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@PhDThesis{Klippel:2017:DiAtPr,
               author = "Klippel, Sandro",
                title = "Modelos de distribui{\c{c}}{\~a}o de esp{\'e}cies de peixes 
                         demersais marinhos da plataforma sul do Brasil: 
                         distribui{\c{c}}{\~a}o atual e proje{\c{c}}{\~o}es futuras em 
                         cen{\'a}rios de mudan{\c{c}}as clim{\'a}ticas",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2017",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2016-08-04",
             keywords = "rela{\c{c}}{\~o}es esp{\'e}cie-ambiente, habitat essencial de 
                         peixes, boosted regression trees, modelos aditivos generalizados, 
                         MAXENT, species-environment relationships, essential fish habitat, 
                         generalized additive model.",
             abstract = "Modelos de distribui{\c{c}}{\~a}o de esp{\'e}cies (MDE) 
                         s{\~a}o ferramentas para a obten{\c{c}}{\~a}o de mapas de 
                         aptid{\~a}o de habitat com base em ocorr{\^e}ncias 
                         hist{\'o}ricas das esp{\'e}cies e vari{\'a}veis ambientais. 
                         Neste trabalho, quatro t{\'e}cnicas diferentes foram comparadas: 
                         O procedimento AquaMaps, o algoritmo de m{\'a}xima entropia 
                         (MAXENT), modelos aditivos generalizados (GAM), e as 
                         \${''}\$Boosted Regression Trees\${''}\$ (BRT). AquaMaps e 
                         MAXENT foram aplicados utilizando apenas dados de presen{\c{c}}a 
                         enquanto GAM e BRT utilizaram dados de 
                         presen{\c{c}}a-aus{\^e}ncia. Usando essas t{\'e}cnicas, foram 
                         desenvolvidos MDEs para 65 peixes marinhos. Dados 
                         extra{\'{\i}}dos de 3.167 lances de arrasto de fundo de 
                         pesquisas cient{\'{\i}}ficas realizadas na plataforma sul do 
                         Brasil foram utilizados nos modelos. A capacidade preditiva dos 
                         modelos foi avaliada usando a {\'a}rea sob a curva 
                         caracter{\'{\i}}stica de opera{\c{c}}{\~a}o do receptor (AUC) 
                         e valida{\c{c}}{\~a}o cruzada. Embora as abordagens de 
                         presen{\c{c}}a-aus{\^e}ncia tenham produzido um n{\'u}mero 
                         maior de modelos com boa capacidade preditiva do que as 
                         t{\'e}cnicas que utilizam apenas dados de presen{\c{c}}a; alguns 
                         modelos que utilizam apenas dados de presen{\c{c}}a tiveram um 
                         desempenho equivalente. MDEs foram usadas para projetar a 
                         {\'a}rea de distribui{\c{c}}{\~a}o de peixes demersais marinhos 
                         sob diferentes cen{\'a}rios de aquecimento das {\'a}guas do 
                         Atl{\^a}ntico Sudoeste. As mudan{\c{c}}as projetadas para 
                         2\$^{o}\$C de aquecimento incluem uma redu{\c{c}}{\~a}o de 
                         15-34\% no habitat adequado para tr{\^e}s esp{\'e}cies 
                         comercialmente importantes de peixes marinhos na plataforma sul do 
                         Brasil, considerando prefer{\^e}ncias t{\'e}rmicas e de 
                         profundidade, e um deslocamento para o sul do centro da {\'a}rea 
                         de distribui{\c{c}}{\~a}o que chega a 200 km. A 
                         redu{\c{c}}{\~a}o e o deslocamento das {\'a}reas de 
                         distribui{\c{c}}{\~a}o dessas esp{\'e}cies, que s{\~a}o os 
                         principais recursos demersais explorados pela pesca comercial na 
                         regi{\~a}o, pode reduzir a disponibilidade das esp{\'e}cies para 
                         as frotas nacionais com graves consequ{\^e}ncias econ{\^o}micas 
                         e sociais. Al{\'e}m disso, foram desenvolvidos MDEs para cinco 
                         esp{\'e}cies de elasmobr{\^a}nquios amea{\c{c}}adas, e 
                         comparados com o conhecimento atual sobre essas esp{\'e}cies. 
                         Esses modelos fizeram previs{\~o}es razo{\'a}veis usando a 
                         grande cobertura espacial e temporal de sensoriamento remoto. Tal 
                         desempenho {\'e} particularmente {\'u}til para restringir 
                         {\'a}reas de pesca, ou mesmo para deslocar os pescadores para 
                         {\'a}reas com menor probabilidade de capturas acidentais. 
                         ABSTRACT: Species distribution models (SDM) are tools to obtain 
                         habitat suitability maps based on historical species occurrences 
                         and environmental variables. In this work, four different 
                         techniques were compared: The AquaMaps procedure, maximum entropy 
                         algorithm (MAXENT), general additive models (GAM), and Boosted 
                         Regression Trees (BRT). AquaMaps and MAXENT were applied using 
                         presence-only data whereas GAM and BRT used presence-absence data. 
                         Using these techniques, SDMs for 65 marine fishes were developed. 
                         Data drawn from 3,167 bottom trawl hauls of scientific surveys 
                         carried out on South Brazil Shelf were used in the models. The 
                         predictive ability of the models was assessed using the area under 
                         receiver operating characteristic curve (AUC) and 10-fold 
                         cross-validation. While presence-absence approaches have been 
                         found to produce more models with greater predictive ability than 
                         presence-only approaches; some presence-only models can perform 
                         almost as well. SDMs were used to project the distribution area of 
                         marine demersal fishes under different warming scenarios of the 
                         Southwestern Atlantic waters. The projected changes under 
                         2\$^{o}\$C warming include a decrease of 1534\% in suitable 
                         habitat for three commercially important marine fish species in 
                         Southern Brazil, considering thermal and depth preferences, and a 
                         shift to the south of the center of the distribution area that 
                         reaches 200 km. The reduction and displacement of distribution 
                         areas of those species, which are the main demersal resources 
                         exploited by commercial fisheries in the region, may reduce the 
                         availability of those species to national fleets with serious 
                         economic and social consequences. In addition, SDMs for five 
                         endangered elasmobranchs species were developed and compared with 
                         the current knowledge about those species. Those models make 
                         reasonable predictions using the great spatial and temporal 
                         coverage of satellite data. Such performance is particularly 
                         useful to restrict fishing grounds, or even to prompt fishers to 
                         move to areas with lesser probability of incidental catches.",
            committee = "Valeriano, Dalton de Morisson (presidente) and Vinhas, L{\'u}bia 
                         (orientadora) and Kampel, Silvana Amaral (orientadora) and Kampel, 
                         Milton and Santos, Roberta Aguiar dos and Silva, Ant{\^o}nio 
                         Olinto {\'A}vila da",
           copyholder = "SID/SCD",
         englishtitle = "Species distribution models of marine demersal fishes of South 
                         Brazil Shelf: current distribution and future projections under 
                         climate change scenarios",
             language = "pt",
                pages = "295",
                  ibi = "8JMKD3MGP3W34P/3M7GPK5",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34P/3M7GPK5",
           targetfile = "publicacao.pdf",
        urlaccessdate = "24 nov. 2020"
}


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