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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.

Aim Species inhabiting fresh waters are severely affected by climate change and other anthropogenic stressors. Effective management and conservation plans require advances in the accuracy and reliability of species distribution forecasts. Here, we forecast distribution shifts of Salmo trutta based on environmental predictors and examine the effect of using different statistical techniques and varying geographical extents on the performance and extrapolation of the models obtained. Location Watercourses of Ebro, Elbe and Danube river basins (c. 1,041,000 km2; Mediterranean and temperate climates, Europe). Methods The occurrence of S. trutta and variables of climate, land cover and stream topography were assigned to stream reaches. Data obtained were used to build correlative species distribution models (SDMs) and forecasts for future decades (2020s, 2050s and 2080s) under the A1b emissions scenario, using four statistical techniques (generalised linear models, generalised additive models, random forest, and multivariate adaptive regression). Results The SDMs showed an excellent performance. Climate was a better predictor than stream topography, while land cover characteristics were not necessary to improve performance. Forecasts predict the distribution of S. trutta to become increasingly restricted over time. The geographical extent of data had a weak impact on model performance and gain/loss values, but better species response curves were generated using data from all three basins collectively. By 2080, 64% of the stream reaches sampled will be unsuitable habitats for S. trutta, with Elbe basin being the most affected, and virtually no new habitats will be gained in any basin. Main conclusions More reliable predictions are obtained when the geographical data used for modelling approximate the environmental range where the species is present. Future research incorporating both correlative and mechanistic approaches may increase robustness and accuracy of predictions.

Highlights 31 diversity in eleven organism groups across five aquatic ecosystems was quantified 32  land use alone explained little variation in aquatic biodiversity 33  geo-climatic (natural) descriptors explained significantly more variation 34 and groundwater. In addition, nine geo-climatic descriptors (e.g. latitude, longitude, 48 precipitation) were used to disentangle land use effects from those of natural drivers of 49 biodiversity. Using a variance partitioning scheme based on boosted regression trees and 50 generalised linear regression modelling, we sought: i) to partition the unique, shared and 51 unexplained variation in the metrics explained by both groups of descriptor variables, ii) to 52 quantify the contribution of each descriptor variable to biodiversity variation in the most 53 parsimonious regression model and iii) to identify interactions of land use and natural 54 descriptors. The variation in biodiversity uniquely described by land use was consistently low 55 across both ecosystem types and organism groups. In contrast, geo-climatic descriptors 56 uniquely, and jointly with land use, explained significantly more variance in all 39 57 biodiversity metrics tested. Regression models revealed significant interactions between geo-58 climatic descriptors and land use for a third of the models, with interactions accounting for up 59 to 17% of the model's deviance. However, no consistent patterns were observed related to the 60 type of biodiversity metric and organism group considered. Subdividing data according to the 61 strongest geo-climatic gradient in each dataset aimed to reduce the strength of natural 62 descriptors relative to land use. Although data sub-setting can highlight land use effects on 63 freshwater biodiversity, sub-setting our data often failed to produce stronger land use effects. 64There was no increase in spatial congruence in the subsets, suggesting that the observed land 65 use effects were not dependent upon the spatial extent of the subsets.

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.