Peter D. F. Isles

Characterization of Organic Phosphorus Form and Bioavailability in Lake Sediments using P Nuclear Magnetic Resonance and Enzymatic Hydrolysis.

Lake sediments are known to be a significant source of phosphorus (P) to plankton populations under certain biogeochemical conditions; however, the contribution of sediment organic P (P) to internal P loads remains poorly understood. We investigated P speciation and bioavailability in sediments collected over multiple months from a shallow, eutrophic bay in Lake Champlain (Missisquoi Bay, VT) using solution P nuclear magnetic resonance (NMR) spectroscopy and enzymatic hydrolysis (EH) analysis of sediments collected during years with (2008) and without (2007) algal blooms. Sediments collected during bloom onset (July) and peak bloom (August) months contained the largest proportion of enzyme-labile P, whereas pre- and postbloom sediments were primarily composed of nonlabile P. Monoester P to diester P ratios changed with respect to depth, particularly during bloom periods. Monoester P and DNA accumulation, likely from settling particulate matter, began at the onset of the bloom and continued into October 2008 during the postbloom period. The disappearance of inositol hexakisphosphate stereoisomers and the generation of orthophosphate at lower sediment depths was also evident in August 2008. Principal components analysis of EH and NMR species proportions confirmed differences between sediment cores collected during bloom onset and peak bloom, compared with pre- and postbloom sediments. Large enzyme-labile and P species proportions corresponded to increased sediment P flux and reduced manganese and iron species in porewater. These findings suggest that interseasonal changes in P speciation may influence P mobility in sediments and contribute to important feedback dynamics between biological productivity and sediment water interface geochemistry.

The mobility of phosphorus, iron, and manganese through the sediment–water continuum of a shallow eutrophic freshwater lake under stratified and mixed water-column conditions

The management of external nutrient inputs to eutrophic systems can be confounded due to a persistent pool of phosphorus (P) in lake sediments. The behaviors of P and trace metals depend largely on the reductive dissolution of amorphous iron (Fe) and manganese (Mn) (oxy)hydroxides in sediments; however, a holistic understanding of these dynamics in relation to the broader ecological and hydrodynamic conditions of the system remains elusive. We used a high-frequency monitoring approach to develop a comprehensive conceptual model of P, Mn, and Fe dynamics across the sediment water continuum of a shallow bay in Lake Champlain (Missisquoi Bay, USA). The greatest release of sediment P, Mn, and Fe occurred under stable hydrodynamic conditions, particularly during the onset of the cyanobacterial bloom and was associated with low available P and the accumulation of soluble Mn and Fe above the sediment–water interface (SWI). During the warmest part of the season, bloom severity and sediment P release was partially regulated by hydrodynamic drivers, which changed on hourly time scales to affect redox conditions at the SWI and bottom water concentrations of soluble P, Mn, and Fe. A geochemically distinct increase in soluble P and Fe concentrations, but not Mn, marked the influence of riverine inputs during a late season storm disturbance. Despite continued depletion of the reactive sediment P and metals pool into the bloom period, declining temperatures and a well-mixed water column resulted in bloom senescence and the return of P, Mn, and Fe to surface sediments. The closed cycling of P and metals in Missisquoi Bay poses a significant challenge for the long-term removal of P from this system. Multiple time-scale measures of physical and biogeochemical changes provide a basis for understanding P and trace metals behavior across sediments and the water column, which shape seasonally variable cyanobacterial blooms in shallow eutrophic systems.

Coupled impacts of climate and land use change across a river–lake continuum: insights from an integrated assessment model of Lake Champlain’s Missisquoi Basin, 2000–2040

Global climate change (GCC) is projected to bring higher-intensity precipitation and highervariability temperature regimes to theNortheasternUnited States. The interactive effects of GCCwith anthropogenic land use and land cover changes (LULCCs) are unknown for watershed level hydrological dynamics and nutrientfluxes to freshwater lakes. Increased nutrient fluxes can promote harmful algal blooms, also exacerbated bywarmerwater temperatures due toGCC. To address the complex interactions of climate, land and humans, we developed a cascading integrated assessment model to test the impacts of GCC and LULCCon the hydrological regime, water temperature, water quality, bloomduration and severity through 2040 in transnational Lake Champlain’sMissisquoi Bay. Temperature and precipitation inputs were statistically downscaled from four global circulation models (GCMs) for three Representative Concentration Pathways. An agent-basedmodel was used to generate four LULCC scenarios. Combined climate and LULCC scenarios drove a distributed hydrologicalmodel to estimate river discharge and nutrient input to the lake. Lake nutrient dynamics were simulatedwith a 3Dhydrodynamic-biogeochemicalmodel.We find acceleratedGCC could drastically limit landmanagement options tomaintainwater quality, but the nature and severity of this impact varies dramatically byGCMandGCC scenario.

The potential of high-frequency profiling to assess vertical and seasonal patterns of phytoplankton dynamics in lakes: An extension of the Plankton Ecology Group (PEG) model

Abstract The use of high-frequency sensors on profiling buoys to investigate physical, chemical, and biological processes in lakes is increasing rapidly. Profiling buoys with automated winches and sensors that collect high-frequency chlorophyll fluorescence (ChlF) profiles in 11 lakes in the Global Lake Ecological Observatory Network (GLEON) allowed the study of the vertical and temporal distribution of ChlF, including the formation of subsurface chlorophyll maxima (SSCM). The effectiveness of 3 methods for sampling phytoplankton distributions in lakes, including (1) manual profiles, (2) single-depth buoys, and (3) profiling buoys were assessed. High frequency ChlF surface data and profiles were compared to predictions from the Plankton Ecology Group (PEG) model. The depth-integrated ChlF dynamics measured by the profiling buoy data revealed a greater complexity that neither conventional sampling nor the generalized PEG model captured. Conventional sampling techniques would have missed the SSCM in 7 of 11 study lakes. Although surface-only ChlF data underestimated average water column ChlF, at times by nearly 2-fold in 4 of the lakes, overall there was a remarkable similarity between surface and mean water column data. Contrary to the PEG models proposed negligible role for physical control of phytoplankton during the growing season, thermal structure and light availability were closely associated with ChlF seasonal depth distribution. Thus, an extension of the PEG model is proposed, with a new conceptual framework that explicitly includes physical metrics to better predict SSCM formation in lakes and highlight when profiling buoys are especially informative.

Modeling the drivers of interannual variability in cyanobacterial bloom severity using self-organizing maps and high-frequency data

Abstract It is well established that cyanobacteria populations in shallow lakes exhibit dramatic fluctuations on both interannual and intraannual timescales; however, despite extensive research, disentangling the drivers of interannual variability in bloom severity has proved challenging. Critical thresholds of abiotic drivers such as wind, irradiance, air temperature, and tributary inputs may control the development and collapse of blooms, but these thresholds are difficult to identify in large and complex datasets. In this study, we compared high-frequency estimates of oxygen metabolism in a shallow bay of Lake Champlain to concurrent measurements of physical and chemical parameters over 3 years with very different bloom dynamics. We clustered the data using supervised and unsupervised self-organizing maps to identify the environmental drivers associated with key stages of bloom development. We then used threshold analysis to identify subtle yet important thresholds of thermal stratification that drive transitions between bloom growth and decline. We found that extended periods with near-surface temperature differentials above 0.20 °C were associated with the initial development of bloom conditions, and subsequent frequency and timing of wind mixing events had a strong influence on interannual variability in bloom severity. The methods developed here can be widely applied to other high frequency lake monitoring datasets to identify critical thresholds controlling bloom development.