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      Strategies to optimize modeling habitat suitability of Bertholletia excelsa in the Pan‐Amazonia


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          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.


          Amazon region, South America.


          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.


          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 km 2, 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.


          Innovation on fit models using collaborative model‐building process. Ecological knowledge supporting modeling techniques. Suitability habitat model to support conservation and planting to important Amazon species.

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          Most cited references 43

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          Hyperdominance in the Amazonian tree flora.

          The vast extent of the Amazon Basin has historically restricted the study of its tree communities to the local and regional scales. Here, we provide empirical data on the commonness, rarity, and richness of lowland tree species across the entire Amazon Basin and Guiana Shield (Amazonia), collected in 1170 tree plots in all major forest types. Extrapolations suggest that Amazonia harbors roughly 16,000 tree species, of which just 227 (1.4%) account for half of all trees. Most of these are habitat specialists and only dominant in one or two regions of the basin. We discuss some implications of the finding that a small group of species--less diverse than the North American tree flora--accounts for half of the world's most diverse tree community.
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            SoilGrids1km — Global Soil Information Based on Automated Mapping

            Background Soils are widely recognized as a non-renewable natural resource and as biophysical carbon sinks. As such, there is a growing requirement for global soil information. Although several global soil information systems already exist, these tend to suffer from inconsistencies and limited spatial detail. Methodology/Principal Findings We present SoilGrids1km — a global 3D soil information system at 1 km resolution — containing spatial predictions for a selection of soil properties (at six standard depths): soil organic carbon (g kg−1), soil pH, sand, silt and clay fractions (%), bulk density (kg m−3), cation-exchange capacity (cmol+/kg), coarse fragments (%), soil organic carbon stock (t ha−1), depth to bedrock (cm), World Reference Base soil groups, and USDA Soil Taxonomy suborders. Our predictions are based on global spatial prediction models which we fitted, per soil variable, using a compilation of major international soil profile databases (ca. 110,000 soil profiles), and a selection of ca. 75 global environmental covariates representing soil forming factors. Results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices (based on MODIS images), lithology, and taxonomic mapping units derived from conventional soil survey (Harmonized World Soil Database). Prediction accuracies assessed using 5–fold cross-validation were between 23–51%. Conclusions/Significance SoilGrids1km provide an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available. Some of the main limitations of the current version of SoilGrids1km are: (1) weak relationships between soil properties/classes and explanatory variables due to scale mismatches, (2) difficulty to obtain covariates that capture soil forming factors, (3) low sampling density and spatial clustering of soil profile locations. However, as the SoilGrids system is highly automated and flexible, increasingly accurate predictions can be generated as new input data become available. SoilGrids1km are available for download via http://soilgrids.org under a Creative Commons Non Commercial license.
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              Making better Maxentmodels of species distributions: complexity, overfitting and evaluation


                Author and article information

                Ecol Evol
                Ecol Evol
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                25 October 2019
                November 2019
                : 9
                : 22 ( doiID: 10.1002/ece3.v9.22 )
                : 12623-12638
                [ 1 ] Environmental Analysis and Geoprocessing Laboratory CENA University of Sao Paulo Sao Paulo Brazil
                [ 2 ] Department of Biological Sciences University of Montréal Montreal QC Canada
                [ 3 ] Agrometeorology Laboratory EMBRAPA Eastern Amazon Santarem Brazil
                [ 4 ] Forest Research and Development EMBRAPA Amapá Macapá Brazil
                [ 5 ] Bioversity International Regional Office for the Americas Lima Peru
                Author notes
                [* ] Correspondence

                Daiana C. M. Tourne, Environmental Analysis and Geoprocessing Laboratory, CENA, University of Sao Paulo, Box 96, 13416‐903 Piracicaba, SP, Brazil.

                Email: monteiro.dca@ 123456gmail.com

                © 2019 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 8, Tables: 0, Pages: 16, Words: 10661
                Funded by: São Paulo Research Foundation (FAPESP),São Paulo, Brazil
                Award ID: 2015/04749‐1
                Original Research
                Original Research
                Custom metadata
                November 2019
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.2 mode:remove_FC converted:25.11.2019


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