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

The shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiver sity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxo nomic 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 be yond 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 un equally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the coun try. 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.Fil: Gomes da Silva, Janaina. Jardim Botânico do Rio de Janeiro: Rio de Janeiro, BrasilFil: Filardi, Fabiana L.R. Jardim Botânico do Rio de Janeiro; BrasilFil: Barbosa, María Regina de V. Universidade Federal da Paraíba: Joao Pessoa; BrasilFil: Baumgratz, José Fernando Andrade. Jardim Botânico do Rio de Janeiro; BrasilFil: de Mattos Bicudo, Carlos Eduardo. Instituto de Botânica. Núcleo de Pesquisa em Ecologia; BrasilFil: Cavalcanti, Taciana. Empresa Brasileira de Pesquisa Agropecuária Recursos Genéticos e Biotecnologia; BrasilFil: Coelho, Marcus. Prefeitura Municipal de Campinas; BrasilFil: Ferreira da Costa, Andrea. Federal University of Rio de Janeiro. Museu Nacional. Department of Botany; BrasilFil: Costa, Denise. Instituto de Pesquisas Jardim Botanico do Rio de Janeiro; BrasilFil: Dalcin, Eduardo C. Rio de Janeiro Botanical Garden Research Institute; BrasilFil: Labiak, Paulo. Universidade Federal do Parana; BrasilFil: Cavalcante de Lima, Haroldo. Jardim Botânico do Rio de Janeiro; BrasilFil: Lohmann, Lucia. Universidade de São Paulo; BrasilFil: Maia, Leonor. Universidade Federal de Pernambuco; BrasilFil: Mansano, Vidal de Freitas. Instituto de Pesquisas Jardim Botânico do Rio de Janeiro; Brasil. Jardim Botânico do Rio de Janeiro; BrasilFil: Menezes, Mariângela. Federal University of Rio de Janeiro. Museu Nacional. Department of Botany; BrasilFil: Morim, Marli. Instituto de Pesquisas Jardim Botânico do Rio de Janeiro; BrasilFil: Moura, Carlos Wallace do Nascimento. Universidade Estadual de Feira de Santana. Department of Biological Science; BrasilFil: Lughadha, Eimear NIck. Royal Botanic Gardens; Reino UnidoFil: Peralta, Denilson. Instituto de Pesquisas Ambientais; BrazilFil: Prado, Jefferson. Instituto de Pesquisas Ambientais; BrasilFil: Roque, Nádia. Universidade Federal da Bahia; BrasilFil: Stehmann, Joao. Universidade Federal de Minas Gerais; BrasilFil: da Silva Sylvestre, Lana. Universidade Federal do Rio de Janeiro; BrasilFil: Trierveiler-Pereira, Larissa. Universidade Estadual de Maringá. Departamento de Análises Clínicas e Biomedicina; BrasilFil: Walter, Bruno Machado Teles. EMBRAPA Cenargen Brasília; BrasilFil: Zimbrão, Geraldo. Universidade Federal do Rio de Janeiro; BrasilFil: Forzza, Rafaela C. Jardim Botânico do Rio de Janeiro; BrasilFil: Morales, Matías. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Morón. Facultad de Agronomía y Ciencias Agroalimentarias; Argentin

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Modeling, Mining and Analysis of Multi-Relational Scientific Social Network

Social networks are dynamic social structures consisting of individuals or organizations, usually represented by nodes tied by one or more relationship type. Analyzing these structures enables us to detect several inter and intra connections between people in and outside their organizations. In this context, we construct a multi-relational scientific social network where researchers may have four different types of relationships with each other. We adopt some criteria such as relationship age in order to assign a weight to relationships and to enable the modeling of a scientific social network as close as possible to reality. Using clustering techniques with maximum flow measure, we identify the social structure and research communities in a way that allows us to evaluate the knowledge flow in the Brazilian scientific community

Modeling, Mining and Analysis of Multi-Relational Scientific Social Network

Abstract: Social networks are dynamic social structures consisting of individuals or organizations, usually represented by nodes tied by one or more relationship type. Analyzing these structures enables us to detect several inter and intra connections between people in and outside their organizations. In this context, we construct a multi-relational scientific social network where researchers may have four different types of relationships with each other. We adopt some criteria such as relationship age in order to assign a weight to relationships and to enable the modeling of a scientific social network as close as possible to reality. Using clustering techniques with maximum flow measure, we identify the social structure and research communities in a way that allows us to evaluate the knowledge flow in the Brazilian scientific community

Programa de Engenharia de Sistemas e Computação The Temporal R-Tree

It’s a well-known fact that the new GIS applications need to keep track of temporal information. However, the most known spatial index structure, the R-Tree and its variants, does not preserve the MBRs evolution. A first but inefficient approach is to add one dimension to data space in order to store time. In this work, we propose an alternative approach: a new index structure called Temporal R-Tree that deals with spatiotemporal data. The Temporal R-Tree, or TR-Tree, allows the retrieving of present and past states of data. There is little data duplication, which is guaranteed by the block copying mechanism. The retrieving time is comparable to the original R-Tree. 1

Approximate Query Processing in Spatial Databases Using Raster Signatures

Traditional query processing provides exact answers to queries. However, in many applications, the response time of exact answers is often longer than what is acceptable. Approximate query processing has emerged as an alternative approach to give to the user an answer in a short time. The goal is to provide an estimated result in one order of magnitude less time than the time to compute the exact answer. There is a large set of techniques for approximate query processing; however, most of them are only suitable for traditional data. This work proposes new algorithms for a set of spatial operations that can be processed approximately using 4CRS (Four-Color Raster Signature).Pages: 53-7

Approximate Query Processing in Spatial Databases Using Raster Signatures

Abstract. Traditional query processing provides exact answers to queries. However, in many applications, the response time of exact answers is often longer than what is acceptable. Approximate query processing has emerged as an alternative approach to give to the user an answer in a short time. The goal is to provide an estimated result in one order of magnitude less time than the time to compute the exact answer. There is a large set of techniques for approximate query processing; however, most of them are only suitable for traditional data. This work proposes new algorithms for a set of spatial operations that can be processed approximately using 4CRS (Four-Color Raster Signature). Resumo. Processamento tradicional de consultas visa prover respostas exatas para consultas; todavia, em muitas aplicações, o tempo de uma resposta exata é frequentemente muito maior do que o desejado. Processamento aproximado de consultas tem surgido como uma abordagem alternativa para processar consultas em um curto período de tempo, retornando uma resposta estimada para o usuário. Existem várias técnicas para processamento aproximado de consultas; todavia, muitas delas são aplicáveis apenas a dados tradicionais. Este trabalho propõe novos algoritmos para operações espaciais que podem ser processadas de forma aproximada usando a assinatura 4CRS (Assinatura Raster de Quatro Cores). 1

Efficient Processing of Spatiotemporal Joins 1

Abstract. Among other operations, a spatiotemporal DBMS should efficiently answer the spatiotemporal join. This paper presents an evaluation of spatiotemporal join algorithms using these new structures, particularly a partially persistent R-Tree called Temporal R-Tree and the 2+3D R-Tree. Starting from spatial join algorithms, we present algorithms for processing spatiotemporal joins over time instants and intervals on both spatiotemporal data structures. Finally, we implement and test these new algorithms with a couple of generated spatiotemporal data sets. Our experiments show that our algorithms ´ performance is good even in extreme cases, showing its good scalability – especially for the TR-Tree

Approximate spatial query processing using raster signatures

Nowadays, the database characteristics, such as the huge volume of data, the complexity of the queries, and even the data availability, can demand minutes or hours to process a query. On the other hand, in many cases it may be enough to the user to get a fast approximate answer, since it has the desired precision. The challenge to give to the user an exact query answer within a reasonable time becomes even bigger in the spatial database field. This work proposes the use of the Four Color Raster Signature (4CRS) for approximate query processing. The main goal is to reduce the time required to process a query executing it on approximate data (4CRS signature) instead of accessing the real datasets. The experimental tests demonstrated the good results of our proposal. Considering the test of the most important algorithm, the time required to process an approximate query answer has average of 7.22% of the time to get an exact answer, the disk accesses have average of 7.04% and the average error is 1% related to exact processing. Besides the 4CRS storage requirements are also quite small, which has an average of only 3.57% of the space required to store the real datasets.Pages: 403-42

A Raster Approximation for Processing of Polyline Joins

The main subject of spatial joins is polygons and polylines. The processing of spatial joins can be greatly improved by the use of filters that reduce the need for examining the exact geometry of spatial objects in order to find the intersecting ones. Approximations of candidate pairs of spatial objects are examined using such filters. As a result, three possible sets of answers are identified: the positive one, composed of intersecting pairs; the negative one, composed of non-intersecting pairs; and the inconclusive one, composed of the remaining pairs of candidates. To identify all the intersecting pairs of spatial objects with inconclusive answers, it is necessary to have access to the representation of them so that an exact geometry test can take place. This is true for both polygons and polylines. There are many approximations designed for polygons, but few of them are suitable for approximating polylines. This article presents a polyline approximation for spatial join processing, which we call raster signature for polylines. The performance of a filter using this approximation was evaluated with real world datasets. The results showed that our approach, when compared to other approaches presented in the related literature, reduced the inconclusive answers by a factor of more than two. As a result, the need for retrieving the representation of polylines and carrying out exact geometry tests is reduced by a factor of more than two, as well. 1