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		<isbn>978-85-17-00088-1</isbn>
		<label>59861</label>
		<citationkey>MottaBragSilvChri:2017:AvDeMo</citationkey>
		<title>Avaliação do desempenho de modelos de distribuição potencial da espécie Wunderlichia azulenzis</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
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		<size>552 KiB</size>
		<author>Motta, Alline Zagnoli Villela,</author>
		<author>Braga, Sollano Rabelo,</author>
		<author>Silva, Nathalia Drummond Marques da,</author>
		<author>Christofaro, Cristiano,</author>
		<electronicmailaddress>allinezvm@gmail.com</electronicmailaddress>
		<editor>Gherardi, Douglas Francisco Marcolino,</editor>
		<editor>Aragão, Luiz Eduardo Oliveira e Cruz de,</editor>
		<e-mailaddress>daniela.seki@inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Sensoriamento Remoto, 18 (SBSR)</conferencename>
		<conferencelocation>Santos</conferencelocation>
		<date>28-31 maio 2017</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>6874-6881</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>Potential distribution models, when allowing the occurrence mapping of species, can be a powerful tool for conservation of natural resources programs. The objective of this study is to evaluate the performance of many modeling algorithms utilizing distribution data of the Wunderlichia azulenzis species. The species is listed in the Ministry of Environment''s National list of endangered species of flora in the Caatinga biome. Two groups of algorithms, classified according to two types of entry data (presence and absence), were evaluated using the Area Under the Curve - AUC. From the registered occurrences for the species on database Global Biodiversity Information Facility  GBIF, and utilizing six temperature and precipitation variables selected from the Worldclim project, species distribution maps were created. Six different algorithms were used to create the distribution maps of the species. The Mahalanobis Distance (0,978) and the Random Forest (0,0993) algorithms presented the greatest AUC values among its respective groups, while the Bioclim (0,931) and General Linear Model - GLM (0,807) algorithms presented the lowest values. The algorithms that are a part of the group of models that use only presence registers (Bioclim, Domain and Mahalanobis Distance) were considered efficient.</abstract>
		<area>SRE</area>
		<type>Monitoramento e modelagem ambiental</type>
		<language>pt</language>
		<targetfile>59861.pdf</targetfile>
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