysOuD61z9r8OtEtruK8jluzJG4kXMi4JBz/drqwMgfnXQ6TqQ lTrap6fIwipWanqZ9jY3EF7JcTeWN8aoFRi3Qk9wPWn/AGy6kMrQW0JjjdkDPMVJwcHgKe49avE7 w2OMd6oQ2coikKXRVJHZ9pQEgk5P61x4mhXo4eNPAWTT69te9zWmoLSWxatLhbu0gnVSqyxrIAeo yM/1qXGeO3Wq9lALSFbdSxSNFRN3XAGOfyqUNtlMbdCMqfWvSJIGklM7wRw79qqzZYL1zj+Rp1rO XtvubXVmQrnPIODXD+OPHV54Q1+OG2tIJxcWyOTKTxhnHGK6XwhqL6x4dg1KREjkuWd2RDwuWPFR GTcmuh1VMNOFGNZrSRsxpsyOMk5JqFYfLSQRtwScA+tUfFGtP4e8P3GpRQrK0WMIxwDk1zPgfx3c eLNVmtJLGKBIojMWVySeQP61TavYiOHqzpOrFe6t2dNBPJZalcedbTP5kcYBjAbpuz396fDNqP2U PaW7iEPO0ownmMWdgoTdkAqcE54we/a5cBSVbHzH5Qa5WTxQ1rcTo2mwyiJ2XJkwTgkc/KfSolWp 0HzTdr6HJKcKb5pO1zZsW8SG7UXe4xB4P+WcKqVxiXPGfft7elb9iyrYQkkAbapeVC7lhHGCe20V b3o0SoVYADkA/wCe1dPNc2Mm71fTYtVnS9ZUdURQGjLccnsD60zTZoZdOk8uPdC80pUbeCpdu3pW XrmgX9/q095F5AicKAGcgjAA9K0rSJ9I8P7LnDGCKR28s5zyW4/CuKEqrrTUo+70fc5oOftZXWnQ njsNOiZJE0yBHUgqywKCD6ggVdEy46N+VcXonxJ0jXNWtdPggu0lk+VS6jGfzrspLmOBgpz0zXQn fVHZVo1KUuWorM5u58IarM9wFmtGWRnIdnYE5JOSNvXn1rtHiVlA6BemKZ9nkA+W5lz74I/lTftB jG25XZ/tryp/woo4alRcnTVuZ3ZVWvUqpKbvbYiYbSR6UKu5gB3qVY425EykexqWOEK24HPFXysx sQXlmJrKeONRvaNlUE4GSOKryW91cPbloRGI5d5O8HjaR/WtSk7CiVOMmm+hSk0rIpxQkhxnGGwK VoZB2z9KnTHmyjvw36Y/pUnUVTiibFIxv/dP5U0AsQoGT2q/VbzXlJ8mPcvQsxwD9PUUuQLDo4QB k4JHfqB9KkeMMvJNRZvP7kH/AH0f8KUNdgcxxH/dc/4VVhkLoFcqpyTTShjO08VJ5M8r7jti57fN mlkindlUyoccbtmD/PFRyCsc7qMRGtPK0Ejxm3RQyxFxkM5PQH1FWNFhKQXJeN0WScsgZSuRhecf hWsbaVGyQJlHodp/LpTHZi3yQyY9XGK5IYGFPEyxKb5pK3l0/wAu5PJaXMN9hSEYYnHBFHlyg/M2 3PYD/GlCSA/K+70DD/CuiwyrLZ28rlp7eGQ4A3OgbH50+OFYZNlvBHFHyQqjaPc4H1qcBlYeahVW yMg7vpWa2sRAOUt7iRYmZd67QDtJB6tntUValKglKq0k+/cUnZEus7F0r96QIxNDvJ6bfMXOfbGa rRvpsmqWi6XJblsOZfIYH5cd8ds4rYjkWaFJEztdQw+hpw/KqcbyTLUrKxhXR04axcLf3EUYEcYj WSfZ/ezgZHtU9iyLpLSWz7ot020q24YDtgg1rdqPauSlhHDEzrubal06L0G5XilY8W8N67rdz4n0 6GfU71onuFDK8rEEZ6HmvZXXI39wcioXt4YSZI4Yw2Cc7R1xUxRx1lOPoK6adNxVm7mNODirN3MP W/DGja9drdalYieSNBEp3spAyT2I7mtPR7G10vTY7Gzj8qKLICZJxk56mrPlggAZG09evWmpxcDd xg7ePQ9P1FaJK5u6k5RUG9EVtb0q21rSZrG7DmGTG7YcHg9qyfDng3R/Dl1Ndad5/mSx+W3mOCMZ B9PauiuWEYXccZYDA6moXdBPlDh9pYoRjIHU/qPzpW6gqk1FwTdn06E7KDH8wrnLnwjb3F1I63Vw olYuw+U4JOTjitt9Sson2y3USOOqlhkUqXMYgMrSL5WCwkz8u31zWc4U6mkknYxlCM9JK48YRsY5 B/Opc5bBx0HWqceo6fcuiQ3tvI7j5VSVST+tWhkqfUdK1RZTWe6uLu8iR4USGQIu6MsTlFbn5h/e qNVfVfD80cjBHkWSIsi9OSuQDUz6dDJdNOrzoZTufZKyhiAADgHHQCrFtDHb24giXCKSOTn3NcVC liY16k6k7wfwrsU3GystTgvDnwvt9D1q21NdTe4MOT5TQ7c8Y65ruLiBZ5d2/GBt6U2clbmziR2Q SyENt7jYx/mBUV2DBNtF5KuRnBK/4V1XjDQMRip1Z81V3Zv5YDn+X/16b5gzg4ye3P8AhRRXShMZ 5UBOTDH/AN8im/Zoh8yF4v8AdJA/I8UUUAU1uZ1kAFxCFd3VElJLPszuIwPanx6kZJAqmJ/uZC7w QGOAeVGf/rUUUwLTOqXXJ6qB06c8US3AjUsqlwvUjp/9eiikAnlSzDEzBUP8Cd/qf8KsAADAHA6U UUAFLRRSAazYP4ZpFHfn8f8AP+cUUUAOoPSiigCKaMyAEDkUqQqoDY+bFFFFluADAlAJ7ZH9f6Vh XGgXCrc+TcxESySOA0ZyNzFjzntmiisMThaOJio1o3S1+ZLipaMvW9tPBbxxCItsQLncOcDFSFZ0 HzQnHqpzRRW3KirDdzY/1Mv/AHwaQyKvLBlHqVIooo5EKxHK6PGQHX04NPM6HnJI9QDRRS5UACVT 90Mw9Qpp0rxunD7WHQsNv86KKaikNDJJf3lrcCJpQj7ikZGfukdyBwSO9E8z3mpwzG1lhSOF0Pml eSxU8bSf7pooqW7XQjIkt7mO8uWS2kkjdwysrL02KO5HcGprixuZvC11arGDPLayIqFh94qcDPTq fWiiuPD4GlSrTrRveW4oRUZcyPK/A/hDXNJ8W2l1f2EkMChtzsRgcGvZo98n+rUt7ngfnRRXZCnG KsjrxeJniqntKm/kPFvKoA80Aj0XpxSeRIDkSj8VooqjnsKbGCWPbOkU2ecSYOPwPSozo9j0+wQH /tiD/SiinYD/2Q==