Biased-corrected richness estimates for the Amazonian tree flora

Amazonian forests are extraordinarily diverse, but the estimated species richness is very much debated. Here, we apply an ensemble of parametric estimators and a novel technique that includes conspecific spatial aggregation to an extended database of forest plots with up-to-date taxonomy. We show that the species abundance distribution of Amazonia is best approximated by a logseries with aggregated individuals, where aggregation increases with rarity. By averaging several methods to estimate total richness, we confirm that over 15,000 tree species are expected to occur in Amazonia. We also show that using ten times the number of plots would result in an increase to just ~50% of those 15,000 estimated species. To get a more complete sample of all tree species, rigorous field campaigns may be needed but the number of trees in Amazonia will remain an estimate for years to come.

Rarity of monodominance in hyperdiverse Amazonian forests

Tropical forests are known for their high diversity. Yet, forest patches do occur in the tropics where a single tree species is dominant. Such “monodominant” forests are known from all of the main tropical regions. For Amazonia, we sampled the occurrence of monodominance in a massive, basin-wide database of forest-inventory plots from the Amazon Tree Diversity Network (ATDN). Utilizing a simple defining metric of at least half of the trees ≥ 10 cm diameter belonging to one species, we found only a few occurrences of monodominance in Amazonia, and the phenomenon was not significantly linked to previously hypothesized life history traits such wood density, seed mass, ectomycorrhizal associations, or Rhizobium nodulation. In our analysis, coppicing (the formation of sprouts at the base of the tree or on roots) was the only trait significantly linked to monodominance. While at specific locales coppicing or ectomycorrhizal associations may confer a considerable advantage to a tree species and lead to its monodominance, very few species have these traits. Mining of the ATDN dataset suggests that monodominance is quite rare in Amazonia, and may be linked primarily to edaphic factors.

Species Distribution Modelling: Contrasting presence-only models with plot abundance data

Species distribution models (SDMs) are widely used in ecology and conservation. Presence-only SDMs such as MaxEnt frequently use natural history collections (NHCs) as occurrence data, given their huge numbers and accessibility. NHCs are often spatially biased which may generate inaccuracies in SDMs. Here, we test how the distribution of NHCs and MaxEnt predictions relates to a spatial abundance model, based on a large plot dataset for Amazonian tree species, using inverse distance weighting (IDW). We also propose a new pipeline to deal with inconsistencies in NHCs and to limit the area of occupancy of the species. We found a significant but weak positive relationship between the distribution of NHCs and IDW for 66% of the species. The relationship between SDMs and IDW was also significant but weakly positive for 95% of the species, and sensitivity for both analyses was high. Furthermore, the pipeline removed half of the NHCs records. Presence-only SDM applications should consider this limitation, especially for large biodiversity assessments projects, when they are automatically generated without subsequent checking. Our pipeline provides a conservative estimate of a species’ area of occupancy, within an area slightly larger than its extent of occurrence, compatible to e.g. IUCN red list assessments.

High airborne PM2.5 chlorine concentrations link to diabetes surge in Portugal

Since 1995 airborne particles have been sampled near Lisbon and analysed by PIXE at ITN. On the Summer of 2004 extremely high concentrations of 14 microg/m(3) of chlorine in PM2.5 were determined in a week average sample. Later in 2004 and in 2005 similar events occurred. A 12 year database of PIXE data on airborne elemental concentrations (1995 to 2006) compiled on 2007 was then analysed for PM2.5 chlorine concentrations above 1 microg/m(3), and showed that the number of this type of events per year is increasing since 1995 up until the present. A quest for time coincident abnormal health data reports was carried out and revealed a 30% raise in diabetes mellitus incidence from 2003 to 2004 followed by a 20% raise from 2004 to 2005. After a first short publication at the XIth Int. PIXE Conference in 2007 (Reis et al., 2007a) the problem remained live. Taking into account new insights into the problem, recently published data, and biomonitoring data that were previously not considered, it was possible to establish a highly probable link between the abnormally high values of PM2.5 chlorine measured in the Lisbon area and the surge in diabetes mellitus incidence in Portugal in 2004 and 2005. Data, reasoning, possible mechanisms and conclusions regarding this link are reported in the present paper.