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

I Diretrizes do Grupo de Estudos em Cardiogeriatria da Sociedade Brasileira de Cardiologia

O idoso apresenta características próprias na manifestação das doenças, na resposta à terapêutica e no efeito colateral dos medicamentos. Constitui um grupo de maior risco para o aparecimento das doenças degenerativas, em geral, e cardiovasculares, em particular, além de apresentar maior número de comorbidades

Reconstruction and simulation of neocortical microcircuitry

We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm3 containing ∼31,000 neurons, and patch-clamp studies identify 55 layer-specific morphological and 207 morpho-electrical neuron subtypes. When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ∼8 million connections with ∼37 million synapses. Simulations reproduce an array of in vitro and in vivo experiments without parameter tuning. Additionally, we find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms. The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies

One sixth of Amazonian tree diversity is dependent on river floodplains

Amazonia’s floodplain system is the largest and most biodiverse on Earth. Although forests are crucial to the ecological integrity of floodplains, our understanding of their species composition and how this may differ from surrounding forest types is still far too limited, particularly as changing inundation regimes begin to reshape floodplain tree communities and the critical ecosystem functions they underpin. Here we address this gap by taking a spatially explicit look at Amazonia-wide patterns of tree-species turnover and ecological specialization of the region’s floodplain forests. We show that the majority of Amazonian tree species can inhabit floodplains, and about a sixth of Amazonian tree diversity is ecologically specialized on floodplains. The degree of specialization in floodplain communities is driven by regional flood patterns, with the most compositionally differentiated floodplain forests located centrally within the fluvial network and contingent on the most extraordinary flood magnitudes regionally. Our results provide a spatially explicit view of ecological specialization of floodplain forest communities and expose the need for whole-basin hydrological integrity to protect the Amazon’s tree diversity and its function

The Maximum Entropy Formalism of statistical mechanics in a biological application: a quantitative analysis of tropical forest ecology

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain almost ten times more of local relative abundances then constraints based on either directional or stabilizing selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics

The Maximum Entropy Formalism of statistical mechanics in a biological application: a quantitative analysis of tropical forest ecology

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain almost ten times more of local relative abundances then constraints based on either directional or stabilizing selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics

Mapping density, diversity and species-richness of the Amazon tree flora

Files for "Mapping density, diversity and species-richness of the Amazon tree flora"PlotData.csv: Plot metadata and diversity data, needed to run the R-scripts, plus references.PlotsAbiotic.csv: Abiotic metadata for all plots, needed to run R-scriptsAmazonLowLandForestRaisg.csv: Coordinate file of Amazon forest (0.1 degree), withing Raisg boundary.SoterRaisg.asc: Raster PlotsAbiotic.csv: Abiotic metadata for all plots, needed to run R-scriptsTreeDiversityFunctions.R: Functions need to run R-scriptTreeDiversityScript.R: R-script to create all outputTreeDensity.asc: raster file of estimated tree density of Amazon forestTreeDiversity.asc: raster file of fisher's alpha (0.1 degree) of Amazon forest TreeRichness_ha.asc: raster file of species richness/ha (0.1 degree) of Amazon forest TreeDiversityPoster01.tif: High resolution poster of tree diversity (Fisher's alpha) of Amazon forestTreeDiversityPoster02.tif: High resolution poster of tree species richness/ha of Amazon fores

Data from: Over 10,000 Pre-Columbian earthworks are still hidden throughout Amazonia

Dataset: This set of data and R computer codes were used to create the predictive model, figures, and develop analysis on the manuscript "Over 10,000 Pre-Columbian earthworks are still hidden throughout Amazonia" submitted to Science journal as a research article (DOI: ...ade2541). Please read the materials and methods sections on the manuscript supplementary materials, along with the data provided in the "Database" folder, to ensure reproducibility. Earthwork Predictive Model: The Inhomogeneous Poisson Process (IPP) model fit was performed using the 'fit_bayesPO' function of the 'bayesPO' library in R version 4.0.2. The model was developed by the author of the package Guido Alberti Moreira. Figures: Figures created from R computer codes presented on the Main text are inside the "MainText_figures" folder, and Supplementary material figures are inside the "SuppMaterial_figures" folder. Please utilize the instructions in the supplementary material in conjunction with the data in the "database" folder to ensure reproducibility. Dataset usage: It is free to use, but if you use this dataset in your work, please make sure to cite the repository and our paper properly. We also welcome users to invite us for collaboration. For the use of this dataset, please cite: Peripato, V. et al. Data from: Over 10,000 Pre-Columbian earthworks are still hidden throughout Amazonia (2023). DOI: 10.5281/zenodo.7750985. https://doi.org/10.5281/zenodo.775098