Trends in Lake Erie phytoplankton biomass and community structure during a 20-year period of rapid environmental change

Since the 1990s, Lake Erie has experienced resurgent eutrophication due in part to climate change-driven increases in precipitation, which have combined with increasingly intensive agricultural practices in the region to produce excessive nutrient runoff into the lake. Harmful blooms of the cyanobacterium Microcystis aeruginosa (“Microcystis”) in Lake Erie’s western and central basins (WB and CB, respectively) have been a highly visible consequence of this eutrophication, however few studies have characterized intra- or interannual trends in less abundant, though likely more edible, phytoplankton taxa over the last 25 years. Here, we used the 20-year Lake Erie Plankton Abundance Study (LEPAS) dataset to quantify intra- and interannual trends in the dynamics of six major phytoplankton groups in the WB and CB during 1995–2015. Cyanobacteria biomass in the WB increased >1000-fold during this period, while biomass of all other major taxa groups increased between 10- and 100-fold. Early summer (June–July) and spring (May) communities saw more modest directional change in the biomass of both edible and less-edible taxa as well as community structure. Around 2008, the CB also began to experience Microcystis blooms concurrent with those in the WB, with similar, though less dramatic consequences for phytoplankton community structure and edible biomass. The biomass of several phytoplankton groups exhibited intra-annual oscillations with a ∼5-year period. The mechanisms underlying changes in the phytoplankton community structure and their consequences for higher trophic levels are not well understood, however increases in edible phytoplankton may be sustaining long-term upward trends in many zooplankton taxa.     

Assessing phytoplankton structure and spatio-temporal dynamics in a freshwater ecosystem using a powerful multiway statistical analysis

Phytoplankton dynamics and diversity are particularly difficult to analyze, especially when (i) the scale of the analysis is situated at the species level, (ii) such a diversity is high, (iii) the study covers several seasons, and (iv) sampling has been performed at many stations of the ecosystem. Fortunately, some powerful statistical methods have been developed with which each species identified can be considered in detailed spatio-temporal analyses. The Partial Triadic Analysis, a method issued from the STATIS family, was applied on a dataset corresponding to 6 stations of the largest French reservoir (Reservoir Marne) sampled 22 times over two years (2006-2007) between March and September. Three key sampling periods that were consistent with those exhibited with the Plankton Ecology Group model (i.e. early spring, late spring-early summer, late summer-early autumn) were unambiguously recognized, with some specific species associated with each of them. Furthermore, a potential reference sampling station was identified among all stations investigated, an information very relevant to both scientists and water managers. It remains that 3 other stations could also be monitored, regularly or from time to time, because of specific phytoplankton characteristics.     

Identification and simulation of a spatial ecological model in a lake with fractal boundary

We propose a 2D ecological model of phytoplankton dynamics accounting for the distribution and the evolution of algae in a large basin located in the Amazonian region. The model is described by a set of reaction-drift-diffusion equations and is driven by several exogenous inputs, such as wind velocity and direction, water temperature and solar radiation. Due to the roughness of the domain, a preliminary boundary extraction with a curvelet algorithm is performed. Then, the model is simulated in an approximated domain, where the contour has been reconstructed by estimating a set of Recurrent Fractal Interpolation Functions, aimed at preserving its fractal structure. Simulations are combined with time and space chlorophyll-a data in order to estimate the parameters of the model. The proposed algorithm is based on an iterative two-step identification procedure, where reaction parameters are recovered first and then used for estimating diffusion and transport parameters. Comparison results at different accuracy approximations and before and after the algorithm implementation are presented and discussed.     

Proposals for inhibiting abundant phytoplankton growth at the head of a river reservoir

In a river reservoir, the Ishitegawa Dam Reservoir in Japan, sites of abundant phytoplankton growth were investigated in connection with the water movement in the reservoir from January 1982 to January 1984 by comparing the distributions of chlorophyll a, water temperature, and coliform bacteria (Project A). The results distinguished three types of phytoplankton growth, of which one type, the abundant surface growth at the head of the reservoir, was most frequently observed. This case was considered to be caused by mixing of the surface lake water with the inflowing nutrient-rich river water at the shallow lake-head. Subsequently the effects of dissolved calcium and magnesium on the chlorophyll a and phytoplanktonic particulate phosphorus concentrations at the head of the reservoir were investigated until December 1987 (Project B). A trophic index, named the Ca-Mg index, was found which correlated positively with the chlorophyll a and phytoplanktonic particulate phosphorus concentrations in a logarithmic scale and varied in relation to the changes in the same index of the inflowing river water. Based on the ecological results of these two projects, two proposals are presented for inhibiting abundant phytoplankton growth at the head of a river reservoir. Proposal A: make the head deep and, in addition, create a strong near-bottom underflow of the inflowing river water. Proposal B: make a bypass in order to cut off the inflow of river water in the case of a high Ca-Mg index.     

U.S. EPA Great Lakes National Program Office monitoring of the Laurentian Great Lakes: Insights from 40 years of data collection

The U.S. EPA Great Lakes National Program Office (GLNPO) implements long-term monitoring programs to assess Great Lakes ecosystem status and trends for many interrelated ecosystem components, including offshore water quality as well as offshore phytoplankton, zooplankton and benthos; chemical contaminants in air, sediments, and predator fish; hypoxia in Lake Erie's central basin; and coastal wetland health. These programs are conducted in fulfillment of Clean Water Act mandates and Great Lakes Water Quality Agreement commitments. This special issue presents findings from GLNPO's Great Lakes Biology Monitoring Program, Great Lakes Water Quality Monitoring Program, Lake Erie Dissolved Oxygen Monitoring Program, Integrated Atmospheric Deposition Network, Great Lakes Fish Monitoring and Surveillance Program, and Great Lakes Sediment Surveillance Program. These GLNPO programs have generated temporal and spatial datasets for all five Great Lakes that form the basis for assessment of the state of these lakes, including trends in nutrients, key biological indicators, and contaminants in air, sediments and fish. These datasets are used by researchers and managers across the Great Lakes basin for investigating physical, chemical and biological drivers of ongoing ecosystem changes; some of these analyses are presented in this special issue, along with discussion of new methods and approaches for monitoring.     

Spatio-temporal variation of microcystins and its relationship to biotic and abiotic factors in Hongze Lake,China

An investigation of three particle-associated microcystin (MC) congeners (MC-LR, MC-RR, and MC-YR) was performed in 2013 from August to November in Hongze Lake, China. All MCs congeners exhibited significant spatial and temporal variability. Particle-associated MCs were commonly found in this lake, but MC-LR was present at low levels (0.075–1.252 μg/L). All three MC congeners were found to be significantly correlated with non-toxic phytoplankton (e.g., chlorophyte, cryptophyte and diatoms), while only MC-YR and total MC were correlated with cyanobacteria biomass. Copepods, rotifers and three genera of Cladocera were positively correlated with MC concentrations, but feeding habits of these zooplankton species might have different effects on different MC congeners and total MC distributions. Linear mixed effects (LME) model results showed that the interactions between cyanobacteria and other non-toxic phytoplankton or zooplankton species may drive MCs dynamics. Physiochemical parameters also may affect MC congener variability individually or through their effects on toxic cyanobacteria. The results of this study suggested that MCs distribution in Hongze Lake was site-specific and could be influenced by biotic and abiotic factors both individually and through their interactions.     

藻类和悬浮物的光谱识别研究

针对藻类和悬浮物浓度测量仪,利用纯种藻和混合藻种的光谱数据,通过分析研究,提出了辨识的方法和途径。     

Distribution of Diatoms in Lake Superior Sediments

Four maps showing semiquantitative abundance patterns of diatoms throughout Lake Superior were made by examination of 170 cores at sediment depths of 0–1, 10, 20, and 50 cm. These maps show a decrease in diatom abundance with sediment depth and an absence of diatoms in most open-lake sediments. Diatoms in surficial sediments are most abundant in Jive regions in Lake Superior: Thunder Bay, Keweenaw Bay, the Thunder Bay and northshore troughs, and a region to the southwest of Isle Royale. Only three of these five regions still show relatively abundant diatoms at the 50-cm sediment depth. Diatom abundance in the sediments is positively correlated with sedimentation rates, water depth, and proximity to shore. Nearshore deep-water troughs act as depositional traps for diatoms in western Lake Superior and Keweenaw Bay. Later transport of diatoms by waves and currents generally erases any relationship between diatom abundance in the sediments and diatom biomass in the overlying water. Diatom abundances at 10- and 20-cm depths correlate positively with sedimentation rates, suggesting that biogenous and terrigenous components are eroded, transported, and redeposited as a mixture, rather than being hydraulically separated. Diatoms are abundant at 0–1 cm in regions of both low and high sedimentation rates.     

Research on Great Lakes Algal Communities: Problems from the Past,Lessons for the Future

Review of research on algal communities in the Laurentian Great Lakes shows significant progress in delineating this component of the biota and understanding the factors which regulate its composition and distribution. Unaddressed or poorly addressed problems remain in certain fundamental research areas and future progress will depend largely on how quickly and thoroughly these problems are reduced. It is clear that there is no useful distinction between basic and applied research in this area and best progress will be achieved through a balanced approach.     

A three component classification of phytoplankton absorption spectra: Application to ocean-color data

A method is presented to identify absorption characteristics of three optically-distinct phytoplankton classes from a suite of measurements of total phytoplankton absorption coefficient and chlorophyll-a concentration by successive application of the two-population absorption model of Sathyendranath et al. (2001) and Devred et al. (2006a). The total phytoplankton absorption coefficient at multiple wavelengths is expressed as the weighted sum of the absorption coefficients of each class at those wavelengths. The resultant system of equations is solved under some constraints to derive the fraction of each class present in any given sample of seawater, given the spectrum of total phytoplankton absorption coefficient. When applied to a large database, the results compare well with phytoplankton size-classes derived from pigment composition, so that we can assume that the three phytoplankton classes derived from absorption coefficients are representative of the pico-, nano- and microphytoplankton size classes. A modification is proposed to the pigment-based phytoplankton size classification of Uitz et al. (2006) to account for the effect of fucoxanthin associated with nanophytoplankton. Comparison between satellite and in situ data demonstrates the potential of satellite ocean-color data to yield the distribution of phytoplankton size classes from space. The algorithm is applied to phytoplankton absorption coefficients derived from remotely-sensed reflectance values collected by SeaWiFS over the Northwest Atlantic in 2007. Monthly composites for April, August and November, representative of Spring, Summer and Fall, give synoptic views of the phytoplankton community structure: a Spring bloom dominated by microphytoplankton is followed by a second, less intense, bloom in the Fall dominated by nanophytoplankton. Picophytoplankton are dominant in the study area in Summer.