Abstract
AimAmazon‐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.LocalizationAmazon region, South America.MethodsWe 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.ResultsPrincipal 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 conclusionThe 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.
Highlights
Range‐wide management and conservation of socio‐economically important tree species require a comprehensive understanding of species habitat preferences and the magnitude and nature of anthro‐ pogenic and natural threats to their in situ persistence
Existing knowledge on Amazonian forest species has been poorly integrated within conservation planning frameworks (Addison et al, 2013; Gardner, Barlow, Chazdon, Robert, & Harvey, 2009)
Conservation decision‐making processes in Amazonia can be greatly improved through the inclu‐ sion of species distribution models (SDMs) which is currently not the case in most Amazon countries
Summary
Range‐wide management and conservation of socio‐economically important tree species require a comprehensive understanding of species habitat preferences and the magnitude and nature of anthro‐ pogenic and natural threats to their in situ persistence. The final maps were examined visually by six of twelve Amazon‐ nut experts consulted who were asked to provide feedback on three aspects: (a) whether the model showed predictive power to identify underrepresented areas; (b) whether the distribution of the habitat of the B. excelsa had been well‐represented; (c) whether the most im‐ portant selected variables made ecological sense This information was used in complement to the statistical metrics. This model was fit using records of Amazon‐nut distributed spatially filters at 10 km resolution (557 presence points), regulariza‐ tion multiplier (β = 1.5), feature classes combination (LQHPT), and Group 3 predictors. Our results suggest that under current environmental conditions, suitable habitat for Amazon‐nut is found across 2.3 million km or 32% of the Amazon Biome
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