ORIGINAL PAPER

Factors affecting diatom distribution in floodplain lakesof the southeast Murray Basin, Australia and implicationsfor palaeolimnological studies

Michael A. Reid Æ Ralph W. Ogden

Received: 18 October 2007 / Accepted: 1 July 2008 / Published online: 28 August 2008

� Springer Science+Business Media B.V. 2008

Abstract Diatom assemblages of surface sediments

in 46 billabongs from four river floodplains in the

southeast Murray-Darling Basin, Australia were

sampled to investigate drivers of species distribution.

The principal purpose of the study was to derive

information to aid interpretation of diatom-based

palaeoecological studies of these systems and of

floodplain lakes more generally. Patterns in billabong

diatom assemblages in relation to river reach,

hydrology and farming intensity on surrounding land

were examined, as were correlations with water

quality variables. Seasonal variation in billabong

water quality was high relative to spatial variation,

and spatial patterns in billabong water quality were

weak. In contrast, strong patterns were evident in

diatom assemblages. Three main patterns were

observed: (1) a distinction between billabongs dom-

inated by planktonic diatoms from those dominated

by benthic and attached forms; (2) differences in

diatom assemblages in billabongs on different river

reaches; and (3) differences in assemblages in

billabongs with different hydrology. Of all water

quality variables tested, total phosphorus (TP), total

nitrogen (TN) and pH exerted the strongest indepen-

dent influence on diatom distribution; however, only

TP remained an important variable when species

variation due to river reach, hydrology, and aquatic

plant cover was taken into account. The weak

influence of water quality on diatom distribution is

interpreted as reflecting the dichotomy between

plankton and non-plankton-dominated billabongs,

the influence of hydrology and biogeography, the

lack of strong spatial water quality gradients and the

high degree of temporal variability in water quality.

The findings show that diatom records from billabong

sediments can provide evidence of long-term changes

in the abundance of aquatic macrophytes and hydrol-

ogy. They also suggest that merging calibration data

sets across regions for the purpose of improving

diatom transfer functions for water quality recon-

struction is of limited value for floodplain lakes, and

that performance is more likely to be gained by

boosting site numbers within regions.

Keywords Billabongs � Rivers � Floodplains �Palaeolimnology

Introduction

Along with the other elements of river floodplain

systems in Australia, billabongs have been subject to

a range of anthropogenic stressors over the last

approximately 200 years, including clearance of

M. A. Reid (&)

Riverine Landscapes Research Lab, University

of Canberra, Canberra, ACT 2601, Australia

e-mail: Mike.Reid@canberra.edu.au

R. W. Ogden

eWater Cooperative Research Centre, University

of Canberra, Canberra, ACT 2601, Australia

123

J Paleolimnol (2009) 41:453–470

DOI 10.1007/s10933-008-9236-0

native vegetation, pastoralism, invasive species, flow

regulation and water abstraction. There is little doubt

that these changes have impacted negatively river and

floodplain ecosystems; however, the precise causal

mechanisms and hence appropriate management

actions are not always clear because most changes

were initiated before detailed ecological studies of

these systems began (Reid and Ogden 2006). Palae-

olimnological studies provide one means for gaining

a greater understanding of both the nature of the pre-

European state of these systems as well as the causes

of post-European changes (Thoms et al. 1999; Reid

et al. 2002).

In the past there has been a perception that

sedimentary records from fluvial systems are unreli-

able, due to the dynamics of these environments and

the evidence from numerous studies that floodplain

lakes are relatively short-lived (Eckblad et al. 1977;

Cooper and McHenry 1989; Lewis and Lewin 1983;

Rang and Schouten 1989; Erskine et al. 1992).

However, in recent times there have been increased

efforts to apply palaeoecological approaches to

fluvial systems (e.g. Hay et al. 2000; Michelutti et al.

2001; Schonfelder et al. 2002; Gell et al. 2005a;

Wolfe et al. 2005, 2006). This trend has been

particularly strong in Australia where studies of

floodplain lake (billabong) sediments in the Murray

Basin have proven highly promising (Donnelly et al.

1999; Thoms et al. 1999; Ogden 2000; Gell et al.

2005a; Reid et al. 2007). In addition to providing

records of several thousand years duration (Ogden

et al. 2001), these studies have demonstrated dra-

matic and substantial changes to billabong

ecosystems associated with European settlement

(Ogden 2000; Reid 2002; Tibby et al. 2003; Reid

et al. 2007). In each system, the observed changes

occur over ca 1–2 cm of sediment depth, suggesting

that, despite their relative shallowness (mostly

\2 m), the sediments deposited in these environ-

ments maintain a high level of stratigraphic integrity.

The timing and ultimate causes of these changes

have not always been clear for two principal reasons:

first, the difficulties involved in obtaining reliable

sediment chronologies for the recent past (Gell et al.

2005a); and second, difficulties in interpreting the

proximate drivers of observed stratigraphic changes

due to our poor understanding of the ecology of

common taxa (Reid 2002; Tibby et al. 2003; Leahy

et al. 2005) and of the taphonomic processes leading

to their incorporation in sediment records (Ogden

2000). The purpose of this paper is to address the

second of these issues by examining factors affecting

the current distribution and abundance of diatoms in

billabong sediments. Specifically, the study will

examine the influence of water quality variables such

as nutrients, pH, salinity, turbidity, as well as water

depth, aquatic plant cover, biogeography, local land-

use and hydrology, all of which have been suggested

as important proximate or ultimate drivers of eco-

system change in floodplain lakes in Australia

(Ogden 2000; Gell et al. 2005a, b; Reid et al.

2007). The implications of the results for interpreta-

tion of these and future palaeolimnological records

are discussed.

Methods

The billabong data set

The billabongs included in the study are situated

within the south-eastern portion of the Murray-

Darling Basin, one of the largest drainage systems

in Australia, covering an area of 1,058,800 km2,

roughly 14% of the Australian continent (Fig. 1). The

catchments of the parent rivers of the billabongs

included in the study extend to 2,200 m, the highest

point on the Australian continent. The billabongs

themselves are situated in the lowland reaches at

elevations from just below 300 m asl to around

100 m asl. Rainfall in the study region ranges from

around 750 mm/a at Corryong in the Murray uplands

to the east and Alexandra in the south, down to

444 mm/a at Mathoura in the western portion of the

study area (Fig. 1). Highest rainfall and runoff occur

during winter and spring, with spring discharge

substantially augmented, particularly in the Murray

River, by snowmelt from the high-altitude areas of

their catchments in the Southern Highlands. Although

the headwater regions remain largely forested, the

floodplains themselves have been cleared to varying

degrees for agriculture, mostly pastoralism, and now

consist of grassland and open Eucalyptus camaldul-

ensis and E. largiflorens woodland with isolated

patches of remnant floodplain forest and woodland,

usually in the more frequently flooded areas.

The data set consists of surface sediment diatom

samples and water quality data from 46 billabongs on

454 J Paleolimnol (2009) 41:453–470

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the floodplains of the Murray (30 billabongs), Ovens

(8 billabongs), Kiewa (1 billabong) and Goulburn

(7 billabongs) rivers. A larger data set is created by

the inclusion of physical and chemical data from two

of the Goulburn River billabongs for two separate

monitoring periods and by repeat surface sediment

sampling of these billabongs during the second

monitoring period (Table 1).

Regulation of river flows to provide water for

summer irrigation in downstream areas is affected by

three large dams: Hume Dam on the Murray River,

Dartmouth Dam on the Mitta Mitta River (a tributary

of the Murray) and Eildon Dam on the Goulburn

River (Fig. 1). As a result, the billabongs of the

Murray River below Lake Hume and the Goulburn

River are subject to a regulated hydrological regime.

The effect of this regulation on the flooding regimes

for billabongs associated with these rivers varies. In

areas immediately downstream of the large impound-

ments, billabongs situated close to the mainstream

may receive river input more frequently during the

summer irrigation season as water levels are main-

tained to supply water to downstream irrigators

(Pressey 1986; Maheshwari et al. 1995). Further

away from the mainstream and further downstream

the effect is generally a reduction in flood frequency

as water extraction and flood mitigation take effect

(Pressey 1986; Maheshwari et al. 1995). The variable

Fig. 1 The study area

J Paleolimnol (2009) 41:453–470 455

123

Table 1 Physical features, sampling dates, river reaches and hydrological and farming intensity classes of billabongs included in the

data set

Billabong River/

ReachaMax.

area

(ha)

Max.

depth

(cm)

Min.

depth

(cm)

Mean

vegetation

cover

Hydrology Farming

intensity

Sediment

sampling

date

Survey

period

1 Murr1 3.0 193 97 4.2 Unregulated High 8/93 1/92–3/93

2 Murr1 2.9 136 45 4.3 Unregulated High 8/93 1/92–3/93

3 Murr1 1.3 102 30 4.5 Unregulated High 9/93 1/92–3/93

4 Murr1 7.9 168 51 3.2 Unregulated High 8/93 1/92–3/93

5 Murr1 5.8 242 100 2.3 Unregulated High 9/93 1/92–3/93

6 Murr1 1.5 471 414 2.6 Unregulated High 9/93 1/92–3/93

7 Murr1 0.01 235 94 1.9 Unregulated Low 8/93 1/92–3/93

7a Murr1 0.44 325 193 2.3 Unregulated Low 8/93 1/92–3/93

8 Murr1 0.01 300 101 3.6 Unregulated Low 8/93 1/92–3/93

9 Murr1 3.6 145 122 1.1 Unregulated High 8/93 1/92–3/93

10 Murr1 8 186 68 3.5 Unregulated High 9/93 1/92–3/93

11 Kiewa 1.6 139 78 3.3 Unregulated High 9/93 1/92–3/93

12 Murr2 8 336 50 1.0 Exposed Low 9/93 1/92–3/93

13 Murr2 2.9 199 94 1.9 Isolated High 9/93 1/92–3/93

14 Murr2 4.1 300 121 1.0 Exposed Low 9/93 1/92–3/93

15 Murr2 1.1 187 10 1.7 Isolated High 11/93 1/92–3/93

16 Murr2 7.8 573 191 1.2 Exposed High 9/93 1/92–3/93

17 Murr2 2 40 0 5.0 Isolated High 9/93 1/92–3/93

18 Murr2 1.8 131 0 2.3 Isolated High 9/93 1/92–3/93

19 Murr2 0.06 230 164 3.0 Exposed Low 8/93 1/92–3/93

20 Ovens 1.5 200 50 2.3 Unregulated High 11/93 1/92–3/93

21 Ovens 3.4 188 72 1.8 Unregulated Low 11/93 1/92–3/93

22 Ovens 0.63 210 123 4.2 Unregulated Low 11/93 1/92–3/93

23 Ovens 1.6 203 85 3.5 Unregulated Low 11/93 1/92–3/93

25 Ovens 1.3 153 69 1.8 Unregulated High 11/93 1/92–3/93

26 Ovens 2.8 186 84 1.0 Unregulated High 11/93 1/92–3/93

27 Ovens 3.1 151 71 1.5 Unregulated High 11/93 1/92–3/93

28 Ovens 1.7 167 28 1.2 Unregulated High 11/93 1/92–3/93

29 Murr3 14.6 105 10 4.0 Isolated High 8/93 1/92–3/93

29a Murr3 1.8 447 342 1.3 Isolated Low 8/93 1/92–3/93

30 Murr3 67 111 3 4.4 Isolated High 8/93 1/92–3/93

31 Murr3 2.2 118 9 3.9 Isolated Low 9/93 1/92–3/93

32 Murr3 4.8 362 161 1.1 Isolated Low 9/93 1/92–3/93

33 Murr3 2.9 233 148 1.4 Exposed Low 11/93 1/92–3/93

34 Murr3 2.2 243 99 1.6 Isolated Low 11/93 1/92–3/93

35 Murr3 5.8 487 323 1.0 Exposed Low 11/93 1/92–3/93

36 Murr3 0.6 208 96 1.0 Isolated High 11/93 1/92–3/93

37 Murr3 1.5 50 0 5.0 Isolated High 9/93 1/92–3/93

38 Murr3 5.4 314 122 1.0 Isolated Low 11/93 1/92–3/93

OR Goulb 1.5 300 180 4.0 Isolated High 5/92 5/91–7/92

T1 Goulb 2.8 470 405 2.0 Exposed High 5/92 5/91–7/92

T2 Goulb 1.2 280 150 3.0 Isolated High 5/92 5/91–7/92

456 J Paleolimnol (2009) 41:453–470

123

effects of river regulation on billabong hydrology is

summarised by classification of billabongs on these

regulated river reaches according to the scheme

applied by Pressey (1986) in that author’s inventory

of Murray floodplain wetlands below Lake Hume. In

this scheme, Pressey (1986) classed low-lying bill-

abongs that experience inflow from the mainstream

during the summer irrigation season as hydrological

class 2 billabongs. These billabongs experience more

frequent connection to the river as a result of

regulation and are termed ‘Exposed’ in this study

(Table 1). Billabongs at higher elevations that do not

receive inflow from summer irrigation releases were

classed by Pressey (1986) as hydrological class 3

billabongs and are termed ‘Isolated’ in this study

(Table 1). Because the frequency of medium-sized

floods has been reduced by river regulation (Mahesh-

wari et al. 1995), ‘Isolated’ billabongs now

experience less frequent connection than they did

prior to the onset of the regulated regime (Ogden

1996; Table 1). The Ovens, the Upper Murray and

Kiewa rivers are subject to largely unregulated flow,

although some extraction and inter-basin transfer of

water does occur (Crabb 1997). The billabongs on

these river reaches are classed as ‘Unregulated’

(Unreg; Table 1). Most of the billabongs included

in the study are affected to some degree by agricul-

tural activity, with cattle and sheep grazing being the

most widespread. However, there is variation in the

intensity of agricultural activity at the local scale,

enabling the billabongs to be classed as subject to

high or low intensity agricultural activity, depending

on whether they are situated within cleared grassland

areas or within remnant woodland or forest (Table 1;

Ogden 1996; Reid 1997).

Physical and chemical monitoring

Field sampling of the Goulburn billabongs was

carried out over the period from June 1991 until July

1992 at two-month intervals. Further sampling of two

of these billabongs was carried out monthly between

May 1993 and May 1994 to augment a palaeoeco-

logical study of these billabongs (Reid 1997).

Sampling of the Murray, Ovens and Kiewa River

billabongs was carried out at two-month intervals

over the period from January 1992 until March 1993.

In all cases, electrical conductivity (EC), pH and

water temperature were measured in the field using a

Hanna portable conductivity meter HI 8733 and a

Hanna portable pH meter HI 8424. Additional

chemical variables were measured in the laboratory

using water samples collected in the field. Turbidity

(NTU) was measured using a Hach Turbidimeter,

model 2100A. Total nitrogen (TN) and phosphorus

(TP) and major ions (Na+, Mg2+, Ca2+, K+, HCO3-,

Cl-, SO42-) were analysed at the Murray Darling

Freshwater Research Centre, EML (Chem) Pty. Ltd.

and the Water Studies Centre at Monash University.

Methods are outlined in Ogden (1996) and Reid

(1997).

The water depth of each billabong was measured

on each survey trip at the deepest point in the

billabong. Billabong area was estimated from aerial

photographs or from 1:25,000 topographic maps

(Ogden 1996; Reid 1997). Aquatic macrophyte cover

(Cover) at all but the Callemondah billabongs was

estimated visually using a class-based scale. Five

cover classes were applied: 0–5%, 5–25%, 25–50%,

50–75% and 75–100% (Ogden 1996). Cover at the

Callemondah billabongs was established through line

Table 1 continued

Billabong River/

ReachaMax.

area

(ha)

Max.

depth

(cm)

Min.

depth

(cm)

Mean

vegetation

cover

Hydrology Farming

intensity

Sediment

sampling

date

Survey

period

H Goulb 2.5 340 250 3.0 Isolated High 5/92 5/91–7/92

C1_1 Goulb 2.8 370 270 2.0 Isolated High 5/92 5/91–7/92

C2_1 Goulb 1.5 320 210 3.0 Isolated High 5/92 5/91–7/92

NC Goulb 0.7 200 90 1.0 Isolated High 5/92 5/91–7/92

C1_2 Goulb 2.8 393 310 2.2 Isolated High 5/94 5/93–5/94

C2_2 Goulb 1.5 348 273 2.8 Isolated High 5/94 5/93–5/94

a Murr1 = Murray River above Lake Hume; Murr2 = Murray River between Lake Hume and the Ovens River Junction;

Murr3 = Murray River below the Ovens River Junction

J Paleolimnol (2009) 41:453–470 457

123

intersect surveys and the same cover class scale was

then applied. Submerged, floating and emergent

macrophytes were included in the cover estimates.

Formal floristic surveys were not carried out, except

at the Callemondah billabongs; however, the com-

mon aquatic taxa throughout the system included

Myriophyllum spp. L., Potamogeton tricarinatus

A.Benn., Juncus ingens N.A.Wakef., Eleocharis

sphacelata R.Br., E. acuta R.Br. and Azolla pinnata

R.Br.

Surface sediment sampling

Surface sediments were sampled for diatom analysis

at the end of the 12-month sampling period used in

each sampling program described above. Samples

were taken from the deepest section of each billabong

using soft sediment corers designed to minimise

disturbance of surface sediment layers. The upper

1 cm of sediment was extruded and sectioned in the

field to minimise disturbance and maintain strati-

graphic integrity. If the surface layer was seen to have

been disturbed during the coring process, the material

was discarded and a new sample taken.

Preparation of samples for diatom analysis fol-

lowed the methods outlined in Battarbee (1986).

Identification and enumeration of diatoms was carried

out using an Olympus BH-2 with Nomarski differ-

ential interference contrast and standard taxonomic

references (Patrick and Reimer 1966, 1975; Germain

1981; Krammer and Lange-Bertalot 1986, 1988,

1991a, b). At least 300 diatom valves were counted

for each sample.

Data analysis

Water quality variables measured over the course of

the sampling period were summarised by the median,

mean, maximum and minimum values recorded

during the full sampling period (these are denoted

in the text by the subscripts ‘med’, ‘mean’, ‘max’ and

‘min’, respectively). Data were log 10 transformed

where necessary to overcome problems of skewness

and non-linear relationships between parameters such

as total nitrogen, total phosphorus, turbidity and

water depth. Relationships between water quality

variables were explored using Pearson correlations

and Principal Components Analysis based on median

values. Diatom species abundances are expressed as a

percentage of the total number of valves recorded in

each sample.

Similarity matrices were calculated for water

quality profiles and diatom species assemblages using

the PRIMER computer program (Version 6.1.5,

Primer-E 2006). In the case of the water quality

profiles, the matrix was based on Gower difference

measures calculated using median values. The Gower

measure was used because it includes an implicit

range standardisation, making it an appropriate

measure to apply to environmental data incorporating

a diverse array of variables, each with differing scales

(Belbin and McDonald 1993). For the diatom surface

sediment assemblages, the matrix was based on the

Bray–Curtis similarity measure, which has been

shown to be a robust measure of community-based

data (Faith et al. 1991). These matrices were used to

examine the influence of hydrology, farming inten-

sity, and river reach, using the classifications listed in

Table 1, on billabong water quality and diatom

species distributions using the Analysis of Similarity

(ANOSIM) procedure in PRIMER. This procedure

uses similarity matrices to test for differences

between predetermined groupings (i.e., the river

reach, farming intensity and hydrology classes listed

in Table 1) by comparing the average rank similar-

ities between samples from different groups with the

average rank similarities between samples from the

same groups (Clarke and Warwick 1994).

Relationships between diatom assemblages and

water quality gradients were investigated through

Canonical Correspondence Analysis (CCA) using the

Canoco computer package (Canoco for Windows

4.54 2006). Forward selection was applied to deter-

mine which variables, including median, mean,

maximum and minimum values for each variable,

made a significant contribution (p \ 0.05) to explain-

ing variance in the species data, as tested using a

Monte Carlo permutation test (999 permutations).

Relationships between diatom assemblages and non-

water quality variables (billabong depth, aquatic

vegetation cover, hydrology, river reach and farming

intensity) were also investigated using CCA, again

with forward selection applied. In this instance

hydrology, river reach and farming were represented

as nominal variables in the CCA. Variance partition-

ing (Borcard et al. 1992) was used to investigate the

effects of interactions between the significant envi-

ronmental variables on the diatom assemblage

458 J Paleolimnol (2009) 41:453–470

123

distributions. In order to investigate the effects of

environmental variables exclusive of biogeographic

effects, CCA was also carried out separately on

subsets of billabongs from each of the river reaches

that were shown in ANOSIM to be characterised by

distinct diatom assemblages. Finally, the significance

of differences in the abundance of key diatom taxa

(identified through multivariate analyses) in selected

billabong groupings are tested using analysis of

variance (using SPSS v14.0).

Results

The physical and chemical character of billabongs

Billabongs included in the study were generally small

and shallow. The largest billabong included in the

study has a surface area of 67 ha, while the greatest

depth recorded was 5.72 m. However, the majority of

billabongs are less than 5 ha in maximum extent (41

of 52), with maximum depths of less than 4 m (47).

The mean depth for all billabongs was 177 cm. Depth

ranges for individual billabongs over the course of

sampling were generally around 1 m (Fig. 2), with

the maximum range being 3.82 m.

Water quality data collected from the 48 billabongs

are summarised in the box plots displayed in Fig. 2. As

for water depth, temporal variability in billabong

water chemistry was relatively high (Fig. 2).

The billabongs were moderately to highly turbid,

with individual readings ranging from 1 NTU to 260

at Billabong 38. A strong negative correlation

between turbidity (NTU) and billabong depth (Depth)

is evident (Table 2), suggesting that re-suspension of

surface sediments through wind-generated turbulence

was an important contributor to high turbidity.

Billabong median annual pH ranged from 6.12 to

7.99. The pH appears to reflect the concentration of

base cations in billabong waters, there being a signif-

icant correlation between pH and EC (p = 0.003)

(Table 2). The billabongs in the data set can be classed

as fresh, with median annual EC ranging from

47 ls cm-1 to 400 ls cm-1 (Fig. 2).

The billabongs in the data set ranged from meso to

hypereutrophic, with annual median TP ranging from

10.001.000.100.01100.0010.001.000.100.013002001500.0

383736

3433323130

29a29282726B

illab

on

g

2524232221201918171615141312111098

7a7654321

6420 40302010 10.008.006.00 1,9001,5001,100700300

C1_1

C1_2

C2_1

C2_2

H

NC

T2T1

OR

Depth (m)

Electricalconductivity(µS cm-1) TN (mg-1) TP (mg-1)

Murray 1

Kiewa

Murray 2

Ovens

Murray 3

Goulburn

Water temp (°C) pHTurbidity (NTU)

Fig. 2 Box plots summarising within-billabong range in recorded depth and water quality

J Paleolimnol (2009) 41:453–470 459

123

42 lg l-1 to 990 lg l-1 and TN concentrations

ranging from 0.110 mg l-1 to 4.00 mg l-1. Once

again, the range of within-billabong nutrient concen-

trations was often high. TP correlates strongly with

NTU and Depth (Table 2). Accordingly, shallow

billabongs were characterised by high TP concentra-

tions and high turbidity. Vegetation cover was also

negatively correlated with NTU (Table 2).

The first axis of the PCA carried out on billabong

physical and chemical character reflects variation in

depth, turbidity and TP concentrations, which are all

strongly correlated (Fig. 3). Thus, billabongs scoring

higher on this axis are characterised by greater depth

and water clarity and lower TP concentrations

(Fig. 3). The second axis in the PCA reflects variation

in EC and pH, with billabongs scoring highest on this

axis being characterised by higher values in both

these variables (Fig. 3).

Spatial patterns in billabong water quality

Analysis of Similarity (ANOSIM) carried out on

water quality data shows that only the billabongs of

the Goulburn River, which were characterised by

greater water depth, higher EC and pH and lower

turbidity, are clearly separated from the billabongs

of the remaining reaches (Figs. 3, 4a). There is no

separation of billabongs by hydrological regime

(Fig. 4b); however, when the comparison was

restricted to only Murray 2 billabongs—the single

reach where the hydrological distinction between the

Exposed and Isolated classes is greatest (Maheshwari

et al. 1995)—the difference between these classes

was clear (Global R = 0.844). There was no

significant difference in the water quality of bill-

abongs according to farming intensity on surrounding

land (Global R = -0.033).

Surface sediment diatom assemblages

A total of 306 taxa were identified, although most of

these were very rare, and only those present in

abundances in excess of 5% in a single sample or

greater than 1% in at least four samples (a total of 92

taxa), are considered here and included in the

statistical analyses. The relative abundances of the

more common taxa are indicated in Fig. 5.

The majority of species identified were attached

or motile, benthic species and the majority of

samples were dominated by diatoms that character-

istically display these life habits. The most common

of these include the attached taxa Achnanthidium

minutissimum (Kutz.) Czarnecki and Gomphonema

parvulum Kutzing and the motile benthic spe-

cies Navicula cryptocephala Kutzing. Of these,

A. minutissimum was markedly more abundant

in the non-plankton-dominated billabongs of the

Goulburn, Ovens and Murray 1 river reach flood-

plains. G. parvulum and N. cryptocephala were

more evenly distributed, but were most common in

the billabongs of the Ovens and the Murray 3

(G. parvulum) and the Goulburn River and Murray 1

reach (N. cryptocephala). Cocconeis placentula

Ehrenberg was common in some Goulburn River

billabongs, but was not abundant in the billabongs

of the remaining reaches.

Plankton-dominated assemblages were largely

restricted to billabongs of the Murray River

Table 2 Matrix of Pearson correlation coefficients

Veg covermean Tempmed Depthmed ECmed NTUmed TNmed TPmed pHmed

Veg covermean -0.133 -0.100 0.087 -0.291 0.063 -0.034 -0.144

Tempmed 0.366 0.027 -0.183 0.162 -0.196 0.084 0.081

Depthmed 0.499 0.852 0.025 -0.437 0.247 -0.636 0.354

ECmed 0.558 0.194 0.860 -0.174 0.593 0.122 0.410

NTUmed 0.044 0.252 0.001 0.216 -0.352 0.613 -0.023

TNmed 0.670 0.163 0.077 0.000 0.010 -0.180 0.243

TPmed 0.819 0.553 0.000 0.387 0.000 0.202 -0.062

pHmed 0.328 0.566 0.010 0.003 0.869 0.082 0.661

Correlation coefficients (r) are given above the diagonal cells, p-values are given below, p-values\0.05 are in bold. The variables

NTU, TN and TP were Log 10-transformed before calculating correlations

460 J Paleolimnol (2009) 41:453–470

123

downstream of Lake Hume (Murray 2 and Murray 3).

Aulacoseira subborealis (Nygaard) Denys, Muylaert

& Krammer and Aulacoseira granulata (Ehr.)

Simonsen were the most abundant planktonic diatom

taxa at these sites. The abundance of A. granulata

was highly variable, particularly in the billabongs of

the Murray 2 reach, while the abundance of

Aulacoseira subborealis was more consistent across

Murray 2 and Murray 3 reaches (Fig. 5). Stephan-

odiscus parvus Stoermer & Hakansson, Cyclotella

pseudostelligera and Cyclostephanos tholiformis

Stoermer & Hakansson were occasionally abundant

in Murray billabongs. Aulacoseira crenulata

(Ehrenberg) Thwaites was relatively common in

the sediments of Murray 1 and Ovens billabongs

(Fig. 5).

Spatial patterns in diatom surface sediment

assemblages

Billabong diatom assemblages clearly differed

between most reaches, with only the Murray 1 and

Ovens reaches and the Murray 2 and Murray 3 reaches

returning R-values substantially below 0.5 (Fig. 4a).

On this basis, three distinct reach groupings of

billabongs with regard to diatom assemblages can be

established—the Murray 2 and Murray 3 billabongs,

which were dominated by the two planktonic taxa

Aulacoseira granulata and A. subborealis (Fig. 5), the

combined Murray 1 and Ovens billabongs, which were

dominated by the benthic and epipelic taxa Achnan-

thidium minutissimum, Gomphonema parvulum and

Navicula cryptocephala, and the Goulburn billabongs,

0.1-1.0

water temp

water depth

NTU

pH

EC

TN

TP

veg cover

Environmental variables

River reaches

Murray 1

Murray 2

Murray 3KiewaOvens

Goulburn

Hydrological classUnregulated

Isolated

Exposed

Environmental variables

0.10.1-

water temp

water depth

NTU

pH

EC

TN

TP

veg cover

-1.0

1.0

-1.0

1.0

(a) (b)

Fig. 3 Biplots of environmental variables and sample scores from PCA of billabong water chemistry. Samples classed by reach

(a) and hydrology (b)

J Paleolimnol (2009) 41:453–470 461

123

which were dominated by Achnanthidium minutissi-

mum, Navicula cryptocephala and Cocconeis

placentula (Fig. 5).

Billabong diatom assemblages also differed

between Exposed and Unregulated billabong classes

(Fig. 4b). This difference is driven by the dominance

of Aulacoseira granulata and, to a lesser degree,

A. subborealis in Exposed billabongs. The diatom

assemblages of the two regulated classes were not

clearly separated; however, as for the water quality

data, when the analysis was restricted to the Murray 2

reach only, the two groups were clearly separated

(Global R = 0.771). This distinction can be largely

attributed to differences in the abundance of

A. granulata in Exposed billabongs. ANOVA, com-

paring the abundance of A. granulata in Exposed and

Isolated billabongs of Murray 2 and Murray 3

reaches, shows that A. granulata was more abundant

in Exposed billabongs (F = 6.32, p = 0.02). This

effect was stronger for Murray 2 reach, although the

interaction was not significant and there was also no

difference in the abundance of A. granulata across

reaches (Fig. 6a). No significant effects were detected

when the same analysis was carried out on Aula-

coseira subborealis, the other dominant diatom

species in these reaches (F = 0.429, p = 0.519)

(Fig. 6b). The diatom assemblages of billabongs

subject to high intensity farming and those of

billabongs subject to low intensity farming were not

clearly separated (R = 0.114).

Influence of environmental variables on diatom

assemblages

Forward selection of water quality variables showed

that just three made significant, independent contribu-

tions to explaining variation in the diatom assemblage

data. These were, in order of selection, TNmean, pHmin

and TPmed. In combination, these three variables

explain 15.3% of the variation in the species data.

For the non-water quality variables, four made signif-

icant, independent contributions to explaining species

variance. In order of selection, these variables were

‘Goulburn’ and ‘Unregulated’, vegetation covermin

and ‘Ovens’. In combination these four variables

explain 25.7% of the variation in the species data. With

Water quality

Diatom assemblages

0.00.10.20.30.40.50.60.70.80.91.0

Murray 2Murray 3OvensGoulburn

Murray 1

Murray 2

Murray 3

Ovens

0.00.10.20.30.40.50.60.70.80.91.00.00.10.20.30.40.50.60.70.80.91.00.00.10.20.30.40.50.60.70.80.91.0

Isolated0.0

0.00.10.20.30.40.50.60.70.80.91.0

Exposed

Exposed

Unregulated

0.10.20.30.40.50.60.70.80.91.0

(a)

(b)

Fig. 4 R statistics for pair-

wise comparisons of reach

(a) and hydrological (b)

classifications of billabongs

characterised by physical

and chemical variables and

by diatom assemblages. Rvalues in excess of 0.75

suggest groups are well

separated, values greater

than 0.5 suggest some

overlap, but a clear

difference between groups,

while values less the 0.25

indicate little or no

separation of groups

(Clarke and Warwick 1994)

462 J Paleolimnol (2009) 41:453–470

123

the three water quality and four non-water quality

variables included in CCA, the total species variance

explained increases to 32.4%; however, forward selec-

tion of these seven variables shows that TNmean and

pHmin, which were selected 6th and 7th, respectively,

did not explain a significantly greater amount of species

variation (Table 3). A plot of the CCA ordination using

all seven variables is presented in Fig. 7.

Variance partitioning confirmed the greater

explanatory power of the non-water quality variables,

showing that TNmean, TPmed and pHmin explain only

6.8% of species variation after variation attributable

to ‘Goulburn’, ‘Unregulated’, ‘Ovens’ and vegetation

covermin is removed. In contrast, ‘Goulburn’, ‘Unreg-

ulated’, ‘Ovens’ and vegetation covermin explain

17.1% of the species variation after variance attrib-

utable to TNmean, TPmed and pHmin is removed.

CCA was repeated separately for each of the river

reach groupings that were shown in ANOSIM to be

characterised by distinct diatom assemblages.

1 2 3 4 5 6 77a

89

10 11 12 13 14 15 16 17 18 19 20 21 22 23 25 26 27 28 29

29a 30 31 32 33 34 35 36 3738

ORT1 T2 H

C1_1C2_1

NCC1_2

C2_2

Murray 1

Kiewa

Murray 2

Ovens

Murray 3

Goulburn

0 0 0 0 0 0 0 0 00 0 0

Plankton Facultative plankton Attached Motile benthicB

illab

on

g

20 20 0 20 0 20 400 200 20 40 60 80 200 20 0 0 0 0 020 20 20 20 20 20 20 2040400

Fig. 5 Relative abundances of common diatom taxa found in the billabong data set. Billabongs are arranged broadly upstream to

downstream

J Paleolimnol (2009) 41:453–470 463

123

Forward selection in these analyses showed the

influence of water quality variables to be substan-

tially greater than for the combined data set

(Tables 4–6). In the case of billabongs on the Murray

1, Kiewa and Ovens reaches, the variables TPmin,

pHmed and farming intensity explain 20.12% of the

species variation (Table 4). For the Goulburn bill-

abongs, TPmed, ‘Isolated’, Depthmax and TNmax

explain 58.37% (Table 5), while for the Murray 2

and 3 reaches, TPmed, Vegetation covermin, ECmed,

ECmax, farming intensity and Temperaturemed explain

56.93% of the variation (Table 6).

Discussion

The results of this study indicate that the influence

of water quality variables such as pH, EC, turbidity

and nutrient concentrations on the distribution of

diatom taxa in billabongs across the study area is

relatively small when compared to the influence of

river reach, hydrology and habitat availability (i.e.

whether a billabong is macrophyte or phytoplankton-

0

5

10

15

20

25

30

35

Murray 2 Murray 3 Murray 2 Murray 3

Exposed Isolated

0

10

20

30

40

50

60

Murray 2 Murray 3 Murray 2 Murray 3

Exposed Isolated

(a)

(b)

Fig. 6 Estimated marginal means for Aulacoseira granulata(a) and A. subborealis (b) by river reach (Murray 1 and 2) and

hydrological class (Exposed and Isolated)

Table 3 Results of forward selection in CCA using all sites

Variable k - 1 Variance explained F p

Goulburn 0.259 8.62

Unregulated 0.2239 7.45

TNmean 0.2084 6.94

Vegmin 0.1505 5.01

pHmin 0.1455 4.84

TPmed 0.1334 4.44

Ovens 0.1244 4.14

k - A

Goulburn 0.259 8.62 4.34 0.002

Unregulated 0.3121 10.39 5.77 0.002

TPmed 0.1105 3.68 2.09 0.026

Vegmin 0.1093 3.64 2.12 0.002

Ovens 0.0923 3.07 1.83 0.044

TNmean 0.0506 1.68 1 0.398

pHmin 0.0404 1.34 0.8 0.6

Total 0.947 31.51

The marginal and conditional effects of the significant water

quality (3) and non-water quality (4) variables are shown. The

canonical eigenvalue of each variable, k - 1, indicates the

amount of species variance potentially explained by that

variable alone (the marginal effect). The k - A value indicates

the increase in the sum of all canonical eigenvalues of the

ordination when that variable is added sequentially (the

conditional effect). At each iteration, the variable explaining

the greatest amount of species variance (highest k - A) is

added. F and p values are based on Monte Carlo permutation

tests with 499 permutations and indicate whether the variables

add a significant amount to variance explained

464 J Paleolimnol (2009) 41:453–470

123

dominated). This result contrasts with most studies of

diatom distributions in Australia and elsewhere that

suggest water chemistry exerts the greatest control

over distributions and that biogeographic influences

are relatively minor for these organisms (e.g. Birks

et al. 1990; Dixit et al. 1992; Bennion et al. 1996;

Gell 1997; Tibby and Reid 2004); however, they are

largely consistent with the findings of Hay et al.

(2000), which also highlighted the influence of

hydrology and macrophyte abundance on diatom

assemblages in floodplain lakes of the Mackenzie

Delta in the Northwest Territories, Canada.

Diatom habitat grouping

Planktonic taxa

Attached taxaMotile benthic taxa

Environmental variables

Nominal Environmental variables

0.10.1-

TPmedUnregulated

Ovens

Veg covermin

GoulburnpH min

TNmean

0.10.1-

River reaches

Murray 1

Murray 2

Murray 3KiewaOvens

Goulburn

Nominal Environmental variables

Environmental variables

TPmed

UnregulatedOvensVeg covermin

Goulburn

pHmin

TNmean

0. 1-0. 1

0. 1-0. 1

(a) (b)

Fig. 7 Biplot of seven environmental variables and sample (a) and species scores (b) on axes 1 & 2 from CCA ordination using the

50-sample billabong data set. Scaling focused on inter-species distances

Table 4 Results of forward selection in CCA using Murray 1,

Ovens and Kiewa billabongs only (all unregulated sites)

Variable k - 1 Variance explained F p

TPmin 0.2928 11.35

pHmed 0.2635 10.21

k - A

TPmin 0.2928 11.35 2.3 0.028

pHmed 0.2259 8.76 1.86 0.022

Total 0.519 20.11

See Table 3 header for explanation of the forward selection

process and derived values

J Paleolimnol (2009) 41:453–470 465

123

Studies that have shown a predominant influence

of water chemistry on diatom distributions support

the notion that improved characterisation of diatom

optima and tolerances can be best achieved through

creation of large regional data sets that maximise the

length of the gradient of interest and minimise bias in

sampling across the gradient (Gasse et al. 1995;

Bennion et al. 1996). The assumption underlying this

view is that the cosmopolitan nature of diatom

distributions reduces the capacity of biogeographic

factors to introduce noise into observed distribution

patterns. The results of this study suggest that, for

these billabongs at least, this assumption does not

hold and that individual river reaches have distinct

diatom floras associated with them.

Reach-scale spatial patterns are evident in the water

quality of billabongs included in this survey. However,

these patterns are not strong and only the Goulburn

River billabongs, with generally higher EC and pH and

lower TP, clearly differ from the remaining bill-

abongs. This seems to be largely a reflection of the

local geology in the vicinity of the Goulburn bill-

abongs which includes Devonian marine sediments

incorporating limestone (Reid 1997). Similarly,

although the ‘Exposed’ billabongs form a tight

grouping (Fig. 3b), neither this nor the other hydro-

logical groupings have clearly different water quality.

The lack of strong spatial patterns in water quality may

have been influenced by the fact that the surveys of

most billabongs were conducted during a year when

rainfall was 60% above average (Ogden 1996).

Flooding occurred during the year that the billabongs

were sampled and it is possible that differences

between ‘Exposed’ and ‘Isolated’ billabongs, for

example, would have been more marked if no flooding

had occurred. However, the flooding that occurred

during the sampling period cannot account for the lack

of separation between billabongs on reaches that are

not hydrologically connected such as the Ovens and

Murray 1 reaches and the Ovens and Murray 2 reaches

(Fig. 4a). Thus, the general pattern that emerges is that

the location of a billabong, be it at a regional or local

scale, does not appear to exert a strong influence over

the water chemistry as recorded in this study.

In contrast to the relatively weak spatial patterns in

billabong water quality, the spatial patterns in diatom

assemblages are strong. A fundamental distinction

can be made between billabongs that are dominated

by planktonic diatoms (predominantly Murray 2 and

Murray 3 billabongs) and those dominated by

attached and benthic taxa (predominantly billabongs

located on the remaining river reaches). Previous

studies of the distribution of cladoceran remains in

most of the billabongs included in this data set also

showed strong distinctions between billabongs with

assemblages dominated by planktonic taxa and those

dominated by plant-associated taxa (Ogden 2000).

This pattern was attributed to feedback mechanisms

acting to maintain planktonic dominance and limit

growth of submerged plants in large, relatively deep

Table 5 Results of forward selection in CCA using Goulburn

billabongs only

Variable k - 1 Variance explained F p

TPmed 0.3699 25.49

Depthmax 0.2976 20.51

Isolated 0.1885 12.99

k - A

TPmed 0.3699 25.49 2.4 0.01

Isolated 0.2201 15.17 1.53 0.046

Depthmax 0.257 17.71 2.13 0.024

Total 0.847 58.37

See Table 3 header for explanation of the forward selection

process and derived values

Table 6 Results of forward selection in CCA using Murray 2

and 3 billabongs only

Variable k - 1 Variance explained F p

TPmed 0.2178 20.00

Veg covermin 0.1575 14.46

ECmed 0.1591 14.61

ECmax 0.182 16.71

High farm 0.1843 16.92

Tempmed 0.0842 7.73

k - A

TPmed 0.2178 20.00 4.25 0.002

Veg covermin 0.1014 9.31 2.11 0.002

ECmed 0.0813 7.47 1.77 0.022

ECmax 0.0752 6.91 1.72 0.04

High farm 0.0731 6.71 1.76 0.032

Tempmed 0.071 6.52 1.81 0.024

Total 0.62 56.93

See Table 3 header for explanation of the forward selection

process and derived values

466 J Paleolimnol (2009) 41:453–470

123

billabongs, in accordance with the theory of alterna-

tive stable states (Scheffer et al. 1993; Ogden 2000).

According to the theory of alternative stable states,

a clear-water, macrophyte-dominated state will per-

sist in shallow lakes under low nutrient conditions

and a turbid, phytoplankton-dominated state will

persist under high nutrient concentrations; however,

either state can persist at intermediate nutrient

concentrations as a result of feedback processes that

act to maintain competitive dominance of macro-

phytes or phytoplankton (Scheffer et al. 1993, 2001).

Billabongs are shallow lakes and, assuming they are

subject to alternative stable states, very different

diatom floras would be expected to occur in macro-

phyte-rich and macrophyte-poor billabongs even in

the absence of strong differences in water quality,

except in relation to light.

By repeating CCA separately on the distinct groups

of billabongs (Tables 4–6), the more subtle species

responses to environmental gradients are drawn out. In

this respect, the results of this study support the

findings of a recent study carried out on stream and

river diatoms in SE Australia that found strong regional

differences in diatom communities between those of

upland and lowland areas. Philibert et al. (2006) also

found that even within these broad groupings, geospa-

tial variables such as altitude, latitude and longitude

remained important predictors of diatom distributions.

Importantly, however, Philibert et al. (2006) inter-

preted the importance of these geospatial variables as

an artefact of covariance with water quality vari-

ables—for example the strong east–west climatic and

salinity gradients that exist in south east Australia.

While such covariance is likely to have some impor-

tance in the case of billabong diatom assemblages—

particularly with regard to the Goulburn billabongs—it

appears that hydrology, billabong morphometry and

biogeographic effects associated with reach-scale

population dynamics are more important.

A further contrast with the results from the

Philibert et al. (2006) study is that diatom distribu-

tions in the billabongs in this data set appear to be

more strongly influenced by TP than pH, EC and TN.

TP was consistently shown to exert the strongest

influence on diatom distribution within the reach

groupings (Tables 4–6), supporting the notion that

the apparent influence of pH and TN on diatom

distributions in the full data set (Table 3) was due to

the confounding of these variables with reach groups.

A further factor that may have contributed to weak

water quality–species relationships in the full data set

is the low level of spatial, relative to temporal,

variability in the water quality data. Low spatial

variability means that the environmental gradients

incorporated in the data set are short (Fig. 2) and this

will result in less species turnover (Bennion et al.

1996). On the other hand, high temporal variability

ensures that diatom assemblages are dominated by

tolerant taxa and/or a mixture of taxa with a broad

range in optima (Gell 1997). In combination, these

effects will limit the strength of species–environment

relationships. It is also possible that the combination

of high temporal and low spatial variability in water

quality has contributed to the relatively strong

geographical distinctions in billabong diatom assem-

blages. High temporal variability in water quality

means that conditions within individual billabongs

are likely to become periodically unsuitable for

specific taxa. Under these conditions, the abundance

of taxa within individual billabongs may be more

strongly influenced by the close proximity and

sporadic hydrological connection to re-colonisation

sources (i.e. nearby billabongs and the river) than by

the absolute optima of competing taxa. In this way,

subtle differences in the water quality of billabongs

within certain reaches may be magnified to create

quite distinct diatom assemblages.

ANOVAs carried out on the dominant species

within the Murray 2 and 3 reaches suggest that the

abundance of Aulacoseira granulata is influenced by

whether or not the billabong experiences regular

connection to the mainstream, particularly within the

Murray 2 reach. Given that A. granulata is the

dominant planktonic alga in the Murray River below

Lake Hume (Sullivan et al. 1988; Hotzel 1997), it is

likely that this distinction reflects, at least in part, the

direct input of this species from the river. Influx of

riverine diatoms to floodplain lakes through hydro-

logical connection was also observed by Hay et al.

(2000) in the Mackenzie River Delta, and by Van den

Brink et al. (1994) on the Rhine and Meuse Rivers.

Aulacoseira granulata has a relatively high sinking

rate and thus requires high turbulence to maintain

position in the water column (Lund 1964; Kilham

et al. 1986; Reynolds 1988). Studies of Murray River

potamoplankton show that A. granulata is less

abundant where turbulence is reduced, presumably

as a result of settling (Hotzel and Croome 1996;

J Paleolimnol (2009) 41:453–470 467

123

Bormans and Webster 1999). Thus, the reduced

abundance of A. granulata in the isolated billabongs

of Murray 2 and Murray 3 reaches may reflect

reduced riverine input and its competitive disadvan-

tage under conditions where turbulence is low.

Although connection to the main channel may be

the most parsimonious explanation for the contrasting

abundances of A. granulata in ‘Exposed’ and

‘Isolated’ billabongs, it remains possible that other

factors such as water quality or depth may be

important. Further studies are required to test these

relationships, for example, through the use of sedi-

ment traps and direct sampling of plankton

communities in ‘Exposed’ and ‘Isolated’ billabongs

as well as in the river itself over a time series that

incorporates periods when ‘Exposed’ billabongs are

both disconnected and connected to the main channel.

At this stage, however, with the evidence at hand, it

seems likely that the sediments of ‘Exposed’ bill-

abongs preserve elements of the mainstream diatom

flora and so could provide a record of hydrological

change (Hay et al. 2000; Michelutti et al. 2001) as

well as changes to the broader river ecosystem.

Conclusions and implications for

palaeolimnological study

Although the results of this study indicate that pH,

TN and TP are the most important water quality

variables controlling diatom distribution in bill-

abongs, the influence of pH and TN on diatom

distribution appears to be secondary to whether or not

a billabong is phytoplankton- or macrophyte-domi-

nated or the river reach on which the billabong is

located. The dichotomy between phytoplankton and

macrophyte dominance means that billabongs of

similar water quality can support very different

diatom assemblages and hence a relatively small

proportion of the variation in species assemblages can

be attributed to water quality gradients. A high degree

of temporal relative to spatial variability in the

physical and chemical character of individual bill-

abongs is also likely to contribute to the relatively

weak species responses to water quality gradients.

Distinctions between the assemblages of billabongs

of different rivers and river reaches suggest that

distribution is also confounded by hydrological and

biogeographic factors.

This study has provided a broad picture of the

environmental variables, mechanisms and processes

that drive the distribution of diatoms in billabongs

within the study region. Many uncertainties remain,

largely as a result of confounding effects that are

inherent in the use of species distributions to inform

on species preferences. Nevertheless, the information

gained from this study will allow for these uncer-

tainties to be addressed through more focused

distributional and experimental studies.

These patterns have several important implications

for palaeolimnological studies. First, diatom-based

transfer functions have been established for pH and

EC using data from the region (Tibby et al. 2003,

2007; Tibby and Reid 2004; Philibert et al. 2006), the

results of this study indicate that there may also be

scope to develop a transfer function for reconstruct-

ing TP. Second, there may be value in developing

separate transfer functions for phytoplankton- and

macrophyte-dominated billabongs and for billabongs

on different reaches. By separating phytoplankton-

and macrophyte-dominated billabongs and different

reaches, important sources of confounding in relation

to habitat and biogeography can be eliminated. It is

possible that these effects are of particular impor-

tance in floodplain lakes (e.g. Hay et al. 2000;

Michelutti et al. 2001) because of the episodic

hydrological connections that occur across water

bodies of the same river reaches due to flooding. This

issue needs to be further examined through spatially

explicit hierarchical sampling of billabongs to iden-

tify the spatial scale at which the various potential

drivers of diatom distributions (water quality, mor-

phometry, population dynamics and hydrology)

operate. Finally, the distribution of diatoms in

billabongs of the lower Murray reaches suggests that

key elements of the river flora appear to be preserved

in billabong sediments. This greatly enhances the

scope of reconstructions. The Murray River supports

a large phytoplankton population (dominated by the

diatom Aulacoseira granulata) that accounts for up to

84% of the primary productivity of the river (Gawne

et al. 2007). Evidence that this phytoplankton assem-

blage is reflected in billabong sediments raises the

possibility that changes in this community may be

tracked through time. The rivers of the Murray-

Darling system are widely held to have undergone

profound ecological change as a result of changing

land use and river regulation. However, much of the

468 J Paleolimnol (2009) 41:453–470

123

evidence for change is either anecdotal or suffers

from a disturbing lack of temporal perspective

(Thoms et al. 1999; Reid and Ogden 2006). The

possibility that palaeolimnological approaches could

offer empirical evidence of change within river

systems extending over centuries is one that is well

worth pursuing.

Acknowledgements This research was supported by

Australian Post-Graduate Research Awards provided to the

authors, and by a research grant from the Murray-Darling

Freshwater Research Centre. The School of Geography and

Environmental Science, Monash University and the Department

of Biogeography and Geomorphology, Australian National

University provided field and lab support. The authors also

thank the many landholders who allowed access to the billabongs

on their properties, Damien Smith for tireless field assistance,

Gerry Quinn for valuable statistical advice, and finally, Peter

Kershaw and John Tibby for their helpful comments on earlier

drafts.

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