20220613 13232000 1.0 ISO 19139 Metadata Implementation Specification GML3.2 TRUE Essa base de dados delimita as áreas prioritárias para a conservação da Mata Atlântica, indicando o custo de conservação, a importância biológica e a prioridade para as ações de conservação.O dado original, separado por bioma, foi unido em um único arquivo e recortado pelo limite do Estado de Pernambuco. Campos da tabela de atributos: ID_AP – Código numérico da área prioritária (AP) para conservação da Mata Atlântica. Cod_AP – Código da área prioritária (AP) para conservação da Mata Atlântica. Custo_Soma – Custo relativo, para cada AP, para conservação da Mata Atlântica, calculado a partir de variáveis econômicas, ambientais e sociais. Area_ha – Tamanho em hectares das Aps.NumMarxan – Número médio de vezes que as Unidades de planejamento (UPs) dentro das APs foram selecionadas como prioritárias pelo Marxan nas 100 rodadas de criação de cenários. ImportBio – Importância biológica das APs com base no NumMarxan (frequência média que as Unidades de planejamento (UPs) dentro das APs foram selecionadas como prioritárias nas análises usando o Marxan). Custo_CAT – Categorização, em três classes, do custo relativo para a conservação da Mata Atlântica em cada AP (Custo_Soma). Prioridade – Prioridade de ação de conservação para cada área prioritária, definido com base no custo para a conservação e na importância biológica. AcaoPrincipal – Código da ação principal de conservação das APs. APs_Prioridade Essa base de dados delimita as áreas prioritárias para a conservação da Mata Atlântica, indicando o custo de conservação, a importância biológica e a prioridade para as ações de conservação. MMA, GPSI, Apac, ÁreasEspeciais_Áreas_PrioritáriasParaConservaçãoDaBiodiversidade_Importância /9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCACFAMgDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACmSSLEhZzgdB 7nsB70+oJRIbmHEMckWTuYnDRnHBHrnp2x70AVrXVoryRI4objzDy6vGV8sc8kng8jHBP6GpLvVb CxuIbe7vIYJZziJJHClz7Z61h6nd32leITfzCd9OCBcK424O0YwTjduIPrjPOBUF/otj43l0zU0u WiWyk/exGI7ieG27sggj1GRzWMa124faXR9V39DaFON05v3e6NO58WaZaX0tpMZ1ki++fKJCnGQP X07Y5rYgnjuYI54W3RyKGVvUGszVvDthqcFw32SBbuSNgs23B3EcEkfQc9R2rI0DQNV03Uw80kcd oF3tHDIxDucjBHAJwFJYj0HPaHOtGryuN4vqunrd6/IXLTcL3s/zOuooHT0orpMgopofMjIVYY6E jg01p4UnSBpY1mkBKRlgGYDqQO+MigCSiiq819a28qxSzqsjEAJ1PPAJA6D3PFJyUVdsCZ3EaFmD ED+6pY/kKrWNxdXMTPc2TWp3kBHcMxUdG+XI59O1W6KYCAg9DmloAA6UUAFFFFABRRXlXxW1zUrS +tLO0uZ7eAwszmKQpvJJBB9RgfrUVJqEeZnRhcPLE1o0ouzZ6G3iHRkuntX1WyWdOGjadQQfzrRV ldQykMpGQQcgivlNh3AOe/evU/hV4g1a4P8AYkaQyWsH75pZWbKISAVXqOpyAcd65qOL558jVj28 wyD6rh/bwnzW30t89+561RRRXYfOBRRRQAUUUUAUNY046rpz2yy+Ux5DYyM+h9j7Yqj4e0i90p7h bidXibbsVGJGe5wenYe9btZ97qyWMoWW0u2jyMzRxhkXPc85/T6ZrCpSp86rS3XUtVJKLgtmaFRX FzDaxebPKscecbmOBTBf2rWz3Kzo0KfedTkD64+o/OmQ6lZXTPGk6Fg2wo42nP0OCa09pG6Sau9i bFpWV0V0YMrDIIOQRS1gT+JCGeW3tvMtISd75+aTHUIOnvkntjHet8HIyOlZ0cTSrOUacruLs/Jg 01ueWeNNG1rQLPVNZt/Et4kM8wfyUZg25n+VQQflAU4znnGMV5tpeu3mma7BrAcz3UTFszMx3ZBB yc57nvzXrvxWsL+/8Pq1tBK8FsfOlZJOMdOU74znPbBrxGOKSZisUbuQCxCjJAHJP/1+1cWL5o1F yn3XD6pVsHN1rPo9EtLW1Z6LY+JviB4sONMXYiy5M0UYRF4A2lm4OOuOTye2AO+1CVYVigD/AG7V o0EUsoUxAHY2JDgEA5x378cgCuC+G2neIYo5Lm1FzFp9wNyFCgWRlOM/N0APsScHg449L0/w5BEj S33+kXEpZn3YIVmPJU4BB6c/jxmmnVqw5Yr3tHeS0+S6v+rnzebOlDEypUoxUYv7O79X+BnaFeWd nPcXF1qEe5hHAqNIWI5JJHcqWYjLcgg89h00tu008TmeRUjO7YhwGPue49qgl0ewlt4YGtk8uHbs A4wAc4z3B7g8HvVLxB4q0zw0bRb+QhrmTYqqMlR3c+w4/pmuuhCVGkoTd7W1/wAzzIwlVnywV2+i NuqOsavaaHpkuoXrMIIiobaMnkgDA/GpxdwvY/bIm82HyzIDGM7hjPFeJePPF7+LFEVjZ3KafZtu eR16seAWA6egyautVVON+p2ZdgJYyuobRvq+3/B7Hq9xrcep6JnR723ivryNhZrdN5bE5IztIzxg np29K8rt/iL4n0bXjbarcrcx283k3ERjTPynDYIA564PeuQ03WtQ0i/jvbO4KXEalVZgGwDnPB47 k/WrOjzWk+urqGtXYMSyedKHiMrTHqV2j16HJGM1wyxLqNcrs/wPqKORxwkantoqcWtNLyv2Xy/z PpVHEkauOjAGsTW/COleIdQtbvUY3kNujIIwxCtkjr9OfzrMtfiX4ZnWFRcyxl0yVMDHy8cYO0Gt G68XabGn+is12xXKGP7mccZY/wBM12VK1GMeaclY+QjSxNGpZJxl80+xiavofhrSNJbSIdO/1pja bYCHdFOc7yDnleg/SsW2un8I+K7K20iOF9M1S4SF22JuYg4JUq5PG7uB24qC/v7m7uXknuLidlUk qoPHU4HIHPAA6n862PBtnb3+qLNLbyGS0/eo8vLKTuUK3b1I78c9K8jD436ziuWC93o9n5/f8j0Z OVKm3Wble903e7ez9Voeh0UUV7x4oUyWaOCJpZXCIvVmPAp9ZN1p9/fTt5txDHCj7ogqbu3BPTkc 9yPasq05wg3TjzPtovzGrX1KUuq6peuyabbn5SDyACAOzFuBnI4Azj8x0SklFLLtYjkZzg1BZWos 7VYQ24jJZsY3E9TirFTh4VIwvVleT37LyXl+IO3QKwpNUS9uHsp7UiAzmEuJSrBg3ynGBxkdj6Vu 1zsk2nTa0s8yeXDGdyTbsRvJgHLDHBHY55x9Kyxc3HkSmo3a36rqhxMn7RZ3N/8AZLLUJnScY8yR MRyt02nbjhu5wR6Vf1TTfsOmNPLc73ZkDRswCknjCcZznGO/HvVRtOTS9WW9sLWe+tmjMlvHCCwV +PvPntzgc9fauls9HtLSUT4ee4GcTztvcZ64J+717Yrko5fSqJqtBaXStfRdLdn10Nakoq3I3/wT OXRYbvR1+yzEl4wNsjF0Pqpz82Cc9/w4xWppVo9jpkFrI+5o1253FuM8DJ5OBgZPXFXKZK/lxs+x nwM7UGSa9CnhqNKTnTik3vbS/wAtr+e5i23uY3itFl0SSGXP2aUlZsMynbgnGVIPOAPTnoelcQmh vo8ksdlpE1lhfNlaORnwqqefMJxxk8DHJ5Hp0/iHxFE0V3plvEZJt/k7hggHbnI91bbwfwrnLm/v bwSLc3k8qycOpbCn/gIwP0+teTmeLo03aU3rp7ttLXvrur6fd6no4OnWaaW29nez87dToNB8TQwW rRaldYKtsjUQMWUDj5to79s88HNA8S35137OohaBrgQqu0jKlgA2eucEH0IPSuV2ABQp2KD0GMEd x9PU1unw/A2l2U15qcNlLIoBWYgBlB+QD5lIO3Ax7djknnwuOxOLXLS05Wuu62d2+vUuth6VF3n1 v9/kdfFq1jNqVzp8dwpurYK00eD8obpz0ryz4v6J5N3a6ysrN5/7l0Y8LgcY/XP4Vwurx6tbX9/9 rNwDNOwmf5gkrBs5z0IyQfxFdKfhn4hn0a1uopBI8pJkt5XKGMDgMd2O36Y6161SrKtGUFE9nCYG jl1aliJV0k/LfRX16f8ADGv4P0/xfrmg24tddjsdKRjGqR48wAHnBAyDyep9K9WaxtpLN7R4g8Lr tcP8xYYxyTyT7nmuY8E6VH4a8M3caTfapElaSVBhSjbFzGSTjIx1zj8q0vCviJ/E2mG/OnyWkW4r GXcMJMdSOnQ8dK6qS5YpPc8HHVPbVJ1KaXInpZJb7X6t6dbnD6p8LdLstVspUvmFnPcpCbaQkMc9 drAHngnkY9xUOvfCe4F28uhCAWwQYimmJdm79VwPzr1K5sLe7uLWeZNz2rmSLJ4DEEZ/Imo9WvGs NMmuU2b0xgODgkkDHH1qJUaUYuTVluaRzbGtxXtG+lm9P+D6vUx9P0Tw94YsI1FnHG82FPnBXlYk 9Ce+M9uKy9f8Py2t001hbsbaQZbaC+xs88ZzjvxVCys5tYv1hd1kmZjLI7YGAT8zAfj0H8q7S5tt RVw8NzI4WMKCCoYnuSpG0k+vFeW61PH0JSdNuCelt35rb+vMzqc+GqJ815Pc4XVdPfT76WzeQnAy kgGCykdRn8Rn2rf8AaULGwvLvzZJDdSgAuc5CDbn65yPwre+wR6nZxjVbSN5VY5DAdicdCevBxmr 0UUcEKRRIEjRQqqowAB2rfBZdHD1ZVYP3ZbK2xnWxbq01BrXv3H0UUV6pxhRRRQAVheJ9ZuNGtrS W2RJZJZ9hiYZLLtYnHIxjA5/xrdrhdZd9a8Xx6b58NukatGjNmQucBmwBjrwDk/wH6HnxVWVOn+7 +J6L1/rU1owUpe9styhPrGoX2swXAlSKV5Y4rdFYhBk8AnuCeTxk/gK6F7a4W7MMEF1PgPuDRhFU BgFG4nBzyRgkjHQdKktNGGj6laLbwyXM07MZbuRRtgjUZwB2ZiQPpk9gK6SvPjlar0XHGvmk3f02 2Na1WPMvZKyRT0uzew02G3kkaSRQS7sQSWJJPYdzVyiivXjFRSitkcoUUUUwM6XSI5ddh1TzCGjj 2FNowx5wc/8AAj+lcdq2iXOlxSXV1co6GXajclpM5JLH+HgEk816FTJYYp4zHNGkiHkq6gj8jXFi sBRxEHGS7v52tc6KOJnSd1/SPKWEZH76JZYgQXjbIDgc4OOmR27da7KHQJ9S029fUlhiu7wKEAjz 5Cr9wdefUjPPSta10HSrKQyQWUQfcWDON5U+xbOB7DitGufLsueFhKE5XT6WNMVilWknFWsUoNLt otMgsJUFzDCFA89Q3TocdOO3pVPxLrtv4e0n7Vc2s9zG7+SI4VB5IOM+g4xnnrWzVLVdPOp2Ythc Nb/vEcyIoLDawPGcgHjrg16lrKyOaLXMubY8Z0zXNMtNde4shqQtJnK2tlJInlGVgQwZem3LL+Fd x8NtJ1ywsZZ9SuWS3mZylk0WCrZHz57A4PHTGKzfDfgS6g8RaiNbgadY0zZXvmk7SSeVH94ZzyOC OOua63w3pOpeH7dNMmuVvrFB+6nPySR/7JXkEehz+FYUoyunLzPWx1elyyhSad+Xz012duml9m35 aGF4mPih9eJ8MazC/mpsktSUPklOp+bIBOT6H8hW94esdZfRGtfFDW11Nv4Kc7lGCN3AGcitSLTb WC/kvY02zSAhiDwckE8fgKt1cISu3P8A4Fjz54i9JUlFadba/eV0sbWKZJo7eNJEUopRduAcZHH0 H5VYoorU5gooooAKKKKACiiigAqk+j6a90LlrC288EnzPKAY5BBye+QT19au0UAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH//Z