Goiás State University

Phytoplankton communities are sensitive to global change drivers, yet the combined effects of warming and nutrient enrichment remain insufficiently explored in tropical freshwater ecosystems. Here, we tested the individual and interactive effects of increased temperature and eutrophication on phytoplankton structure using outdoor mesocosms in a tropical region. We hypothesized that both stressors would influence community responses, promoting higher phytoplankton density, increased chlorophyll-a, reduced species richness, and shifts toward tolerant Chlorophyta and Cyanobacteria, with stronger effects expected when warming and nutrient enrichment occurred together. We monitored limnological conditions and phytoplankton composition to capture short-term ecological responses. Nutrient enrichment triggered pronounced changes in water conditions, including higher turbidity, conductivity, dissolved oxygen, and chlorophyll-a, accompanied by clear shifts in phytoplankton density and species composition. Warming alone produced subtle effects but slightly reinforced eutrophication-driven increases in chlorophyll-a and influenced some limnological variables. Species richness remained stable across treatments, while Chlorophyta dominated under nutrient-rich conditions, reflecting their advantage in tropical eutrophic environments. Overall, eutrophication was the primary driver of phytoplankton responses, whereas warming acted as a secondary, modulatory factor. These findings highlight the heightened vulnerability of tropical freshwater ecosystems to nutrient enrichment and underscore the need to integrate nutrient reduction policies with climate change adaptation in freshwater management.

Air temperature is a critical climatic variable for agricultural production, influenced by multiple environmental factors and exhibiting pronounced spatial variability. In large agricultural regions of Brazil, its characterization is constrained by the low density and uneven distribution of meteorological stations. Despite the increasing applications of deep learning artificial neural networks, the importance of variable selection and model complexity at the monthly scale remains unclear in the Central-West region of Brazil. This study evaluated the applicability of deep learning models to estimate monthly maximum (Tmax), mean (Tm), and minimum (Tmin) air temperatures using long-term surface meteorological data from 81 weather stations covering the period 1990–2020. Nine explanatory variable scenarios (S1–S9), combining geographic and meteorological predictors, were assessed using deep learning architectures with different hidden layer configurations (M1, M2, and M3), and model performance was evaluated using training, validation, and test datasets. The results indicate that scenarios S1, S2, S3, S4, and S6 yielded more consistent estimates across months and network configurations, with a larger proportion of models achieving R² ≥ 70

In this work, a new sulfamerazine-based Ni complex was synthesized, namely tris(ethylenediamine-κ2N, N’)-nickel(II) bis(sulfamerazinate) hydrate (abbreviated as compound (I) herein), and characterized by single-crystal X-ray diffraction. Compound (I) crystallizes in the monoclinic C2/c space group. A detailed Hirshfeld surface (HS) analysis of (I) showed that its crystal structure is stabilized by the presence of O─H∙∙∙O, O─H∙∙∙N, N─H∙∙∙O and N─H∙∙∙N H-bonds, in addition to C─H∙∙∙H─C and C─H···π intermolecular interactions. The theoretical modeling revealed the significant influence of the crystalline environment, with a molecular dipole moment increase of 20.7

Cantharellus is an important genus of edible fungi with a worldwide distribution, whose species are recognized for their exceptional taste, beauty, and unique texture. Species of Cantharellus are terrestrial ectomycorrhizal fungi valued for their nutritional and medicinal properties, with demonstrated therapeutic potential due to their anti-inflammatory, antimicrobial, and antigenotoxic activities. Cantharellus guyanensis, originally described in 1854 from French Guiana, grows on soil and is currently known from restricted areas in South America, including the Amazon and Atlantic forests in Brazil. Appreciated in gourmet cuisine, C. guyanensis is reported here for the first time in the Cerrado biome. The species was confirmed through morphological and phylogenetic analysis (ITS, LSU and TEF1-α genetic markers), and the voucher is deposited in the fungaria of the Universidade Estadual de Goiás (HUEG-Fungi) and Universidade Federal da Paraíba (JPB). Its nutritional potential determined by bromatological analysis revealed good storage stability, maintaining its high concentration of general digestible nutrients and low lipid contents. This record expands knowledge about the distribution of the genus among Brazilian biomes and reveals an excellent alternative food resource for the Cerrado.

Taxonomic uncertainty is prevalent across many biological groups. Yet, it remains overlooked in ecology, evolution, and conservation, leading to potential misinterpretations of biodiversity patterns. Here, we argue that this uncertainty emerges from the interaction between biological processes shaping natural lineages and human efforts to name and classify them. Based on this, we propose a set of metrics to quantify confidence in species boundaries and to track the history of taxonomic change and stability. We show how these metrics can be embedded into biodiversity analyses, from mapping uncertainty across taxa and geographic regions to assigning weights to species and deriving more realistic error ranges in ecological models. Making taxonomic uncertainty explicit advances biodiversity science and strengthens conservation decisions.