Quantitative estimates of water tables and soil moisture in Holocene peatlands from testate amoebae

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1998 8: 261The HoloceneWendy A. Woodland, Dan J. Charman and Peter C. Sims

Quantitative estimates of water tables and soil moisture in Holocene peatlands from testate amoebae

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The Holocene 8,3 (1998) pp. 261–273

Quantitative estimates of water tablesand soil moisture in Holocene peatlandsfrom testate amoebaeWendy A. Woodland,1 Dan J. Charman2 and Peter C. Sims2

(1School of Geography and Environmental Management, University of the Westof England, Bristol BS16 1QY, UK;2Department of Geographical Sciences,University of Plymouth, Plymouth PL4 8AA, UK)

Received 4 December 1996; revised manuscript accepted 9 September 1997

Abstract: Changes in surface wetness on Holocene ombrotrophic mires have principally been estimated fromplant macrofossils and humification. Testate amoebae (Protozoa: Rhizopoda) provide an additional techniqueand have the potential to provide improved quantitative estimates of water-table depths and soil moisture. Therelationship between hydrology and testate amoebae assemblages from 163 samples on nine British mires isexplored using canonical correspondence analysis (CCA). Mean annual water-table depth and percentage soilmoisture are two of the most important environmental variables related to the distribution of testate amoebaewithin peat. Transfer functions for these variables are developed using four underlying models; weighted aver-aging (WA), tolerance downweighted weighted averaging (WA-Tol), weighted averaging partial least squares(WA-PLS) and partial least squares (PLS). In ‘jack-knifed’ validation, WA produced the lowest predictionerrors for water table, but was outperformed by WA-Tol for percentage moisture. WA and WA-Tol basedtransfer functions are then applied to a fossil data set from Bolton Fell Moss, Cumbria. This methodologyoffers a new technique for reconstructing surface wetness changes on British ombrotrophic and oligotrophicmires and provides data in terms of a meaningful environmental parameter. The cosmopolitan distribution oftestate amoebae species suggests that the technique has a much wider geographical potential.

Key words: Ombrotrophic peatlands, palaeohydrology, surface wetness, testate amoebae, Protozoa, Rhizopoda,water tables, soil moisture.

Introduction

There has been much discussion of surface wetness changes inpeatlands since the publication of the Blytt-Sernander schemelinking peat stratigraphy with climate change (Sernander, 1908).Many studies concentrated on the use of peat stratigraphy withfield descriptions of humification and gross plant macrofossil con-tent (Granlund, 1932; Walker and Walker, 1961). Later refine-ments of plant macrofossil analyses included semiquantitativeestimates of abundance (Barber, 1981) and the application of indi-ces of wetness toSphagnumremains (Dupont, 1986) and otherplant macrofossils (Haslam, 1987). More recently, Barberet al.(1994a) used axis one scores from canonical correspondenceanalyses as a more sophisticated indicator of wetness fluctuationsin raised mires. There has also been considerable progress in themeasurement and application of peat humification, with colori-metric assays of the concentration of organic acids (Blackford andChambers, 1993). These have been used to identify periods wherehumification changes occur simultaneously across two or moresites which are hydrologically unconnected, implying a climatic

Arnold 1998 0959-6836(98)HL223RP

cause (Blackford and Chambers, 1991; 1995). However, despitethese improvements, both techniques remain essentiallysemiquantitative as there is no modern training set from whichabsolute palaeoindicator values are derived. Thus the magnitudesof inferred changes in surface wetness remain difficult to interpretin terms of any meaningful environmental parameter such aswater table or moisture content of the substrate.

There are also specific problems with one or other of thesetechniques, such as the lack of potential modern analogues forSphagnum imbricatumpeats (Stonemanet al., 1993) and specieseffects on humification (Blackford and Chambers, 1993). How-ever, there is still considerable potential for the use of thesemethods in combination (Barberet al., 1994b) which will helpresolve difficulties with using individual approaches. A furthertechnique which has been used with some success is based ontestate amoebae or ‘rhizopods’ (Tolonen, 1986; Van der Molenand Hoekstra, 1988). Warner and Charman (1994) introduce con-siderable improvements in this technique and indicate its appli-cation to mires in Britain.

Testate amoebae are microscopic animals, typically between

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262 The Holocene 8 (1998)

20 mm and 250mm in size, which inhabit the surface layers ofpeatlands and other moist soils, as well as living in the benthicenvironment of freshwater lakes. Testate amoebae depend onwater to live because they use an unprotected cell membrane forfeeding (Warner, 1990). As a result they are confined to thin waterfilms between soil particles, plant leaves and roots in terrestrialenvironments where they feed on other microorganisms as wellas on each other. Small, flattened tests and an ability to encystare two features which enable some species to tolerate relativelydry periods. Variable morphology seems to be a strong factor inthe relative ability of different species to survive dry conditionsbut it does not entirely explain the species distribution in other-wise similar environments.

In Sphagnumpeatlands, testate amoebae are a major componentof the microfauna with up to 1.6× 107 individuals m−2 (Heal,1962). Their advantages as indicators of peatland palaeohydrologyare principally as follows.

(1) Substrate wetness is the primary control on species abundanceand community composition (Jung, 1936; Scho¨nborn, 1963;Meisterfeld, 1977).

(2) They are identifiable to species level in most cases(Tolonen, 1986).

(3) As discrete individuals, modern and fossil communities aredirectly comparable. Although Lousier and Parkinson (1981)demonstrated differential decay rates for the empty tests ofselected testate amoebae in woodland soils, this is not thoughtto operate in peat. Therefore fossilization is unlikely to affectthe relative proportions of species present, unlike plant macro-fossils for example, although further work is required to con-firm this.

(4) Community diversity is high, typically around 15species/4 cm3 sample.

However, studies which have used testate amoebae to aid recon-struction of mire surface wetness have so far mostly been basedon qualitative estimates of the hydrological preferences of individ-ual species (Tolonen, 1966; Aaby, 1976; Tolonenet al., 1985;Van der Molen and Hoekstra, 1988; Warner, 1991; Van der Molenet al., 1995). More recently, more precise estimates of optimumwater tables and soil moisture levels have been published for sev-eral regions in North America and Scandinavia (Charman andWarner, 1992; Tolonenet al., 1992) and these have been used tocalculate transfer functions for fossil assemblages (Warner andCharman, 1994). However, these studies are still restricted geo-graphically and, more importantly, they generally rely on water-table and moisture measurements at only one period in the year.Thus while hydrological data associated with species assemblagesis comparable between samples within individual data sets, it isdifficult to compare between data sets where samples were takenat different times. Furthermore, there has been no exploration ofthe different species response models now available (Jugginsetal., 1994; Birks, 1995). This paper presents modern species datafrom Britain which addresses the issue of single water-tablesamples and presents the results of validation tests on four differ-ent transfer functions. The optimum transfer function is appliedto a ‘test’ set of fossil data from Bolton Fell Moss, Cumbria.

Methods

Nine peatland sites from across Britain (Figure 1) were selectedas a representative range of relatively undamaged mires. Since theultimate aim of the study was to provide transfer functions forpalaeoclimatic studies on ombrotrophic (precipitation-dependent)mires, all sites were of low nutrient status (oligotrophic). Most ofthe sites are raised or blanket mires with the exception of ChartleyMoss and Llyn Mire which are oligotrophic schwingmoors. These

Figure 1 Locations of the nine sites where modern testate amoebaeassemblages were studied and the position of Bolton Fell Moss, Cumbria.

have comparable pH values to the raised and blanket mires,although conductivity is slightly elevated at Chatley Moss (Table1). In order to be able to establish mean annual water tables ateach sampling location, only sites with at least three years’ water-table data out of the last ten were used. The exception to this wasTor Royal Bog, Dartmoor, where water tables were monitoredover a period of 15 months, of which the 12 months from June1993 to May 1994 were used as the annual period (Woodland,1996). At Tor Royal, water-table monitoring sites were estab-lished and testate amoebae assemblages were sampled at exactlythe same locations. In order to avoid disturbing monitoring equip-ment and to avoid the local influence of the installations on waterchemistry and testate amoebae assemblages at the other sites, aseries of similar replicate microsites were selected from acrossthe mire. Water tables at the monitored locations were comparedwith water tables at equivalent microsites on the day of sampling.In most cases the difference between water tables was less than1 cm, except on a few hummock sites with low water tables wherethe difference was up to 3 cm, although still within 20% of themonitored value. Up to six replicates for each monitored water-table site were sampled. This yielded a total of 163 samples withsufficient numbers of amoebae tests present.

At each sampling location, vegetation cover was recorded aspercentage abundance. All vascular plants and bryophytes wereidentified to species level. An 11-cm diameter sharpened steel cyl-inder marked the sampling location and then scissors were used



Wendy A. Woodland et al.: Testate amoebae and the reconstruction of the surface wetness of peatlands 263

Table 1 Details of sites sampled for the modern testate amoebae assemblages

Site name National grid Area (ha) Mire type Mean pH Mean conductivity Details of hydrological monitoringreference (mScm−1) programmes

Tor Royal, Dartmoor SX 601731 58 Upland raised mire 4.46 46 June 1993–September 1994. This study.Water-table (recorded every 12 hours) andwater-chemistry data (collectedfortnightly).

Strathy Bogs NC 790555 909 Blanket mire 4.07 99 May 1988–September 1990. University ofDundee. Weekly records of water-tabledepth.

Coladoir Bog NM 550292 52 Oceanic blanket 4.42 46 1984–1989. Scottish Natural Heritage.Daily records of water-table depth.

Dun Moss NO 169559 133 Upland raised mire 4.46 65 December 1989–July 1992. University ofDundee. Weekly records of water-tabledepth.

Butterburn Flow NY 675761 400 Upland raised mire 4.35 45 May 1988–May 1991. University ofDundee. Weekly records of water-tabledepth.

Glasson Moss NY 238604 101 Lowland raised 4.41 88 1989–1992. English Nature. Fortnightlyrecords of water-table depth.

Chartley Moss SK 025281 40 Schwingmoor 4.28 122 1989–1992. University of Nottingham.Weekly records of water-table depth andwater chemistry.

Borth Bog SN 640995 3339 Estuarine raised 4.37 88 1989–1992. Countryside Council forWales. Daily records of water-table depth.

Llyn Mire SO 016553 35 Schwingmoor 4.28 70 1985–1989. Institute of Hydrology. Dailyrecords of water-table depth.

to cut through fibrous surface roots before the cylinder was pushedin to a depth of about 30 cm. The sampling procedures of Tolonenet al. (1992) were followed where the surface growth of greenshoots of vascular plants and the green capitulum ofSphagnummosses is removed and the remaining portion of upright mossstems and roots is retained for analysis. Although living testateamoebae exhibit vertical zonation in the upper parts ofSphagnummosses, fossil assemblages become integrated as theSphagnummosses collapse at the base of the acrotelm. Some species whichmainly inhabit the green capitulum ofSphagnum(e.g.Placocistaspinosa), are found very rarely in the fossil record, suggestingthat this element of the fauna does not form a significant part ofthe death assemblage, and therefore this part of the moss sampleis removed prior to analysis. Depending on the peat-forming com-munity, the sampled section may be the next 1–10 cm of the sam-ple. The moss stems and peat below this depth were discarded.Field pH, conductivity and temperature of the mire water weremeasured and a water sample taken for ionic determinations (Cl−,SO2−

4 , Ca2+ and Mg2+).In the laboratory, samples were cut vertically into two equal

portions. One half was weighed, dried at 105°C for eight hoursand reweighed for calculation of bulk density and % moisture.Subsamples of 4–5 cm3 were taken from the other half of the sam-ple, weighed and prepared according to Tolonen (1986), with themodification of soaking samples overnight to aid disaggregationand the use of a 300-mm mesh sieve. This smaller sieve sizereduces the amount of extraneous plant matter but still allowseven the largest tests (c. 250mm) to pass through. Microsievingwas not required for the modern samples (cf. Hendon and Char-man, 1997).

Tests were identified to the lowest taxonomic level possibleusing de Graaf (1956), Grospietsch (1958), Corbet (1973), Ogdenand Hedley (1980) and slides from the Penard collection in theBritish Museum (Natural History). Normally 150 individuals werecounted as this was shown to record most species present (Figure2), although occasionally this total could not be achieved. Allsamples with counts below 100 were excluded from further analy-

Figure 2 Cumulative species diversity for samples in the modern Britishdata set, shown as the mean,± 2 standard errors.

sis. Counts were expressed as percentage of the total fauna. Con-centrations were not used to develop transfer functions since theycannot be directly related to fossil concentrations.

Results

Community variability and relationship withenvironmental variablesOther studies have shown that hydrology is the main influence oncommunity assemblage (e.g. Charman and Warner, 1992; Tolonenet al., 1992), but it was important to confirm this relationship forthe British data set. Canonical correspondence analysis was usedto relate species to environment (ter Braak, 1988). Figure 3ashows the resulting ordination of environmental variables, includ-ing host vegetation, and Figure 3b shows the associated species




Figure 3a Canonical correspondence analysis ordination for environmental variables and host plant species.

ordination. Correlation coefficients for each of the variables withthe species axes are given in Table 2. Of the environmental vari-ables, axis 1 is most strongly associated withMenyanthes trifoli-ata, which is positioned at the extreme end of axis 1, associatedwith high percentage soil moisture and high water tables (other‘dry’ species, such asErica tetralix and Calluna vulgaris, arelocated at the other end of axis 1). Mean annual water-table depthis also significantly correlated with axis 1. Axis 2 is correlatedwith water chemistry (as expressed by Ca2+ and SO2−

4 ) andSphag-num recurvum. pH is not significantly correlated with axis 2.However, as all the sites are oligotrophic, the range of waterchemistry measured is relatively narrow. As a result the relation-ships shown with axis 2 are probably not ecologically meaningful.None of the species which are normally found in more enrichednutrient conditions (e.g.Quadrulella symmetrica) were present inany abundance in the surface samples. Moisture was correlatedwith axis 1, but the correlation coefficient was not statisticallysignificant. However, a partial analysis using moisture aloneresulted in a correlation of r= 0.727 (p, 0.01) and explained51% of the variance. This suggests that the hydrological para-meters percentage moisture and depth to water table are suitablefor constructing transfer functions.

The testate amoebae species are primarily separated along axis1, with severalDifflugia and Arcella species associated with the

wetter end of the axis and species such asTrigonopyxis arculaandHeleopera sylvaticaassociated with drier locations. These generaltrends are in line with other studies of modern testate assemblagesin other parts of the world (Charman and Warner, 1992; Tolonenet al., 1992). It is beyond the scope of this paper to make detailedcomparisons with other data sets.

Derivation and performance of transfer functionsThe strong relationships between testate amoebae assemblagesand hydrological parameters provide a good basis for the calcu-lation of transfer functions for water tables and percentage moist-ure. Transfer functions have been most commonly used for dia-toms and lake chemistry (Birkset al., 1990; Fritzet al., 1991;1993; Hall and Smol, 1992; Jugginset al., 1994) but a wide rangeof applications have been found (see examples in Birks, 1995)and there are now a number of methods which can be applied tosuch data (Jugginset al., 1994; Birks, 1995). Previous work ontestate amoebae is limited to simple weighted averaging (Warnerand Charman, 1994) and a major aim of this paper is to assesspotential improvements from other approaches. Four methodswere evaluated here using the computer program Calibrate(Juggins, unpublished). Weighted averaging regression (WA) isthe simplest of the unimodal techniques and has given goodresults in a number of studies (e.g. Oksanenet al., 1988; Birks




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Table 2 Characteristics of axes 1 and 2 and correlation coefficients withenvironmental in the British data set, following canonicalcorrespondence analysis

Variable Axis 1 Axis 2

Eigenvalue 0.275 0.220% variance explained 15.500 12.400

Menyanthes trifoliata 0.602*** 0.005Sphagnum cuspidatum 0.471** 0.201Mean annual water table 0.342*−0.165Calluna vulgaris −0.308 −0.149% moisture content 0.291 −0.158Eriophorumspp. −0.253 0.221Temperature 0.208 −0.341*Erica tetralix −0.207* 0.119Bulk density −0.198 0.456**Sphagnum magellanicum −0.151 0.089S. auriculatum −0.144 −0.085Myrica gale 0.143 −0.004Open water 0.136 0.068Juncusspp. −0.120 0.078Conductivity 0.109 −0.375*Molinia caerulea −0.100 0.026Ca2+ −0.084 −0.521***pH 0.084 0.123Sphagnum capillifolium −0.078 0.085Cl− 0.072 −0.470**SO2−

4 −0.064 −0.501***S. recurvum 0.047 −0.665***Carexspp. −0.032 −0.150Mg2+ 0.027 −0.349**Polytrichum commune −0.027 0.014Sphagnum papillosum 0.015 0.044Sphagnum imbricatum 0.007 0.084S. pulchrum −0.003 0.040Hypnum cupresseforme 0.002 0.089

*** p , 0.01; ** p , 0.05; * p, 0.10.

et al., 1990; Jugginset al., 1994). Tolerance downweightedweighted averaging (WA-Tol) is a modified form of this and hasgiven some improvements in performance (e.g. Juggins, 1992)with smaller prediction errors. Partial least squares (PLS) is a lin-ear method and has not been widely applied in palaeoecology (terBraak and Juggins, 1993). Weighted averaging partial leastsquares (WA-PLS) was presented as a refinement of WA by terBraak and Juggins (1993) as it goes through several regressioncycles. If only the first component is retained, performance issimilar to WA but may be enhanced by up to 60% if two orthree components are used (Birks, 1995). Each of these methodsis described in more detail by Birks (1995) and their relative per-formance in developing transfer functions is discussed in Wood-land (1996).

The models used here have been assessed by two validationmethods. First, a simple validation was utilized which applies themodel to the same data from which it was derived. Errors betweenobserved and predicted values are termed ‘apparent’ errors. Amore robust technique of validation is a variant of cross-validationknown as ‘jack-knifing’ (Jugginset al., 1994) where a new modelis generated from all data except one sample and then applied tothat sample. This is repeated n times (where n= number ofsamples). Thus, the data used to generate the model are separatefrom the data to which they are applied. Errors between observedand predicted values are termed ‘prediction’ errors and these are amore reliable estimate of model performance. Performances wereassessed from the root mean square of the errors (RMSE) andmaximum bias along the ecological gradient. For the latter data,the ecological gradient is divided into sections and the mean bias

within each section calculated. The maximum bias is the highestof these values (Birks, 1995). Table 3 shows the results for eachmodel. RMSEP values for WA and WA-PLS are almost equalfor percentage moisture, although WA gives lower maximum biasfigures. WA, WA-PLS and WA-Tol also have almost equal valuesfor RMSEP and maximum bias for water table. The data structureappears to be similar to that of diatoms and water chemistry whereWA-based methods also perform well (Jugginset al., 1994; terBraak and Juggins, 1993).

Figure 4 shows observed values plotted against ‘jack-knifed’predicted values from WA for water table and moisture. Theseplots clearly show that there are some samples where predictedvalues are very different from observed values and Table 4 detailsthese differences. The differences are attributable to various fac-tors. Water chemistry can exert a strong influence on testate amoe-bae assemblages (Charman and Warner, 1992; Tolonenet al.,1992) and there were some samples (from Chartley Moss) withsufficient nutrient enrichment to alter species composition fromthat expected in less base-rich sites with similar water-table andsoil-moisture levels. Other samples had extremely low watertables by comparison with the rest of the samples. These are thevery high hummock tops and the unreliable estimates of hydrolog-ical values result from there being no samples with measuredvalues intermediate between these and the rest of the data. A thirdcategory of samples was excluded as testate amoebae assemblagesseemed to be related to unusual vegetation types rather thanhydrology. In a further six samples particularly unusual testateamoebae assemblages were present. Sample D2 from ChartleyMoss, for example, was dominated byAmphitrema wrightianumand Arcella discoides, two ‘wet’ species which are indicative of.95% soil moisture (Figure 5a). Consequently, the model over-estimated moisture content for this sample. Finally, a number ofthe samples were excluded from the soil moisture data set simplybecause they appeared to have very low or very high observedvalues. This can only be attributed to the problems of compar-ability of soil moisture measurements taken at different times ondifferent sites.

Birks et al. (1990) identified such ‘rogue’ samples in a diatomdata set and excluded them by a data screening exercise; a similarprocess was followed here. The mean annual water tables in this

Table 3 Performance of the four regression models ranked according toRMSE values for the entire data set for water table and moisture. Apparent(RMSE) and jack-knifed (RMSEP) prediction errors are both given

Method RMSE Max bias Method RMSE Max bias

Moisture(%)

Apparent Prediction

PLS 4.88 8.75 WA-PLS 7.83 21.50WA-PLS 4.96 8.98 WA 7.84 16.07WA-Tol 6.25 16.36 PLS 8.80 12.40WA 6.33 16.07 WA-Tol 9.07 22.47

Water table(cm)

Apparent Prediction

PLS 4.29 8.81 WA-Tol 5.71 12.28WA-PLS 4.31 9.04 WA 5.74 12.30WA 5.01 10.44 WA-PLS 5.74 12.30WA-Tol 5.10 11.30 PLS 5.86 12.20




Figure 4 Observed and predicted values for the testate amoebae sampleshased on jack-knifed estimates from (a) WA for mean annual water tableand (b) WA-Tol for percentage soil moisture (n= 163).

study represent several years’ data, while soil moisture is derivedfrom single measurements made during the early autumn. Thus,the soil-moisture data are less representative of typical peatlandhydrology than water-table depth and may impede the perform-ance of the functions. Accordingly, separate soil-moisture andwater-table data sets were derived from the original data set.Samples were excluded where the difference between observedand predicted values exceeded 5% and 9 cm for soil moisture andwater table respectively. In both cases, this is one-fifth of the mea-sured range in the data set. This removed three samples from thewater-table data set and 29 samples from the soil-moisture dataset, resulting in only a minor alteration of the water-table data set.Table 5 shows that this data-filtering exercise yielded considerableimprovement in RMSEP and maximum bias, as well as alteringthe relative performances of the transfer functions. This time,WA-PLS and WA produced the smallest prediction errors forwater table, while WA-Tol performed best for soil moisture. WAand WA-PLS transfer functions performed almost identically forwater-table and percentage moisture, despite the use of six compo-nents for WA-PLS. In palaeoecological reconstructions, it is not

Table 4 (a) Details of the outlier samples excluded from the final transferfunctions for moisture content based on observed and model predictedvalues of moisture. Possible reasons for the poor predictions are alsosuggested. See text for further explanation

Sample Possible reason for outlier Observed Modelpredicted

Butterburn Flow low observed value 69.54% 84.57%C5 conductivity/unusual testate 94.88% 88.94%Chartley Moss C4 amoebae assemblageChartley Moss conductivity/unusual testate 92.84% 101.19%D2 amoebae assemblageColadoir Bog A1 low observed value 63.00% 84.63%Coladoir Bog A3 low observed value 65.56% 90.79%Coladoir Bog A4 low observed value 72.12% 83.98%Coladoir Bog A5 low observed value 63.92% 85.96%Coladoir Bog A6 low observed value 65.48% 90.82%Coladoir Bog B1 low observed value 76.15% 84.57%Coladoir Bog B6 vegetation (Menyanthes 95.34% 103.45%

trifoliata)Coladoir Bog C1 low observed value 76.04% 82.39%Coladoir Bog C2 high observed value 96.96% 91.07%Coladoir Bog E2 vegetation (Erica 95.02% 81.59%

tetralix)/high observed valueDun Moss A1 high observed value 97.06% 90.28%Strathy Bogs B1 low observed value 74.97% 84.43%Strathy Bogs B2 high observed value 94.68% 87.71%Strathy Bogs B3 high observed value 95.68% 87.39%Strathy Bogs C1 low observed value 74.58% 85.08%Strathy Bogs C2 low observed value 74.43% 85.07%Strathy Bogs C3 low observed value 74.85% 85.40%Strathy Bogs C4 low observed value 79.05% 97.80%Strathy Bogs C5 low observed value 75.24% 81.44%Strathy Bogs D5 high observed value 97.09% 88.75%Tor Royal A1 low observed value 63.12% 92.63%Tor Royal A3 low observed value 65.56% 93.02%Tor Royal A4 low observed value 72.12% 80.98%Tor Royal C3 high observed value 95.49% 89.22%Tor Royal D6 unusual testate amoebae 79.05% 93.80%

assemblageTor Royal F1 unusual testate amoebae 84.93% 92.99%

assemblage

(b) Details of the outlier samples excluded from the final transfer functionsfor water table based on observed and model predicted values of watertable depth. Possible reasons for the poor predictions are also suggested.See text for further explanation

Sample Possible reason for outlier Observed Modelpredicted

Tor Royal C1 unusually deep water table−23 cm −8.42 cmTor Royal E1 unusually deep water table−40 cm −18 cmTor Royal F1 unusually deep water table−46 cm −13 cm

uncommon for WA-PLS to outperform WA (e.g. ter Braak andJuggins, 1993; Jugginset al., 1994), particularly in noisy datasets (ter Braak and Juggins, 1993). WA is, however, advantageousbecause it is relatively simple computationally and easily inter-preted ecologically. As a result, it is used widely in palaeoecologi-cal reconstructions (e.g. Hall and Smol, 1992; Joneset al., 1993;Warner and Charman, 1994).

Using the WA and WA-Tol transfer functions, RMSEP isreduced to 3.93 cm for water table and 3.44% for moisturerespectively. This suggests that for fossil samples where there isa very good match between fossil and modern assemblages, water-table and moisture predictions will be within± 3.9 cm and 3.4%




Figure 5a Individual species’ optima and tolerances for mean annual water table using the Calibrate modelling program. Only species occurring in. 16samples (10% of the total data set) are shown.




Figure 5b Individual species’ optima and tolerances for percentage soil moisture derived from WA and WA-Tol respectively, using the Calibrate modellingprogram. Only species occurring in. 16 samples (10% of the total data set) are shown.




Table 5 Revised jack-knifed prediction RMSEP and maximum bias forthe British data set derived from various regression models, following theexclusion of rogue samples. Prediction RMSEPs from the first regressionexercise are shown in brackets

Model Water table depth (cm) Moisture content (%)

RMSEP Maximum bias RMSEP Maximum bias

WA 3.93 7.13 3.67 8.76(5.74) (12.30) (7.84) (16.07)

WA-Tol 4.07 7.63 3.44 7.81(5.71) (12.28) (9.07) (22.47)

WA-PLS 3.93 7.13 3.67 8.78(5.74) (12.30) (7.83) (21.50)

PLS 4.17 7.13 3.68 8.67(5.86) (12.20) (8.80) (12.40)

respectively. The individual species’ optima used in the transferfunctions are shown in Figure 5, together with their tolerances.

Application of transfer functions:Bolton Fell Moss, Cumbria

As an example of the application of the transfer functions, a shortprofile from Bolton Fell Moss, Cumbria, has been analysed. Thissite has been the location for a number of studies on surface wet-ness changes in peatlands (Barber, 1981; Barberet al., 1994a;1994b; 1994c) and is therefore an ideal place to compare theresults of different palaeoecological techniques. However,detailed comparisons will not be made here as they will be thesubject of a future paper. Samples of 2-cm3 at 2 cm intervals wereremoved from the top 100 cm of monolith J1 (Barberet al.,

Figure 6 Correspondence analysis of modern estate amoebae samples from the British data set and fossil samples from Bolton Fell Moss, Cumbria. Emptycircles represent modern samples; filled circles represent fossil samples.

1994a; 1994b). The contained testate amoebae were prepared andcounted in the same way as the modern samples. To provide acomparison of fossil and modern assemblages, the percentagevalues from both data sets were analysed together by uncon-strained correspondence analysis (CA). Figure 6 shows theresulting sample ordination. The overlap between the fossil andmodern data is generally very good, although there are a few fossilsamples at the upper end of axis 1 for which there are no overlap-ping modern samples.

Water tables and percent moisture were reconstructed from theedited modern data set using the WA-Tol transfer function forpercent moisture and WA for water table. The results of thistogether with the percentage species data are shown in Figure 7.The diagram has been zoned using CONISS as part of the TILIAprogram (Grimm, 1991). Radiocarbon dates suggest a relativelyuniform accumulation rate of 12.4 years cm−1 (Barber et al.,1994a).

Throughout BFM 1 and BFM 2, the water table fluctuatesmarkedly between 2 and 7 cm, masking an underlying trendtowards wetter conditions. This trend is paralleled by the moisturecurve, which is less changeable. After fluctuating conditions at thebase of BFM 3, a sustained wet period follows when soil moisturereaches 98% and the water table lies 0.5 cm below the bog sur-face. This is followed by a rapid drying phase when the watertable drops to−7 cm, marking one of the two driest parts of thecore. Thereafter, the water table experiences a more rapid recov-ery than moisture content into BFM 4. Apart from the abruptreversal to very dry conditions in the top half of BFM 4, whenmoisture falls to 90% and water table to−7 cm, this is a compara-tively stable hydrological phase, with good agreement betweenthe curves. BFM 5 marks a return to fluctuating soil moisture con-ditions which is not paralleled by the water-table curve, althoughboth curves do indicate a return to wetter conditions. During thetransition from BFM 5 to BFM 6 the water table begins to falland this continues through the greater part of BFM 6. There is a




Figure 7 Testate amoebae diagram for Bolton Fell Moss withreconstructed mean annual water table and percentage soil moisture fromthe WA and WA-Tol transfer functions. Upper and lower limits ofestimates are the mean species tolerance for each sample.

slight delay before the moisture curve follows suit, early inBFM 6. The uppermost two samples suggest a recovery in watertables and moisture.

The agreement between percentage moisture and water-tablecurves is generally quite good except for several places whichhave been highlighted above. The reason for this apparent mis-match can be found in the individual species’ optima (Figure 5)which suggest that there is not complete correspondence betweenthese two parameters for all species. For exampleHyalospheniaelegans, a species which is normally considered to be a ‘wet’indicator, has a high optimum water table but a relatively lowmoisture optimum. A similar pattern is found forNebela parvula,another low moisture species. A possible explanation is that thesespecies tend to be found on hummock tops which are dominatedby Sphagnum. While water tables can be very low due to thehummock height, theSphagnumis effective at retaining waterimmediately below the surface. It is also possible that species withhigh water-table optima and lower moisture optima are more com-monly found on vascular plants and non-Sphagnumbryophyteswhich are less effective at retaining water. The fact that some ofthe thin, small compressed tests such asEuglypha species arefound in this group provides some support for this idea, but furtherinvestigation of relationships with plant species is necessary toprovide further evidence for this effect.

Another explanation may be found in the difference betweenthe way percentage moisture and water tables were measured forthe modern data set. Water tables are annual averages while per-centage moisture are single measurements made during the earlyautumn. Clearly this will result in a less reliable data set for per-centage moisture and, although steps were taken to remove roguesoil moisture data, discrepancies may still arise between the recon-structed hydrological curves.

Conclusions

There are a number of conclusions from this study which havesignificant implications for reconstructions of surface wetnessconditions on oligotrophic and ombrotrophic peatlands in Britainand elsewhere.

(1) The results of the large survey of testate amoebae assem-blages in British mires reported here demonstrate clearly thatthese organisms are good indicators of surface wetness con-ditions, whether this is measured as soil moisture or watertable. There are different individual species responses to thesevariables but this may be due to lack of long-term mean datafor soil moisture at least in part. As a result, the relationshipwith mean annual water table is considered to be a morereliable paramter on which to base reconstructions of palaeo-hydrological conditions. The use of mean annual data forwater tables is a major advance on previous work to character-ise hydrological preferences of testate amoebae species.

(2) Four different transfer functions for water-table depth and soilmoisture from testate amoebae are presented. In jack-knifedvalidation of the filtered data set, tolerance downweightedweighted averaging (WA-Tol) performed best for moisture,while weighted averaging (WA) gave the lowest predictionerrors for water table, although the performance was identicalto weighted averaging partial least squares (WA-PLS). Theprediction errors were considerably improved when using adata set filtered to remove rogue samples with atypicalobserved fauna and/or hydrological parameters. The RMSEPof predicted values using ‘jack-knifing’ shows that watertables can be predicted to within± 3.9 cm and soil moisture towithin ± 3.4%. However this assumes a good match betweenmodern and fossil species assemblages.

(3) The case study of a short profile from Bolton Fell Moss,




Cumbria, provides an example of reconstructed water tablesand soil moisture for the past 1500 years from a raised miresite. Such reconstructions provide a new technique for esti-mating fluctuations in mire surface wetness. In combinationwith complimentary approaches such as plant macrofossil andhumification analyses, these should enable higher-precision,more accurate palaeohydrological curves for British mires. Inparticular the magnitude of changes can now be assessedquantitatively in terms of a meaningful environmental vari-able rather than as an abstract relative measure. The problemsand potential of such multiproxy approaches will be addressedin a future paper.

The present study is limited to British mires, but similarrelationships with hydrology have been shown for testate amoebaeassemblages in several other parts of the northern hemisphere(Charman and Warner, 1992; Tolonenet al., 1992; 1994; Warnerand Charman, 1994) and preliminary studies show similar trendsin the southern hemisphere (Charman, 1997). In contrast withhigher plant species and even with bryophytes, testate amoebaeare highly cosmopolitan and there are few species which are notcommon to mire environments across Europe and North America.This fact offers considerable scope for establishing comparabledata sets of modern and fossil assemblages from across a verybroad region in a way that is impossible for plant-based data.However, the problem with most other data sets, with the excep-tion of a few samples from Minnesota, USA (Warner and Char-man, 1994), and Finland (Tolonenet al., 1992), is that no meanannual data are available, thus making the combination of datafrom continental or intercontinental areas difficult. Further workneeds to be done to address this problem and to assess the varia-bility of the ecological relationships concerned. Broadening thegeographical scale will increase the diversity of modern analoguesavailable for the interpretation of fossil assemblages and will pro-vide a unique way of obtaining comparisons of Holocene palaeo-hydrology of peatlands which can be applied in palaeoclimaticstudies.

Acknowledgements

This work was carried during the tenure of a NERC ResearchStudentship GT4/92/19/G (Wendy Woodland). Steve Juggins pro-vided his unpublished program ‘Calibrate’ and advice on its useand Keith Barber made samples from Bolton Fell Moss available.Hydrological data were supplied by Olivia Bragg (Dun Moss, But-terburn Flow, Strathy Bogs), Countryside Council for Wales(Borth Bog, Llyn Mire), Scottish Natural Heritage (Coladoir Bog)and English Nature (Chartley Moss, Glasson Moss). Thanks alsoto the landowners of all sites for access and permission to sample.

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Quantitative estimates of water tables and soil moisture in Holocene peatlands from testate amoebae