Species distribution models for Amazonian trees have mostly been produced at scales and resolutions that are too broad and coarse for practical use in either conservation or forestry. On the other hand, several studies have shown that elevation and the medium‐resolution remote sensing data available via Landsat imagery can be successfully used to detect differences in plant species composition in Amazonia. Therefore, it seems likely that the same data can also be used to predict geographical distributions of individual taxa. Here we use remotely sensed data and a maximum entropy algorithm (MaxEnt) to generate landscape‐scale distribution models at 30‐m‐resolution for five economically important timber tree genera (Apuleia, Amburana, Crepidospermum, Dipteryx, and Manilkara). Individual Landsat Thematic Mapper bands and normalized difference vegetation index yielded acceptable model performance, and the use of averaging filters (3 × 3 and 5 × 5 pixel low‐pass filters) improved model performance further. Including elevation as a predictor also improved model performance for all the genera. Our results suggest that it is possible to use Landsat bands and elevation as predictors for modeling the potential distribution of tree species in lowland Amazonia at a fine enough resolution to facilitate the practical management of forest resources.

Recognition of the spatial variation in tree species composition is a necessary precondition for wise management and conservation of forests. In the Peruvian Amazonia, this goal is not yet achieved mostly because adequate species inventory data has been lacking. The recently started Peruvian national forest inventory (INFFS) is expected to change the situation. Here, we analyzed genus-level variation, summarized through non-metric multidimensional scaling (NMDS), in a set of 157 INFFS inventory plots in lowland to low mountain rain forests (<2000 m above sea level) using Landsat satellite imagery and climatic, edaphic, and elevation data as predictor variables. Genus-level floristic patterns have earlier been found to be indicative of species-level patterns. In correlation tests, the floristic variation of tree genera was most strongly related to Landsat variables and secondly to climatic variables. We used random forest regression, under varying criteria of feature selection and cross-validation, to predict the floristic composition on the basis of Landsat and environmental data. The best model explained >60% of the variation along NMDS axes 1 and 2 and 40% of the variation along NMDS axis 3. We used this model to predict the three NMDS dimensions at a 450-m resolution over all of the Peruvian Amazonia and classified the pixels into 10 floristic classes using k-means classification. An indicator analysis identified statistically significant indicator genera for 8 out of the 10 classes. The results are congruent with earlier studies, suggesting that the approach is robust and can be applied to other tropical regions, which is useful for reducing research gaps and for identifying suitable areas for conservation.

This is an open access article under the terms of the Creat ive Commo ns Attri butio n-NonCo mmerc ial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

No abstract