Dataset on seston and zooplankton fatty-acid compositions, zooplankton and phytoplankton biomass, and environmental conditions of coastal and offshore waters of the northern Baltic Sea

We analyzed the taxonomic and fatty-acid (FA) compositions of phytoplankton and zooplankton, and the environmental conditions at three coastal and offshore stations of the northern Baltic Sea. Plankton samples for FA analyses were collected under the framework of sampling campaigns of the Swedish National Marine Monitoring program in September 2017. Monitoring data of phytoplankton and zooplankton biomass, and environmental variables at each station were extracted from the Swedish Meteorological and Hydrological Institute database (https://sharkweb.smhi.se/). Monthly phytoplankton biomass at each station in July-September 2017 was aggregated by class (i.e., chyrsophytes, cryptophytes, dinoflagellates, diatoms, euglenophytes, cyanobacteria, etc.). Zooplankton biomass in September 2017 was aggregated by major taxa (i.e., Acartia sp. [Calanoida], Eurytemora affinis [Calanoida], Cladocera, Limnocalanus macrurus and other copepods (i.e. excluding Eurytemora and Acartia)). Environmental variables monthly monitored in January-October 2017 included salinity, concentrations of dissolved organic carbon, humic substances, total nitrogen and total phosphorus. These variables were measured from 0 to 10 m depth below water surface, and the depth-integrated averages were used for data analyses. Seston and zooplankton (Eurytemora affinis, Acartia sp. and Cladocera) FA compositions were analyzed using gas chromatography and mass spectroscopy (GC–MS). Our dataset could provide new insights into how taxonomic composition and biochemical quality of the planktonic food chains change with the environmental conditions in subarctic marine ecosystems

Spatiotemporal carbon, nitrogen and phosphorus stoichiometry in planktonic food web in a northern coastal area

The concentrations of ambient nutrients and dissolved organic carbon (DOC) in northern coastal ecosystems often show large variations, due to the spatiotemporal differences in terrestial inputs. How these variations affect the stoichiometry of coastal planktonic organisms is, however, poorly known. Here we assessed the spatiotemporal variability of C, nitrogen (N), and phosphorous (P) concentrations of the seawater on the elemental stoichiometry of seston and dominant mesozooplankton taxa in a coastal area of the northern Baltic Sea. The freshwater inflow peaked in spring following the snowmelt and brought a significant amount of DOC, but not N and P to the coastal system. DOC was the main environmental descriptor for seston C:N stoichiometry. The C:N ratio of seston from 0.7 to 50 mu m and mesozooplankton followed the temporal pattern of water C:N ratio, while the temporal trend of bacteria C:N showed an opposite pattern. Our results also indicated that the C:N ratio of seawater controlled both seston and mesozooplankton C:N ratios. Our findings imply that inflows of terrestrial DOC alter the stoichiometry and reduce the nutritional quality of planktonic food webs in northern coastal ecosystems

Fatty-acid based assessment of benthic food-web responses to multiple stressors in a large river system

Rivers are often exposed to multiple stressors, such as nutrients and contaminants, whose impacts on the river food webs may not be distinguished by sole assessment of biological community structures. We examined the benthic algal assemblages and the fatty acids (FA) of benthic macroinvertebrates in the lower Athabasca River in Canada, aiming to assess the changes in algal support and nutritional quality of the benthic food web in response to cumulative exposure to natural bitumen, municipal sewage discharge (hereafter, "sewage"), and oil sands mining ("mining"). Data show that the decline in water quality (increases in nutrient concentrations and total suspended solids) was associated with decreases in benthic diatom abundance, and was driven mainly by sewageinduced nutrient enrichment. Responses in nutritional quality of benthic macroinvertebrates, indicated by their polyunsaturated FA (PUFA) concentrations, were taxon- and stressor-specific. Nutritional quality of the larval dragonfly predator, Ophiogomphus, decreased nonlinearly with decreasing benthic diatom abundance and was lowest at the sewage-affected sites, although exposure to natural bitumen also resulted in reduced Ophiogomphus PUFA concentrations. In contrast, the PUFA concentrations of mayfly grazers/collector-gatherers were not affected by natural bitumen exposure, and were higher at the sewage and sewage+mining sites. The PUFA concentrations of the shredder Pteronarcys larvae did not change with cumulative exposure to the stressors. Sediment metal and polycyclic aromatic compound concentrations were not associated with the macroinvertebrate FA changes. Overall, we provide evidence that sewage induced reduction in trophic support by PUFA-rich diatoms, and was the predominant driver of the observed changes in FA composition and nutritional quality of the benthic macroinvertebrates. Fatty-acid metrics are useful to untangle effects of concurrent stressors, but the assessment outcomes depend on the functional feeding guilds used. A food-web perspective using multiple trophic levels and feeding guilds supports a more holistic assessment of the stressor impacts

Phytoplankton biomass in northern lakes reveals a complex response to global change

Global change may introduce fundamental alterations in phytoplankton biomass and community structure that can alter the productivity of northern lakes. In this study, we utilized Swedish and Finnish monitoring data from lakes that are spatially (135 lakes) and temporally (1995-2019, 110 lakes) extensive to assess how phytoplankton biomass (PB) of dominant phytoplankton groups related to changes in water temperature, pH and key nutrients [total phosphorus (TP), total nitrogen (TN), total organic carbon (TOC), iron (Fe)] along spatial (Fennoscandia) and temporal (25 years) gradients. Using a machine learning approach, we found that TP was the most important determinant of total PB and biomass of a specific species of Raphidophyceae - Gonyostomum semen - and Cyanobacteria (both typically with adverse impacts on food-webs and water quality) in spatial analyses, while Fe and pH were second in importance for G. semen and TN and pH were second and third in importance for Cyanobacteria. However, in temporal analyses, decreasing Fe and increasing pH and TOC were associated with a decrease in G. semen and an increase in Cyanobacteria. In addition, in many lakes increasing TOC seemed to have generated browning to an extent that significantly reduced PB. The identified discrepancy between the spatial and temporal results suggests that substitutions of data for space-for-time may not be adequate to characterize long-term effects of global change on phytoplankton. Further, we found that total PB exhibited contrasting temporal trends (increasing in northern- and decreasing in southern Fennoscandia), with the decline in total PB being more pronounced than the increase. Among phytoplankton, G. semen biomass showed the strongest decline, while cyanobacterial biomass showed the strongest increase over 25 years. Our findings suggest that progressing browning and changes in Fe and pH promote significant temporal changes in PB and shifts in phytoplankton community structures in northern lakes

Declining calcium concentration drives shifts toward smaller and less nutritious zooplankton in northern lakes

Zooplankton community composition of northern lakes is changing due to the interactive effects of climate change and recovery from acidification, yet limited data are available to assess these changes combined. Here, we built a database using archives of temperature, water chemistry and zooplankton data from 60 Scandinavian lakes that represent broad spatial and temporal gradients in key parameters: temperature, calcium (Ca), total phosphorus (TP), total organic carbon (TOC), and pH. Using machine learning techniques, we found that Ca was the most important determinant of the relative abundance of all zooplankton groups studied, while pH was second, and TOC third in importance. Further, we found that Ca is declining in almost all lakes, and we detected a critical Ca threshold in lake water of 1.3 mg L-1, below which the relative abundance of zooplankton shifts toward dominance of Holopedium gibberum and small cladocerans at the expense of Daphnia and copepods. Our findings suggest that low Ca concentrations may shape zooplankton communities, and that current trajectories of Ca decline could promote widespread changes in pelagic food webs as zooplankton are important trophic links from phytoplankton to fish and different zooplankton species play different roles in this context.Among five environmental variables tested, we found that low lake calcium (Ca) concentrations shape most zooplankton communities in northern lakes. When lake water Ca falls below a threshold of 1.3 mg L-1, the relative abundance of zooplankton shifts toward dominance of Holopedium and small cladocerans at the expense of Daphnia and cyclopoid and calanoid copepods. The current trajectory of Ca declines found in northern lakes implies community shifts toward dominance of smaller and less nutritious zooplankton. Ca declines potentially have strong repercussions for lake food webs and productivity as zooplankton are important trophic links from phytoplankton to fish.imag

Retention of essential fatty acids in fish differs by species, habitat use and nutritional quality of prey

Algae-produced long-chain polyunsaturated fatty acids (LC-PUFA; with >= 20 carbon atoms) are key biomolecules for consumer production and animal health. They are transferred to higher trophic levels and accumulated in food chains. However, LC-PUFA accumulation in consumers and their trophic transfer vary with the diet quality and the physiological demand for LC-PUFA of consumers. The goal of this study was to investigate spatial and taxonomic differences in LC-PUFA retention of coastal fish predators that potentially differ in their habitat use (benthic versus pelagic) and prey quality. We analyzed the fatty acid (FA) composition of common fish species, namely roach and European perch, as well as their potential prey from benthic and pelagic habitats in three bays of the northern Baltic Sea. We then assessed whether the fish LC-PUFA retention differed between species and among the study bays with different diet quality, that is, LC-PUFA availability. Our data indicated taxon-specific differences in the retention of LC-PUFA and their precursor FA in fish (i.e., short-chain PUFA with <20 carbon atoms). Perch did not show any spatial variation in the retention of all these FA, while roach showed spatial differences in the retention of docosahexaenoic acid (DHA) and their precursor FA, but not eicosapentaenoic acid (EPA). Data suggest that diet quality and trophic reliance on benthic prey underlay the DHA retention differences in roach. Although the PUFA supply might differ among sites, the low spatial variation in LC-PUFA content of perch and roach indicates that both fishes were able to selectively retain dietary LC-PUFA. Climate change together with other existing human-caused environmental stressors are expected to alter the algal assemblages and lower their LC-PUFA supply for aquatic food webs. Our findings imply that these stressors will pose heterogeneous impacts on different fish predators. We advocate further investigations on how environmental changes would affect the nutritional quality of the basal trophic level, and their subsequent impacts on LC-PUFA retention, trophic ecology, and performance of individual fish species

Variation in fatty acid content among benthic invertebrates in a seasonally driven system

At temperate latitudes where seasonal changing environmental conditions strongly affect the magnitude, duration and species composition of pelagic primary production, macrobenthic organisms living below the photic zone rely on the sedimentation of organic matter as their primary energy source. The succession from nutritious spring blooms to summer cyanobacteria is assumed to reduce food quality for benthic primary consumers and their fatty acid (FA) profiles. In contrast, we find low seasonal variability in FA content of five benthic macroinvertebrates spanning two trophic levels in the Baltic Sea, a system with high seasonal variation in phytoplankton species composition. However, levels of the major FA groups vary greatly between benthic species. The results suggest that benthic macroinvertebrates have evolved FA metabolism adapted to degraded sedimenting material. Moreover, our study shows that species composition of benthic macrofauna rather than seasonal changing conditions affect availability of essential nutrients to higher trophic levels

Structured Bayesian Compression for Deep Neural Networks Based on The Turbo-VBI Approach

With the growth of neural network size, model compression has attracted increasing interest in recent research. As one of the most common techniques, pruning has been studied for a long time. By exploiting the structured sparsity of the neural network, existing methods can prune neurons instead of individual weights. However, in most existing pruning methods, surviving neurons are randomly connected in the neural network without any structure, and the non-zero weights within each neuron are also randomly distributed. Such irregular sparse structure can cause very high control overhead and irregular memory access for the hardware and even increase the neural network computational complexity. In this paper, we propose a three-layer hierarchical prior to promote a more regular sparse structure during pruning. The proposed three-layer hierarchical prior can achieve per-neuron weight-level structured sparsity and neuron-level structured sparsity. We derive an efficient Turbo-variational Bayesian inferencing (Turbo-VBI) algorithm to solve the resulting model compression problem with the proposed prior. The proposed Turbo-VBI algorithm has low complexity and can support more general priors than existing model compression algorithms. Simulation results show that our proposed algorithm can promote a more regular structure in the pruned neural networks while achieving even better performance in terms of compression rate and inferencing accuracy compared with the baselines