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Registro Completo |
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Biblioteca(s): |
Embrapa Acre. |
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Data corrente: |
26/06/2019 |
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Data da última atualização: |
16/11/2023 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
FIGUEIREDO, S. M. de M.; FIGUEIREDO, E. O. |
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Afiliação: |
Symone Maria de Melo Figueiredo, Universidade Federal do Acre (Ufac); EVANDRO ORFANO FIGUEIREDO, CPAF-AC. |
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Título: |
Espacialização de espécies florestais por classe diamétrica usando máxima entropia no sudoeste da Amazônia. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 19., 2019, Santos, SP. Anais... São José dos Campos: INPE, 2019. |
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Páginas: |
4 p. |
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ISBN: |
978-85-17-00097-3 |
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Idioma: |
Português |
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Conteúdo: |
O objetivo do estudo foi analisar a predição da distribuição de espécies florestais madeireiras, em escala local, utilizando dados de ocorrência agrupados por classe diamétrica. Para estimar a distribuição foi utilizado o método de máxima entropia (Maxent) e as ocorrências são de inventário florestal de planos de manejo. Foram selecionadas seis variáveis preditoras, por espécie, pelo método de todas as regressões possíveis. Os modelos tiveram em média bom desempenho (AUC = 0,7; taxa de omissão = 8,8%), demonstrando a viabilidade de se predizer a distribuição de espécies por classe diamétrica. De acordo com os modelos, Astonium lecointei, Clarisia racemosa e Ceiba pentandra com diâmetro a altura do peito (DAP) ≥ 100 cm têm maior probabilidade de ocorrer em locais com altitudes mais elevadas do terreno. Esse procedimento de modelagem é eficiente para ampliar o conhecimento sobre as preferências de habitat e a distribuição geográfica de espécies na paisagem. The aim of the study was to analyze the predicting the distribution of forest tree species, on a local scale, using occurrence data grouped by diameter class. To estimate the distribution of species was used the maximum entropy method (Maxent) and the occurrence data are forest management plan. Six predictor variables were selected by species by method of all possible regressions. The models, by species and diameter class, had an average good performance (AUC = 0.7; omission rate = 8.8%), demonstrate the viability of predicting distribution of species by diameter class. According to the models, trees Astonium lecointei, Clarisia racemosa and Ceiba pentandra with diameter at breast height (DBH) ≥ 100 cm are more likely to occur in localized at higher elevations. This modeling procedure is efficient to increase knowledge about habitat preferences and geographical distribution of species in the landscape. MenosO objetivo do estudo foi analisar a predição da distribuição de espécies florestais madeireiras, em escala local, utilizando dados de ocorrência agrupados por classe diamétrica. Para estimar a distribuição foi utilizado o método de máxima entropia (Maxent) e as ocorrências são de inventário florestal de planos de manejo. Foram selecionadas seis variáveis preditoras, por espécie, pelo método de todas as regressões possíveis. Os modelos tiveram em média bom desempenho (AUC = 0,7; taxa de omissão = 8,8%), demonstrando a viabilidade de se predizer a distribuição de espécies por classe diamétrica. De acordo com os modelos, Astonium lecointei, Clarisia racemosa e Ceiba pentandra com diâmetro a altura do peito (DAP) ≥ 100 cm têm maior probabilidade de ocorrer em locais com altitudes mais elevadas do terreno. Esse procedimento de modelagem é eficiente para ampliar o conhecimento sobre as preferências de habitat e a distribuição geográfica de espécies na paisagem. The aim of the study was to analyze the predicting the distribution of forest tree species, on a local scale, using occurrence data grouped by diameter class. To estimate the distribution of species was used the maximum entropy method (Maxent) and the occurrence data are forest management plan. Six predictor variables were selected by species by method of all possible regressions. The models, by species and diameter class, had an average good performance (AUC = 0.7; omission rate = 8.8%), demonstrate the viability of ... Mostrar Tudo |
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Palavras-Chave: |
Acre; Amazonia Occidental; Amazônia Ocidental; Árboles forestales; Biodiversidad; Embrapa Acre; Instituto de Meio Ambiente do Acre; Inventario forestal; Manejo florestal; Manejo forestal; Maximum Entropy Method (Maxent); Método da Máxima Entropia (Maxent); Modeflora; Prácticas de conservación; Predição; Predicción; Sistemas de información geográfica; Teledetección; Western Amazon. |
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Thesagro: |
Administração Florestal; Árvore Florestal; Biodiversidade; Biogeografia; Conservação; Dendrometria; Diâmetro; Inventário Florestal; Sensoriamento Remoto; Sistema de Informação Geográfica. |
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Thesaurus Nal: |
Biodiversity; Biogeography; Conservation practices; Forest inventory; Forest management; Forest mensuration; Forest trees; Geographic information systems; Prediction; Remote sensing. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/198850/1/26804.pdf
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Marc: |
LEADER 03845nam a2200613 a 4500
001 2110086
005 2023-11-16
008 2019 bl uuuu u00u1 u #d
020 $a978-85-17-00097-3
100 1 $aFIGUEIREDO, S. M. de M.
245 $aEspacialização de espécies florestais por classe diamétrica usando máxima entropia no sudoeste da Amazônia.$h[electronic resource]
260 $aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 19., 2019, Santos, SP. Anais... São José dos Campos: INPE$c2019
300 $a4 p.
520 $aO objetivo do estudo foi analisar a predição da distribuição de espécies florestais madeireiras, em escala local, utilizando dados de ocorrência agrupados por classe diamétrica. Para estimar a distribuição foi utilizado o método de máxima entropia (Maxent) e as ocorrências são de inventário florestal de planos de manejo. Foram selecionadas seis variáveis preditoras, por espécie, pelo método de todas as regressões possíveis. Os modelos tiveram em média bom desempenho (AUC = 0,7; taxa de omissão = 8,8%), demonstrando a viabilidade de se predizer a distribuição de espécies por classe diamétrica. De acordo com os modelos, Astonium lecointei, Clarisia racemosa e Ceiba pentandra com diâmetro a altura do peito (DAP) ≥ 100 cm têm maior probabilidade de ocorrer em locais com altitudes mais elevadas do terreno. Esse procedimento de modelagem é eficiente para ampliar o conhecimento sobre as preferências de habitat e a distribuição geográfica de espécies na paisagem. The aim of the study was to analyze the predicting the distribution of forest tree species, on a local scale, using occurrence data grouped by diameter class. To estimate the distribution of species was used the maximum entropy method (Maxent) and the occurrence data are forest management plan. Six predictor variables were selected by species by method of all possible regressions. The models, by species and diameter class, had an average good performance (AUC = 0.7; omission rate = 8.8%), demonstrate the viability of predicting distribution of species by diameter class. According to the models, trees Astonium lecointei, Clarisia racemosa and Ceiba pentandra with diameter at breast height (DBH) ≥ 100 cm are more likely to occur in localized at higher elevations. This modeling procedure is efficient to increase knowledge about habitat preferences and geographical distribution of species in the landscape.
650 $aBiodiversity
650 $aBiogeography
650 $aConservation practices
650 $aForest inventory
650 $aForest management
650 $aForest mensuration
650 $aForest trees
650 $aGeographic information systems
650 $aPrediction
650 $aRemote sensing
650 $aAdministração Florestal
650 $aÁrvore Florestal
650 $aBiodiversidade
650 $aBiogeografia
650 $aConservação
650 $aDendrometria
650 $aDiâmetro
650 $aInventário Florestal
650 $aSensoriamento Remoto
650 $aSistema de Informação Geográfica
653 $aAcre
653 $aAmazonia Occidental
653 $aAmazônia Ocidental
653 $aÁrboles forestales
653 $aBiodiversidad
653 $aEmbrapa Acre
653 $aInstituto de Meio Ambiente do Acre
653 $aInventario forestal
653 $aManejo florestal
653 $aManejo forestal
653 $aMaximum Entropy Method (Maxent)
653 $aMétodo da Máxima Entropia (Maxent)
653 $aModeflora
653 $aPrácticas de conservación
653 $aPredição
653 $aPredicción
653 $aSistemas de información geográfica
653 $aTeledetección
653 $aWestern Amazon
700 1 $aFIGUEIREDO, E. O.
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Registro original: |
Embrapa Acre (CPAF-AC) |
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Registro Completo
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Biblioteca(s): |
Embrapa Gado de Corte; Embrapa Pecuária Sudeste. |
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Data corrente: |
07/12/2021 |
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Data da última atualização: |
20/12/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
MARTINS, F. B.; MORAES, A. C. L.; AONO, A. H.; FERREIRA, R. C. U.; CHIARI, L.; SIMEÃO, R. M.; BARRIOS, S. C. L.; SANTOS, M. F.; JANK, L.; VALLE, C. B. do; VIGNA, B. B. Z.; SOUZA, A. P. DE. |
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Afiliação: |
FELIPE BITENCOURT MARTINS, Center for Molecular Biology and Genetic Engineering; ALINE COSTA LIMA MORAES, Center for Molecular Biology and Genetic Engineering; ALEXANDRE HILD AONO, Center for Molecular Biology and Genetic Engineering; REBECCA CAROLINE ULBRICHT FERREIRA, Center for Molecular Biology and Genetic Engineering; LUCIMARA CHIARI, CNPGC; ROSANGELA MARIA SIMEAO, CNPGC; SANZIO CARVALHO LIMA BARRIOS, CNPGC; MATEUS FIGUEIREDO SANTOS, CNPGC; LIANA JANK, CNPGC; CACILDA BORGES DO VALLE, CNPGC; BIANCA BACCILI ZANOTTO VIGNA, CPPSE; ANETE PEREIRA DE SOUZA, Center for Molecular Biology and Genetic Engineering; UNICAMP. |
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Título: |
A semi-automated SNP-based approach for contaminant identification in biparental Polyploid Populations of tropical forage grasses. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Frontiers in Plant Science, v.12, article 737919, 2021. |
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Páginas: |
19 p. |
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DOI: |
https://doi.org/10.3389/fpls.2021.737919 |
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Idioma: |
Inglês |
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Conteúdo: |
Artificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs. MenosArtificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made ava... Mostrar Tudo |
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Palavras-Chave: |
Allele dosage; Apomictic clones; Clustering analysis; GBS; Half sibling; Self fertilization; Shiny. |
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Thesaurus NAL: |
Principal component analysis. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/228605/1/SemiAutomatedSNP.pdf
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Marc: |
LEADER 02794naa a2200373 a 4500
001 2138071
005 2021-12-20
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3389/fpls.2021.737919$2DOI
100 1 $aMARTINS, F. B.
245 $aA semi-automated SNP-based approach for contaminant identification in biparental Polyploid Populations of tropical forage grasses.$h[electronic resource]
260 $c2021
300 $a19 p.
520 $aArtificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs.
650 $aPrincipal component analysis
653 $aAllele dosage
653 $aApomictic clones
653 $aClustering analysis
653 $aGBS
653 $aHalf sibling
653 $aSelf fertilization
653 $aShiny
700 1 $aMORAES, A. C. L.
700 1 $aAONO, A. H.
700 1 $aFERREIRA, R. C. U.
700 1 $aCHIARI, L.
700 1 $aSIMEÃO, R. M.
700 1 $aBARRIOS, S. C. L.
700 1 $aSANTOS, M. F.
700 1 $aJANK, L.
700 1 $aVALLE, C. B. do
700 1 $aVIGNA, B. B. Z.
700 1 $aSOUZA, A. P. DE
773 $tFrontiers in Plant Science$gv.12, article 737919, 2021.
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Embrapa Gado de Corte (CNPGC) |
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