Linking shifts in bacterial community with changes in dissolved organic matter pool in a tropical lake

Bacterioplankton communities have a pivotal role in the global carbon cycle. Still the interaction between microbial community and dissolved organic matter (DOM) in freshwater ecosystems remains poorly understood. Here, we report results from a 12-day mesocosm study performed in the epilimnion of a tropical lake, in which inorganic nutrients and allochthonous DOM were supplemented under full light and shading. Although the production of autochthonous DOM triggered by nutrient addition was the dominant driver of changes in bacterial community structure, temporal covariations between DOM optical proxies and bacterial community structure revealed a strong influence of community shifts on DOM fate. Community shifts were coupled to a successional stepwise alteration of the DOM pool, with different fractions being selectively consumed by specific taxa Typical freshwater clades as Limnohabitans and Sporichthyaceae were associated with consumption of low molecular weight carbon, whereas Gammaproteobacteria and Flavobacteria utilized higher molecular weight carbon, indicating differences in DOM preference among Glades. Importantly. Verrucomicrobiaceae were important in the turnover of freshly produced autochthonous DOM, ultimately affecting light availability and dissolved organic carbon concentrations. Our findings suggest that taxonomically defined bacterial assemblages play definite roles when influencing DOM fate, either by changing specific fractions of the DOM pool or by regulating light availability and DOC levels. (C) 2019 Elsevier B.V. All rights reserved.Peer reviewe

Using near-term forecasts and uncertainty partitioning to improve predictions of low-frequency cyanobacterial events

Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water and air quality. Importantly, ecological forecasts can identify where uncertainty enters the forecasting system, which is necessary to refine and improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance (uncertainty) introduced by different sources, including specification of the model structure, errors in driver data, and estimation of initial state conditions. Uncertainty partitioning could be particularly useful in improving forecasts of high-density cyanobacterial events, which are difficult to predict and present a persistent challenge for lake managers. Cyanobacteria can produce toxic or unsightly surface scums and advance warning of these events could help managers mitigate water quality issues. Here, we calibrate fourteen Bayesian state-space models to evaluate different hypotheses about cyanobacterial growth using data from eight summers of weekly cyanobacteria density samples in an oligotrophic (low nutrient) lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We identify dominant sources of uncertainty for near-term (one-week to four-week) forecasts of G. echinulata densities over two years. Water temperature was an important predictor in calibration and at the four-week forecast horizon. However, no environmental covariates improved over a simple autoregressive (AR) model at the one-week horizon. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and often did not capture rare peak density occurrences, indicating that significant explanatory variables in calibration are not always effective for near-term forecasting of low-frequency events. Uncertainty partitioning revealed that model process specification and initial conditions uncertainty dominated forecasts at both time horizons. These findings suggest that observed densities result from both growth and movement of G. echinulata, and that imperfect observations as well as spatial misalignment of environmental data and cyanobacteria observations affect forecast skill. Future research efforts should prioritize long-term studies to refine process understanding and increased sampling frequency and replication to better define initial conditions. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.Accepted manuscrip

Wildfire smoke impacts lake ecosystems.

Publication status: PublishedFunder: U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, and Water Power Technologies Office at Oak Ridge National Laboratory (ORNL); doi: http://dx.doi.org/10.13039/100000015Wildfire activity is increasing globally. The resulting smoke plumes can travel hundreds to thousands of kilometers, reflecting or scattering sunlight and depositing particles within ecosystems. Several key physical, chemical, and biological processes in lakes are controlled by factors affected by smoke. The spatial and temporal scales of lake exposure to smoke are extensive and under-recognized. We introduce the concept of the lake smoke-day, or the number of days any given lake is exposed to smoke in any given fire season, and quantify the total lake smoke-day exposure in North America from 2019 to 2021. Because smoke can be transported at continental to intercontinental scales, even regions that may not typically experience direct burning of landscapes by wildfire are at risk of smoke exposure. We found that 99.3% of North America was covered by smoke, affecting a total of 1,333,687 lakes ≥10 ha. An incredible 98.9% of lakes experienced at least 10 smoke-days a year, with 89.6% of lakes receiving over 30 lake smoke-days, and lakes in some regions experiencing up to 4 months of cumulative smoke-days. Herein we review the mechanisms through which smoke and ash can affect lakes by altering the amount and spectral composition of incoming solar radiation and depositing carbon, nutrients, or toxic compounds that could alter chemical conditions and impact biota. We develop a conceptual framework that synthesizes known and theoretical impacts of smoke on lakes to guide future research. Finally, we identify emerging research priorities that can help us better understand how lakes will be affected by smoke as wildfire activity increases due to climate change and other anthropogenic activities