Alterações antropogênicas nos remanescentes de vegetação natural (RVN) de savana do estado de São Paulo no ano de 2009.

Deforestation in São Paulo State was mainly driven by sugarcane and coffee crops during the XVIII and XIX centuries which caused a significant fragmentation in native forests. More recently, between the 1970?s and 1980?s decades the native forest fragmentation process was intensified and also the savannah area was drastically reduced due to sugarcane expansion for ethanol production. Much native vegetation was deteriorated and fragmented into smaller disconnected portions during this period. This work was performed within the scope of a technical cooperative agreement between the Environmental Secretary of São Paulo State (SMA) and the National Institute for Space Research (INPE). It consisted in monitoring monthly the land cover changes of 7,000 polygons of remanescent native vegetation (RVN) in the São Paulo State. The environmental assessment of the vegetation cover status was performed based on mesoregions for better discussion of the geographical distribution of the deforested RVN's from May to December 2009. The results showed that Bauru and Ribeirão Preto mesoregions presented the highest land cover changes with 19.4 and 12.1 km², respectively

Combinatorial ECM Arrays Identify Cooperative Roles for Matricellular Proteins in Enhancing the Generation of TH+ Neurons From Human Pluripotent Cells

The development of efficient cell culture strategies for the generation of dopaminergic neurons is an important goal for transplantation-based approaches to treat Parkinson’s disease. To identify extracellular matrix molecules that enhance differentiation and might be used in these cell cultures we have used micro-contact printed arrays on glass slides presenting 190 combinations of 19 extracellular matrix molecules selected on the basis of their expression during embryonic development of the ventral midbrain. Using long-term neuroepithelial stem cells (Lt-NES), this approach identified a number of matricellular proteins that enhanced differentiation, with the combination of Sparc, Sparc-like (Sparc-l1) and Nell2 increasing the number of tyrosine hydroxylase+ neurons derived from Lt-NES cells and, critically for further translation, human pluripotent stem cells

Molecular Diversity of Midbrain Development in Mouse, Human, and Stem Cells.

Understanding human embryonic ventral midbrain is of major interest for Parkinson's disease. However, the cell types, their gene expression dynamics, and their relationship to commonly used rodent models remain to be defined. We performed single-cell RNA sequencing to examine ventral midbrain development in human and mouse. We found 25 molecularly defined human cell types, including five subtypes of radial glia-like cells and four progenitors. In the mouse, two mature fetal dopaminergic neuron subtypes diversified into five adult classes during postnatal development. Cell types and gene expression were generally conserved across species, but with clear differences in cell proliferation, developmental timing, and dopaminergic neuron development. Additionally, we developed a method to quantitatively assess the fidelity of dopaminergic neurons derived from human pluripotent stem cells, at a single-cell level. Thus, our study provides insight into the molecular programs controlling human midbrain development and provides a foundation for the development of cell replacement therapies.All authors were supported by EU FP7 grant DDPDGENES. S.L. was supported by European Research Council grant 261063 (BRAINCELL), Knut and Alice Wallenberg Foundation grant 2015.0041, Swedish Research Council (STARGET), and the Swedish Foundation for Strategic Research (RIF14-0057). A.Z. was supported by the Human Frontier Science Program. E.A. was supported by Swedish Research Council (VR projects: 2011-3116 and 2011-3318), Swedish Foundation for Strategic Research (SRL program), and Karolinska Institutet (SFO Thematic Center in Stem cells and Regenerative Medicine). E.A. and R.A.B. were supported by the EU FP7 grant NeuroStemcellRepair. R.A.B. was also supported by an NIHR Biomedical Research Centre award to the University of Cambridge/Addenbrookes Hospital. iCell dopaminergic neurons were a generous gift from Cellular Dynamics International. Single-cell RNA-seq servic0es were provided by the Eukaryotic Single-cell Genomics facility and the National Genomics Infrastructure at Science for Life Laboratory.This is the final version of the article. It first appeared from Elsevier via https://doi.org/10.1016/j.cell.2016.09.02

The Amazon Epiphyte Network: A First Glimpse Into Continental-Scale Patterns of Amazonian Vascular Epiphyte Assemblages

Epiphytes are still an understudied plant group in Amazonia. The aim of this study was to identify distributional patterns and conservation priorities for vascular epiphyte assemblages (VEA) across Amazonia. We compiled the largest Amazonian epiphyte plot database to date, through a multinational collaborative effort of 22 researchers and 32 field sites located across four Amazonian countries – the Amazonian Epiphyte Network (AEN). We addressed the following continental-scale questions by utilizing the AEN database comprising 96,448 epiphyte individuals, belonging to 518 vascular taxa, and growing on 10,907 tree individuals (phorophytes). Our objectives here are, first, to present a qualitative evaluation of the geographic distribution of the study sites and highlight regional lacunae as priorities for future quantitative inventories. Second, to present the floristic patterns for Amazonia-wide VEA and third, to combine multivariate analyses and rank abundance curves, controlled by major Amazonian habitat types, to determine how VEA vary geographically and ecologically based on major Amazonian habitat types. Three of the most striking patterns found are that: (1) VEA are spatially structured as floristic similarity decays with geographic distance; (2) a core group of 22 oligarchic taxa account for more than a half of all individuals; and (3) extensive floristic sampling gaps still exist, mainly across the highly threatened southern Amazonian deforestation belt. This work represents a first step toward unveiling distributional pattern of Amazonian VEA, which is important to guide future questions on ecology and species distribution ranges of VEA once the collaborative database grows allowing a clearer view of patterns

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

Amazon tree dominance across forest strata

The forests of Amazonia are among the most biodiverse plant communities on Earth. Given the immediate threats posed by climate and land-use change, an improved understanding of how this extraordinary biodiversity is spatially organized is urgently required to develop effective conservation strategies. Most Amazonian tree species are extremely rare but a few are common across the region. Indeed, just 227 ‘hyperdominant’ species account for >50% of all individuals >10 cm diameter at 1.3 m in height. Yet, the degree to which the phenomenon of hyperdominance is sensitive to tree size, the extent to which the composition of dominant species changes with size class and how evolutionary history constrains tree hyperdominance, all remain unknown. Here, we use a large floristic dataset to show that, while hyperdominance is a universal phenomenon across forest strata, different species dominate the forest understory, midstory and canopy. We further find that, although species belonging to a range of phylogenetically dispersed lineages have become hyperdominant in small size classes, hyperdominants in large size classes are restricted to a few lineages. Our results demonstrate that it is essential to consider all forest strata to understand regional patterns of dominance and composition in Amazonia. More generally, through the lens of 654 hyperdominant species, we outline a tractable pathway for understanding the functioning of half of Amazonian forests across vertical strata and geographical locations

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

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