A Bayesian palaeoenvironmental transfer function model for acidified lakes
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A Bayesian approach to palaeoecological environmental reconstruction deriving from the unimodal responses generally exhibited by organisms to an environmental gradient is described. The approach uses Bayesian model selection to calculate a collection of probability-weighted, species-specific response curves (SRCs) for each taxon within a training set, with an explicit treatment for zero abundances. These SRCs are used to reconstruct the environmental variable from sub-fossilised assemblages. The approach enables a substantial increase in computational efficiency (several orders of magnitude) over existing Bayesian methodologies. The model is developed from the Surface Water Acidification Programme (SWAP) training set and is demonstrated to exhibit comparable predictive power to existing Weighted Averaging and Maximum Likelihood methodologies, though with improvements in bias; the additional explanatory power of the Bayesian approach lies in an explicit calculation of uncertainty for each individual reconstruction. The model is applied to reconstruct the Holocene acidification history of the Round Loch of Glenhead, including a reconstruction of recent recovery derived from sediment trap data.
The Bayesian reconstructions display similar trends to conventional (Weighted Averaging Partial Least Squares) reconstructions but provide a better reconstruction of extreme pH and are more sensitive to small changes in diatom assemblages. The validity of the posteriors as an apparently meaningful representation of assemblage-specific uncertainty and the high computational efficiency of the approach open up the possibility of highly constrained multiproxy reconstructions.
KeywordsEnvironmental reconstruction Transfer functions Bayesian model selection Diatoms Acidification
We are grateful to Devinder Sivia, John Birks and Richard Telford for useful discussions. We are additionally grateful for the constructive comments of both referees which have substantially improved the paper.
- Battarbee RW, Juggins S, Gasse F, Anderson NJ, Bennion H, Cameron NG, Ryves DB, Pailles C, Chalie F, Telford R (2001) European Diatom Database (EDDI). An information system for palaeoenvironmental reconstruction. ECRC Research Report No. 81, 94 ppGoogle Scholar
- Birks HJB (1995) Quantitative palaeoenvironmental reconstructions. In: Maddy D, Brew JS (eds) Statistical modelling of quaternary science data. Technical guide 5. Quaternary Research Association, Cambridge, pp 161–254Google Scholar
- Birks HJB (2003) Quantitative palaeoenvironmental reconstructions from Holocene biological data. In: Mackay AW, Battarbee RW, Birks HJB, Oldfield F (eds) Global change in the Holocene. Hodder Arnold, New York, pp 107–123Google Scholar
- Birks HH, Birks HJB (2003) Reconstructing Holocene climates from pollen and plant macrofossils. In: Mackay AW, Battarbee RW, Birks HJB, Oldfield F (eds) Global change in the Holocene. Hodder Arnold, New York, pp 342–357Google Scholar
- Box CEP, Tiao GC (1992) Bayesian inference in statistical analysis. Wiley-Interscience, New York, pp 608Google Scholar
- Imbrie J, Kipp NG (1971) A new micropaleontological method for quantitative paleoclimatology: application to a late Pleistocene Carribean core. In: Turekian KK (ed) The late cenozoic glacial ages. Yale University Press, New Haven and London, pp 71–181Google Scholar
- Jones VJ, Flower RJ (1986) Spatial and temporal variability in periphytic diatom communities: palaeoecological significance in an acidified lake. In: Smol JP, Battarbee RW, Davis RB, Meriläinen J (eds) Diatoms and Lake Acidity. Dr W. Junk Publishers, Dordrecht, pp 87–94Google Scholar
- Juggins S (2003) C2 user guide. Software for ecological and palaeoecological data analysis and visualisation. University of Newcastle, Newcastle upon Tyne, UK, 69 ppGoogle Scholar
- Rymer L (1978) The use of uniformitariansim and analogy in palaeoecology, particularly pollen analysis. In: D Walker JC Guppy (eds) Biology and quaternary environments. Australian Academy of Sciences, Canberra, pp 245–258Google Scholar
- Stevenson AC, Juggins S, Birks HJB, Anderson DS, Anderson NJ, Battarbee RW, Berge F, Davis RB, Flower RJ, Haworth EY, Jones VJ, Kingston VJ, Kreiser AM, Line JM, Munro MAR, Renberg I (1991) The surface waters acidification project palaeolimnology program: modern diatom/lake-water chemistry set. ENSIS, London, 86 ppGoogle Scholar
- ter Braak CJF (1995) Non linear models for multivariate statistical calibration and their use in palaeoecology; a comparison of inverse k-nearest neighhbours, partial least squares and weighted averaging partial least squares, and classical approaches. Chemomet Intell Lab 28:165–180CrossRefGoogle Scholar
- ter Braak CJF, Juggins S, Birks HJB, van der Voet H (1993) Weighted averaging partial least squares regression (WA-PLS): definition and comparison with other methods for species-environment calibration. In: GP Patil CR Roa (eds) Multivariate environmental statistics. Elsevier Science Publishers, Amsterdam, pp 525–560Google Scholar