Palaeolimnological diatom data comprise counts of many species expressed as percentages for each sample. Reconstruction of past lake-water pH from such data involves two steps; (i) regression, where responses of modern diatom abundances to pH are modelled and (ii) calibration where the modelled responses are used to infer pH from diatom assemblages preserved in lake sediments. In view of the highly multivariate nature of diatom data, the strongly nonlinear response of diatoms to pH, and the abundance of zero values in the data, a compromise between ecological realism and computational feasability is essential. The two numerical approaches used are (i) the computationally demanding but formal statistical approach of maximum likelihood (ML) Gaussian logit regression and calibration and (ii) the computationally straightforward but heuristic approach of weighted averaging (WA) regression and calibration. When the Surface Water Acidification Project (SWAP) modern training set of 178 lakes is reduced by data-screening to 167 lakes, WA gives superior results in terms of lowest root mean squared errors of prediction in cross-validation. Bootstrapping is also used to derive prediction errors, not only for the training set as a whole but also for individual pH reconstructions by WA for stratigraphic samples from Round Loch of Glenhead, southwest Scotland covering the last 10 000 years. These reconstructions are evaluated in terms of lack-of-fit to pH and analogue measures and are interpreted in terms of rate of change by using bootstrapping of the reconstructed pH time-series.

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Diatoms and pH Reconstruction

Long-term increases in surface water dissolved organic carbon: Observations, possible causes and environmental impacts

Weighted averaging regression and calibration form a simple, yet powerful method for reconstructing environmental variables from species assemblages. Based on the concepts of niche-space partitioning and ecological optima of species (indicator values), it performs well with noisy, species-rich data that cover a long ecological gradient (>3 SD units). Partial least squares regression is a linear method for multivariate calibration that is popular in chemometrics as a robust alternative to principal component regression. It successively selects linear components so as to maximize predictive power. In this paper the ideas of the two methods are combined. It is shown that the weighted averaging method is a form of partial least squares regression applied to transformed data that uses the first PLS-component only. The new combined method, ast squares, consists of using further components, namely as many as are useful in terms of predictive power. The further components utilize the residual structure in the species data to improve the species parameters (‘optima’) in the final weighted averaging predictor. Simulations show that the new method can give 70% reduction in prediction error in data sets with low noise, but only a small reduction in noisy data sets. In three real data sets of diatom assemblages collected for the reconstruction of acidity and salinity, the reduction in prediction error was zero, 19% and 32%.

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Weighted Averaging Partial Least-Squares Regression (Wa-Pls) - an Improved Method for Reconstructing Environmental Variables from Species Assemblages

In this article we review the biological effects of Al, primarily with respect to the chemical factors controlling Al bioavailability and toxicity, and how its biological effects are best predicted. Our intent is not to duplicate recent reviews on Al chemistry or toxicity, but rather to update the literature since these reviews were published, and to focus on Al speciation and other external chemical influences on Al bioavailability to freshwater biota. Briefly, we first review Al chemistry, with a specific focus on understanding, as well as measuring, Al chemical species of importance to aquatic biota. Next we more comprehensively review Al toxicity and bioavailability to freshwater algae, with a thorough analysis of the relationships between speciation and toxicity, the role of important chemical complexing agents such as P, Si, and organic carbon, as well as the potential for Al to impact algal community structure. A third section reviews the more sparse literature on aquatic higher plants; the fourth ...

The Bioavailability and Toxicity of Aluminum in Aquatic Environments

Preface to the second edition. About the author. 1 There is no substitute for water. 2 How long is long?. 3 Sediments: an ecosystem's memory. 4 Retrieving the sedimentary archive and establishing the geochronological clock: collecting and dating sediment cores. 5 Reading the records stored in sediments: the present is a key to the past. 6 The paleolimnologist's Rosetta Stone: calibrating indicators to environmental variables using surface-sediment training sets. 7 Acidification: finding the "smoking gun". 8 Metals, technological development, and the environment. 9 Persistent organic pollutants: industrially synthesized chemicals "hopping" across the planet. 10 Mercury - "the metal that slipped away". 11 Eutrophication: the environmental consequences of over-fertilization. 12 Erosion: tracking the accelerated movement of material from land to water. 13 Species invasions, biomanipulations, and extirpations. 14 Greenhouse gas emissions and a changing atmosphere: tracking the effects of climatic change on water resources. 15 Ozone depletion, acid rain, and climatic warming: the problems of multiple stressors. 16 New problems, new challenges. 17 Paleolimnology: a window on the past, a key to our future. Glossary. References. Index

Pollution of Lakes and Rivers: A Paleoenvironmental Perspective

137Cs and 210Pb dating of sediments from soft-water lakes in New England (U.S.A.) and Scandinavia, a failure of 137Cs dating

The Surface Waters Acidification Project Palaeolimnology Programme: Modern Diatom / Lake-Water Chemistry Data-Set

The hypothesis that loss of dissolved organic matter from lake water accompanies acidification1,2 has been investigated palaeolimnologically for Hovvatn (pH 4.4; TOC (total organic carbon) 3.2 p.p.m.) and Holmvatn (pH 4.7; TOC 2.2 p.p.m.) in southern Norway. Regression equations3,4 that relate sedimentary diatom remains separately to pH and TOC were applied to diatom counts from 210Pb-dated cores from the lakes. The results indicate that in pre-industrial times both lakes were acidic (pH 4.9–5.1, no bicarbonate alkalinity) and relatively humic (TOC 6–9 p.p.m.). This acidification started about 1920 at Howatn and in the 1940s at Holmvatn, and was accompanied by TOC decreases of 3–6 p.p.m. This supports the hypothesis and suggests that the acidification of such lakes transformed them from organic weak acid dominated to mineral strong acid dominated. It also implies that acidification decreased the availability of organic ligands for binding potentially toxic aluminium.

Palaeolimnological evidence that lake acidification is accompanied by loss of organic matter

The pH history of lakes can be inferred from diatom remains in dated sediment cores. To derive transfer functions for pH inference in acidic lakes, we counted diatoms in surface-sediment from 31 soft-water lakes in n. New England (NE) and 36 in Norway (N), covering pH 4.4–7.1. Cluster analysis of each data set indicates that pH 6 is an upper limit for a group of similar diatom assemblages. For each set, we developed multiple linear regressions to relate three versions of the diatom data to pH of surface-waters: (1) relative frequencies of selected diatom taxa, (2) the first principal component (1 PC) of these frequencies, and (3) the frequencies of Hustedt pH groups. Also, simple linear regressions were developed for two versions: (1) Index B and (2) Index Alpha, both based on pH groups. Regressions were run separately for lakes with pH ≤ 6; these are most relevant for pH inference in acidic lakes. The best regressions (N: taxa & 1 PC taxa) have r2 0.69–0.91 and Se 0.24–0.31 pH units, the worst (NE: log alpha) have r2 0.27–0.57 and Se 0.51. In all cases, errors for NE are greater than N, partly due to greater diversity of NE lakes. Regressions based on pH groups (directly & by indices) have smaller r2 and larger Se than those based on taxa and 1 taxa. The Index Alpha is least useful because its requirement for alkaline diatom units is unsatisfied at many acidic lakes. Regressions based on taxa may give erratic pH inferences due to sensitivity to unusual frequencies of individual taxa; this effect is reduced by using 1 PC taxa. Four regressions based on pH ≤ 6 lakes were used for inferring pH in a 210Pb dated core from Nedre Malmesvatn, N (now pH 4.6). There is good agreement among three of the four (not for the regression based directly on taxa) that there has been a decrease of ca. 0.6 pH units starting in the late 1800's.

Methods of pH calibration of sedimentary diatom remains for reconstructing history of pH in lakes

The diatom data used for reconstructing pH within the Surface Water Acidification Project (SWAP) came from several different laboratories. The laboratories used agreed nomenclature and standardized identifications by using quality control techniques. A diatom database (DISCO) stored and processed counts and site information.

Dennis S. Anderson

Papers

137Cs and 210Pb dating of sediments from soft-water lakes in New England (U.S.A.) and Scandinavia, a failure of 137Cs dating

The Surface Waters Acidification Project Palaeolimnology Programme: Modern Diatom / Lake-Water Chemistry Data-Set

Palaeolimnological evidence that lake acidification is accompanied by loss of organic matter

Methods of pH calibration of sedimentary diatom remains for reconstructing history of pH in lakes

Diatom quality control and data handling