R. M. Wright

Optimising the design of passage facilities to restore fluvial connectivity for juvenile European eel (Anguilla anguilla) is a key priority within conservation efforts for the species, across the majority of its freshwater range. Employing an experimental setup that simulated gravity-fed upstream eel passes, this study demonstrated that novel V-profile passes, which incorporate two lateral slopes (15°), performed better than laterally flat passes over the flow rates tested (0.2–0.6 L s⁻¹). For the small eel size used (60–80 mm length), the bristle substrate consistently outperformed studs, but the lateral slope had a greater effect on passage metrics than the substrate choice. Our findings strongly support the use of V-shaped channels for upstream migrating eel at fish passage facilities, particularly in scenarios where flow rates may be elevated and/or fluctuating, such as for gravity-fed passes.

The European eel (Anguilla anguilla) is critically endangered (according to the most recent IUCN assessment) and has suffered a 95% decline in recruitment since the 1980s, attributed in part to factors occurring during the marine phases of its life-cycle. As an adult, the European eel undertakes the longest spawning migration of all anguillid eels, a distance of 5000 to 10,000 km across the Atlantic Ocean to the Sargasso Sea. However, despite the passage of almost 100 years since Johannes Schmidt proposed the Sargasso Sea as the breeding place of European eels on the basis of larval surveys, no eggs or spawning adults have ever been sampled there to confirm this. Fundamental questions therefore remain about the oceanic migration of adult eels, including navigation mechanisms, the routes taken, timings of arrival, swimming speed and spawning locations. We attached satellite tags to 26 eels from rivers in the Azores archipelago and tracked them for periods between 40 and 366 days at speeds between 3 and 12 km day⁻¹, and provide the first direct evidence of adult European eels reaching their presumed breeding place in the Sargasso Sea.

Recent legislative approaches to improve the quality of rivers have resulted in the design and implementation of extensive and intensive monitoring programmes that are costly and time consuming. An important component of assessing the ecological status of a water body as required by the Water Framework Directive is characterising the hydromorphology. Recent advances in autonomous operation and the spatial coverage of monitoring systems enables more rapid 3D models of the river environment to be produced. This study presents a Structure from Motion (SfM) semi-autonomous based framework for the estimation of key reach hydromorphological measures such as water surface area, wetted water width, bank height, bank slope and bank-full width, using in-channel stereo-imagery. The framework relies on a stereo-camera that could be positioned on an autonomous boat. The proposed approach is demonstrated along three 40 m long reaches with differing hydromorphological characteristics. Results indicated that optimal stereo-camera settings need to be selected based on the river appearance. Results also indicated that the characteristics of the reach have an impact on the estimation of the hydromorphological measures; densely vegetated banks, presence of debris and sinuosity along the reach increased the overall error in hydromorphological measure estimation. The results obtained highlight a potential way forward towards the autonomous monitoring of freshwater ecosystems.

The multiple protocols that have been developed to characterize river hydromorphology, partly in response to legislative drivers such as the European Union Water Framework Directive (EU WFD), make the comparison of results obtained in different countries challenging. Recent studies have analyzed the comparability of existing methods, with remote sensing based approaches being proposed as a potential means of harmonizing hydromorphological characterization protocols. However, the resolution achieved by remote sensing products may not be sufficient to assess some of the key hydromorphological features that are required to allow an accurate characterization. Methodologies based on high resolution aerial photography taken from Unmanned Aerial Vehicles (UAVs) have been proposed by several authors as potential approaches to overcome these limitations. Here, we explore the applicability of an existing UAV based framework for hydromorphological characterization to three different fluvial settings representing some of the distinct ecoregions defined by the WFD geographical intercalibration groups (GIGs). The framework is based on the automated recognition of hydromorphological features via tested and validated Artificial Neural Networks (ANNs). Results show that the framework is transferable to the Central-Baltic and Mediterranean GIGs with accuracies in feature identification above 70%. Accuracies of 50% are achieved when the framework is implemented in the Very Large Rivers GIG. The framework successfully identified vegetation, deep water, shallow water, riffles, side bars and shadows for the majority of the reaches. However, further algorithm development is required to ensure a wider range of features (e.g., chutes, structures and erosion) are accurately identified. This study also highlights the need to develop an objective and fit for purpose hydromorphological characterization framework to be adopted within all EU member states to facilitate comparison of results.

Existing regulatory frameworks aiming to improve the quality of rivers place hydromorphology as a key factor in the assessment of hydrology, morphology and river continuity. The majority of available methods for hydromorphological characterisation rely on the identification of homogeneous areas (i.e., features) of flow, vegetation and substrate. For that purpose, aerial imagery is used to identify existing features through either visual observation or automated classification techniques. There is evidence to believe that the success in feature identification relies on the resolution of the imagery used. However, little effort has yet been made to quantify the uncertainty in feature identification associated with the resolution of the aerial imagery. This paper contributes to address this gap in knowledge by contrasting results in automated hydromorphological feature identification from unmanned aerial vehicles (UAV) aerial imagery captured at three resolutions (2.5 cm, 5 cm and 10 cm) along a 1.4 km river reach. The results show that resolution plays a key role in the accuracy and variety of features identified, with larger identification errors observed for riffles and side bars. This in turn has an impact on the ecological characterisation of the river reach. The research shows that UAV technology could be essential for unbiased hydromorphological assessment.