Adjacency and Area Explain Species Bioregional Shifts in Neotropical Palms

Environmental and geographical variables are known drivers of community assembly, however their influence on phylogenetic structure and phylogenetic beta diversity of lineages within different bioregions is not well-understood. Using Neotropical palms as a model, we investigate how environmental and geographical variables affect the assembly of lineages into bioregions across an evolutionary time scale. We also determine lineage shifts between tropical (TRF) and non-tropical (non-TRF) forests. Our results identify that distance and area explain phylogenetic dissimilarity among bioregions. Lineages in smaller bioregions are a subset of larger bioregions and contribute significantly to the nestedness component of phylogenetic dissimilarity, here interpreted as evidence for a bioregional shift. We found a significant tendency of habitat shifts occurring preferentially between TRF and non-TRF bioregions (31 shifts) than from non-TRF to TRF (24) or from TRF to TRF (11) and non-TRF to non-TRF (9). Our results also present cases where low dissimilarity is found between TRF and non-TRF bioregions. Most bioregions showed phylogenetic clustering and larger bioregions tended to be more clustered than smaller ones, with a higher species turnover component of phylogenetic dissimilarity. However, phylogenetic structure did not differ between TRF and non-TRF bioregions and diversification rates were higher in only two lineages, Attaleinae and Bactridinae, which are widespread and overabundant in both TRF and non-TRF bioregions. Area and distance significantly affected Neotropical palm community assembly and contributed more than environmental variables. Despite palms being emblematic humid forest elements, we found multiple shifts from humid to dry bioregions, showing that palms are also important components of these environments

The role of environmental filtering, geographic distance and dispersal barriers in shaping the turnover of plant and animal species in Amazonia

To determine the effect of rivers, environmental conditions, and isolation by distance on the distribution of species in Amazonia. Location: Brazilian Amazonia. Time period: Current. Major taxa studied: Birds, fishes, bats, ants, termites, butterflies, ferns + lycophytes, gingers and palms. We compiled a unique dataset of biotic and abiotic information from 822 plots spread over the Brazilian Amazon. We evaluated the effects of environment, geographic distance and dispersal barriers (rivers) on assemblage composition of animal and plant taxa using multivariate techniques and distance- and raw-data-based regression approaches. Environmental variables (soil/water), geographic distance, and rivers were associated with the distribution of most taxa. The wide and relatively old Amazon River tended to determine differences in community composition for most biological groups. Despite this association, environment and geographic distance were generally more important than rivers in explaining the changes in species composition. The results from multi-taxa comparisons suggest that variation in community composition in Amazonia reflects both dispersal limitation (isolation by distance or by large rivers) and the adaptation of species to local environmental conditions. Larger and older river barriers influenced the distribution of species. However, in general this effect is weaker than the effects of environmental gradients or geographical distance at broad scales in Amazonia, but the relative importance of each of these processes varies among biological groups

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 un derstanding 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,5–7 vast areas of the tropics remain understudied.8–11 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 underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities 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 or ganism 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 ne glected 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 lostinfo:eu-repo/semantics/publishedVersio

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

'Torque teno sus virus' 1 and 2 viral loads in faeces of porcine circovirus 2-positive pigs

Recently, studies have suggested an association between the 'Torque teno sus virus' (TTSuV) and the 'Porcine circovirus-2' (PCV2) in PCV2-associated disease cases. The aim of this study was to verify TTSuVs loads in pig faeces from PCV2-positive animals with and without diarrhea from PCVAD-affected and PCV2-unvaccinated herds. A total of 80 faecal samples were collected individually from nursery and grow-finish pigs with (n = 40) or without (n = 40) diarrhea. The samples were tested for PCV2 and TTSuVs by using DNA binding dye SYBR Green quantitative polymerase chain reaction (qPCR). 'Torque teno sus virus k2' (TTSuVk2) load in the faeces was significantly higher in the nursery pigs with diarrhea, and these pigs also exhibited significantly higher PCV2 (

Shvo's catalyst in chemoenzymatic dynamic kinetic resolution of amines-inner or outer sphere mechanism?

Evidence is provided for the inner-sphere mechanism with actual metal coordination of the racemic amine in the crucial hydrogen transfer step promoted by Shvo's catalyst of the chemoenzymatic dynamic kinetic resolution (DKR) of amines. Key intermediates involved in this H-transfer step were intercepted and continuously monitored by electrospray ionization mass spectrometry (ESI-MS) and characterized by their dissociation chemistries via ESI-MS/MS. © 2013 The Royal Society of Chemistry