Peatland pole forest is the most carbon-dense ecosystem in Amazonia, but its spatial distribution and species composition are poorly known. To address this knowledge gap, we quantified variation in the floristic composition, peat thickness, and the amount of carbon stored above and below ground of 102 forest plots and 53 transects in northern Peruvian Amazonia. This large dataset includes 571 ground reference points of peat thickness measurements across six ecosystem types. These field data were also used to generate a new land-cover classification based on multiple satellite products using a random forest classification. Peatland pole forests are floristically distinctive and dominated by thin-stemmed woody species such as Pachira nitida (Malvaceae), Platycarpum loretense (Rubiaceae), and Hevea guianensis (Euphorbiaceae). In contrast, palm swamps and open peatlands are dominated by Mauritia flexuosa (Arecaceae). Peatland pole forests have high peat thickness (274 ± 22 cm, mean ± 95% CI, n = 184) similar to open peatlands (282 ± 46 cm, n = 46), but greater than palm swamps (161 ± 17 cm, n = 220) and seasonally-flooded forest, terra firme, and white-sand forest where peat is rare or absent. As a result, peatland pole forest has exceptional carbon density (1,133 ± 93 Mg C ha−1). The new sites expand the known distribution of peatland pole forest by 61% within the Pastaza-Marañón Foreland basin, mainly alongside the Tigre river, to cover a total of 7540 km2 in northern Peruvian Amazonia. However, only 15% of the pole forest area is within a protected area, whilst an additional 26% lies within indigenous territories. The current low levels of protection and forest degradation but high threat from road paving projects makes the Tigre river basin a priority for conservation. The long-term conservation of peatland pole forests has the potential to make a large contribution towards international commitments to mitigate climate change.

The forests of western Amazonia are among the most diverse tree communities on Earth, yet this exceptional diversity is distributed highly unevenly within and among communities. In particular, a small number of dominant species account for the majority of individuals, whereas the large majority of species are locally and regionally extremely scarce. By definition, dominant species contribute little to local species richness (alpha diversity), yet the importance of dominant species in structuring patterns of spatial floristic turnover (beta diversity) has not been investigated. Here, using a network of 207 forest inventory plots, we explore the role of dominant species in determining regional patterns of beta diversity (community-level floristic turnover and distance-decay relationships) across a range of habitat types in northern lowland Peru. Of the 2,031 recorded species in our data set, only 99 of them accounted for 50% of individuals. Using these 99 species, it was possible to reconstruct the overall features of regional beta diversity patterns, including the location and dispersion of habitat types in multivariate space, and distance-decay relationships. In fact, our analysis demonstrated that regional patterns of beta diversity were better maintained by the 99 dominant species than by the 1,932 others, whether quantified using species-abundance data or species presence-absence data. Our results reveal that dominant species are normally common only in a single forest type. Therefore, dominant species play a key role in structuring western Amazonian tree communities, which in turn has important implications, both practically for designing effective protected areas, and more generally for understanding the determinants of beta diversity patterns.

Mapping plant species at the regional scale to provide information for ecologists and forest managers is a challenge for the remote sensing community. Here, we use a deep learning algorithm called U-net and very high-resolution multispectral images (0.5 m) from GeoEye satellite to identify, segment and map canopy palms over ∼3000 km 2 of Amazonian forest. The map was used to analyse the spatial distribution of canopy palm trees and its relation to human disturbance and edaphic conditions. The overall accuracy of the map was 95.5% and the F1-score was 0.7. Canopy palm trees covered 6.4% of the forest canopy and were distributed in more than two million patches that can represent one or more individuals. The density of canopy palms is affected by human disturbance. The post-disturbance density in secondary forests seems to be related to the type of disturbance, being higher in abandoned pasture areas and lower in forests that have been cut once and abandoned. Additionally, analysis of palm trees’ distribution shows that their abundance is controlled naturally by local soil water content, avoiding both flooded and waterlogged areas near rivers and dry areas on the top of the hills. They show two preferential habitats, in the low elevation above the large rivers, and in the slope directly below the hill tops. Overall, their distribution over the region indicates a relatively pristine landscape, albeit within a forest that is critically endangered because of its location between two deforestation fronts and because of illegal cutting. New tree species distribution data, such as the map of all adult canopy palms produced in this work, are urgently needed to support Amazon species inventory and to understand their distribution and diversity.

Sustainable management of non-timber forest products such as palm fruits is crucial for the long-term conservation of intact forest. A major limitation to expanding sustainable management of palms has been the need for precise information about the resources at scales of tens to hundreds of hectares, while typical ground-based surveys only sample small areas. In recent years, small unmanned aerial vehicles (UAVs) have become an important tool for mapping forest areas as they are cheap and easy to transport, and they provide high spatial resolution imagery of remote areas. We developed an object-based classification workflow for RGB UAV imagery which aims to identify and delineate palm tree crowns in the tropical rainforest by combining image processing and GIS functionalities using color and textural information in an integrative way to show one of the potential uses of UAVs in tropical forests. Ten permanent forest plots with 1170 reference palm trees were assessed from October to December 2017. The results indicate that palm tree crowns could be clearly identified and, in some cases, quantified following the workflow. The best results were obtained using the random forest classifier with an 85% overall accuracy and 0.82 kappa index.