Predicting presence and absence of trout (Salmo trutta) in Iran.

Species distribution modelling, as a central issue in freshwater ecology, is an important tool for conservation and management of aquatic ecosystems. The brown trout (Salmo trutta) is a sensitive species which reacts to habitat changes induced by human impacts. Therefore, the identification of suitable habitats is essential. This study explores the potential distribution of brown trout by a species distribution modelling approach for Iran. Furthermore, modelling results are compared to the distribution described in the literature. Areas outside the currently known distribution which may offer potential habitats for brown trout are identified. The species distribution modelling was based on five different modelling techniques: Generalised Linear Model, Generalised Additive Model, Generalised Boosting Model, Classification Tree Analysis and Random Forests, which are finally summarised in an ensemble forecasting approach. We considered four environmental descriptors at the local scale (slope, bankfull width, wetted width, and elevation) and three climatic parameters (mean air temperature, range of air temperature and annual precipitation) which were extracted on three different spatial extents (1/5/10 km). The performance of all models was excellent (≥0.8) according to the TSS (True Skill Statistic) criterion. Slope, mean and range of air temperature were the most important variables in predicting brown trout occurrence. Presented results deepen the knowledge about distribution patterns of brown trout in Iran. Moreover, this study gives a basic background for the future development of assessment methods for riverine ecosystems in Iran.

LaRiMo - A simple and efficient GIS-based approach for large-scale morphological assessment of large European rivers.

Large rivers cover and function over large spatial extents. Accordingly, the detailed assessment of their morphology is complex. Here, a methodology is presented to assess large rivers' morphology (LaRiMo) based on free datasets of geographic information systems. This approach could help to achieve a comparable, transboundary assessment of large river morphology to support the implementation of the European Water Framework Directive. The analyses are based on seven parameters describing processes and characteristics related to large river morphology. These parameters are evaluated for and compared between the rivers Danube, Elbe and Loire. A significantly higher amount of gravel and sand bars within the river bed strongly supported the overall higher morphological status of the Loire. A PCA highlighted that the parameters Wetlands, Active Riparian Zones and Free Flowing Sections similarly described a good morphological situation. In contrast, Canal, Dams and Impervious Surface indicated bad morphological conditions. Finally, the approach was successfully validated with data from a detailed, field-based morphological assessment for the Danube. LaRiMo represents an efficient and cost-effective approach to assess large river morphology across large extents. This method provides comparable results across countries and regions.

Quantified biotic and abiotic responses to multiple stress in freshwater, marine and ground waters.

We reviewed 219 papers and built an inventory of 532 items of ecological evidence on multiple stressor impacts in rivers, lakes, transitional and coastal waters, as well as groundwaters. Our review revealed that, despite the existence of a huge conceptual knowledge base in aquatic ecology, few studies actually provide quantitative evidence on multi-stress effects. Nutrient stress was involved in 71% to 98% of multi-stress situations in the three types of surface water environments, and in 42% of those in groundwaters. However, their impact manifested differently along the groundwater-river-lake-transitional-coastal continuum, mainly determined by the different hydro-morphological features of these ecosystems. The reviewed papers addressed two-stressor combinations most frequently (42%), corresponding with the actual status-quo of pressures acting on European surface waters as reported by the Member States in the WISE WFD Database (EEA, 2015). Across all biological groups analysed, higher explanatory power of the stress-effect models was discernible for lakes under multi-stressor compared to single stressor conditions, but generally lower for coastal and transitional waters. Across all aquatic environments, the explanatory power of stress-effect models for fish increased when multi-stressor conditions were taken into account in the analysis, qualifying this organism group as a useful indicator of multi-stress effects. In contrast, the explanatory power of models using benthic flora decreased under conditions of multiple stress.