Multi-biologic group analysis for an ecosystem response to longitudinal river regulation gradients.

This work assesses the effects of river regulation on the diversity of different instream and riparian biological communities along a relieve gradient of disturbance in regulated rivers. Two case studies in Portugal were used, with different river regulation typology (downstream of run-of-river and reservoir dams), where regulated and free-flowing river stretches were surveyed for riparian vegetation, macrophytes, bryophytes, macroalgae, diatoms and macroinvertebrates. The assessment of the regulation effects on biological communities was approached by both biological and functional diversity analysis. Results of this investigation endorse river regulation as a major factor differentiating fluvial biological communities through an artificial environmental filtering that governs species assemblages by accentuating species traits related to river regulation tolerance. Communities' response to regulation gradient seem to be similar and insensitive to river regulation typology. Biological communities respond to this regulation gradient with different sensibilities and rates of response, with riparian vegetation and macroinvertebrates being the most responsive to river regulation and its gradient. Richness appears to be the best indicator for general fluvial ecological quality facing river regulation. Nevertheless, there are high correlations between the biological and functional diversity indices of different biological groups, which denotes biological connections indicative of a cascade of effects leading to an indirect influence of river regulation even on non-responsive facets of communities' biological and functional diversities. These results highlight the necessary holistic perspective of the fluvial system when assessing the effects of river regulation and the proposal of restoration measures.

A taxonomy-free approach based on machine learning to assess the quality of rivers with diatoms.

Diatoms are a compulsory biological quality element in the ecological assessment of rivers according to the Water Framework Directive. The application of current official indices requires the identification of individuals to species or lower rank under a microscope based on the valve morphology. This is a highly time-consuming task, often susceptible of disagreements among analysts. In alternative, the use of DNA metabarcoding combined with High-Throughput Sequencing (HTS) has been proposed. The sequences obtained from environmental DNA are clustered into Operational Taxonomic Units (OTUs), which can be assigned to a taxon using reference databases, and from there calculate biotic indices. However, there is still a high percentage of unassigned OTUs to species due to the incompleteness of reference libraries. Alternatively, we tested a new taxonomy-free approach based on diatom community samples to assess rivers. A combination of three machine learning techniques is used to build models that predict diatom OTUs expected in test sites, under reference conditions, from environmental data. The Observed/Expected OTUs ratio indicates the deviation from reference condition and is converted into a quality class. This approach was never used with diatoms neither with OTUs data. To evaluate its efficiency, we built a model based on OTUs lists (HYDGEN) and another based on taxa lists from morphological identification (HYDMORPH), and also calculated a biotic index (IPS). The models were trained and tested with data from 81 sites (44 reference sites) from central Portugal. Both models were considered accurate (linear regression for Observed and Expected richness: R2 ≈ 0.7, interception ≈ 0.8) and sensitive to global anthropogenic disturbance (Rs2 > 0.30 p < 0.006 for global disturbance). Yet, the HYDGEN model based on molecular data was sensitive to more types of pressures (such as, changes in land use and habitat quality), which gives promising insights to its use for bioassessment of rivers.