Depth diversity gradients of macrophytes: shape, drivers and recent shifts

Investigating diversity gradients helps to understand biodiversity drivers and threats. However, one diversity gradient is seldomly assessed, namely how plant species distribute along the depth gradient of lakes. Here, we provide the first in-depth characterization of depth diversity gradients (DDG) of submerged macrophytes across different lakes. We characterize the DDG for additive richness components (alpha, beta, gamma), assess environmental drivers and address temporal change over recent years. We take advantage of yet the largest dataset of macrophyte occurrence along lake depth (274 depth transects across 28 deep lakes) as well as of physio-chemical measurements (12 deep lakes from 2006 to 2017 across Bavaria), provided publicly online by the Bavarian State Office for the Environment. We found a high variability in DDG shapes across the study lakes. The DDG for alpha and gamma richness are predominantly hump-shaped, while beta richness shows a decreasing DDG. Generalized additive mixed-effect models indicate that the maximum alpha richness within the depth transect (R) is significantly influenced by lake area only, whereas for the corresponding depth (D) are influenced by light quality, light quantity and layering depth. Most observed DDGs seem generally stable over recent years. However, for single lakes we found significant linear trends for Rmax and Dmax going into different directions. The observed hump-shaped DDGs agree with three competing hypotheses: the mid-domain effect, the mean-disturbance hypothesis, and the mean-productivity hypothesis. The DDG amplitude seems driven by lake area (thus following known species-area relationships), whereas skewness depended on physio-chemical factors, mainly water transparency and layering depth. Our results provide insights for conservation strategies and for mechanistic frameworks to disentangle competing explanatory hypotheses for the DDG.

Coupling particle tracking model and satellite data for trajectories prediction

<p>Various physical, chemical and biological processes take place three- dimensionally in deep lakes, regulated by complex boundary conditions. Propelled by the rapid development of equipment, technology and computational power, the understanding of deep lakes has steadily advanced. In particular hydrodynamic monitoring and simulation studies have benefitted from combining field observation, numerical simulation and other emerging techniques such as remote sensing. In contrast, water quality parameters are less well investigated by this combination of tools. In this study, we integrate remote sensing techniques with a Lagrangian particle tracking model for lake water quality simulations. Specifically, our goal was to establish a successive individual-based model for health-relevant microorganisms in Lake Geneva. To this end, we combined remote sensing images from the current Sentinel 2 and Sentinel 3 satellites and Delft3D hydrodynamic and particle tracking models. Total suspended matter (TSM), which can both be detected by satellites and simulated by numerical models, is chosen as a parameter of concern. Concentration of TSM in Lake Geneva deduced from remote sensing images is used as observation to compare with particle tracking simulation to support the validation of the numerical model. On the other hand, the model allows to bridge gaps in satellite observations due to cloud coverage. Point source releasing and lake-wide dynamic pattern of TSM are employed as scenario studies to indicate the validation of our particle tracking model, focusing on time spans between 1 to 10 days. Our findings demonstrate that remote sensing images can serve to calibrate and validate the particle tracking water quality model, and in return, the particle tracking model provides the possibilities for data inference and interpolation between satellite images. The flexibility of the Lagrangian particle tracking method poses more possibilities to incorporate flow independent movement, mortality and growth of micro-organisms. It is expected that a more universal and accurate tool for water quality simulation can be created which will facilitate decision making.</p>

The influence of water depth and forcing factors on the performances of Machine Learning approaches for the simulation of lake surface water temperature

<p>Some attempts to predict water temperature in lakes by means of machine learning (ML) approaches have been pursued in recent years, relying on the performances that ML showed in many different contexts. The existing literature is focused on specific applications, and does not provide a general framework. Therefore, we systematically tested the role of different forcing factors on the accuracy of the simulation of lake surface water temperature (LSWT), comparing ML results with those obtained for a synthetic case study by means of a physically-based one-dimensional model, GLM. Among the available supervised ML tools, we considered artificial neural network (ANN) with back propagation, one of the most common and successful methods.</p><p>In our modelling exercise, we found that the two most important factors influencing the ability of ML to predict LSWT in temperate climates are air temperature (AT) and the day of the year (DOY). All the other meteorological inputs provide only minor improvements if considered additionally to AT and DOY, while they cannot be used as single predictors. The analysis showed that an important role is played by lake depth because a larger volume per unit of surface area implies a larger heat capacity of the lake, which smooths the temporal evolution of LSWT.  Such a filtering behaviour of deep lakes is not reproduced by standard ML methods, and requires an ad hoc pre-processing of AT input, which needs to be averaged with a proper time window. Moreover, while shallow lakes tend to be relatively well-mixed also in summer, deeper lakes can develop a strong stratification that tends to isolate the surface layer, modifying the thermally reactive volume and thus affecting the temporal evolution of LSWT. These considerations suggest that the physical dynamics of lakes, and especially of deep lakes, needs to be carefully considered also when adopting “black-box” approaches such as ML.</p><p> </p>

Diversity and Structure of Pelagic Zooplankton (Crustacea, Rotifera) in NE Poland

This study presents the diversity and structure of pelagic zooplankton in north-eastern Poland. The research was conducted in 47 lakes with different trophic conditions in the middle of summer. Samples were collected close to the deepest part of the lakes to avoid the diverse benthic and littoral zones. We found 119 zooplankton species of which 32 were Cladocera, 16 were Cyclopoida, 4 were Calanoida, and 67 were Rotifera. We determined which species occurred most frequently in the region, as well as the species that were characteristic of different trophic conditions. We also recorded the presence of eight cold-adapted species which some of them are considered as glacial relicts (e.g., Eurytemora lacustris, Heterocope appendiculata, Cyclops lacustris). Our research revealed potential glacial refugia for planktonic species in 14 lakes of NE Poland. Our study suggests that the presence of stenotherm species may be an excellent indicator of the ecological status of deep lakes and could be considered in lake monitoring programs. Furthermore, we did not find Bythotrephes longimanus which has been reported from Poland. Instead, we found that B. brevimanus was the most common representative of the genus in the study area.

The water column of the Yamal tundra lakes as a microbial filter preventing methane emission

Microbiological, molecular ecological, biogeochemical, and isotope geochemical research was carried out in four lakes of the central part of the Yamal Peninsula in the area of continuous permafrost. Two of them were large (73.6 and 118.6 ha) and deep (up to 10.6 and 12.3 m) mature lakes embedded into all geomorphological levels of the peninsula, and two others were smaller (3.2 and 4.2 ha) shallow (up to 2.3 and 1.8 m) lakes which appeared as a result of thermokarst on constitutional (segregated) ground ice. We collected samples in August 2019. The Yamal tundra lakes exhibited high phytoplankton production (340–1200 mg C m−2 day−1) during the short summer season. Allochthonous and autochthonous, both particulate and dissolved organic matter was deposited to the bottom sediments, where methane production occurred due to anaerobic degradation (90–1000 µmol СН4 dm−3). The rates of hydrogenotrophic methanogenesis appeared to be higher in the sediments of deep lakes than in those of the shallow ones. In the sediments of all lakes, Methanoregula and Methanosaeta were predominant components of the archaeal methanogenic community. Methane oxidation (1.4–9.9 µmol dm−3 day−1) occurred in the upper sediment layers simultaneously with methanogenesis. Methylobacter tundripaludum (family Methylococcaceae) predominated in the methanotrophic community of the sediments and the water column. The activity of methanotrophic bacteria in deep mature lakes resulted in a decrease of the dissolved methane concentration in lake water from 0.8–4.1 µmol CH4 L−1 to 0.4 µmol CH4 L−1, while in shallow thermokarst lakes the geochemical effect of methanotrophs was much less pronounced. Thus, only small shallow Yamal lakes may contribute significantly to the overall diffusive methane emissions from the water surface during the warm summer season. The water column of large deep lakes on Yamal acts, however, as a microbial filter preventing methane emission into the atmosphere.