Distribution of benthic diatom assemblages in Tasmanian highland ...

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Distribution of benthic diatom assemblages in Tasmanian highland lakes and their possible use as indicators of environmental changes Wim Vyverman, Ruth Vyverman, Vijaya S. Rajendran, and Peter Tyler

Abstract: Classification and gradient analyses of littoral diatom assemblages and 37 chemical and physical variables in littoral sediments of 76 Tasmanian dystrophic and (ultra-)oligotrophic highland lakes indicate strong west–east gradients in pH, calcium concentration, sodium concentration, alkalinity, and gilvin, which are underlain by an oceanic–inland gradient and geological discontinuities. Two major clusters of lakes are recognised, concordant with the recognition on chemical and optical criteria of western and eastern limnological provinces and coinciding with a steep gradient in mean annual rainfall and major geological changes. Changes in species composition occur gradually across the corridor separating the two provinces. Highly significant inference models can be constructed for pH and calcium concentration and related variables, which can be used in palaeolimnological reconstructions in Tasmanian highland lakes. Résumé : D’après une classification et des analyses de gradient des assemblages de diatomées du littoral et de 37 variables chimiques et physiques observées dans des sédiments littoraux de 76 lacs des hautes terres de Tasmanie, de type dystrophique et (ultra-)oligotrophe, il existe de forts gradients ouest–est de pH, de calcium, d’alcalinité et d’absorbance optique (gilvin), qui sont sous-tendus par un gradient des caractéristiques océaniques et intérieures et des discontinuités géologiques. On distingue deux grappes principales de lacs, qui correspondent à la présence, d’après des critères chimiques et optiques des provinces limnologiques de l’ouest et de l’est, et qui coïncident avec un gradient prononcé des précipitations annuelles moyennes et des grands changements géologiques. On note généralement des changements graduels dans la composition spécifique en travers du corridor séparant les deux provinces. On peut construire des modèles d’inférence très significatifs à partir du pH, du calcium et de variables connexes, et ceux-ci peuvent servir à reconstructions paléolimnologiques sur les lacs des hautes terres de Tasmanie. [Traduit par la Rédaction]

Introduction Over the past decade quantitative models for inferring environmental factors from diatom assemblages of surface sediments have become a regular tool in regional palaeoecological reconstructions (Dixit et al. 1993; Charles and Smol 1994; Davis et al. 1994). The majority of these studies deal with the northern hemisphere and comparable studies from the tropics and the temperate parts of the southern hemisphere are few (e.g., Servant-Vildary and Roux 1990; Vyverman and Sabbe 1995). Only in very recent years has the use of diatoms as environmental indicators gained substantial attention in Australia (e.g., Tudor et al. 1991), which may be explained partly by the lack of regional diatom floras and of information on the auto- and syn-ecology of freshwater diatoms, though reports on both are in preparation (Vyverman et al. 1995; W. Vyverman, Received April 5, 1995. Accepted September 13, 1995. J12860 W. Vyverman,1 R. Vyverman, V.S. Rajendran, and P. Tyler. Faculty of Aquatic Sciences and Natural Resources Management, Deakin University, P.O. Box 423, Warrnambool 3280, Victoria, Australia. 1

Author to whom all correspondence should be sent at the following address: Department of Botany, University of Gent, K.L. Ledeganckstraat, 35, B-9000 Gent, Belgium. e-mail: Wim=Vyverman%resbot%Biomse.RUG.ac.be

Can. J. Fish. Aquat. Sci. 53: 493–508 (1996).

R. Vyverman, V.S. Rajendran, P. Kew, and P. Tyler, unpublished data; H.U. Ling, W. Vyverman, and P. Tyler, unpublished data). Tasmania (Fig. 1) is situated on the southeastern edge of the Australian continental plate. It is characterized by low levels of atmospheric pollution, attributable to southwesterly prevailing winds from the Southern Ocean, to the low population density, and to the absence of large-scale industrial activities (Cameron et al. 1993). The central and southwestern highlands of Tasmania constitute a region of great limnological and biological diversity, designated as a World Heritage Area (Tyler 1992). More than 4000 lakes and tarns of mainly glacial origin, scattered over much of the highland regions, have been the subject of more than 20 years of limnological research (for a summary see Tyler 1992). The geomorphology is to a large extent shaped by glacial activity that took place about 15 000 years ago. The aims of this study were threefold. The first was to study the composition and distribution of diatom assemblages of recent surface sediments in relation to present-day environmental conditions and to provide a baseline for future monitoring of environmental changes. The second aim was to test the hypothesis (Tyler 1992) that the composition of the algal flora, in this case the diatoms, changes across Tyler’s Line, the division between the eastern and western limnological provinces. The third was to construct inference models for selected environmental variables on the basis of the abundance and distribution of the diatom species. © 1996 NRC Canada

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Can. J. Fish. Aquat. Sci. Vol. 53, 1996

Fig. 1. The study area and location of the 76 study lakes in Tasmania. Lake numbers correspond to the numbers given in Table 1. The 1200-mm isohyet is indicated by a solid line; the approximate western edge of the Jurassic dolerite is indicated by a dashed line. The approximate position of the first TWINSPAN division is indicated by a dotted line.

Materials and methods Study sites and sample collection Two main limnological regions have been recognised in the Tasmanian highlands, referred to as the western and eastern limnological provinces respectively, characterized by differences in optical and chemical properties, underlain by edaphic, climatic, geological, and vegetational differences (Tyler 1992). The western province receives more or less constant, high precipitation (between 2000 and 3500 mm⋅year–1). Siliceous rocks of Precambrian, Cambrian, and Ordovician origin are the prevalent rock types. The vegetation consists of Nothofagus rain forest or Gymnoschoenus sedgeland in the lower regions and sedgeland and (sub-)alpine vegetation in the more elevated areas. Soils are predominantly podzols, moor peats, and alpine humus. The lakes in the western province are moderately to highly dystrophic, have low pH and alkalinity, and are of the “crepuscular red window” optical type characteristic of lakes with high concentrations of humic compounds (Bowling et al. 1986; Tyler 1992). The eastern province receives less rainfall (between 800 and 2000 mm⋅year–1), more seasonally distributed. Jurassic dolerite is the dominant rock type along with Tertiary basalts and the vegetation consists of sclerophyll forest and alpine heathland; the soils are mainly

alpine humus. It comprises the vast, fairly flat area of the Central Plateau where most of the 4000 lakes are located, and a number of isolated ranges more to the south. Eastern lakes typically are slightly acidic, higher in alkalinity than western lakes; they are (ultra-)oligotrophic and have “green window” optical properties (Tyler 1992; W. Vyverman, R. Vyverman, V.S. Rajendran, P. Kew, and P. Tyler, unpublished data). The transition zone between the western and eastern provinces is now known as Tyler’s line (Shiel at al. 1989; Mesibov 1994) though Tyler (1992) sees it as more of a corridor of change. It lies approximately between the two lines denoting the Jurassic edge and the 1200-mm isohyet (Fig. 1). Sediments of Permian origin, mainly sand- and mud-stones, occur here along with Jurassic dolerite. Except for the introduction of trout in much of the eastern province, the flooding of highland lakes for the production of hydroelectricity (Tyler 1974, 1976) and some local disturbance by ecotourism, the majority of the limnological environments are still in a virtually pristine condition. Lakes were chosen so as to cover the widest possible range of limnological and environmental conditions of the study area. The present analysis comprises 76 lakes for which complete water chemistry data were available (Table 1). Surface sediment samples for diatom analysis and water samples © 1996 NRC Canada

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Vyverman et al. Table 1. Selected morphometric and chemical characteristics of the 76 lakes and tarns (with their numbers, used throughout the text, figures, and TWINSPAN clusters (Table 3)). Lake Dora Rolleston Selina Spicer Spicer 1 Ceres Cygnus Fortuna Oberon Square Crooked Lake Crooked Tarn Curly Gordon Tarn 1 Gordon Tarn 5 Lavara Tarn Rhona Wurrawena Artists Pool Crater Dove Hanson Lilla Rodway Twisted Lake Ada Chr W1 Chr W4 Chr W6 Pine Lake Arthur Tarn Esperance Hartz Ladies’ Tarn Osborne Perry Belcher Belcher Tarn Belton Dobson Eagle Tarn James Tarn Johnstons Tarn Newdegate Nichols Robert Tarn Seal Twisted Tarn Webster Judd Saint Clair Crescent Solomon’s Jewels Jerusalem Tarn Jerusalem1138 Stretcher

Lake No.

Elevation (m)

Area (m)

Catchment (ha)

pH

g440a (m–1)

Geological context

TWINSPAN cluster

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56

756 556 630 694 720 780 880 680 840 880 815 815 880 490 490 980 875 885 1060 1135 934 1001 992 919 1116 1148 1160 1160 1160 1189 1060 960 950 960 890 930 900 900 1000 1025 1035 1150 1202 1138 978 1195 906 1120 829 590 737 826 1185 1110 1080 1115

37.0 64.0 22.0 67.0 9.0 1.0 1.0 4.0 15.0 4.0 1.0 0.1 20.0 0.1 0.1 0.3 9.0 1.0 0.5 20.0 91.0 7.0 7.0 21.0 1.0 223.0 0.1 0.1 0.1 9.0 0.1 3.0 19.0 1.0 3.0 3.0 8.0 0.1 21.0 5.0 0.5 0.5 3.0 5.0 6.0 0.5 34.0 2.8 24.0 177.0 2437.0 2316.0 2.0 0.4 1.0 5.7

241.0 779.0 448.0 355.0 87.0 138.0 26.0 73.0 87.0 58.0 33.0 1.0 391.0 0.1 0.1 12.0 114.0 29.0 11.0 106.0 466.0 60.0 34.0 138.0 17.0 3 359.0 2.1 2.0 3.2 756.0 30.1 79.0 103.0 29.0 65.0 56.0 563.0 0.1 165.0 129.0 8.0 16.0 81.0 88.0 141.0 28.0 256.0 119.0 842.0 1 008.0 27 591.0 5 096.0 28.0 400.0 302.0 46.0

4.4 4.3 4.2 4.3 3.9 4.0 4.5 4.0 4.2 4.2 3.9 3.8 4.4 4.2 4.2 4.1 4.0 4.8 4.2 4.2 4.4 4.0 4.3 4.9 5.1 6.1 5.1 5.1 5.0 6.4 5.2 5.8 5.6 5.9 5.1 5.5 5.7 4.3 5.7 6.6 6.4 5.5 6.0 5.4 5.9 5.5 6.5 5.9 6.6 5.9 5.9 6.6 5.9 6.4 6.4 6.3

5.965 7.293 5.671 4.644 6.391 6.650 6.074 10.133 4.030 3.080 6.477 11.200 8.176 14.642 10.760 8.008 3.460 5.640 6.305 3.656 3.656 6.448 8.262 2.735 2.706 0.317 0.633 0.345 0.864 0.546 1.094 0.403 0.633 1.094 1.036 0.825 1.555 6.794 1.727 0.691 0.735 1.871 0.403 1.132 0.259 0.633 0.949 0.547 1.154 1.336 0.864 1.612 0.633 0.543 0.535 0.233

Cambr Ordov, Cambr Ordov Cambr Cambr Prec Prec Prec Prec Prec Ordov Ordov Prec Ordov Ordov Ordov Ordov Ordov Prec Prec Prec Prec Prec Prec Prec Juras Juras Juras Juras Juras Juras Juras Juras Juras Juras Juras Perm Perm Perm Juras Juras Juras Juras Juras Juras Juras Juras Juras Juras Prec, Juras, Perm Juras, Trias Juras, Trias, Tert Juras Juras Juras Juras

*11 *11 *11 *11 *11 *11 *11 *11 *11 *11 *11 *11 *11 *11 *11 *11 *11 *11 *11 *11 *11 *11 *11 *11 *11 *010 *010 *010 *010 *010 *010 *010 *010 *11 *010 *011 *010 *011 *010 *010 *010 *010 *010 *010 *010 *011 *010 *010 *010 *011 *10 *00 *10 *011 *010 *011

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Table 1 (concluded). Lake Stretcher B Loane Adelaide Adelaide Tarn Sappho W Sappho Rim Lake Ophion Persephone Tartarus Elysia Leuce Cyane Swallows Nest Lake Ooze Lake Pigsty Ponds Reservoir 1 Reservoir 2 Shadow Lake Forgotten Lake

Lake No.

Elevation (m)

Area (m)

57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76

1130 1125 1055 1066 1135 1134 1084 1137 1100 1056 1133 1109 1137 857 870 725 783 776 954 966

1.0 3.0 235.0 0.04 1.0 90.0 9.0 2.5 0.7 3.0 4.4 0.6 1.0 0.9 1.0 0.7 5.0 5.0 17.0 19.0

Catchment (ha) 155.0 25.0 929.0 1.0 30.0 214.0 257.0 150.0 46.0 124.0 177.0 46.0 75.0 72.0 30.0 38.0 160.0 160.0 372.0 193.0

pH

g440a (m–1)

6.0 6.6 6.3 6.3 6.4 6.4 6.4 6.5 5.6 6.6 6.3 6.1 6.3 5.4 5.6 4.9 4.7 4.6 6.0 6.1

0.233 0.233 0.173 0.245 0.214 0.036 0.140 0.288 0.288 0.288 0.345 0.288 0.288 0.518 0.288 2.706 3.109 2.879 0.864 1.152

Geological context Juras Juras Juras Juras Juras Juras Juras Juras Juras Juras Juras Juras Juras Perm, Juras Juras, Trias Perm, Trias Perm, Trias Perm, Trias Juras, Trias Juras

TWINSPAN cluster *011 *011 *011 *10 *10 *010 *10 *10 *10 *10 *10 *10 *10 *011 *011 *10 *10 *10 *010 *010

Note: Abbreviations for the geological substrates are as follows: Cambr, Cambrian acid with intermediate volcanic rocks with locally basic-intermediate volcanics; Ordov, Ordovician siliceous conglomerate, shallow-water quartzose sandstone and siltstone; Prec, Precambrian metamorphic rocks of mainly metaquartzite and pelitic sequences; Juras, Jurassic dolerite (a mafic igneous rock) and related rock types; Perm, Permian mudstone and sandstone; Trias, Triassic siltstone, sandstone and mudstone; Tert, Tertiary basalt and related rock types. a Gilvin (g440, Kirk 1976) is used here as a measure of the optical absorbance of the water and as an indication of the total amount of dissolved organic matter.

for chemical analyses were collected in February–March, August, and November–December 1994 from the littoral zone of the lakes. All sediment samples were taken between 0.5 and 1 m below the water surface, by pushing a short perspex cylinder into the sediment. For each lake, five replicate samples were mixed to account for patchiness in the distribution of the diatoms. Only the upper 1–2 cm of sediment was collected, representing a mixture of living cells and recent diatom remains. Because of the remoteness of most lakes, it was not possible to take sediment cores in deeper parts of the lakes using standard surface corers. The upper first few centimetres of sediment cores of Lake Nicholls and Lake Curly, collected in May and June 1991 (Cameron et al. 1993), were also used in the present analyses. Water samples for chemical analyses were taken between 0.3 and 0.5 m below the water surface. Bicarbonate alkalinity, pH, conductivity, and gilvin were determined as soon as possible after collecting. Gilvin was determined spectrophotometrically, after filtration of the sample through 0.45-µm membrane filters, as the absorption at 440 nm measured in 4-cm cuvettes. Bicarbonate alkalinity was determined titrimetrically. Major anions and cations were analysed by flame emission, atomic absorption spectroscopy, and (or) high-performance liquid chromatography (HPLC). Complete analytical details for water chemistry will be given elsewhere (W. Vyverman, R. Vyverman, V.S. Rajendran, P. Kew, and P. Tyler, unpublished data). Environmental variables A total of 37 environmental variables (Tables 1, 2, and 5), including physico-chemical characteristics of the lakes and a number of catchment characteristics, were used to interpret the variation in species composition. The water chemistry values used in the statistical analyses are arithmetic averages of available data for each lake, collected over the past 20 years. The number of analyses available per lake varied between 1 and 10; for most lakes complete chemical data were

available from 1 or 2 sampling occasions only. A synoptic paper dealing with lake water characteristics in relation to geological, pedological, and climatic factors in more than 200 Tasmanian highland lakes will be published elsewhere (W. Vyverman, R. Vyverman, V.S. Rajendran, P. Kew, and P. Tyler, unpublished data). Catchment area and lake surface area were calculated from 1 : 100 000 and 1 : 25 000 topographic maps (Tasmaps, Lands Department, Hobart, Tasmania), using a digitiser and the computer program AUTOCAD. Catchment and lake area are only approximate for several of the lakes on the Central Plateau, because of the small altitudinal differences between them, highly variable lake water levels, and poorly understood local hydrology. As a rule, maximum-possible areas were used in these cases. Variables relating to geology and vegetation type were expressed as the percentage of the lake’s catchment occupied by each of the different variables. The data were calculated from geological and vegetational maps of Tasmania (Kirkpatrick and Dickinson 1984). Data on mean annual rainfall (MAR) were derived from isohyet distribution maps published by the Tasmanian Mapping Bureau (1988) and were recorded as ordinal variables using 6 classes (1, 500 –1000 mm⋅year –1; 2, 1000 –1500 mm⋅year –1; 3, 1500–2000 mm⋅year–1; 4, 2000–2500 mm⋅year–1; 5, 2500–3000 mm⋅year–1; 6, 3000–3500 mm⋅year–1). Unfortunately we did not have soil data other than a general classification (Pemberton 1989); all lake catchments have podzols, moor peats, and (or) (sub-)alpine humus soils as the dominant soil type. These data were not used in the analyses. Thirteen of the 37 environmental variables (Table 2) were used as active variables in the ordination analyses. The remaining 24 variables, including the relative abundance of the major cations and anions, mean annual rainfall, the percent cover of the catchment area by the 10 major vegetation types, and the 8 geological variables were used as passive environmental variables for the further interpretation of the ordinations. Several of these variables had high inflation factors in a preliminary canonical correspondence analysis (CCA) ordination © 1996 NRC Canada

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Vyverman et al. Table 2. Morphometric and chemical characteristics of the 76 Tasmanian highland lakes. Environmental variable Elevation (m) Surface area (ha) Catchment area (ha) Conductivity (µS⋅cm–1) pH g440 (m–1) Turbidity (NTU) Sodium (µequiv.⋅L–1) Potassium (µequiv.⋅L–1) Calcium (µequiv.⋅L–1) Magnesium (µequiv.⋅L–1) Chloride (µequiv.⋅L–1) Alkalinity (µequiv.⋅L–1) Sulfate (µequiv.⋅L–1) Silica (µequiv.⋅L–1) Percent [Na++K+] Percent [Ca2+] Percent [Mg2+] Percent [Cl–] Percent [HCO3–] Percent [ SO42–]

A A A A A A — A A A A A A A — P P P P P P

Minimum

Maximum

Median

Mean

N

490 0.04 0.1 13.5 3.8 0.036 0.2 45.5 0.3 8.0 17.3 38.0 0.0 0.0 0.0 27.0 5.0 8.5 21.9 0.0 0.0

1202 2437.0 27591 100.2 7.1 14.642 4.5 405.5 23.8 202.5 242.2 331.9 473.8 38.5 6.4 80.9 42.8 38.1 98.0 73.9 20.7

972 3.0 87 26.9 5.5 1.094 0.6 115.3 6.4 35.3 34.5 135.5 36.5 14.0 0.8 65.4 16.6 18.0 66.8 20.8 7.3

957 80.4 636 29.2 5.3 2.744 1.0 124.9 6.3 42.8 45.4 139.4 59.5 15.2 1.5 61.8 19.0 19.6 68.0 24.7 7.3

76 76 76 76 76 76 55 76 76 76 76 76 76 76 38 76 76 76 76 76 76

Note: Variables that were used as active explanatory variables for the distribution of surface sedimentary diatoms are indicated with an A. Variables that were included as passive variables are indicated with a P. Turbidity and silica were not included in the statistical analyses as insufficient data were available for many lakes.

using all variables as active variables, indicating multicollinearity with other environmental variables (ter Braak 1988a). Although several passive variables were as strong as or stronger than the active variables, they were not included as active variables because we aimed to construct quantitative models for ecologically significant variables such as pH and the concentration of major ions. Sample preparation, identification, and enumeration Homogeneous aliquots of sediment were digested in a solution of hydrogen peroxide and acetic acid (Schrader 1973). After centrifugation with distilled water, cleaned diatom suspensions were settled and dried on microscope cover slips, which were then mounted on glass slides using Naphrax. Diatom taxonomy and nomenclature mainly followed Krammer and Lange-Bertalot (1986–1991), the PIRLA Diatom Iconograph (Camburn et al. 1984–1986), Vyverman (1991), and numerous smaller publications. As there are few regional taxonomic publications (e.g., Hustedt 1955; Haworth and Tyler 1993), a major part of the present work included a taxonomic study of the Tasmanian diatom flora. Taxa that could not be identified beyond the generic level were indicated as temporary species and require further taxonomic investigation. Complete taxonomic details are published elsewhere (Vyverman et al. 1995). Diatoms were identified and enumerated along transects of a microscope slide using a Zeiss Axioplan microscope equipped with phase-contrast and Nomarski interference optics. Between 300 and 500 valves were identified from each of the 76 surface sediments and were expressed as relative abundances (Battarbee 1986). The diatom data base and reference slides are kept at the University of Gent (Belgium). Numerical analysis Classification Classification was done by two-way indicator species analysis (TWINSPAN, Hill 1979) to group samples with similar community

composition into a hierarchical structure of clusters and subclusters. The following cut levels were used in the construction of pseudospecies: 0.0 1.0 2.0 5.0 10.0 20.0. All pseudospecies were given equal weight. The analyses were done on both the initial nontransformed data set including 245 taxa and on the screened data set with ln(x + 1) transformed abundances of 157 taxa (see below). The two analyses resulted in the same classification. Ordination Diatom taxa present with a relative abundance of greater than 1% in at least two of the lakes were included in the numerical analyses and in the development of inference models. Of a total of 245 taxa, 157 taxa met this criterion. Species data were transformed prior to the ordinations using ln(x + 1) transformation. Detrended correspondence analysis (DCA), with detrending by segments, and CCA were used to examine the principal patterns of floristic variation in the initial data set. Rare species were down-weighted in all ordinations (ter Braak 1988a). A series of preliminary CCAs was run to identify outlying samples, following the criteria summarized in Birks et al. (1990) and Hall and Smol (1992). A total of 13 active environmental variables was used in the CCA to examine the major direction of variation in the species data as indicated by DCA. Twenty-four passive environmental variables were used in the further interpretation of the data. All variables had skewed distributions and, except for pH, were log transformed (log(x + 1)) prior to the ordination (Zar 1984). A series of CCAs with forward selection of environmental factors and unrestricted Monte Carlo permutation tests (99 permutations, P ≤ 0.05) were used to select the minimal number of variables explaining the largest amount of variation in the species data. The relative contribution of the environmental variables to the ordination axes was evaluated by the canonical coefficients (significance of approximate t tests) and intraset correlations (Jongman et al. 1987; ter Braak 1988a). Unrestricted Monte Carlo permutation tests (99 permutations, P ≤ 0.05) were used to test the statistical significance of the first two ordination axes. All ordinations were performed using the computer © 1996 NRC Canada

498 program CANOCO (ter Braak 1988a), version 3.1 (ter Braak 1990a, 1990b). Regression The optima and tolerances of diatom taxa to the pH and other environmental gradients were estimated from weighted averaging regression (WA), using the computer program WACALIB (version 3.3, Line et al. 1994). Weighted averaging uses the weighted average and weighted standard deviation as approximations of the taxon’s optimum and tolerance, respectively (Birks et al. 1990). The partial strength and independence of environmental variables were assessed by a series of CCAs and partial CCAs (ter Braak 1988b), using the variables of interest either as sole environmental variables in a constrained CCA or as (co-)variables in a partial CCA. The ratio of the two first eigenvalues (λ1/λ2) was used to evaluate the importance and dependence of the variables tested. Unrestricted Monte Carlo permutation tests (99 permutations) were used to assess the significance of the ordination axes (P ≤ 0.05). The tolerances of taxa were considered in the WA models by down-weighting each taxon by their respective variances, a technique that is known as WA with tolerance down-weighting (WA(tol)) (ter Braak and Van Dam 1989). Classical and inverse regressions were compared to assess the degree of shrinkage in the original gradients (Birks et al. 1990). The predictive power of the models was assessed by inspection of the correlation coefficient between the observed and inferred environmental variables, the apparent root-mean-squared error (RMSE) of prediction and the bootstrap RMSE of prediction (RMSEboot) (Efron and Gong 1983; Birks et al. 1990).

Results General characteristics of the study lakes Although lake surface area and catchment area cover a wide range, most lakes are relatively small (Table 1). All but five lakes are smaller than 100 ha. For the majority of the lakes, no data were available on basin morphometry, but a similar wide range can be expected, from shallow tarns (less than 0.4 m deep) to the deep glacial lakes such as Lake St. Clair (170 m deep). The lakes cover a pH range from highly acidic (3.8) to circumneutral (7.1) and have very low to elevated gilvin values and generally low turbidities. Conductivity is low in the western lakes and low to very low in the dilute lakes of the eastern plateau, except for Lake Crescent, which has medium electrolyte contents. Chloride and bicarbonate are the major anions, all lakes being comparatively low in sulfate. Silica is generally low in most lakes for which data are available. The ionic composition of the lake water reflects a west–east gradient from lakes with a composition close to that of seawater in the west to lakes with a composition closer to that of World Average Freshwater in the east. Several variables are significantly intercorrelated (Table 3), indicating a close relationship between gilvin, pH, bicarbonate alkalinity, calcium concentration, mean annual rainfall, the proportion of chloride and sodium + potassium, and the geological context (Jurassic dolerite and Precambrian siliceous rocks). Diatom classification and synecology Of a total of 245 taxa recorded in the cell counts, about 35 could not be identified to the species level or, in a few cases, even to the generic level. Apart from the many cosmopolitan and northern–montane taxa, several taxa are possibly endemic to Tasmania, such as Cymbella tasmaniensis Hustedt, Cym-


bella angustiformis Hust., Actinella tasmaniensis Hustedt, and Cyclotella tasmanica Haworth & Tyler. To this list can possibly be added a number of new taxa, currently being described (W. Vyverman, K. Sabbe, and R. Vyverman, submitted for publication; W. Vyverman, K. Sabbe, D. Mann, R. Vyverman, and D. Hodgeson, submitted for publication). Several of them (e.g., sp. 1, sp. 2, Biremis sp. 1, Navicula sp. 9) are relatively abundant and widespread in the lakes studied. In addition there are some taxa, such as Eunotia camelus Ehrenberg and its varieties, and Stauroneis pachycephala Cleve, which are usually found in tropical and subtropical habitats and occur in several of the generally cool highland lakes. Frustulia rhomboides (Ehr.) De Toni, Navicula subtilissima Cleve, and Brachysira serians (Breb. ex Kütz.) Round & Mann were the most common taxa and occurred in more than 90% of the lakes. Frustulia rhomboides was present in all lakes though only in very low numbers in Lake Crescent. The first TWINSPAN division roughly coincides with the separation of optically dark, humic-stained, western lakes (cluster *1) from the green window lakes more to the east (cluster *0). Achnanthes cf. didyma, Achnanthes minutissima, and Achnanthes cf. rossii (Table 4) are the indicator taxa for the latter cluster, while Eunotia sp. 7 and Euntoia incisa are characteristic of the western lakes. In the cluster of eastern lakes, Lake Crescent (cluster *00) is separated from the remaining (ultra-)oligotrophic lakes (cluster *01). It differs from all lakes further west on the Central Plateau in being eutrophic (Cheng and Tyler 1976), which is reflected by its deviant species composition dominated by Fragilaria pinnata, Fragilaria tenera, and Navicula cryptocephala, which are absent or rare in the other lakes. Cluster *01 can be further subdivided into two subgroups (*010 and *011). Cluster *011 comprises a group of lakes situated along the eastern margin of the limnological divide. It is characterized by high abundances of Actinella sp. 2, Eunotia bilunaris, and Eunotia tenella and by the absence of Cymbella cesatii, Cymbella tasmaniensis, and Navicula pseudoscutiformis. The latter three species are indicators of cluster *010, consisting of the many lakes situated more to the east of the divide, which is also characterized by high relative abundances of Achnanthes cf. rossii and Achnanthes minutissima. The western lakes of main cluster *1 can be further subdivided into two groups (*10 and *11). Cluster *10 comprises a group of lakes near the western edge of the limnological divide, situated mainly on Jurassic dolerite, while cluster *11 consists of the more western lakes situated mainly on metamorphic and sedimentary rocks of Precambrian, Cambrian, and Ordovician origin. Species 1, Fragilaria virescens, Actinella tasmaniensis, and Brachysira serians are indicators for cluster *10 by their higher relative abundances. Eunotia sp. 2, Eunotia incisa, Eunotia septentrionalis, and Eunotia sp. 1 and sp. 2 are indicators for cluster *11 comprising the most acidic and dystrophic lakes in the west of Tasmania, with an ionic composition close to that of seawater and with very low to zero alkalinity. The geographical position of the main clusters thus agrees well with the general west–east gradient in environmental characteristics (Fig. 1, Tables 2, 5). Interestingly, Lake Judd, situated amidst dark western lakes, receives its water from the dolerite-capped Mount Anne massif, which forms an outcrop in a region dominated by Precambrian quartzitic rocks. This is © 1996 NRC Canada

1.00 –0.22 –0.20 0.59 –0.14 –0.40 –0.57 0.53 –0.66 –0.48 –0.37 –0.30 0.25 0.45 –0.13 0.06 –0.61 –0.47 0.41 0.48 0.09 0.23

ALT

1.00 0.85 0.06 0.02 –0.12 –0.09 0.11 –0.08 –0.20 0.02 –0.15 0.03 0.10 0.08 0.23 –0.15 –0.12 0.13 0.15 0.01 0.03

ARE

1.00 0.08 –0.03 –0.17 –0.07 0.22 –0.14 –0.27 –0.01 –0.28 0.13 0.20 0.14 0.27 –0.20 –0.21 0.23 0.25 0.15 0.17

CAT

1.00 –0.55 –0.51 –0.29 0.86 –0.89 –0.21 –0.42 –0.51 0.64 0.70 0.25 0.23 –0.45 –0.69 0.70 0.72 0.25 0.24

JUR

1.00 0.28 –0.04 –0.51 0.47 –0.07 0.20 0.30 –0.49 –0.44 –0.25 –0.10 0.14 0.41 –0.41 –0.39 –0.05 0.03 1.00 0.12 –0.47 0.49 0.40 –0.09 0.61 –0.62 –0.72 –0.23 –0.25 0.37 0.61 –0.50 –0.51 0.20 0.28

PRE MAR

1.00 –0.32 0.39 0.61 0.51 –0.08 0.19 –0.17 0.56 0.17 0.68 0.10 –0.15 –0.28 0.15 –0.16

K25

1.00 –0.89 –0.25 –0.53 –0.58 0.66 0.73 0.29 0.28 –0.47 –0.74 0.79 0.81 0.35 0.33

pH

1.00 0.19 0.44 0.44 –0.59 –0.66 –0.17 –0.14 0.44 0.65 –0.74 –0.75 –0.28 –0.27

g440

1.00 0.15 0.45 –0.05 –0.44 0.21 –0.32 0.85 0.43 –0.22 –0.35 0.25 0.01

Na

1.00 0.06 –0.03 –0.16 0.18 0.07 0.45 0.26 –0.28 –0.35 –0.18 –0.34

Ca

%Ca

Mg

%Mg

Cl

%Cl

1.00 –0.82 1.00 –0.83 0.90 1.00 –0.69 0.66 0.38 1.00 –0.74 0.42 0.37 0.80 1.00 0.41 –0.14 –0.49 0.24 –0.18 1.00 0.84 –0.74 –0.80 –0.46 –0.50 0.59 1.00 –0.65 0.65 0.66 0.46 0.45 –0.40 –0.82 –0.64 0.60 0.68 0.35 0.42 –0.55 –0.83 –0.09 0.16 0.03 0.28 0.15 0.17 –0.21 0.05 –0.06 –0.04 –0.02 0.03 –0.09 –0.13

K %NaK

1.00 0.98 0.26 0.16

1.00 0.20 0.17

HCO %HCO

1.00 0.92

1.00

SO4 %SO4

Note: Abbreviations used are as follows: ALT, elevation; AR, lake surface area; CAT, lake catchment area; JUR, Jurassic dolerite; PRE, Precambrian metamorphic rocks; MAR, mean annual rainfall; K25, conductivity; g440, gilvin; Na, sodium; K, potassium; %NaK, percentage of sodium and potassium; Ca, calcium; %Ca, percentage of calcium; Mg, magnesium; %Mg, percentage of magnesium; Cl, chloride; %Cl, percentage of chloride; HCO, alkalinity; %HCO, percentage of alkalinity; SO, sulfate; %SO, percentage of sulfate.

ALT ARE CAT JUR PRE MAR K25 pH g440 Na K %NaK Ca %Ca Mg %Mg Cl %Cl HCO %HCO SO4 %SO4

Table 3. Product-moment correlation matrix of the main environmental variables used in this study.

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Table 4. Indicator and preferential taxa for the main lake clusters identified from the TWINSPAN analysis.

No.

Indicator taxon

No. occ.

3 5 11 13 17 18 22 23 24 26 29 30 31 38 41 42 45 48 51 52 55 57 59 70 75 76 79 88 90 91 95 96 97 103 104 105 106 107 112 113 121 129 132 133 134 135 137 157 159 160 166 168 198 205 209

Achnanthes delicatula (Kütz.) Grun. Achnanthes cf. rossii Hust. Achnanthes minutissima Kütz. Achnanthes cf. didyma Hust. Actinella sp. 2 Actinella tasmaniensis Hust. sp. 1 sp. 2 Aulacoseira distans (Ehr.) Sim. Melosira arentii (Kolbe) Nag. & Kob. Brachysira serians (Breb. ex Kütz.) Round & Mann Brachysira styriaca (Grun. in V.H.) R. Ross Brachysira microcephala (Grun.) Comp. Cyclotella stelligera Cleve & Grun. Cymbella cesatii (Rab.) Grun. Cymbella cistula(Ehr.) Kirch. Cymbella cuspidata Kütz. Cymbella gracilis (Ehr.) Kütz. Cymbella microcephala Grun. Cymbella minuta Hilse Cymbella silesiaca Bleisch Cymbella tasmaniensis Hust. Diatomella balfourniana Grev. Eunotia exigua (Breb.) Rab. Eunotia incisa Greg. Eunotia bilunaris (Ehr.) Mills Eunotia naegelii Mig. Eunotia septentrionalis Oestr. Eunotia sp. 1 Eunotia sp. 2 Eunotia sp. 7 Eunotia paludosa Grun. Fragilaria capucina Desmaz. Fragilaria nanana Lange-Bert. Fragilaria pinnata Ehr. Fragilaria virescens Ralfs Frustulia magaliesmontana Choln. Frustulia rhomboides (Ehr.) De Toni Gomphonema cf. contraturris L.-B. & Reich. Gomphonema cf. clevei Fricke Navicula cf. pseudosilicula Hust. Navicula cf. concentrica Cart. Navicula pseudoscutiformis Hust. Navicula cryptocephala Kütz. Navicula cryptotenella Lange-Bert. Navicula difficillima Hust. Navicula gottlandica Grun. Navicula subtilissima Cleve Navicula viridula (Kütz.) Ehr. Biremis sp. 1 Navicula sp. 9 Nitzschia dissipata (Kütz.) Grun. Stenopterobia curvula (W. Smith) Kram. Fragilaria tenera (W. Smith) Lange-Bert. Tabellaria flocculosa (Roth) Kütz.

24 34 47 45 46 17 43 40 23 33 71 41 40 14 28 22 22 37 26 18 22 9 13 36 57 43 54 32 39 39 53 58 14 13 15 43 23 76 15 20 16 16 14 2 19 22 44 72 23 33 30 22 30 2 40

Relative abundancea

pH opt.

pH tol.

[Ca] opt.

[Ca] tol.

*00

*010

*011

*10

*11

5.80 5.84 5.81 5.83 4.95 5.80 5.70 4.83 5.44 4.94 5.40 5.82 5.65 5.78 5.91 5.98 5.89 5.79 5.82 5.83 5.81 5.79 5.77 4.93 4.76 5.59 4.94 4.32 4.68 4.60 4.93 5.19 5.70 4.60 5.53 5.61 4.45 5.36 6.00 5.92 5.75 5.89 5.76 — 5.85 4.22 5.87 5.40 5.75 5.68 5.63 5.84 5.92 — 5.73

0.71 0.55b 0.61b 0.56b 0.93 0.76 0.73 0.84 0.76 0.94 0.91 0.67 0.62 0.51 0.54b 0.50b 0.49b 0.63 0.65 0.53 0.58 0.58 0.48 1.05 0.83 0.84 0.91 0.59b 0.84 0.75 0.97 0.96 0.56 0.48 0.61 0.84 0.49b 0.91 0.39b 0.57b 0.49 0.56 0.54 — 0.46 0.31b 0.57b 0.91 0.70 0.71 0.93 0.59b 0.61b — 0.73

56.08 62.86 64.24 60.30 25.68 33.84 42.02 28.92 45.64 33.65 38.11 45.21 59.96 77.46 63.37 76.63 68.26 58.65 71.80 54.01 65.09 48.20 65.82 28.49 26.50 34.42 26.23 19.37 22.76 27.66 26.60 28.50 62.84 24.17 60.21 38.26 19.86 41.14 58.66 64.33 47.06 56.66 64.54 — 70.85 18.89 55.78 40.56 57.25 45.81 34.02 63.38 51.58 — 44.26

48.20 40.48 44.48 38.61 12.22b 9.90 25.75 23.95b 28.63 35.01 30.21 31.16 44.13 50.38 41.28 55.99 45.46 23.67b 56.42 19.05 40.52 17.63 43.14 19.31 21.01b 16.93 18.29b 11.37b 15.80b 26.35b 17.15b 15.27b 36.14 12.78 46.40 27.80 11.95b 33.41 33.46 56.50 22.31 44.51 40.83 — 49.93 10.30b 34.80 29.55 40.16 24.21 12.87 45.97 42.79 — 32.12

— — 3.6 3.6 — — — — — — — — — 1.2 — — — — — — — — — — — — — — — — — — — — 42.7 3.6 — — — 0.3 — — — 4.6 — — — — — — — — — 26.5 0.3

0.4 5.5 7.8 14.2 — — 0.3 0.2 0.8 0.2 2.1 1.4 2.9 0.3 1.3 0.9 0.3 1.1 1.3 0.7 0.4 0.6 0.4 0.1 0.2 0.2 0.2 — 0.1 0.3 0.3 0.2 0.9 — 2.7 0.6 — 17.8 0.2 0.3 0.3 0.3 0.6 0.01 1.0 — 3.9 7.5 0.5 0.3 — 0.3 0.3 0.01 2.3

0.8 1.1 3.7 23.5 2.6 0.1 0.7 1.2 0.7 0.1 3.4 3.5 1.4 — 0.5 0.2 0.2 0.6 0.4 — 0.1 — 0.1 0.2 1.0 2.0 0.9 0.1 0.2 1.2 0.8 4.0 — — 0.2 2.0 — 10.6 0.6 — 0.4 0.1 — — 0.1 — 4.4 3.7 0.4 1.6 0.3 0.1 0.2 — 9.0

1.3 0.8 0.3 0.4 4.4 1.8 1.2 0.4 2.3 0.5 10.7 2.5 0.3 — 0.1 — — 0.3 0.1 0.1 0.1 — — 0.6 3.5 2.7 1.1 0.2 0.6 0.2 8.0 2.9 — 0.3 — 6.3 — 18.1 — — — 0.1 — — — 0.1 0.6 6.3 0.1 1.0 1.6 0.1 0.1 — 1.0

0.1 — 0.2 0.1 6.4 — 0.1 2.7 0.2 1.7 4.4 0.4 0.3 — — — — — 0.1 — — — — 1.6 10.8 0.7 3.9 3.7 6.1 3.5 6.6 2.8 — 2.5 — 0.4 2.6 16.8 — — — — — — — 4.9 — 4.9 — 0.1 0.3 0.1 — — 1.2

Note: For each taxon the number of occurrences, the weighted mean (WA optima) and tolerance pH and calcium concentration (all calculated using the screened data set of 75 lakes) and the mean relative abundance per TWINSPAN cluster are given. a The number of samples for each TWINSPAN cluster is as follows: *00, 1; *010, 24; *011, 11; *10, 14; and *11, 26. b Taxon fulfilling the criteria to be used as an indicator taxon for pH or calcium concentration.

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Vyverman et al. Table 5. Physico-chemical, climatic, and geological characteristics of the five main TWINSPAN clusters based on the diatom species composition of 76 Tasmanian lakes. *11 Elevation (m above sea level) Surface area (km2) Catchment area (km2) Conductivity (µS⋅cm–1) pH g440 (m–1) Sodium (µequiv.⋅L–1) Postassium (µequiv.⋅L–1) Calcium (µequiv.⋅L–1) Magnesium (µequiv.⋅L–1) Chloride (µequiv.⋅L–1) Alkalinity (µequiv.⋅L–1) Sulfate (µequiv.⋅L–1) Rainfall class Jurassic (%) Precambrian (%) Cambrian (%) Ordovician (%) Permian (%) Tertiary (%) Triassic (%)

*10

*011

*010

Mean

Range

Mean

Range

Mean

Range

Mean

Range

*00

837 0.16 1.44 30.6 4.4 6.34 130.5 8.3 19.9 33.3 165.0 12.3 12.8 4.9 3.8 50.0 12.7 25.8 0 0 0

490–1135 0.001–0.91 0.01–7.8 15.5–47.0 3.8–5.9 1.09–15.20 69.6–243.0 2.0–23.7 5.5–60.7 4.0–115.0 51.0–398.0 0.0–130.0 0.0–32.0 4.0–6.0 0–100.0 0–100.0 0–100.0 0–100.0 — — —

1012 1.76 20.5 22.7 5.6 0.93 91.1 12.9 37.7 40.5 124.2 46.1 21.4 4.2 75.7 0 0 0 14.3 0 10.0

725–1185 0.001–24.4 0.01–275.9 11.8–40.0 4.6–6.8 0.17–3.11 48.0–243.5 3.0–26.0 25.0–90.0 16.0–66.0 28.0–304.8 12.0–143.0 2.0–70.0 3.0–5.0 0 –100.0 — — — 0 –75.0 — 0 –50

988 0.39 2.5 25.8 5.5 1.25 120.8 5.5 40.6 41.0 135.3 77.7 17.5 3.8 71.1 6.5 0 0 17.5 0 4.5

590–1195 0.001–2.35 0.01–10.1 15.1–42.5 4.3–6.2 0.17–6.79 69.5–287.1 1.6–9.6 25.0–61.0 16.0–90.5 57.5–336.7 0.0–144.0 0.0–38.5 3.0–4.0 0.0–100.0 0 –71.0 — — 0–100 — 0–50

1045 0.21 3.5 29.0 5.8 0.81 99.2 7.5 68.7 56.6 116.2 110.6 15.6 3.0 89.6 0 0 0 8.3 0 2.0

829–1202 0.001–2.23 0.01–33.6 16.1–59.2 5.0–6.6 0.04–1.87 45.5–163.1 2.7–23.8 20.8–162.3 16.0–177.0 38.0–190.4 0.0–350.0 0.0–31.3 2.0–4.0 0.0–100.0 — — — 0–100.0 — 0–50

826 23.2 50.9 110.1 7.1 1.61 382.8 16.6 250.0 230.3 310.6 416.4 37.5 1.0 60.0 0 0 0 20.0 20.0 0

Note: The chemical data for individual lakes are based on single analyses or are mean values of multiple measurements. The number of lakes in each cluster is as follows: *11, 26; *10, 14; *0.11, 11; *010, 24; *00, 1.

confirmed by its classification with other lakes on dolerite east of Tyler’s Line (cluster *011), a finding that was already suspected on optical criteria (Tyler 1992). Ladies’ Tarn, situated on dolerite rock in the Hartz mountains, has elevated gilvin values and a peaty bottom only partially in contact with the Jurassic bedrock and is classified along with the western lakes (cluster *11). The other lakes occurring in the transition zone between the western and eastern provinces are classified in cluster *010, *011, or *10. Ordination analyses In a preliminary DCA of the 76 lakes, Lake Crescent was identified as an outlier as it had a very high score along the first ordination axis, confirming its deviant species composition as shown in the TWINSPAN classification. Consequently, this lake was excluded from all subsequent ordinations. The eigenvalues for axes 1 and 2 (0.45 and 0.11, respectively) of a subsequent DCA ordination using the remaining 75 lakes indicated good dispersion of the diatoms on these axes. These two axes explain 23.7% of the cumulative variance in the diatom data, comparable to similar data sets such as the SWAP (Surface Water Acidification Project, Stevenson et al. 1991), PIRLA (Paleoecological Investigation of Recent Lake Acidification, Dixit et al. 1993) and Arctic (Pienitz et al. 1995) training sets, but lower than the data set used to infer temperature from Papua New Guinean diatom assemblages (Vyverman and Sabbe 1995). The main variation in species composition seems to be along the first axis, as suggested by the ratio of the eigenvalues (λ1/λ2 = 4.1). In a CCA ordination constrained to the 13 active environmental variables (Table 6), canonical axes 1 (λ1 = 0.35) and 2

(λ2 = 0.12, λ1/λ2 = 2.9) explained 19.6% of the cumultative variance in the species data. The species–environment correlations were high (0.891 and 0.853 for axes 1 and 2, respectively) and Monte Carlo permutation tests (99 unrestricted permutations) showed that both axes were significant (p = 0.01). The eigenvalue of the second CCA axis was somewhat higher than in the DCA ordination, suggesting a slight arch effect on this axis (ter Braak 1988a). A DCCA ordination (see also Dixit et al. 1993) on the same data partly removed this effect, but did not have any further effect on the interpretation of the data. Moreover, detrending is not needed in a constrained analysis if only the essential environmental variables are included (ter Braak and Prentice 1988); these were identified in a following CCA using forward selection of the environmental variables. Comparison of the position of species and site scores on the two first CCA and DCA axes showed that species and samples occupied similar positions along this axis in both ordinations, but they were somewhat more dispersed along the second CCA axis. This indicates that a large portion of the explainable variation in the weighted averages of the species data was accounted for by the measured environmental variables. The first CCA axis corresponded to a gradient in pH, g440, calcium concentration, and alkalinity as indicated by the intraset correlations (Table 6) and reflected a similar gradient of the following passive variables: mean annual rainfall, the percent cover by Jurassic dolerite, and the relative proportion of the concentrations of sodium and potassium, calcium, and chloride and alkalinity. The second CCA axis was poorly correlated with most variables except for potassium and the labyrinth subalpine complex (Table 6), which may indicate that © 1996 NRC Canada

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Table 6. Interset correlations of active and passive environmental variables with species axes as determined by a CCA ordination of 75 lakes using 13 active variables and 24 passive variables. Axis 1

Axis 2

Axis 3

Axis 4

Active variables Elevation Surface area Catchment Conductivity pH g440 Sodium Potassium Calcium Magnesium Chloride Alkalinity Sulfate

–0.52 –0.01 –0.10 0.15 –0.73 0.72 0.34 0.15 –0.75 –0.43 0.40 –0.71 –0.05

0.16 0.14 0.08 –0.23 0.40 –0.40 0.30 –0.52 –0.06 –0.18 0.03 0.12 0.22

–0.18 0.37 0.40 0.41 –0.08 –0.03 0.19 0.43 0.14 0.15 0.30 –0.08 0.10

0.17 0.38 0.22 –0.04 0.15 –0.15 0.38 –0.34 –0.02 0.07 –0.33 0.07 0.13

Passive variables Jurassic dolerite Triassic Tertiary Permian Precambrian Cambrian Ordovician Mean annual rainfall Western alpine complex Central alpine complex Eastern alpine complex High subalpine complex Western central subalpine complex Labyrinth subalpine complex Lake Wilks subalpine complex Eucalyptus coccifera complex Nothofagus rain forest Gymnoschoenus moorland %NaK %Ca %Mg %Cl %HCO3 %SO4

–0.75 –0.06 0.12 –0.08 0.49 0.33 0.32 0.76 0.45 –0.34 –0.25 –0.26 –0.18 0.01 0.26 –0.31 –0.30 0.45 0.71 –0.81 –0.45 0.71 –0.72 0.03

0.28 0.26 –0.08 0.14 –0.21 –0.17 –0.13 0.19 0.00 –0.12 –0.17 –0.21 0.31 0.62 –0.13 –0.14 –0.10 –0.22 0.29 –0.08 –0.30 0.11 0.11 0.28

0.01 0.18 –0.11 0.08 –0.00 0.05 –0.05 –0.19 0.06 0.16 0.10 0.02 –0.47 0.23 –0.21 0.14 0.20 0.13 0.08 –0.06 –0.11 0.09 –0.14 –0.00

0.14 –0.07 –0.07 –0.07 –0.37 0.55 0.17 0.23 0.02 –0.35 –0.11 –0.09 0.13 0.09 –0.36 –0.14 0.08 0.28 –0.01 –0.05 0.06 –0.05 0.09 0.18

Note: The eigenvalues were as follows: axis 1, λ1 = 0.35; axis 2, λ2 = 0.12; axis 3, λ3 = 0.06; axis 4, λ4 = 0.05.

similar environmental factors determine community structure both in terrestrial vegetation and benthic diatom communities. Forward selection and Monte Carlo permutation (99 iterations) identified 3 active environmental variables accounting for 55.7% of the variance explained by the original 13 variables: pH (32.9%), calcium concentration (12.7%), and sodium concentration (10.1%) (Fig. 2). A similarly high portion of the variance was explained when gilvin and alkalinity were selected instead of pH and calcium concentration. The eigenvalues, significant for axes 1 and 2, were 0.32 and 0.10 (λ1/λ2 = 2.9), respectively, and both axes were significant (p = 0.01). The eigenvalues for axes 3 and 4 were much smaller (0.05 and 0.04, respectively). The canonical coefficients were significant for the three selected environmental variables and for both axes. The intra-set correlations were

strongest for pH and calcium concentration with the first axis (Table 7); sodium concentration was strongly correlated with the third canonical axis (data not shown) but only weakly with the first axis. The intraset correlations of the remaining variables were similar to those in the initial CCA. The position of the samples in the ordination (Fig. 2A) plane of the first two axes agreed well with the TWINSPAN clusters discussed above, although cluster *11 was somewhat more separated from the remaining clusters. In the species’ ordination (Fig. 2B), acidophilic taxa are in the left part of the diagram, whereas more neutrophilic taxa are more to the right, which is in agreement with the main changes in the principal environmental factors. A series of constrained CCAs was done using a single environmental variable; the ratio of the first constrained © 1996 NRC Canada

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Vyverman et al. Fig. 2. Canonical correspondence analysis (CCA) ordination based on 75 lakes showing significant forward selected environmental variables (arrows) in a biplot of (A) lake scores and (B) diatom taxa scores. Passive environmental variables are shown in the inset (C). Only the indicator taxa identified in the TWINSPAN analysis are given and correspond to those in Table 3. Lake numbers correspond to those in Table 1.

Table 7. CCA axes 1 and 2 canonical coefficients, their t values, and intraset correlations of the environmental variables selected by the forward selection option in CCA. Axis 1

pH Sodium Calcium

Axis 2

Canonical coefficient

t

Intraset correlation

Canonical coefficient

t

Intraset correlation

0.41 –0.28 0.60

3.97* –3.59* 5.94*

0.74 –0.35 0.75

1.26 0.67 –0.93

9.91* 7.08* –7.55*

0.38 0.33 –0.11

*Significant at p = 0.05.

eigenvalue to the second unconstrained eigenvalue was used as an indication of the relative importance of that variable in explaining the species data (ter Braak 1988a) (Table 8). From these analyses, calcium concentration and pH were identified as the most important variables. Although all three variables were significant (p < 0.05), sodium concentration was less important as judged from the smaller ratio of the first and second eigenvalues. Other variables, such as gilvin and alkalinity, were also strong variables in explaining a large portion of the variance in the species data, and could be used instead of pH (gilvin) or calcium concentration (alkalinity) (Table 8). Partial CCAs with covariables further showed that pH, calcium concentration, and sodium concentration each contributed (partially) independently to the variation in the species

data (Table 8) when the two other forward selected variables were used as covariables. Lake water chemistry inference models Inference models can be constructed for pH, calcium concentration, and their related variables as they are strong and partially independent variables in explaining the variation in the species data. A major question is to what extent lake catchment characteristics and climate-related factors control lake water chemistry. Apart from soil conditions, retention time, and other hydrological characteristics of the lake catchments (characteristics that were unfortunately not available for any of the studied lakes), the geological context and rainfall regime are likely to influence lake water chemistry. A series of © 1996 NRC Canada

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Table 8. Canonical correspondence and partial canonical correspondence analysis results from tests of the strength and partial independence of selected environmental variables. Variable included

Covariable

pH g440 Alkalinity Calcium Sodium Jurassic dolerite Rainfall

— — — — — — —

λ1

λ1/λ2

p

0.26 0.26 0.24 0.25 0.09 0.27 0.28

0.89 0.88 0.86 1.10 0.22 1.00 1.26

0.01 0.01 0.01 0.01 0.01 0.01 0.01

CCA

Partial CCA with three selected variables pH Na, Ca 0.11 0.62 0.01 Calcium pH, Na 0.12 0.66 0.01 Sodium pH, Ca 0.08 0.45 0.01 Partial CCA restricted to pH, rainfall, and Jurassic dolerite pH Rainfall 0.14 0.74 0.01 Rainfall pH 0.16 0.82 0.01 pH Dolerite 0.06 0.22 0.01 Dolerite pH 0.06 0.22 0.01 Calcium Rainfall 0.08 0.45 0.01 Rainfall Calcium 0.10 0.58 0.01 Calcium Dolerite 0.09 0.40 0.01 Dolerite Calcium 0.10 0.47 0.01

constrained and partial CCAs was designed with mean annual rainfall and Jurassic dolerite as respective covariables to get an indication of the partial dependence of pH and calcium concentration with respect to these variables (Table 8). The results presented here should be considered with some caution, given the very skewed distribution of the variable Jurassic dolerite. Equally, although the various classes of mean annual rainfall were all well represented in our data set, it relies on interpolated data, as for most regions in the study area rainfall readings were not available. Important local variations in rainfall regime can be expected as a result of local topography and the orientation of the ranges where the lakes occur. When rainfall was taken as a covariable, the ratio of the first two eigenvalues of a CCA constrained to pH did not alter greatly, whereas when the effect of dolerite was partialled out, the importance of pH explaining the remaining variation along the first axis was much smaller although still significant. The effect of rainfall regime on the lake water concentration of calcium seemed to be more important than for pH, and was comparable to the effect of dolerite on the calcium concentration. These results would suggest that both dolerite and mean annual rainfall partly account for the variation in diatom species composition attributable to pH and calcium concentration; and that the effect of dolerite is more important than mean annual rainfall. Inference models (Figs. 3, 4) were constructed for calcium concentration and pH using classical and inverse WA regression and calibration with (WA(tol)) and without (simple WA) tolerance down-weighting (Table 9). All models using inverse deshrinking produced a highly significant trend in the residuals (r2adj between 0.22 and 0.35, p < 0.001, df = 73), overesti-

mating low values and underestimating high values of both pH and calcium concentration. Classical regression models for pH and calcium concentration using tolerance down-weighting were superior to the simple WA models (Figs. 3 and 4) and produced a higher correlation coefficient and a lower apparent (and bootstrap) RMSE of prediction, whereas the bootstrapped RMSE (1000 bootstrap cycles) was higher than for the simple WA models. The relatively large prediction error (for pH: RMSEsi1 = 0.429, RMSEs2 = 0.575; for calcium concentration: RMSEsi1 = 0.140, RMSEs2 = 0.233) of these models suggests, however, that a considerable error is involved in calculating the optima and (or) tolerances of the taxa, in addition to the natural variation in the data set (Birks et al. 1990). The pH model was stronger than the model for inferring calcium concentration as judged from the r2adj value. A similar model for pH using nontransformed species data had an r2adj value of 0.75 and apparent RMSE of 0.52, which is only slightly less efficient than the model based on transformed species data and in agreement with other studies (Cumming and Smol 1993; Cumming et al. 1994). Taxa that could be used as potential indicator taxa for pH and (or) calcium concentration were selected using the criteria discussed in Stevenson et al. (1991) (Table 3). Fourteen taxa have narrow tolerances for pH, and except for Eunotia septentrionalis, Frustulia magaliesmontana, and Navicula difficillima, all are indicative of slightly acidic conditions. Twelve taxa seem to be particularly sensitive to calcium levels; all taxa, except for Cymbella gracilis, are typical for more dystrophic lakes and are associated with low calcium levels.

Discussion This study gives further evidence of the unique limnological and biological diversity of the Tasmanian highland lakes. The diatom flora is diverse and comprises an interesting mixture of cosmopolitan taxa typical of oligo- and dys-trophic waters, rare nordic–alpine species, a number of (sub-)tropical taxa, and probably some endemic taxa. The occurrence of tropical taxa in Tasmania is interesting from a biogeographical point of view and has also been observed for desmids (Vyverman 1995). Noteworthy is the widespread distribution of rare diatoms such as Actinella spp. Several new taxa await formal description (W. Vyverman and R. Vyverman, unpublished data; W. Vyverman, K. Sabbe, and R. Vyverman, submitted for publication; W. Vyverman, K. Sabbe, D. Mann, R. Vyverman, and D. Hodgson, submitted for publication); their widespread distribution in many of the western and (or) eastern lakes confirms the unique character of this region. These data strongly suggest that although many of the taxa found are also typical for similar lakes in the northern hemisphere, there is a distinct regional floristic element in the Tasmanian diatom flora, as there is for other algal groups and for invertebrates (Tyler 1995). This endemism highlights the need for regional models using diatoms as environmental indicators. Our results provide a baseline for further studies exploring the causal effects of environmental conditions on benthic diatom communities as well as a framework for future monitoring studies. The first TWINSPAN division confirms the existence of two floristically distinct limnological regions, with dystrophic lakes in the western province and (ultra-)oligotrophic lakes in the eastern province (Fig. 1). That the division is © 1996 NRC Canada

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Vyverman et al. Fig. 3. Plots of measured lake water pH versus the inferred pH (A, B) and pH residuals (C, D) for two diatom predictive models: (A, C) weighted averaging with tolerance down-weighting (WA(tol)), and (B, D) simple weighted averaging (WA).

better seen as a corridor of change rather than as an abrupt discontinuity (Tyler 1992) is supported by the intermediate TWINSPAN clusters (*11 and *011) that comprise sets of lakes close to but on either side of Tyler’s Line. The classification of neighbouring lakes (such as those on Mt. La Perouse (lakes 70–74), Hartz Mountains (lakes 31–36), and Mt. Field (lakes 37–49)) into different clusters (Fig. 1, Table 2) supports earlier views (Tyler 1992) that organisms may be more sensitive indicators of limnological change than physico-chemical criteria. The answer to why there is an east–west disjunction in the distribution of aquatic organisms must lie in the intermediate lakes in this corridor. The epithet Tyler’s Line has been used recently to indicate a zone of major faunistic changes (e.g., Shiel et al. 1989; Mesibov 1994). In these studies, however, major changes seem to occur more to the west or east of the median position of the limnological divide, perhaps suggesting different responses to the main geological and climatological gradients. The ordination analysis provides further details of the relationships between the physico-chemical environment and the diatom species composition in these lakes. Among the measured lake water characteristics, pH and the concentrations of

calcium and sodium account for most of the explained variation in the species data set and reflect similar gradients in several other chemical characteristics, such as alkalinity, gilvin, and the relative contribution of calcium concentration, chloride concentration, and alkalinity. This indicates the existence of major gradients from western lakes, with a composition close to that of seawater, to the eastern lakes, which have relatively higher calcium concentrations and alkalinity. These gradients can be partially explained by similar gradients in mean annual rainfall and by geological differences. The effect of geology on lake water chemistry is well known (e.g., Wright and Henriksen 1978; Sutcliffe and Carrick 1983), although soil characteristics may mask its effect (e.g., Hornung et al. 1990). For Tasmania, however, no data are available on weathering rates of the various geological substrates, nor on the relationships between rock and soil type under different climatic conditions. Clearly, much further research is needed to study these relationships and their effect on the hydrochemical characteristics of the lake catchments, soil development, and lake water chemistry and hence on the benthic algal flora. Statistically significant inference models based on changes in sedimentary diatom assemblages can be constructed for © 1996 NRC Canada

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Fig. 4. Plots of measured lake water calcium concentration (as log(Ca + 1)) versus inferred calcium concentration (A, B) and residuals (C, D) for two diatom predictive models: (A, C) weighted averaging with tolerance down-weighting (WA(tol)), and (B, D) simple weighted averaging (WA).

Table 9. Results of the inference models for pH and calcium based on weighted averaging with (WA(tol)) and without (WA) tolerance down-weighting of diatom species composition in 75 Tasmanian highland lakes, using classical or inverse regression. Calibration type

Deshrinking type

r2adj

RMSE

RMSEboot

Model pH WA WA(tol) WA WA(tol)

Classical Classical Inverse Inverse

0.68 0.74 0.68 0.74

0.62 0.53 0.51 0.45

0.66 0.72 0.64 0.72

Model log(Ca+1) WA WA(tol) WA WA(tol)

Classical Classical Inverse Inverse

0.60 0.63 0.60 0.63

0.239 0.226 0.186 0.179

0.246 0.314 0.209 0.272

several physico-chemical factors such as calcium concentration and pH and can be used in the reconstruction of these variables from the diatom remains in lake sediments of the

Tasmanian highland lakes. Our inference model for pH is less robust than similar models constructed in other regions (e.g., Stevenson et al. 1991; Dixit et al. 1993), but it is comparable to models using scaled chrysophytes (e.g., Cumming et al. 1992). Possible reasons for this lower efficiency may be related to the sampling strategy used and inherent to the direction and interaction of other environmental variables. Firstly, as the samples were taken from the littoral zone of the lakes, local variations in species composition caused by factors other than the measured variables may have caused additional variation in the species data set. Secondly, neither pH nor calcium concentration seemed to have an overriding effect on the species composition, as indicated by the ratio of the first constrained eigenvalue to the other unconstrained eigenvalues (ter Braak 1988b). The predominantly west–east gradients of most of the measured environmental variables render a statistical analysis of their partial effects on diatom species composition a difficult task, which is partly due to the imperfection of the currently available software (see also Dixit et al. 1993). Furthermore, more standardized and critical water chemistry analyses in these dilute waters are needed, especially for © 1996 NRC Canada

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Vyverman et al.

pH and alkalinity, and would probably result in a further improvement of the model. Of particular importance are the humic substances, because there is a close correlation between pH and gilvin, a feature that is quite different from the major diatom data bases used in palaeolimnological reconstructions (e.g., Stevenson et al. 1991). Most Tasmanian highland lakes are poorly buffered and thus very susceptible to lake acidification. Given the range in pH from acidic to circumneutral lakes and the minimal atmospheric pollution, it is an ideal region to study the effect of pH on the distribution of benthic algal communities in relation to geological, climatic, and physical gradients in a geographically small and largely undisturbed region. The present study is in agreement with other studies in that major changes in species composition occur in the pH range of 5 to 6 (e.g., Stokes et al. 1990; Olaveson and Nalewajko 1994). Species richness seems to be positively correlated with pH (Fig. 5), although there is important scatter in the data, probably attributable to other environmental conditions. Bradbury (1986), Markgraf et al. (1986), and Cameron et al. (1993) noted that there have been important changes in the diatom assemblages from western and eastern Tasmanian highland lakes, suggesting shifts in pH, alkalinity, and water level since the end of the last ice age and they interpreted these changes as a result of climatic change. The models presented here, when used within a pH range of 3.8–6.6 and a calcium range of 8–203 µequiv.⋅L–1, can be used to study quantitatively these changes in lake environments. Although it is reasonable to expect an indirect influence of rainfall on lake water pH through the formation of humic substances, as a source of acidity, and inversely an increased buffering capacity and increased pH during periods of lower rainfall and increased evaporation, it remains a major challenge to demonstrate a causal link between local climatic conditions and lake water characteristics such as pH, gilvin, alkalinity, and calcium concentration in a geologically diverse environment.

Acknowledgements W.V. is a senior research fellow of the Belgian National Fund for Scientific Research. This work was made possible by a postdoctoral fellowship at Deakin University and was financially supported by grants from the Australian Research Council, the Commonwealth Scientific and Industrial Research Organisation – University of Tasmania joint research grants scheme, and the Tasmanian National Parks and Wildlife Service. We are greatly indebted to the Centre for Aquatic Resources Utilisation and Management, Deakin University, for further financial support. P. Jones is thanked for his assistance with the fieldwork and fruitful discussions.

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