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

Konkurenční analýza stavebního spoření

Seznámení s problematikou stavebního spoření a jeho využití při řešení bytové situace. Porovnání současných podmínek s podmínkami platnými do 31.12.2003. Zhodnocení dané problematiky u jednotlivých staveních spořitelen. Zhodnocení SS do budoucna a jeho dopady do státního rozpočtu

Brazilian Flora 2020: Leveraging the power of a collaborative scientific network

International audienceThe shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiversity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxonomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world's known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world's most biodiverse countries. We further identify collection gaps and summarize future goals that extend beyond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still unequally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the country. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora

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