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Different intensities and directions of hyporheic water exchange in habitats of aquatic Ranunculus species in rivers—a case study in Poland
Diversification of macrophytes within aquatic nature-based solutions (NBS) developing under urban environmental conditions across European cities
Understanding ecohydrology and biodiversity in aquatic nature-based solutions in urban streams and ponds through an integrative multi-tracer approach
Abstract. Rapid urbanization and climate change affect ecohydrology, biodiversity, and water quality in urban freshwaters. Aquatic nature-based solutions (aquaNBSs) are being widely implemented to address some of the ecological and hydrological challenges that threaten urban biodiversity and water security. However, there is still a lack of process-based evidence of ecohydrological interactions in urban aquaNBSs and their relationship to water quality and quantity issues at the ecosystem level. Through a novel, integrative multi-tracer approach using stable water isotopes, hydrochemistry, and environmental DNA we sought to disentangle the effects of urbanization and hydroclimate on ecohydrological dynamics in urban aquaNBSs and understand ecohydrological functioning and the future resilience of urban freshwaters. Stable isotopes and microbial data reflected a strong influence of urban water sources (i.e., treated effluent, urban surface runoff) across stream NBSs. The results show potential limitations of aquaNBS impacts on water quality and biodiversity in effluent-impacted streams, as microbial signatures are biased towards potentially pathogenic bacteria. Urban ponds appear to be more sensitive to hydroclimate perturbations, resulting in increased microbial turnover and lower microbial diversity than expected. Furthermore, assessment of macrophytes revealed low diversity and richness of aquatic plants in both urban streams and ponds, further challenging the effectiveness of NBSs in contributing to aquatic diversity. This also demonstrates the need to adequately consider aquatic organisms in planned restoration projects, particularly those implemented in urban ecosystems, in terms of habitat requirements. Our findings emphasize the utility of integrated tracer approaches to explore the interface between ecology and hydrology and provide insights into the ecohydrologic functioning of aquaNBSs and their potential limitations. We illustrate the benefit of coupling ecological and hydrological perspectives to support future NBS design and applications that consider the interactions between water and the ecosystem more effectively.
Predicting freshwater biological quality using macrophytes: A comparison of empirical modelling approaches
AbstractDifficulties have hampered bioassessment in southern European rivers due to limited reference data and the unclear impact of multiple interacting stressors on plant communities. Predictive modelling may help overcome this limitation by aggregating different pressures affecting aquatic organisms and showing the most influential factors. We assembled a dataset of 292 Mediterranean sampling locations on perennial rivers and streams (mainland Portugal) with macrophyte and environmental data. We compared models based on multiple linear regression (MLR), boosted regression trees (BRT) and artificial neural networks (ANNs). Secondarily, we investigated the relationship between two macrophyte indices grounded in distinct conceptual premises (the Riparian Vegetation Index — RVI, and the Macrophyte Biological Index for Rivers — IBMR) and a set of environmental variables, including climatic conditions, geographical characteristics, land use, water chemistry and habitat quality of rivers. The quality of models for the IBMR was superior to those for the RVI in all cases, which indicates a better ecological linkage of IBMR with the stressor and abiotic variables. The IBMR using ANN outperformed the BRT models, for which the r-Pearson correlation coefficients were 0.877 and 0.801, and the normalised root mean square errors were 10.0 and 11.3, respectively. Variable importance analysis revealed that longitude and geology, hydrological/climatic conditions, water body size and land use had the highest impact on the IBMR model predictions. Despite the differences in the quality of the models, all showed similar importance to individual input variables, although in a different order. Despite some difficulties in model training for ANNs, our findings suggest that BRT and ANNs can be used to assess ecological quality, and for decision-making on the environmental management of rivers.