Origins of carbon sustaining the growth of whitefish Coregonus lavaretus early larval stages in Lake Annecy: insights from fatty-acid biomarkers.

International audienceThe hypothesis that diatom carbon (C) produced during the spring peak supported spring zooplankton production and, ultimately, the growth of Coregonus lavaretus early larval stages from March to May 2006 in Lake Annecy, France, was tested using gut content analyses and fatty acid biomarkers. Gut content results showed that C. lavaretus larvae from stages 1 to 4 preferentially fed on copepods with Daphnia sp. only a minor proportion of larval diet. The levels of diatom-marker fatty acids (C16:1n-7 and C20:5n-3) were high in Daphnia sp., but lower in both copepods and C. lavaretus larvae from stages 0 to 4. These results indicated that the spring diatom biomass was actually grazed by Daphnia sp., but, contrary to what was expected, the spring bloom was not the only C source supporting copepods secondary production and, consequently, the growth of C. lavaretus early larval stages. In contrast, levels of terrestrial fatty acid marker (C24:0) were low in Daphnia sp. but high in copepods and C. lavaretus larvae, indicating a significant contribution of terrestrial carbon to copepods and, ultimately, to the growth of C. lavaretus early larval stages

Can we detect ecosystem critical transitions and signals of changing resilience from paleo-ecological records?

Nonlinear responses to changing external pressures are increasingly studied in real-world ecosystems. However, as many of the changes observed by ecologists extend beyond the monitoring record, the occurrence of critical transitions, where the system is pushed from one equilibrium state to another, remains difficult to detect. Paleo-ecological records thus represent a unique opportunity to expand our temporal perspective to consider regime shifts and critical transitions, and whether such events are the exception rather than the rule. Yet, sediment core records can be affected by their own biases, such as sediment mixing or compression, with unknown consequences for the statistics commonly used to assess regime shifts, resilience, or critical transitions. To address this shortcoming, we developed a protocol to simulate paleolimnological records undergoing regime shifts or critical transitions to alternate states and tested, using both simulated and real core records, how mixing and compression affected our ability to detect past abrupt shifts. The smoothing that is built into paleolimnological data sets apparently interfered with the signal of rolling window indicators, especially autocorrelation. We thus turned to time-varying autoregressions (online dynamic linear models, DLMs; and time-varying autoregressive state-space models, TVARSS) to evaluate the possibility of detecting regime shifts and critical transitions in simulated and real core records. For the real cores, we examined both varved (annually laminated sediments) and non-varved cores, as the former have limited mixing issues. Our results show that state-space models can be used to detect regime shifts and critical transitions in some paleolimnological data, especially when the signal-to-noise ratio is strong. However, if the records are noisy, the online DLM and TVARSS have limitations for detecting critical transitions in sediment records

Estimating stable isotope turnover rates of epidermal mucus and dorsal muscle for an omnivorous fish using a diet-switch experiment

© 2018, The Author(s). Stable isotope (SI) analysis studies rely on knowledge of isotopic turnover rates and trophic-step discrimination factors. Epidermal mucus (‘mucus’) potentially provides an alternative SI ‘tissue’ to dorsal muscle that can be collected non-invasively and non-destructively. Here, a diet-switch experiment using the omnivorous fish Cyprinus carpio and plant- and fish-based formulated feeds compared SI data between mucus and muscle, including their isotopic discrimination factors and turnover rates (as functions of time T and mass G, at isotopic half-life (50) and equilibrium (95)). Mucus isotope data differed significantly and predictively from muscle data. The fastest δ13C turnover rate was for mucus in fish on the plant-based diet (T50: 17 days, T95: 74 days; G50: 1.08(BM), G95: 1.40(BM)). Muscle turnover rates were longer for the same fish (T50: 44 days, T95: 190 days; G50: 1.13(BM), G95: 1.68(BM)). Longer half-lives resulted in both tissues from the fish-based diet. δ13C discrimination factors varied by diet and tissue (plant-based: 3.11–3.28‰; fishmeal: 1.28–2.13‰). Mucus SI data did not differ between live and frozen fish. These results suggest that mucus SI half-lives provide comparable data to muscle, and can be used as a non-destructive alternative tissue in fish-based SI studies

Physical and chemical impacts of a major storm on a temperate lake: a taste of things to come?

Extreme weather can have a substantial influence on lakes and is expected to become more frequent with climate change. We explored the influence of one particular extreme event, Storm Ophelia, on the physical and chemical environment of England's largest lake, Windermere. We found that the substantial influence of Ophelia on meteorological conditions at Windermere, in particular wind speed, resulted in a 25-fold increase (relative to the study-period average) in the wind energy flux at the lake-air interface. Following Ophelia, there was a short-lived mixing event in which the Schmidt stability decreased by over 100 Jm-2 and the thermocline deepened by over 10 m during a 12-hour period. As a result of changes to the strength of stratification, Ophelia also changed the internal seiche regime of Windermere with the dominant seiche period increasing from ~17 h pre-storm to ~21 h post-storm. Following Ophelia, there was an upwelling of cold and low-oxygenated waters at the southern-end of the lake. This had a substantial influence on the main outflow of Windermere, the River Leven, where dissolved oxygen concentrations decreased by ~48 %, from 9.3 mg L-1 to 4.8 mg L-1, while at the mid-lake monitoring station in Windermere, it decreased by only ~3%. This study illustrates that the response of a lake to extreme weather can cause important effects downstream, the influence of which may not be evident at the lake surface. To understand the impact of future extreme events fully, the whole lake and downstream-river system need to be studied together

Are flood-driven turbidity currents hot spots for priming effect in lakes?

In deep stratified lakes, such as Lake Geneva, flood-driven turbidity currents are thought to contribute to the replenishment of deep oxygen by significant transport of river waters saturated with oxygen into the hypolimnion. The overarching aim of this study was to test this long-standing hypothesis directly. It combines direct observational data collected during an extreme flooding event that occurred in May 2015 with dark bioassays designed to evaluate the consequences of river-borne inputs for the hypolimnetic respiration. The exceptional precipitation events of May 2015 caused floods with an annual return time for the Rhône River, the dominant tributary of Lake Geneva, and with 50-year return time for the Dranse River, the second-most important tributary. Sediment-loaded river flows generated turbidity currents plunging into the lake hypolimnion. The observed river intrusions contributed to the redistribution of dissolved oxygen, with no net gain, when occurring in the lowermost hypolimnetic layer. In the uppermost hypolimnion above the last deep-mixing event, the intrusions coincided with a net oxygen deficit. Consistent with field observations, dark bioassays showed that 1 to 50 % substitution of riverine organic matter to deep (< 200 m) hypolimnetic water did not affect microbial respiration, while the addition of 1 to 10 % of riverine water to the uppermost hypolimnetic waters resulted in a respiration over-yielding, i.e. excess respiration of both river-borne and lacustrine organic matter. The results of our study conflict with the hypothesis that flood-driven turbidity currents necessarily increase hypolimnetic oxygen stocks in Lake Geneva. In contrast, results show that flood-driven turbidity currents can be potential hot spots for priming effect in lakes

Causal networks reveal the dominance of bottom-up interactions in large, deep lakes

International audienceEcological dynamics often exhibit significant temporal variability and sudden shifts that characterize their non-equilibrium and nonlinear nature, challenging our ability to understand and predict their trajectories. Among a set of ecological time series originating from the long-term monitoring of three large and deep lakes, nonlinear forecasting methods (Simplex projection and S-map) indicated that most of the time series exhibited hallmarks of complex dynamics in the form of nonlinear behaviors. Convergent Cross Mapping (CCM) was used to estimate the causal relationships among these time series by considering different time lags. The significant causal relationships were then used to construct causal networks from which nodes were characterized using PageRank and CheiRank. For the three lakes, the dominance of bottom-up control was revealed and was mostly indirect (i.e., nutrient-forcing zooplankton). This result likely evidences the transitivity of the causal relationships obtained by CCM as well as the mixed phytoplankton diet of zooplankton species limiting the identification of causal relationships among these two ecological components. Complementarily, the consistence of causal relationships for the different time lags may highlight a temporal transitivity by which the instantaneous causal signal was transmitted over time. The dual representation of both PageRank and CheiRank provided a straightforward classification of each node and enabled their thorough implications in the information flow within the causal networks. The complementary use of CCM and network metrics constituted an efficient way to delineate ecological causation using a high-resolution time series, for which linear methods performed poorly, and provided insights into the dynamic hierarchy of the different ecological variables in aquatic ecosystems

Accounting for surface waves improves gas flux estimation at high wind speed in a large lake

The gas transfer velocity (k) is a major source of uncertainty when assessing the magnitude of lake gas exchange with the atmosphere. For the diversity of existing empirical and process-based k models, the transfer velocity increases with the level of turbulence near the air-water interface. However, predictions for k can vary by a factor of 2 among different models. Near-surface turbulence results from the action of wind shear, surface waves and buoyancy-driven convection. Wind shear has long been identified as a key driver, while recent lake studies have shifted the focus towards the role of convection, particularly in small lakes. In large lakes, wind fetch can however be long enough to generate surface waves and contribute to enhance gas transfer, as widely recognised in oceanographic studies. Here, field values for gas transfer velocity were computed in a large hardwater lake, Lake Geneva, from CO2 fluxes measured with an automated (forced diffusion) flux chamber and CO2 partial pressure measured with high frequency sensors. k estimates were compared to a set of reference limnological and oceanic k models. Our analysis reveals that accounting for surface waves generated during windy events significantly improves the accuracy of k estimates in this large lake. The improved k model is then used to compute k over a one-year time-period. Results show that episodic extreme events with surface waves (6 % occurrence, significant wave height > 0.4 m) can generate more than 20 % of annual cumulative k and more than 25 % of annual net CO2 fluxes in Lake Geneva. We conclude that for lakes whose fetch can exceed 15 km, k-models need to integrate the effect of surface waves