Lake Types Research Articles

Uncertainties on the combined use of ICESat and ICESat-2 observations to monitor lake levels

Lake water level is an important variable to indicate lake hydrological balances and climate change impacts. Benefiting from the launch of the laser altimeters ICESat and ICESat-2, higher spatial-resolution elevation measurements have opened new possibilities for monitoring lake levels globally over the past two decades. However, uncertainties on the combined use of two-generation satellite laser measurements have not yet been investigated specifically. This study aimed to summarize the important technique notes on water level data processing by integrating the ICESat and ICESat-2 altimetry measurements. We mainly focused on the effect of geoid height, water masks for extracting altimetry footprints, and the 9-year data gap between the two generations of satellites on water level change estimates. We compared the influences of the above three factors in different situations by selecting typical lakes worldwide as study cases. The results showed that: (1) In the combination of ICESat and ICESat-2 products, geoid heights need to be recalculated for each footprint based on its longitude and latitude in order to replace the geoid values of the original products when calculating orthometric heights. It is necessary because the default geoids in both generations of products (ICESat and ICESat-2) exhibit a systematic deviation; (2) To balance the accuracy and efficiency, the small water mask in the low-level year is recommended to extract the potential footprints in comparison with the laborious processing of time-varying water masks; (3) The 9-year data gap between ICESat and ICESat-2 observations may cause inevitable overestimations or underestimations of the long-term change rate of lake levels with a non-linear trajectory, yet it has few effects on lakes with (near) linear trending or fluctuating changes.

Effects of lakeshore landcover types and environmental factors on microplastic distribution in lakes on the Inner Mongolia Plateau, China

Microplastic pollution in freshwater environments has received increasing attention. However, limited research on the occurrence and distribution of microplastics in plateau lakes. This study investigated the microplastic characteristics and influencing factors in lakes with different land cover types on the Inner Mongolia Plateau. Results showed that microplastic abundance ranged from 0.5 to 12.6 items/L in water and 50–325 items/kg in sediments. Microplastics in water were predominantly polypropylene (50.5%), fragments (40.5%), and 50–200 µm (66.7%). High-density (27.9%), fibrous (69.3%), and large-sized microplastics (47.7%) were retained primarily in lake sediments. The highest microplastic abundance in water was found in cropland lakes and grassland lakes, while that in sediments was in descending order of desert lakes > cropland lakes > grassland lakes > forest-grassland lakes. Differences among lake types suggest that agriculture, tourism, and atmospheric transport may be critical microplastic sources. Microplastic distribution was positively correlated with farmland and artificial surface coverage, showing that land cover types related to human activities could exacerbate microplastic pollution in lakes. Redundancy analysis showed that ammonia nitrogen and pH were the key physicochemical factors affecting microplastic distribution in lakes, indicating the potential sources of microplastics in lakes and the uniqueness of microplastic occurrence characteristics in desert saline-alkaline lakes, respectively.

Setting nutrient boundaries to protect aquatic communities: The importance of comparing observed and predicted classifications using measures derived from a confusion matrix

Defining nutrient thresholds that protect and support the ecological integrity of aquatic ecosystems is a fundamental step in maintaining their natural biodiversity and preserving their resilience. With increasing catchment pressures and climate change, it is more important than ever to develop clear methods to establish thresholds for status classification and management of waters. This must often be achieved using complex data and should be robust to interference from additional pressures as well as ameliorating or confounding conditions. We use both artificial and real data to examine challenges in setting nutrient thresholds in unbalanced and skewed data. We found significant advantages to using binary logistic regression over other techniques. However, one of the key challenges is objectively selecting a probability from which to derive the nutrient threshold. For this purpose, the examination of the proportions of matching and mismatching status classifications of nutrients and a biological quality element using a confusion matrix is a key step that should be more widely adopted in threshold selection. We examined a large array of statistical measures of classification accuracy and their performance over combinations of skewness and imbalance in the data. The most appropriate threshold probability is a compromise between maximising overall classification accuracy and reducing mismatches expressed as commission (false positives) without excessive omission (false negatives). An application to a lake type indicated total phosphorus thresholds that would be around 50 μg l−1 lower than the threshold achieved by an ‘unguided’ approach, indicating that this approach is a very significant development meriting attention from national authorities responsible for water management.

Retrieving Lake Chla concentration from remote Sensing: Sampling time matters

Remote sensing is a promising technology for global water eutrophication monitoring, and the quantity and representativeness of the observed samples influence its accuracy. Due to limitations in high costs in situ monitoring, current remote sensing of lakes often relies on models trained with water quality samples from a single season. However, the generalization ability of these single-season trained models to other seasons has not been extensively studied. In this study, focusing on 38 major lakes in the Yangtze River Basin, we utilized monthly chlorophyll-a (Chla) monitoring data and Sentinel-2 image datasets from 2016 to 2021 to assess the impact of different seasonal data-trained models on the performance of Chla concentration retrieval in lakes. The results indicate that: (a) The sampling time for lake Chla significantly affects the performance of the retrieval model. Based on the seasonal data, the trained models show an R2 range of 0.523 to 0.699, with a Bias ranging from −7.06 % to 7.74 %, while the models trained with full-season samples perform between the various seasonal models. (b) When models built with seasonal data are applied to full-season data retrieval, there is a noticeable decrease in performance. The R2 of the spring-winter data trained model drops by 18.2 %, with RMSL and MAPE increasing by 11.4 % and 4.2 %, respectively. Similarly, the model trained from summer-autumn data shows a 4.02 % decrease in R2, with RMSLE and MAPE rising by 2.76 % and 13.56 %, respectively. This suggests that using models established from seasonal samples to infer full-season Chla concentrations can amplify errors. (c) Compared to seasonal models, full-season models based on different seasonal sampling exhibit better performance (R2 = 0.585, RMSLE = 0.337, Bias = -3.12 %, MAPE = 34.71 %). R2, RMSLE, and MAPE are all superior to seasonal models. Therefore, when performing full-time and long-term retrieval of Chla concentrations in water bodies, it is necessary to use data sampled in different seasons to reduce the errors introduced by seasonal sampling. (d) Chla retrieval models were established for 38 typical lakes and reservoirs in the Yangtze River Basin using the full-time dataset, revealing that 25 % (7/28) of the lakes and 20 % (2/10) of the reservoirs showed significant alteration trends. The number of lakes with increasing Chla concentrations exceeded those with decreasing concentrations, indicating that there is still a substantial risk of eutrophication in the lakes and reservoirs of the Yangtze River Basin.

Congruence of water column, contemporary and pre‐industrial sediment cladoceran assemblages in 85 Canadian lakes of contrasting human impact levels

Abstract Crustacean zooplankton occupy a key position as primary consumers within lake food webs, acting as early indicators of wider ecosystem shifts that can cascade to higher trophic levels. Their communities can be assessed via water column net hauls or via sediment samples containing preserved subfossil remnants from various time periods; the latter probably integrate multiple years of data, habitats within lakes, and seasonal patterns. The degree to which such samples do or do not correspond has never been assessed across a wide variety of lake types and gradients. As part of a larger sampling of over 600 lakes across lake‐rich Canada, 85 lakes were selected along a gradient of human impact, according to contemporary catchment land use, to analyse cladoceran zooplankton assemblage relatedness among snapshot water column, contemporary and pre‐industrial sediment samples. We used a combination of ordinations and matrix correlations (RV coefficients), along with partial redundancy analyses, to determine the degree to which the different sample types were comparable and whether they responded similarly to >30 environmental variables. We also compared conclusions obtained using the full cladoceran assemblages versus pelagic‐only taxa and whether our results varied according to degree of human impact. Overall, while all sample types were correlated, congruence was highest between contemporary and pre‐industrial sediment sample types. This result could arise from the under‐representation of littoral taxa in water column samples relative to those from the sediment. In high impacted catchments, however, we observed greater similarity between water column and contemporary sediment samples, as water quality stressors potentially structure assemblages in parallel directions over time. In these modern‐day sample types and among measured variables, we identified lake depth and pH as the most important structuring variables across Canada. In this first study comparing cladoceran assemblages in water column, contemporary and pre‐industrial sediments across many lakes, we conclude that water column and sediment samples respond similarly to environmental drivers but should not be considered entirely analogous or redundant.

Effects of water environmental factors and antibiotics on bacterial community in urban landscape lakes

The presence of antibiotics can affect the natural microbial community and exert selective pressure on the environment's microorganisms. This study focused on three types of urban landscape lakes in Xi'an that were closely related to human activities. By combining basic water quality indicators, antibiotic occurrence status, bacterial communities and their potential metabolic functions, Spearman correlation coefficient and redundancy analysis were used to explore the relationship between them, and further explore the impact mechanism of environmental factors and antibiotics on bacterial community structure. The results showed that ofloxacin, erythromycin, and roxithromycin were the main types of antibiotics in the three landscape lakes, with low ecological risks, and there was a clear clustering of antibiotic occurrence. Proteobacteria was the most abundant bacterial phylum, and each lake had its own unique dominant bacteria, which indicates that they are influenced by varying water sources, pollution, and other nearby environments. Statistical analysis showed that pH and nitrogen nutrients were the most critical environmental factors affecting bacterial communities (P<0.01), while tetracyclines and lincomycins were the antibiotics that had a significant impact on bacterial communities (P<0.05). Antibiotics mainly promote defense- and signal transduction-related functions, and inhibit the metabolic activity of bacterial communities. However, the impact of antibiotics on bacterial diversity, community structure, and potential metabolic function in the three urban lakes was less than that of environmental factors. These results help to clarify the mechanism and degree of impact of different interference factors (environmental factors, conventional pollutants, and antibiotics) on bacterial communities in the water environment and are important for the management of urban landscape lake water environments.

Ecosystem health risk assessment of lakes in the Inner Mongolian Plateau based on the coupled AHP-SOM-CGT model

The ecosystem health risk assessment of lake basins can provide a crucial foundation and support for the sustainable development of ecosystems in the arid regions of northern China. This study adopts the Driver-Pressure-State-Impact-Response (DPSIR) framework to select evaluation indicators, and utilizes a coupled model of the Analytic Hierarchy Process (AHP)-Self-Organizing Mapping Neural Network Algorithm (SOM)-Combinatorial Game Theory Algorithm (CGT) to determine the weight values of each indicator, and ultimately calculate the Ecosystem Health Index (EHI) of a typical lake basin from 1990 to 2020. AHP and SOM are employed to determine the subjective and objective weights of the evaluation indicators, respectively. While, the CGT is used to establish a balance between these two weights. This ensures that the final weight values embody both the subjective preferences of the decision-makers and the objective characteristics inherent in the data. The calculations show that the EHI of the basin fluctuated from a healthy state (69.8) in 1990 to a sub-healthy state (47.0) in 2020, although the dominant factors influencing EHI varied across different stages, the decline in the Driver and Impact health indices emerged as the primary reason for the decrease in EHI. Before 2009, driven by socio-economic development, the area of irrigated land and water withdrawals in the basin increased significantly, which led to an increase in ecosystem pressures, resulting in a rapid decrease in EHI. After 2009, the increase in per capita water resources and water-saving irrigation facilities reduced the pressure on the ecological system. the increased ecological water use and afforestation area improved the ecosystem’s status, leading to an increase in the vegetation cover within the basin and a gradual upward trend in EHI.

Determining the spatiotemporal variation, sources, and ecological processes of antibiotic resistance genes in a typical lake of the middle reaches of the Yangtze River

Antibiotic resistance genes (ARGs) are emerging environmental pollutants, influenced by complex regulatory factors. River-lake systems act as natural reservoirs for ARGs and provide an ideal model for studying their regulatory mechanisms. This study employed high-throughput quantitative PCR, high-throughput sequencing, correlation analyses, and model predictions to investigate the dynamics of ARGs and their influencing factors in Liangzi Lake, located in the mid-reaches of the Yangtze River. The research specifically centered on three environmental components: lake water, sediment, and river water. Results indicated that the ARGs from eight major antibiotic classes, displaying distinct seasonal distribution patterns. In comparison to the sediment, the water phase demonstrated a higher diversity of ARGs, with the highest level of ARGs sharing observed between lake and river waters (approximately 83.7 %). Furthermore, seasonal variations significantly influenced the distributions of both ARGs and bacterial communities. The diversity of ARGs was highest during the summer and autumn, and specific bacterial species exhibited robust correlations with ARGs (including matA/mel, aac (6′)-Ib-03, and blaROB). It is worth noting that environmental attributes and bacterial diversity had the most substantial impact on the dynamic changes in ARGs. Lastly, source tracking analysis pinpointed that sediment as the primary source of ARGs in lake water, constituting 45 % to 48 % of the total ARGs. Our study provides a comprehensive analysis of ARGs and their influencing factors in the river-lake system of the middle reaches of the Yangtze River, with Liangzi Lake as a representative case.

Salinity is an important factor in carbon emissions from an inland lake in arid region

Saline lakes, serving as the ultimate destination for most hydrological systems, accumulate substantial amounts of nutrients and organic matter from basins, and act as vast carbon reservoirs. These lakes exhibit exceptionally active biogeochemical cycling processes of carbon dioxide (CO2) and methane (CH4), and constitute integral components of the global carbon cycle. However, understanding of greenhouse gas emissions from saline lakes remains unclear mostly due to scarce data. In this study, we obtained CO2 and CH4 diffusive fluxes and biogeochemical parameters during ice-free period of 2021 at Bosten Lake, which is a representative inland saline lake located in China's arid region. Results revealed that Bosten Lake was a significant source of atmospheric gas carbon emissions, with average diffusion emissions of 12.645 ± 3.475 mmol m−2 d−1 for CO2 and 0.279 ± 0.069 mmol m−2 d−1 for CH4. Temporally, field measurements found a positive correlation between conductivity (Spc, a proxy of salinity) and CO2 emissions (R2 = 0.50, p < 0.01). Furthermore, the CH4 diffusive fluxes increased with the trophic state index (TSI, R2 = 0.31, p < 0.01). Spatially, exogenous inputs led to the spatial heterogeneity of carbon emissions. Our results highlighted that temporal variations in salinity constitute a crucial factor influencing CO2 emissions, and the saline lake has greater global warming potential compared to freshwater. The study provides an in-depth analysis of greenhouse gas emissions and driving factors in saline lakes of arid regions, and supports a further understanding of the carbon cycle in different types of lakes.

Exposure, Distribution, and Ecological Risk of Four New Bisphenol Analogs in the Typical Lake Region of Taihu Lake

Exposure, Distribution, and Ecological Risk of Four New Bisphenol Analogs in the Typical Lake Region of Taihu Lake

Functional ecological metrics of good ecological quality – Needs of managers and data from pristine lakes

Anthropogenic eutrophication and overfishing of top predators have put our lakes in danger, and coping mechanisms like The European Water Framework Directive have been put in place to mitigatetheireffects. However, the discussion on the means we employ to monitor lake ecosystems and establish their ecological status is ongoing. Importantly, it has been suggested that universal, functional indicators of healthy reference ecosystems could be identified, measured and used for comparison purposes across ecological types of lakes. An equally important issue is to develop such metrics that can be employed by lake managers, who are the end users of the scientific data when planning everyday management practices. We employed bioenergetic food web approach with Ecological Network Analysis to assess organic matter fluxes and food web structure in 13 lakes in Latvia – including 3 pristine bog lakes - and combined the acquired functional descriptors with simple and well-known ecological descriptors. We conclude that information on the functioning of pristine lakes provides a potentially valuable tool for setting functional reference conditions for lake management. We also found that the ecological parameters - the percentage of piscivorous fish and the abundance of cyanobacteria - correlate well with the functional food web descriptors. Thus, they could be valuable tools to both managers and scientists when evaluating lake ecological status across the boundaries of ecological types.

Mountain glaciers influence biogeochemical and ecological characteristics of high‐elevation lakes across the northern Rocky Mountains, USA

AbstractMountain glaciers are retreating rapidly due to climate change, leading to the formation of downstream lakes. However, little is known about the physical and biogeochemical conditions in these lakes across a range of glacial influence. We surveyed alpine lakes fed by both glacial and snowpack meltwaters and those fed by snowpack alone to compare nutrient concentrations, stoichiometry, water clarity, chlorophyll, and zooplankton communities. Total phosphorus (TP) and soluble reactive phosphorus were two times higher in glacial lakes than in non‐glacial lakes, while nitrate concentrations were three times higher. However, organic carbon concentrations in glacial lakes were two times lower than in non‐glacial lakes. The carbon‐to‐phosphorus ratio and the nitrogen‐to‐phosphorus ratio of lake seston increased with water clarity in glacial lakes, suggesting that turbidity from glacial flour increases light limitation and increases stoichiometric food quality for zooplankton in newly formed lakes. However, chlorophyll a concentrations did not differ between lake types. Through structural equation modeling, we found that glaciers exhibit a bidirectional association with nitrate and TP concentrations, perhaps mediated through landscape vegetation and lake clarity. Zooplankton communities in high‐turbidity glacial lakes were largely composed of cyclopoid copepods and rotifers (i.e., non‐filter feeders), while non‐glacial lakes tended to be dominated by calanoid copepods and cladocerans (i.e., filter feeders). Our results show that glacier‐influenced lakes have biogeochemical and ecological characteristics distinct from snow‐fed mountain lakes. Sustained studies are needed to assess the dynamics of these unique features as the influence of the alpine cryosphere fades under ongoing climate change.

Analysis of Lake Shoreline Evolution Characteristics Based on Object Increments

Evolutionary changes in shallow lake shorelines can significantly impact wetland biodiversity transformation. This paper aims to further elucidate the wetland evolution process by investigating the temporal and spatial characteristics and rules governing lake shoreline evolution. Departing from traditional analyses of wetland area and shoreline length changes, this paper presents a comprehensive approach to quantifying typical lake shoreline evolution patterns using the concept of object increments. These evolutionary patterns are classified into four types: “expansion”, “shrinkage”, “appearance”, and “disappearance”. Using Shengjin Lake as a case study, Landsat images from 2001 to 2020 were used to extract the lake shoreline. The temporal series characteristics of different evolution patterns, the laws at the patch scale, and the characteristics of evolution direction were analyzed. The key findings are as follows. (1) The evolution of Shengjin Lake’s shoreline from 2001 to 2020 was primarily characterized by “expansion” or “shrinkage” patterns with a clear negative correlation between them. The “appearance” and “disappearance” of lake shorelines were rare. (2) The evolutionary patterns of “expansion” or “shrinkage” mainly occurred in smaller patches with a large number, while the “appearance” and “disappearance” of lake shorelines occurred mostly in larger patches with a small number, and there were no occurrences in certain years. (3) The “expansion” evolutionary pattern was more dominant in the northeast and east-by-northeast regions, while the “shrinkage” evolutionary pattern varied across the southwest and west-by-southwest regions. In conclusion, the analysis of shoreline evolution’s temporal and spatial characteristics, based on spatiotemporal object increments, can quantitatively elucidate the lake wetland evolution process and offers a novel perspective for future research on lake wetlands.

Determining whether an 18/16O technology threshold exists in estimating lake primary productivity and associated metabolism

To determine lake carbon (C) cycling storage, transport, and transformation functions, it is necessary to understand metabolic lacustrine changes and associated drivers, all of which are premised on accurate metabolic estimates. The continuous improvement in conventional metabolic lake methods and the emergence of novel relevant methods in recent years have led to confusion as to their usage. Although 18/16O technology has recently been more widely used, its applicability remains controversial among researchers. The Qinghai–Tibet Plateau (QTP) is an exceptional location for such experimental studies, primarily due to its many different natural lake types. This study used 18/16O technology to investigate 19 lakes of different salinity (0.0 ∼ 196.0 ‰), area (5.0 ∼ 4345.9 km2), and altitude grades (2549.9 ∼ 4541.8 m) on the QTP to determine their specific metabolism. Results show that 18/16O technology is not suitable for use for high saline and low dissolved oxygen lakes (i.e., a salinity level greater than 35‰ and a dissolved oxygen level < 4 mg L-1). Under low dissolved oxygen conditions caused by high salinity, alterations in isotopic fractionation will cause 18/16O technology to fail. These findings can be used as a reference for estimating lake metabolism as well as researching nutrient cycling while also promoting metabolic estimation method improvements.

Assessment of Landsat-8 and Sentinel-2 Water Indices: A Case Study in the Southwest of the Buenos Aires Province (Argentina).

The accuracy assessment of three different Normalized Difference Water indices (NDWIs) was performed in La Salada, a typical lake in the Pampean region. Data were gathered during April 2019, a period in which floods occurred in a large area in the Southwest of the Buenos Aires Province (Argentina). The accuracy of the estimations using spaceborne medium-resolution multi-spectral imaging and the reliability of three NDWIs to highlight shallow water features in satellite images were evaluated using a high-resolution airbone imagery as ground truth. We show that these indices computed using Landsat-8 and Sentinel-2 imagery are only loosely correlated to the actual flooded area in shallow waters. Indeed, NDWI values vary significantly depending on the satellite mission used and the type of index computed.

Algae distribution according to locality in saline coastal water bodies of the Pryazov National Nature Park (Ukraine)

The study focuses on the distribution of algae in the saline coastal water bodies of the Pryazov National Nature Park according to their habitat preferences. The research revealed that aquatic species form the basis of algal flora. Among them, diatoms are predominant in terms of species richness and frequency of occurrence. In the lake type estuaries and ephemeral water bodies, an increased number of amphibious species was found, which can be attributed to the varying inundation conditions of these water bodies. Among the amphibious species Cyanoprokaryota representatives dominate. Hierarchical clustering of the water bodies demonstrated a connection between the species composition of algae and the type of water bodies, which is consistent with their genesis and transformation.

An Integrated Framework for Remote Sensing Assessment of the Trophic State of Large Lakes

The trophic state is an important factor reflecting the health state of lake ecosystems. To accurately assess the trophic state of large lakes, an integrated framework was developed by combining remote sensing data, field monitoring data, machine learning algorithms, and optimization algorithms. First, key meteorological and environmental factors from in situ monitoring were combined with remotely sensed reflectance data and statistical analysis was used to determine the main factors influencing the trophic state. Second, a trophic state index (TSI) inversion model was constructed using a machine learning algorithm, and this was then optimized using the sparrow search algorithm (SSA) based on a backpropagation neural network (BP-NN) to establish an SSA-BP-NN model. Third, a typical lake in China (Hongze Lake) was chosen as the case study. The application results show that, when the key environmental factors (pH, temperature, average wind speed, and sediment content) and the band combination data from Sentinel-2/MSI were used as input variables, the performance of the model was improved (R2 = 0.936, RMSE = 1.133, MAPE = 1.660%, MAD = 0.604). Compared with the performance prior to optimization (R2 = 0.834, RMSE = 1.790, MAPE = 2.679%, MAD = 1.030), the accuracy of the model was improved by 12.2%. It is worth noting that this framework could accurately identify water bodies in different trophic states. Finally, based on this framework, we mapped the spatial distribution of TSI in Hongze Lake in different seasons from 2019 to 2020 and analyzed its variation characteristics. The framework can combine regional special feature factors influenced by a complex environment with S-2/MSI data to achieve an assessment accuracy of over 90% for TSI in sensitive waters and has strong applicability and robustness.

Lake Ice Simulation and Evaluation for a Typical Lake on the Tibetan Plateau

This study aims to simulate the lake ice conditions in the Nam Co lake using a lake ice model, which is a one-dimensional physics-based model that utilizes enthalpy as the predictor variable. We modified the air density schemes within the model to improve the accuracy of the lake ice simulation. Additionally, the process of lake ice sublimation was included, and the effect of lake water salinity on the freezing point was considered. Using the improved lake ice model, we simulated lake surface water temperature, lake ice thickness, and interannual variations in lake ice phenology, and we compared these results with observations at Nam Co. The results demonstrate that the improved model better reproduces the lake surface water temperature, lake ice thickness, and lake ice phenology at Nam Co. Additionally, the thin air density affects lake processes by weakening sensible heat and latent heat, which ultimately leads to a delayed ice-on date and a slightly earlier ice-free date in Nam Co. This study contributes to an enhanced understanding of the freeze–thaw processes in Nam Co and reduces the biases in lake ice simulation on the Tibetan Plateau through the lake model improvement.

The recovery of crustacean zooplankton from acidification depends on lake type.

Acidification has harmed freshwater ecosystems in Northern Europe since the early 1900s. Stricter regulations aimed at decreasing acidic emissions have improved surface-water chemistry since the late 1980s but the recovery of biotic communities has not been consistent. Generally, the recovery of flora and fauna has been documented only for a few lakes or regions and large-scale assessments of long-term dynamics of biotic communities due to improved water quality are still lacking. This study investigates a large biomonitoring dataset of pelagic and littoral crustacean zooplankton (Cladocera and Copepoda) from 142 acid-sensitive lakes in Norway spanning 24 years (1997-2020). The aims were to assess the changes in zooplankton communities through time, compare patterns of changes across lake types (defined based on calcium and humic content), and identify correlations between abiotic and biological variables. Our results indicate chemical and biological recovery after acidification, as shown by a general increase in pH, acid neutralizing capacity, changes in community composition and increases in the total number of species, number of acid-sensitive species and functional richness through time. However, the zooplankton responses differ across lake types. This indicates that the concentration of calcium (or alkalinity) and total organic carbon (or humic substances) are important factors for the recovery. Therefore, assessment methods and management tools should be adapted to the diverse lake types. Long-term monitoring of freshwater ecosystems is needed to fully comprehend the recovery dynamics of biotic communities from acidification.

Geochemical, sedimentological and microbial diversity in two thermokarst lakes of far Eastern Siberia

Thermokarst lakes are important conduits for organic carbon sequestration, soil organic matter (soil-OM) decomposition and release of atmospheric greenhouse gases in the Arctic. They can be classified as either floating-ice lakes, which sustain a zone of unfrozen sediment (talik) at the lakebed year-round, or as bedfast-ice lakes, which freeze all the way to the lakebed in winter. Another key characteristic of thermokarst lakes are their eroding shorelines, depending on the surrounding landscape, they can play a major role in supplying the lakebeds with sediment and OM. These differences in winter ice regime and eroding shorelines are key factors which determine the quantity and quality of OM in thermokarst lake sediments. We used an array of physical, geochemical, and microbiological tools to identify the differences in the environmental conditions, sedimentary characteristics, carbon stocks and microbial community compositions in the sediments of a bedfast-ice and a floating-ice lake in Far East Siberia with different eroding shorelines. Our data show strong differences across most of the measured parameters between the two lakes. For example, the floating-ice lake contains considerably lower amounts of sediment organic matter and dissolved organic carbon, both of which also appear to be more degraded in comparison to the bedfast-ice lake, based on their stable carbon isotope composition (δ13C). We also document clear differences in the microbial community composition, for both archaea and bacteria. We identified the lake water depth (bedfast-ice vs. floating-ice) and shoreline erosion to be the two most likely main drivers of the sedimentary, microbial and biogeochemical diversity in thermokarst lakes. With ongoing climate warming, it is likely that an increasing number of lakes will shift from a bedfast- to a floating-ice state, and that increasing levels of shoreline erosion will supply the lakes with sediments. Yet, still little is known about the physical, biogeochemical and microbial differences in the sediments of these lake types and how different eroding shorelines impact these lake systems.