Habitat mapping of riverine fish by means of hydromorphological tools

Abstract

Hydromorphological features of rivers, such as flow depth, flow velocity, and the composition of bed material play a crucial role in the habitat selection of fish. Although these basic hydromorphological parameters can be determined with high spatial and temporal resolution using state‐of‐the‐art investigation methods, only few studies deal with the connection of habitat parameters and abundance of fishes (i.e., habitat modelling) in large rivers. The aim of this study is to fill this gap by introducing the so‐called habitat maps connecting the results of 3D hydrodynamic simulations and results of offshore fish ecological surveys. The steps of habitat modelling are introduced through the case study of the Danube River focusing on evaluating the habitat use and suitability of two fish species representative to this section of the river (i.e., the round goby Neogobius melanostomus and the Danube streber Zingel streber). A novel method for mapping the bed material composition, playing a crucial role in the habitat assessment, is also presented along the numerical modelling.

Environmental drivers of benthic fish assemblages and fish‐habitat associations in offshore areas of a very large river

“…The CART analysis further highlighted that its abundance was the highest in relatively slow flowing areas with higher portion of rough substratum (gravel and pebble). This result is consistent with our former, more local scale study, which directly quantified offshore habitat preference curves for the species (Baranya et al, 2018). The habitat use of the racer goby differed to some degree from the round goby.…”

“…Further, variation in velocity was coupled with variation in mean depth and substratum composition (i.e., Shannon diversity of sediment composition) and higher proportion of silt material. These results thus show that offshore areas which are mostly considered as homogenous mesoscale-level units in fish habitat evaluations (Baranya et al, 2018;Habersack, Tritthart, Liedermann, & Hauer, 2014;Wegscheider, Linnansaari, & Curry, 2020) do show some clearly recognizable environmental heterogeneity. However, the hierarchical evaluations and visualization of physical data also show that most offshore environmental heterogeneity occurs at the macroscale (10 4 -10 5 m) that is between larger river segments in the Middle-Danube, and that within mesoscale level (i.e., between sample units within transects) environmental heterogeneity is relatively low offshore.…”

“…Percentage substrate composition of the units were visually estimated based on three grab samples collected in the upper, middle and lower portion of the sampling unit. The following categories were distinguished (Baranya et al, 2018): silt (<0.25 mm), sand (0.25-2 mm), gravel or fine gravel (2-8 mm), pebble or rough gravel (8-64 mm), cobble or stone (>64 mm), marl and/or clay (rather cemented very fine silty material). Shannon diversity of the substrate material was also calculated based on the percentage data of substrate categories.…”

A numerical modeling‐based predictive methodology for the assessment of the impacts of ship waves on YOY fish

“…Suitability index (SI) curves express the habitat suitability of a given species based on different abiotic parameters. In riverine conditions, the most relevant variables are usually related to prevailing flow conditions, such water depth and flow velocity; however, other parameters, for example, water temperature or substrate material (sediments) are also frequently incorporated in habitat suitability assessments (e.g., Baranya et al (2018); Yao et al (2017)). Water depths along the employed slice model are determined by the topography of the bed, thus considered to be more or less constant for large areas of the bathymetry over time, even though high‐frequency surface elevation fluctuations clearly occur as a results of wave propagation.…”

“…In case of larger rivers, especially if river training works (e.g., groins) or complex flow features (due to, e.g., tributary inflow) are present, the proper numerical representation of the flow often requires more sophisticated 3D models, offering the reliable prediction of near‐bed flow conditions, which is reportedly of high importance from the ecological perspective (Shen & Diplas, 2008) and is essential from the aspect of sediment transport as well (Baranya, Olsen, & Józsa, 2015; Török, Józsa, & Baranya, 2019; Tritthart, Liedermann, Schober, & Habersack, 2011). Examples for 3D habitat modeling are usually restricted to shorter river sections (Baranya et al, 2018; Habersack et al, 2014), mostly due to high computational costs.…”