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Page 1: Modern pollen–vegetation relationships in western Tasmania, Australia

nology 146 (2007) 146–168www.elsevier.com/locate/revpalbo

Review of Palaeobotany and Paly

Modern pollen–vegetation relationships in westernTasmania, Australia

Michael-Shawn Fletcher ⁎, Ian Thomas

School of Anthropology, Geography and Environmental Studies, University of Melbourne, Parkville, 3010, Australia

Received 24 November 2006; received in revised form 16 March 2007; accepted 21 March 2007Available online 1 April 2007

Abstract

This paper describes the results of a modern pollen survey of plant communities in western Tasmania, Australia. Sampledcommunities occur in the main vegetation types: alpine/subalpine, rainforest and moorland. We show that despite the over-representation of rainforest trees in the regional pollen rain, vegetation type and some communities can be distinguished usingpollen analysis. Temperature (altitude) and fire frequency are significantly correlated with the ordination axes, consistent with theecology of the region, indicating that pollen composition is a good reflection of vegetation and that pollen spectra can be effectivelyused to reconstruct changes in these environmental parameters. Moorland communities are clearly distinguished by ordinationanalysis. Seasonality is significantly correlated with moorland community type. Although percentage cover of the major plant taxacorrelates significantly in most cases with pollen percentages, the high variability means that quantitative estimates of vegetationcover from pollen data alone are not possible.© 2007 Elsevier B.V. All rights reserved.

Keywords: Australia; western Tasmania; Palynology; modern pollen rain; modern pollen–vegetation relationships; moorland; fire

1. Introduction

Pollen analysis is the most widely used tool forreconstructing past vegetation. Central to the employmentof pollen analysis as a palaeoecological tool is theassumption that relative pollen abundance in sediments isin some way proportional to the relative abundance ofplants in a given landscape (Erdtman, 1943). Plants employvarious means of dispersing pollen and, as such, pollenproduction and dispersal varies greatly between species(Tauber, 1965; Faegri, 1966). It follows that an under-standing of the relationship between vegetation andmodernpollen rain is a vital and necessary first step before

⁎ Corresponding author.E-mail address: [email protected] (M.-S. Fletcher).

0034-6667/$ - see front matter © 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.revpalbo.2007.03.002

attempting to interpret fossil pollen spectra (Janssen, 1973).There is a considerable body of research concerningmodern (surface) pollen and its relationship to vegetationfor northern hemisphere plant communities (e.g. Wright,1967; Gaillard et al., 1984; Prentice, 1985; Gaillard et al.,1992; Sugita et al., 1999; Bunting, 2003). The forests ofEurope and North America have received particularattention (e.g. Janssen, 1984; Bartlein et al., 1986; Prentice,1986; Prentice and Webb, 1986; Seppa et al., 2004) and agood understanding of regional and local scale pollen–vegetation relationships has been garnered for these regionsusing both intuitive and objective means (e.g. Prentice,1980; Birks and Gordon, 1985; Overpeck et al., 1985;Bartlein et al., 1986; Birks, 1994; Seppa et al., 2004).

Australia, on the other hand, has a comparativelysparse coverage of published modern pollen literature

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(Dodson, 1977, 1982, 1983; Dodson and Myers, 1986;Kershaw, 1993a,b; Crowley et al., 1994; Kershaw andBulman, 1994; D'costa and Kershaw, 1997; Luly, 1997;Shimeld and Colhoun, 2001; Pickett et al., 2004) witheven fewer making use of objective numerical analyses(Dodson, 1983; Dodson and Myers, 1986; Kershaw andBulman, 1994). Dodson (1983) provided a regional scalequalitative assessment of pollen representation for anumber of southeast Australian pollen types (compiledfrom a variety of sources), identifying three classes ofpollen representation: over-, well-, and under-repre-sented, and presenting a means to differentiate vegeta-tion type using numerical analyses. Since then, therehave been a number of attempts to use modern pollenrepresentation, calibrated against the bioclimatic envel-opes of single (e.g.Markgraf et al., 1986; McKenzie andBusby, 1992; Kershaw, 1993a,b; Thomas, 1998) andmultiple (Kershaw and Nix, 1988) plant taxa, to derivepalaeoclimatic estimates from pollen diagrams. Inaddition, a number of regional indicator pollen typesfor temperature and precipitation have been identified forsoutheast Australia using large modern pollen datasetsplotted along major climatic gradients (Kershaw et al.,1994; D'costa and Kershaw, 1997).

Many publications provide information on modernpollen composition around fossil sites (e.g. Hope, 1974;Ladd, 1979; Macphail, 1979; Colhoun and van de Geer,1986; Harle et al., 1999), but there is a lack of syntheticstudies aimed at identifying regional pollen–vegetationrelationships (Dodson, 1983; Crowley et al., 1994;Kershaw et al., 1994; D'costa and Kershaw, 1997;Pickett et al., 2004) and the reliability of pollen spectrafor predicting Australian vegetation communities islargely untested. In a recent paper we (Fletcher andThomas, 2007) used multivariate analysis of a limitedmodern pollen dataset from western Tasmania, insoutheast Australia, to accurately predict the occurrenceof plant communities and applied these results to theinterpretation of Holocene vegetation change. We wereprompted by a lack of modern pollen literature for theregion and by the failure of a recent regional synthesis toscry the dominant lowland vegetation type in westernTasmania: treeless buttongrass moorland (Pickett et al.,2004). We demonstrated a clear potential for the use ofobjective numerical analyses to correctly predictvegetation type from pollen, despite the over-represen-tation of a few key tree species in the pollen rain and theabundance of pollen types with poor taxonomicresolution (Fletcher and Thomas, 2007).

In this paper we: (1) expand upon our earlier analysis,strengthening its predictive power; (2) quantitativelyassess the representativeness of important pollen types;

(3) compare vegetation data with pollen representation;(4) identify the dominant environmental correlatesdetermining modern pollen composition; and (5) discussthe implications of the results for Quaternary research inwestern Tasmania.

2. The study area

2.1. Environment

Tasmania is a large island located to the south ofmainland Australia between 41° and 43°S. WesternTasmania is a mountainous and perennially wet regionwhere orographic rain deposits 1500–3500 mm rainannually and average annual temperatures range be-tween 5 and 7 °C in winter and between 14 and 16 °C insummer (Nunez, 1979). Precipitation exceeds evapora-tion for most of the year and the climate is classed assuperhumid (Fig. 1, Gentilli, 1972). Two main climaticgradients operate in western Tasmania, a precipitationgradient from west (wet) to east (drier) and a latitudinalgradient manifest in temperature and seasonalitydifferences between the north (warmer-seasonal) andsouth (cooler-aseasonal) (see Fig. 1).

Precipitation in the study region is dictated byorography with the west coast rarely experiencinglong periods of moisture deficit, while water stress iscommon in the rain-shadows cast over the midlands andeastern Tasmania (Gentilli, 1972). Latitudinally, Tasma-nia lies near the boundary of two major weathersystems: the mid- to high-latitude low pressure(cyclonic) and the subtropical high pressure (anticy-clonic) systems. Annual migration of these systemsrelieves the northwest from the maritime westerlies forlonger periods than the southwest, and incursions of hotand dry continental air in to the northwest are commonin summer (Nunez, 1979).

Soils in western Tasmania are usually highly infertileand acidic. Large areas of predominantly quartziticbedrock are overlain by moor and forest peats on whichare developed the whole spectrum of plant communitiesfound in the region. Except in rare situations, and in themost general sense of open versus forest vegetation, soiltype is not a useful predictor of plant community type(Bowman et al., 1986; Pemberton, 1989).

2.2. Vegetation

The main determinants of vegetation type in westernTasmania are altitude and fire frequency (Jackson, 1968;Bowman and Jackson, 1981; Kirkpatrick, 1982).Lowland vegetation (excluding coastal vegetation)

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Fig. 1. Map of the study area showing the distribution of vegetation communities, climatic classification (Gentilli, 1972) and climate graphs fromselected meteorological stations (Australian Bureau of Meteorology).

148 M.-S. Fletcher, I. Thomas / Review of Palaeobotany and Palynology 146 (2007) 146–168

exists along a continuum dictated by fire frequency,where rainforest prevails in the absence of fire(N400 year interval) and buttongrass moorland, asedge and low scrub dominated community, developsin areas of high (b20 year interval) fire frequency(Jackson, 1968; Bowman and Jackson, 1981). Button-grass moorland is the dominant vegetation type (N47%of the lowlands), reflecting the widespread influence offire in the region (Kirkpatrick and Dickinson, 1984;Brown, 1999). The principle seral stages in thesuccession from moorland to rainforest are: wet scrub,

wet sclerophyll forest and mixed forest (Jackson, 1968).The transition from moorland to alpine vegetation isabrupt on mountains, due to the microclimatic effects ofpersistent cloud ceilings (Kirkpatrick et al., 1996), whilethe transition from rainforest to alpine vegetation isoften diffuse with many rainforest species persistingabove the climatic treeline as dwarfed (krummoltz)shrubs in alpine communities (Kirkpatrick, 1982).Alpine sedgeland (sensu Kirkpatrick and Bridle, 1999)is thought to be a formation that results from thedestruction of shrubs by fire (Kirkpatrick and Bridle,

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1999) and, as such, displays a similar ecology andspecies composition to moorland vegetation (Harris andKitchener, 2005). However, waterlogging and shallowsoils also inhibit the establishment of trees in alpineareas, and alpine sedgeland forms in areas free from fire(Kirkpatrick and Bridle, 1999).

2.2.1. Alpine/subalpineThere is pronounced regionalism in the western and

central Tasmanian alpine flora and the species compo-sition of mountains is often dissimilar (Kirkpatrick,1982; Kirkpatrick and Bridle, 1998). The maritimeclimate of western Tasmania limits the lie of snow torelatively short periods, and alpine grasslands, commonin the alpine flora of greater Australia due to the effectsof persistent snow lie, are uncommon (Kirkpatrick,1982). In the absence of fire and waterlogging, woodyconiferous species such as: Microstrobos niphophilus,Microcachrys tetragona, Diselma archerii, Athrotaxisselaginoides and A. cupressoides dominate. Nothofagusgunnii is also an important alpine species and Asteliaalpina is common in alpine wetlands (Kirkpatrick,1982). Alpine sedgeland is dominated by ericad, proteadand asterad shrubs (Kirkpatrick and Bridle, 1999) andsignificant areas of subalpine Eucalyptus forest occur inthe east of the study region where precipitation is lower(Fig. 1). Extensive areas of Sphagnum peatland arerestricted to central Tasmania, with low nutrient statusand high fire frequency prohibiting the establishment ofSphagnum spp. in the west (Whinam et al., 1989).

2.2.2. RainforestRainforest falls within two broad groups related to

altitude: lowland and montane (Jarman and Brown,1983). Montane rainforest often grades in to alpine andsubalpine vegetation, and is dominated by Nothofaguscunninghamii and Athrotaxis species. Lowland rain-forest develops on all substrates in the absence of fireand is dominated by N. cunninghamii, Phyllocladusaspleniifolius, Eucryphia lucida, Atherospermamoschatum and Anodopetalum biglandulosum. Lagar-ostrobos franklinii dominates in many valleys of theperennially wet southwest region (Jarman and Brown,1983).

Lowland and mid-altitude forests are subdivided intothree categories based on structural characteristics (Jar-man and Brown, 1983). Callidendrous rainforests (sensuJarman and Brown, 1983) have a tall structure (canopydominants: N. cunninghamii and/or A. moschatum) withan open and sparse understorey and are largely confinedto the northwest. Thamnic rainforests (sensu Jarman andBrown, 1983) are of moderate height (N. cunninghamii,

P. aspleniifolius, E. lucida and L. franklinii) and have ashrubby understorey (A. biglandulosum, A. glandulosus,Cenahrrhenes nitidum), and along with Implicate rain-forest (sensu Jarman and Brown, 1983), a low and tangledforest type (N. cunninghamii, L. franklinii, P. aspleniifo-lius,Leptospermum spp.,E. lucida), are themost commonin the mid- to southwest. Other important rainforestspecies include Richea pandanifolia, Monotoca glaucaand Acacia spp., while Leptospermum spp. and Mela-leuca squarrosa are common in swamp rainforestassociations (Jarman and Brown, 1983).

2.2.3. Buttongrass moorlandThis vegetation type derives its name from the

dominance of the sedge, Gymnoschoenus sphaeroce-phalus (buttongrass). Peat forms under moorlandvegetation and the western Tasmanian moorlandscomprise Australia's most extensive areas of peatland(Pemberton, 1989). Species diversity is high, with 165vascular plant species identified as typical of thisvegetation type. Taken together, a number of generaare indicative of moorland. These include: Epacris,Sprengelia, Richea, Dracophyllum, Monotoca (Erica-ceae),Melaleuca, Leptospermum, Baeckea (Myrtaceae),Baumea, Schoenus (Cyperaceae), Restio and Empo-disma (Restionaceae) (Jarman et al., 1988).

Floristic differences exist between seasonally (north-west) and perennially (southwest) waterlogged peats.On the perennially waterlogged peat, the dominantspecies include: Agastachys odorata, Baeckea lepto-caulis, Leptospermum nitidum and Monotoca submu-tica, in an association locally referred to as BlanketMoor. Species common to moorland associations in themid- to northwest and areas marginal to the superhumidzone (referred to as Eastern Moor) have a lower speciesdiversity than Blanket Moor. Indicator taxa of EasternMoor include: Baeckea gunniana, Epacris gunnii, He-lichrysum dealbatum and Poaceae species.

3. Methods

3.1. Field methods

Sixty (60) sites were sampled by us for surface pollenanalysis. Twenty-one (21) were used in our previousanalysis (Fletcher and Thomas, 2007), while 39 wereacquired for this investigation. Sampling sites werechosen to represent the major vegetation communitiesacross the region: alpine, rainforest and moorland.Vegetation data were collected at 23 sites using 20 m2

quadrats at forest sites and 20 m line intercept surveys atopen (moorland and alpine sedgeland) sites. At each site

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a species list was compiled and a record was made ofaltitude, latitude and longitude using a hand held GPSdevice (given in Table 1).

Moss polster, lake, lagoon and pond sediments werecollected for the surface pollen analysis. Moss polstersconsist of five or more samples taken intermittentlyalong 20 m transects or five or more samples from 20 m2

quadrats. Lake, lagoon and pond sediments weresampled using the top 1 cm of a D-section (Russian)corer. All samples were treated by the same pollenprocessing procedure outlined by Moore and Webb(1978). At least 300 pollen grains from terrestrialspecies were counted where possible. Taxonomicnomenclature follows Buchanan et al. (1989). Pollendiagrams were constructed using Tilia and TGView(Grimm, 2004).

3.2. Environmental data

Climate data for each site were inferred from thenearest meteorological station. The regional lapse rate of0.63 °C (Nunez, 1988) was applied to sites at altitudesdiffering from the nearest meteorological station. Thedegree of seasonality at sites was gauged by calculatingthe average lowest (PPT-Q1) and highest (PPT-Q4) quartilesof precipitation and average difference between thelowest and highest quartiles of precipitation (PPT-DIFF)each expressed as units of standard deviation from theaverage monthly precipitation at the closest meteoro-logical station. The differences between the highest andlowest quartiles of average monthly humidity (HUM-

Table 1Climate station data calculated for this investigation. Explanation of variableDIFF) are given in units of standard deviation (SD) from the mean monthly p

Climatestation

Latitude Longitude Altitude Average annualprecipitation

Averagmaximtemper

(°S) (°E) (m) (mm) (°C)

ButlersGorge

42.28 146.27 666 1687.3 12.8

CradleValley

41.64 145.94 900 2803.5 11.5

Lake St Clair 42.10 146.22 735 1511.0 12.3Queenstown 42.10 145.54 129 2421.7 19.4Roseberry 41.78 145.54 165 1966.1 16.6SavageRiver

41.49 145.21 352 1935.8 14

Strahan 42.16 145.33 7 1657.5 16.5Strathgordon 42.77 146.04 322 2540.9 14Warratah 41.45 145.53 598 2186.1 12.4Zeehan 41.88 145.33 172 2444.5 15.2

DIFF) and average monthly temperature (TEMP-DIFF) werealso calculated. All climate data were obtained from theAustralian Bureau of Meteorology and are listed inTable 2. Climate stations are sparse, particularly in theremote southwest of Tasmania, and these data are, atbest, approximations of the actual climate at samplingsites.

We calculated a fire interval score (FIRE-INT) based onthe species groups identified by Brown and Podger(1982) along a continuum related to time since the lastfire for southwest Tasmania. Pollen types identifiable tospecies level (13) were given pre-assigned weightsranging from 0 for group 1 (N350 year interval) to 100for group 8 (b1 year interval). We then used weightedaveraging (sensuMcCune and Grace, 2002) to calculatea FIRE-INT score for each surface pollen sample based ona percentage sum recalculated from the 13 selectedspecies. Low FIRE-INT values reflect long fire-freeintervals and high values reflect short fire-free inter-vals. Lagarostrobos franklinii and Atherospermamoschatum were absent from the analysis of Brownand Podger (1982) and were given equal weighting asEucryphia lucida based on the similar fire ecologies ofthese species. Fire susceptibility scores (FIRE-SUS) foreach site are based on the continuum of fire sensitivitiesof Tasmanian vegetation identified by Pyrke andMarsden-Smedley (2005): extreme (1); very high (2);high (3); moderate (4); and low (5). In this scheme, a plantcommunity which is highly sensitive to fire is unlikely toburn and has a low susceptibility to fire. The FIRE-SUS

score was calculated by expressing the fire sensitivity

codes is given in Section 3.2. Precipitation data (PPT-Q1, PPT-Q4 and PPT-

recipitation at each climate station

e annualumature

Average annualminimumtemperature

PPT-Q1 PPT-

Q4

PPT-

DIFF

HUM-

DIFF

TEMP-

DIFF

(C°) (SDunits)

(SDunits)

(SDunits)

(%) (°C)

2.9 −0.64 1.15 1.79 16.00 9.30

2.8 −0.78 1.51 2.29 19.25 10.10

3.8 −0.59 1.19 1.78 10.13 9.607.9 −0.72 1.25 1.97 13.13 8.787 −0.65 1.47 2.12 16.50 8.906.4 −0.85 1.37 2.22 14.00 9.15

7.8 −0.71 1.40 2.10 11.50 7.456.2 −0.65 1.27 1.93 16.25 8.503.6 −0.82 1.25 2.07 14.50 8.756.3 −0.82 1.20 2.02 15.13 7.43

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Table 2Sample numbers, environmental data and literature source of surface pollen sites used in this study

Siteno.

Site name Latitude Longitude Altitude Temperature Precipitation FIRE-

INT

FIRE-

SUS

Vegetation Sample type Source

(°S) (°E) (m) (°C) (mm)

1 Tarn Shelf 42.70 146.50 1158 4.9 1717 0.41 20 Alpine heath Lake sediment Macphail, 1979; D'costaand Kershaw, 1997

2 Upper Lake Wurawina 42.50 146.25 1040 5.7 2043 1.92 20 Alpine heath Lake sediment Macphail, 1986; D'costaand Kershaw, 1997

3 Eagle Tarn 42.70 146.58 1033 5.7 1555 0.96 20 Subalpine open forest Lake sediment Macphail, 1979; D'costaand Kershaw, 1997

4 Tarn Shelf, Tyndall Range 41.90 145.62 1000 6.3 3194 0.99 20 Alpine heath Lake sediment Macphail, 1979; D'costaand Kershaw, 1997

5 Mt Field 1 42.70 146.63 1000 6.0 990 0.00 20 Subalpine open forest Moss polster6 Mt Field 2 42.70 146.63 1000 6.0 990 1.28 80 Alpine sedgeland Moss polster7 Mt Field 3 42.70 146.63 1000 6.0 990 2.33 80 Alpine sedgeland Moss polster8 Beaties Tarn 42.70 146.63 990 6.0 1475 2.78 20 Subalpine open forest Lake sediment Macphail, 1979; D'costa

and Kershaw, 19979 Adamsons Peak 43.40 146.82 960 5.8 1884 0.27 20 Alpine heath Lake sediment Macphail, 1979; D'costa

and Kershaw, 199710 Mt Read 1 41.90 145.55 960 6.8 3220 1.01 80 Alpine heath Moss polster11 Lake Dove 1 41.70 145.95 934 10.7 2798 0.45 20 Buttongrass moorland–

subalpine forestLake sediment Dyson, 1995; D'costa

and Kershaw, 199712 Lake Johnson 41.90 145.55 900 6.9 3220 1.05 20 Subalpine closed forest Lake sediment Anker et al., 2001; D'costa

and Kershaw, 199713 Above Lake Johnson 41.90 145.55 900 6.9 3220 1.53 80 Alpine heath Moss polster14 Ooze Lake 43.50 146.72 880 6.2 1939 0.00 20 Alpine heath Lake sediment Macphail and Colhoun, 1985;

D'costa and Kershaw, 199715 Mt Read 2 41.90 145.55 800 7.2 3220 0.51 20 Subalpine closed forest Moss polster16 Burns Plain 1 42.10 146.21 730 6.9 1511 13.16 40 Subalpine open forest Moss polster17 Lake Dove 2 41.70 145.95 940 6.9 2798 15.86 100 Buttongrass moorland Pond sediment18 Lake Dove 3 41.70 145.95 940 6.9 2798 24.59 100 Buttongrass moorland Moss polster19 Lake Dove 4 41.70 145.95 940 6.9 2798 17.62 100 Buttongrass moorland Moss polster20 Cradle Link Road 1 41.54 145.89 820 7.3 2803 17.39 100 Buttongrass moorland Moss polster21 Cradle Link Road 2 41.54 145.89 820 7.3 2803 55.76 100 Buttongrass moorland Moss polster22 Cradle Link Road 3 41.54 145.89 820 7.3 2803 16.81 100 Buttongrass moorland Moss polster23 Cradle Link Road 4 41.54 145.89 820 7.3 2803 10.45 100 Buttongrass moorland Moss polster24 Cradle Link Road 5 41.54 145.89 820 7.3 2803 8.00 100 Buttongrass moorland Moss polster25 Vale of Belvoir 8c 41.54 145.89 820 7.3 2803 0.00 100 Buttongrass moorland Moss polster26 Vale of Belvoir 1 41.54 145.89 820 7.3 2803 6.02 100 Buttongrass moorland Moss polster27 Vale of Belvoir 2 41.54 145.89 820 7.3 2803 36.45 100 Buttongrass moorland Moss polster28 Vale of Belvoir 8b 41.54 145.89 820 7.3 2803 3.26 100 Buttongrass moorland Moss polster29 Vale of Belvoir 6 41.54 145.89 820 7.3 2803 1.28 100 Buttongrass moorland Moss polster30 Vale of Belvoir 8a 41.54 145.89 820 7.3 2803 11.70 100 Buttongrass moorland Moss polster31 Vale of Bevoir 7 41.54 145.89 820 7.3 2803 1.52 100 Buttongrass moorland Moss polster

(continued on next page)

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Table 2 (continued)

Siteno.

Site name Latitude Longitude Altitude Temperature Precipitation FIRE-

INT

FIRE-

SUS

Vegetation Sample type Source

(°S) (°E) (m) (°C) (mm)

32 Northwest Moorland 41.44 145.61 800 7.3 2186 9.43 100 Buttongrass moorland Moss polster33 Moxon Saddle 41.90 145.62 760 7.5 3100 7.76 100 Buttongrass moorland Lagoon

sediment34 Mt Anne 7 42.95 146.31 750 7.0 2540 7.61 100 Buttongrass moorland Moss polster35 Burns Plain 2 42.10 146.21 730 6.9 1511 29.65 100 Buttongrass moorland Moss polster36 Burns Plain 3 42.10 146.21 730 6.9 1511 33.86 100 Buttongrass moorland Moss polster37 Burns Plain 4 42.10 146.21 730 6.9 1511 54.76 100 Buttongrass moorland Moss polster38 Burns Plain 5 42.10 146.21 730 6.9 1511 49.00 100 Buttongrass moorland Moss polster39 Poets Hill 41.90 145.58 620 8.5 3225 1.64 100 Buttongrass moorland Lake sediment Colhoun, 1992; D'costa

and Kershaw, 199740 Mt Anne 4 42.95 146.31 600 8.0 2540 21.31 100 Buttongrass moorland Moss polster41 Lake Selina 1 41.90 145.63 540 8.9 3200 5.45 100 Buttongrass moorland Lake sediment Colhoun et al., 1999; D'costa

and Kershaw, 199742 Lake Selina 2 41.90 145.63 540 8.9 3200 18.83 100 Buttongrass moorland Moss polster43 Mt Anne 2 42.95 146.31 500 9.0 2540 24.66 100 Buttongrass moorland Moss polster44 Boco Valley 41.65 145.60 450 10.7 2500 49.72 100 Buttongrass moorland Moss polster45 Artists Hill 42.20 145.98 450 8.0 2849 5.63 100 Buttongrass moorland Moss polster46 Mt Anne 1 42.95 146.31 400 10.0 2540 16.32 100 Buttongrass moorland Moss polster47 Stonehaven Creek 42.21 145.99 380 8.6 2810 15.27 100 Buttongrass moorland Moss polster48 West Coast Highway 42.21 145.99 380 8.6 2810 7.02 100 Buttongrass moorland Moss polster49 Harlequin Hill 1 42.95 146.31 320 9.0 2540 40.28 100 Buttongrass moorland Lagoon

sediment50 Harlequin Hill 2 42.95 146.31 320 9.0 2540 33.82 100 Buttongrass moorland Moss polster51 Harlequin Hill 3 42.95 146.31 320 9.0 2540 29.49 100 Buttongrass moorland Moss polster52 Smelter Creek 42.20 145.63 200 10.6 2877 2.86 100 Buttongrass moorland Peat Colhoun et al., 1992; D'costa

and Kershaw, 199753 King River 42.20 145.65 200 10.6 2832 1.64 100 Buttongrass moorland Lake sediment van de Geer et al., 1991; D'costa

and Kershaw, 199754 Governer Bog 42.20 145.65 180 10.7 2829 3.61 100 Buttongrass moorland Peat Colhoun et al., 1991; D'costa

and Kershaw, 199755 Darwin Crater 42.30 145.67 180 10.7 2803 18.33 100 Buttongrass moorland Peat Colhoun and van de Geer, 1988;

D'costa and Kershaw, 199756 Piney Creek 1 42.95 146.36 172 11.1 2444 6.36 80 Buttongrass moorland Moss polster57 Piney Creek 2 42.95 146.36 172 11.1 2444 2.00 100 Buttongrass moorland Moss polster58 Piney Creek 3 42.95 146.36 172 11.1 2444 2.23 100 Buttongrass moorland Moss polster59 Piney Creek 4 42.95 146.36 172 11.1 2444 13.16 100 Buttongrass moorland Moss polster60 Piney Creek 5 42.95 146.36 172 11.1 2444 17.26 100 Buttongrass moorland Moss polster61 Piney Creek 6 42.95 146.36 172 11.1 2444 11.25 100 Buttongrass moorland Moss polster62 Hardwood River Valley 42.95 146.31 50 12.0 2540 26.77 100 Buttongrass moorland Pond sediment63 Melaleuca Inlet 43.30 146.08 10 11.5 2261 7.47 100 Buttongrass moorland Peat Thomas, 1995a,b; D'costa

and Kershaw, 199764 Mill Bay 1 42.14 145.30 5 11.8 1474 13.43 100 Buttongrass moorland Moss polster

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65 Mill Bay 2 42.14 145.30 5 11.8 1474 19.61 100 Buttongrass moorland Moss polster66 Tullabardine Dam 41.67 145.65 230 10.7 1936 7.78 60 Wet sclerophyll forest Peat Colhoun and van de Geer, 1986;

D'costa and Kershaw, 199767 Cradle Link Road Forest 41.54 145.89 840 11.0 2803 0.00 40 Subalpine forest Moss polster68 Lake Vera 42.30 145.87 560 8.6 2849 0.31 20 Rainforest Lake sediment Macphail, 1979; D'costa

and Kershaw, 199769 Lake Timk 42.90 146.45 545 8.5 1869 2.37 40 Rainforest Swamp

sedimentHarle, 1989; D'costaand Kershaw, 1997

70 Piney Creek Forest 42.95 146.36 172 11.1 2444 0.10 20 Rainforest Moss polster71 Newall Creek 42.20 145.50 140 11.1 2450 0.25 20 Rainforest Lake sediment van de Geer et al., 1989;

D'costa and Kershaw, 199772 Lake Chisholm 1 41.13 145.06 120 12.0 1500 0.00 40 Rainforest Moss polster73 Lake Chisholm 2 41.13 145.06 120 12.0 1500 0.00 40 Rainforest Moss polster74 Perched Lake 1 42.56 145.68 35 11.7 2430 0.00 20 Rainforest Moss polster75 Perched Lake 3 42.56 145.68 35 11.7 2430 0.00 20 Rainforest Moss polster76 Perched Lake 4 42.56 145.68 35 11.7 2430 0.00 20 Rainforest Moss polster77 Perched Lake 5 42.56 145.68 35 11.7 2430 0.00 20 Rainforest Moss polster78 Perched Lake 6 42.56 145.68 35 11.7 2430 0.00 20 Rainforest Moss polster79 Perched Lake 7 42.56 145.68 35 11.7 2430 0.00 20 Rainforest Moss polster80 Perched Lake 8 42.56 145.68 35 11.7 2430 0.72 20 Rainforest Moss polster81 Lake Fiddler LQ 8 42.50 145.68 5 11.7 2430 3.37 20 Rainforest Moss polster Harle et al. (1999)82 Lake Fiddler LQ 7 42.50 145.68 5 11.7 2430 2.79 20 Rainforest Moss polster Harle et al. (1999)83 Lake Fiddler LQ 6 42.50 145.68 5 11.7 2430 0.74 20 Rainforest Moss polster Harle et al. (1999)84 Lake Fiddler LQ 5 42.50 145.68 5 11.7 2430 1.41 20 Rainforest Moss polster Harle et al. (1999)85 Lake Fiddler LQ 4 42.50 145.68 5 11.7 2430 3.53 20 Rainforest Moss polster Harle et al. (1999)86 Lake Fiddler LQ 3 42.50 145.68 5 11.7 2430 1.71 20 Rainforest Moss polster Harle et al. (1999)87 Lake Fiddler LQ 2 42.50 145.68 5 11.7 2430 1.48 20 Rainforest Moss polster Harle et al. (1999)88 Lake Fiddler LQ 1 42.50 145.68 5 11.7 2430 1.37 20 Rainforest Moss polster Harle et al. (1999)89 Lake Fiddler FQ 1 42.50 145.68 5 11.7 2430 1.64 20 Rainforest Moss polster Harle et al. (1999)90 Lake Fiddler FQ 2 42.50 145.68 5 11.7 2430 1.11 20 Rainforest Moss polster Harle et al. (1999)91 Lake Fiddler FQ 3 42.50 145.68 5 11.7 2430 0.92 20 Rainforest Moss polster Harle et al. (1999)92 Lake Fiddler FQ 4 42.50 145.68 5 11.7 2430 1.38 20 Rainforest Moss polster Harle et al. (1999)93 Lake Fiddler FQ 5 42.50 145.68 5 11.7 2430 0.00 20 Rainforest Moss polster Harle et al. (1999)94 Lake Fiddler FQ 6 42.50 145.68 5 11.7 2430 1.02 20 Rainforest Moss polster Harle et al. (1999)95 Lake Fiddler 1 42.50 145.68 5 11.7 2430 1.95 20 Rainforest Lake sediment Harle et al., 1999; D'costa

and Kershaw, 199796 Lake Fiddler 2 42.50 145.68 5 11.7 2430 0.93 20 Rainforest Lake sediment Harle et al. (1999)

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Fig. 2. Dispersability index versus fidelity index scores for major pollen taxa in western Tasmania.

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Fig. 3. Pollen diagram for all 96 surface pollen samples. Samples are grouped according to vegetation type and in decreasing order of altitude andspecies are ordered according to the groupings in Fig. 2.

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Fig. 4. First two axes of the DCA ordination of the total (96 sample) surface pollen dataset. Associated eigenvalues are: axis 1, 0.48; axis 2, 0.3. Thedirections of significantly correlated environmental variables are shown and associated R values are: altitude, 0.757; temperature, 0.752; FIRE-SUS,0.613 (axis 1), 0.735 (axis 2); and FIRE-INT, −0.459 (axis 1), −0.495 (axis 2).

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group number of a plant community (sensu Pyrke andMarsden-Smedley, 2005) as a percentage of the totalnumber of groups (5) so that 100% represents a highsusceptibility and 0% represents a low susceptibility tofire. Both FIRE-INT and FIRE-SUS scores for each surfacesample site are listed in Table 1.

3.3. Data analysis

Ninety-six (96) surface pollen samples were includedin our numerical analysis. Sixty (60) surface samplescollected by us and 36 modern pollen spectra importedfrom other studies, 21 from D'costa and Kershaw's(1997) modern pollen database for southeast Australiaand 15 digitised from published pollen diagrams fromthe region (Table 1).

The following modifications were made to the pollendata prior to numerical analysis:

i. Leptospermum/Baeckea type and Leptospermumtype, and Melaleuca squarrosa and M. squameawere grouped into Leptospermum and Melaleuca

respectively to facilitate the comparison between ourdata and imported data.

ii. Taxa with five or less occurrences or that failed toregister at a percentage level of 1% or more wereexcluded from the analysis (sensu McGlone andMoar, 1997), the remaining pollen sum included 32taxa and percentages were calculated after exclusions.

To asses the representation of pollen types, fidelityand dispersability indices were calculated (sensuMcGlone and Meurk, 2000). The fidelity indexrepresents the number of sites where a given taxonwas recorded in both the vegetation and surface pollenspectra, expressed as a percentage of the total number ofsites in which the taxon was present in the vegetation.The dispersability index represents the number of siteswhere a given taxon was recorded in the surface pollenspectra but not in the vegetation, expressed as apercentage of all sites where the plant is absent. Forthe imported datasets, species lists provided in therelevant publications were used and sites were excludedwhere there was insufficient information.

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Detrended correspondence analysis (DCA) (Hill andGauch, 1980) was performed on the entire surfacesample dataset using the DECORANA computerprogram in PCOrd Version 4.25 (McCune and Mefford,1999). A separate DCA ordination was performed on asubset of 48 sites from buttongrass moorland vegetation.Ordination results were compared with an analysis bynon-metric multidimensional scaling (NMDS) (sensuEjrnaes, 2000). The overall patterns were very similar inboth the DCA and NMDS, with a clearer separationdisplayed in the DCA.

4. Results and discussion

4.1. Fidelity and dispersability

The plot of fidelity and dispersability (Fig. 2)identifies four groups displaying different pollen repre-sentation. Nomenclature of the groups follows that usedby Dodson (1983) and Deng et al. (2006):

1. Over-represented pollen types: have high fidelity(N80%) and dispersability (N90%) index scores and,

Fig. 5. First two axes of the DCA ordination of the total (96 sample) surface0.48; axis 2, 0.3. The directions of significantly correlated environmentatemperature, 0.752; FIRE-SUS, 0.613 (axis 1), 0.735 (axis 2); and FIRE-INT, −0

as such, are pollen types which are producedabundantly and are dispersed long distances fromtheir source. Nothofagus cunninghamii and Phyllo-cladus aspleniifolius are over-represented in thepollen rain of western Tasmania.

2. Well-represented pollen types: have high fidelity(N80%) and moderate dispersability (33–66%).Poaceae and Eucalyptus are sometimes over-repre-sented and are at the high end of dispersability forthis group, as is Monotoca. Allocasuarina, Aster-aceae and Leptospermum are moderately represented(dispersability index 37–43%). These pollen typesare dispersed in lesser distances, and moderate tohigh values indicate a local source.

3. Under-represented pollen types: are poorly dispersed(b28%) pollen types with high fidelity (70–100%).There is a strong correlation between the presenceof these pollen types in surface pollen and vegetationand many of these are good indicators of local vege-tation. Lagarostrobos franklinii, Melaleuca, Bauerarubioides, Athrotaxis, Eucryphia/Anodopetalum, Mi-crocachrys tetragona,Gymnoschoenus sphaerocepha-lus, Nothofagus gunnii, Microstrobos niphophilus,

pollen dataset showing pollen taxa. Associated eigenvalues are: axis 1,l variables are shown and associated R values are: altitude, 0.757;.459 (axis 1), −0.495 (axis 2).

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Banksia marginata, Ecriaceae, Agastachys odorataand Astelia alpina are all included in this group.

4. Severely under-represented: Atherosperma moscha-tum pollen has a low fidelity (41%) and a very lowdispersability (11%). This pollen type is often absentfrom surface samples when it is present in localvegetation.

5. Other species: Pomaderris apetala is present in 78%of our samples despite an absence from thevegetation of all sites and is over-represented(group 1). Other pollen types absent from vegetation,but present in surface samples are: Ampereaxiphoclada (1–2), Tasmannia lanceolata (2–3),Acacia (3–4), Dodonea viscosa (1–2), Plantago

Fig. 6. Box-plots of modern pollen rain percentages for the main vegetationThe box encloses the first and third quartiles, the vertical line indicates the meOutliers are marked with ⁎ and extreme values are denoted with �.

(1–2), Coprosma (2–3), Chenopodiaceae (1) andPodocarpus (3–4). Grouping (bracketed number) ofthese pollen types follows Dodson (1983) andpersonal experience.

4.2. Pollen–vegetation relationships

A clear relationship between vegetation type andlocally dispersed pollen types is evident in the pollendiagram in Fig. 3. The DCA ordination of the entiresurface sample dataset equates remarkably well withvegetation type (Figs. 4 and 5). Two alpine sites overlapwith moorland surface samples, due to poor taxonomicresolution of their dominant pollen types and are

types in western Tasmania: alpine/subalpine, rainforest and moorland.dian value and the horizontal line represents the typical range of values.

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Fig. 6 (continued).

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discussed in Section 4.2.1. Axis 1 (eigenvalue 0.48) andaxis 2 (0.3) account for 78% of the variation in themodern pollen dataset, while altitude and temperaturecorrelate significantly with axis 1 (R=0.757 and 0.752respectively) clearly indicating that altitude and itsrelationship to temperature is the main determinantaffecting pollen composition in the region.

A secondary influence of fire on pollen compositioncan be inferred from the significant correlations betweenboth ordination axes and the FIRE-SUS (R=0.613 and0.735 for axes 1 and 2 respectively) and FIRE-INT scores(R=−0.459 and −0.495). FIRE-SUS and FIRE-INT areobliquely related to the ordination axes (Figs. 4 and 5),with the surface samples separating into four broadgroups relating to altitude and fire: (1) fire-sensitive(lowland) rainforest; (2) fire-sensitive alpine andsubalpine; (3) fire-tolerant moorland; and (4) fire-

tolerant alpine sedgeland vegetation (Figs. 4 and 5).There is some continuity between fire-sensitive alpineand fire-tolerant moorland sites, reflecting the pollencomposition of alpine sites above mountain slopesvegetated by moorland (sites 2 and 11) and the input ofalpine pollen types into some high altitude moorlandsites (sites 17–19, 33 and 39). The only surface samplefrom wet sclerophyll forest (site 66) occupies anintermediate position between fire-tolerant moorlandand fire-sensitive rainforest sites, consistent with theecology of this transitional community (Jackson, 1968).

Summary statistics for the percent representation ofselected pollen types in each vegetation type are shownin Fig. 6. We have plotted percent pollen values againstpercent vegetation cover and calculated R2 values forthe linear regressions of the more abundant species withgood pollen representation (Fig. 7). The gradient of the

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regression line is an indication of the pollen tovegetation ratio at sampling sites, while the y-interceptis a guide to the proportion of pollen originating outsidevegetation sampling plots.

4.2.1. Community representation

4.2.1.1. Alpine/subalpine sites. Pollen representation,fire susceptibility (FIRE-SUS) and the inferred time sincethe last fire (FIRE-INT) is varies in this group (Figs. 3 and

Fig. 7. Percent pollen versus percent vegetation of common taxa. L

6, Table 2), reflecting regional variation in thecomposition of alpine communities in western Tasmania(Kirkpatrick, 1982) and the relationship betweenabutting lowland vegetation and fire. All sites showsignificant values of over-represented lowland treespecies Nothofagus cunninghamii (2–45%) and Phyl-locladus aspleniifolius (1–26%) (Figs. 3 and 6). Theidentification of alpine vegetation from pollen spectra isdependent on the presence of either Astelia alpina (0.5–12.5%), Nothofagus gunnii (1–33%), Microstrobos

inear regression lines, R2 and y-intercept values are shown.

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Fig. 8. Pollen diagram for 48 moorland samples. Samples are grouped according to moorland type and arranged in order of decreasing altitude.

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niphophilus (1–45%) and/or Microcachrys tetragona(1–45%). Athrotaxis (1–47%), Poaceae (1–14%) andAsteraceae (1–14%) are also important, while Euca-lyptus pollen (3–71%) is well-represented in subalpinesites in central Tasmania where precipitation is reduced(Fig. 3 and Table 2).

Alpine sedgeland (sensu Kirkpatrick and Bridle,1999) represents a high altitude expression of moorlandin the ordination space (Fig. 4), and is consistent with itsenvironmental correlates of fire and waterlogging.Alpine sedgeland is differentiated from other alpinecommunities by significant values of Gleichenia alpina(53–72%) and an absence of Leptospermum,Melaleucaand Gymnoschoenus sphaerocephalus pollen discrimi-nates alpine sedgeland frommoorland sites (Fig. 3). Sites13 and 15 overlap with moorland in the ordination (Fig.4): site 15 contains 5% Microcachrys tetragona pollen(under-represented) and can be confidently identified asalpine; and site 13 lacks alpine indicator pollen types andis dominated by Ecriaceae and Asteraceae species.

4.2.1.2. Rainforest sites. This vegetation type recordslow FIRE-SUS and FIRE-INT scores (Table 1 and Fig. 4) inkeeping with the low fire frequency experienced by

Fig. 9. First two axes of the DCA ordination of the 48 moorland surface samplof significantly correlated environmental variables are shown and associatedDIFF, 0.634; PPT-Q4, 0.634; TEMP-DIFF, 0.625; and PPT-DIFF, 0.606.

rainforest vegetation. Pollen spectra from rainforest sitesare characterised by moderate to high values of under-represented rainforest species: Lagarostrobos franklinii(7–76%), Eucryphia/Anodopetalum (2–49%) andAtherosperma moschatum (0.4–2.5%). Nothofaguscunninghamii (9–84%) pollen dominates sites where itis dominant in the vegetation (Fig. 7). Phyllocladusaspleniifolius (1.5–33%) shows consistent but variedrepresentation and Leptospermum (1–15%) and Mela-leuca (1–15%) are common in swamp forest samples.

4.2.1.3. Buttongrass moorland sites. A gradationfrom Blanket Moor to Eastern Moor is apparent inthe moorland modern pollen diagram (Fig. 8).Moorland type equates well with the first two axes ofthe second ordination (Fig. 9, eigenvalues of 0.35 and0.15 for axes 1 and 2 respectively) and it is evident thatpollen composition from moorland sites really reflectsmoorland community type. The significant correlationof altitude (R=0.756), latitude (0.721), average annualtemperature (0.653), HUM-DIFF (0.634), PPT-Q4 (0.629),TEMP-DIFF (0.625), and PPT-DIFF (0.606) with axis 1 ofthe ordination (Fig. 9) imply that these factors areimportant determinants of the duration of waterlogging

es. Associated eigenvalues are: axis 1, 0.35; axis 2, 0.15. The directionsR values are: altitude, 0.756; latitude, 0.721; temperature, 0.653; HUM-

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and, hence, the composition of moorland species.Thus, it is possible to identify moorland type and inferchanges in seasonality and/or altitude using moorlandpollen representation.

All buttongrass moorland sites collected by us containpollen from Gymnoschoenus sphaerocephalus (1–29%)and we have previously suggested that an absence of thispollen type from other studies is likely to be the result ofan omission of, or a failure to identify this pollen type(Fletcher and Thomas, 2007). Ecriaceae (1–47%) is theonly other pollen type consistently represented in allmoorland surface samples. All moorland sites displayhigh susceptibility to fire and a pollen compositionreflecting a short fire-free interval (Table 2 and Fig. 4).Box-plot analysis for each moorland type (Fig. 10)identifies moorland type indicators: Blanket Moor ischaracterised by G. sphaerocephalus (1–9%),Melaleuca(1–40%), Leptospermum (1–25%), Ecriaceae (4–45%),Banksia marginata (0.5–30%), Agastachys odorata(0.5–20%) andMonotoca (1–20%) (Figs. 8–10);whereasEastern Moor contains G. sphaerocephalus (1–28%),Poaceae (1–49%), Asteraceae (1–24%), Astelia alpina(0.5–9%) and Tasmannia lanceolata (0.5–10%)(Figs. 8–10).

Fig. 10. Box-plots of modern pollen rain percentages from moorland commuand third quartiles, the vertical line indicates the median value and the horizonand extreme values are denoted with �.

4.2.2. Representation of significant taxa

4.2.2.1. Nothofagus cunninghamii. This anemophi-lous species is over-represented in the regional pollenrain of western Tasmania (Fig. 2), due to its good pollendispersal capabilities, the extensive mosaics of rainforestin the lowlands and the persistence of this species abovethe treeline at many alpine sites. Median (maximum)values of 22% (49%) and 20% (46%) in alpine/subalpineand moorland samples respectively (Fig. 6), and the y-intercept value of 14% (Fig. 7), indicate that a significantproportion of this pollen type originates outside of thelocal vegetation at sampling sites. Where this species ispresent in the local vegetation, there is a goodrelationship between its abundance in pollen andvegetation (Fig. 7). In the absence of locally over-represented rainforest pollen types (such as L. franklinii,Fig. 7 and discussed in Section 4.2.2.4), we agree withMacphail (1979) that Nothofagus values exceeding 40%in pollen spectra indicate local presence.

4.2.2.2. Phyllocladus aspleniifolius. This pollen type,like N. cunninghamii, is over-represented in the regionalpollen rain (Figs. 2, 3, and 6) and is a significant

nity types: Eastern Moor and Blanket Moor. The box encloses the firsttal line represents the typical range of values. Outliers are marked with ⁎

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component of surface pollen spectra from all vegetationtypes (Figs. 3 and 6). Regression analysis (although notsignificant at the P=b0.05 level) suggests that ca. 8% ofthe pollen sum is composed of P. aspleniifoliuswhen thisspecies is absent from the vegetation (Fig. 5), with up to26% imported into some open vegetation sites (Figs. 3and 6). This species is usually under-represented where itoccurs in the vegetation at sampling sites (Fig. 7).

4.2.2.3. Eucalyptus. Our results indicate that Euca-lyptus pollen is well-represented in the regional modernpollen rain of western Tasmania (Fig. 2). High values ofthis pollen type are likely to indicate a local source, whilemoderate values can indicate a source that is local orproximal to the sampling site. Colhoun and Shmeild(2002) report a rapid decline in Eucalyptus pollenoutside of Eucalyptus dominated forest on the CentralPlateau of Tasmania, in accordance with our results thatindicate that this taxon can contribute up to 30% of thepollen sum at sites where it is absent from the vegetation(Fig. 7). Eucalyptus species are ubiquitous acrossTasmania and it is probable that the frequent occurrenceof this pollen type in surface samples where Eucalyptusspecies are absent from the local vegetation is a result ofthis ubiquity rather than a tendency toward over-representation, a notion supported by the zoophilouspollen dispersal in this genera. Where Eucalyptus ispresent in the local vegetation, there is a tendency towardunder-representation in the pollen rain (Fig. 7), althoughthis relationship is highly variable and dependent onspecies composition at sampling sites (Colhoun andGoede, 1979). In western Tasmania, Eucalyptus pollenattains its greatest representation in subalpine vegetationtoward the drier east (Figs. 3 and 6, Table 2) and valuesbetween 30 and 66% are common in subalpine woodlandvegetation (Fig. 3). This may be a useful threshold forinferring local subalpine vegetation, especially whenalpine indicators are present. However, the poortaxonomic resolution of Eucalyptus pollen and thebroad ecological range of Eucalyptus species precludeconfident inferences based on this pollen type alone.

4.2.2.4. Lagarostrobos franklinii. Whilst producingthe saccate pollen grains typical of many anemophilousconifer species, L. franklinii is under-represented in theregional pollen rain (Fig. 2) and over-represented where itoccurs in the local vegetation (Fig. 7). This is highlightedby the dominance of L. franklinii pollen (75%) at arainforest site (site 70), despite this species comprisingonly 13% of the local vegetation. Pollen values fall tob4% in a surface sample frommoorland vegetation 100meast of site 70 (down wind from the dominant westerlies)

and continue to decrease (3–0.2%) along a 10 km transecteast through open moorland. This species inhabits manyriparian zones in western Tasmania and deposition downstream into non-rainforest vegetation accounts for theanomalously large percentage values at some moorlandsites. This pollen type, as suggested for all under-represented pollen types in Section 4.1, is a good indicatorof local vegetation type.

4.2.2.5. Gymnoschoenus sphaerocephalus. G. sphaer-ocephalus is poorly represented in the pollen rain atmoorland sites (Fig. 7) and has low dispersability (5%)(Fig. 2). This species does have a high fidelity score(100%) in samples we collected and, like L. franklinii forrainforest, G. sphaerocephalus pollen is a good andreliable indicator of moorland vegetation due to itswidespread occurrence in the vegetation and high fidelity.Lower G. sphaerocephalus values in Blanket Moorrelative to Eastern Moor vegetation (Fig. 10) are aconsequence of greater species diversity in this vegetationtype (Jarman et al., 1988) diluting the pollen signal of thisspecies.

4.2.2.6. Melaleuca. Melaleuca pollen is under-repre-sented (Fig. 2) and its presence in pollen spectra indicatesa likely presence in local vegetation.Melaleuca pollen islargely confined to lowland sites in western Tasmania(Figs. 3, 5, and 6) and there is little difference betweenrepresentation in (swamp) rainforest and moorlandvegetation (Fig. 6). Despite this, in the presence/absenceof rainforest and moorland indicators, Melaleuca pollencan provide important information about the composi-tion of local vegetation. Blanket Moor vegetation re-cords higher values of this pollen type than EasternMoor (Figs. 8 and 10), consistent with the ecology ofM. squamea (a common moorland species tolerant ofhigh fire frequency and waterlogging) (Jarman et al.,1988), while Melaleuca pollen in rainforest pollenspectra (likely to be M. squarrosa) is a good indicatorof damp conditions.

4.2.2.7. Poaceae. Inferences based on moderatevalues of this pollen type should be treated with cautiondue to the tendency toward over-representation (Fig. 2and Dodson, 1983). This pollen type is typically foundin areas with low precipitation in the southeastAustralian region (Kershaw et al., 1994; D'costa andKershaw, 1997) and is largely confined to easternTasmania and drier parts of the west. Eastern Moorsamples contain significant amounts of Poaceae pollen(1–49%) (Figs. 8 and 10) and this pollen type is apotential indicator of Eastern Moor vegetation, where

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seasonality and reduced precipitation facilitates thepresence of grass species in local vegetation.

4.2.2.8. Ericaceae. Ericaceae species in westernTasmania are predominantly represented by membersof the sub-family Styphelioideae. Ericaceae species arewidespread in all vegetation types in western Tasmania(Fig. 3) and their pollen grains are rarely dispersed farfrom their source plant (Fig. 2). Rainforest sitesgenerally have low representation of this pollen type(1–5%), with high values usually indicative of openvegetation.

4.2.2.9. Monotoca. Monotoca is one of the fewEcriaceae genera with pollen grains (triads) identifiablebeyond the family level.M. glacuca is largely confined toforest vegetation, but it is also present throughoutmoorland on dry copses that represent seral stages towardrainforest, whileM. submutica is common in low altitudeBlanket Moor vegetation (Jarman et al., 1988). Pollenfrom many rainforest sites contains significantMonotocapollen (1–50%), while Blanket Moor sites contain low tomoderate values (1–9%) and it is absent from EasternMoor (Figs. 3, 6, 8, and 10).

4.2.2.10. Alpine species. Microstrobos niphophilus,Microcachrys tetragona and Nothofagus gunnii are allalpine woody taxa identifiable to species level in pollenspectra. These pollen types, along with Athrotaxis/Di-selma pollen, are under-represented in the regional pollenrain, but are faithful indicators of alpine vegetation(Fig. 2). Astelia alpina pollen is also poorly dispersed butwell-represented where it comprises part of the vegetationand is a good indicator of alpine wetland sites.

4.2.2.11. Proteaceous shrubs. Agastachys odorataand Banksia marginata are both entomophilous shrubsthat are under-represented in the regional pollen rain.These species are listed as moorland indicators byJarman et al. (1988) and their pollen is largely restrictedto surface samples from moorland vegetation (Fig. 3),although A. odorata may be present as an Implicaterainforest understorey species where it is a minorcomponent of the pollen (Fig. 3).

4.3. Modern pollen spectra in western Tasmania

In western Tasmania, regionally important treespecies, Nothofagus cunninghamii and Phyllocladusaspleniifolius, are over-represented in the pollen rain,and the dominant vegetation community, open button-grass moorland, has gone undetected in attempts to

predict regional vegetation from pollen spectra (Pickettet al., 2004). Our quantitative classification of pollentypes into groups relating to their representation is ingood agreement with Dodson's (1983) qualitativeassessment of important southeast Australian pollentypes and there is a clear relationship between under-represented pollen taxa and vegetation type (Figs. 3 and8). Pollen from alpine species, Lagarostrobos frankliniiand Gymnoschoenus sphaerocephalus are clear indica-tors of alpine/subalpine, rainforest and moorlandvegetation respectively, while values exceeding 40%of N. cunninghamii and 25% of P. aspleniifolius arelikely to represent extant individuals and rainforestvegetation.

A clear separation of vegetation type by ordinationanalysis (Figs. 4 and 9) occurs irrespective of the surfacesample type used in this analysis. Surface sample typehas been shown to influence the pollen signal ofvegetation, due largely to changes in the source area ofpollen arriving at different sampling site types, thedifferent dispersal modes of pollen types and/orreworking of pollen from catchment soils into lakebasins (Prentice, 1986; Cundill, 1991; Davis, 2000;Vermoere et al., 2000; Wilmshurst and McGlone, 2005).We used moss polsters, and lake, lagoon and pondsediments in our analysis and the separation ofvegetation type irrespective of sample type speaks ofthe clear differences in pollen representation betweenthe major vegetation types of western Tasmania. Thisindicates that our analysis is robust with respect tovarying sample type and can be applied to both modernand fossil pollen spectra regardless of sample orsediment type.

4.4. Palaeoecological implications

A number of studies have derived palaeoclimaticestimates from fossil pollen spectra by calibrating thebioclimatic profiles of plant species, based on modernspecies distributions, with their modern pollen repre-sentation (e.g. Kershaw and Nix, 1988; Kershaw,1993a,b; Kershaw and Bulman, 1994; D'costa andKershaw, 1997; McKenzie, 2002). N. cunninghamii hasreceived particular attention in this regard (Busby,1986) and estimates of Holocene temperature changeshave been derived from pollen diagrams acrosssoutheastern Australia, including western Tasmania,based on trends in this species (McKenzie and Busby,1992; McKenzie, 2002). While a single taxon approachis useful for approximating climatic envelopes frompollen spectra, it is subject to biases in the climaticprofile of a species resulting from factors such as

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dispersal ability, vicariance, fire and local environmen-tal factors. Importantly, modern species distributionsmay not be indicative of the actual climatic envelope ofa species (McKenzie and Busby, 1992).

Analyses that focus on changes in a range of speciesin pollen spectra (using multivariate techniques), suchas the present study, produce less precise estimates ofclimate from pollen spectra, but are useful in determin-ing the direction of change in significantly correlatedenvironmental variables. This approach is particularlyuseful in western Tasmania, where climate-sensitiverainforest species have not fulfilled their potentialclimatic range due to the widespread influence of fire(Jackson, 1968; Bowman and Jackson, 1981; Brownand Podger, 1982). We have shown that it may bepossible to predict the direction of change for a numberof variables including temperature, seasonality and firefrequency from pollen spectra using a two-stepreconstructive process. Firstly, broad vegetation type,direction of temperature change and relationship to firefrequency can be inferred from fossil pollen spectra (orunknown modern spectra). Secondly, the compositionof moorland pollen spectra, which comprise themajority of (Holocene) western Tasmanian fossil pollenspectra (see Fletcher and Thomas, 2007 for discussionon this), can provide information about changes inimportant climatic variables, such as seasonality.

The implications of this study for Tasmanianpalaeoecological research, where there has been atendency to focus on the response of climate-sensitivetree species to climate change (Markgraf et al., 1986;Colhoun, 1996; Colhoun et al., 1999; Colhoun, 2000 andreferences therein), are significant. It is now possible toredress the imbalance in the palaeoecological literaturetoward the over-estimation of rainforest cover (seeFletcher and Thomas, 2007). It is also possible to inferthe influence of fire on past vegetation communities, acontentious topic in the broader Australian environment(Horton, 1982; Flannery, 1994) and western Tasmania(Cosgrove et al., 1990; Thomas, 1993; Cosgrove, 1995;Thomas, 1995a,b; Cosgrove, 1999), independent ofcarbonised particle analysis.

Our results show clear promise in identifying latitudi-nal shifts of the front between the circumpolar westerliesand subtropical high pressure systems, and changesassociated with the seasonal distribution of climaticvariables influenced by regional inter-annual climatephenomena such as the El Nino-Southern Oscillation andthe Antarctic Circumpolar Wave. In a forthcoming paperwe discuss in detail these climatic phenomena and presenta model which describes temperature and precipitationchanges for the entire Holocene of western Tasmania.

The results presented here constitute the firstdescriptive and quantitative assessment of pollen–vegetation relationships in western Tasmania and oneof the few regional scale modern pollen analysesperformed in Australia. We have previously indicatedthat surface pollen spectra can be used to accuratelypredict vegetation type, despite the influence of over-represented pollen types, and have applied this to theinterpretation of local vegetation change in a Holocenepollen diagram from moorland vegetation (Fletcher andThomas, 2007). The present study strengthens thepredictive power of our earlier analysis and identifiestrends in pollen representation that are consistent withthe main environmental determinants of western Tasma-nian vegetation (altitude, fire and seasonality) thus,vindicating the role of pollen analysis for the recon-struction of vegetation, climate and fire histories for theregion.

Acknowledgements

This study was generously supported by the MazdaFoundation and the Australian Institute of NuclearScience and Engineering. M.F. was in receipt of anAustralian Postgraduate Award during the period ofresearch.Wewould like to thank JohnMarsden-Smedleyand the Tasmanian Department of Primary IndustriesWater and Environment for useful discussions and aid insite selection. Thanks to Simon Connor, Libby Rumpffand Megan Fitzgerald for field work assistance and weare grateful to Eric Colhoun and an anonymous refereefor their useful comments on the manuscript.

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