Resumo Sistemas de informação são fundamentais para o gerenciamento dos acervos biológicos das instituições de pesquisas em biodiversidade, uma vez que elas vêm fazendo investimentos significativos nos processos de informatização e digitalização de suas coleções. Os sistemas de bancos de dados de herbários e de jardins botânicos têm evoluído no sentido de disponibilizar online os dados de exsicatas e das coleções correlatas, além de suas imagens. Neste trabalho é apresentada a nova versão do Jabot, o sistema de gerenciamento de coleções botânicas desenvolvido no Jardim Botânico do Rio de Janeiro. O sistema reflete hoje o conhecimento adquirido por uma equipe multidisciplinar composta de botânicos e profissionais da área de computação, em uma década de uso intensivo no gerenciamento do conteúdo digital e na curadoria do herbário.
Fully contained within the Atlantic Forest Biome, the state of Rio de Janeiro represents one of the greatest areas of diversity of vegetation physiognomy, habitats and plant species, including endemics. The flora of Rio de Janeiro state is recognized as one of the richest in the country and the state as an important center of endemism. These features have encouraged diferent botanical studies, highligthing Vellozo, Cysneiros, Glaziou, Gardner, among others, as precursor naturalists in exploration native forests. It is presented physiographic features of the state and brief retrospective of the Flora of Rio de Janeiro state project, which began in 2001, and consolidated in 2007 with a checklist prepared and made available online, and in 2011, with the implantation of the Catálogo de espécies de plantas vasculares e briófitas da flora do estado do Rio de Janeiro, with the participation of over 150 participants working online. Currently 334 families, 1,821 genera and, 8,203 species, subspecies and varieties have been recorded to the state, 1,740 of which are endemic. We highlight the vegetation formations, municipalities, conservation unities, and families of angiosperms, bryophytes, ferns and lycophytes families most diverse, as well as comments about monographic treatments already published. This special volume of Rodriguésia dedicated to the flora of Rio de Janeiro state presentes 19 families with 76 species of Angiosperms -Aizoaceae, Alismataceae, Asparagaceae, Ceratophyllaceae, Cabombaceae, Haloragaceae, Hydrocharitaceae, Hydroleaceae, Juncaginaceae, Lentibulariaceae, Mayacaceae, Menyanthaceae, Molluginaceae, Myristicaceae, Nymphaeaceae, Pontederiaceae, Potamogetonaceae, Rhizophoraceae e Typhaceae. Key words: Angiosperms, Bryophytes, endemic, Gymnosperms, Lycophytes, Ferns. ResumoO estado do Rio de Janeiro, inserido no Bioma Mata Atlântica, representa uma área com alta diversidade de paisagens vegetacionais, habitats e espécies de plantas, incluindo várias endêmicas. A flora do estado do Rio de Janeiro é reconhecidamente uma das mais ricas do país e o estado apontado como um importante centro de endemismo. Tais características têm incentivado os mais diferentes estudos botânicos, destacando Vellozo, Cysneiros, Glaziou, Gardner, entre outros, como naturalistas precursores na exploração das florestas nativas. Apresentam-se, de modo geral, aspectos fisiográficos do estado e uma breve retrospectiva do projeto Flora do estado do Rio de Janeiro, iniciado em 2001, que culminou, em 2007, com a elaboração online do checklist da flora do estado, e em 2011, com a implantação do Catálogo de espécies de plantas vasculares e briófitas da flora do estado do Rio de Janeiro, no qual mais de 150 colaboradores trabalharam online. Essas iniciativas revelam números bem expressivos da diversidade da flora fluminense e a necessidade de se prosseguir nos estudos taxonômicos dos grupos botânicos que compõem a flora. Atualmente, são registradas para o estado do Rio de Janeiro 334 famílias, 1.821 gêneros e 8.203 espécies, subesp...
The unprecedented size of the human population, along with its associated economic activities, has an ever‐increasing impact on global environments. Across the world, countries are concerned about the growing resource consumption and the capacity of ecosystems to provide resources. To effectively conserve biodiversity, it is essential to make indicators and knowledge openly available to decision‐makers in ways that they can effectively use them. The development and deployment of tools and techniques to generate these indicators require having access to trustworthy data from biological collections, field surveys and automated sensors, molecular data, and historic academic literature. The transformation of these raw data into synthesized information that is fit for use requires going through many refinement steps. The methodologies and techniques applied to manage and analyze these data constitute an area usually called biodiversity informatics. Biodiversity data follow a life cycle consisting of planning, collection, certification, description, preservation, discovery, integration, and analysis. Researchers, whether producers or consumers of biodiversity data, will likely perform activities related to at least one of these steps. This article explores each stage of the life cycle of biodiversity data, discussing its methodologies, tools, and challenges. This article is categorized under: Algorithmic Development > Biological Data Mining
The continuous growth of biodiversity databases has led to a search for techniques that can assist researchers. This paper presents a method for the analysis of occurrences of pairs and groups of species that aims to identify patterns in co-occurrences through the application of association rules of data mining. We propose, implement and evaluate a tool to help ecologists formulate and validate hypotheses regarding cooccurrence between two or more species. To validate our approach, we analyzed the occurrence of species with a dataset from the 50-ha Forest Dynamics Project on Barro Colorado Island (BCI). Three case studies were developed based on this tropical forest to evaluate patterns of positive and negative correlation. Our tool can be used to point co-occurrence in a multi-scale form and for multi-species, simultaneously, accelerating the identification process for the Spatial Point Pattern Analysis. This paper demonstrates that data mining, which has been used successfully in applications such as business and consumer profile analysis, can be a useful resource in ecology.
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