The Amazon river is a major source of terrestrially derived organic carbon to the tropical Atlantic Ocean. Field, satellite and a vertically generalized production model data were used to estimate empirical surface salinity and fit an inverse logit function to investigate the limiting effect of salinity on the productivity in the Amazon river plume. Satellite data included Moderate Resolution Imaging Spectroradiometer, Soil Moisture and Ocean Salinity, and Aquarius missions. Previous empirical surface salinity models have relied on a very narrow range of salinity values and satellite data to estimate the spatial extent of the river plume. The empirical surface salinity model presented here extended the range of salinity values and captures all the main surface mesoscale features, particularly those related to the main path of the low‐salinity water. We also show that it is possible to gain new insights on the spatiotemporal variability of the Amazon river plume by improving the empirical surface salinity and expanding its sampling period with the aid of remote sensing data. The variability of primary productivity is dominated by the subannual (6 month) and annual (12 month) frequency bands. Low‐salinity river water influences surface primary productivity continuously during the year through mechanisms associated with the western tropical Atlantic circulation and vertical mixing.

The Amazon River low‐salinity plume takes part in important ocean and atmosphere processes that influence climate. In the last three decades, the intensification of the hydrological cycle has increased the interannual variability of total freshwater discharged into the ocean. However, the feedback mechanisms of the Amazon River plume acting on this intensification are not fully understood. Correlation maps and multiple regression analysis applied to 16 years of satellite data and river flow measurements indicate that a positive precipitation trend of 15 mm/year in the western Amazon basin follows the long‐term warming of the tropical Atlantic. This increased the total amount of freshwater discharged into the ocean and reduced the Amazon River plume salinity by 3.5% per year in the main plume water export pathway. Based on these results we propose a process‐oriented model of the feedback process that explains the intensification of the Amazon hydrological cycle.

Abiotic and biotic factors are known drivers that modulate community assembly from a regional species pool. Recent evidence has highlighted the intrinsic role of phylogenetic history on communities' response to the environment. Understanding its exact role poses a challenge because community assembly is embedded in a spatio‐temporal context where dispersal capabilities and biotic interactions may also determine species niches, especially in isolated oceanic islands. We unravelled how reef fish abundances from four oceanic islands in the southwestern Atlantic responded to environmental variability through seven years considering their phylogenetic history, functional traits and species co‐occurrence patterns. Species response to environmental variation was assessed through a multivariate hierarchical generalized linear mixed model that allows the inclusion of spatio‐temporal random effects, fitted with Bayesian inference. We found a strong phylogenetic signal (0.98) and a relatively low variance in abundance explained by functional traits, from around 30% in spring to 33% in summer, based on a posterior probability > 0.9. The most important environmental factor was surface chlorophyll‐a concentration, a proxy for primary productivity, explaining up to 23% of abundance variance. The global spatial and temporal effects on abundance were also low, with a maximum of 18% for sampling sites in spring. Our study offers a synthesis of the influence of complex phylogenetic history and geographical isolation on reef fish species niches in isolated oceanic islands, gaining new insights into how assembly processes have shaped these isolated communities.

Wide swath synthetic aperture radar (SAR) has been commonly used for oceanographic applications, such as maritime safety, fisheries management and coastal monitoring, including dark target detection. However, the incidence angle (θ) effect on these data makes a direct visualization and classification of oceanographic targets troublesome. The recent availability of free-of-charge Sentinel-1 C-band data have the potential to assess methods for correcting the θ in wide swath SAR images, an issue that still remains to be undertaken. The goal of this work was to test two methods for correcting the θ effect of two Sentinel-1 SAR imaging modes and assess their implications for dark target detection in a sector of the South Atlantic Ocean. We assessed the empirical modelling and square cosine-based methods for correcting the θ effect on co and cross-polarization from the Interferometric Wide Swath (IW) and Extra Wide Swath (EW) imaging modes. The results showed that the empirical modelling method improved the visualization and classification of dark targets for the co-polarized bands from both the imaging modes. However, further studies are necessary to test the potential of empirical modelling for correcting the θ for other different oceanic environments and SAR frequencies.

We present a novel approach to structure and interpret long-term environmental monitoring data, by combining the Adverse Outcome Pathway (AOP) concept with probabilistic causal modeling. We applied this framework to a monitoring program following the 2015 tailings dam collapse in southeastern Brazil. Six years of data (2018-2024) spanning river-to-sea gradients included dissolved metal(loid)s, fish bioaccumulation, and sub-individual biomarkers. Metal(loid)s in water were first integrated into an index representing spatial variation in toxic pressure. Within the AOP framework, biomarkers of antioxidant defense, oxidative damage, and tissue injury were linked to a growth-related outcome using piecewise structural equation mixed-effects models, accounting for species-level and temporal variability. Toxic pressure was consistently higher in the river and decreased towards the sea. Biomarker responses reflected these gradients: in the river, metallothionein, lipid peroxidation, and histopathological lesions were strongly associated with toxic pressure and specific metals, whereas in marine environments, responses were weaker and more variable. Across models, species and temporal variability accounted for a substantial share of explained variance, highlighting the importance of mixed-effects approaches to unravel biological heterogeneity. Lesions were consistently linked to oxidative stress and, in the freshwater, negatively associated with fish condition index, suggesting that tissue-level damage can scale up to growth impairment. Overall, responses revealed both expected and unexpected patterns, highlighting the context-dependent nature of defense mechanisms under mixed-metal exposure and the value of integrated approaches to environmental monitoring.