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

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          Abstract

          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 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.

          Abstract

          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 references43

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          Making better Maxentmodels of species distributions: complexity, overfitting and evaluation

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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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              Spatial filtering to reduce sampling bias can improve the performance of ecological niche models

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                Author and article information

                Contributors
                monteiro.dca@gmail.com
                Journal
                Ecol Evol
                Ecol Evol
                10.1002/(ISSN)2045-7758
                ECE3
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2045-7758
                25 October 2019
                November 2019
                : 9
                : 22 ( doiID: 10.1002/ece3.v9.22 )
                : 12623-12638
                Affiliations
                [ 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

                Author information
                https://orcid.org/0000-0002-0083-2179
                https://orcid.org/0000-0003-2567-6747
                https://orcid.org/0000-0001-7639-7217
                https://orcid.org/0000-0003-3893-3781
                https://orcid.org/0000-0003-2702-5614
                https://orcid.org/0000-0002-7838-6228
                Article
                ECE35726
                10.1002/ece3.5726
                6875584
                31788202
                36a415dc-f19b-496f-b2b9-9d544ed06980
                © 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.

                History
                : 08 April 2019
                : 03 September 2019
                : 13 September 2019
                Page count
                Figures: 8, Tables: 0, Pages: 16, Words: 10661
                Funding
                Funded by: São Paulo Research Foundation (FAPESP),São Paulo, Brazil
                Award ID: 2015/04749‐1
                Categories
                Original Research
                Original Research
                Custom metadata
                2.0
                November 2019
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.2 mode:remove_FC converted:25.11.2019

                Evolutionary Biology
                amazon‐nut,brazil‐nut,expert knowledge,maximum entropy,model evaluation,principal component analysis,protected amazonian species,spatial filtering,species distribution model

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