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In a way to counter criticism on low cost-effective conventional activated sludge (AS) technology, waste stabilization ponds (WSPs) offer a valid alternative for wastewater treatment due to their simple and inexpensive operation. To evaluate this alternative with respect to its robustness and resilience capacity, we perform in silico experiments of different peak-load scenarios in two mathematical models representing the two systems. A systematic process of quality assurance for these virtual experiments is implemented, including sensitivity and identifiability analysis, with non-linear error propagation. Moreover, model calibration of a 210-day real experiment with 31 days of increased load was added to the evaluation. Generally speaking, increased-load scenarios run in silico showed that WSP systems are more resilient towards intermediate disturbances, hence, are suitable to treat not only municipal wastewater, but also industrial wastewater, such as poultry wastewater, and paperboard wastewater. However, when disturbances are extreme (over 7000 mg COD·L−1), the common design of the natural system fails to perform better than AS. Besides, the application of sensitivity analysis reveals the most influential parameters on the performance of the two systems. In the AS system, parameters related to autotrophic bacteria have the highest influence on the dynamics of particulate organic matter, while nitrogen removal is largely driven by nitrification and denitrification. Conversely, with an insignificant contribution of heterotrophs, the nutrient removal in the pond system is mostly done by algal assimilation. Furthermore, this systematic model-based analysis proved to be a suitable means for investigating the maximum load of wastewater treatment systems, and from that avoiding environmental problems and high economic costs for cleaning surface waters after severe overload events.

Freshwater ecosystems are among the most threatened ecosystems on Earth. Effective conservation strategies are essential to reverse this trend and should be based on sound knowledge of biodiversity patterns and the main drivers structuring them. In this study, we investigated the role of environmental and dispersal-connectivity controls on freshwater diatom and fish communities’ variability. We used 441 biological samples obtained from Spanish biomonitoring datasets, which cover a highly variable environmental gradient across the national river network. We compared the taxonomic and trait-based spatial dependency of the two biotic groups using distance-decay relationships and variation partitioning with spatially constrained randomisations. Our findings showed that most of the diatoms and fish biological variation was attributed to pure spatial and spatially structured environmental variation. Compared to diatoms, fish community composition presented a stronger spatial dependency, likely because of their weaker dispersal ability. In addition, broad-scale environmental characteristics showed a higher predictive capacity for fish assemblages’ variation. Trait-based similarities presented lower spatial dependency than taxonomic datasets, indicating that they are less susceptible to dispersal-connectivity effects. These findings contribute to understand the mechanisms underlying river community assembly at large spatial scales (i.e., at and beyond the river network) and point out the importance of dispersal-connectivity processes, which are usually neglected in traditional niche-based biomonitoring programmes but can influence their outcomes (e.g., masking the detection of anthropogenic impacts). Therefore, the integration of the dispersal-connectivity component, as well as information on organisms’ dispersal abilities, are crucial when establishing effective conservation objectives and designing biomonitoring strategies.