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Abstract Protecting freshwater biodiversity is considered an ultimate challenge but depends on reliable surveys of species distribution and abundance which eDNA metabarcoding (environmental DNA metabarcoding) may offer. To do so, a better understanding of the sources of temporal variation among species eDNA abundance and of data transformation in eDNA metabarcoding studies is needed. Here, we show that transformation based on relative abundance is critical to suitable analyses of eDNA metabarcoding data and that Hellinger transformation performed slightly better than other methods. Furthermore, we show that site localities significantly explain eDNA metabarcoding variation, while no variation is explained by time of sampling. This indicates that species communities vary more spatially than temporally within a dendritic system composed of small rivers. We then further documented the community structure in the St. Charles River (Québec City, Canada) and six of its tributaries. This revealed the existence of eight species communities explaining 82.1% of eDNA read variation within this river network. Moreover, variation in environmental variables among sites explained 53.0% of eDNA reads, while sampling events and temporal environmental variation explained no eDNA metabarcoding variation. Altogether, this supports the claim that eDNA metabarcoding is a powerful tool to document and monitor fish communities in watersheds composed of small river dendritic systems.

We developed a holistic intelligent decision-support system (IDSS) to provide decision-support for all steps in planning, managing and optimizing water quality monitoring programs (WQMPs). The IDSS is connected to a previously developed database, EnkiTM. The IDSS integrates tacit and explicit knowledge on WQMPs to standardize decision making and to make decisions transparent and transferable. The optimization features of the IDSS were tested on a lake and a river WQMP from two case studies in Canada. We illustrate how the IDSS provides decision support to understanding the underlying rationale of the existing WQMPs, validating and storing data, selecting optimization procedures proposed in the literature, applying the optimization procedures and finalizing the optimization procedure. We demonstrated that the IDSS/EnkiTM is necessary to take and document decisions during all phases of a WQMP to obtain a clear idea of when and why changes are made and determine actionable tasks in the optimization process.