We aimed to predict cyanobacteria biomass and nitrate (NO3-) concentrations in Lake Võrtsjärv, a large, shallow, and eutrophic lake in Estonia. We used a model chain based on the succession of a mechanistic (INCA-N) model and an empirical, generalized linear model. INCA-N model calibration and validation was performed with long term climate and catchment parameters. We constructed twelve scenarios as combinations of climate forcing from the Intergovernmental Panel on Climate Change (IPCC, 3 scenarios), land conversion (forest to agriculture, 2 scenarios), and fertilizer use (2 scenarios). Models predicted 46% of the variance of cyanobacteria biomass and 65% of that of NO3- concentrations. The model chain simulated that scenarios comprising both forest conversion to agricultural lands and a greater use of fertilizer per surface area unit would cause increases in lacustrine NO3- (up to twice the historical mean) and cyanobacteria biomass (up to a four-fold increase compared to the historical mean). The changes in NO3- concentrations and cyanobacteria biomass were more pronounced in low and moderate warming scenarios than in high warming scenarios because of increased denitrification rates in a warmer climate. Our findings show the importance of reducing anthropogenic pressures on lake catchments in order to reduce harmful pollutant and microalgae proliferation, and highlight the counterintuitive effects of multiple stressor interactions on lake functioning.

Ecosystem models that measure the impact of quantitative interactions between trophic levels are widely used tools in ecosystem studies and fishery management. We constructed a mass-balance trophic model using an Ecopath with Ecosim (EwE) modelling suite for large shallow Lake Võrtsjärv, Estonia. The model was calibrated for 36 years (1983–2018) and included 23 functional groups. We examined trophic relationships, functional group interactions, energy fluxes, and keystone groups having a high impact on the ecosystem relative to their biomass. We tested 6 hypothetical scenarios based on future biomass changes for the major functional groups (phytoplankton, zooplankton, macrozoobenthos, piscivorous fish, and bream) for 20 years. The output of the predictive scenarios showed that the biomass changes of planktonic groups would affect the whole food web. Among consumers, macrozoobenthos was crucial for the food web balance because a reduction of their biomass would also reduce the biomass of the fish community. Changes in fish catches would cause minimal biomass difference in other groups. While increased fishing pressure on large piscivores would have a marked effect on the rest of the food web, the reduction of nonpiscivorous fish like bream would have little effect. The results suggested a positive relationship between the biomass of small phytoplankton and fish, alluding to the prevalence of bottom-up trophic processes. These outcomes could be helpful for assessing trophic dynamics in shallow lakes and important aspects for fisheries and ecosystem management.

Climate change shows itself in many different ways on marine life. The fishery is also a part of marine life and affected by climate change-driven weather conditions directly or indirectly. In the present study, relationships between commercial species (grey mullet -approximate to 90% of total capture-and gilthead seabream) that were captured from lagoon traps in Koycegiz lagoon (Turkey) and local weather conditions were analysed. The machine learning method Random Forests (RF) was used to pre-select the model predictors. RF results showed that while temperature-related parameters, cloudy days, and wind speed were the most effective parameters, precipitation-parameters were the least important parameters for these two species catch. Generalized linear models (GLMs) were applied to each fish species with the best pre-selected parameters, with the resulting equation being used for future prediction of the two fish species. Future prediction of predictors was calculated by monthly autoregressive integrated moving average (ARIMA) and 20th/80th percentile intervals were used as the scenarios. Simulations showed that an increase in some weather parameters (wind speed, seawater temperature, maximum air temperature, cloudy days) lead to an increase in grey mullet and (wind speed) gilthead seabream catch. Models proved that the impact of the weather parameters differs for those two targeted fish species although they live in the same environment. We recommend that individual fish species (and/or catch) should be used in the models, not the whole fish yield. Moreover, the model can also be used for non-commercial species in ecosystem-based studies. Changes in weather parameters due to climate change should be monitored to make proper decision on fishery management. (C) 2021 Elsevier B.V. All rights reserved.

Monitoring heavy metal contaminants in fish is important for the assessment of environmental quality as well as food safety. In this study, European eel samples were collected from Lake Köyceğiz and Lake Võrtsjärv in 2017 and 2018. The concentrations of Mn, Cd, Zn, Pb, and Cu metals were measured by using GF-AAS in four selected tissues of eel, including liver, gill, skin, and muscle in both lakes. The pollution index (Pi, MPI) values were calculated for both lakes and the health risk for consumers was assessed for both adults and children in Turkey and Estonia. The estimated weekly intake (EWI), hazard index (HI), and lifetime cancer risk values (CRs) for the metals were calculated for both lakes. According to the results of this study, a significant difference was determined between the metal concentrations (especially Cu, Cd, and Pb) in the tissues of the eel samples taken from the two lakes. These results show that besides the pollution levels in the aquatic environment, physiological needs and metabolic activities in different habitats have a significant effect on metal accumulation in eels. In addition, HI was found to be < 1 for both adult and child consumers in both lakes, which indicates that consumers would not experience non-carcinogenic health effects. However, the values of CR for Pb and Cd were found negligible in Lake Köyceğiz, while the CR value for Pb was found to be very close to the danger limits in Lake Võrtsjärv.

Shallow lakes are globally the most numerous water bodies and are sensitive to external perturbations, including eutrophication and climate change, which threaten their functioning. Extreme events, such as heat waves (HWs), are expected to become more frequent with global warming. To elucidate the effects of nutrients, warming, and HWs on zooplankton community structure, we conducted an experiment in 24 flow-through mesocosms (1.9 m in diameter, 1.0 m deep) imitating shallow lakes. The mesocosms have two nutrient levels (high (HN) and low (LN)) crossed with three temperature scenarios based on the Intergovernmental Panel on Climate Change (IPCC) projections of likely warming scenarios (unheated, A2, and A2 + 50%). The mesocosms had been running continuously with these treatments for 11 years prior to the HW simulation, which consisted of an additional 5 °C increase in temperature applied from 1 July to 1 August 2014. The results showed that nutrient effects on the zooplankton community composition and abundance were greater than temperature effects for the period before, during, and after the HW. Before the HW, taxon richness was higher, and functional group diversity and evenness were lower in HN than in LN. We also found a lower biomass of large Cladocera and a lower zooplankton: phytoplankton ratio, indicating higher fish predation in HN than in LN. Concerning the temperature treatment, we found some indication of higher fish predation with warming in LN, but no clear effects in HN. There was a positive nutrient and warming interaction for the biomass of total zooplankton, large and small Copepoda, and the zooplankton: phytoplankton ratio during the HW, which was attributed to recorded HW-induced fish kill. The pattern after the HW largely followed the HW response. Our results suggest a strong nutrient effect on zooplankton, while the effect of temperature treatment and the 5 °C HW was comparatively modest, and the changes likely largely reflected changes in predation.

Changes in the ice phenology, seasonal temperature and extreme events are consistent evidence of climate change effect on lakes. In this study, we analyzed multiannual variability, determined long-term trends and detected changes in the frequency of extreme events in the surface water temperature (LSWT) of Lake Peipsi (Estonia/Russia) for nearly seven decades (1950-2018) and aimed to trace how the LSWT responded to the climate change. Dynamic water temperature parameters were calculated using the smoothed water temperature curve fitted to daily water temperatures. Our results showed that, although the average LSWT did not increase significantly on an annual basis since 1950 it rose rapidly in the winter season during the last decade (similar to +0.5 degrees C). Ice formation exhibited a marked (similar to 15 days) delay since 2007 resulting in a longer open water period. Extreme LSWT events did not occur more frequently. We noticed however significant fluctuating in winter LSWT in time series, starting from 2007 and also causing an increase in stochasticity. The consequences of the on-going winter warming and changes of ice cover phenology are expected to be crucial for Lake Peipsi ecosystem functioning and impact on lake biota, especially temperature-sensitive native fishes.

Relationships between biomass and ecological factors including trophic interactions were examined to understand the dynamics of six fish species in Lake Võrtsjärv, a large shallow eutrophic lake located in Estonia (north-eastern Europe). The database contained initially 31 predictive variables that were monitored in situ for nearly forty years. The strongest predictive variables were selected by three parallel approaches: single correlation (Pearson), a multivariate method (Co-inertia analyses), and a machine learning algorithm (Random Forests), followed by a Generalized Least Squares model to determine meaningful relationships with fish biomass. Models with both additive and interactive effects were constructed. The results revealed that the indicators of degraded ecological conditions (high cyanobacteria biomass and their proportion in total phytoplankton, high summer temperature, high nutrient concentration) were negatively correlated to fish biomass. Benthic macroinvertebrates and other biotic predictors (biomass of specific fish prey and predators) were also important contributors to fish biomass dynamics. Together, abiotic and biotic factors explained 40–60% of the variance of fish biomass, depending of the species. Our findings suggest that both abiotic and biotic factors control fish biomass changes in this eutrophic lake.

We numerically explored the effects of long-term water level changes on biotic biomass and spatial distribution of fish in a large shallow lake. We calibrated Ecospace model (Ecopath with Ecosim modelling suite) with data from various functional groups (ranging from phytoplankton to piscivorous fish), and considered 14 different habitats. Two scenarios representing, respectively, a long-term water-level increase and decrease by 1 m were constructed and run for a period of thirty eight years (1979-2016). The results showed a very uneven spatial distribution of fish biomass in the lake, with the highest concentration in the southern basin. The 1 m decrease scenario caused a diminution in the biomass of all groups but piscivorous fish. The 1 m increase scenario saw a weak decrease in most species biomass. Consequently, in both scenarios, long-term water level changes would be generally detrimental to the lake biota. In the context of more frequent climate-induced hydrological fluctuations, we encourage the use of these simulations as effective tools for future prediction and assessment of ecosystem-based fisheries management and ecological status maintenance of shallow lakes. (C) 2020 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

Restocking of European eel (Anguilla anguilla) is a widespread practice throughout Europe. Conditions during restocking activities and mortality related to restocking practices have been discussed, however, factors affecting these restocked populations afterwards are mostly not considered. In this study we used a machine learning method followed by generalized linear model to analyze long time eel restocking, commercial fishery and environmental data from Lake Vortsjarv, Estonia, to detect whether significant relationships exist within these data. It was found that environmental parameters can have an effect on the commercial eel yield both retrospectively and during the particular fishing year. Considering that 7-year old eel was the most common age group in commercial catch, we introduced a 7-year gap between eel restocking and yield to study the most important abiotic and biotic factors during the first year of eel restocking that have an effect on the yield. According to our results, cyanobacterial biomass and summer water temperature during the year of restocking had the strongest negative impact on the yield 7 years after, while the number of restocked individuals and copepod biomass had a positive effect. During particular fishing year, however, the yield was most notably positively affected by total phosphorous concentration, number of individuals restocked 7 years before and metazooplankton biomass in the lake.

Phytoplankton usually responds directly and fast to environmental fluctuations, making them useful indicators of lake ecosystem changes caused by various stressors. Here, we examined the phytoplankton community composition before, during, and after a simulated 1-month heat wave in a mesocosm facility in Silkeborg, Denmark. The experiment was conducted over three contrasting temperature scenarios (ambient (A0), Intergovernmental Panel on Climate Change A2 scenario (circa +3 °C, A2) and A2+ %50 (circa +4.5 °C, A2+)) crossed with two nutrient levels (low (LN) and high (HN)) with four replicates. The facility includes 24 mesocosms mimicking shallow lakes, which at the time of our experiment had run without interruption for 11 years. The 1-month heat wave effect was simulated by increasing the temperature by 5 °C (1 July to 1 August) in A2 and A2+, while A0 was not additionally heated. Throughout the study, HN treatments were mostly dominated by Cyanobacteria, whereas LN treatments were richer in genera and mostly dominated by Chlorophyta. Linear mixed model analyses revealed that high nutrient conditions were the most important structuring factor, which, regardless of temperature treatments and heat waves, increased total phytoplankton, Chlorophyta, Bacillariophyta, and Cyanobacteria biomasses and decreased genus richness and the grazing pressure of zooplankton. The effect of temperature was, however, modest. The effect of warming on the phytoplankton community was not significant before the heat wave, yet during the heat wave it became significant, especially in LN-A2+, and negative interaction effects between nutrient and A2+ warming were recorded. These warming effects continued after the heat wave, as also evidenced by Co-inertia analyses. In contrast to the prevailing theory stating that more diverse ecosystems would be more stable, HN were less affected by the heat wave disturbance, most likely because the dominant phytoplankton group cyanobacteria is adapted to high nutrient conditions and also benefits from increased temperature. We did not find any significant change in phytoplankton size diversity, but size evenness decreased in HN as a result of an increase in the smallest and largest size classes simultaneously. We conclude that the phytoplankton community was most strongly affected by the nutrient level, but less sensitive to changes in both temperature treatments and the heat wave simulation in these systems, which have been adapted for a long time to different temperatures. Moreover, the temperature and heat wave effects were observed mostly in LN systems, indicating that the sensitivity of phytoplankton community structure to high temperatures is dependent on nutrient availability.

Essential and non-essential total eleven metals and metalloids (Al, B, Cr, Co, Cd, Cu, Fe, Pb, Mn, Ni and Zn) concentrations were determined by ICP-AES in muscle of Squalius fellowesii (Gunther, 1868), between November of 2013 and June of 2014, on chosen four stations on Tersakan and Saricay streams. Average concentrations (mu g g(-1) wet weight) in Tersakan: Al (60.81 +/- 58.51), B (27.13 +/- 12.42), Co (0.07 +/- 0.05), Cd (0.01 +/- 0.02), Cu (1.44 +/- 0.51), Cr (0.75 +/- 0.54), Fe (33.49 +/- 30.69), Mn (2.19 +/- 1.35), Ni (0.98 +/- 1.27), Zn (20.71 +/- 20.16) and in Saricay: Al (33.82 +/- 37.19), B(8.15 +/- 8.90), Co (0.04 +/- 0.035), Cd (0.01 +/- 0.01), Cu (0.90 +/- 0.44), Cr (0.40 +/- 0.27), Fe (12.97 +/- 8.17), Mn (3.75 +/- 2.045), Ni (0.54 +/- 0.82), Zn (19.05 +/- 10.65) were found. Lead (Pb) was found below detection limits in all stations or seasons while Al accumulation was found highest. The effects of water quality parameters on essential and non-essential metal-metalloid accumulations in fish tissue were indicated by PCA and Co-inertia analyses. Concentrations of observed non-essential metals did not exceed the consumption limits. The evaluation of the data obtained from the study, in Tersakan and Saricay streams, reveal that the ecological balance can be changed in a negative way in case of continuation of the pollution of these two streams.

The concentrations of nine metals (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn), individual total metal load (IMBI) values and metal pollution index (MPI) were determined in water, sediment and European chub, Squalius cephalus (Linnaeus, 1758) inhabiting Saricay Stream Turkey. A total of sixty European chub samples and twelve sediment and water samples were taken and analysed seasonally between June 2011 and May 2012. Heavy metals were analysed by ICP-AES. The distribution of the IMBI values ranged from 0.040 to 0.418. Distribution patterns of IMBI in seasons and stations of European chub follow the sequence: spring> winter> summer> autumn, Station III> I> II, respectively. Result of high IMBI values in all seasons and stations can be explained by the fact that increasing MPI value of Zn, Fe and Mn. Among the heavy metals studied Cd and Co were below the detection limits in most seasons. The heavy metal concentrations in the edible tissue of European chub were compared with the tolerable national and international values in fish. The results obtained, showed that the heavy metal concentrations in edible tissue were excessive and were not safe within the limits for human consumption.