Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-f554764f5-8cg97 Total loading time: 0 Render date: 2025-04-09T00:15:05.567Z Has data issue: false hasContentIssue false

3 - Numerical methods for the analysis of diatom assemblage data

from Part I - Introduction

Published online by Cambridge University Press:  05 June 2012

By
H. John B. Birks
Edited by
John P. Smol  and
Eugene F. Stoermer
H. John B. Birks
Affiliation:
University of Bergen
John P. Smol
Affiliation:
Queen's University, Ontario
Eugene F. Stoermer
Affiliation:
University of Michigan, Ann Arbor
Get access

Summary

Introduction

Research involving diatom assemblages, both modern and fossil, has expanded enormously in recent decades. Because many of the questions asked in such research are quantitative in character (e.g. what was the lake-water pH at AD 1850, what are the major environmental gradients determining the modern diatom assemblages in a set of lakes on the Isle of Skye), there has been a similar development and application of numerical methods appropriate for the quantitative analysis of diatom assemblage data.

Despite diatom ecology and paleoecology being over 100 years old, the relevant statistical methods for assessing the inherent uncertainties associated with diatom counts were only relatively recently developed (in the context of pollen counting) by Mosimann (1965). The application of multivariate data analytical techniques such as cluster analysis, principal components analysis, and correspondence analysis to diatom assemblage data began in the early 1970s. With the upsurge of interest in the mid 1980s in diatom ecology and paleoecology in response to research on the causes of surface-water acidification in Europe and North America, the development and application of data analytical techniques such as canonical correspondence analysis (ter Braak, 1985, 1986) and weighted-averaging regression and calibration (ter Braak & van Dam, 1989) in diatom research expanded greatly (Birks, 1998). Such techniques are now widely used items in the diatomist's tool-kit (Smol et al., 2011 and chapters in this volume).

Type
Chapter
Information
The Diatoms
Applications for the Environmental and Earth Sciences
 , pp. 23 - 54
Publisher: Cambridge University Press
Print publication year: 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

Aitchinson, J. (1986). The Statistical Analysis of Compositional Data, Chapman and Hall, London.CrossRefGoogle Scholar
Anderson, M. J. & Willis, T. J. (2003). Canonical analysis of principal co-ordinates: a useful method of constrained ordination for ecology. Ecology, 84, 511–25.CrossRefGoogle Scholar
Anderson, N. J. & Korsman, T. (1990). Land-use change and lake acidification: Iron Age de-settlement in northern Sweden as a pre-industrial analogue. Philosophical Transactions of the Royal Society of London B, 327, 373–6.CrossRefGoogle Scholar
Anderson, N. J., Odgaard, B. V., Segerström, U. & Renberg, I. (1996). Climate–lake interactions recorded in varved sediments from a Swedish boreal forest lake. Global Change Biology, 2, 399–405.CrossRefGoogle Scholar
Anderson, N. J., Renberg, I., & Segerstrom, U. (1995). Diatom production response to the development of early agriculture in a boreal forest lake-catchment (Kassjön, northern Sweden). Journal of Ecology, 88, 809–22.CrossRefGoogle Scholar
Ball, I. R. (1975). Nature and formulation of biogeographic hypotheses. Systematic Zoology, 24, 407–30.CrossRefGoogle Scholar
Battarbee, R. W. (1990). The causes of lake acidification, with special reference to the role of acidification. Philosophical Transactions of the Royal Society of London B, 327, 339–47.CrossRefGoogle Scholar
Battarbee, R. W., Jones, V. J., Flower, R. J., et al. (2001a). Diatoms. In Tracking Environmental Change Using Lake Sediments, Volume 3: Terrestrial, Algal, and Siliceous Indicators, ed. Smol, J. P., Birks, H. J. B., & Last, W. M., Kluwer Academic Publishers: Dordrecht, pp. 155–202.Google Scholar
Battarbee, R. W., Juggins, S., Gasse, F., et al. (2001b). European Diatom Database (EDDI). An information system for palaeoenvironmental reconstruction. Environmental Change Research Centre, University College London, pp. 210.
Bennett, K. D. (1994). Confidence intervals for age estimates and deposition times in late-Quaternary sediment sequences. The Holocene, 4, 337–48.CrossRefGoogle Scholar
Bennett, K. D. (1996). Determination of the number of zones in a biostratigraphical sequence. New Phytologist, 132, 155–70.CrossRefGoogle Scholar
Besse-Lototskaya, A., Verdonschot, P. F. M., & Sinkeldam, J. A. (2006). Uncertainty in diatom assessment: sampling, identification and counting varition. Hydrobiologia, 566, 247–60.CrossRefGoogle Scholar
Bigler, C., Larocque, I., Peglar, S. M., Birks, H. J. B, & Hall, R. I. (2002). Quantitative multiproxy assessment of long-term patterns of Holocene environmental change from a small lake near Abisko, northern Sweden. Holocene, 12, 481–96.CrossRefGoogle Scholar
Birks, H. H., Battarbee, R. W., & Birks, H. J. B. (2000). The development of the aquatic ecosystem at Krakenes Lake, western Norway, during the late glacial and early Holocene – a synthesis. Journal of Paleolimnology, 23, 91–114.CrossRefGoogle Scholar
Birks, H. H. & Birks, H. J. B. (2006). Multi-proxy studies in palaeolimnology. Vegetation History and Archaeobotany, 15, 235–51.CrossRefGoogle Scholar
Birks, H. J. B. (1985). Recent and possible future mathematical developments in quantitative paleoecology. Palaeogeography Palaeoclimatology Palaeoecology, 50, 107–47.CrossRefGoogle Scholar
Birks, H. J. B. (1987). Multivariate-analysis of stratigraphic data in geology – a review. Chemometrics and Intelligent Laboratory Systems, 2, 109–26.CrossRefGoogle Scholar
Birks, H. J. B. (1992). Some reflections on the application of numerical methods in Quaternary palaeoecology. Publications of the Kareliàn Institute, University of Joensuu, 102, 7–20.Google Scholar
Birks, H. J. B. (1995). Quantitative palaeoenvironmental reconstructions. In Statistical Modelling of Quaternary Science Data. Technical Guide 5, ed. Maddy, D. & Brew, J. S., Cambridge: Quaternary Research Association pp. 161–254.Google Scholar
Birks, H. J. B. (1997). Environmental change in Britain – a long-term palaeoecological perspective. In Britain's Natural Environment: a State of the Nation Review, ed. Mackay, A. W. & Murlis, J., London: Ensis Publications, pp. 23–8.Google Scholar
Birks, H. J. B. (1998). Numerical tools in palaeolimnology – progress, potentialities, and problems. Journal of Paleolimnology, 20, 307–32.CrossRefGoogle Scholar
Birks, H. J. B. (2001). Maximum likelihood environmental calibration and the computer program WACALIB – a correction. Journal of Paleolimnology, 25, 111–15.CrossRefGoogle Scholar
Birks, H. J. B. (2003). Quantitative palaeoenvironmental reconstructions from Holocene biological data. Global Change in the Holocene, ed. Mackay, A. W., Battarbee, R. W., Birks, H. J. B. & Oldfield, F., London: Arnold, pp. 342–57.Google Scholar
Birks, H. J. B. (2007). Estimating the amount of compositional change in late-Quaternary pollen-stratigraphical data. Vegetation History and Archaeobotany, 16, 197–202.CrossRefGoogle Scholar
Birks, H. J. B. (2008). Ordination – an ever-expanding tool-kit for ecologists? Bulletin of the British Ecological Society, 39, 31–3.Google Scholar
Birks, H. J. B. (2011). Stratigraphical data analysis. In Tracking Environmental Change Using Lake Sediments, Volume 5: Data Handling and Numerical Techniques, eds. Birks, H. J. B., Lotter, A. F., Juggins, S. & Smol, J. P., Dordrecht: Springer (in press).Google Scholar
Birks, H. J. B., Berge, F., Boyle, J. F. & Cumming, B. F. (1990c). A palaeoecological test of the land use hypothesis for recent lake acidification in south west Norway using hill top lakes. Journal of Paleolimnology, 4, 69–85.CrossRefGoogle Scholar
Birks, H. J. B. & Birks, H. H. (2008). Biological responses to rapid climate changes at the Younger Dryas–Holocene transition at Kråkenes, western Norway. The Holocene, 18, 19–30.CrossRefGoogle Scholar
Birks, H. J. B. & Gordon, A. D. (1985). Numerical Methods in Quaternary Pollen Analysis, London: Academic Press.Google Scholar
Birks, H. J. B., Juggins, S. & Line, J. M. (1990b). Lake surface-water chemistry reconstructions from palaeolimnological data. In The Surface Waters Acidification Programme, ed. Mason, B. J., Cambridge: Cambridge University Press, pp. 301–13.Google Scholar
Birks, H. J. B. & Line, J. M. (1992). The use of rarefraction analysis for estimating palynological richness from Quaternary pollen-analytical data. The Holocene, 2, 1–10.CrossRefGoogle Scholar
Birks, H. J. B., Line, J. M., Juggins, S., Stevenson, A. C., & Braak, C. J. F. (1990a). Diatoms and pH reconstruction. Philosophical Transactions of the Royal Society of London Series B – Biological Sciences, 327, 263–78.CrossRefGoogle Scholar
Birks, H. J. B. & Lotter, A. F. (1994). The impact of the Laacher See Volcano (11000 yr B.P.) on terrestrial vegetation and diatoms. Journal of Paleolimnology, 11, 313–22.CrossRefGoogle Scholar
Birks, H. J. B., Lotter, A. F., Juggins, S., & Smol, J. P. (eds.) (2011). Tracking Environmental Change Using Lake Sediments, Volume 5: Data Handling and Numerical Techniques, Dordrecht: Springer (in press).Google Scholar
Birks, H. J. B. & Seppä, H. (2004). Pollen-based reconstructions of late-Quaternary climate in Europe – progress, problems, and pitfalls. Acta Palaeobotanica, 44, 317–34.Google Scholar
Blaauw, M. & Heegaard, E. (2011). Estimation of age–depth relationships. In Tracking Environmental Change Using Lake Sediments, Volume 5: Data Handling and Numerical Techniques, ed. Birks, H. J. B., Lotter, A. F., Juggins, S., & Smol, J. P., Dordrecht: Springer (in press).Google Scholar
Bolker, B. M., Brooks, M. E., Clark, C. J., et al. (2009). Generalized liner mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution, 24, 127–35.CrossRefGoogle Scholar
Bookstein, F., Chernoff, B., Elder, R., et al. (1985). Morphometrics in Evolutionary Biology, Philadelphia: Academy of National Sciences of Philadelphia Special Publication.Google Scholar
Borcard, D., Legendre, P., & Drapeau, P. (1992). Partialling out the spatial component of ecological variation. Ecology, 73, 1045–55.CrossRefGoogle Scholar
Bouchard, G., Gajewski, K., & Hamilton, P. B. (2004). Freshwater diatom biogeography in the Canadian Arctic archipelago. Journal of Biogeography, 31, 1955–73.CrossRefGoogle Scholar
Bradshaw, E. G., Rasmussen, P., & Odgaard, B. V. (2005). Mid- to late-Holocene land-use change and lake development at Dallund Sø, Denmark: synthesis of multiproxy data, linking land and lake. The Holocene, 15, 1152–1162.CrossRefGoogle Scholar
Brown, K. J., Clark, J. S., Grimm, E. C., et al. (2005). Fire cycles in North American interior grasslands and their relation to prairie drought. Proceedings of the National Academy of Sciences of the USA, 102, 8865–70.CrossRefGoogle ScholarPubMed
Cade, B. S. & Noon, B. (2003). A gentle introduction to quantile regression for ecologists. Frontiers in Ecology and the Environment, 1, 412–20.CrossRefGoogle Scholar
Cade, B. S., Noon, B. R., & Flather, C. H. (2005). Quantile regression reveals hidden bias and uncertainty in habitat models. Ecology, 86, 786–800.CrossRefGoogle Scholar
Cameron, N. G., Birks, H. J. B., Jones, V. J., et al. (1999). Surface-sediment and epilithic diatom pH calibration sets for remote European mountain lakes (AL:PE Project) and their comparison with the Surface Waters Acidification Programme (SWAP) calibration set. Journal of Paleolimnology, 22, 291–317.CrossRefGoogle Scholar
Cardinale, B. J., Hillebrand, H., & Charles, D. F. (2006). Geographic patterns of diversity in streams are predicted by a multivariate model of disturbance and productivity. Journal of Ecology, 94, 609–18.CrossRefGoogle Scholar
Chamberlain, T. C. (1965). The method of multiple working hypotheses. Science, 148, 754–9.CrossRefGoogle Scholar
Chambers, J. M., Cleveland, W. S., Kleiner, B., & Tukey, P. A. (1983). Graphical Methods for Data Analysis, Monterey, CA: Wadsworth.Google Scholar
Chaudhuri, P. & Marron, J. S. (1999). SiZer for exploration of structures in curves. Journal of the American Statistical Association, 94, 807–23.CrossRefGoogle Scholar
Clark, J. S. (2005). Why environmental scientists are becoming Bayesians. Ecology Letters, 8, 2–14.CrossRefGoogle Scholar
Clark, J. S. & Gelfand, A. E. (2006a). A future for models and data in environmental science. Trends in Ecology and Evolution, 21, 375–380.CrossRefGoogle ScholarPubMed
Clark, J. S. & Gelfand, A. E. (eds.) (2006b). Hierarchical Modelling for the Environmental Sciences, Oxford: Oxford University Press.Google Scholar
Claude, J. (2008). Morphometrics with R, New York, NY: Springer.Google Scholar
Cleveland, W. A. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74, 829–36.CrossRefGoogle Scholar
Cleveland, W. A. & Devlin, S. J. (1988). Locally weighted regression: an approach to regression analysis by local fitting. Journal of the American Statistical Association, 83, 596–610.CrossRefGoogle Scholar
Cleveland, W. S. (1993). Visualizing Data, Murray Hill, NJ: AT&T Bell Laboratories.Google Scholar
Cleveland, W. S. (1994). The Elements of Graphing Data, Murray Hill, NJ: AT&T Bell Laboratories.Google Scholar
Cutler, D. R., Edwards, T. C., Beard, K. H., et al. (2007). Random forests for classification in ecology. Ecology, 88, 2783–92.CrossRefGoogle ScholarPubMed
Davis, J. C. (2002). Statistics and Data Analysis in Geology, New York, NY: Wiley.Google Scholar
De'ath, G. (2002). Multivariate regression trees: a new technique for modeling species–environment relationships. Ecology, 83, 1105–17.Google Scholar
De'ath, G. (2007). Boosted trees for ecological modeling and prediction. Ecology, 88, 243–51.CrossRefGoogle ScholarPubMed
De'ath, G. & Fabricus, K. E. (2000). Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 81, 3178–92.CrossRefGoogle Scholar
Deevey, E. S. (1969). Coaxing history to conduct experiments. BioScience, 19, 40–3.CrossRefGoogle Scholar
Dixit, S. S., Smol, J. P., Charles, D. F., et al. (1999). Assessing water quality changes in the lakes of the northeastern United States using sediment diatoms. Canadian Journal of Fisheries and Aquatic Science, 56, 131–52.CrossRefGoogle Scholar
du Buf, J. M. H. & Bayer, M. M. (2002). Automatic Diatom Identification, Singapore: World Scientific Publishing.CrossRefGoogle Scholar
du Buf, J. M. H., Bayer, M. M., Droop, S. J. M., et al. (1999). Diatom identification: a double challenge called ADIAC. Proceedings of the 10th International Conference on Image Analysis and Processing, Venice, Italy.CrossRef
Dutilleul, P., Cumming, B. F., & Lontoc-Roy, M. (2011). Auto-correlogram and periodogram analyses of palaeolimnological temporal series from lakes in central and western North America to assess shifts in drought conditions. In Tracking Environmental Change Using Lake Sediments, Volume 5: Data Handling and Numerical Techniques, ed. Birks, H. J. B., Lotter, A. F., Juggins, S., & Smol, J. P., Dordrecht: Springer (in press).Google Scholar
Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802–13.CrossRefGoogle ScholarPubMed
Ellison, A. M. (2004). Bayesian inference in ecology. Ecology Letters, 7, 509–20.CrossRefGoogle Scholar
Everitt, B. (1978). Graphical Techniques for Multivariate Data, London: Heinemann.Google Scholar
Ferguson, C. A., Carvalho, L., Scott, E. M., Bowman, A. W., & Kirika, A. (2008). Assessing ecological responses to environmental change using statistical models. Journal of Applied Ecology, 45, 193–203.CrossRefGoogle Scholar
Fielding, A. H. (2007). Cluster and Classification Techniques for the Biosciences, Cambridge: Cambridge University Press.Google Scholar
Finlay, B. J., Monaghan, E. B., & Maberly, S. C. (2002). Hypothesis: the rate and scale of dispersal of freshwater diatom species is a function of their global abundance. Protist, 153, 261–73.CrossRefGoogle ScholarPubMed
Flenley, J. (2003). Some prospects for lake sediment analysis in the 21st century. Quaternary International, 105, 77–80.CrossRefGoogle Scholar
Fox, J. (2002). An R and S-Plus Companion to Applied Regression, Thousand Oaks, CA: Sage Publishers.Google Scholar
Fritz, S. C. (1990). Twentieth-century salinity and water-level fluctuations in Devil's Lake, North Dakota: test of a diatom-based transfer function. Limnology and Oceanography, 35, 1171–81.CrossRefGoogle Scholar
Gabriel, K. R. (2002). Goodness of fit of biplots and correspondence analysis. Biometrika, 89, 423–36.CrossRefGoogle Scholar
Gauch, H. G. (1982). Noise reduction by eigenvector ordination. Ecology, 63, 1643–9.CrossRefGoogle Scholar
Ginn, B. K., Cumming, B. F., & Smol, J. P. (2007). Diatom-based environmental inferences and model comparisons from 494 northeastern North American lakes. Journal of Phycology, 43, 647–61.CrossRefGoogle Scholar
Godínez-Domínguez, E. & Freire, F. (2003). Information-theoretic approach for selection of spatial and temporal models of community organization. Marine Ecology Progress Series, 253, 17–24.CrossRefGoogle Scholar
Gordon, A. D. (1999). Classification, Boca Raton, FL: Chapman & Hall/CRC Press.Google Scholar
Grimm, E. C. (1987). CONISS – a FORTRAN-77 program for stratigraphically constrained cluster-analysis by the method of incremental sum of squares. Computers & Geosciences, 13, 13–35.CrossRefGoogle Scholar
Grimm, E. C. & Jacobson, G. L. (1992). Fossil-pollen evidence for abrupt climate changes during the past 18 000 years in eastern North America. Climate Dynamics, 6, 179–84.CrossRefGoogle Scholar
Guiot, J. & Vernal, A. (2007). Transfer functions: methods for quantitative paleoceanography based on microfossils. In Proxies in Late Cenozoic Paleoceanography, ed. Hillaire-Marcel, C. & Vernal, A., Amstedam: Elsevier, pp. 523–63.CrossRefGoogle Scholar
Haberle, S. G., Tibby, J., Dimitriadis, S., & Heijnis, H. (2006). The impact of European occupation on terrestrial and aquatic dynamics in an Australian tropical rain forest. Journal of Ecology, 94, 987–1102CrossRefGoogle Scholar
Hall, R. I., Leavitt, P. R., Quinlan, R., Dixit, A. S., & Smol, J. P. (1999). Effects of agriculture, urbanization, and climate on water quality in the northern Great Plains. Limnology and Oceanography, 44, 736–56.CrossRefGoogle Scholar
Hammer, Ø. & Harper, D. A. T. (2006). Paleontological Data Analysis, Oxford: Blackwell.Google Scholar
Hao, L. & Naiman, D. Q. (2007). Quantile Regression, Thousand Oaks, CA: Sage Publishers.CrossRefGoogle Scholar
Hausmann, S. & Lotter, A. F. (2001). Morphological variation within the diatom taxon Cyclotella comensis and its importance for quantitative temperature reconstructions. Freshwater Biology, 46, 1323–33.CrossRefGoogle Scholar
Heegaard, E. (2002). The outer border and central border for species environmental relationships estimated by non-parametric generalised additive models. Ecological Modelling, 157, 131–9.CrossRefGoogle Scholar
Heegaard, E., Birks, H. J. B., & Telford, R. J. (2005). Relationships between calibrated ages and depth in stratigraphical sequences: an estimation procedure by mixed-effect regression. The Holocene, 15, 612–18.CrossRefGoogle Scholar
Heegaard, E., Lotter, A. F., & Birks, H. J. B. (2006). Aquatic biota and the detection of climate change: are there consistent aquatic ecotones? Journal of Paleolimnology, 35, 507–18.CrossRefGoogle Scholar
Heino, J., Ilmonen, J., Kotanen, J., et al. (2009). Surveying biodiversity in protected and managed areas: algae, macrophytes and macroinvertebrates in boreal forest streams. Ecological Indicators, 9, 1179–87.CrossRefGoogle Scholar
Heino, J. & Soinenen, J. (2005). Assembly rules and community models for unicellular organisms: patterns in diatoms of boreal streams. Freshwater Biology, 50, 567–77.CrossRefGoogle Scholar
Hewitt, C. N. (ed.) (1992). Methods of Environmental Data Analysis, London: Elsevier.Google Scholar
Hill, M. O. & Gauch, H. G. (1980). Detrended correspondence analysis, an improved ordination technique. Vegetatio, 42, 47–58.CrossRefGoogle Scholar
Holden, P. B., Mackay, A. M., & Simpson, G. L. (2008). Bayesian palaeoenvironmental transfer function model for acidified lakes. Journal of Paleolimnology, 39, 551–66.CrossRefGoogle Scholar
Holmström, L. & Erästö, P. (2002). Making inferences about past environmental change using smoothing in multiple timescales. Computational Statistics and Data Analysis, 41, 289–309.CrossRefGoogle Scholar
Hübener, T., Dressler, M., Schwarz, A., Langner, K., & Adler, S. (2008). Dynamic adjustment of training set (“moving-window” reconstruction) by using transfer functions in paleolimnology – a new approach. Journal of Paleolimnology, 40, 79–95.CrossRefGoogle Scholar
Huisman, J., Olff, H. & Fresco, L. F. M. (1993). A hierarchical set of models for species response models. Journal of Vegetation Science, 4, 37–46.CrossRefGoogle Scholar
Jackson, D. A. (1993). Stopping rules in principal components analysis: a comparison of heuristical and statistical approaches. Ecology, 74, 2204–14.CrossRefGoogle Scholar
Jacobson, G. L. & Grimm, E. C. (1986). A numerical-analysis of Holocene forest and prairie vegetation in central Minnesota. Ecology, 67, 958–66.CrossRefGoogle Scholar
Janssen, C. R. & Birks, H. J. B. (1994). Recurrent Groups of Pollen Types in Time. Review of Palaeobotany and Palynology, 82, 165–73.CrossRefGoogle Scholar
Jolliffe, I. T. (1986). Principal Components Analysis, New York, NY: Springer.CrossRefGoogle Scholar
Jones, V. J. & Juggins, S. (1995). The construction of a diatom-based chlorophyll a transfer function and its application at three lakes on Signy Islands (Maritime Antarctic) subject to differing degrees of nutrient enrichment. Freshwater Biology, 34, 433–45.CrossRefGoogle Scholar
Jongman, R. H. G., Braak, C. J. F., & Tongren, O. F. R. (1987). Data Analysis in Community and Landscape Ecology, Wageningen: Pudoc. (Reissued in 1995 by Cambridge University Press, Cambridge).Google Scholar
Juggins, S. (1996). The PALICLAS database. Memorie dell'Instituto Italiano di idrobiologia, 55, 321–8.Google Scholar
Juggins, S. (2005). C2 User Guide. Software for ecological and palaeoecological data analysis and visualisation, University of Newcastle, Newcastle-upon-Tyne.
Juggins, S. & Birks, H. J. B. (2011). Quantitative environmental reconstructions from biostratigraphical data. In Tracking Environmental Change Using Lake Sediments, Volume 5: Data Handling and Numerical Techniques, ed. Birks, H. J. B., Lotter, A. F., Juggins, S. & Smol, J. P., Dordrecht: Springer (in press).Google Scholar
Julius, M. L., Estabrook, G. F., Edlund, M. B., & Stoermer, E. F. (1997). Recognition of taxonomically significant clusters near the species level, using computationally intense methods, with examples from the Stephanodiscus niagarae complex. Journal of Phycology, 33, 1049–54.CrossRefGoogle Scholar
Kelly, M. G., Bayer, M. M., Hürlmann, J., & Telford, R. J. (2002). Human error and quality assurance in diatom analysis. In Automatic Diatom Identification, ed. du Buf, H. & Bayer, M. M., Singapore: World Scientific Publishing, pp. 75–91.CrossRefGoogle Scholar
Kelly, M., Juggins, S., Guthrie, R., et al. (2008). Assessment of ecological status in UK rivers using diatoms. Freshwater Biology, 53, 403–22.Google Scholar
Kelly, M. & Telford, R. J. (2007). Common freshwater diatoms of Britain and Ireland: an interactive identification key (CD ROM). Environment Agency, UK.
Kingston, J. C. & Pappas, J. L. (2009). Quantitative shape analysis as a diagnostic and prescriptive tool in determining Fragilarioaforma (Bacillariophyta) taxon status. Nova Hedwigia, 135, 103–19.Google Scholar
Kociolek, J. P. & Stoermer, E. F. (1986). Phylogenetic relationships and classification of monoraphid diatoms based on phenetic and cladistic methodologies. Phycologia, 25, 297–303.CrossRefGoogle Scholar
Kociolek, J. P. & Stoermer, E. F. (1988). A preliminary investigation of the phylogenetic relationships among the freshwater, apical pore field-bearing cymbelloid and gomphonemoid diatoms (Bacillariophyceae). Journal of Phycology, 24, 377–85.CrossRefGoogle Scholar
Korhola, A., Weckström, J., Holmström, L., & Erästö, P. (2000). A quantitative Holocene climatic record from diatoms in northern Fennoscandia. Quaternary Research, 54, 284–94.CrossRefGoogle Scholar
Korsman, T., Renberg, I., & Anderson, N. J. (1994). A palaeolimnological test of the influence of Norway spruce (Picea abies) immigration on lake-water acidity. The Holocene, 4, 132–40.CrossRefGoogle Scholar
Korsman, T. & Segerström, U. (1998). Forest fire and lake-water acidity in a northern Swedish boreal area: Holocene changes in lake-water quality at Makkassjon. Journal of Ecology, 86, 113–24.CrossRefGoogle Scholar
Kreiser, A. M. & Battarbee, R. W. (1988). Analytical quality control (AQC) in diatom analysis. Proceedings of Nordic Diatomist Meeting, Stockholm, June 10–12, 1987, ed. Miller, U. & Robertsson, A. M., University of Stockholm, Dept Quaternary Research Report, pp. 41–4.Google Scholar
Laird, K. R. & Cumming, B. F. (2001). A regional paleolimnological assessment of the impact of clear-cutting on lakes from the central interior of British Columbia. Canadian Journal of Fisheries and Aquatic Science, 58, 492–505.CrossRefGoogle Scholar
Laird, K. R., Cumming, B. F., & Nordin, R. (2001). A regional paleolimnological assessment of the impact of clear-cutting on lakes from the west coast of Vancouver Island, British Columbia. Canadian Journal of Fisheries and Aquatic Science, 58, 479–91.CrossRefGoogle Scholar
Laird, K. R., Cumming, B. F., Wunsum, S., et al. (2003). Large-scale shifts in moisture regimes from lake records across the Northern Plains of North America during the past two millennia. Proceedings of the National Academy of Sciences of the USA, 100, 2483–8.CrossRefGoogle Scholar
Laird, K. R., Fritz, S. C., Cumming, B. F., & Grimm, E. C. (1998). Early-Holocene limnological and climate variability in the Northern Great Plains. The Holocene, 8, 275–85.CrossRefGoogle Scholar
Laird, K. R., Fritz, S. C., Maasch, K. A., & Cumming, B. F. (1996). Greater drought intensity and frequency before AD 1200 in the Northern Great Plains, USA. Nature, 384, 552–4.CrossRefGoogle Scholar
Legendre, P. & Anderson, M. J. (1999). Distance-based redundancy analysis: testing multispecies responses in multifactorial ecological experiments. Ecological Monographs, 69, 1–24.CrossRefGoogle Scholar
Legendre, P. & Birks, H. J. B. (2011a). Clustering and partitioning. In Tracking Environmental Change Using Lake Sediments, Volume 5: Data Handling and Numerical Techniques, ed. Birks, H. J. B., Lotter, A. F., Juggins, S., & Smol, J. P., Dordrecht: Springer (in press).Google Scholar
Legendre, P. & Birks, H. J. B. (2011b). From classical to canonical ordination. In Tracking Environmental Change Using Lake Sediments, Volume 5: Data Handling and Numerical Techniques, ed. Birks, H. J. B., Lotter, A. F., Juggins, S., & Smol, J. P., Dordrecht: Springer (in press).Google Scholar
Legendre, P. & Legendre, L. (1998). Numerical Ecology, Amsterdam: Elsevier.Google Scholar
Lepš, J. & Šmilauer, P. (2003). Multivariate Analysis of Ecological Data using CANOCO, Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Lotter, A. F. (1998). The recent eutrophication of Baldeggersee (Switzerland) as assessed by fossil diatom assemblages. Holocene, 8, 395–405.CrossRefGoogle Scholar
Lotter, A. F., Ammann, B., & Sturm, M. (1992). Rates of change and chronological problems during the late-glacial period. Climate Dynamics, 6, 233–9.CrossRefGoogle Scholar
Lotter, A. F. & Anderson, N. J. (2011). Limnological responses to environmental changes at inter-annual to decadal time-scales. In Tracking Environmental Change Using Lake Sediments, Volume 5: Data Handling and Numerical Techniques, ed. Birks, H. J. B., Lotter, A. F., Juggins, S. & Smol, J. P., Dordrecht: Springer (in press).Google Scholar
Lotter, A. F. & Birks, H. J. B. (1993). The impact of the Laacher See tephra on terrestrial and aquatic ecosystems in the Black-Forest, Southern Germany. Journal of Quaternary Science, 8, 263–76.CrossRefGoogle Scholar
Lotter, A. F. & Birks, H. J. B. (1997). The separation of the influence of nutrients and climate on the varve time series of Baldeggersee, Switzerland. Aquatic Sciences, 59, 362–75.CrossRefGoogle Scholar
Lotter, A. F. & Birks, H. J. B. (2003). The Holocene palaeolimnology of Sagistalsee and its environmental history – a synthesis. Journal of Paleolimnology, 30, 333–42.CrossRefGoogle Scholar
Lotter, A. F., Birks, H. J. B., Hofmann, W., & Marchetto, A. (1997). Modern diatom, cladocera, chironomid, and chrysophyte cyst assemblages as quantitative indicators for the reconstruction of past environmental conditions in the Alps. 1. Climate. Journal of Paleolimnology, 18, 395–420.CrossRefGoogle Scholar
Lotter, A. F., Birks, H. J. B., & Zolitschka, B. (1995). Late-Glacial pollen and diatom changes in response to two different environmental perturbations – volcanic-eruption and Younger Dryas cooling. Journal of Paleolimnology, 14, 23–47.CrossRefGoogle Scholar
Maher, L. J., Heiri, O., & Lotter, A. F. (2011). Assessment of uncertainties associated with palaeolimnological laboratory methods and microfossil analysis. In Tracking Environmental Change Using Lake Sediments, Volume 5: Data Handling and Numerical Techniques, ed. Birks, H. J. B., Lotter, A. F., Juggins, S., & Smol, J. P., Dordrecht: Springer (in press).Google Scholar
Manly, B. J. F. (2007). Randomization, Bootstrap, and Monte Carlo Methods in Biology, London: Chapman & Hall/CRC Press.Google Scholar
McCarthy, M. A. (2007). Bayesian Methods in Ecology, Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Millar, R. B., Anderson, M. J., & Zunun, G. (2005). Fitting nonlinear environmental gradients to community data: a general distance-based approach. Ecology, 86, 2245–51.CrossRefGoogle Scholar
Mosimann, J. E. (1965). Statistical methods for the pollen analyst: multinomial and negative multinomial techniques. In Handbook of Paleontological Techniques, ed. Kummel, B. & Raup, D., San Francisco, CA: W.H. Freeman.Google Scholar
Mou, D. & Stoermer, E. F. (1992). Separating Tabellaria (Bacillariophyceae) shape groups based on Fourier descriptors. Journal of Phycology, 28, 386–95.CrossRefGoogle Scholar
Munro, M. A. R., Kreiser, A. M., Battarbee, R. W., et al. (1990). Diatom quality control and data handling. Philosophical Transactions of the Royal Society of London Series B – Biological Sciences, 327, 257–61.CrossRefGoogle Scholar
Odgaard, B. V. (1994). The Holocene vegetation history of northern West Jutland, Denmark. Opera Botanica, 123, 1–171.Google Scholar
Oksanen, J. (2004). Multivariate analysis in ecology – lecture notes. See http://cc.oulu.fi/∼jarioksa/opetus/metodi/index.html (accessed 25 January 2010).
Oksanen, J., Kindt, R., Legendre, P., et al. (2008). VEGAN: community ecology package. R package version 1.13–1. http://cran.r-project.org/, http://vegan.r-forge.r-project.org/.
Oksanen, J. & Minchin, P. R. (2002). Continuum theory revisited: what shape are species responses along ecological gradients? Ecological Modelling, 157, 119–29.CrossRefGoogle Scholar
Pappas, J. L., Fowler, G. W., & Stoermer, E. F. (2001). Calculating shape descriptors from Fourier analysis: shape analysis of Asterionella (Heterokontophyta, Bacillariophyceae). Phycologia, 40, 440–56.CrossRefGoogle Scholar
Pappas, J. L. & Stoermer, E. F. (1995). Multidimensional analysis of diatom morphologic and morphometric phenotypic variation and relation to niche. Ecoscience, 2, 357–67.CrossRefGoogle Scholar
Pappas, J. L. & Stoermer, E. F. (1996). Formulation of a method to count number of individuals representative of number of species in algal communities. Journal of Phycology, 32, 693–6.CrossRefGoogle Scholar
Pappas, J. L. & Stoermer, E. F. (2001). Fourier shape analysis and fuzzy measure shape group differentiation of Great Lakes Asterionella Hassall (Heterokontophyta, Bacillariophyceae). In Proceedings of the Sixteenth International Diatom Symposium, ed. Economou-Amilli, A., Athens: Amvrosiou Press, pp. 485–501.Google Scholar
Paradis, E. (2006). Analysis of Phylogenetics and Evolution with R, New York, NY: Springer.Google Scholar
Passy, S. I. (2007). Differential cell size optimization strategies produce distinct diatom richness–body size relationships in stream benthos and plankton. Journal of Ecology, 95, 745–54.CrossRefGoogle Scholar
Passy, S. I. (2008a). Species size and distribution jointly and differentially determine diatom densities in US streams. Ecology, 89, 475–84.CrossRefGoogle Scholar
Passy, S. I. (2008b). Continental diatom biodiversity in stream benthos declines as more nutrients become limiting. Proceedings of the National Academy of Sciences of the USA, 105, 9663–7.CrossRefGoogle ScholarPubMed
Passy, S. I. (2009). The relationship between local and regional diatom richness is mediated by the local and regional environment. Global Ecology and Biogeography, 18, 383–91.CrossRefGoogle Scholar
Passy, S. I. & Legendre, P. (2006a). Are algal communities driven toward maximum biomass? Proceedings of the Royal Society B, 273, 2667–74.CrossRefGoogle ScholarPubMed
Passy, S. I. & Legendre, P. (2006b). Power-law relationships among hierarchical taxonomic categories in algae reveal a new paradox of the plankton. Global Ecology and Biogeography, 15, 528–35.CrossRefGoogle Scholar
Patrick, R. (1949). A proposed biological measure of stream conditions based on a survey of the Conestaga Basin, Lancaster County, Pennsylvania. Proceedings of the National Academy of Sciences of the USA, 101, 277–341.Google Scholar
Patrick, R., Hohn, M. H., & Wallace, J. H. (1954). A new method for determining the pattern of the diatom flora. Notulae Naturae, 259, 1–12.Google Scholar
Patrick, R. & Strawbridge, D. (1963). Variation in the structure of natural diatom communities. American Naturalist, 97, 51–7.CrossRefGoogle Scholar
Pelánková, B., Kunes, P., Chytry, M., et al. (2008). The relationships of modern pollen spectra to vegetation and climate along a steppe–forest–tundra transition in southern Siberia, explored by decision trees. The Holocene, 18, 1259–71.CrossRefGoogle Scholar
Peres-Neto, P. R., Legendre, P., Dray, S., & Borcard, D. (2006). Variation partitioning of species data matrices: estimation and comparison of fractions. Ecology, 87, 2614–25.CrossRefGoogle ScholarPubMed
Peters, J., Baets, B., Verhoest, N. E. C., et al. (2007). Random forests as a tool for ecohydrological distribution modelling. Ecological Modelling, 207, 304–18.CrossRefGoogle Scholar
Potapova, M. G. & Charles, D. F. (2002). Benthic diatoms in USA rivers: distribution along spatial and environmental gradients. Journal of Biogeography, 29, 167–87.CrossRefGoogle Scholar
Prasad, A. M., Iverson, L. R., & Liaw, A. (2006). Newer classification and regression tree techniques: bagging and random forests for ecological predictions. Ecosystems, 9, 181–99.CrossRefGoogle Scholar
Racca, J. M. J., Philibert, A., Racca, R., & Prairie, Y. T. (2001). A comparison between diatom-based pH inference models using artificial neural networks (ANN), weighted averaging (WA) and weighted averaging partial least squares (WA-PLS) regressions. Journal of Paleolimnology, 26, 411–22.CrossRefGoogle Scholar
Racca, J. M. J., Wild, M., Birks, H. J. B., & Prairie, Y. T. (2003). Separating wheat from chaff: diatom taxon selection using an artificial neural network pruning algorithm. Journal of Paleolimnology, 29, 123–33.CrossRefGoogle Scholar
Renberg, I. & Battarbee, R. W. (1990). The SWAP Palaeolimnology Programme: a synthesis. In The Surface Waters Acidification Programme, ed. Mason, B. J., Cambridge: Cambridge University Press, pp. 281–300.Google Scholar
Renberg, I., Korsman, T., & Birks, H. J. B. (1993). Prehistoric increases in the pH of acid-sensitive Swedish lakes caused by land-use changes. Nature, 362, 824–7.CrossRefGoogle Scholar
Reyment, R. A. (1991). Multidimensional Palaeobiology, Oxford: Pergamon Press.Google Scholar
Reyment, R. A. & Savazzi, E. (1999). Aspects of Multivariate Statistical Analyses in Geology, Amsterdam: Elsevier.Google Scholar
Rühland, K., Paterson, A. M., & Smol, J. P. (2008). Hemispheric-scale patterns of climate-related shifts in planktonic diatoms from North American and European lakes. Global Change Biology, 14, 2470–5.Google Scholar
Rusak, J. A., Leavitt, P. R., & McGowan, S. (2004). Millennial-scale relationships of diatom species richness and production in two prairie lakes. Limnology and Oceanography, 49, 1290–9.CrossRefGoogle Scholar
Schelske, C. L. & Stoermer, E. F. (1971). Eutrophication, silica depletion, and predicted changes in algal quality in Lake Michigan. Science, 173, 423–4.CrossRefGoogle ScholarPubMed
Schelske, C. L., Stoermer, E. F., Conley, D. J., Robbins, J. A., & Glover, R. M. (1983). Early eutrophication of the lower Great Lakes: new evidence from biogenic silica in the sediments. Science, 222, 320–2.CrossRefGoogle Scholar
Schröder, H. K., Andersen, H. E., & Kiehl, K. (2005). Rejecting the mean: estimating the response of fen plant species to environmental factors by non-linear quantile regression. Journal of Vegetation Science, 16, 373–82.CrossRefGoogle Scholar
Schulz, M. & Mudelsee, M. (2002). REDFIT: estimating red-noise spectra directly from unevenly spaced paleoclimatic time series. Computations and Geoscience, 28, 421–6.CrossRefGoogle Scholar
Schulz, M. & Stattegger, K. (1997). SPECTRUM: spectral analysis of unevenly spaced paleoclimatic time series. Computations and Geoscience, 23, 929–945.CrossRefGoogle Scholar
Simpson, G. L. (2007). Analogue methods in palaeoecology: using the analogue package. Journal of Statistical Software, 22, 1–29.CrossRefGoogle Scholar
Šmilauer, P. & Birks, H. J. B. (1995). The use of generalised additive models in the description of diatom–environment response surfaces. Geological Survey of Denmark (DGU) Service Report, 7, 42–7.Google Scholar
Smol, J. P. (2008). Pollution of Lakes and Rivers, Oxford: Blackwell.Google Scholar
Smol, J. P., Birks, H. J. B., Lotter, A. F., & Juggins, S. (2011). The march towards the quantitative analysis of palaeolimnological data. In Tracking Environmental Change Using Lake Sediments, Volume 5: Data Handling and Numerical Techniques, ed. Birks, H. J. B., Lotter, A. F., Juggins, S. & Smol, J. P., Dordrecht: Springer (in press).Google Scholar
Smol, J. P., Wolfe, A. P., Birks, H. J. B., et al. (2005). Climate-driven regime shifts in the biological communities of arctic lakes. Proceedings of the National Academy of Sciences of the USA, 102, 4397–402.CrossRefGoogle ScholarPubMed
Soinenen, J. & Eloranta, P. (2004). Seasonal persistence and stability of diatom communities in rivers: are there habitat specific differences? European Journal of Phycology, 39, 153–60.CrossRefGoogle Scholar
Soinenen, J. & Heino, J. (2005). Relationships between local population persistence, local abundance and regional occupancy of species: distribution patterns of diatoms in boreal streams. Journal of Biogeography, 32, 1971–8.CrossRefGoogle Scholar
Soinenen, J., Heino, J., Kokocinski, M., & Muotka, T. (2009). Local–regional diversity relationship varies with spatial scale in lotic diatoms. Journal of Biogeography, 36, 720–7.CrossRefGoogle Scholar
Soinenen, J. & Kokocinski, M. (2006). Regional diatom body size distributions in streams: does size vary along environmental, spatial and diversity gradients? Ecoscience, 13, 271–4.CrossRefGoogle Scholar
Soinenen, J., Paavola, R., & Muotka, T. (2004). Benthic diatom communities in boreal streams: community structure in relation to environmental and spatial gradients. Ecography, 27, 330–42.CrossRefGoogle Scholar
Sokal, R. R. & Rohlf, F. J. (1995). Biometry – The Principles and Practice of Statistics in Biological Research, New York, NY: W.H. Freeman.Google Scholar
Sonderegger, D. L., Wang, H., Clements, W. H., & Noon, B. R. (2009). Using SiZer to detect thresholds in ecological data. Frontiers in Ecology and the Environment, 7, 190–5.CrossRefGoogle Scholar
St Jacques, J.-M., Cumming, B. F., & Smol, J. P. (2009). A 900-year diatom and chrysophyte record of spring mixing and summer stratification from varved Lake Mina, west-central Minnesota, USA. The Holocene, 19, 537–47.CrossRefGoogle Scholar
Stevenson, A. C., Juggins, S., Birks, H. J. B., et al. (1991). The Surface Waters Acidification Project Palaeolimnology Programme: Modern Diatom/Lake-Water Chemistry Data-Set, London: ENSIS Publishing.Google Scholar
Stoermer, E. F. (2001). Diatom taxonomy for paleolimnologists. Journal of Paleolimnology, 25, 393–8.CrossRefGoogle Scholar
Stoermer, E. F. & Ladewski, T. B. (1976). Apparent optimal temperatures for the occurrence of some common phytoplankton species in southern Lake Michigan. University of Michigan, Great Lakes Research Division, Publication 18.
Stoermer, E. F. & Ladewski, T. B. (1982). Quantitative analysis of shape variation in type and modern populations of Gomphoneis herculeana. Nova Hedwigia, 73, 347–86.Google Scholar
Stoermer, E. F., Qi, Y.-Z., & Ladewski, T. B. (1986). A quantitative investigation of shape variation in Didymosphenia (Lyngb.) M Schmidt. Phycologia, 25, 494–502.CrossRefGoogle Scholar
Stoermer, E. F., Wolin, J. A., Schelske, C. L., & Conley, D. J. (1985). An assessment of ecological changes during the recent history of Lake Ontario based on siliceous microfossils preserved in the sediments. Journal of Phycology, 21, 257–76.CrossRefGoogle Scholar
Telford, R. J., Andersson, C., Birks, H. J. B., & Juggins, S. (2004). Biases in the estimation of transfer function prediction errors. Paleoceanography, 19, PA4014, doi: 10.1029/2004PA001072.CrossRefGoogle Scholar
Telford, R. J. & Birks, H. J. B. (2005). The secret assumption of transfer functions: problems with spatial autocorrelation in evaluating model performance. Quaternary Science Reviews, 24, 2173–9.CrossRefGoogle Scholar
Telford, R. J. & Birks, H. J. B. (2009). Design and evaluation of transfer functions in spatially structured environments. Quaternary Science Reviews, 28, 1309–16.CrossRefGoogle Scholar
Telford, R. J., Vandvik, V., & Birks, H. J. B. (2006). Dispersal limitations matter for microbial morphospecies. Science, 312, 1015.CrossRefGoogle ScholarPubMed
Braak, C. J. F. (1985). Correspondence analysis of incidence and abundance data: properties in terms of a unimodal response model. Biometrics, 41, 859–73.CrossRefGoogle Scholar
Braak, C. J. F. (1986). Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology, 67, 1167–79.CrossRefGoogle Scholar
Braak, C. J. F. (1987a). Ordination. In Data Analysis in Community and Landscape Ecology, ed. Jongman, R. H. G., Braak, C. J. F., & Tongren, O. F. R., Wageningen: Pudoc, pp. 91–173.Google Scholar
Braak, C. J. F. (1987b). Unimodal models to relate species to environment. Unpublished Ph.D. thesis, University of Wageningen.
Braak, C. J. F. (1994). Canonical community ordination. Part I: basic theory and linear methods. Ecoscience, 1, 127–40.CrossRefGoogle Scholar
Braak, C. J. F. (1995). Non-linear methods for multivariate statistical calibration and their use in palaeoecology: a comparison of inverse (k-nearest neighbours, partial least squares and weighted averaging partial least squares) and classical approaches. Chemometrics and Intelligent Laboratory Systems, 28, 165–80.CrossRefGoogle Scholar
Braak, C. J. F. (1996). Unimodal Models to Relate Species to Environment, Wageningen: DLO-Agricultural Mathematics Group.Google Scholar
Braak, C. J. F. & Barendregt, L. G. (1986). Weighted averaging of species indicator values: its efficiency in environmental calibration. Mathematical Biosciences, 78, 57–72.CrossRefGoogle Scholar
Braak, C. J. F. & Juggins, S. (1993). Weighted averaging partial least-squares regression (WA-PLS) – an improved method for reconstructing environmental variables from species assemblages. Hydrobiologia, 269/270, 485–502.CrossRefGoogle Scholar
Braak, C. J. F., Juggins, S., Birks, H. J. B., & Voet, H. (1993). Weighted averaging partial least squares regression (WA-PLS): definition and comparison with other methods for species–environment calibration. In Multivariate Environmental Statistics, ed. Patil, G. P. & Rao, C. R.), Amsterdam: Elsevier, pp. 529–560.Google Scholar
Braak, C. J. F. & Looman, C. W. N. (1986). Weighted averaging, logit regression and the Gaussian response model. Vegetatio, 65, 3–11.CrossRefGoogle Scholar
Braak, C. J. F. & Prentice, I. C. (1988). A theory of gradient analysis. Advances in Ecological Research, 18, 271–317.CrossRefGoogle Scholar
Braak, C. J. F. & Šmilauer, P. (2002). CANOCO Reference Manual and CanoDraw for Windows User's Guide: Software for Canonical Community Ordination (version 4.5), Ithaca, NY: Microcomputer Power.Google Scholar
Braak, C. J. F. & Dam, H. (1989). Inferring pH from diatoms – a comparison of old and new calibration methods. Hydrobiologia, 178, 209–23.CrossRefGoogle Scholar
Braak, C. J. F. & Verdonschot, P. F. M. (1995). Canonical correspondence analysis and related multivariate methods in aquatic ecology. Aquatic Sciences, 57, 255–89.CrossRefGoogle Scholar
Theriot, E. C. (1987). Principal component analysis and taxonomic interpretation of environmentally related variation in silification in Stephanodiscus (Bacillariophyceae). British Phycological Journal, 22, 359–73.CrossRefGoogle Scholar
Theriot, E. C., Fritz, S. C., Whitlock, C., & Conley, D. J. (2006). Late Quaternary rapid morphological evolution of an endemic diatom in Yellowstone Lake, Wyoming. Paleobiology, 32, 38–54.CrossRefGoogle Scholar
Theriot, E. C., Håkansson, H., & Stoermer, E. F. (1988). Morphometric analysis of Stephanodiscus alpinus (Bacillariophyceae) and its morphology as an indicator of lake trophic status. Phycologia, 27, 485–93.CrossRefGoogle Scholar
Theriot, E. C. & Stoermer, E. F. (1984). Principal component analysis of character variation in Stephanodiscus niagarae Ehrenb.: morphological variation related to lake trophic status. In Proceedings of the Seventh Diatom Symposium 1982, ed. Mann, D. G., Königstein: Koeltz Scientific Publishers, pp. 91–111.Google Scholar
Torrence, C. & Compo, G. P. (1999). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79, 61–78.2.0.CO;2>CrossRefGoogle Scholar
Tufte, E. R. (1983). The Visual Display of Quantitative Information, Cheshire, CT: Graphics Press.Google Scholar
Tyler, P. A. (1996). Endemism in freshwater algae. Hydrobiologia, 336, 127–35.CrossRefGoogle Scholar
Brink, P. J. & Braak, C. J. F. (1998). Multivariate analysis of stress in experimental ecosystems by principal response curves and similarity analysis. Aquatic Ecology, 32, 163–78.CrossRefGoogle Scholar
Voet, H. (1994). Comparing the predictive accuracy of models using a simple randomization test. Chemometrics and Intelligent Laboratory Systems, 25, 313–23.CrossRefGoogle Scholar
Vanormelingen, P., Verleyen, E., & Vyverman, W. (2008). The diversity and distribution of diatoms: from cosmopolitanism to narrow endemism. Biodiversity and Conservation, 17, 393–405.CrossRefGoogle Scholar
Vyverman, W., Verleyen, E., Sabbe, K., et al. (2007). Historical processes constrain patterns in global diatom diversity. Ecology, 88, 1924–31.CrossRefGoogle ScholarPubMed
Warton, D. I. (2008). Raw data graphing: an informative but under-utilized tool for the analysis of multivariate abundances. Australian Ecology, 33, 290–300.CrossRefGoogle Scholar
Weckström, J. & Korhola, A. (2001). Patterns in the distribution, composition and diversity of diatom assemblages in relation to ecoclimatic factors in Arctic Lapland. Journal of Biogeography, 28, 31–45.CrossRefGoogle Scholar
Williams, D. M. (1985). Morphology, taxonomy and inter-relationships of the ribbed araphid diatoms from the genera Diatoma and Meridion (Diatomaceae: Bacillariophyta). Bibliotheca Diatomologica, 8, 1–228.Google Scholar
Witt, A. & Schumann, A. Y. (2005). Holocene climate variability on millennial scales recorded in Greenland ice cores. Non-linear Processes in Geophysics, 12, 345–52.CrossRefGoogle Scholar
Wolfe, A. P. (1997). On diatom concentrations in lake sediments: results from an inter-laboratory comparison and other tests performed on a uniform sample. Journal of Paleolimnology, 18, 261–8.CrossRefGoogle Scholar
Yee, T. W. (2004). A new technique for maximum-likelihood canonical Gaussian ordination. Ecological Monographs, 74, 685–701.CrossRefGoogle Scholar
Yee, T. W. (2006). Constrained additive ordination. Ecology, 97, 203–13.CrossRefGoogle Scholar
Yee, T. W. & Mitchell, N. D. (1991). Generalized additive models in plant ecology. Journal of Vegetation Science, 2, 587–602.CrossRefGoogle Scholar
Yuan, L. L. (2004). Assigning macroinvertebrate tolerance classification using generalised additive models. Freshwater Biology, 49, 662–77.CrossRefGoogle Scholar
Yuan, L. R. (2007a). Maximum likelihood method for predicting environmental conditions from assemblage composition: the R package bio.infer. Journal of Statistical Software, 22, 1–20.CrossRefGoogle Scholar
Yuan, L. R. (2007b). Using biological assemblage composition to infer the values of covarying environmental factors. Freshwater Biology, 52, 1159–75.CrossRefGoogle Scholar
Zuur, A. F., Ieno, E. N., & Smith, G. M. (2007). Analyzing Ecological Data, New York, NY: Springer.CrossRefGoogle Scholar
Zuur, A. F., Ieno, E. N., Walker, N. J., Savelier, A. A., & Smith, G. M. (2009). Mixed Effect Models and Extensions in Ecology with R, New York, NY: Springer.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×