E.G. Bellinger

Freshwater algae : identification and use as bioindicators

Preface. Copyright Acknowledgements. 1 Introduction to Freshwater Algae. 1.1 General introduction. 1.1.1 Algae - An overview. 1.1.2 Algae as primary producers. 1.1.3 Freshwater environments. 1.1.4 Planktonic and benthic algae. 1.1.5 Size and shape. 1.2 Taxonomic variation - the major groups of algae. 1.2.1 Microscopical appearance. 1.2.2 Biochemistry and cell structure. 1.2.3 Molecular characteristics and identification. 1.3 Blue-green algae. 1.3.1 Cytology. 1.3.2 Morphological and taxonomic diversity. 1.3.3 Ecology. 1.3.4 Blue-green algae as bio-indicators. 1.4 Green algae. 1.4.1 Cytology. 1.4.2 Morphological diversity. 1.4.3 Ecology. 1.4.4 Green algae as bioindicators. 1.5 Euglenoids. 1.5.1 Cytology. 1.5.2 Morphological diversity. 1.5.3 Ecology. 1.5.4 Euglenoids as bioindicators. 1.6 Yellow-green algae. 1.6.1 Cytology. 1.6.2 Morphological diversity. 1.6.3 Ecology. 1.6.4 Yellow-green algae as bioindicators. 1.7 Dinoflagellates. 1.7.1 Cytology. 1.7.2 Morphological diversity. 1.7.3 Ecology. 1.8 Cryptomonads. 1.8.1 Cytology. 1.8.2 Comparison with euglenoid algae. 1.8.3 Biodiversity. 1.8.4 Ecology. 1.8.5 Cryptomonads as bioindicators. 1.9 Chrysophytes. 1.9.1 Cytology. 1.9.2 Morphological diversity. 1.9.3 Ecology. 1.9.4 Chrysophytes as bioindicators. 1.10 Diatoms. 1.10.1 Cytology. 1.10.2 Morphological diversity. 1.10.3 Ecology. 1.10.4 Diatoms as bioindicators. 1.11 Red algae. 1.12 Brown algae. 2 Sampling, Biomass Estimation and Counts of FreshwaterAlgae. A Planktonic Algae. 2.1 Protocol for collection. 2.1.1 Standing water phytoplankton. 2.1.1 River phytoplankton. 2.2 Mode of collection. 2.2.1 Phytoplankton trawl net. 2.2.2 Volume samplers. 2.2.3 Integrated sampling. 2.2.4. Sediment traps. 2.3 Phytoplankton biomass. 2.3.1 Turbidity. 2.3.2 Dry weight and ash-free dry weight. 2.3.3 Pigment concentrations. 2.4 Flow cytometry: automated analysis of phytoplanktonpopulations. 2.5 Microscope counts of species populations. 2.5.1 Sample preservation and processing. 2.5.2 Species counts. 2.5.3 Conversion of species counts to biovolumes. 2.5.4 Chemical cleaning of diatoms. 2.6 Diversity within single-species populations. 2.6.1 Molecular analysis. 2.6.2 Analytical microscopical techniques. B Non-Planktonic Algae. 2.7 Deep water benthic algae. 2.7.1 Benthic-pelagic coupling. 2.7.2 Benthic algae and sediment stability. 2.7.3 Invertebrate grazing of benthic algae. 2.8 Shallow water communities. 2.8.1 Substrate. 2.8.2 Algal communities. 2.9 Algal biofilms. 2.9.1 Mucilaginous biofilms. 2.9.2 Biomass. 2.9.3 Taxonomic composition. 2.9.4 Matrix structure. 2.10 Periphyton - algal mats. 2.10.1 Inorganic substratum. 2.10.2 Plant surfaces. 3 Algae as bioindicators. 3.1 Bioindicators and water quality. 3.1.1 Biomarkers and bioindicators. 3.1.2 Characteristics of bioindicators. 3.1.3 Biological monitoring versus chemical measurements. 3.1.4 Monitoring water quality: objectives. 3.2 Lakes. 3.2.1 Contemporary planktonic and attached algae asbioindicators. 3.2.2 Fossil algae as bioindicators: lake sediment analysis. 3.2.3 Water quality parameters: inorganic and organic nutrients,acidity and heavy metals. 3.3 Wetlands. 3.4 Rivers. 3.4.1 The periphyton community. 3.4.2 River diatoms. 3.4.3 Evaluation of the diatom community. 3.4.4 Human impacts and diatom indices. 3.4.5 Calculation of diatom indices. 3.4.6 Practical applications of diatom indices. 3.5 Estuaries. 3.5.1 Ecosystem complexity. 3.5.2 Algae as estuarine bioindicators. 4 A Key to the More Frequently Occurring FreshwaterAlgae. 4.1 Introduction to the key. 4.1.1 Using the key. 4.1.2 Morphological groupings. 4.2 Key to the main genera and species. 4.3 List of algae included and their occurrence in the key. 4.4 Algal identification: bibliography. Glossary. References. Index.

Patterns of abundance and dominance of the phytoplankton of Rostherne Mere, England: Evidence from an 18-year data set

Rostherne Mere is a small but relatively deep, turbid and highly eutrophic lake. In recent years, the productivity and dynamics of its phytoplankton have been particularly sensitive to the interaction between light income and thermal stability. Analysis reveals that interannual differences in the size and duration of phytoplankton crops, as well as in the predominating species, are not random but that there is a reproducible coherence with weather-generated external forcing. Severe light-limitation restricts phytoplankton development outside the stratified period, though delayed stratification in spring may promote relatively large diatom crops. In summer,Microcystis will usually dominate provided its recruitment period coincides with high water clarity. OtherwiseCeratium generally dominates. Examples of extremely stable stratification leading toScenedesmus dominance and of persistent episodes of summer mixing favouringOscillatoria dominance are found to agree well with previous matrix models.

The levels of metals in dock-yard sediments with particular reference to the contributions from ship-bottom paints

Abstract Levels of the metals copper, lead, tin and zinc have been determined in a range of shipbottom paints by atomic absorption spectrophotometry. Copper, lead and zinc have been similarly determined in concentrated nitric acid digests and 1 M ammonium acetate extracts of sediments taken from enclosed dock-basins at Liverpool, Tilbury and Manchester. At each of the dock-yards concentrations of all the metals in nitric acid extracts were elevated in the vicinity of dry-docks. These elevations are believed to be due to the presence of residues from the ship-bottom paints. The amounts of copper and zinc, which are major components of antifoulant paints, in the ammonium acetate extracts correlate closely with the levels in the nitric acid digests; lead, which is found mainly in anticorrosive and primer paints, does not. Direct toxic effects of the metals to marine organisms are considered, but are not thought to be important in view of the large dilution and dispersed capacities usually available. The possibility of organisms developing genetic resistance to heavy metals in enclosed dock-basins is discussed. It is noted that many of the dock-yard organisms are components of the ship-fouling ecosystem, so that the premature exposure to the toxins of antifoulant paints may result in the development of toxin resistance of considerable economic importance.

Indirect regulation rule for consecutive stages of ecological succession

Abstract The lake ecosystem model ‘Rostherne’ allowed a theoretical insight into delayed causal relationships in aquatic ecosystems. Model simulations were used to demonstrate the possibility of influencing a species dominant at a later stage of ecological succession, by alleviating growth limitation of a different species, dominant at an earlier stage. Such delayed relationships are characteristic of various types of systems (including ecological), and can be illustrated by using a simple Stella model presented here. The stated indirect regulation rule for consecutive stages of ecological succession provides an important theoretical basis both for certain ecological manipulations and for the better understanding of various environmental relationships. It should, therefore, prove useful for theoretical analysis of system dynamics, studies of terrestrial and aquatic ecosystems, management of natural resources, Environmental Assessment and Auditing.

Expansion of the model 'Rostherne' for fish and zooplankton: Role of top-down effects in modifying the prevailing pattern of ecosystem functioning

Abstract This paper presents a new version of the well-established aquatic ecosystem model ‘Rostherne’, incorporating lake fish and zooplankton. The model currently comprises differential and algebraic equations describing processes and forcing functions most important for a freshwater ecosystem. These include seasonal changes of solar radiation and water temperature, processes of algal and cyanobacterial population dynamics and nutrient uptake, water and chemical budgeting, stratification of the water column and sedimentation of suspended particles, and dynamics of detritus and its chemical constituents. Although it was shown previously for some years that a reasonable simulation of changes in most state variables could be achieved without accounting for fish and zooplankton, in other years, consideration of these compartments improved the fit between observations and model simulations dramatically. This was particularly true for the year 1998, when the high zooplankton numbers in spring appeared to prevent any significant development of the diatom population. The latter, however, bloomed during the first part of the summer (following a decrease in zooplankton counts) causing an unusually delayed major drop in ambient Si levels. As biogoechemical cycles of Si, P and N are interconnected through the dynamics of the primary producers, such a delay has serious implications for the functioning of an aquatic ecosystem model. Since changes in zooplankton dynamics are heavily dependent on the variability in fish recruitment, simultaneous consideration of the two components is helpful both for modelling overall dynamics of the lake ecosystem and for implementation of the biogeochemical regulation proposed previously.

Interrelations between Si and P biogeochemical cycles—a new approach to the solution of the eutrophication problem

Because the biogeochemical cycles of P and Si in temperate lakes are strongly connected by the dynamics of primary producers, it should be possible to influence the former cycle by causing changes in the latter. It is shown using the mathematical model ‘Rostherne’ that winter levels of ambient Si have a major influence both on spring levels of ambient P and on the summer cyanobacterial maxima. Additions of Si to the lakes could be used for the fine regulation of the biogeochemical balance and may prescribe a recipe for improvement of water quality, as well as a new solution to the problem of eutrophication. Copyright

Application of SEM XRMA data to lake ecosystem modelling

The model Rostherne represents the first attempt to apply SEM XRMA (scanning electron microscopy X-ray microanalysis) data to lake ecosystem modelling. It considers subsystems proved to be most important for Rostherne mere (Cheshire. UK) with incorporation of uptake dependency of one nutrient upon internal deficiency in another. The model showed a reasonable fit (R 2 = 0.87, P < 0.001) between measured data and simulation curves for most of the considered variables (i.e. P, Si, chlorophyll-a and algal concentrations in the lake water, nutrient mass fractions of algal cells, etc.) and could, therefore. have been used to estimate some parameters and variables which were not measured otherwise (e.g. sedimentation and growth rates, etc.). The possibility of incorporating alternative expressions for the processes considered is discussed and tasks for future research in relation to coupling of various submodels with the proposed submodel of nutrient uptake are envisaged.

Examination of the phytoplankton of Rostherne Mere using a simulation mathematical model

Changes of phytoplankton populations in Rostherne Mere in 1996 were examined by means of simulation mathematical models. Simple models, solely based on Monod or Michaelis Menten equations, failed to give a reasonable simulation of the phytoplankton succession. A more complex model Rostherne (version 1.1a) calibrated on an extensive set of XRMA and conventional data, however, proved to be useful both for prediction of the outcome of the spring and summer competition and for the estimation of values of certain non-measured variables. It also helped to identify the limiting factors for different times of the year. Alteration of the simulated magnitude of the spring diatom bloom had a major influence on summer cyanobacterial maxima, demonstrating fine regulation of the biogeochemical balance within the modelled system.

A note on the use of algal sizes in estimates of population standing crops

Counts of cell numbers of phytoplankton algae of differing sizes do not accurately reflect the biomass of the population present. For a better estimate it is suggested that cell numbers be multiplied by the average cell volume for each species present. Data on cell volumes for certain species are given and compared with results from other authors. Surface areas and surface area to volume ratios are also given and the importance of these parameters noted.

Elemental composition of phytoplankton in a subtropical lake: X-ray microanalytical studies on the dominant algae Spirulina platensis (Cyanophyta) and Cyclotella meneghiniana (Bacillariophyceae)

Studies were carried out on the phytoplankton of a subtropical polluted lake (Lake Maryut, Egypt) over a 1 year sampling period. The elemental composition of two major algal constituents-Spirulina platensis (Cyanophyta) and Cyclotella meneghiniana (Bacillariophyceae)—was determined from mixed plankton samples using electron-probe X-ray microanalysis. X-ray emission spectra obtained from S. platensis revealed a wide range of detectable elements. Clear positive (e.g. K-P, Na-Cl) and negative (e.g. Na-S, P-Cl) statistical correlations were interpreted in terms of elemental associations and compartmentation within the cell. C. meneghiniana had a broadly similar range of elements, but differed in the additional presence of Al and frequent peaks of Fe and Cu. The high concentration of Si reduced the detectability of other elements, causing a general reduction in mass fraction levels. Comparison of correlation coefficients derived from four sets of data revealed a clear pattern of elemental occurrence in the cel...