10 research outputs found

    Macroinvertebrates inhabiting the tank leaf terrestrial and epiphyte bromeliads at Reserva Adolpho Ducke, Manaus, Amazonas

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    The aim of this work was to investigate the diversity of macroinvertebrates and also verify if the abundance and diversity of Diptera were influenced by the abiotic factors. The samples were collected from the epiphytic and terrestrial bromeliads G. brasiliensis (1 and 3m) in wet and dry seasons at Reserva Adolpho Ducke analyzed total of 144 samples were analyzed from a total of 15,238 individuals collected. These conatined 14,097 insects and, among these, 8,258 were immature Diptera, represented by eight most abundant families: Chironomidae, Ceratopogonidae and Culicidae. The relationship of Diptera diversity was influenced by the seasons and stratifications (p= 0.01); the abundance was influenced by the volume of water (p= 0.02) and the relationship between the season and volume of water in the terrestrial bromeliads was significant (p= 0.01). This study represented the first contribution to knowledge of community of macroinvertebrates associated to bromeliads G. brasiliensis in Central Amazon

    Pervasive gaps in Amazonian ecological research

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    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
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