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dc.contributor.authorTOURNE, D. C. M.eng
dc.contributor.authorBALLESTER, M. V. R.eng
dc.contributor.authorJAMES, P. M. A.eng
dc.contributor.authorMARTORANO, L. G.eng
dc.contributor.authorGUEDES, M. C.eng
dc.contributor.authorTHOMAS, E.eng
dc.date.accessioned2019-11-18T18:07:57Z-
dc.date.available2019-11-18T18:07:57Z-
dc.date.created2019-11-18
dc.date.issued2019
dc.identifier.citationEcology and Evolution, p. 1-16, Oct. 2019.eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1114461-
dc.descriptionAim: 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.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectExpert knowledgeeng
dc.subjectMaximum entropyeng
dc.subjectModel evaluationeng
dc.subjectProtected Amazonian specieseng
dc.subjectSpatial filteringeng
dc.subjectSpecies distribution modeleng
dc.subjectConhecimento especializadoeng
dc.subjectEntropia máximaeng
dc.subjectAvaliação de modeloeng
dc.subjectAnálise de componentes principaiseng
dc.subjectFiltragem espacialeng
dc.subjectModelo de distribuição de espécieeng
dc.titleStrategies to optimize modeling habitat suitability of Bertholletia excelsa in the Pan-Amazonia.eng
dc.typeArtigo de periódicoeng
dc.date.updated2019-11-18T18:07:57Z
dc.subject.thesagroCastanhaeng
dc.subject.nalthesaurusPrincipal component analysiseng
riaa.ainfo.id1114461eng
riaa.ainfo.lastupdate2019-11-18
dc.identifier.doihttps://doi.org/10.1002/ece3.5726eng
dc.contributor.institutionDAIANA 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.eng
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