Temporal dynamics of organic matter, hyphomycetes and invertebrate communities in a Brazilian savanna stream

Abstract Leaf litter breakdown is an important process in riparian ecosystems, regulated by the concomitant fluctuations of allochthonous organic matter input (quality and quantity), the environmental conditions, and the decomposer community. Our objective was to assess the effects of temporal variability of litter quantity and quality over the stream's decomposer community. We hypothesized that the litter effects over the decomposer community would be overruled by Cerrado's harsh environmental conditions. Precipitation fluctuations, especially during dry and rain seasons, did modify the litterfall periodicity, but not the average organic matter entering the system or the litterfall triggers. Fifteen riparian species were identified contributing with organic matter into the stream, however, Richeria grandis contributed with 48% of litter biomass, helping explain the nutritional intra-annual balance given by the litter chemistry, that would be determinant for ecosystem stability. Higher aquatic hyphomycetes sporulation rates and invertebrate density during the dry season suggest that the decomposer community required a more stable environment (consistent low current) in order to colonize and exploit leaf litter. Our results point out that physical fragmentation was the predominant driver of litter breakdown for our system, due to high decomposition rates, litter remaining mass correlated negatively with precipitation, and low decomposer abundance and activity. Invertebrate collectors' abundance was negatively correlated with litter remaining mass and showed no temporal variation, suggesting that this functional group may have benefited from the particulate organic matter produced by physical fragmentation. Therefore, annual temporal variations on Brazilian savanna stream systems may drive the functioning of the ecosystem

A new classification of the long-horned caddisflies (Trichoptera: Leptoceridae) based on molecular data

Background: Leptoceridae are among the three largest families of Trichoptera (caddisflies). The current classification is founded on a phylogenetic work from the 1980's, based on morphological characters from adult males, i.e. wing venation, tibial spur formula and genital morphology. In order to get a new opinion about the relationships within the family, we undertook a molecular study of the family based on sequences from five genes, mitochondrial COI and the four nuclear genes CAD, EF-1 alpha, IDH and POL. Results: The resulting phylogenetic hypotheses are more or less congruent with the morphologically based classification, with most genera and tribes recovered as monophyletic, but with some major differences. For monophyly of the two subfamilies Triplectidinae and Leptocerinae, one tribe of each was removed and elevated to subfamily status; however monophyly of some genera and tribes is in question. All clades except Leptocerinae, were stable across different analysis methods. Conclusions: We elevate the tribes Grumichellini and Leptorussini to subfamily status, Grumichellinae and Leptorussinae, respectively. We also propose the synonymies of Ptochoecetis with Oecetis and Condocerus with Hudsonema.authorCount :

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 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 or ganism 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 ne glected 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 lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

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