QUANTITATIVE METHODS IN PALAEOECOLOGY AND PALAEOCLIMATOLOGY

Class 3

Analysis of Stratigraphical Data

Espegrend August 2008

Introduction to temporal stratigraphical dataSingle sequence

Partitioning or zonationSequence splittingRate-of-change analysisGradient analysis and summarisationAnalogue matchingRelationships between two or more sets of variables in same

sequenceTwo or more sequences

Sequence comparison and correlationCombined scalingDifference diagramsMapping

Locally weighted regression (LOWESS)INQUA Commission for the Study of the HoloceneSummary

CONTENTS

INTRODUCTIONIn ecology, analysis of quadrats, lakes, streams, etc. Assume no autocorrelation, namely cannot predict the values of a variable at some point in space from known values at other sampling points.

PALAEOCOLOGY – fixed sample order in time.

strong autocorrelation – temporal autocorrelation

STRATIGRAPHICAL DATA

biostratigraphic, lithostratigraphic, geochemical, geophysical, morphometric, isotopic

multivariate

continuous or discontinuous time series

ordering very important – display, partitioning, trends, interpretation

Numerical Techniques in Palaeoecology

Range of numerical of data-analytical techniques available for the summarisation, synthesis, and interpretation of palaeoecological dataMain purposes

1. Detect major patterns in complex data

2. Summarise data in terms of fossil zones, major trends, and groups of fossil types that covary

3. Identify ‘hidden’ features of data such as statistically significant splits in individual curves, rates of change, etc

4. Interpretation of data in terms of modern analogues (vegetation types) and past environment (e.g. climate)

5. Aid comparison and correlation of sequences from 1 or more sites

6. Display fossil data as maps to explore spatial patternsNumerical techniques are a useful part of the

palaeoecologist’s ‘tool-kit’

SINGLE SEQUENCE

Useful for:

1) description

2) discussion and interpretation

3) comparisons in time and space

 

“sediment body with a broadly similar composition that differs from underlying and overlying sediment bodies in the kind and/or amount of its composition”.

Zonation or Partitioning of Stratigraphical Data

CONSTRAINED CLASSIFICATIONS

1) Constrained agglomerative procedures CONSLINK

CONISS 

2) Constrained binary divisive procedures

Partition into g groups by placing g – 1 boundaries.

Number of possibilities

Compared with non-constrained situation.

Criteria – within-group sum-of-squares or variance SPLITLSQ

– within-group information SPLITINF

n n gg 1 1 2 for

2 11n

n

i

m

k ikikik qpp

1 1

log

3) Constrained optimal divisive analysis OPTIMAL

 

2 group______________________________

3 group

4 group

4) Variable barriers approachBARRIER

All methods in one program: ZONE

n1

n1

n1n2

n2 n3

Pollen diagram and numerical zonation analyses for the complete Abernethy Forest 1974 data set.

Birks & Gordon 1985

CONISS = constrained incremental sum-of-squares (= constrained Word's minimum variance)

What about CONISS in TILIA?

TILIAZONE

OPTIMAL SUM OF SQUARES PARTITIONS OF THE ABERNETHY FOREST 1974 DATA

Number of groups g (zones)

Percentage of total sum-of-

squares

Markers

2 59.3 15

3 28.4 15 32

4 18.9 15 33 41

5 14.7 15 33 41 45

6 10.6 15 32 34 41 45

7 8.1 15 26 32 34 41 45

8 5.8 8 15 26 32 34 41 45

9 4.7 8 15 24 29 32 34 41 45

10 3.9 8 15 24 29 32 33 34 41 45

ZONE

K D Bennett (1996) Determination of the number of zones in a biostratigraphical sequence. New Phytologist 132, 155-170

Broken stick model

Pn ir

i k

n

1 1

BSTICK

HOW MANY ZONES?

Pollen percentage diagram plotted against depth. Lithostratigraphic column is represented; symbols are based on Troels-Smith (1995).

Tzedakis 1994

Ioannina Basin

Ioannina Basin

Tzedakis 1994

Variance accounted for by the nth zone as a proportion of the total variance (fluctuating curve) compared with values from a broken-stick model (smooth curve):

(a) randomized data set,

(b) original data set.

Zonation method: binary divisive using the information content statistic.

Data set; Ioannina.

Original data

Broken stick model

BSTICK

Bennett 1996

Sequence Splitting

Walker & Wilson 1978 J Biogeog 5, 1–21

Walker & Pittelkow 1981 J Biogeog 8, 37–51

SPLIT, SPLIT2

BOUND2

Need statistically ‘independent’ curves

  Pollen influx (grains cm–2 year–1)

PCA or CA or DCA axes CANOCO

Aitchison log-ratio transformation LOGRATIO

i

ikik p

pZ log

m

k

iki m

pp1

loglogwhere

Correlograms of sequence splits with charcoal, inorganic matter and total pollen influxes for three sections of the pollen record. The vertical scales give correlations; the horizontal scales give time lag in years (assuming a sampling interval of 50 years).

Amount of palynological compositional change per unit time.

Calculate dissimilarity between pollen assemblages of two adjacent samples and standardise to constant time unit, e.g. 250 14C years.

Jacobson & Grimm 1986 Ecology 67, 958-966

Grimm & Jacobson 1992 Climate Dynamics 6, 179-184

RATEPOLPOLSTACK

(TILIA)

Rate Of Change Analysis

Graph of distance (number of standard deviations) moved every 100 yr in the first three dimensions of the ordination vs age. Greater distance indicates greater change in pollen spectra in 100yr.Jacobson & Grimm 1986

Graph of distance (number of standard deviations) moved every 100 yr in the first three dimensions of the ordination vs. age. Greater distance indicates greater change in pollen spectra in 100 yr.

MANY PROXIES, ONE SITE

Chord distance between samples at Solsø, Skånsø, and Kragsø, calculated on smoothed data with 35 taxa and interpolated at 400 year and 1,000 year intervals.

- fertile

- poor

- poor

ONE PROXY, MANY SITES

Pollen percentages from Loch Lang, Western Isles, plotted against age (radiocarbon years BP). Data from Bennett (1990).

Pollen percentages from Hockham Mere, eastern England, plotted against age (radiocarbon years BP). Data from Bennett (1983).

Comparison of Holocene rates of change at Loch Lang and Hockham Mere, with 2 - 2 dissimilarity coefficient on unsmoothed data, with a radiocarbon timescale.

High rates of change at Hockham Mere

Rate x5 that at Loch Lang

Data Summarisation by Ordination or Gradient Analysis of Single

Sequence

Ordination methods CA/DCA or PCA

joint plot biplot

   

Sample summary CA/DCA/PCA

 

Species arrangementCCA

CA = correspondence analysis

DCA = detrended correspondence analysis

PCA = principal components analysis

CCA = canonical correspondence analysis

CANOCOR

Biplot of the Kirchner Marsh data; C2 = 0.746. The lengths of the Picea and Quercus vectors have been scaled down relative to the other vectors. Stratigraphically neighbouring levels are joined by a line.

PCA Biplot 74.6%

Gordon 1982

Correspondence analysis representation of the Kirchner Marsh data; C2 = 0.620. Stratigraphically neighbouring levels are joined by a line.

CA Joint Plot 62%

Gordon 1982

Stratigraphical plot of sample scores on the first correspondence analysis axis (left) and of rarefaction estimate of richness (E(Sn)) (right) for Diss Mere, England. Major pollen-stratigraphical and cultural levels are also shown. The vertical axis is depth (cm). The scale for sample scores runs from –1.0 (left) to + 1.2 (right).

DCS axes 1 and 2 for a south Finnish pollen sequence plotted (right) in relation to time.

The 1st and 2nd axis of the Detrended Correspondence Analysis for Laguna Oprasa and Laguna Facil plotted against calibrated calendar age (cal yr BP). The 1st axis contrasts taxa from warmer forested sites with cooler herbaceous sites. The 2nd axis contrasts taxa preferring wetter sites with those preferring drier sites

Haberle & Bennett 2005

Percentage pollen and spore diagram from Abernethy Forest, Inverness-shire. The percentages are plotted against time, the age of each sample having been estimated from the deposition time. Nomenclatural conventions follow Birks (1973a) unless stated in Appendix 1. The sediment lithology is indicated on the left side, using the symbols of Troels-Smith (1995). The pollen sum, P, includes all non-aquatic taxa. Aquatic taxa, pteridophytes, and algae are calculated on the basis of P + group as indicated.

Species arrangement

Pollen types re-arranged on the basis of the weighted average for depth

CANOCOTRAN

Analogue Analysis

Modern training set – similar taxonomy

  – similar sedimentary environment

 

Compare fossil sample 1 with all modern samples, use appropriate DC, find sample in modern set ‘most like’ (i.e. lowest DC) fossil sample 1, call it ‘closest analogue’, repeat for fossil sample 2, etc.

Overpeck et al. 1985 Quat Res 23, 87–108

 

ANALOG

MATCH

MAT

Compare fossil sample i with modern sample j.

Calculate similarity between i and j

Sij

Find modern sample with highest similarity 'ANALOGUE‘

Repeat for all fossil samples

Repeat for all modern samples

? Evaluation

Dissimilarity coefficients, radiocarbon dates, pollen zones, and vegetation types represented by the top ten analogues from the Lake West Okoboji site.

Maps of squared chord distance values with modern samples at selected time intervals

Plots of the minimum squared chord-distance for each fossil spectrum at each of the eight sites.

A schematic representation of how fossil diatom zones/samples in a sediment core from an acidified lake can be compared numerically with modern surface sediment samples collected from potential modern analogue lakes. In this space-for-time model the vertical axis represents sedimentary diatom zones defined by depth and time; the horizontal axis represents spatially distributed modern analogue lakes and the dotted lines indicate good floristic matches (dij = <0.65), as defined by the mean squared Chi-squared estimate of dissimilarity (SCD, see text).

Flower et al. 1997

Flower Flower et alet al. 1997. 1997

Comparison and Correlation Between Time Series

Two or more stratigraphical sets of variables from same sequence.

Are the temporal patterns similar?

(1) Separate ordinations

Oscillation log - likelihood G-test or 2 test

(2) Constrained ordinations

Pollen data - 3 or 4 ordination axes or major patterns of variation Y

Chemical data - 3 or 4 ordination axes X

Depth as a covariable

Does 'chemistry' explain or predict 'pollen'? i.e. is variance in Y well explained by X?

Lotter et al., 1992 J. Quat. Sci. Pollen 16O/18O (depth)

34% 16% 12%

79% 12% 4% 1%

Comparison and Correlation Between Time Series

Two or more stratigraphical sets of variables from same sequence.

Are the temporal patterns similar?

(1) Separate ordinations

Oscillation log - likelihood G-test or 2 test

(2) Constrained ordinations

Pollen data - 3 or 4 ordination axes or major patterns of variation Y

Chemical data - 3 or 4 ordination axes X

Depth as a covariable

Does 'chemistry' explain or predict 'pollen'? i.e. is variance in Y well explained by X?

Lotter et al., 1992 J. Quat. Sci. Pollen 16O/18O (depth)

Pollen, oxygen-isotope stratigraphy, and sediment composition of Aegelsee core AE-1 (after Wegmüller and Lotter 1990)

Pollen and oxygen-isotope stratigraphy of Gerzensee core G-III (after Eicher and Siegenthaler 1976)

Is there a statistically significant relationship between the pollen stratigraphy and the stable-isotope record?

Summary of the results from detrended correspondence analysis (DCA) of late-glacial pollen spectra from five sequences. The percentage variance represented by each DCA axis is listed.

Reduce pollen data to DCA axes. Use these then as ‘responses’

Site No. of samples

No. of taxa

DCA Axis

1 2 3 4

Aegelsee AE-1 100 26 57.2 12.0 2.3 1.4

Aegelsee AE-3 54 32 44.3 3.3 1.5 1.4

Gerzensee G-III 65 28 37.6 4.0 1.2 0.9

Faulenseemoos 62 25 44.1 18.8 5.0 3.8

Rotsee RL-250 44 23 38.2 13.3 3.1 2.3

Results of redundancy analysis and partial redundancy analysis permutation tests for the significance of axis 1 when oxygen isotopes and depth are predictor variables, when oxygen is the only predictor, and when oxygen isotopes are the predictor variable and depth is a covariable.

Site Predictor variable: 18O

and depth

Predictor variable: 18O

Covariable: depth

Predictor variable:

18O

Number of response

variables (DCA axes)

Pollen DCA axes

Aegelsee AE-1

0.01a 0.01a 0.02a 2

Aegelsee AE-3

0.01a 0.16 0.20 1

Gerzensee G-III

0.01a 0.46 0.57 1

Faulenseemoos

0.01a 0.01a 0.01a 3

Rotsee RL-250

0.01a 0.21 0.08 2

a Significant at p< 0.05

Lotter et al. 1992

Regional zones, description of common features, interpretation, detection of unique features.

Sequence comparison and correlation.

Sequence slotting SLOTSEQ

FITSEQ

CONSSLOT

 

Combined scaling of two or more sequences. CANOCO

Difference diagrams

Mapping procedures

ANALYSIS OF TWO OR MORE SEQUENCES

S2 (B1, B2, ..., B7), illustrating the contributions to the measure of discordance (S1, S2) and the 'length' of the sequences, (S1, S2).

The results of sequence-slotting of the Wolf Creek and Horseshoe Lake pollen sequences ( = 2.095). Radiocarbon dates for the pollen zone boundaries are also given, expressed as radiocarbon years before present (BP).

SLOTSEQ

Birks & Gordon 1985

Slotting of the sequences S1 (A1, A2, ..., A10) and

Sequence Comparison and Correlation

Comparison of oxygen-isotope records from Swiss lakes Aegelsee (AE-3), Faulenseemoos (FSM) and Gerzensee (G-III) with the Greenland Dye 3 record (Dansgaard et al. 1982). LST marks the position of the Laacher See Tephra (11,000 yr BP). Letters and numbers mark the position of synchronous events (for details see text).

AE-3 FSM G III Dye 3

Psi values for pair-wise sequence slotting of the stable-isotope stratigraphy at five Swiss late-glacial sites and the Dye 3 site in Greenland. Values above the diagonal are constrained slotting, using the three major shifts shown in previous figure; values below the diagonal are for sequence slotting in the absence of any external constraints. The mean 18O and standard deviation for each sequence is also listed. CONSLOXY

Lotter et al 1992

Fugla Ness, Shetland

Combined Scaling or Ordinations

Pollen diagram from Sel Ayre showing the frequencies of all determinable and indeterminable pollen and spores expressed as percentages of total pollen and spores (P).

Abbreviations: undiff. = undifferentiated, indet = indeterminable.

The 1st and 2nd axis of the Detrended Correspondence Analysis for Laguna Oprasa and Laguna Facil plotted against calibrated calendar age (cal yr BP). The 1st axis contrasts taxa from warmer forested sites with cooler herbaceous sites. The 2nd axis contrasts taxa preferring wetter sites with those preferring drier sites

Haberle & Bennett, 2004

Comparison of Bjärsjöholmssjön and Färskesjön using principal component analysis. The mean scores of the local pollen zones and the ranges of the sample scores in each zone are plotted on the first and second principal components, and are joined up in stratigraphic order. The Blekinge regional pollen assemblage zones are also shown.

Birks & Berglund 1979

Comparison of Färskesjön and Lösensjön using principal component analysis. The mean scores of the local pollen zones and the ranges of the sample scores in each zone are plotted on the first and second principal components, and are joined up in stratigraphic order. The regional pollen assemblage zones are also shown.

Pollen percentage diagram of selected taxa plotted against depth. Lithostratigraphic symbols are based on Troels-Smith (1995). For correlations and ages see Tzedakis (1993, 1994).

Tzedakis & Bennett 1995

5e

7c

9c

11a + b + c

Pollen percentage diagrams of selected arboreal taxa of the Metsovon, Zista, Pamvotis and Dodoni I and II forest periods of Ioannina 249

5e

7c

9c

11a + b + c

Tzedakis & Bennett 1995

Solar insolation values of mid-month day for selected periods at latitude 39º40'N. Values are given for July and January extremes and July minus January for each interglacial period calculated at thousand year intervals. Values are expressed in cal cm2 day-1. In parentheses are percentage differences from 10 ka values. Timing of extreme insolation excursions also given. Data from a computer program written by N.G. Pisias, based on Berger (1978). Chronology based on Imbrie et al. (1984) and Martinson et al. (1987)

Combined plot of sample scores on the first two principal components for Metsovon, Zista, Pamvotis, and Dodoni I forest periods. Asterisks indicate the base of the intervals considered.

Results of comparison of vegetation and climatic signatures of different interglacial periods. '+' sign means similar and '-' means different. First sign refers to climate and second to vegetation character.

Different climate, similar pollen in one comparison

Tzedakis & Bennett, 1995

In multi-proxy studies (e.g. pollen, diatoms, chironomids, etc. studied on the same core), important question is ‘are the major stratigraphical patterns of variation (‘signal’) the same in all proxies?’

Laguna Facil, southern Chile

Massaferro et al. 2005 Quaternary Science Reviews 24: 2510-2522

Pollen and chironomids studied on the same core

Simplified each data-set to the first ordination axes of a correspondence analysis (CA) and a principal components analysis (PCA) for both data-sets

Multi-proxy studies

Massaferro et al. 2005

Chironomid stratigraphy

Massaferro et al. 2005

Pollen stratigraphy

Massaferro et al. 2005

Can detect similarities in both proxies and differences

1. Major change in both prior to 14,700 cal yr BP.

2. Changes in the chironomids tend to lag behind changes in the pollen. Perhaps a chironomid response to changes in vegetation (tree canopy and forest type) or lake chemistry, resulting from changes in catchment soils as a result of vegetational change.

3. At about 7200 cal yr BP, chironomids change before the pollen. May be a response to climate change.

4. Strong correlations between the charcoal stratigraphy and pollen and chironomid stratigraphies. Probable importance of fire and/or vulcanism in influencing both vegetational and limnological dynamics.

Charc

o

al

Massaferro et al. 2005

Can use ordination methods to summarise several palaeoecological proxies and to compare with other proxies

Major changes between pre-European period (A)

and European settlement (B)

Lake Euramoo, NE Queensland, last 800 years

Haberle et al. 2006

Tested how well different proxies ‘predict’ or ‘explain’ (in a statistical sense) other proxies

Only proxy that significantly predicted other proxies was pollen that predicted changes in diatoms (25.4%) and chironomids (15.4%)

Illustrates the importance of catchment and its vegetation on the lake and its biota

Difference Diagrams

Pollen percentage difference diagram for the Hockham Mere and Stow Bedon sequences for selected taxa, plotted against radiocarbon age. Note different percentage scale for each taxon.

Location of the two coring sites, Rezina Marsh and Gramousti Lake, in relation to

altitude.

Pollen percentage difference diagram to compare results between the pollen percentage values of selected taxa at Rezina Marsh and Gramousti Lake. The values are plotted against an estimated time scale and have been calculated at a time interval of 250 yr. Values to the right of the axis (blue) indicate a higher recorded percentage of a taxon at Rezina Marsh, values to the left (red) indicate a higher recorded percentage of the taxon at Gramousti Lake.

Distribution in northern England of maximum

values for pollen of Tilia during the period 5000 to 3000 BC

Mapping

Maps of pollen frequencies 5000 years BP

Pinus Betula

Maps of pollen frequencies 5000 years BP

Ulmus Corylus

Maps of pollen frequencies 5000 years BP

Quercus Tilia

Map of pollen frequencies 5000 years BP

Alnus

Map of scores of pollen spectra on 1st principal component, 5000 years BP

Map of scores of pollen spectra on 2nd principal component, 5000 years BP

Map of scores of pollen spectra on 3rd principal component, 5000 years BP

Provisional map of wood-land types for the British Isles 5000 years ago.

Vegetation regions reconstructed from pollen data for 9000, 6000, 3000, and 0 yr BP

LOCALLY WEIGHTED REGRESSION

W.S. Cleveland LOWESS locally weighted regression or LOESS scatterplot smoothing

May be unreasonable to expect a single functional relationship between Y and X throughout range of X.

(Running averages for time-series – smooth by average of yt-1, y, yt+1 or add weights to yt-1, y, yt+1)

(A) Survival rate (angularly transformed) of tadpoles in a single enclosure plotted as a function of the average body mass of the survivors in the enclosure. Data from Travis (1983). Line indicates the normal least-squares regression. (B) Residuals from the linear regression depicted in part A plotted as a function of the independent variable, average body mass.

Linear

(A) DATA from previous graph A with a line depicting a least-square quadratic model. (B) Data from previous graph A with a line depicting LOWESS regression model with f = 0.67. (C) Data from previous graph A with a line depicting a LOWESS regression model with f = 0.33.

Quadratic LOWESS LOWESS

LOWESS - more general

1. Decide how “smooth” the fitted relationship should be.

2. Each observation given a weight depending on distance to observation x1 for all adjacent points considered.

3. Fit simple linear regression for adjacent points using weighted least squares.

4. Repeat for all observations.

5. Calculate residuals (difference between observed and fitted y).

6. Estimate robustness weights based on residuals, so that well-fitted points have high weight.

7. Repeat LOWESS procedure but with new weights based on robustness weights and distance weights.

Repeat for different degree of smoothness, to find “optimal” smoother. R

How the LOESS smoother works. The shaded region indicates the window of values around the target value (arrow). A weighted linear regression (broken line) is computed, using weights given by the 'tri-cube' function (dotted curve). Repeating this process for all target values gives the solid curve.

tri-cube function

linear regression

target value

Round Loch of Glenhead

LOWESS curve

INQUA COMMISSION FOR THE STUDY OF THE

HOLOCENE

Working Group on Data-Handling Methods

To get newsletters, software, etc.

http://www.chrono.qub.ac.uk/inqua/

SUMMARY

1. A range of robust numerical techniques are now available to assist in the partitioning, summarisation, synthesis, and interpretation of palaeoecological data

2. Gradient analysis or ordination help to detect the major patterns of variation in complex palaeoecological data

3. More specialised techniques like sequence-splitting, rate-of-change analysis, and analogue analysis can be useful in particular research studies

4. There are several techniques for summarising patterns of similarity and dissimilarity at two or more sites

5. Palaeoecological data can be mapped at a range of spatial scales

6. Locally weighted regression (LOWESS) is a useful tool for highlighting ‘signal’ in noisy stratigraphical data