calibrating indicators to environmental variables using surface-sediment training sets
Copyright © 2008 by DBS

Contents
• Using biology to infer environmental conditions
• Surface-sediment calibration or training sets
• Assessing the influence of environmental variables on species distributions and the development of quantitative transfer functions
• Assumptions of quantitative inferences
• Exploring the values of interpreting ‘annually integrated ecology’: some questions and answers regarding surface-sediment calibration sets

The Paleolimnologists Rosetta Stone
Using Biology to infer Environmental Conditions
• Distribution and abundances of organisms are controlled by physical, chemical and biological factors
• Fossil communities can be used to infer past environments
– Assuming ecological characteristics have not changed with time

The Paleolimnologists Rosetta Stone
Using Biology to infer Environmental Conditions
Match the organism with its environment

The Paleolimnologists Rosetta Stone
Using Biology to infer Environmental Conditions
• Specialists – organisms are specific indicators of environmental conditions
• Generalists – organisms with less well defined optima and tolerances
• Paleolimnologists have to deal with 100’s of species
• Unlike polar bears and camels the ecological characteristics of our indicator species are poorly known

The Paleolimnologists Rosetta Stone
Surface-sediment Calibration or Training Sets
• Calibration sets involve sampling for a suite of environmental variables which are then related to species preserved in the surface sediments
– Uses the known present-day ecology of organisms to infer past environments
– Method for estimating an organisms optima and tolerances e.g. temperature, pH, nutrients, oxygen levels, water chemistry etc.
See Dixit et al, 1993; Charles, 1990

B) Top 1 cm of sediments + indicators
(e.g. diatom valves, chironomid head capsules, etc.)
C) Data Matrix’s
(i) environmental data
The Paleolimnologists Rosetta Stone
Surface-sediment Calibration or Training Sets
+
A) Appropriate calibration lakes and associated limnological data required that spans the range of environmental conditions we are interested in
D) Statistical techniques environmental variables that are highly related to the species abundance are used in the development of transfer functions that include estimates of species parameters (e.g. optima, tolerances) based on this training set

The Paleolimnologists Rosetta Stone
Surface-sediment Calibration or Training Sets e.g. PIRLA-II acidification project
( P aleoecological I nvestigation of R ecent L ake A cidification )
Calibration lakes: typically ~ 40 lakes
- used 71 lakes with pH 4.4 to 7.8
Ecological indicators:
- Top 1 cm gives representative sample of the lake-catchment system
- Gives empirical evidence of present-day limnological characteristics
Transfer function: two data-sets are combined
- present-day organisms are used to infer environmental variables from microfossils
- e.g. infer lake pH from chrysophyte species composition

The Paleolimnologists Rosetta Stone
Assessing the Influence of Environmental Variables
• Most organisms respond to environmental gradients in a unimodal fashion
Optimum (u)
Track a species from -50
°C to +50 ° C there is one temperature where the species is most abundant
Tolerance (t)
Generalists have broad curves (large t), specialists have narrow tolerances
Estimated from SD of curve
Gaussian unimodal curve for a hypothetical species showing the optimum ( u ) and tolerance ( t ) to an environmental variable.
Modified from Jongman et al.
(1995)

The Paleolimnologists Rosetta Stone
Assessing the Influence of Environmental Variables
• What would a species response curve look like for a polar bear (stenothermal)? For a human (eurythermal)?

The Paleolimnologists Rosetta Stone
Assessing the Influence of Environmental Variables
Adriondack lake study
Chrysophytes:
– Mallomonas hindonii scales common at low pH
–
Synura curtispina scales common at high pH
– M. crassisquama optimum at pH 7 with broad unimodal curve (generalist)
Cumming et al, 1992

The Paleolimnologists Rosetta Stone
Assessing the Influence of Environmental Variables
• Not all taxa show unimodal response curves (see Birks, 1995)
• If sampled environmental gradient is not long enough certain taxa may appear to respond linearly
The same Gaussian unimodal curve shown in Figure 6.2, except in this case only a small part (shaded area) of the environmental gradient (i.e. only lakes that had a pH between 4.5 and 5.8) was used in the training set. In this example, the abundance of this hypothetical taxon would appear to be linearly related to lakewater pH between 4.5 and 5.8, and so linear statistical techniques would be most appropriate for this analysis. However, if the entire pH gradient was sampled, then this taxon would exhibit a unimodal response to pH, and unimodal techniques would be most appropriate.

The Paleolimnologists Rosetta Stone unimodal response curves
(see Birks, 1995)
If sampled environmental gradient is not long enough certain taxa may appear to respond linearly

The Paleolimnologists Rosetta Stone
Assessing the Influence of Environmental Variables
•
Which environmental variables exert the most influence on the distribution of taxa?
• Transfer function will be only as good as the species-environmental factor relationship
•
In order to use an organism to reconstruct a specific environmental variable their distribution must be influenced by that variable (often linearly related)
•
Multivariate ordination and regression techniques are used

The Paleolimnologists Rosetta Stone
Assessing the Influence of Environmental Variables
• Unimodal response
– Gradient analysis: Indirect (correspondence analysis, CA) and direct
(canonical correspondence analysis, CCA)
•
Linnear response
– Indirect (Principal components analysis, PCA) or direct (redundancy analysis, RDA)
• Method is chosen based on looking at scatter plot of data
• Indirect techniques - reveal patterns within species data
• Direct techniques - species are modeled to environmental predictors analogous to simple multiple regression (except all species are included at the same time)

The Paleolimnologists Rosetta Stone
Assessing the Influence of Environmental Variables
•
Example CCA plot for 149 diatom taxa from 37 calibration lakes
Canonical correspondence analysis (CCA) ordination plots of (A) 149 diatom taxa identified from the surface sediments of 37 Adirondack Park calibration lakes. Individual diatom taxa are shown as triangles.
The four significant environmental variables are shown as arrows. B) Same CCA biplot, but this time showing the position of the 37 calibration lakes (marked by numbers) as they plot on the ordination.
Modified from Kingston et al.
(1992).

The Paleolimnologists Rosetta Stone
Assessing the Influence of Environmental Variables
High Al, low pH diatoms
CCA identified 4 significant variables
Higher pH diatoms
Orientation of arrows show correlations e.g. pH correlates with axis 1 e.g. DOC and Al correlates with Axis 2
Low pH, high DOC diatoms

The Paleolimnologists Rosetta Stone
Assessing the Influence of Environmental Variables
High Al, low pH lakes
CCA identified 4 significant variables
Higher pH lakes
Orientation of arrows show correlations e.g. pH correlates with axis 1 e.g. DOC and Al correlates with Axis 2
Low pH, high DOC lakes

The Paleolimnologists Rosetta Stone
Assessing the Influence of Environmental Variables
• Once it is determined which variable has greatest influence on species distribution
• Model taxa to these gradients and construct transfer function

A) Selection of a modern set of
The Paleolimnologists Rosetta Stone both environmental variables relates distribution and
Assessing the Influence of Environmental Variables abundance of indicator to and species assemblages
(often from the surface sediments) are collected the environmental variable of interest
C) lakes from which the species responses were estimated are used to generate the inference model (thus producing an overly optimistic r 2 )
B) The regression step, where species response curves are estimated based on their distributions in the calibration set of lakes
D) an independent test set of lakes is used to generate the inference model

The Paleolimnologists Rosetta Stone
Assessing the Influence of Environmental Variables
The relationship between measured and diatom-inferred lakewater pH
(A) and plot for pH residuals (B) for a 71 lake surface sediment calibration set from Adirondack Park, New York. Figures C and D show the relationships for lakewater acid neutralizing capacity (ANC).

The Paleolimnologists Rosetta Stone
Assumptions of Quantitative Inferences
1.
Taxa used in the training set are systematically related to the environment in which they live
2. The Environmental variable is related to, or is linearly related to, an ecologically important determinant of the ecosystem under study
3. The mathematical methods used to model the responses of taxa to environmental variables are adequate
4. Other environmental variables, besides those of interest, have negligible influences on the inferences
5. The taxa used in the training set are well represented in the downcore paleolimnological assessment, and their ecological responses to the environmental variables of interest have not changed over time

The Paleolimnologists Rosetta Stone
Summary
• Large numbers of biological organisms are preserved in sediments
• Before ecological conditions can be reconstructed ecological optima and tolerances of the indicators must be estimated e.g. paleolimnologist can determine that a particular organism is more common in acidic or high nutrient waters
• Surface-sediment calibration sets are an extension of this idea which use statistics to reconstruct past environmental conditions

References
• Birks, H.J.B. (1995) Quantitative paleoenvironmental reconstructions. In Maddy, D. and
Brew, J.S. (eds.), Statistical Modelling of Quaternary Science Data. Quaternary Research
Association, Cambridge, pp. 161-254.
• Birks, H.J.B. (1998) Numerical tools in paleolimnology – progress, potentialities, and problems. Journal of Paleolimnology , Vol. 20, pp. 307-322.
• Bradbury, J.P. (1988) A climatic-limnologic model of diatom succession for paleolimnological interpretation of varved sediments at Elk Lake, Minnesota. Journal of
Paleolimnology , Vol. 1, pp. 115-131.
• Brinkhurst, R.O. (1974) The Benthos of Lakes . Macmillan, London.
• Charles, D.F. and Smol, J.P. (1990) The PIRLA II project: regional assessment of lake acidification trends. Verhandlungen der Internationalen Vereinigung von Limnologen , Vol.
24, pp. 474-480.
• Charles, D.F. (1990) A check list for describing and documenting diatom and chrysophyte calibration data sets and equations for inferring water chemistry. Journal of
Paleolimnology , Vol. 3, pp. 175-178.

References
• Dixit, S.S., Cumming, B.F., Kingston, J.C., Smol, J.P., Birks, H.J.B., Uutala, A.J., Charles,
D.F. and Camburn, K. (1993) Diatom assemblages from Adriondack lakes (N.Y., USA) and the development of inference models for retrospective environmental assessment.
Journal of Paleolimnology , Vol. 8, pp. 27-47.
• Fritz, S.F., Cumming, B.F., Gasse, F. and Laird, K. (1999) Diatoms as indicators of hydrologic and climatic change in saline lakes. In Stoermer, E.F. and Smol, J.P. (eds.),
The Diatoms: Applications for the Environmental and Earth Sciences . Cambridge
University Press, Cambridge, pp. 41-72.
• Glew, J. (1991) Miniature gravity corer for recovering short sediment cores. Journal of
Paleolimnology , Vol. 5, p. 285-287.
• Hutchinson, G.E. (1958) Concluding remarks. Cold Spring harbor Symposium on
Quantitative Biology , Vol. 22, pp. 415-427.
• Jackson, S.T. and Overpeck, J.T. (2000) Responses of plant populations and communities to environmental changes in the late Quaternary. Paleobiology , Vol. 26 (Suppl. 4), pp.
194-200.
• Jongman, R.H.G., ter Braak, C.J.F. and van Tongeren, OF.R. (eds.) (1995) Data Analysis in Community and Landscape Ecology . Cambridge University Press, Cambridge.

References
• Kingston, J.C., Birks, H.J.B., Uutala, A.J., Cumming, B.F. and Smol, J.P. (1992)
Assessing trends in fishery resources and lake water aluminum for paleolimnological analyses of siliceous algae. Canadian Journal of Fisheries and Aquatic Sciences , Vol. 49, pp. 116-127.
• Line, J.M., ter Braak, C.J.F. and Birks, H.J.R. (1994) WACALIB version 3.3: a computer intensive program to reconstruct environmental variables from fossil assemblages by weighted averaging and to derive sample-specific errors of prediction. Journal of
Paleolimnology , Vol. 10, pp. 147-152.
• Silver, P.A. and Hamer, J.S. (1992) Seasonal periodicity of Chrysophyceae and
Synurophyceae in a small New England lake: implications for paleolimnological research.
Journal of Phycology , Vol. 28, pp. 186-198.
• Smol, J.P. (1990) Are we building enough bridges between paleolimnology and aquatic ecology? Hydrobiologia , Vol. 214, pp. 201-206.
• ter Braak, C.J.F. (1986) Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology, Vol. 67, pp. 1167-1179.