Insights into regional patterns of Amazonian forest structure, diversity, and dominance from three large terra-firme forest dynamics plots

We analyze forest structure, diversity, and dominance in three large-scale Amazonian forest dynamics plots located in Northwestern (Yasuni and Amacayacu) and central (Manaus) Amazonia, to evaluate their consistency with prevailing wisdom regarding geographic variation and the shape of species abundance distributions, and to assess the robustness of among-site patterns to plot area, minimum tree size, and treatment of morphospecies. We utilized data for 441,088 trees (DBH ≥1 cm) in three 25-ha forest dynamics plots. Manaus had significantly higher biomass and mean wood density than Yasuni and Amacayacu. At the 1-ha scale, species richness averaged 649 for trees ≥1 cm DBH, and was lower in Amacayacu than in Manaus or Yasuni; however, at the 25-ha scale the rankings shifted, with Yasuni < Amacayacu < Manaus. Within each site, Fisher’s alpha initially increased with plot area to 1–10 ha, and then showed divergent patterns at larger areas depending on the site and minimum size. Abundance distributions were better fit by lognormal than by logseries distributions. Results were robust to the treatment of morphospecies. Overall, regional patterns in Amazonian tree species diversity vary with the spatial scale of analysis and the minimum tree size. The minimum area to capture local diversity is 2 ha for trees ≥1 cm DBH, or 10 ha for trees ≥10 cm DBH. The underlying species abundance distribution for Amazonian tree communities is lognormal, consistent with the idea that the rarest species have not yet been sampled. Enhanced sampling intensity is needed to fill the still large voids we have in plant diversity in Amazon forests. © 2016, Springer Science+Business Media Dordrecht

Low Phylogenetic Beta Diversity and Geographic Neo-endemism in Amazonian White-sand Forests

Over the past three decades, many small-scale floristic studies of white-sand forests across the Amazon basin have been published. Nonetheless, a basin-wide description of both taxonomic and phylogenetic alpha and beta diversity at regional scales has never been achieved. We present a complete floristic analysis of white-sand forests across the Amazon basin including both taxonomic and phylogenetic diversity. We found strong regional differences in the signal of phylogenetic community structure with both overall and regional Net Relatedness Index and Nearest Taxon Index values found to be significantly positive leading to a pattern of phylogenetic clustering. Additionally, we found high taxonomic dissimilarity but low phylogenetic dissimilarity in pairwise community comparisons. These results suggest that recent diversification has played an important role in the assembly of white-sand forests causing geographic neo-endemism patterns at the regional scale

Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8-50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha-1) at spatial scales ranging from 5 to 250 m (0.025-6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20-400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise. © Author(s) 2014