Remotely sensed data — Spectral patterns that might indicate biotic differentiation are identified visually and with object-based image analysis in our Landsat image. Predictions about the degree of floristic and edaphic distinctness among patches, as well as abruptness and permeability of the identified boundaries is made on the basis of the spectral data and tested with field data. Rigorous testing of the predictive power of the Landsat mosaic in different contexts is one of our main aims. For instance, initial analyses combining Landsat and field data will assess how well the spectral values from the mosaic can predict soil properties and species occurrence, turnover, and richness patterns of the focal plant groups across Amazonia. We already know that species differ in their optima and ranges along edaphic gradients (Cámara-Leret et al. 2017; Tuomisto 2006; Zuquim et al. 2014), but so far lack of reliable environmental data layers has made it difficult to apply this knowledge for making predictive maps. Following the initial analyses, we identify the most important biogeographical boundaries and ecological subdivisions in Amazonia. We use the maximum entropy approach to produce species distribution models for each focal species from the different plant groups (Elith et al. 2011). Various environmental data layers, including the Landsat mosaic, are used to predict suitable areas for each target plant species. Then we assess whether the currently existing network of conservation areas (including indigenous reserves) covers the identified biotope diversity within Amazonia, and whether any biotically unique areas seem to be threatened by imminent deforestation.

Remotely sensed data — Spectral patterns that might indicate biotic differentiation are identified visually and with object-based image analysis in our Landsat image. Predictions about the degree of floristic and edaphic distinctness among patches, as well as abruptness and permeability of the identified boundaries is made on the basis of the spectral data and tested with field data. Rigorous testing of the predictive power of the Landsat mosaic in different contexts is one of our main aims. For instance, initial analyses combining Landsat and field data will assess how well the spectral values from the mosaic can predict soil properties and species occurrence, turnover, and richness patterns of the focal plant groups across Amazonia. We already know that species differ in their optima and ranges along edaphic gradients (Cámara-Leret et al. 2017; Tuomisto 2006; Zuquim et al. 2014), but so far lack of reliable environmental data layers has made it difficult to apply this knowledge for making predictive maps. Following the initial analyses, we identify the most important biogeographical boundaries and ecological subdivisions in Amazonia. We use the maximum entropy approach to produce species distribution models for each focal species from the different plant groups (Elith et al. 2011). Various environmental data layers, including the Landsat mosaic, are used to predict suitable areas for each target plant species. Then we assess whether the currently existing network of conservation areas (including indigenous reserves) covers the identified biotope diversity within Amazonia, and whether any biotically unique areas seem to be threatened by imminent deforestation.