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Freshwaters are inhabited by a remarkable variety of species from most taxonomic groups that have evolved on Earth, but they are also a source of human opportunities, requirements, and conflicts. Strong human impacts have restricted the number and variety of freshwaters and polluted and disturbed many others. This article discusses the range of human impacts and their consequences for species richness and functional diversity of individual lakes and streams. Special emphasis is placed on freshwaters in Europe and North America and their intensely cultivated regions. Biological invasions and climatic changes are not dealt with because of lack of space and paucity of general predictions.

Lake morphometric features like surface area, volume, mean, and maximum depth are important predictors of many physical, biological, and ecological processes. Lake bathymetric maps that present the lake basin contours are thus an integral part of limnological investigations. Accurate but cumbersome traditional bathymetric surveys measure the depth using a lead line or echosounder. Recently, airborne bathymetric mapping using imagery or laser scanning has been attempted in shallow freshwater and coastal habitats. However, these methods depend on the ability of light to penetrate the water column, which can be problematic in eutrophic lakes and shallow lakes. To alleviate these issues, we developed and tested a deep learning model (based on the U-net) using data from 153 lakes in Denmark to predict bathymetry using the topography of the surrounding terrain. The deep learning model performed much better (pixel-wise mean absolute error: validation set = 1.75 and test set = 2.15 m) than baseline interpolation approaches (validation set = 3.12 m). In addition, the deep learning model generated more realistic bathymetry maps that did not suffer from interpolation artifacts. We find that the model performance improves slightly with increasing model size (number of trainable parameters) and the extent of the surrounding terrain. In addition, our pretraining procedure improved performance and reduced the time for model convergence. Because the model only relies on digital elevation data which are widely available, it can be fine-tuned and used to predict lake bathymetry in other geographical regions., Lake morphometric features like surface area, volume, mean, and maximum depth are important predictors of many physical, biological, and ecological processes. Lake bathymetric maps that present the lake basin contours are thus an integral part of limnological investigations. Accurate but cumbersome traditional bathymetric surveys measure the depth using a lead line or echosounder. Recently, airborne bathymetric mapping using imagery or laser scanning has been attempted in shallow freshwater and coastal habitats. However, these methods depend on the ability of light to penetrate the water column, which can be problematic in eutrophic lakes and shallow lakes. To alleviate these issues, we developed and tested a deep learning model (based on the U-net) using data from 153 lakes in Denmark to predict bathymetry using the topography of the surrounding terrain. The deep learning model performed much better (pixel-wise mean absolute error: validation set = 1.75 and test set = 2.15 m) than baseline interpolation approaches (validation set = 3.12 m). In addition, the deep learning model generated more realistic bathymetry maps that did not suffer from interpolation artifacts. We find that the model performance improves slightly with increasing model size (number of trainable parameters) and the extent of the surrounding terrain. In addition, our pretraining procedure improved performance and reduced the time for model convergence. Because the model only relies on digital elevation data which are widely available, it can be fine-tuned and used to predict lake bathymetry in other geographical regions.

Freshwater ecosystems have experienced a great loss of biodiversity in the recent century due to eutrophication and loss of habitats, particularly in agricultural lowlands. Some of these ecosystems have recently been re-established, or entirely new lakes have been created to improve nutrient removal, biodiversity, and recreation. Fish have an important structuring role in lake ecosystems, but little is known about the temporal development of fish species richness or composition in new or re-established lakes, especially in comparison with natural lakes. We investigated the influence of environmental variables and landscape features on fish species richness and fish assemblage in drainage basins and in 34 new (between 0 and 99 years) and 193 natural lakes in Denmark, using structural equation modelling. Fish species richness in drainage basins is influenced primarily by basin elevation, lake area, and salinity at the basin outlet; low-salinity coastal regions are important migration pathways for many freshwater fish species, not just for anadromous or catadromous species. Land use does not appear to influence richness at the drainage basin scale, indicating that the influence of anthropogenic activities is minor or that significant effects thereof occurred a long time ago (decades to centuries). The drainage basin richness defines the apparent pool of species that may colonise lakes within the basin, but the actual number of species is particularly influenced by stream network connectivity. Disconnected lakes have fewer species, a deficit that is sustained over long time scales. In addition to the influence of stream connectivity, lake characteristics such as elevation, surface area, and water chemistry (alkalinity and pH) are also important predictors of fish species richness, whereas we did not find any effect of lake nutrient concentrations. The direct effect of land use was not evaluated. Ordination analysis shows that fish assemblage depends on b, Freshwater ecosystems have experienced a great loss of biodiversity in the recent century due to eutrophication and loss of habitats, particularly in agricultural lowlands. Some of these ecosystems have recently been re-established, or entirely new lakes have been created to improve nutrient removal, biodiversity, and recreation. Fish have an important structuring role in lake ecosystems, but little is known about the temporal development of fish species richness or composition in new or re-established lakes, especially in comparison with natural lakes. We investigated the influence of environmental variables and landscape features on fish species richness and fish assemblage in drainage basins and in 34 new (between 0 and 99 years) and 193 natural lakes in Denmark, using structural equation modelling. Fish species richness in drainage basins is influenced primarily by basin elevation, lake area, and salinity at the basin outlet; low-salinity coastal regions are important migration pathways for many freshwater fish species, not just for anadromous or catadromous species. Land use does not appear to influence richness at the drainage basin scale, indicating that the influence of anthropogenic activities is minor or that significant effects thereof occurred a long time ago (decades to centuries). The drainage basin richness defines the apparent pool of species that may colonise lakes within the basin, but the actual number of species is particularly influenced by stream network connectivity. Disconnected lakes have fewer species, a deficit that is sustained over long time scales. In addition to the influence of stream connectivity, lake characteristics such as elevation, surface area, and water chemistry (alkalinity and pH) are also important predictors of fish species richness, whereas we did not find any effect of lake nutrient concentrations. The direct effect of land use was not evaluated. Ordination analysis shows that fish assemblage depends on both lake type

De første målinger fra Lyngsø dokumenterer ikke kun mængden af metan og CO2, der forsvinder fra søen, men også hvornår på dagen og hvor i søen.