O período da contaminação com petróleo influencia a rebrota de Echinochloa polystachya (H.B.K.) Hitchcock em solo de várzea da Amazônia central?

Several factors may influence the impact of oil on the environment. However, although it is understood that the effect of pollutants may change throughout the year according to seasonal variations in environmental parameters, this effect is poorly studied in the tropical region. The effects of Urucu's crude oil on the vegetative propagation and growth of Echinochloa polystachya were evaluated in a 63 days period, in two experiments, "A" (July-September) and "B" (September-November) planting the species in a greenhouse. In both experiments parts of stems were placed in 2 L of várzea soil contaminated by 6 oil doses, ranging from 0 to 0.231 L oil m-2 soil. In response to dosage increase there was a decrease of total biomass, ratio of live /total biomass, the leaf length and number of leaves. The period of planting influenced the response of plants to the dosage applied due to climate change, with negative effects in the "B" period of higher temperatures. We concluded that the exposure period influence the vegetative propagation and growth of the seedlings, being a spill in the period of the higher temperatures more dangerous for this specie

Influência das condições do habitat sobre a estrutura de herbáceas aquáticas na região do Lago Catalão, Manaus, AM

Na Amazônia, as herbáceas aquáticas são encontradas em todas as tipologias de água, mas sua abundância pode ser influenciada pelas condições limnológicas de cada ambiente. Assim, este trabalho teve como objetivo avaliar a influência das condições do habitat sobre a estrutura de herbáceas aquáticas na região do Lago Catalão, Manaus, AM. Foram avaliadas sete transecções em ambientes de água branca (AB), nove em água decantada (AD) e sete em água mista (AM). Em cada transecção foram identificadas as herbáceas aquáticas, estimada a área de cobertura relativa, calculada a frequência de ocorrência e avaliadas as variáveis turbidez, condutividade elétrica, pH e profundidade. A variação na profundidade indicou que os ambientes de AD e AM eram mais profundos do que AB, já águas mais ácidas e com menor condutividade foram registradas na AM. Foram registrados 32 táxons de herbáceas aquáticas sendo as espécies mais frequentes Paspalum repens, Salvinia auriculata, Pistia stratiotes e Lemna valdiviana. Entre as espécies levantadas, 50% foram comuns aos três ambientes. Por outro lado, algumas espécies ocorreram exclusivamente em ambientes de AB, em AD e em AM. Na AB foram mais frequentes as formas flutuantes, que também apresentaram a maior cobertura neste ambiente; na AD e na AM as emersas apresentaram maior frequência e cobertura. A AM apresentou maior riqueza de herbáceas aquáticas em relação aos demais ambientes. As variações registradas indicam que as condições limnológicas dos rios de água branca e preta podem determinar a estrutura da comunidade de herbáceas aquáticas, mesmo em pequenas escalas espaciais

A new data-driven map predicts substantial undocumented peatland areas in Amazonia

Tropical peatlands are among the most carbon-dense terrestrial ecosystems yet recorded. Collectively, they comprise a large but highly uncertain reservoir of the global carbon cycle, with wide-ranging estimates of their global area (441 025-1700 000 km²) and below-ground carbon storage (105-288 Pg C). Substantial gaps remain in our understanding of peatland distribution in some key regions, including most of tropical South America. Here we compile 2413 ground reference points in and around Amazonian peatlands and use them alongside a stack of remote sensing products in a random forest model to generate the first field-data-driven model of peatland distribution across the Amazon basin. Our model predicts a total Amazonian peatland extent of 251 015 km² (95th percentile confidence interval: 128 671-373 359), greater than that of the Congo basin, but around 30% smaller than a recent model-derived estimate of peatland area across Amazonia. The model performs relatively well against point observations but spatial gaps in the ground reference dataset mean that model uncertainty remains high, particularly in parts of Brazil and Bolivia. For example, we predict significant peatland areas in northern Peru with relatively high confidence, while peatland areas in the Rio Negro basin and adjacent south-western Orinoco basin which have previously been predicted to hold Campinarana or white sand forests, are predicted with greater uncertainty. Similarly, we predict large areas of peatlands in Bolivia, surprisingly given the strong climatic seasonality found over most of the country. Very little field data exists with which to quantitatively assess the accuracy of our map in these regions. Data gaps such as these should be a high priority for new field sampling. This new map can facilitate future research into the vulnerability of peatlands to climate change and anthropogenic impacts, which is likely to vary spatially across the Amazon basin