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| 165. | | FERREIRA, G.; VACONCELOS, C. da C.; RIBEIRO, G. G.; COSTA, J. B. P.; GUEDES, A. C. L.; GUEDES, M. C. Propagulos de Cordia Tetrandra Aubl. (Boraginaceae) em floresta de várzea no estuário Amazônico. In: CONGRESO LATINOAMERICANO DE BOTÁNICA, 11.; CONGRESSO NACIONAL DE BOTÂNICA, 65.; ENCONTRO REGIONAL DE BOTÂNICOS - MG, BA, ES, 34., 2014, Salvador. Botânica na América Latina: conhecimento, interação e difusão. Brasília, DF: SBS, 2014.| Biblioteca(s): Embrapa Amapá. |
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| Registros recuperados : 321 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Amapá; Embrapa Amazônia Oriental. |
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Data corrente: |
18/11/2019 |
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Data da última atualização: |
07/12/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
TOURNE, D. C. M.; BALLESTER, M. V. R.; JAMES, P. M. A.; MARTORANO, L. G.; GUEDES, M. C.; THOMAS, E. |
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Afiliação: |
DAIANA C. M. TOURNE, USP; MARIA V. R. BALLESTER, USP; PATRICK M. A. JAMES, UNIVERSITY OF MONTRÉAL; LUCIETA GUERREIRO MARTORANO, CPATU; MARCELINO CARNEIRO GUEDES, CPAF-AP; EVERT THOMAS, BIOVERSITY INTERNATIONAL, REGIONAL OFFICE FOR THE AMERICAS. |
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Título: |
Strategies to optimize modeling habitat suitability of Bertholletia excelsa in the Pan-Amazonia. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Ecology and Evolution, v. 9, n. 22, p. 12357-12960, Nov. 2019. |
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DOI: |
https://doi.org/10.1002/ece3.5726 |
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Idioma: |
Inglês |
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Conteúdo: |
Aim: Amazon-nut (Bertholletia excelsa) is a hyperdominant and protected tree species, playing a keystone role in nutrient cycling and ecosystem service provision in Amazonia. Our main goal was to develop a robust habitat suitability model of Amazon-nut and to identify the most important predictor variables to support conservation and tree planting decisions. Localization: Amazon region, South America. Methods: We collected 3,325 unique Amazon-nut records and assembled >100 spatial predictor variables organized across climatic, edaphic, and geophysical categories. We compared suitability models using variables (a) selected through statistical techniques; (b) recommended by experts; and (c) integrating both approaches (a and b). We applied different spatial filtering scenarios to reduce overfitting. We additionally fine-tuned MAXENT settings to our data. The best model was selected through quantitative and qualitative assessments. Results: Principal component analysis based on expert recommendations was the most appropriate method for predictor selection. Elevation, coarse soil fragments, clay, slope, and annual potential evapotranspiration were the most important predictors. Their relative contribution to the best model amounted to 75%. Filtering of the presences within a radius of 10 km displayed lowest overfitting, a satisfactory omission rate and the most symmetric distribution curve. Our findings suggest that under current environmental conditions, suitable habitat for Amazon-nut is found across 2.3 million km2, that is, 32% of the Amazon Biome. Main conclusion: The combination of statistical techniques with expert knowledge improved the quality of our suitability model. Topographic and soil variables were the most important predictors. The combination of predictor variable selection, fine-tuning of model parameters and spatial filtering was critical for the construction of a reliable habitat suitability model. MenosAim: Amazon-nut (Bertholletia excelsa) is a hyperdominant and protected tree species, playing a keystone role in nutrient cycling and ecosystem service provision in Amazonia. Our main goal was to develop a robust habitat suitability model of Amazon-nut and to identify the most important predictor variables to support conservation and tree planting decisions. Localization: Amazon region, South America. Methods: We collected 3,325 unique Amazon-nut records and assembled >100 spatial predictor variables organized across climatic, edaphic, and geophysical categories. We compared suitability models using variables (a) selected through statistical techniques; (b) recommended by experts; and (c) integrating both approaches (a and b). We applied different spatial filtering scenarios to reduce overfitting. We additionally fine-tuned MAXENT settings to our data. The best model was selected through quantitative and qualitative assessments. Results: Principal component analysis based on expert recommendations was the most appropriate method for predictor selection. Elevation, coarse soil fragments, clay, slope, and annual potential evapotranspiration were the most important predictors. Their relative contribution to the best model amounted to 75%. Filtering of the presences within a radius of 10 km displayed lowest overfitting, a satisfactory omission rate and the most symmetric distribution curve. Our findings suggest that under current environmental conditions, suitable habitat for Am... Mostrar Tudo |
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Palavras-Chave: |
Análise de componentes principais; Avaliação de modelo; Conhecimento especializado; Entropia máxima; Expert knowledge; Filtragem espacial; Maximum entropy; Model evaluation; Modelo de distribuição de espécie; Protected Amazonian species; Spatial filtering; Species distribution model. |
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Thesagro: |
Castanha. |
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Thesaurus NAL: |
Principal component analysis. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1159297/1/Strategies-to-optimize.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/204836/1/CPAF-AP-2019-Strategies-to-optimize-modeling-habitat.pdf
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Marc: |
LEADER 03112naa a2200361 a 4500
001 2159297
005 2023-12-07
008 2019 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1002/ece3.5726$2DOI
100 1 $aTOURNE, D. C. M.
245 $aStrategies to optimize modeling habitat suitability of Bertholletia excelsa in the Pan-Amazonia.$h[electronic resource]
260 $c2019
520 $aAim: Amazon-nut (Bertholletia excelsa) is a hyperdominant and protected tree species, playing a keystone role in nutrient cycling and ecosystem service provision in Amazonia. Our main goal was to develop a robust habitat suitability model of Amazon-nut and to identify the most important predictor variables to support conservation and tree planting decisions. Localization: Amazon region, South America. Methods: We collected 3,325 unique Amazon-nut records and assembled >100 spatial predictor variables organized across climatic, edaphic, and geophysical categories. We compared suitability models using variables (a) selected through statistical techniques; (b) recommended by experts; and (c) integrating both approaches (a and b). We applied different spatial filtering scenarios to reduce overfitting. We additionally fine-tuned MAXENT settings to our data. The best model was selected through quantitative and qualitative assessments. Results: Principal component analysis based on expert recommendations was the most appropriate method for predictor selection. Elevation, coarse soil fragments, clay, slope, and annual potential evapotranspiration were the most important predictors. Their relative contribution to the best model amounted to 75%. Filtering of the presences within a radius of 10 km displayed lowest overfitting, a satisfactory omission rate and the most symmetric distribution curve. Our findings suggest that under current environmental conditions, suitable habitat for Amazon-nut is found across 2.3 million km2, that is, 32% of the Amazon Biome. Main conclusion: The combination of statistical techniques with expert knowledge improved the quality of our suitability model. Topographic and soil variables were the most important predictors. The combination of predictor variable selection, fine-tuning of model parameters and spatial filtering was critical for the construction of a reliable habitat suitability model.
650 $aPrincipal component analysis
650 $aCastanha
653 $aAnálise de componentes principais
653 $aAvaliação de modelo
653 $aConhecimento especializado
653 $aEntropia máxima
653 $aExpert knowledge
653 $aFiltragem espacial
653 $aMaximum entropy
653 $aModel evaluation
653 $aModelo de distribuição de espécie
653 $aProtected Amazonian species
653 $aSpatial filtering
653 $aSpecies distribution model
700 1 $aBALLESTER, M. V. R.
700 1 $aJAMES, P. M. A.
700 1 $aMARTORANO, L. G.
700 1 $aGUEDES, M. C.
700 1 $aTHOMAS, E.
773 $tEcology and Evolution$gv. 9, n. 22, p. 12357-12960, Nov. 2019.
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Embrapa Amazônia Oriental (CPATU) |
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