Forested Riparian Zones Provide Important Habitat for Fish in Urban Streams

Combined effects of river hydromorphological disturbances on macroinvertebrate communities: Multispatial scales analysis of central European rivers

Hydro-morphological threats impact the natural physical characteristics of river ecosystems, such as flow regimes, sediment transport, and channel morphology. These negative effects can occur at multiple scales, ranging from local microhabitats to geographic regions. Understanding these interactions can be useful for an integrated conservation approach and is needed for effective freshwater management. The aim of the study was to elucidate the combined effects of hydro-morphological threats on macroinvertebrates at three spatial scales: macroscale, including whole catchments, mesoscale (hydro-morphological changes in individual river sections) and the microscale, describing the microhabitat conditions of European rivers. The diversity and trophic structure of 1120 local macroinvertebrate communities in 28 catchments of various hydro-morphological disturbance levels, ranging from 0 to 2400 m asl, were analyzed. The response of macroinvertebrates to the main disturbance gradient differed between mountain and lowland communities. Random forest analysis indicated that the most important predictor of the ecological, diversity, and trophic indices was described by flow rate reduction. Generalized additive mixed models showed that decreased flow combined with river incision explained most of the variation in macroinvertebrate indices. Our results emphasize that based on multi-spatial scale analysis, hydro-morphological threats are very important factors in invertebrates biodiversity loss. Thus, to implement effective river management, we should pay more attention to the combined effects of geomorphological threats.

A Bayesian Belief Network learning tool integrates multi-scale effects of riparian buffers on stream invertebrates

Riparian forest buffers have multiple benefits for biodiversity and ecosystem services in both freshwater and terrestrial habitats but are rarely implemented in water ecosystem management, partly reflecting the lack of information on the effectiveness of this measure. In this context, social learning is valuable to inform stakeholders of the efficacy of riparian vegetation in mitigating stream degradation. We aim to develop a Bayesian belief network (BBN) model for application as a learning tool to simulate and assess the reach- and segment-scale effects of riparian vegetation properties and land use on instream invertebrates. We surveyed reach-scale riparian conditions, extracted segment-scale riparian and subcatchment land use information from geographic information system data, and collected macroinvertebrate samples from four catchments in Europe (Belgium, Norway, Romania, and Sweden). We modelled the ecological condition based on the Average Score Per Taxon (ASPT) index, a macroinvertebrate-based index widely used in European bioassessment, as a function of different riparian variables using the BBN modelling approach. The results of the model simulations provided insights into the usefulness of riparian vegetation attributes in enhancing the ecological condition, with reach-scale riparian vegetation quality associated with the strongest improvements in ecological status. Specifically, reach-scale buffer vegetation of score 3 (i.e. moderate quality) generally results in the highest probability of a good ASPT score (99-100%). In contrast, a site with a narrow width of riparian trees and a small area of trees with reach-scale buffer vegetation of score 1 (i.e. low quality) predicts a high probability of a bad ASPT score (74%). The strengths of the BBN model are the ease of interpretation, fast simulation, ability to explicitly indicate uncertainty in model outcomes, and interactivity. These merits point to the potential use of the BBN model in workshop activities to stimulate key learning processes that help inform the management of riparian zones.

Regression Tree Analysis for Stream Biological Indicators Considering Spatial Autocorrelation

Multiple studies have been conducted to identify the complex and diverse relationships between stream ecosystems and land cover. However, these studies did not consider spatial dependency inherent from the systemic structure of streams. Therefore, the present study aimed to analyze the relationship between green/urban areas and topographical variables with biological indicators using regression tree analysis, which considered spatial autocorrelation at two different scales. The results of the principal components analysis suggested that the topographical variables exhibited the highest weights among all components, including biological indicators. Moran′s I values verified spatial autocorrelation of biological indicators; additionally, trophic diatom index, benthic macroinvertebrate index, and fish assessment index values were greater than 0.7. The results of spatial autocorrelation analysis suggested that a significant spatial dependency existed between environmental and biological indicators. Regression tree analysis was conducted for each indicator to compensate for the occurrence of autocorrelation; subsequently, the slope in riparian areas was the first criterion of differentiation for biological condition datasets in all regression trees. These findings suggest that considering spatial autocorrelation for statistical analyses of stream ecosystems, riparian proximity, and topographical characteristics for land use planning around the streams is essential to maintain the healthy biological conditions of streams.