Local trade, spatial occurrence and conservation of hypostomus soniae (siluriformes, loricariidae), an ornamental fish endemic to the tapajos river, Brazil.

Hypostomus soniae is a small sized armored catfish endemic to the Tapajos River basin and ranked as one of the most exploited ornamental fish in the Santarem export marketplace. This study aims to evaluate distributional patterns of Hypostomus soniae and contribute to the species conservation in the face of development of the ornamental fish trade in the Amazon region. We compiled data associated with geographic coordinates in public repositories, supplemented with original field records. We compared our data to published records in the literature and museum collections to check for accuracy. To investigate the fishery and commercialization of H. soniae, we conducted interviews with ornamental fish stakeholders from the local trade. We also made direct observations in the fishing sites and export facilities in Santarem. A cluster analysis of the geolocation data was carried out to explore the spatial distribution patterns. The volume of captures and exportation of H. soniae decreased during the period 2020-2023. The occurrence of H. soniae was associated with annual rainfall ranging from 2,000 mm to 2,500 mm and concentrated in two municipalities of the State of Mato Grosso and two of the Para State. The species distribution area has been threatened, unfortunately, by fishermen who do not respect the laws that support artisanal fishing in the Amazon.

Strategies to optimize modeling habitat suitability of Bertholletia excelsa in the Pan-Amazonia.

AIM: Amazon-nut (Bertholletia excelsa) is a hyperdominant and protected tree species, playing a keystone role in nutrient cycling and ecosystem service provision in Amazonia. Our main goal was to develop a robust habitat suitability model of Amazon-nut and to identify the most important predictor variables to support conservation and tree planting decisions. LOCALIZATION: Amazon region, South America. METHODS: We collected 3,325 unique Amazon-nut records and assembled >100 spatial predictor variables organized across climatic, edaphic, and geophysical categories. We compared suitability models using variables (a) selected through statistical techniques; (b) recommended by experts; and (c) integrating both approaches (a and b). We applied different spatial filtering scenarios to reduce overfitting. We additionally fine-tuned MAXENT settings to our data. The best model was selected through quantitative and qualitative assessments. RESULTS: Principal component analysis based on expert recommendations was the most appropriate method for predictor selection. Elevation, coarse soil fragments, clay, slope, and annual potential evapotranspiration were the most important predictors. Their relative contribution to the best model amounted to 75%. Filtering of the presences within a radius of 10 km displayed lowest overfitting, a satisfactory omission rate and the most symmetric distribution curve. Our findings suggest that under current environmental conditions, suitable habitat for Amazon-nut is found across 2.3 million km2, that is, 32% of the Amazon Biome. MAIN CONCLUSION: The combination of statistical techniques with expert knowledge improved the quality of our suitability model. Topographic and soil variables were the most important predictors. The combination of predictor variable selection, fine-tuning of model parameters and spatial filtering was critical for the construction of a reliable habitat suitability model.

Impact of agricultural expansion on water footprint in the Amazon under climate change scenarios.

Agricultural expansion and intensification are main drivers of land-use change in Brazil. Soybean is the major crop under expansion in the area. Soybean production involves large amounts of water and fertiliser that act as sources of contamination with potentially negative impacts on adjacent water bodies. These impacts might be intensified by projected climate change in tropical areas. A Water Footprint Assessment (WFA) serves as a tool to assess environmental impacts of water and fertiliser use. The aim of this study was to understand potential impacts on environmental sustainability of agricultural intensification close to a protected forest area of the Amazon under climate change. We carried out a WFA to calculate the water footprint (WF) related to soybean production, Glycine max, to understand the sustainability of the WF in the Tapajós river basin, a region in the Brazilian Amazon with large expansion and intensification of soybean. Based on global datasets, environmental hotspots - potentially unsustainable WF areas - were identified and spatially plotted in both baseline scenario (2010) and projection into 2050 through the use of a land-use change scenario that includes climate change effects. Results show green and grey WF values in 2050 increased by 304% and 268%, respectively. More than one-third of the watersheds doubled their grey WF in 2050. Soybean production in 2010 lies within sustainability limits. However, current soybean expansion and intensification trends lead to large impacts in relation to water pollution and water use, affecting protected areas. Areas not impacted in terms of water pollution dropped by 20.6% in 2050 for the whole catchment, while unsustainability increased 8.1%. Management practices such as water consumption regulations to stimulate efficient water use, reduction of crop water use and evapotranspiration, and optimal fertiliser application control could be key factors in achieving sustainability within a river basin.