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

THE SERO-CONVERSION AND EVALUATION OF RENAL ALTERATIONS IN DOGS INFECTED BY Leishmania (Infantum) chagasi

This study investigated the sero-conversion period in which dogs from endemic areas test positive for visceral leishmaniasis (VL) as well as the early post-infection period in which renal alterations are observed. Dogs that were initially negative for Canine Visceral Leishmaniasis (CVL) were clinically evaluated every three months by serological, parasitological and biochemical tests until sero-conversion was confirmed, and six months later a subsequent evaluation was performed. Samples of kidney tissues were processed and stained with Hematoxylin and Eosin (H&E), Periodic Acid Schiff (PAS) and Massons trichrome stain and lesions were classified based on the WHO criteria. Of the 40 dogs that initially tested negative for VL, 25 (62.5%) exhibited positive serological tests during the study period. Of these 25 dogs, 15 (60%) tested positive within three months, five (20%) tested positive within six months and five (20%) tested positive within nine months. The dogs exhibited antibody titers between 1:40 and 1:80 and 72% of the dogs exhibited clinical symptoms. The Leishmania antigen was present in the kidneys of recently infected dogs. We found higher levels of total protein and globulin as well as lower levels of albumin in the infected dogs when compared to the control dogs. Additionally, infected dogs presented levels of urea and creatinine that were higher than those of the uninfected dogs. Glomerulonephritis was detected in some of the dogs examined in this study. These data suggest that in Teresina, the sero-conversion for VL occurs quickly and showed that the infected dogs presented abnormal serum proteins, as well as structural and functional alterations in the kidneys during the early post-infection period

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost